Green energy management data application system

Abstract

According to the green energy management data application system provided by the invention, through integration of multi-link business data, comprehensive and accurate energy market information and enterprise energy consumption analysis are provided, a basis is provided for enterprise energy purchase decision making, enterprise energy consumption efficiency is improved, resource waste is reduced, and digital transformation of the energy industry is promoted; the problems that an existing system cannot solve the problems that an energy purchasing decision is lack of data support, enterprise energy consumption efficiency is low, and effective evaluation and optimization means are lacked are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy data processing and management, and particularly to a green energy management data application system. Background Art

[0002] In the field of energy management, currently enterprises face many challenges in energy procurement decision-making and energy consumption management. When making energy procurement decisions, it is difficult for enterprises to obtain comprehensive and accurate multi-dimensional information such as the price fluctuation trend of the energy market, the energy quality of suppliers, and stability. They mostly rely on limited channels and empirical judgments, lacking systematic data support, resulting in ineffective control of procurement costs and limited economic benefits and competitiveness. For example, traditional energy procurement methods are mainly based on past cooperation relationships or limited market research, and cannot accurately grasp the dynamic changes in the market.

[0003] In terms of enterprise energy consumption management, for heat-consuming enterprises, there is a lack of effective means to accurately identify uneven heat consumption, and it is difficult to comprehensively explore the room for improving the energy utilization efficiency of boiler rooms. There is a lack of intelligent solutions in aspects such as equipment operation optimization, performance evaluation and improvement of combustion systems and make-up water systems, resulting in problems such as low combustion efficiency and water resource waste; for enterprises waiting to consume energy, there is a lack of an objective and scientific yardstick to evaluate heat suppliers, it is difficult to accurately select a suitable steam boiler, and there is a lack of data support for energy-saving space and optimization directions, and it is impossible to effectively reduce energy costs and improve production efficiency. Currently, systems for data receipt and management of energy problems generally manage data applications through data collection, data analysis, and result output. For example, the publicly disclosed patent 2024104150660 discloses an energy data collection device and its energy comprehensive monitoring and management system, which analyzes energy problems through a data receiving module, a data storage module, a data analysis module, and a user interface module. However, it has problems such as lack of data support for energy procurement decisions, low energy consumption efficiency of enterprises, and lack of effective evaluation and optimization means. Therefore, it is particularly important to solve the problems that the existing system cannot solve, such as lack of data support for energy procurement decisions, low energy consumption efficiency of enterprises, and lack of effective evaluation and optimization means. Summary of the Invention

[0004] The purpose of the present invention is to provide a green energy management data application system, which integrates business data of multiple links, provides comprehensive and accurate energy market information and enterprise energy consumption analysis, provides a basis for enterprise energy procurement decisions, improves enterprise energy consumption efficiency, reduces resource waste, promotes the digital transformation of the energy industry, and solves the problems that the existing system cannot solve, such as lack of data support for energy procurement decisions, low energy consumption efficiency of enterprises, and lack of effective evaluation and optimization means.

[0005] The present invention provides a green energy management data application system, including a data acquisition module, a data integration and analysis module, a decision support and optimization module, and a user interface module. The relevant data is collected by the data acquisition module and transmitted to the data integration and analysis module for integration processing, and algorithms are used for calculation. The calculation results are input into the decision support and optimization module to generate simulation decisions and optimization suggestions, and the analysis results and optimization suggestions are displayed to the user through the user interface module.

[0006] A further improvement lies in that: the data acquisition module includes a green waste collection module, a boiler and locomotive operation module, and an environment and environmental protection module, and different data is collected through the green waste collection module, the boiler and locomotive operation module, and the environment and environmental protection module.

[0007] A further improvement lies in that: the data integration and analysis module includes a data storage module, a data cleaning and transformation module, a data integration module, an energy market analysis module, and an enterprise energy consumption analysis module. The data collected by the data acquisition module is received and stored by the data storage module. The data in the data storage module is processed by the data cleaning and transformation module, and finally integrated by the data integration module and sent to the energy market analysis module and the enterprise energy consumption analysis module for analysis of different calculation results. Finally, the analyzed data is input into the decision support and optimization module.

[0008] A further improvement lies in that: the user interface module includes a data visualization interface and a user operation interface. The decision-making and optimization information is displayed through the data visualization interface, and the system is operated through the user operation interface.

[0009] A further improvement lies in that: the algorithms in the data integration and analysis module include analyzing the price fluctuation trend of the energy market, the energy quality data of different suppliers, the energy consumption law of the enterprise itself, and evaluating the energy stability and environmental protection index information of each supplier.

[0010] A further improvement lies in that: the data integration and analysis module accurately identifies the uneven heat consumption phenomenon for heat-using enterprises, comprehensively evaluates the energy utilization efficiency of the boiler house, and analyzes the performance of the combustion system and the make-up water system.

[0011] Data acquisition module: Collect relevant data from various business links such as green waste collection, boiler and locomotive operation, emergency plans for sudden environmental events, environmental protection, and sewage discharge, ensuring that the data sources are extensive and comprehensive, providing a basis for subsequent analysis.

[0012] Data Integration and Analysis Module: Integrates and processes the collected data, applies data analysis algorithms to deeply explore the data value. It includes analyzing the price fluctuation trends in the energy market, the energy quality data of different suppliers, the energy consumption patterns of the enterprise itself, and evaluating key information such as the energy stability and environmental protection indicators of each supplier; for heat-using enterprises, accurately identifying the uneven heat consumption phenomenon, comprehensively evaluating the energy utilization efficiency of the boiler room, and analyzing the performance of the combustion system and the water replenishment system.

[0013] Decision Support and Optimization Module: Based on the data analysis results, provides strong evidence for the enterprise's energy procurement decision-making, helps select high-quality and reasonably priced energy suppliers; provides intelligent transformation solutions for heat-using enterprises, such as optimizing equipment operation parameters, improving the performance of the combustion system and the water replenishment system to increase combustion efficiency and reduce water waste; provides a scientific yardstick for evaluating heat suppliers for enterprises waiting to use energy, assists in selecting suitable steam boilers, and analyzes the energy-saving space and optimization directions.

[0014] User Interaction Interface: Presents the analysis results and optimization suggestions of the data product in an intuitive and easy-to-understand manner, facilitating enterprise users to view and use, ensuring that enterprises can effectively utilize the data product for energy management decision-making.

[0015] The beneficial effects of the present invention are: (1) Enhancing the scientific nature of energy procurement decision-making By integrating multi-dimensional energy data, enterprises can accurately grasp the price fluctuation trends in the energy market and avoid high-price procurement caused by lagging or incomplete information. For example, in the application of a certain pilot enterprise, by analyzing historical price data and real-time market dynamics through the "Green Energy Heat Chain Treasure" system, the procurement cost was successfully reduced by about 15% during energy procurement.

[0016] Based on the evaluation of key information such as the energy quality, stability, and environmental protection indicators of suppliers, enterprises can select better-quality suppliers, ensure the reliability and stability of energy supply, reduce production losses caused by energy quality problems, and increase the overall production efficiency by about 20%.

[0017] (2) Improving the energy utilization efficiency of enterprises For heat-using enterprises, after accurately identifying the uneven heat consumption phenomenon and optimizing the equipment operation parameters, the energy utilization efficiency of the boiler room has been significantly improved. Through actual tests, the combustion efficiency has increased by about 25% on average, effectively reducing energy waste and lowering energy costs. At the same time, the evaluation and improvement of the performance of the water replenishment system have reduced water waste by about 30%.

[0018] Enterprises waiting to use energy can scientifically evaluate heat suppliers with the help of the system, select suitable steam boilers and implement energy-saving optimization measures, resulting in a reduction of about 20% in energy costs, a significant improvement in production benefits, and a shortening of the product production cycle by about 10%.

[0019] (3) Promoting the digital transformation of the industry It provides an example of a data-driven management model for the energy industry, stimulates the industry's attention to the application and innovation of energy data, encourages more enterprises to attach importance to energy data management, and accelerates the digital transformation process of the entire industry.

[0020] It promotes the circulation and sharing of data in the energy field, breaks data islands, maximizes the release of data value, and provides strong support for the market-oriented allocation of data elements in the energy industry. Description of the Drawings

[0021] Figure 1 It is the system module diagram of the present invention.

[0022] Figure 2 It is the overall system architecture and processing flow chart of the present invention. Detailed Implementation Manner

[0023] To deepen the understanding of the present invention, the present invention will be further described in detail below in conjunction with embodiments. These embodiments are only used to explain the present invention and do not limit the protection scope of the present invention.

[0024] Embodiment 1: Optimization Implementation of Energy Procurement Decision (1) Data Collection In the link of green waste collection, data such as waste weight, volume, and collection location are collected through sensors installed on transport vehicles and sorting centers. The data is transmitted to the data collection module. The sensor accuracy reaches ±1%, and the data collection frequency is once a day.

[0025] Data such as equipment operation time and fault records are obtained from the boiler and locomotive operation record systems, and the data integrity rate reaches over 99%. At the same time, emission data is obtained from environmental protection monitoring equipment, and permit index data is obtained from the pollutant discharge permit management system, etc.

[0026] (2) Data Integration and Analysis The data collection module transmits various types of data to the data integration and analysis module, and uses big data processing technology to clean, classify, and integrate the data. The time series data in different formats is unified into a standard format to ensure data accuracy and consistency.

[0027] The data analysis algorithm is used to analyze the price fluctuation trend of the energy market. Real-time price data is obtained by docking with a professional energy market data platform, and prediction analysis is carried out in combination with historical price data. The prediction accuracy rate reaches over 85%.

[0028] Evaluate the energy quality of different suppliers, establish a supplier evaluation model according to indicators such as heating stability, etc. The model comprehensively considers 10 key indicators, and the weights are determined by the analytic hierarchy process.

[0029] (3) Decision Support Based on the analysis results, an energy procurement decision report is generated for the enterprise, recommending high-quality suppliers and procurement opportunities. After a certain enterprise applied it, the procurement cost was reduced by 18% and the energy supply stability was improved by 30%.

[0030] Example 2: Implementation of energy efficiency improvement for heat-using enterprises (1) Identification of heat imbalance and efficiency evaluation The data acquisition module continuously collects temperature and heat distribution data of each area of ​​the boiler room through distributed temperature sensors. The sensor accuracy is ±0.3℃ and the collection range covers the entire boiler room.

[0031] The data integration and analysis module uses thermal imaging analysis technology to process the collected data, generate thermal distribution maps, and intuitively display the imbalance of heat use. At the same time, the energy utilization efficiency of the boiler room is evaluated, and an energy efficiency evaluation model is established. The model considers eight key factors such as combustion efficiency and heat transfer efficiency to calculate the overall energy utilization efficiency of the boiler room. Compared with the industry average, the company's energy utilization efficiency has increased by 28%.

[0032] (2) Implementation of intelligent transformation plan Based on the evaluation results, the decision support and optimization module provides enterprises with equipment operation parameter optimization solutions, such as adjusting the burner air-fuel ratio, optimizing the boiler water circulation speed, etc. The equipment operation parameters are automatically adjusted through the intelligent control system with an adjustment accuracy of ±1%.

[0033] After evaluating the performance of the combustion system and water supply system, improvement measures were proposed, such as replacing high-efficiency burners and optimizing the control logic of the water supply system. After the implementation of the improvement measures, the combustion efficiency increased by 26% and the water waste was reduced by 32%.

[0034] Example 3: Evaluation and selection of heating suppliers for energy-using enterprises (1) Establishment of data collection and evaluation standards for heat suppliers Collect information from historical heating data, equipment parameters, maintenance records, etc. provided by heating suppliers, and conduct comprehensive analysis based on other user feedback data. The historical heating data collection spans more than 3 years to ensure that the data is representative.

[0035] A standard evaluation system for heat suppliers has been established, including five dimensions: heat supply stability (weight 30%), energy efficiency (weight 25%), environmental protection indicators (weight 20%), service quality (weight 15%), and price rationality (weight 10%). Each dimension is broken down into a number of specific indicators, and the indicator weights are determined through expert scoring and statistical analysis of actual data.

[0036] (2) Evaluation and selection of heating suppliers The data integration and analysis module comprehensively evaluates and scores heat suppliers based on the collected data and the evaluation standard system, and generates a heat supplier evaluation report for energy-consuming enterprises to be served. Enterprises select suitable steam boiler heat suppliers according to the report, and the selection accuracy rate reaches over 95%.

[0037] During the subsequent cooperation process, continuously monitor the actual heat supply situation of heat suppliers, and optimize and adjust the evaluation criteria and models according to the actual data to ensure the accuracy and timeliness of the evaluation results.

Claims

1. A green energy management data application system, characterized by: It includes data acquisition module, data integration and analysis module, decision support and optimization module, and user interaction interface module. Relevant data is collected through the data acquisition module and transmitted to the data integration and analysis module for integration and processing. Algorithms are used for calculation, and the calculation results are input into the decision support and optimization module to generate simulated decisions and optimization suggestions. The analysis results and optimization suggestions are displayed to users through the user interaction interface module.

2. A green energy management data application system as claimed in claim 1, characterized in that: The data collection module includes a greening waste collection module, a boiler and locomotive operation module, and an environment and environmental protection module, and collects different data through the greening waste collection module, the boiler and locomotive operation module, and the environment and environmental protection module.

3. A green energy management data application system as claimed in claim 1, characterized in that: The data integration and analysis module includes a data storage module, a data cleaning and conversion module, a data integration module, an energy market analysis module and an enterprise energy consumption analysis module. The data storage module receives and stores the data collected by the data acquisition module. The data in the data storage module is processed by the data cleaning and conversion module, and finally integrated by the data integration module and sent to the energy market analysis module and the enterprise energy consumption analysis module for analysis of different calculation results. Finally, the analyzed data is input into the decision support and optimization module.

4. A green energy management data application system as claimed in claim 1, characterized in that: The user interaction interface module includes a data visualization interface and a user operation interface. The decision and optimization information are displayed through the data visualization interface, and the system is operated through the user operation interface.

5. A green energy management data application system as claimed in claim 1 or 3, characterized in that: The algorithms in the data integration and analysis module include analyzing the energy market price fluctuation trend, energy quality data of different suppliers, the energy consumption patterns of the enterprise itself, and evaluating the energy stability and environmental protection index information of each supplier.

6. A green energy management data application system as claimed in claim 5, characterized in that: The data integration and analysis module is targeted at heat-using enterprises, accurately identifies heat imbalance, comprehensively evaluates boiler room energy efficiency, and analyzes the performance of the combustion system and water supply system.

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