I. Project Introduction The emergence of industrial big data has provided a new research perspective for tapping into the energy-saving potential of enterprises and assessing their comprehensive energy efficiency. Deep learning based on massive multi-source heterogeneous energy metering big data can refine the analysis of comprehensive energy efficiency at various levels of production processes, deeply mining the potential for improving comprehensive energy efficiency from different scales such as equipment, procedures, and systems. At the equipment level, optimal process parameters or control parameters are identified to improve the energy conversion rate of power supply equipment and the energy utilization rate of energy-consuming equipment based on equipment measurement data and operating conditions; at the procedure level, the correspondence between product characteristics and energy efficiency is explored, optimizing the connection relationship and production rhythm between procedures while meeting product quality and production requirements, reducing energy waste caused by low-load operation of equipment; at the system level, long-term energy efficiency plans oriented towards energy saving are excavated, according to the overall energy efficiency situation of the enterprise and its development planning, Through big data analysis, optimize energy supply solutions and reasonably match the development requirements of enterprise production capacity. Typical cases: (1) Shanghai Chlor-Alkali Chemical Co., Ltd. - Electrolysis Cell Energy Saving Potential Analysis Applying big data analysis to the F2 ion membrane workshop of the company's electrochemicals plant, by analyzing a large amount of production condition data of electrolysis cells, find out the important key factors affecting the electricity consumption per ton of caustic soda and alkali concentration, thereby providing the best operating parameters for the electrolysis cells, so as to reduce the electricity consumption of electrolysis as much as possible while ensuring the quality and yield of caustic soda. (2) Shanghai Heavy Machinery Factory Co., Ltd. - Heat Treatment Process Energy Consumption Prediction Analyzing the energy consumption data of the company's power department and forging plant, predicting the energy consumption of heating equipment under different process schemes, providing decision-making basis for the company to formulate the optimal process scheme oriented to energy saving. (3) Baosteel Industrial Furnace Engineering Technology Co., Ltd. - Analysis of Energy Saving Potential in Steel Industry Heating Furnaces: Constructing a heating furnace industrial big data ontology model to achieve comprehensive management of multi-source heterogeneous heating furnace big data based on semantic web; using neural network deep learning to construct a heating furnace process model with ton steel energy consumption as output, and applying genetic algorithm to obtain the optimal equipment and process parameters for reducing ton steel energy consumption of heating furnaces. (4) Shanghai Baosteel Energy Conservation and Environmental Protection Technology Co., Ltd. - Analysis of Energy Saving Potential in Waste Heat Boilers: Using semantic web technology for industrial big data integration of waste heat boilers. Taking the main steam production and dust emission of waste heat boilers as the analysis objects, using Sequential Minimal Optimization (SMO) algorithm to construct steam and dust models of waste heat boilers. Taking the maximum main steam flow rate and minimum outlet flue gas dust content as optimization objectives, The multi-objective particle swarm algorithm is adopted to optimize the operating parameters of waste heat boilers, providing a reference for adjusting the working conditions. II. Project industrialization prospects and application fields With the rapid development of internet technology, energy equipment can achieve end-to-end connectivity and generate massive amounts of data. The internet will integrate into every link of energy industrial production, including the collection, analysis, sharing, and processing of energy data, thereby bringing about tremendous changes in energy production, transmission, storage, and usage patterns, giving rise to an energy internet system. Compared to traditional energy systems, the entire energy efficiency of the energy internet system will rely more on the deep mining and processing of data. Facing the data characteristics of the energy internet system, research on big data analysis technology for the energy internet is conducted. Through the in-depth analysis and processing of energy big data, the potential for energy saving in enterprises is explored, and the dynamic allocation of energy production, transmission, and consumption is adjusted. Enhance the efficiency of the entire energy industry and the efficiency of energy use. The results are applicable to manufacturing enterprises of various scales and production characteristics. III. Expected Cooperation Methods and Investment: Adopt a cooperation model that combines industry, academia, and research institutions. 2-year investment: 2 million yuan, to carry out the collection, organization, and analysis of big data resources such as energy metering for typical production equipment and processes, mine the correlations between data, and provide enterprises with energy system optimization potential for energy saving.