Comprehensive energy data analysis method and system based on intelligent platform

Abstract

The invention belongs to the field of comprehensive energy data, and particularly relates to a comprehensive energy data analysis method and system based on an intelligent platform. The data acquisition module is used for acquiring comprehensive energy data through various acquisition technologies, including energy production data, energy transmission data, energy consumption data and equipment basic information; the code storage module is used for classifying and coding the comprehensive energy data and then uploading the comprehensive energy data to the distributed database; the monitoring management module is used for performing quality monitoring and life cycle management on the data in the database; the calculation and analysis module is used for performing calculation and analysis on the data in the database to obtain a comprehensive analysis result and an energy management index; the result display module is used for displaying the obtained comprehensive analysis result to a user on terminal equipment by generating a graph, a report, an instrument panel and a map; and the implementation and application module is used for implementing specific energy management and decision-making functions according to the energy management indexes.

Description

Technical Field

[0001] The present invention belongs to the field of integrated energy data, and specifically relates to an integrated energy data analysis method and system based on an intelligent platform. Background Art

[0002] With the global energy structure transformation and the advancement of energy conservation and emission reduction goals, traditional energy management models are no longer able to meet the needs of complex energy systems. In this context, efficient collection and analysis of energy data has become critical.

[0003] The underlying technology behind traditional energy data management solutions is limited by technological advancements and energy management concepts. In the early days, energy management relied heavily on manual record-keeping, requiring staff to regularly visit energy production, transmission, and consumption sites to manually record various energy data, such as electricity, water, and gas meter readings, as well as equipment operating status. With the subsequent advancement of information technology, some companies began adopting simple automated data collection equipment and stand-alone data management software. However, these systems were often independent and lacked data exchange capabilities.

[0004] In terms of storage management, traditional stand-alone data management software has limited storage capacity, making it difficult to cope with the rapid growth of energy data. Data security is also poor, and data can be easily lost if hardware equipment is damaged. Furthermore, the lack of effective data quality monitoring and lifecycle management mechanisms prevents data integrity and availability from being guaranteed, leading to a large accumulation of invalid or erroneous data, which hinders subsequent analysis.

[0005] Traditional solutions for calculation, analysis, and result presentation rely heavily on manual statistics and simple spreadsheet processing. These methods are unable to deeply mine and analyze complex energy data, making it difficult to identify potential issues and optimization opportunities in energy usage. The presentation of results is often monotonous and lacks intuitiveness and interactivity, making it difficult for managers to quickly access key information and make informed decisions. In practice, due to a lack of systematic data support and intelligent analysis, energy management and decision-making are often based on empirical judgments, lacking scientific accuracy and precision. This leads to significant energy waste and inefficient management. Summary of the Invention

[0006] In view of this, the present invention aims to propose a comprehensive energy data analysis method and system based on an intelligent platform, which collects energy production data, energy transmission speed data, and energy consumption speed data through various collection technologies, and classifies and encodes the collected data and uploads it to a distributed database. At the same time, the data is quality-monitored and lifecycle-managed, and the data in the database is calculated and analyzed to obtain a comprehensive analysis result of energy use. The comprehensive analysis results obtained are displayed to users on terminal devices by generating graphics, reports, dashboards, and maps. According to the displayed energy data and analysis results, specific energy management and decision-making functions are implemented, which effectively solves the problems mentioned in the background technology.

[0007] The object of the present invention can be achieved by the following technical solution: a comprehensive energy data analysis system based on an intelligent platform, characterized by comprising: The data collection module is used to collect comprehensive energy data through various collection technologies, including energy production data, energy transmission data, energy consumption data and basic equipment information; The coding and storage module is used to classify and encode the comprehensive energy data and upload it to the distributed database; Monitoring and management module, used to perform quality monitoring and life cycle management on data in the database; The calculation and analysis module is used to perform calculation and analysis on the data in the database to obtain comprehensive analysis results and energy management indicators; The result display module is used to display the comprehensive analysis results to users on terminal devices by generating graphics, reports, dashboards, and maps; Implementation application module is used to implement specific energy management and decision-making functions based on energy management indicators.

[0008] A comprehensive energy data analysis method based on an intelligent platform, characterized by comprising: S1. Collect comprehensive energy data through various collection technologies, including energy production data, energy transmission data, energy consumption data and basic equipment information; S2. Classify and encode the comprehensive energy data and upload it to the distributed database; S3, perform quality monitoring and lifecycle management on the data in the database; S4. Perform calculation and analysis on the data in the database to obtain comprehensive analysis results and energy management indicators; S5. Generate comprehensive analysis results in graphics, reports, dashboards, and maps, and display them to users on terminal devices; S6. Implement specific energy management and decision-making functions based on energy management indicators.

[0009] The comprehensive energy data are specifically as follows: energy production data includes primary energy input and secondary energy output; energy transmission data includes energy input, energy output, transmission method, and transmission path; energy consumption data includes total energy consumption and total industrial output value; equipment information includes basic equipment information, operation and maintenance information, energy-consuming equipment type, and energy efficiency health status.

[0010] The quality monitoring method is as follows: eliminate duplicates and outliers, verify integrity and logical consistency; set indicators, compare standard values ​​or historical data, and mark problematic data; trace the source of the problem, correct erroneous data and record the processing process; track data fluctuations in real time through threshold alarms and trend analysis, and trigger response mechanisms; regularly evaluate monitoring models, iterate rules, and improve data quality.

[0011] Lifecycle management refers to: standardizing the collection process and labeling metadata during the creation phase; hierarchical management and encryption protection based on frequency of use during the storage phase; using permission control to ensure the safe circulation of data during the use phase, combining analysis to mine the value of data, and archiving historical data according to rules; for data that exceeds the retention period, irreversible deletion or physical destruction is completed in accordance with compliance processes, and operational audit records are retained.

[0012] The method of performing computational analysis on the data is: after cleaning and preprocessing the data, the data is subjected to energy consumption analysis, energy efficiency analysis, and equipment health analysis, and the energy usage and change patterns, utilization efficiency, equipment failure and remaining life prediction are obtained.

[0013] The energy consumption analysis refers to: analyzing the total energy consumption, consumption structure, and consumption trends; counting the total energy consumption by time dimension, generating trend charts to observe changes in the total amount, identifying peak and low energy consumption periods, and analyzing their correlation with production activities and weather; counting the consumption proportions by energy type, including electricity, natural gas, fuel oil, and renewable energy, drawing pie charts or stacked bar charts, and breaking down energy consumption by department or equipment to compare efficiency differences between departments; based on historical data, using time series models to fit energy consumption trends, identifying long-term trends or cyclical fluctuations, and establishing regression models between energy consumption and external variables, analyzing driving factors, and combining business planning to predict future energy consumption and structural changes, and setting energy-saving targets.

[0014] The energy efficiency analysis refers to: analyzing the production efficiency, transmission efficiency, and consumption efficiency of energy; Production efficiency can be expressed as: ; Where V1 is the production efficiency, M is the secondary energy output, and m is the primary energy input; The transmission efficiency can be expressed as: ; Where V2 is the transmission efficiency, N is the energy input, and n is the energy output; Consumption efficiency can be expressed as: ; Among them, V3 is consumption efficiency, K is total energy consumption, and H is total industrial output value.

[0015] The energy management and decision-making functions refer to various applications including energy monitoring, energy scheduling, energy optimization, energy forecasting, and energy management.

[0016] Combining all the above technical solutions, the present invention has the following positive effects: 1. In terms of data processing, the data collection module and its collection steps utilize various collection technologies to comprehensively capture energy production, transmission, consumption data, and equipment information. This is more efficient and accurate than traditional manual collection, avoiding data loss and delays. The storage management module and its corresponding steps implement data classification, coding, and quality monitoring, addressing the chaotic and poor data storage issues of traditional solutions and ensuring data integrity and availability. The computational analysis module and its steps conduct in-depth computational analysis of massive amounts of data to uncover potential patterns in energy usage, overcoming the limitations of traditional manual statistics that make it difficult to identify optimization opportunities.

[0017] 2. At the display and application level, the result display module presents analysis results in a variety of forms, which is more intuitive and easier for users to obtain key information than the traditional single table display; the implementation application module implements energy management and decision-making based on the displayed data and analysis results, changing the traditional experience-based decision-making model, realizing scientific and precise energy management, improving energy utilization efficiency, and helping energy conservation and emission reduction and enterprise cost reduction and efficiency improvement.

[0018] 3. The system and method build an intelligent energy management system that is compatible with a variety of energy data and equipment, adapts to the complex and changing needs of the energy system, provides strong support for the digital transformation of the energy industry, and promotes the development of energy management towards intelligence and integration. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0020] Figure 1 This is a system framework diagram of the system of the present invention.

[0021] Figure 2 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] See also Figure 1 As shown, the present invention proposes a comprehensive energy data analysis system based on an intelligent platform, which includes a data acquisition module, a coding storage module, a monitoring and management module, a calculation and analysis module, a result display module, and an implementation and application module.

[0024] In a more specific application of the present invention, a complete data acquisition network can be built in the data acquisition module through various acquisition technologies. At the energy equipment production end, the intelligent power generation metering device can accurately collect various energy production data in real time, including steam flow and power generation of thermal power generation, and wind speed and power generation of wind power generation, by relying on high-precision metering chips and advanced communication modules. All of these data can be recorded quickly and accurately. At the same time, various sensors, such as temperature sensors, pressure sensors, and vibration sensors, work closely with the intelligent power generation metering device to monitor equipment operating status parameters, including the operating temperature of the power generation equipment, internal pressure changes, and equipment vibration frequency, providing multi-dimensional data for analyzing equipment health and energy production efficiency.

[0025] On energy transmission lines, for fluid energy such as natural gas and oil, fluid transmission speed monitoring devices are deployed. They use ultrasonic and electromagnetic induction technologies to accurately measure the flow rate, flow rate and pressure of the fluid in the pipeline, and timely grasp the dynamic changes of fluid energy during transmission. For power transmission, power transmission status monitoring devices use advanced power electronics and communication technologies to monitor the voltage, current, power factor and harmonic parameters of the transmission line in real time. By analyzing these data, the power transmission efficiency can be effectively evaluated, and problems with line loss and power quality can be discovered to ensure stable power transmission.

[0026] At the energy consumption end, smart metering devices, such as smart electricity, water, and gas meters, offer metering capabilities and two-way communication, enabling real-time upload of energy consumption data to the system platform. IoT sensors further expand the breadth and depth of data collection. In commercial buildings, by deploying temperature, humidity, light, and occupancy sensors, combined with energy consumption data collected by smart metering devices, it is possible to analyze the impact of various environmental factors on energy consumption, such as the relationship between indoor temperature regulation and air conditioning energy consumption, and the correlation between natural light intensity and lighting energy consumption. This provides a comprehensive data foundation for refined energy management and energy conservation optimization.

[0027] In the coding and storage module, collected comprehensive energy data is categorized and encoded. At the classification level, data is divided into four basic categories: production, transmission, consumption, and equipment information, based on the operational processes of the energy system. Further subdivided, energy production data can be broken down by energy type, with thermal power data, wind power data, and photovoltaic data each classified separately. Energy transmission data can be divided into natural gas pipeline transmission data and power line transmission data based on the medium used. Energy consumption data is categorized by the end-use scenario, such as industrial, commercial, and residential. Basic equipment information, including equipment model, manufacturer, and installation time, is categorized separately.

[0028] The encoding process can use a combination of "letters + numbers" to construct encoding rules. Taking energy production data as an example, "SD-HD-001" can be used to represent the first thermal power production data. SD is the pinyin initials of "production" and serves as the first-level category identifier; HD stands for thermal power and is the second-level sub-category identifier; and 001 is a serial number used to distinguish different records within the same data type. Energy transmission data can be coded as "CS-DL-002," where CS represents transmission, DL represents electricity, and 002 is the sequence number.

[0029] Distributed databases include: relational databases, which are used to store structured data, including basic equipment information, user information, and energy metering data, and use SQL language for data query and management; non-relational databases, which are used to store unstructured data and semi-structured data, including equipment log data, text files, and image files, and use NoSQL technology for data storage and management; data warehouses, which are used to store historical energy data, provide data support for data analysis and mining, and use data mart technology and data cube technology for data organization and management.

[0030] The quality monitoring method is to eliminate duplicates and outliers, verify integrity and logical consistency; set indicators, compare standard values ​​or historical data, and mark problem data; trace the source of the problem, correct erroneous data and record the processing process; track data fluctuations in real time through threshold alarms and trend analysis, and trigger response mechanisms; regularly evaluate monitoring models, iterate rules, and improve data quality.

[0031] The purpose of quality monitoring is to monitor the integrity of the data, check whether the data is lost, whether the fields are missing, and whether the collection is interrupted; monitor the accuracy of the data, check whether the data truly reflects the physical quantity and there are no sensor failures, transmission errors or human errors; monitor the consistency of the data, check whether the data logic across systems and devices is consistent and there are no contradictions or conflicts.

[0032] Lifecycle management refers to standardizing the collection process and labeling metadata during the creation phase; hierarchical management and encryption protection based on frequency of use during the storage phase; using permission control to ensure the safe circulation of data during the use phase, combining analysis to explore the value of data, and archiving historical data according to rules; for data that exceeds the retention period, irreversible deletion or physical destruction is completed in accordance with compliance processes, and operational audit records are retained.

[0033] The coding storage module and the monitoring and management module classify and encode collected data before uploading it to a distributed database. Combined with data quality monitoring and lifecycle management, they enable orderly data storage and efficient utilization. Classification and coding prioritize data within the database, facilitating rapid retrieval and access. Quality monitoring promptly identifies data anomalies, ensuring data accuracy and reliability. Lifecycle management rationally plans data storage duration and processing methods, preventing the accumulation of invalid data and reducing storage costs. Ultimately, this provides a solid, high-quality data foundation for subsequent energy data analysis and decision-making, enhancing the scientific and refined nature of integrated energy management.

[0034] In the analysis and calculation module, the method of calculating and analyzing the data is as follows: after cleaning and preprocessing the data, the data is analyzed for energy consumption, energy efficiency, and equipment health, and the energy usage and change patterns, utilization efficiency, equipment failure, and remaining life prediction are obtained.

[0035] The energy consumption analysis refers to: analyzing the total energy consumption, consumption structure, and consumption trends; counting the total energy consumption by time dimension, generating trend charts to observe changes in the total amount, identifying peak and low energy consumption periods, and analyzing their correlation with production activities and weather; counting the consumption proportions by energy type, including electricity, natural gas, fuel oil, and renewable energy, drawing pie charts or stacked bar charts, and breaking down energy consumption by department or equipment to compare efficiency differences between departments; based on historical data, using time series models to fit energy consumption trends, identifying long-term trends or cyclical fluctuations, and establishing regression models between energy consumption and external variables, analyzing driving factors, and combining business planning to predict future energy consumption and structural changes, and setting energy-saving targets.

[0036] Energy efficiency analysis refers to: analyzing the production efficiency, transmission efficiency, and consumption efficiency of energy; Production efficiency can be expressed as: ; Where V1 is the production efficiency, M is the secondary energy output, and m is the primary energy input.

[0037] Clarify the efficiency level of converting primary energy into secondary energy, identify energy losses caused by process defects and equipment aging, and provide a basis for production process optimization and equipment upgrades.

[0038] The transmission efficiency can be expressed as: ; Where V2 is the transmission efficiency, N is the energy input, and n is the energy output; Accurately locate loss nodes in the energy transmission process, such as grid line loss and pipeline leakage, evaluate the rationality of transmission technology and pipeline network layout, and help reduce transmission losses and optimize transmission routes.

[0039] Consumption efficiency can be expressed as: ; Among them, V3 is consumption efficiency, K is total energy consumption, and H is total industrial output value.

[0040] What needs to be specifically explained in this embodiment is the production efficiency, transmission efficiency and consumption efficiency calculated based on the specification, the correlation between production efficiency, transmission efficiency and consumption efficiency is quantified through a dynamic weight allocation model (such as the entropy weight method), and the LSTM neural network is trained with historical data to predict energy utilization efficiency.

[0041] Understand the impact of terminal equipment energy consumption intensity, energy usage structure and operating habits on energy efficiency, identify high-energy-consuming equipment and inefficient energy usage scenarios, and provide direction for energy-saving transformation such as replacing high-efficiency motors and optimizing operation strategies.

[0042] In one specific example, a power plant generated 6 million kilowatt-hours of electricity per month using 8,000 tons of standard coal. Using the equation 10,000 kilowatt-hours = 3.2 tons of standard coal, 6 million kilowatt-hours equates to 1,920 tons of standard coal, resulting in a production efficiency of only 24%. Investigations revealed that a significant amount of heat energy was not being effectively converted into electricity due to incomplete boiler combustion and aging turbine blades.

[0043] The power plant transmits 6 million kilowatt-hours of electricity to the industrial park via high-voltage transmission lines. The industrial park actually receives 5.58 million kilowatt-hours, a transmission efficiency of 93%. Monitoring of the transmission line sections revealed a high line loss rate of 5% on a 20-kilometer section of aging lines, far exceeding normal levels. Analysis revealed that the high resistance was caused by aging insulation and thin wires.

[0044] The total monthly energy consumption (K) of enterprises within the industrial park is equivalent to 5,000 tons of standard coal, generating a total industrial output value (H) of 120 million yuan. Each ton of standard coal consumed generates 24,000 yuan in output value. Further analysis revealed that a particular mechanical processing enterprise within the park accounted for 35% of total energy consumption, but its energy consumption per unit of output was 1.8 times the park average. This enterprise utilized a large number of outdated motors and traditional cutting machine tools, which were energy-intensive and often idle.

[0045] Equipment health analysis mainly predicts equipment failures and remaining life through a multi-dimensional comprehensive analysis of equipment operating status data, historical failure data, performance parameters and environmental factors.

[0046] What needs to be specifically explained in this embodiment is that the random forest algorithm is used to input equipment operating status data, historical failure data, performance parameters and environmental factors as multi-dimensional analysis data, and output equipment failure probability and remaining life prediction.

[0047] The analysis and calculation module can accurately analyze energy usage and changing patterns through data cleaning, preprocessing and multi-dimensional analysis, explore inefficient energy utilization and improve efficiency, predict equipment failures in advance, and predict remaining life to optimize maintenance strategies, thereby providing comprehensive and reliable data support for refined energy management and scientific decision-making, achieving cost reduction, efficiency improvement and sustainable development.

[0048] In the terminal equipment display section, the results of in-depth analysis of energy data are presented intuitively through a variety of forms such as graphics, reports, dashboards, and maps, helping users quickly grasp the core information. Line charts and bar charts are used to present the time trend of energy usage, clearly showing the fluctuations in energy consumption in different time periods and regions; heat maps are used to intuitively mark the energy utilization efficiency of each area, with red highlighted areas representing low efficiency and green areas representing high efficiency areas, allowing users to quickly locate problems; the equipment health dashboard uses dynamic data cards to display the equipment operating status, fault warning probability, and remaining life prediction in real time, and uses Gantt charts to simulate the equipment life cycle, providing an intuitive basis for maintenance decisions. These visual display methods make complex energy data easy to understand and use, significantly improving users' decision-making efficiency and management level.

[0049] Based on the displayed energy data and analysis results, various applications such as energy monitoring, energy scheduling, energy optimization, energy forecasting, and energy management can be realized.

[0050] The energy monitoring link monitors the production, transmission and consumption of energy in real time, displays the operating status of equipment and changes in energy data in real time, promptly detects abnormal situations and issues early warnings; queries and analyzes historical records of energy data to understand the operating trends and changing patterns of energy, and provides historical data support for energy management.

[0051] The energy dispatching link rationally arranges energy production plans based on energy demand forecasts and the operating status of energy production equipment to ensure a stable supply of energy; it rationally adjusts energy transmission plans based on the operating status of the energy transmission network and the distribution of energy demand to improve energy transmission efficiency and reliability.

[0052] The energy optimization phase analyzes the operating data of energy equipment to identify areas for equipment optimization, proposes equipment maintenance, transformation, and upgrade plans, and improves the energy efficiency of the equipment. It also analyzes and optimizes the overall operation of the energy system, such as optimizing the layout of the energy network, improving energy conversion efficiency, and optimizing energy storage and utilization, to achieve overall optimization of the energy system.

[0053] The energy forecasting phase predicts energy demand in the future based on historical energy data, economic development data, population growth data, and meteorological data, providing a basis for energy planning and decision-making; it predicts energy supply in the future based on energy resource reserves, development progress, and production capacity, providing a reference for energy supply security.

[0054] The energy management process generates various energy statistical reports, such as energy consumption reports and energy efficiency reports, to provide data support for energy management; establishes an energy management indicator system, such as energy consumption indicators and energy efficiency indicators, to evaluate and assess energy management performance; audits and evaluates energy usage, identifies problems and deficiencies in energy management, proposes improvement measures and suggestions, and improves the level of energy management.

[0055] like Figure 2 As shown, the present invention also provides a comprehensive energy data analysis method based on an intelligent platform, comprising: S1. Collect comprehensive energy data through various collection technologies, including energy production data, energy transmission data, energy consumption data and basic equipment information; S2. Classify and encode the comprehensive energy data and upload it to the distributed database; S3, perform quality monitoring and lifecycle management on the data in the database; S4. Perform calculation and analysis on the data in the database to obtain comprehensive analysis results and energy management indicators; S5. Generate comprehensive analysis results in graphics, reports, dashboards, and maps, and display them to users on terminal devices; S6. Implement specific energy management and decision-making functions based on energy management indicators.

[0056] Secondly: The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to conventional designs. The same embodiment and different embodiments of the present invention may be combined with each other without conflict. Finally: The above description is only a preferred embodiment of the present invention and is 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 scope of protection of the present invention.

Claims

1. A comprehensive energy data analysis system based on an intelligent platform, characterized in that: include: The data collection module is used to collect comprehensive energy data through various collection technologies, including energy production data, energy transmission data, energy consumption data and basic equipment information; The coding and storage module is used to classify and encode the comprehensive energy data and upload it to the distributed database; Monitoring and management module, used to perform quality monitoring and life cycle management on data in the database; The calculation and analysis module is used to perform calculation and analysis on the data in the database to obtain comprehensive analysis results and energy management indicators; The result display module is used to display the comprehensive analysis results to users on terminal devices by generating graphics, reports, dashboards, and maps; Implementation application module is used to implement specific energy management and decision-making functions based on energy management indicators.

2. The integrated energy data analysis system based on an intelligent platform according to claim 1, characterized in that: The comprehensive energy data are specifically as follows: energy production data includes primary energy input and secondary energy output; energy transmission data includes energy input, energy output, transmission method, and transmission path; energy consumption data includes total energy consumption and total industrial output value; equipment information includes basic equipment information, operation and maintenance information, energy-consuming equipment type, and energy efficiency health status.

3. The integrated energy data analysis system based on an intelligent platform according to claim 1, characterized in that: The quality control method is to eliminate duplicates and outliers, and verify integrity and logical consistency; Set indicators, compare with standard values ​​or historical data, and mark problematic data; Trace the source of the problem, correct the incorrect data and record the processing process; Track data fluctuations in real time through threshold alarms and trend analysis, triggering response mechanisms; Regularly evaluate monitoring models, iterate rules, and improve data quality.

4. The integrated energy data analysis system based on an intelligent platform according to claim 1, characterized in that: Lifecycle management refers to: standardizing the collection process and labeling metadata during the creation phase; hierarchical management and encryption protection based on frequency of use during the storage phase; using permission control to ensure the safe circulation of data during the use phase, combining analysis to mine the value of data, and archiving historical data according to rules; for data that exceeds the retention period, irreversible deletion or physical destruction is completed in accordance with compliance processes, and operational audit records are retained.

5. The integrated energy data analysis system based on an intelligent platform according to claim 1, characterized in that: The method of performing computational analysis on the data is: after cleaning and preprocessing the data, the data is subjected to energy consumption analysis, energy efficiency analysis, and equipment health analysis, and the energy usage and change patterns, utilization efficiency, equipment failure and remaining life prediction are obtained.

6. The integrated energy data analysis system based on an intelligent platform according to claim 5, characterized in that: The energy consumption analysis refers to: analyzing the total energy consumption, consumption structure, and consumption trends; counting the total energy consumption by time dimension, generating trend charts to observe changes in the total amount, identifying peak and low energy consumption periods, and analyzing their correlation with production activities and weather; counting the consumption proportions by energy type, including electricity, natural gas, fuel oil, and renewable energy, drawing pie charts or stacked bar charts, and breaking down energy consumption by department or equipment to compare efficiency differences between departments; based on historical data, using time series models to fit energy consumption trends, identifying long-term trends or cyclical fluctuations, and establishing regression models between energy consumption and external variables, analyzing driving factors, and combining business planning to predict future energy consumption and structural changes, and setting energy-saving targets.

7. The integrated energy data analysis system based on an intelligent platform according to claim 5, characterized in that: The energy efficiency analysis refers to: analyzing the production efficiency, transmission efficiency, and consumption efficiency of energy; Production efficiency can be expressed as: ; Where V1 is the production efficiency, M is the secondary energy output, and m is the primary energy input; The transmission efficiency can be expressed as: ; Where V2 is the transmission efficiency, N is the energy input, and n is the energy output; Consumption efficiency can be expressed as: ; Among them, V3 is consumption efficiency, K is total energy consumption, and H is total industrial output value.

8. The comprehensive energy data analysis method based on an intelligent platform according to claim 1, characterized in that: The energy management and decision-making functions refer to various applications including energy monitoring, energy scheduling, energy optimization, energy forecasting, and energy management.

9. A comprehensive energy data analysis method based on an intelligent platform, characterized in that: include: S1. Collect comprehensive energy data through various collection technologies, including energy production data, energy transmission data, energy consumption data and basic equipment information; S2. Classify and encode the comprehensive energy data and upload it to the distributed database; S3, perform quality monitoring and lifecycle management on the data in the database; S4. Perform calculation and analysis on the data in the database to obtain comprehensive analysis results and energy management indicators; S5. Generate comprehensive analysis results in graphics, reports, dashboards, and maps, and display them to users on terminal devices; S6. Implement specific energy management and decision-making functions based on energy management indicators.

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