Energy data management system and method based on simulation platform

Abstract

The present invention discloses an energy data management system and method based on a simulation platform, belonging to the technical field of energy management. The system comprises a data acquisition module, an energy analysis and optimization module, a simulation prediction module, an automation control module and a data visualization module. The data acquisition module is responsible for collecting real-time energy data from various data sources; the energy analysis and optimization module is responsible for analyzing the collected energy data and adjusting the equipment operation parameters according to the analysis results; the simulation prediction module is responsible for establishing a simulation model of the energy system based on the collected energy data to predict the energy consumption; the automation control module is responsible for optimizing and regulating energy consumption by controlling the operation status and parameters of the equipment; the data visualization module is responsible for visually displaying the collected data and analysis results to provide a user-friendly interface and realize human-computer interaction.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management, and in particular to an energy data management system and method based on a simulation platform. Background Art

[0002] Energy refers to the material or energy form that exists in nature and can be transformed and used in various activities such as production, transportation, lighting, heating, driving machinery, etc. Energy is an important material basis for supporting the development and survival of human society. It exists in various forms, including but not limited to fossil energy, nuclear energy, hydropower, wind energy, solar energy, bioenergy, etc.; in the process of human social development, energy is widely used in various fields of production and life, such as industrial production, transportation, home heating, electricity production, etc.; the use of energy directly affects the development of social economy and environmental sustainability; therefore, the efficient use of energy and the development and utilization of renewable energy have become one of the important issues of today's society and environmental protection.

[0003] In the entire life cycle of traditional refrigeration room construction, the adjustment of operating parameters of refrigeration equipment usually relies on manual intervention or fixed time intervals, lacking real-time and personalization, making it impossible to accurately predict and adjust energy consumption, resulting in energy waste.

[0004] Therefore, current energy data management requires a more advanced and integrated system to solve these problems, and the energy data management system and method based on a simulation platform of the present invention aims to provide a new and more efficient solution. Summary of the invention

[0005] The purpose of the present invention is to provide an energy data management system and method solution based on a simulation platform to solve the problems raised in the above background technology.

[0006] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: an energy data management system based on a simulation platform, comprising a data acquisition module, an energy analysis and optimization module, a simulation prediction module, an automation control module and a data visualization module;

[0007] The data acquisition module is responsible for collecting the operating data and environmental data of different equipment in the refrigeration room, and transmitting the collected data to the energy analysis and optimization module and the simulation prediction module; the energy analysis and optimization module is responsible for analyzing the collected energy data, outputting instructions for adjusting the equipment operating parameters according to the analysis results, and transmitting the instruction information to the automation control module; the simulation prediction module is responsible for establishing a simulation model of the energy system based on the collected energy data to predict the energy consumption, and transmitting the predicted energy consumption to the data visualization module; the automation control module is responsible for optimizing and regulating the refrigeration room equipment by controlling the operating status and parameters of the equipment; the data visualization module is responsible for visually displaying the collected energy data and its analysis results, and providing users with an interactive graphical interface.

[0008] The data acquisition module includes an equipment data acquisition unit and an environment data acquisition unit;

[0009] The equipment data acquisition unit is used to collect the output power, working efficiency and running time of different equipment; the environmental data acquisition unit is responsible for collecting the temperature data in the refrigeration room and the required temperature data of the chilled water.

[0010] The equipment data acquisition unit connects to various refrigeration equipment and collects key parameters such as output power, work efficiency and running time in real time. This data is essential for understanding the performance and operating status of the equipment. By monitoring and analyzing equipment data, the system can promptly detect equipment failures, optimize equipment operating parameters to improve efficiency, and formulate more effective maintenance plans to extend equipment life. The environmental data acquisition unit is responsible for collecting environmental data of the refrigeration room, including temperature data and chilled water demand temperature data. This data is essential for the energy consumption and efficiency of the refrigeration room; by real-time monitoring and analysis of environmental data, the system can adjust the operating parameters of the refrigeration equipment according to actual needs to maintain a stable temperature in the refrigeration room and ensure that the demand temperature of chilled water is met. In addition, environmental data can also be used to predict future energy consumption and help formulate more effective energy management strategies.

[0011] The energy analysis and optimization module includes a data cleaning and processing unit, an energy data analysis unit and an operation parameter adjustment unit;

[0012] The data cleaning and processing unit is used to clean and process the temperature data collected in the refrigeration room; the energy data analysis unit is used to analyze the collected energy data and identify the patterns and trends of energy consumption based on the time series prediction algorithm; the operating parameter adjustment unit is used to adjust the operating parameters of the refrigeration room equipment in real time according to the results of the energy data analysis.

[0013] The simulation prediction module includes an energy simulation modeling unit and an energy consumption prediction unit;

[0014] The energy simulation modeling unit is used to establish a simulation model of the energy system based on the collected energy data, including equipment and system topology; the energy consumption prediction unit is used to use the established simulation model and predict energy consumption.

[0015] The energy simulation modeling unit builds a simulation model of the energy system based on the collected real-time energy data and using advanced modeling technology. These models cover the topological structure, operating parameters and mutual correlation of various energy devices and systems. Through simulation, users can fully understand the operating characteristics of the energy system, including the interaction between devices, the energy transmission process and the stability of the system, thereby providing an important reference for subsequent energy optimization and decision-making. The energy consumption prediction unit uses the established simulation model, combined with historical energy data and future prediction information, to predict energy consumption in the future. Through the simulation of the energy system, the prediction unit can simulate the energy consumption in different scenarios, and predict the changing trend of future energy demand and the magnitude of energy consumption based on factors such as system status, external environmental factors and user needs, to help users make corresponding energy management decisions, including resource allocation, scheduling arrangements and energy-saving optimization strategies.

[0016] The automation control module includes an equipment operation control unit and an automation decision-making unit;

[0017] The equipment operation control unit is used to optimize and regulate energy consumption by controlling the operating status and parameters of the equipment; the automated decision-making unit is used to obtain energy consumption prediction results in real time. If the energy consumption prediction results are greater than the preset standard energy consumption, the operation of the refrigeration room equipment is automatically terminated.

[0018] The data visualization module includes a data display unit and a user interaction interface unit;

[0019] The data display unit is used to display the collected energy data, analysis results and prediction information; the user interaction interface unit is used to perform human-computer interaction, allowing users to query, analyze and operate energy data.

[0020] The data display unit is an important component that presents the energy data, analysis results and forecast information obtained from the data acquisition module to the user in a visual form such as graphics and charts. Through various visualization methods, such as line graphs, bar graphs, pie charts, etc., users can intuitively understand the energy usage, trend changes and performance of key indicators, so as to better understand the energy consumption situation and optimization potential. The user interaction interface unit is a window for users to interact with the system. Through various operations and instructions on the interface, users can query, analyze and operate energy data to achieve personalized energy management and optimization. The interface usually has a friendly graphical interface and intuitive operation mode, allowing users to easily browse data, adjust parameters, generate reports, etc., so as to better participate in the energy management process.

[0021] An energy data management method based on a simulation platform, the energy data management method comprising the following steps:

[0022] S1. Collect equipment data and environmental data in the refrigeration room, and clean and process the collected environmental data;

[0023] S2. Calculate adjustment parameters based on environmental data, and automatically adjust output power parameters of the device based on the adjustment parameters and device data;

[0024] S3. In the simulation platform, predict the future energy consumption of the refrigeration room according to the adjusted output power parameters;

[0025] S4. Visualize the collected energy data and its analysis results.

[0026] In step S1, the equipment data and environmental data in the refrigeration room are collected in the following manner:

[0027] First, determine the startup time of the equipment to be collected in the refrigeration room, and collect the equipment data in real time based on the startup time of the equipment to be collected. The equipment data is obtained in real time from the monitoring equipment, and the environmental data is obtained in real time from the local data storage device;

[0028] The data cleaning and processing of the collected environmental data includes data cleaning and processing of the temperature data in the refrigeration room. The data cleaning process adopts the following dynamic interval interpolation method:

[0029] Assume that the temperature data set collected in the refrigeration room is {x1,x2,…,x n}, where x1 represents the first temperature data in the temperature data set in the refrigeration room, x n represents the nth temperature data in the temperature data set in the refrigeration room, and the missing value x for the i-th data i, i = 1, 2, ..., n, first define a dynamic interval [L, R], where L represents the number of steps to the left, R represents the number of steps to the right, and search for the two nearest non-missing values ​​x in the interval [L, R]. j and x u , where j<i<u, according to the non-missing value x j and x u Calculate missing values ​​x i The calculation formula is as follows:

[0030]

[0031] Finally, the missing values ​​are filled to obtain the temperature data set in the refrigeration room after cleaning.

[0032] In step S2, the adjustment parameter is calculated based on the temperature data in the following manner:

[0033] First, obtain the output power of the current refrigeration equipment as P cu , the actual required chilled water temperature in the refrigeration room is T in , the refrigeration room temperature is P out , the calculation formula of the adjustment parameter k is as follows:

[0034]

[0035] Among them, the adjustment parameter k is a parameter for adjusting the output power of the energy supply equipment, e is a natural constant, α is a control parameter used to adjust the degree of influence of the temperature difference change on the adjustment parameter k, and α∈[0,1];

[0036] The output power parameter P of the equipment is automatically controlled based on the adjustment parameters and equipment data out The calculation is done using the following formula:

[0037] P out =P cu ×(1+k).

[0038] In step S3, in the simulation platform, the energy consumption is predicted based on the adjusted output power data and is calculated using the following formula:

[0039]

[0040] Where E is the total energy consumption of the refrigeration room equipment, b is the number of equipment in the refrigeration room, N a is the working efficiency of the ath device, P out_a is the output power data of the ath device after adjustment, Δt a is the running time of the ath device, η ais the energy consumption coefficient of the ath device under unit output power;

[0041] In step S4, the collected energy data and analysis results are visualized, including displaying environmental data and energy forecasts during the refrigeration process, and the environmental data is represented in the form of a curve graph, and the energy forecast is represented in the form of a statistical chart.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0043] 1. The system adjusts the equipment operating parameters according to the analysis results to achieve intelligent optimization of energy consumption. Compared with the traditional static energy management method, the system can dynamically adjust the equipment operating parameters according to real-time data and analysis results, thereby improving energy utilization efficiency;

[0044] 2. The simulation prediction module can build a simulation model of the energy system based on the collected energy data and predict the energy consumption in the future. This enables energy managers to make adjustments and optimizations in advance to avoid mismatches between energy supply and demand;

[0045] 3. The automation control module can optimize and regulate energy consumption by controlling the operating status and parameters of the equipment. Compared with the traditional manual control method, the system can achieve more accurate and timely energy control, improving the efficiency and accuracy of energy management;

[0046] 4. The data visualization module can visualize the collected data and analysis results, and provide a user-friendly interface to achieve human-computer interaction. This enables energy managers to more intuitively understand energy usage and analysis results, so as to better formulate energy management strategies and decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0048] Figure 1 It is a schematic diagram of the module composition of an energy data management system based on a simulation platform of the present invention;

[0049] Figure 2 It is a schematic flow chart of the steps of an energy data management method based on a simulation platform of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0051] like Figure 1-2 As shown, the present invention provides the following technical solutions:

[0052] An energy data management system based on a simulation platform, comprising a data acquisition module, an energy analysis and optimization module, a simulation prediction module, an automation control module and a data visualization module;

[0053] The data acquisition module is responsible for collecting the operating data and environmental data of different equipment in the refrigeration room, and transmitting the collected data to the energy analysis and optimization module and the simulation prediction module; the energy analysis and optimization module is responsible for analyzing the collected energy data, outputting instructions for adjusting the equipment operating parameters according to the analysis results, and transmitting the instruction information to the automation control module; the simulation prediction module is responsible for establishing a simulation model of the energy system based on the collected energy data to predict the energy consumption, and transmitting the predicted energy consumption to the data visualization module; the automation control module is responsible for optimizing and regulating the refrigeration room equipment by controlling the operating status and parameters of the equipment; the data visualization module is responsible for visually displaying the collected energy data and its analysis results, and providing users with an interactive graphical interface.

[0054] The data acquisition module includes an equipment data acquisition unit and an environment data acquisition unit;

[0055] The equipment data collection unit is used to collect the output power, working efficiency and running time of different equipment; the environmental data collection unit is responsible for collecting the temperature data in the refrigeration room and the required temperature data of the chilled water.

[0056] The equipment data acquisition unit connects to various refrigeration equipment and collects key parameters such as output power, work efficiency and running time in real time. This data is essential for understanding the performance and operating status of the equipment. By monitoring and analyzing equipment data, the system can promptly detect equipment failures, optimize equipment operating parameters to improve efficiency, and formulate more effective maintenance plans to extend equipment life. The environmental data acquisition unit is responsible for collecting environmental data of the refrigeration room, including temperature data and chilled water demand temperature data. This data is essential for the energy consumption and efficiency of the refrigeration room; by real-time monitoring and analysis of environmental data, the system can adjust the operating parameters of the refrigeration equipment according to actual needs to maintain a stable temperature in the refrigeration room and ensure that the demand temperature of chilled water is met. In addition, environmental data can also be used to predict future energy needs and help formulate more effective energy management strategies.

[0057] The energy analysis and optimization module includes a data cleaning and processing unit, an energy data analysis unit, and an operation parameter adjustment unit;

[0058] The data cleaning and processing unit is used to clean and process the temperature data collected in the refrigeration room; the energy data analysis unit is used to analyze the collected energy data and identify the patterns and trends of energy consumption based on the time series prediction algorithm; the operating parameter adjustment unit is used to adjust the operating parameters of the refrigeration room equipment in real time according to the results of the energy data analysis.

[0059] The simulation prediction module includes an energy simulation modeling unit and an energy consumption prediction unit;

[0060] The energy simulation modeling unit is used to establish a simulation model of the energy system based on the collected energy data, including equipment and system topology; the energy consumption prediction unit is used to use the established simulation model and predict energy consumption.

[0061] The energy simulation modeling unit builds a simulation model of the energy system based on the collected real-time energy data and using advanced modeling technology. These models cover the topological structure, operating parameters and mutual correlation of various energy devices and systems. Through simulation, users can fully understand the operating characteristics of the energy system, including the interaction between devices, the energy transmission process and the stability of the system, thereby providing an important reference for subsequent energy optimization and decision-making. The energy consumption prediction unit uses the established simulation model, combined with historical energy data and future prediction information, to predict energy consumption in the future. Through the simulation of the energy system, the prediction unit can simulate the energy consumption in different scenarios, and predict the changing trend of future energy demand and the magnitude of energy consumption based on factors such as system status, external environmental factors and user needs, to help users make corresponding energy management decisions, including resource allocation, scheduling arrangements and energy-saving optimization strategies.

[0062] The automation control module includes an equipment operation control unit and an automation decision-making unit;

[0063] The equipment operation control unit is used to optimize and regulate energy consumption by controlling the operating status and parameters of the equipment; the automated decision-making unit is used to obtain energy consumption forecast results in real time. If the energy consumption forecast result is greater than the preset standard energy consumption, the operation of the refrigeration room equipment is automatically terminated.

[0064] The data visualization module includes a data display unit and a user interaction interface unit;

[0065] The data display unit is used to display the collected energy data, analysis results and prediction information; the user interaction interface unit is used for human-computer interaction, allowing users to query, analyze and operate energy data.

[0066] The data display unit is an important component that presents the energy data, analysis results and forecast information obtained from the data acquisition module to the user in a visual form such as graphics and charts. Through various visualization methods, such as line graphs, bar graphs, pie charts, etc., users can intuitively understand the energy usage, trend changes and performance of key indicators, so as to better understand the energy consumption situation and optimization potential. The user interaction interface unit is a window for users to interact with the system. Through various operations and instructions on the interface, users can query, analyze and operate energy data to achieve personalized energy management and optimization. The interface usually has a friendly graphical interface and intuitive operation mode, allowing users to easily browse data, adjust parameters, generate reports, etc., so as to better participate in the energy management process.

[0067] An energy data management method based on a simulation platform, the energy data management method comprising the following steps:

[0068] S1. Collect equipment data and environmental data in the refrigeration room, and clean and process the collected environmental data;

[0069] S2. Calculate adjustment parameters based on environmental data, and automatically adjust output power parameters of the device based on the adjustment parameters and device data;

[0070] S3. In the simulation platform, predict the future energy consumption of the refrigeration room according to the adjusted output power parameters;

[0071] S4. Visualize the collected energy data and its analysis results.

[0072] In step S1, the equipment data and environmental data in the refrigeration room are collected in the following manner:

[0073] First, determine the startup time of the equipment to be collected in the refrigeration room, and collect the equipment data in real time based on the startup time of the equipment to be collected. The equipment data is obtained in real time from the monitoring equipment, and the environmental data is obtained in real time from the local data storage device;

[0074] The collected environmental data is cleaned and processed, including the temperature data in the refrigeration room. The data cleaning process uses the following dynamic interval interpolation method:

[0075] Assume that the temperature data set collected in the refrigeration room is {x1,x2,…,x n}, where x1 represents the first temperature data in the temperature data set in the refrigeration room, x n represents the nth temperature data in the temperature data set in the refrigeration room, and the missing value x for the i-th data i , i = 1, 2, ..., n, first define a dynamic interval [L, R], where L represents the number of steps to the left, R represents the number of steps to the right, and search for the two nearest non-missing values ​​x in the interval [L, R]. j and x u , where j<i<u, according to the non-missing value x j and x u Calculate missing values ​​x i The calculation formula is as follows:

[0076]

[0077] Finally, the missing values ​​are filled to obtain the temperature data set in the refrigeration room after cleaning.

[0078] In step S2, the adjustment parameter is calculated based on the temperature data using the following calculation method:

[0079] First, obtain the output power of the current refrigeration equipment as P cu , the actual required chilled water temperature in the refrigeration room is T in , the refrigeration room temperature is T out , the calculation formula of the adjustment parameter k is as follows:

[0080]

[0081] Among them, the adjustment parameter k is a parameter for adjusting the output power of the energy supply equipment, e is a natural constant, α is a control parameter used to adjust the degree of influence of the temperature difference change on the adjustment parameter k, and α∈[0,1];

[0082] Automatically control the output power parameter P of the equipment based on adjustment parameters and equipment data out The calculation is done using the following formula:

[0083] P out =P cu ×(1+k).

[0084] In step S3, in the simulation platform, the energy consumption is predicted based on the adjusted output power data using the following formula:

[0085]

[0086] Where E is the total energy consumption of the refrigeration room equipment, b is the number of equipment in the refrigeration room, N a is the working efficiency of the ath device, P out_a is the output power data of the ath device after adjustment, Δt a is the running time of the ath device, η a is the energy consumption coefficient of the ath device under unit output power;

[0087] In step S4, the collected data and analysis results are visualized, including displaying the environmental data and energy forecast during the refrigeration process, and the environmental data is represented in the form of a curve graph, and the energy forecast is represented in the form of a statistical chart.

[0088] Embodiment 1:

[0089] Assume that the following temperature data set in the refrigeration room is obtained: {20, 25, missing, 30, missing, 40, 45}, and define the dynamic interval as [3, 3], that is, search 3 data to the left and 3 data to the right:

[0090] For the first missing value x3: Search for the two nearest non-missing values ​​x2=25 and x4=30 in the dynamic interval, and substitute these values ​​into the dynamic interval interpolation formula to obtain:

[0091]

[0092] For the second missing value x5: Search for the two nearest non-missing values ​​x4=30 and x6=40 in the dynamic interval, and substitute these values ​​into the dynamic interval interpolation formula to obtain:

[0093]

[0094] Therefore, through the dynamic interval interpolation method, the new temperature data set obtained is {20, 25, 27.5, 30, 35, 40, 45}.

[0095] Assume that the output power P of the current refrigeration equipment cu =100kW, the actual required chilled water temperature T in the refrigeration room in =5℃, refrigeration room temperature T out=25℃, control parameter α=0.5, the number of devices is 2, among which the working efficiency η1 of device 1 is 0.9, the energy consumption coefficient under unit output power is N1=0.8, and the operating time Δt1=10 hours; the working efficiency η2 of device 2 is 0.85, the energy consumption coefficient under unit output power is N2=0.75, and the operating time Δt2=8 hours: First, calculate the adjustment parameter k:

[0096]

[0097] Then, calculate the output power parameter P of the device out :

[0098] P out =P cu ×(1+k)≈100.0045;

[0099] Finally, the energy consumption is predicted based on the obtained output power data:

[0100]

[0101] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0102] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An energy data management method based on a simulation platform, characterized in that: The energy data management method comprises the following steps: S1. Collect equipment data and environmental data in the refrigeration room, and clean and process the collected environmental data; S2. Calculate adjustment parameters based on environmental data, and automatically adjust output power parameters of the device based on the adjustment parameters and device data; S3. In the simulation platform, predict the future energy consumption of the refrigeration room according to the adjusted output power parameters; S4. Visualize the collected energy data and its analysis results; In step S2, the adjustment parameter is calculated based on the environmental data in the following manner: First, obtain the output power of the current refrigeration equipment as P cu , the actual required chilled water temperature in the refrigeration room is T in , the temperature in the refrigeration room is T out , the calculation formula of the adjustment parameter k is as follows: Among them, the adjustment parameter k is a parameter for adjusting the output power of the energy supply equipment, e is a natural constant, α is a control parameter used to adjust the degree of influence of the temperature difference change on the adjustment parameter k, and α∈[0,1]; The output power parameter P of the device is automatically controlled based on the adjustment parameters and device data out The calculation is done using the following formula: P out =P cu ×(1+k)。 2. The energy data management method based on the simulation platform according to claim 1 is characterized in that: In step S1, the equipment data and environmental data in the refrigeration room are collected in the following manner: First, determine the startup time of each device to be collected in the refrigeration room, and collect device data in real time based on the startup time of the device to be collected. The device data is obtained in real time from the monitoring device, and the environmental data is obtained in real time from the local data storage device; The data cleaning and processing of the collected environmental data includes data cleaning and processing of the temperature data in the refrigeration room. The data cleaning process adopts the following dynamic interval interpolation method: Suppose the temperature data set obtained in the refrigeration machine room is {x1, x2, …, x n}, where x1 represents the first temperature data in the temperature data set of the refrigeration machine room, and x n represents the nth temperature data in the temperature data set of the refrigeration machine room. For the missing value x i of the ith temperature data, i = 1, 2, …, n, first define a dynamic interval [L, R], where L represents the number of steps for left search and R represents the number of steps for right search. Search for the two nearest non-missing values x j and x u within the interval [L, R], where j < i < u. According to the non-missing values x j and x u , the calculation formula for the missing value x i is as follows: Finally, the missing values ​​are filled to obtain the temperature data set in the refrigeration room after cleaning.

3. The energy data management method based on the simulation platform according to claim 1 is characterized in that: In step S3, in the simulation platform, the energy consumption is predicted based on the adjusted output power parameters using the following formula: Where E is the total energy consumption of the refrigeration room equipment, b is the number of equipment in the refrigeration room, N a is the working efficiency of the ath device, P out_a is the output power data of the ath device after adjustment, Δt a is the running time of the ath device, η a is the energy consumption coefficient of the ath device under unit output power; In step S4, the collected energy data and analysis results are visualized, including displaying environmental data and energy forecasts during the refrigeration process, and the environmental data is represented in the form of a curve graph, and the energy forecast is represented in the form of a statistical chart.

4. An energy data management system based on a simulation platform, the system is applied to the energy data management method based on a simulation platform according to claim 1, characterized in that: It includes data acquisition module, energy analysis and optimization module, simulation prediction module, automation control module and data visualization module; The data acquisition module is responsible for collecting the operating data and environmental data of different equipment from the refrigeration room, and transmitting the collected data to the energy analysis and optimization module and the simulation prediction module; The energy analysis and optimization module is responsible for analyzing the collected energy data, outputting instructions for adjusting the equipment operating parameters according to the analysis results, and transmitting the instruction information to the automation control module; the simulation prediction module is responsible for establishing a simulation model of the energy system based on the collected energy data to predict energy consumption, and transmitting the predicted energy consumption to the data visualization module; The automation control module is responsible for optimizing and regulating the refrigeration room equipment by controlling the operating status and parameters of the equipment; The data visualization module is responsible for visually displaying the collected energy data and its analysis results, and providing users with an interactive graphical interface.

5. The energy data management system based on the simulation platform according to claim 4 is characterized in that: The data acquisition module includes an equipment data acquisition unit and an environment data acquisition unit; The equipment data acquisition unit is used to collect the output power, working efficiency and running time of different equipment; the environmental data acquisition unit is responsible for collecting the temperature data in the refrigeration room and the required temperature data of the chilled water.

6. The energy data management system based on the simulation platform according to claim 4 is characterized in that: The energy analysis and optimization module includes a data cleaning and processing unit, an energy data analysis unit and an operation parameter adjustment unit; The data cleaning and processing unit is used to clean and process the temperature data collected in the refrigeration room; the energy data analysis unit is used to analyze the collected energy data and use a time series prediction algorithm to identify patterns and trends in energy consumption; the operating parameter adjustment unit is used to adjust the operating parameters of the refrigeration room equipment in real time according to the results of the energy data analysis.

7. The energy data management system based on the simulation platform according to claim 4 is characterized in that: The simulation prediction module includes an energy simulation modeling unit and an energy consumption prediction unit; The energy simulation modeling unit is used to establish a simulation model of the energy system based on the collected energy data, including equipment and system topology; the energy consumption prediction unit is used to predict energy consumption using the established simulation model of the energy system.

8. The energy data management system based on the simulation platform according to claim 4 is characterized in that: The automation control module includes an equipment operation control unit and an automation decision-making unit; The equipment operation control unit is used to optimize and regulate energy consumption by controlling the operating status and parameters of the equipment; the automated decision-making unit is used to obtain energy consumption forecast results in real time. If the energy consumption forecast results are greater than the preset standard energy consumption, the operation of the refrigeration room equipment is automatically terminated.

9. The energy data management system based on the simulation platform according to claim 4, characterized in that: The data visualization module includes a data display unit and a user interaction interface unit; The data display unit is used to display the collected energy data, analysis results and prediction information; The user interaction interface unit is used for human-computer interaction, allowing users to query, analyze and operate energy data.

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