A method and system for simulating energy consumption of a refrigeration room

Abstract

The present invention provides an energy consumption simulation method and system for a refrigeration room, which is applied to the field of energy consumption data processing; by establishing a real-time monitoring and analysis system, the present invention enables the room to effectively integrate scattered energy consumption data, clarify peak and valley load periods, and dynamically adjust energy consumption forecasts and management strategies. This systematic approach combines environmental factors with equipment efficiency, deeply analyzes the energy consumption impact of various load types, ensures that the room can flexibly respond to dynamic changes in equipment, and reduce resource waste and operating costs. At the same time, by identifying key influencing factors and conducting combined load tests, managers can develop targeted cooling strategies and optimization plans, improve energy efficiency, and achieve data-driven decision support, thereby improving the overall operating efficiency and sustainable development capabilities of the room.

Description

Technical Field

[0001] The invention relates to the field of energy consumption data processing, and in particular to an energy consumption simulation method and system for a refrigeration room. Background Art

[0002] Many energy-consuming systems are used in large quantities, such as air conditioners, motors, boilers, etc. They have become the main targets of energy-saving transformation by energy-saving service companies due to their large usage, wide range of use, and large energy-saving potential.

[0003] At present, the energy consumption data of the computer room is usually scattered among various types of equipment and monitoring systems, which makes it difficult to fully reflect the actual energy consumption situation. It performs poorly when dealing with dynamic changes in equipment, especially during load peaks and troughs. Its energy consumption forecast often deviates from the actual value. This inaccuracy may lead to waste of resources and increased costs. Summary of the invention

[0004] The present invention aims to solve the problem that the energy consumption data of a computer room is usually scattered among various types of equipment and monitoring systems, which makes it difficult to fully reflect the actual energy consumption situation and performs poorly in response to dynamic changes in equipment, especially during load peaks and troughs, when the energy consumption forecast often deviates from the actual value. This inaccuracy may lead to waste of resources and increased costs. The present invention provides an energy consumption simulation method and system for a refrigeration room.

[0005] The present invention adopts the following technical means to solve the technical problem:

[0006] The present invention provides an energy consumption simulation method for a refrigeration room, comprising:

[0007] Based on the real-time monitored energy consumption data of the computer room, generate the load period of the equipment in the computer room in the current quarter, wherein the load period specifically includes the peak load period and the low load period;

[0008] Determining whether the predicted energy consumption value in the load period deviates from the preset energy consumption value;

[0009] If so, then according to the pre-entered computer room equipment information, the environmental data in the computer room is identified, the energy consumption impact trend of the computer room equipment is obtained according to the environmental data, the overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend are input into the pre-trained prediction model, and the energy consumption variable information of the computer room equipment is generated, wherein the computer room equipment information specifically includes power, efficiency curve and operation mode, the environmental data specifically includes temperature change, humidity change and seasonal change, and the overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data;

[0010] Determining whether at least two energy consumption variable events are detected in the energy consumption variable information;

[0011] If detected, the load type of the computer room equipment is identified, and the refrigeration working value during different load scenario tests is limited based on the load type. The energy consumption variable events are subjected to a combined load test to obtain energy consumption influencing factors under different load scenarios, wherein the load types specifically include servers, air conditioners, and lighting, the refrigeration working value specifically includes the load working range and load peak and valley values, and the energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication.

[0012] Furthermore, before the step of identifying the environmental data in the computer room, the step further includes:

[0013] Based on the preset equipment working areas in the computer room, identifying the heat generation data of each equipment in the different computer room from the equipment working areas, and dividing the heat dissipation areas of the different equipment in the computer room according to the heat generation data;

[0014] Determining whether the heat dissipation area has at least two identified heat sources;

[0015] If so, activate the preset sensor type according to the heat dissipation area, apply the preset multi-point sampling method, cross-validate the activated sensors, monitor the redundant data of the computer room equipment in real time, and dynamically adjust the sensor position parameters of the activated sensors based on the redundant data, wherein the sensor types specifically include temperature and humidity sensors, airflow sensors and redundant sensors, and the sensor position parameters specifically include sensor height and sensor angle.

[0016] Furthermore, the step of generating the energy consumption variable information of the equipment in the computer room further includes:

[0017] Based on the pre-recorded thermal image data in the computer room, identifying the regional status of different energy consumption areas in the computer room, wherein the regional status specifically includes a high heat density area and a low temperature demand area;

[0018] Determining whether the zone state matches the load resources preset in the energy consumption zone;

[0019] If not, obtain the real-time regional temperature demand of the energy consumption area, identify the energy consumption fluctuation information of the energy consumption area, and dynamically adjust the fan output parameters in the computer room according to the real-time regional temperature demand and the energy consumption fluctuation information, wherein the fan output parameters specifically include fan speed, fan direction, and fan temperature and humidity.

[0020] Furthermore, after the step of obtaining the energy consumption influencing factors under different load scenarios, the method further includes:

[0021] Regularly collect the equipment operation energy consumption data in the computer room based on a preset period, compare the equipment operation energy consumption data with a pre-generated computer room energy consumption baseline, and obtain energy consumption change data;

[0022] Determining whether the energy consumption change data exceeds a preset change threshold;

[0023] If so, obtain the computer room load energy consumption corresponding to the energy consumption change data, detect the load equipment in the computer room based on the computer room load energy consumption, and apply the preset load balancing to evenly distribute the computer room load energy consumption to the load equipment through the computer room server, wherein the load equipment specifically includes loaded equipment and non-load equipment.

[0024] Furthermore, the step of performing a combined load test on the energy consumption variable event further includes:

[0025] Based on the preset fault types, construct a fault load scenario for testing the computer room, wherein the fault types specifically include equipment overheating, network congestion, power failure, and cooling system failure;

[0026] Determine whether the fault load scenario can consume the corresponding energy consumption of the computer room;

[0027] If possible, generate a fault trigger condition for the fault load scenario, run the fault test content of the computer room according to the fault trigger condition, collect the energy consumption change process of the computer room equipment in the fault test content, and obtain the impact coefficient of the fault load scenario on cooling the computer room based on the energy consumption change process.

[0028] Furthermore, the step of determining whether the predicted energy consumption value in the load period deviates from the preset energy consumption value further includes:

[0029] Based on the hierarchical control preset by the computer room for the equipment in the computer room, the load computer room equipment with the upper limit of the load is detected by marking the server in the computer room, wherein the hierarchical control specifically includes local control and global control;

[0030] Determine whether the load room equipment can respond to the room server;

[0031] If possible, the load components in the load room equipment are identified, the load components are temporarily dormant, preset backup components are activated according to the load components, and preset redundant sensors and the backup components are used to maintain the closed-loop operation of the load room equipment.

[0032] Furthermore, the step of generating the load period of the equipment room equipment in the current quarter based on the real-time monitored equipment room energy consumption data further includes:

[0033] Based on the preset functional areas in the computer room, construct the load time period corresponding to the functional areas, wherein the functional areas specifically include a high-performance computing area, a storage area, and a network equipment area;

[0034] Determining whether the load period exceeds a preset period upper limit;

[0035] If so, the cooling strategy of the functional area is dynamically adjusted according to the load period, and the cooling efficiency in the computer room is adaptively optimized through the cooling strategy, wherein the cooling strategy specifically includes regional temperature monitoring and feedback, zoned cooling and resource allocation, and wind speed and direction adjustment.

[0036] The present invention also provides an energy consumption simulation system for a refrigeration room, comprising:

[0037] A generation module, used to generate the load period of the equipment in the computer room in the current quarter based on the real-time monitored energy consumption data of the computer room, wherein the load period specifically includes a load peak period and a load off-peak period;

[0038] A judgment module, used to judge whether the predicted energy consumption value in the load period deviates from the preset energy consumption value;

[0039] An execution module is used to identify environmental data in the computer room according to pre-entered computer room equipment information, obtain the energy consumption impact trend of the computer room equipment according to the environmental data, input the overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend into a pre-trained prediction model, and generate energy consumption variable information of the computer room equipment, wherein the computer room equipment information specifically includes power, efficiency curve and operation mode, the environmental data specifically includes temperature change, humidity change and seasonal change, and the overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data;

[0040] A second judgment module is used to judge whether at least two energy consumption variable events are detected in the energy consumption variable information;

[0041] The second execution module is used to identify the load type of the equipment in the computer room, limit the refrigeration working value during different load scenario tests based on the load type, perform a combined load test on the energy consumption variable events, and obtain energy consumption influencing factors under different load scenarios, wherein the load types specifically include servers, air conditioners and lighting, the refrigeration working value specifically includes the load working range and load peak and valley values, and the energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication.

[0042] Furthermore, it also includes:

[0043] A division module, for identifying heat generation data of various equipment in different equipment rooms from the equipment working areas based on equipment working areas preset in the equipment room, and dividing heat dissipation areas of the different equipment rooms according to the heat generation data;

[0044] A third judgment module is used to judge whether the heat dissipation area has at least two identified heat sources;

[0045] The third execution module is used to activate the preset sensor type according to the heat dissipation area, apply the preset multi-point sampling method, cross-validate the activated sensors, monitor the redundant data of the computer room equipment in real time, and dynamically adjust the sensor position parameters of the activated sensors based on the redundant data, wherein the sensor types specifically include temperature and humidity sensors, airflow sensors and redundant sensors, and the sensor position parameters specifically include sensor height and sensor angle.

[0046] Furthermore, the execution module also includes:

[0047] An identification unit, configured to identify the regional status of different energy consumption areas in the computer room based on the pre-recorded thermal image data in the computer room, wherein the regional status specifically includes a high heat density area and a low temperature demand area;

[0048] A judging unit, used to judge whether the regional state matches the load resources preset in the energy consumption region;

[0049] An execution unit is used to obtain the real-time regional temperature demand of the energy consumption area, identify the energy consumption fluctuation information of the energy consumption area, and dynamically adjust the fan output parameters in the computer room according to the real-time regional temperature demand and the energy consumption fluctuation information, wherein the fan output parameters specifically include fan speed, fan direction and fan temperature and humidity.

[0050] The present invention provides a method and system for simulating energy consumption of a refrigeration room, which has the following beneficial effects:

[0051] By establishing a real-time monitoring and analysis system, the present invention enables the computer room to effectively integrate scattered energy consumption data, identify peak and valley load periods, and dynamically adjust energy consumption forecasts and management strategies. This systematic approach combines environmental factors with equipment efficiency, deeply analyzes the energy consumption impact of various load types, ensures that the computer room can flexibly respond to dynamic changes in equipment, and reduce resource waste and operating costs. At the same time, by identifying key influencing factors and conducting combined load tests, managers can develop targeted cooling strategies and optimization plans, improve energy efficiency, and achieve data-driven decision support, thereby improving the overall operating efficiency and sustainable development capabilities of the computer room. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1A schematic diagram of a flow chart of an embodiment of an energy consumption simulation method for a refrigeration room of the present invention;

[0053] Figure 2 The structure diagram of an embodiment of the energy consumption simulation system of a refrigeration room of the present invention. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The implementation of the objectives, functional features and advantages of the present invention will be further described in conjunction with the embodiments and with reference to the accompanying drawings.

[0055] 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.

[0056] Reference Figure 1 , is a method for simulating energy consumption of a refrigeration room in an embodiment of the present invention, comprising:

[0057] S1: Based on the real-time monitored energy consumption data of the computer room, generate the load period of the equipment in the computer room in the current quarter, wherein the load period specifically includes a load peak period and a load off-peak period;

[0058] S2: Determine whether the predicted energy consumption value in the load period deviates from the preset energy consumption value;

[0059] S3: If yes, then according to the pre-entered computer room equipment information, identify the environmental data in the computer room, obtain the energy consumption impact trend of the computer room equipment according to the environmental data, input the overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend into the pre-trained prediction model, and generate the energy consumption variable information of the computer room equipment, wherein the computer room equipment information specifically includes power, efficiency curve and operation mode, the environmental data specifically includes temperature change, humidity change and seasonal change, and the overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data;

[0060] S4: Determine whether at least two energy consumption variable events are detected in the energy consumption variable information;

[0061] S5: If detected, the load type of the computer room equipment is identified, and the refrigeration working value during different load scenario tests is limited based on the load type. The energy consumption variable events are subjected to a combined load test to obtain energy consumption influencing factors under different load scenarios, wherein the load types specifically include servers, air conditioners, and lighting, the refrigeration working value specifically includes a load working range and load peak and valley values, and the energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication.

[0062] In this embodiment, the system generates the load period of the computer room equipment in the current energy consumption quarter based on the computer room energy consumption data monitored in real time by the computer room terminal. The load period specifically includes the peak load period and the low load period. Then the system determines whether the energy consumption forecast value in these load periods deviates from the preset energy consumption value to execute the corresponding steps; for example, when the system determines that the energy consumption forecast value in these load periods does not deviate from the preset energy consumption value, the system will consider that the current operating status of the computer room is consistent with the expected energy consumption, and the power consumption of the equipment, the cooling system and other resource allocation and usage meet the set standards. The system will maintain continuous monitoring of the equipment and system to ensure that the energy consumption data does not fluctuate abnormally in the subsequent operation, and regular inspection and maintenance Protect equipment to ensure long-term efficient operation, while continuing to implement the current load management and cooling strategies without major adjustments, ensuring that energy efficiency is maintained at a stable level, reducing unnecessary changes and interference, and recording the energy consumption data of this stage and comparing and analyzing it with historical data to find possible optimization space. Although the current energy consumption matches the preset value, the computer room manager can still find potential energy-saving opportunities through data analysis; for example, when the system determines that the energy consumption forecast value in these load periods deviates from the preset energy consumption value, the system will consider that the current operating status of the computer room is inconsistent with the expected energy consumption. The system will identify the machine room equipment according to the pre-entered computer room equipment information, which specifically includes power, efficiency curve and operating mode. The environmental data in the room, which specifically includes temperature changes, humidity changes and seasonal changes, obtains the energy consumption impact trend of the equipment in the computer room based on different environmental data, and inputs the overall energy consumption data, environmental data and energy consumption impact trend within a preset period into the pre-trained prediction model. The overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data, and generates energy consumption variable information predicted for the equipment in the computer room; based on environmental data such as temperature, humidity, season, etc., combined with the power, efficiency curve and operation mode of the equipment, the system comprehensively analyzes the trend of energy consumption changes and optimizes the equipment operation parameters from multiple dimensions. This multi-factor analysis can more accurately reflect the actual operation status and enhance the reliability of the prediction. At the same time, by integrating real-time energy consumption data , environmental data combined with the prediction model, the system can dynamically generate energy consumption variable information to help managers identify which factors lead to energy consumption deviations, so as to make targeted adjustments. This data-driven prediction model improves the intelligence level of energy efficiency management, and the analysis of energy consumption variable information can prevent the equipment from overloading at high loads or running inefficiently at low loads, which can effectively reduce the risk of equipment wear and failure, extend the service life of the equipment, and dynamically adjust the cooling strategy according to environmental data to ensure that the equipment works within the appropriate temperature range to prevent overheating or overcooling, thereby improving equipment stability; then the system determines whether at least two energy consumption variable events are detected to occur simultaneously in these energy consumption variable information, so as to execute the corresponding steps;For example, when the system determines that the predicted energy consumption variable information for the equipment in the computer room does not detect two or more energy consumption variable events at the same time, the system will consider that the current operation of the computer room is relatively stable, and that there are no major events or abnormal situations that may cause changes in energy consumption. The system will continue to monitor the energy consumption data and record these data as a basis for subsequent analysis and optimization, especially during peak load periods or when environmental conditions change. The accumulation of real-time data will help to cope with possible abnormal situations in the future, while maintaining the existing energy efficiency management strategy and continuing to work according to the preset power, cooling and load distribution plans without large-scale adjustments to avoid Interference with normal operation, and by analyzing the current energy consumption variable information, the system can input this data into the prediction model to continuously optimize the future energy consumption prediction capability. Even if no major variable events are detected, the current data can be used to improve the system's prediction accuracy and improve future energy efficiency management effects. For example, when the system determines that the energy consumption variable information predicted for the equipment in the computer room can detect two or more energy consumption variable events at the same time, the system will believe that there are multiple major abnormal events that may cause energy consumption changes in the current operation of the computer room. The system will identify the load type of the equipment in the computer room, which specifically includes servers, air conditioners and lighting. , based on different load types, the cooling working values ​​during different load scenario tests are limited. The cooling working values ​​specifically include the load working range and the load peak and valley values. The energy consumption variable events are combined with load tests to obtain the energy consumption influencing factors under different load scenarios. The energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication; by identifying different load types, the system can tailor different cooling working values ​​for each load type, including the load working range and peak and valley values. This detailed scenario testing and regulation can optimize the operation of the equipment in a targeted manner, reduce excessive equipment work or resource waste, and at the same time By identifying multiple variable events and conducting load scenario tests, the operating parameters of the equipment can be adjusted in time to avoid overload operation of the equipment under abnormal conditions, thereby reducing equipment wear and extending equipment life. In the case of multiple variable events, the system automatically adjusts the operating mode of the equipment so that the equipment always works at the best efficiency, avoiding energy waste and inefficient equipment operation, and improving the overall energy efficiency management level of the computer room. In addition, by combining load testing with multi-dimensional factor analysis, the system can timely discover the source of energy waste and make targeted adjustments. Measures such as optimizing the cooling system and improving equipment efficiency can effectively reduce unnecessary energy consumption and achieve the goal of energy saving and consumption reduction. ;

[0063] It should be noted that, according to the pre-entered computer room equipment information, the environmental data in the computer room is identified, and the energy consumption impact trend of the computer room equipment is obtained based on the environmental data. The overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend are input into the pre-trained prediction model to generate the energy consumption variable information of the computer room equipment. The specific examples are as follows:

[0064] Assume that a data center is located in an area with significant climate change, especially in summer, with outdoor temperatures as high as 35°C and humidity exceeding 60%. This environment poses a great challenge to the energy management of the data center's refrigeration room. The main refrigeration equipment in the room includes air conditioning systems, chillers, and fans. These devices must keep the temperature inside the data center within a reasonable range to ensure the stable operation of servers and other electronic equipment.

[0065] Device information:

[0066] Air conditioning system: Power range is 5kW to 20kW, efficiency is highest when load is above 70%, and it has multiple operating modes including high efficiency mode, low power mode and standby mode;

[0067] Chiller: Power is 15kW, efficiency decreases as ambient temperature increases. When the temperature exceeds 30°C, the efficiency of the chiller decreases by 15%;

[0068] Fan system: Power is 2kW, usually used for auxiliary cooling, wind speed is adjustable;

[0069] Pre-enter equipment information. The system first pre-enters the power, efficiency curve and operation mode of all equipment in the refrigeration room into the energy management system. For example, the system knows how the operating efficiency of air conditioners and chillers will change under different environmental conditions.

[0070] The efficiency curve of the air conditioning system shows that when the temperature exceeds 25°C, the air conditioning load increases and the power demand rises rapidly from the basic 5kW to more than 15kW;

[0071] The efficiency curve of the chiller shows that when the outside temperature exceeds 30°C, its energy efficiency decreases, the power demand increases, and the energy consumption of the equipment increases;

[0072] Real-time collection of environmental data. Through sensors installed in the computer room and outside, the system can obtain real-time environmental data inside and outside the computer room. These environmental data include:

[0073] External temperature: 35°C in summer;

[0074] Internal temperature: The internal temperature of the cooling room was set at 20°C. Sensors detected that the temperature near some servers reached 28°C, indicating that the air conditioning system failed to effectively cover these hot spots.

[0075] Humidity: The current humidity is 65%, which exceeds the efficient operating range of the cooling equipment, causing the equipment to require more energy for dehumidification;

[0076] Energy consumption impact trend analysis: Based on the above environmental data, the system combines equipment information (such as power and efficiency curves) to analyze the energy consumption impact trend of equipment under different environmental conditions, for example:

[0077] Temperature impact: As the external temperature continues to rise, the load on the air conditioning system increases, and the current power demand rises to 18kW, which is 2kW more than the standard mode. The working time of the chiller is also extended from the usual 6 hours to 10 hours, resulting in an increase in daily energy consumption;

[0078] Humidity impact: When humidity exceeds 60%, the air conditioning system requires additional power to operate the dehumidification function. System analysis shows that dehumidification work increases energy consumption by 10%;

[0079] The overall energy consumption data is input and calculated by the prediction model to obtain the overall energy consumption data within the period. The system will generate the overall energy consumption data within a period of time based on the real-time energy consumption data of the equipment in the computer room, historical energy consumption records and the current equipment operation status. This time period is usually pre-set, such as a week or a month. The overall energy consumption data specifically includes:

[0080] Predicted energy consumption data: Based on historical data and a preliminary analysis of current equipment operating conditions, the system predicts the energy consumption of the equipment under the current environment. For example, based on data from similar conditions in the past summer, the system predicts that the air conditioners and chillers in the computer room should consume about 500kWh.

[0081] Actual energy consumption data: Through real-time monitoring, the system obtains the actual energy consumption of the equipment in the computer room. In this example, the actual energy consumption of the air conditioner and chiller is 580kWh, which exceeds the predicted value by 80kWh.

[0082] Environmental data collection: The system uses sensors to obtain real-time environmental data inside and outside the computer room, including:

[0083] Temperature: The current outside temperature is 35°C, and the temperature in some server areas inside the computer room is as high as 28°C, indicating that the air conditioning system is not fully covered;

[0084] Humidity: The external humidity is 65%, which increases the dehumidification burden on the refrigeration equipment and reduces the efficiency of the equipment;

[0085] Energy consumption impact trend analysis: The system evaluates the energy consumption trend of the device in the current environment by analyzing environmental data (such as temperature and humidity) and the power and efficiency curve of the device. For example:

[0086] High temperature effect: As the outside temperature rises, the operating power of the air conditioning system increases from the basic 5kW to 18kW;

[0087] Humidity impact: When the humidity is higher than 60%, the air conditioning dehumidification function starts, consuming more energy, and the system recognizes an additional 10% increase in energy consumption;

[0088] The data is input into the prediction model. The system inputs the overall energy consumption data (predicted and actual), environmental data (temperature, humidity), and equipment energy consumption impact trends into the pre-trained prediction model. The prediction model calculates these data to generate more accurate energy consumption variable information for the next step of operation optimization. For example, the model may indicate:

[0089] Air conditioning and chillers consume 12% more energy than normal due to high temperatures and humidity;

[0090] Abnormal temperatures in specific server areas, poor cooling, and reduced efficiency of cooling equipment;

[0091] Generate energy consumption variable information. The system determines the main factors that cause energy consumption deviation based on the energy consumption variable information calculated by the model, such as:

[0092] Hot spots in high temperature areas: Due to the high temperatures in some server areas (over 28°C), the air conditioning efficiency is significantly reduced;

[0093] Reduced equipment efficiency: The efficiency of the chiller drops by 15% under high temperature conditions, resulting in additional energy consumption;

[0094] Adjust the operating parameters of the air conditioning system. The system automatically adjusts the operating parameters of the air conditioning system according to the actual operating status of different equipment, for example:

[0095] Adjust wind speed and direction: The system detects insufficient air flow in some server areas and optimizes air circulation by increasing wind speed and changing wind direction, which will help reduce temperatures in hot spots and reduce over-running of air conditioners.

[0096] Improve cooling coverage: The air direction of the air conditioner is automatically adjusted to the hot spot area to ensure a more uniform temperature and avoid local overheating.

[0097] Optimize chiller operation time. Since chiller efficiency drops significantly in hot environments, the system automatically recommends running the chiller during cooler hours, such as:

[0098] High-efficiency time period operation: The main working time of the chiller is adjusted to nighttime or lower temperature periods to reduce energy efficiency loss during high temperature periods;

[0099] Reduce use during high temperature periods: When the outside temperature is too high, reduce the continuous operation time of the chiller to reduce additional energy consumption;

[0100] The system starts the auxiliary cooling solution and adjusts the fan system to increase air circulation and further assist cooling, for example:

[0101] Fans accelerate air flow: Increase fan speed in hot spots to quickly remove heat through natural wind effects and mechanical ventilation.

[0102] Zoned cooling: the system dynamically adjusts the allocation of cooling resources according to the temperature requirements of different areas, reducing the workload of air conditioners and chillers;

[0103] Real-time feedback and optimization adjustment. After each adjustment of the equipment operation strategy, the system monitors the energy consumption changes of the equipment in real time and compares the energy consumption data before and after the adjustment. If the system detects that the adjustment measures are effective and the energy consumption gradually returns to normal levels, the system will continue to optimize these adjustments. If the energy consumption still deviates from the preset value, the system will further analyze the new environmental data and equipment status and continue to make dynamic adjustments;

[0104] In summary, the system can identify the energy consumption variables of the equipment and optimize the operating parameters of the equipment based on the input of the overall energy consumption data, environmental data and energy consumption trends within the cycle into the pre-trained prediction model. In the scenario of the refrigeration room, the system effectively reduces the energy waste caused by changes in external temperature and humidity by adjusting the wind speed and direction of the air conditioner, the operating hours of the chiller, and even enabling auxiliary cooling solutions. This process not only optimizes the cooling effect of the equipment, but also avoids excessive energy consumption in the computer room during peak loads, ultimately achieving the goal of reducing operating costs and improving equipment efficiency.

[0105] In this embodiment, before step S3 of identifying the environmental data in the computer room, the following steps are also included:

[0106] S301: Based on a preset equipment working area in the computer room, identifying heat generation data of various equipment in the computer room from the equipment working area, and dividing the heat dissipation areas of the various equipment in the computer room according to the heat generation data;

[0107] S302: Determine whether the heat dissipation area has at least two identified heat sources;

[0108] S303: If so, activate the preset sensor type according to the heat dissipation area, apply the preset multi-point sampling method, cross-validate the activated sensors, monitor the redundant data of the computer room equipment in real time, and dynamically adjust the sensor position parameters of the activated sensors based on the redundant data, wherein the sensor types specifically include temperature and humidity sensors, airflow sensors and redundant sensors, and the sensor position parameters specifically include sensor height and sensor angle.

[0109] In this embodiment, the system identifies the heat generation data of each device in the different computer room from the device working area based on the pre-set device working area in the computer room, divides the required heat dissipation areas of the different computer room devices according to the different heat generation data, and then the system determines whether these heat dissipation areas have at least two identified heat sources to execute the corresponding steps; for example, when the system determines that the heat dissipation areas of the different computer room devices do not have two or more identified heat sources, the system will consider that the heat generation in the area mainly comes from a single device, or the heat load of the device is low, and the heat dissipation demand is relatively simple. The system will maintain the current air speed and temperature settings of the air conditioner or fan, and perform basic heat dissipation according to the needs of the single heat source in the area to avoid waste of resources. At the same time, since there is no complex heat source combination, the system can determine that the area does not require large-scale heat dissipation investment, thereby reducing the excessive operation of air conditioners or other cooling equipment to save energy consumption, and it is still necessary to continuously monitor the equipment status of the area to ensure that it can be adjusted in time when the heat source changes. By continuing to monitor the temperature of the heat dissipation area, once a temperature rise is detected, the system can respond quickly and adjust the cooling strategy; for example, when the system determines that the heat dissipation areas of the different computer room devices have two or more identified heat sources, at this time The system will assume that the heat in the area comes from multiple devices and has a high heat dissipation demand. The system will use the pre-set multi-point sampling method to cross-validate the activated sensors based on the sensor types pre-set in the heat dissipation area, including temperature and humidity sensors, airflow sensors and redundant sensors, and monitor the redundant data of the equipment in the computer room in real time. Based on these redundant data, the sensor position parameters of the activated sensors are dynamically adjusted. The sensor position parameters specifically include sensor height and sensor angle; through multi-point sampling and cross-validation, the system can more accurately capture the temperature, humidity and airflow change data from multiple heat sources, which can ensure that the cooling system can perceive the heat dissipation needs of each device in real time, thereby providing the most effective cooling for each heat source. At the same time, through real-time monitoring of redundant data, the system can dynamically adjust the height and angle of the sensor according to the changes in the heat source to optimize its monitoring capabilities. This dynamic adjustment helps to cope with changes in the operating status of different devices, especially when there is high load or sudden heat increase, and the dynamic adjustment of the sensor position can help the system better distinguish which heat sources have generated greater heat dissipation needs, thereby optimizing resource allocation, and reducing overcooling of low-heat equipment by centrally cooling high-heat areas, avoiding unnecessary energy consumption.

[0110] It should be noted that the preset multi-point sampling method is applied to cross-validate the activated sensors, monitor the redundant data of the equipment in the computer room in real time, and dynamically adjust the sensing position parameters of the activated sensors based on the redundant data. The specific examples are as follows:

[0111] Consider a large refrigeration room with multiple servers, air conditioners, and other auxiliary facilities. These devices generate a lot of heat when in operation, so the room has a high cooling demand. To ensure that the equipment is always at the optimal temperature, the system uses sensors to monitor environmental data (such as temperature, humidity, and airflow) and adjust the cooling strategy in real time.

[0112] The specific process of sampling and cross-validation is that multiple temperature and humidity sensors are installed on the top, side and bottom of each server cabinet in the computer room. Assume that three temperature and humidity sensors (A1, A2 and A3) are installed on the top of server A, and there are also sensors on its bottom to monitor the environment around the server.

[0113] The system obtains temperature and humidity data from these sensors every 5 seconds. The readings of sensors A1, A2, and A3 may be as follows:

[0114] A1 sensor: 35°C;

[0115] A2 sensor: 36°C;

[0116] A3 sensor: 35.5°C;

[0117] In the cross-validation step, the system first compares the data of the three sensors to ensure their data consistency; since the temperature data of A1, A2, and A3 are not much different, the system determines that these data are consistent, indicating that the temperature change in this area is normal;

[0118] However, if the data from sensor A3 is 40°C, the system will detect a significant deviation and sound an alarm to check if there is a problem with the sensor or an abnormal heat source in the area. At this point, the system may ignore the reading from sensor A3 and temporarily rely on the data from A1 and A2 for further judgment.

[0119] Analysis and countermeasures for redundant data. Redundant data refers to multiple sensors monitoring environmental parameters in the same area, such as multiple temperature sensors monitoring the temperature on top of server A. When the system finds deviations between redundant data, it will take different measures to optimize the cooling strategy.

[0120] For example, suppose the data from the top three sensors (A1, A2, A3) of server A are basically the same, but the temperatures reported by the bottom three sensors (B1, B2, B3) are:

[0121] B1 sensor: 25°C;

[0122] B2 sensor: 24.5°C;

[0123] B3 sensor: 26°C;

[0124] The temperature reported by the sensor at the air outlet of the cooling system in the computer room was 18°C, indicating that the cold air flow was mainly concentrated at the bottom of the server and the temperature at the top was relatively high. After comparing these redundant data, the system found that the temperature difference between the top and the bottom was large, and it was inferred that the top was not effectively cooled.

[0125] As a countermeasure, the system can adjust the air outlet angle of the air conditioner to increase the cooling intensity of the top area of ​​server A. At the same time, the system will automatically reduce the cooling intensity of the bottom area to prevent overcooling of the bottom, thereby optimizing energy use.

[0126] For example, the dynamic adjustment of sensor position parameters is as follows: Suppose the temperature on the top of server A is continuously high, and after data analysis, the system believes that the reason is that the airflow does not fully cover the top area. To ensure more accurate monitoring, the system needs to dynamically adjust the position and angle of the sensor.

[0127] Sensor height adjustment: The system found that the heat on top of server A was concentrated in an area 30 cm from the top of the device, while the height of sensor A1 was originally set to 40 cm, which was away from the main heat source. The system automatically adjusted the height of sensor A1 to 30 cm to more accurately capture the temperature changes in this area.

[0128] Sensor angle adjustment: The system detects that after the cold air is blown out from the air conditioner, it mainly flows to the bottom of server A, while the airflow in the top area is insufficient. At this time, the system will adjust the angle of the airflow sensor from 90 degrees (vertical angle) to 45 degrees to better face the top area of ​​the server, thereby detecting whether the cooling air can effectively reach the upper equipment.

[0129] For example, if after adjustment, the system detects that the temperature of top sensors A1, A2, and A3 drops significantly, and the temperature difference with the bottom sensors decreases, it means that the adjustment is successful and the cooling effect of the top area has been significantly improved; the system can also further monitor the readings of the airflow sensor to ensure that the cooling air flows effectively to the top of the server;

[0130] After the adjustment, the feedback and optimization loop shows that the cooling effect has improved after the system dynamically adjusts the sensor position. If the redundant data continues to remain consistent in the next few sampling cycles, the system can confirm that the adjustment is effective; conversely, if there is still a significant temperature difference or inconsistent sensor data after the adjustment, the system may further optimize the working mode of the cooling equipment, such as increasing the wind speed or opening additional cooling channels;

[0131] In addition, the system can also dynamically adjust the overall cooling strategy of the computer room through redundant data, especially during high-load periods, such as when server A and other equipment are working at high load at the same time. The system will monitor multi-point sensor data in real time to ensure that the cooling system operates at optimal efficiency and avoid overheating or overcooling.

[0132] In summary, the system can accurately monitor environmental changes in various areas of the computer room through multi-point sampling, sensor cross-validation, redundant data analysis and dynamic adjustment of sensor positions. Especially in the high-load operating environment of the refrigeration room, this precise monitoring and dynamic adjustment can significantly improve cooling efficiency and reduce the risk of equipment overheating, while avoiding unnecessary energy waste, thereby improving the overall energy efficiency and operational stability of the computer room.

[0133] In this embodiment, the step S3 of generating the energy consumption variable information of the equipment room equipment further includes:

[0134] S31: Based on the pre-recorded thermal image data in the computer room, identifying the regional status of different energy consumption areas in the computer room, wherein the regional status specifically includes a high heat density area and a low temperature demand area;

[0135] S32: Determine whether the regional state matches the load resources preset in the energy consumption area;

[0136] S33: If not, obtain the real-time regional temperature demand of the energy consumption area, identify the energy consumption fluctuation information of the energy consumption area, and dynamically adjust the fan output parameters in the computer room according to the real-time regional temperature demand and the energy consumption fluctuation information, wherein the fan output parameters specifically include fan speed, fan direction, and fan temperature and humidity.

[0137] In this embodiment, the system identifies the regional status of different energy consumption areas in the computer room based on the thermal image data pre-recorded in the computer room. The regional status specifically includes high heat density areas and low temperature demand areas. The system then determines whether these regional statuses match the pre-set load resources of the energy consumption areas to execute corresponding steps; for example, when the system determines that the regional status of different energy consumption areas can match the pre-set load resources, the system will consider that the current cooling strategy and resource allocation of the computer room are consistent with the actual heat demand of each area. The system will monitor the real-time temperature, humidity and other environmental data of these energy consumption areas to ensure that the high heat density areas continue to be within a controllable temperature range and that the low temperature demand areas are not overcooled. If the temperature fluctuations in these areas are within a reasonable range, the system considers that the current cooling solution is effective and matching, and continues to maintain the existing cooling strategy without additional adjustment, which is specifically manifested in that the dynamic allocation of cooling resources (such as air conditioning air volume, wind speed, wind direction, etc.) remains unchanged, and the load changes in these areas are monitored regularly. Although the current regional status matches the load resources, the system will continue to track data changes as the equipment workload fluctuates; for example, when the system determines that the regional status of different energy consumption areas cannot match the pre-set load resources, the system will consider that the current computer room cooling strategy cannot match the actual heat of each area. The system will obtain the real-time regional temperature requirements of the energy consumption areas, identify the energy consumption fluctuation information of the energy consumption areas, and dynamically adjust the fan output parameters in the computer room according to different real-time regional temperature requirements and energy consumption fluctuation information. The fan output parameters specifically include fan speed, fan direction, and fan temperature and humidity. The system dynamically adjusts the fan output parameters based on the real-time temperature requirements of each area, and can accurately match the heat requirements of different areas. This can avoid overheating of high heat density areas due to insufficient cooling, or energy waste due to excessive cooling in low temperature demand areas, such as increasing wind speed and air volume in server-intensive areas, and appropriately reducing them in areas with lighter loads. Cooling resources optimize the overall cooling effect. At the same time, by adjusting the fan output parameters according to the energy consumption fluctuation information, the system can respond quickly when the load fluctuates. For example, when the load in a certain area decreases, the system will automatically reduce the fan speed and air volume in that area, thereby reducing energy consumption and helping to avoid energy waste caused by fixed cooling strategies. Especially during load peaks or troughs, dynamic adjustment can effectively save energy and adjust the cooling strategy in time to prevent performance degradation or failure risks caused by overheating of the equipment, effectively improve the operating stability of the equipment, and continuously optimize the fan output parameters to avoid unnecessary pressure on the equipment due to excessive cooling, thereby extending the life of the equipment. The system can quickly adjust the fan output according to the real-time regional temperature requirements and energy consumption fluctuation information to adapt to the rapid changes in load in different areas. This flexible cooling adjustment enables the system to maintain efficient operation during load peaks and troughs without cooling lags or imbalanced resource allocation.

[0138] In this embodiment, after step S5 of obtaining the energy consumption influencing factors under different load scenarios, the following steps are also included:

[0139] S501: regularly collecting equipment operation energy consumption data in the computer room based on a preset period, comparing the equipment operation energy consumption data with a pre-generated computer room energy consumption baseline, and obtaining energy consumption change data;

[0140] S502: Determine whether the energy consumption change data exceeds a preset change threshold;

[0141] S503: If so, obtain the computer room load energy consumption corresponding to the energy consumption change data, detect the load equipment in the computer room based on the computer room load energy consumption, apply the preset load balancing to evenly distribute the computer room load energy consumption to the load equipment through the computer room server, wherein the load equipment specifically includes loaded equipment and non-load equipment.

[0142] In this embodiment, the system regularly collects the equipment operation energy consumption data in the computer room within a preset period of time, compares the equipment operation energy consumption data with the pre-generated computer room energy consumption baseline, obtains energy consumption change data, and then the system determines whether these energy consumption change data exceed the preset change threshold to execute the corresponding steps; for example, when the system determines that the energy consumption change data does not exceed the preset change threshold, the system will consider that the operation status of the equipment in the computer room is stable, the equipment load matches the preset baseline, and there is no abnormal energy consumption or load fluctuation, and the system will continue to maintain the current equipment operation parameters and energy saving. Strategy to ensure that the equipment operates in the best energy efficiency state. No immediate adjustment is required, such as continuing to maintain the current cooling wind speed, wind direction or air conditioning temperature setting, while continuing to regularly collect and monitor energy consumption data to prevent abnormal fluctuations in the future and maintain the sensitivity of the early warning mechanism. If the data in the future tends to exceed the threshold, the system can issue an early warning signal in time and maintain the current load distribution strategy. There is no need to immediately adjust the load balance or cooling resource allocation. The energy consumption data and operating status of this period are saved as a reference for future analysis or backtracking; for example, when the system determines that the energy consumption change data exceeds The system will consider the operation status of the equipment in the computer room to be unstable when the preset change threshold is reached. The system will obtain the total energy consumption of the computer room load corresponding to the energy consumption change data, detect the loaded equipment in the computer room according to the total energy consumption of the computer room load, and apply the preset load balancing to evenly distribute the total energy consumption of the computer room load to each unloaded equipment through the computer room server; by evenly distributing the total energy consumption of the computer room load to the unloaded equipment, the system can reduce the working pressure of the current loaded equipment and avoid overloading or a sharp increase in energy consumption due to excessive load of a single device. This can effectively balance the load distribution and make the energy consumption of the entire computer room more stable and controllable. , reducing the impact of energy consumption fluctuations on equipment. At the same time, when the energy consumption exceeds the threshold, the system actively detects and redistributes the load, avoiding overheating, overload or failure of some equipment due to long-term high-load operation. It is of great help to the operating stability of key equipment such as servers and refrigeration systems, extending the life of equipment, and improving the reliability of the computer room. In addition, unloaded equipment can fully participate in operating tasks to avoid idle and waste of resources. Through load balancing, the power and energy consumption of all equipment are reasonably allocated, which improves the overall resource utilization of equipment in the computer room, reduces unnecessary electricity consumption, and reduces operating costs.

[0143] In this embodiment, the step S5 of performing a combined load test on the energy consumption variable event further includes:

[0144] S51: constructing a fault load scenario for testing the computer room based on a preset fault type, wherein the fault type specifically includes equipment overheating, network congestion, power failure, and cooling system failure;

[0145] S52: Determine whether the fault load scenario can consume the corresponding energy consumption of the computer room;

[0146] S53: If yes, generate a fault trigger condition for the fault load scenario, run the fault test content of the computer room according to the fault trigger condition, collect the energy consumption change process of the equipment in the computer room in the fault test content, and obtain the influence coefficient of the fault load scenario on cooling the computer room based on the energy consumption change process.

[0147] In this embodiment, the system constructs fault load scenarios for testing the computer room based on pre-set fault types, which specifically include equipment overheating, network congestion, power failure, and cooling system failure. The system then determines whether these fault load scenarios can consume the corresponding energy consumption of the computer room to execute corresponding steps. For example, when the system determines that the fault load scenario being tested cannot consume the corresponding energy consumption of the computer room, the system will consider that the current fault load scenario fails to effectively simulate the energy consumption status in actual operation. The system will check the current preset fault scenario to ensure that it is reasonable and close to reality. For example, when the test equipment is overheated, it should be simulated under specific temperature and load conditions to ensure The scenario setting can truly reflect the energy consumption characteristics under these fault scenarios, while increasing the pressure of the test to ensure that the relevant equipment can reach the expected load in the fault scenario. For example, the operating temperature of the equipment can be increased, the cooling efficiency can be reduced, or the network traffic can be increased in the network congestion test, so that the equipment is closer to the high energy consumption state under the actual operation. If it is found that the cooling system or other redundant protection mechanisms interfere with the load test, the sensitivity or startup threshold of these mechanisms can be temporarily adjusted to allow the test equipment to achieve a higher energy consumption level under preset conditions, including temporarily lowering the cooling response rate of the air conditioner or fan; for example, when the system determines that the fault load scenario being tested can consume the machine The corresponding energy consumption of the computer room. At this time, the system will think that the current fault load scenario can effectively simulate the energy consumption status in real operation. The system will generate the fault trigger conditions of the fault load scenario, and run the fault test content of the computer room according to different fault trigger conditions. In the fault test content, the energy consumption change process of the equipment in the computer room is collected, and the influence coefficient of the fault load scenario on the cooling of the computer room is obtained according to the energy consumption change process. The system can effectively simulate the real energy consumption status in the fault load scenario and generate specific fault trigger conditions to ensure that each simulation is close to the actual fault situation, making the energy consumption assessment of the computer room more accurate and able to truly reflect the energy consumption fluctuation when the fault occurs and its impact on the operation of the equipment. At the same time, by collecting the energy consumption change process in the fault test, the system can identify the cooling demand at the time of the fault and calculate the impact coefficient of the fault on the refrigeration system, which helps to dynamically adjust the refrigeration strategy according to different fault types, ensure that the cooling resources can be efficiently allocated, avoid wasting energy or causing equipment overheating due to excessive or insufficient cooling, and based on the simulation and analysis of different fault triggering conditions, the system can respond quickly and adjust the operating parameters when similar faults occur, improve the computer room's ability to respond to various abnormal conditions, not only shorten the system recovery time, but also minimize energy waste during the fault period and improve the overall operating efficiency.

[0148] It should be noted that the energy consumption change process of the equipment in the computer room is collected in the fault test content, and the influence coefficient of the fault load scenario on cooling the computer room is obtained according to the energy consumption change process. The specific examples are as follows:

[0149] Assume that a data center's cooling room is operating in a high-temperature environment in summer. The servers in the data center are working under continuous high load, and some server areas are overheating. The system's pre-set device operating temperature threshold is 45°C. When this temperature is exceeded, the cooling system will automatically adjust.

[0150] Failure scenario: Due to the increase in external temperature, the cooling system is overloaded and some server areas begin to overheat. The failure scenario simulates insufficient cooling of the server, which causes the temperature to rise, and the data center cooling system needs to cope with this overheating.

[0151] Energy consumption change data collection process, initial state:

[0152] Server temperature: Under high load, the temperature of a server area gradually increases from the normal 40°C to 45°C (preset threshold);

[0153] Cooling system: In the initial state, the fan runs at 50% speed, consumes 10 kW of electricity, and the power consumption of the server is 200W;

[0154] Environmental factors: high external ambient temperature (30°C) and increased humidity;

[0155] Fault trigger: When the temperature in the server area reaches 45°C, the cooling system’s sensors detect overheating and begin dynamic adjustments:

[0156] Increased fan speed: The fan speed is gradually increased to 75%, and the power consumption increases from 10 kW to 15 kW;

[0157] Changes in server energy consumption: As the temperature continues to rise to 50°C, the server power consumption increases from 200W to 250W (server power consumption increases by 10W for every 1°C increase in temperature);

[0158] Other data collection: Temperature and humidity sensors record local humidity increases, airflow sensors detect changes in wind flow, and redundant sensors continuously verify the accuracy of collected data;

[0159] Analysis of energy consumption change data, temperature rise stage:

[0160] Fan response analysis: The system detected that when the temperature rises from 45°C to 50°C, the power consumption of the cooling system fan increases by 1 kW for every 1°C increase; when the fan speed increases from 50% to 75%, the fan power consumption increases by 50%;

[0161] Server energy consumption analysis: As the temperature of the server rises, its power consumption increases linearly. For every 1°C increase, the power consumption increases by 10W. Therefore, during a 5°C temperature increase, the overall power consumption of the server increases by 50W.

[0162] After stabilization, when the server temperature rises to 50°C, the fan runs at maximum load (100%), and the power consumption reaches 20 kW. The cooling system successfully keeps the server temperature below 50°C; the server power consumption reaches 250W and remains stable;

[0163] Calculate the impact coefficient of the cooling system. By analyzing the energy consumption change process, the system derives the impact coefficient, that is, the additional energy consumption of the cooling system when dealing with fault load scenarios:

[0164] Cooling system influence coefficient: Based on the data, the system concluded that for every 1°C increase in temperature, the power consumption of the cooling system fan increased by 10%; when the fan speed was increased to 100%, the total power consumption of the cooling system increased by 10 kW (from 10 kW to 20 kW);

[0165] The server's influence coefficient: For every 1°C increase, the server's power consumption increases by 10W; therefore, the system concludes that the energy consumption increase ratio when the server is overheated is 5%;

[0166] Optimization suggestions for fault load scenarios. Based on the analysis of energy consumption changes, the system generates optimization suggestions:

[0167] Partition cooling strategy: When the server temperature approaches 45°C, the cooling system can increase the cooling power in advance and adopt a partition cooling method to cool only the area with local temperature rise, rather than increasing the cooling power of the entire area; this can save overall energy consumption;

[0168] Dynamic fan adjustment: the system recommends gradually increasing the fan speed in different temperature ranges; for example, when the temperature reaches 42°C, the fan speed can be increased from 50% to 60% in advance to avoid a large increase in cooling power later, which would cause the fan to run at 100%;

[0169] Predictive failure prevention: By collecting energy consumption change data in real time, the system can detect which equipment areas may fail in advance and make adjustments in advance. For example, if the system detects that the temperature in a certain area is rising rapidly, it can proactively allocate more cooling resources.

[0170] To summarize, when the temperature in the server area rises to 45°C, the system will detect changes in temperature, humidity, and fan power through sensor data, analyze the increase in server energy consumption and the response effect of the cooling system, and use real-time collected data to derive the energy consumption impact coefficient of the cooling system under different temperature changes. Based on these data, the system generates optimization suggestions to help save energy and improve cooling effects.

[0171] In this embodiment, the step S2 of determining whether the predicted energy consumption value in the load period deviates from the preset energy consumption value further includes:

[0172] S21: Based on the hierarchical control preset by the computer room for the equipment in the computer room, the load computer room equipment with the upper limit of the load is detected by the computer room server, wherein the hierarchical control specifically includes local control and global control;

[0173] S22: Determine whether the load room equipment can respond to the room server;

[0174] S23: If yes, identify the load components in the load room equipment, put the load components to sleep for a short time, activate the preset backup components according to the load components, and use the preset redundant sensors and the backup components to maintain the closed-loop operation of the load room equipment.

[0175] In this embodiment, the system is based on the hierarchical control pre-set by the computer room for the computer room equipment. The hierarchical control specifically includes local control and global control. The load computer room equipment with the upper limit of the load is detected by the computer room server mark, and then the system determines whether these load computer room equipment can respond to the computer room server to execute the corresponding steps; for example, when the system determines that the load computer room equipment with the upper limit of the load cannot respond to the computer room server, the system will think that the equipment with the upper limit of the load may be overloaded, resulting in its inability to communicate with the server normally. Overload will cause equipment performance degradation, processing delays or even downtime. The system will use fault detection tools to analyze the reasons why the equipment cannot respond, such as checking whether it is a communication failure, hardware failure, or a problem caused by equipment overload. At the same time, if it is detected that the temperature of the equipment rises and the response fails, the system should immediately increase the cooling intensity of the area, increase the fan speed, adjust the wind direction, etc., to ensure that the equipment cools down and returns to normal working temperature, and if the problem is in the communication link, the system can perform network diagnosis to detect whether there are problems such as network delay and data packet loss. If a network problem is detected, communication can be restored by switching to a backup link; for example, when the system determines that the load computer room equipment with the upper limit of the load can respond to the computer room server At this time, the system will think that the equipment with the load reaching the upper limit can still communicate normally. The system will identify the load components in the load room equipment, put the load components into short-term dormancy, activate the corresponding pre-set spare components according to different load components, and use the pre-set redundant sensors and spare components to maintain the closed-loop operation of the load room equipment; the system reduces the continuous load pressure of the equipment by short-term dormancy of the load components, prevents the equipment from overheating or damage due to long-term high load, and extends the service life of the equipment. At the same time, by enabling the pre-set spare components, the system can continue to maintain the closed-loop operation of the equipment without affecting the overall operation. This dynamic switching mechanism ensures that the equipment can maintain stable operation even under high load conditions, reducing system downtime or performance degradation caused by equipment overload, and using redundant sensors to monitor the status of the load room equipment in real time, and can provide accurate feedback and adjustment when the load changes. This feedback loop enables the system to respond quickly to load changes and ensure that the operating status of the equipment is always in the best state. Activating spare components and working with redundant sensors enhances the redundancy and fault tolerance of the system. When the equipment is overloaded, fails or has performance degradation, the spare components can take over in time to avoid system interruption.

[0176] In this embodiment, the step S1 of generating the load period of the equipment room equipment in the current quarter based on the real-time monitored equipment room energy consumption data further includes:

[0177] S11: Based on the preset functional areas in the computer room, construct the load time period corresponding to the functional areas, wherein the functional areas specifically include a high-performance computing area, a storage area, and a network equipment area;

[0178] S12: Determine whether the load period exceeds a preset period upper limit;

[0179] S13: If so, dynamically adjust the cooling strategy of the functional area according to the load period, and adaptively optimize the cooling efficiency in the computer room through the cooling strategy, wherein the cooling strategy specifically includes regional temperature monitoring and feedback, zoned cooling and resource allocation, and wind speed and direction adjustment.

[0180] In this embodiment, the system constructs load time periods corresponding to the functional areas based on the pre-set functional areas in the computer room, which specifically include high-performance computing areas, storage areas, and network equipment areas. The system then determines whether these load time periods exceed the pre-set time period upper limit to execute the corresponding steps; for example, when the system determines that the load time period corresponding to the functional area does not exceed the pre-set time period upper limit, the system will consider that the operation status of the current functional area is normal, the load is within the expected range, and there is no overload or abnormal situation. The system will check the resource utilization of each area. If the load time period is not exceeded, a small optimization can be performed, such as by adjusting the resource allocation of each area, some unused resources are reallocated to areas with lighter loads, thereby improving the overall resource utilization rate. At the same time, real-time monitoring is continuously performed, especially in high-load functional areas, by monitoring the temperature, power, and efficiency of the equipment, to ensure that there is no sudden increase in load, and to issue a warning in time when the load approaches the upper limit, and regularly collect the energy consumption, load distribution and other data of the current functional areas, and compare them with the historical data. If the time period upper limit is not exceeded for multiple operations, the system can fine-tune the preset time period upper limit according to the long-term data trend to ensure Resource allocation will be more accurate in the future. For example, when the system determines that the load period corresponding to the functional area exceeds the preset period upper limit, the system will consider the current functional area to be abnormal in operation. The system will dynamically adjust the cooling strategy of the functional area according to the load period. The cooling strategy specifically includes regional temperature monitoring and feedback, partition cooling and resource allocation, and wind speed and wind direction adjustment. The cooling efficiency in the computer room is adaptively optimized through the cooling strategy. Through regional temperature monitoring and feedback, the system can accurately capture which areas have increased temperatures due to overload operation. By dynamically adjusting the wind speed and wind direction, these areas can be cooled in a targeted manner to avoid equipment performance degradation or damage due to local overheating. At the same time, partition cooling can flexibly allocate cooling resources according to the load requirements of each functional area. This adaptive cooling strategy not only avoids unnecessary cooling waste, but also ensures that high-load areas obtain sufficient cooling, thereby improving energy utilization efficiency as a whole. When the system adjusts the cooling strategy through real-time temperature feedback, it can quickly respond to the over-limit situation of the load period to prevent hardware damage or failure of the equipment due to long-term overload operation. In addition, real-time monitoring and adaptive adjustment can detect abnormal conditions in time, thereby reducing the occurrence of failures.

[0181] Reference Figure 2 , is an energy consumption simulation system for a refrigeration room in one embodiment of the present invention, comprising:

[0182] A generating module 10 is used to generate a load period of the equipment in the equipment room in the current quarter based on the real-time monitored energy consumption data of the equipment room, wherein the load period specifically includes a load peak period and a load low peak period;

[0183] A determination module 20, configured to determine whether the predicted energy consumption value in the load period deviates from a preset energy consumption value;

[0184] The execution module 30 is used to identify the environmental data in the computer room according to the pre-entered computer room equipment information, obtain the energy consumption impact trend of the computer room equipment according to the environmental data, input the overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend into the pre-trained prediction model, and generate the energy consumption variable information of the computer room equipment, wherein the computer room equipment information specifically includes power, efficiency curve and operation mode, the environmental data specifically includes temperature change, humidity change and seasonal change, and the overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data;

[0185] A second determination module 40 is used to determine whether at least two energy consumption variable events are detected in the energy consumption variable information;

[0186] The second execution module 50 is used to identify the load type of the equipment in the computer room, limit the refrigeration working value during different load scenario tests based on the load type, perform a combined load test on the energy consumption variable events, and obtain energy consumption influencing factors under different load scenarios, wherein the load types specifically include servers, air conditioners and lighting, the refrigeration working value specifically includes the load working range and load peak and valley values, and the energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication.

[0187] In this embodiment, the generation module 10 generates the load period of the computer room equipment in the current energy consumption season based on the computer room energy consumption data monitored in real time by the computer room terminal, and the load period specifically includes the load peak period and the load low peak period, and then the judgment module 20 judges whether the energy consumption forecast value in these load periods deviates from the preset energy consumption value to execute the corresponding steps; for example, when the system determines that the energy consumption forecast value in these load periods does not deviate from the preset energy consumption value, the system will consider that the current operating status of the computer room is consistent with the expected energy consumption, and the power consumption of the equipment, the cooling system and other resource allocation and usage meet the set standards. The system will maintain continuous monitoring of the equipment and system to ensure that the energy consumption data does not fluctuate abnormally in the subsequent operation, and regular inspections are carried out. Check and maintain the equipment to ensure long-term efficient operation, while continuing to implement the current load management and cooling strategies without major adjustments, ensuring that energy efficiency is maintained at a stable level, reducing unnecessary changes and interference, and recording the energy consumption data of this stage and comparing and analyzing it with historical data to find possible optimization space. Although the current energy consumption matches the preset value, the computer room manager can still find potential energy-saving opportunities through data analysis; for example, when the system determines that the energy consumption forecast value in these load periods deviates from the preset energy consumption value, the execution module 30 will consider that the current operating status of the computer room is inconsistent with the expected energy consumption. The system will use the pre-entered computer room equipment information, which specifically includes power, efficiency curves and operating modes, Identify the environmental data in the computer room, which specifically includes temperature changes, humidity changes, and seasonal changes. According to different environmental data, obtain the energy consumption impact trend of the equipment in the computer room, and input the overall energy consumption data, environmental data, and energy consumption impact trend within a preset period into the pre-trained prediction model. The overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data, and generate energy consumption variable information predicted for the equipment in the computer room; based on environmental data such as temperature, humidity, and season, combined with the power, efficiency curve, and operation mode of the equipment, the system comprehensively analyzes the trend of energy consumption changes and optimizes the equipment operation parameters from multiple dimensions. This multi-factor analysis can more accurately reflect the actual operation status and enhance the reliability of the prediction. At the same time, by integrating real-time energy consumption data, By combining environmental data with the prediction model, the system can dynamically generate energy consumption variable information to help managers identify factors that cause energy consumption deviations, so as to make targeted adjustments. This data-driven prediction model improves the intelligence level of energy efficiency management, and the analysis of energy consumption variable information can prevent the equipment from overloading at high loads or running inefficiently at low loads, which can effectively reduce the risk of equipment wear and failure, extend the service life of the equipment, and dynamically adjust the cooling strategy according to environmental data to ensure that the equipment works within a suitable temperature range to prevent overheating or overcooling, thereby improving equipment stability; then the second judgment module 40 determines whether at least two energy consumption variable events are detected to occur simultaneously in these energy consumption variable information, so as to execute the corresponding steps;For example, when the system determines that the predicted energy consumption variable information for the equipment in the computer room does not detect two or more energy consumption variable events at the same time, the system will consider that the current operation of the computer room is relatively stable, and that there are no major events or abnormal situations that may cause changes in energy consumption. The system will continue to monitor the energy consumption data and record these data as a basis for subsequent analysis and optimization, especially during peak load periods or when environmental conditions change. The accumulation of real-time data will help to cope with possible abnormal situations in the future, while maintaining the existing energy efficiency management strategy and continuing to work according to the preset power, cooling and load distribution plans without large-scale adjustments to avoid Normal operation is disturbed, and by analyzing the current energy consumption variable information, the system can input this data into the prediction model to continuously optimize the future energy consumption prediction capability. Even if no major variable events are detected, the current data can be used to improve the system's prediction accuracy and improve future energy efficiency management effects. For example, when the system determines that the energy consumption variable information predicted for the equipment in the computer room can detect two or more energy consumption variable events at the same time, the system will believe that there are multiple major abnormal events that may cause energy consumption changes in the current operation of the computer room. The system will identify the load type of the equipment in the computer room, which specifically includes servers, air conditioners, and lighting. Different load types limit the refrigeration working values ​​during different load scenario tests. The refrigeration working values ​​specifically include the load working range and the load peak and valley values. The energy consumption variable events are combined for load testing to obtain the energy consumption influencing factors under different load scenarios. The energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication. The second execution module 50 can tailor different refrigeration working values ​​for each load type by identifying different load types, including the load working range and peak and valley values. This detailed scenario testing and regulation can optimize the operation of the equipment in a targeted manner and reduce excessive equipment work or resource waste. At the same time, by identifying multiple variable events and conducting load scenario tests, the operating parameters of the equipment can be adjusted in time to avoid overload operation of the equipment under abnormal conditions, thereby reducing equipment wear and extending equipment life. In the case of multiple variable events, the system automatically adjusts the operating mode of the equipment so that the equipment always works at the best efficiency, avoiding energy waste and inefficient equipment operation, and improving the overall energy efficiency management level of the computer room. In addition, by combining load testing with multi-dimensional factor analysis, the system can timely discover the source of energy waste, make targeted adjustments, optimize the cooling system, improve equipment efficiency, and other measures to effectively reduce unnecessary energy consumption and achieve the goal of energy saving and consumption reduction. ;

[0188] In this embodiment, it also includes:

[0189] A division module, for identifying heat generation data of various equipment in different equipment rooms from the equipment working areas based on equipment working areas preset in the equipment room, and dividing heat dissipation areas of the different equipment rooms according to the heat generation data;

[0190] A third judgment module is used to judge whether the heat dissipation area has at least two identified heat sources;

[0191] The third execution module is used to activate the preset sensor type according to the heat dissipation area, apply the preset multi-point sampling method, cross-validate the activated sensors, monitor the redundant data of the computer room equipment in real time, and dynamically adjust the sensor position parameters of the activated sensors based on the redundant data, wherein the sensor types specifically include temperature and humidity sensors, airflow sensors and redundant sensors, and the sensor position parameters specifically include sensor height and sensor angle.

[0192] In this embodiment, the system identifies the heat generation data of each device in the different computer room from the device working area based on the pre-set device working area in the computer room, divides the required heat dissipation areas of the different computer room devices according to the different heat generation data, and then the system determines whether these heat dissipation areas have at least two identified heat sources to execute the corresponding steps; for example, when the system determines that the heat dissipation areas of the different computer room devices do not have two or more identified heat sources, the system will consider that the heat generation in the area mainly comes from a single device, or the heat load of the device is low, and the heat dissipation demand is relatively simple. The system will maintain the current air speed and temperature settings of the air conditioner or fan, and perform basic heat dissipation according to the needs of the single heat source in the area to avoid waste of resources. At the same time, since there is no complex heat source combination, the system can determine that the area does not require large-scale heat dissipation investment, thereby reducing the excessive operation of air conditioners or other cooling equipment to save energy consumption, and it is still necessary to continuously monitor the equipment status of the area to ensure that it can be adjusted in time when the heat source changes. By continuing to monitor the temperature of the heat dissipation area, once a temperature rise is detected, the system can respond quickly and adjust the cooling strategy; for example, when the system determines that the heat dissipation areas of the different computer room devices have two or more identified heat sources, at this time The system will assume that the heat in the area comes from multiple devices and has a high heat dissipation demand. The system will use the pre-set multi-point sampling method to cross-validate the activated sensors based on the sensor types pre-set in the heat dissipation area, including temperature and humidity sensors, airflow sensors and redundant sensors, and monitor the redundant data of the equipment in the computer room in real time. Based on these redundant data, the sensor position parameters of the activated sensors are dynamically adjusted. The sensor position parameters specifically include sensor height and sensor angle; through multi-point sampling and cross-validation, the system can more accurately capture the temperature, humidity and airflow change data from multiple heat sources, which can ensure that the cooling system can perceive the heat dissipation needs of each device in real time, thereby providing the most effective cooling for each heat source. At the same time, through real-time monitoring of redundant data, the system can dynamically adjust the height and angle of the sensor according to the changes in the heat source to optimize its monitoring capabilities. This dynamic adjustment helps to cope with changes in the operating status of different devices, especially when there is high load or sudden heat increase, and the dynamic adjustment of the sensor position can help the system better distinguish which heat sources have generated greater heat dissipation needs, thereby optimizing resource allocation, and reducing overcooling of low-heat equipment by centrally cooling high-heat areas, avoiding unnecessary energy consumption.

[0193] In this embodiment, the execution module further includes:

[0194] An identification unit, configured to identify the regional status of different energy consumption areas in the computer room based on the pre-recorded thermal image data in the computer room, wherein the regional status specifically includes a high heat density area and a low temperature demand area;

[0195] A judging unit, used to judge whether the regional state matches the load resources preset in the energy consumption region;

[0196] An execution unit is used to obtain the real-time regional temperature demand of the energy consumption area, identify the energy consumption fluctuation information of the energy consumption area, and dynamically adjust the fan output parameters in the computer room according to the real-time regional temperature demand and the energy consumption fluctuation information, wherein the fan output parameters specifically include fan speed, fan direction and fan temperature and humidity.

[0197] In this embodiment, the system identifies the regional status of different energy consumption areas in the computer room based on the thermal image data pre-recorded in the computer room. The regional status specifically includes high heat density areas and low temperature demand areas. The system then determines whether these regional statuses match the pre-set load resources of the energy consumption areas to execute corresponding steps; for example, when the system determines that the regional status of different energy consumption areas can match the pre-set load resources, the system will consider that the current cooling strategy and resource allocation of the computer room are consistent with the actual heat demand of each area. The system will monitor the real-time temperature, humidity and other environmental data of these energy consumption areas to ensure that the high heat density areas continue to be within a controllable temperature range and that the low temperature demand areas are not overcooled. If the temperature fluctuations in these areas are within a reasonable range, the system considers that the current cooling solution is effective and matching, and continues to maintain the existing cooling strategy without additional adjustment, which is specifically manifested in that the dynamic allocation of cooling resources (such as air conditioning air volume, wind speed, wind direction, etc.) remains unchanged, and the load changes in these areas are monitored regularly. Although the current regional status matches the load resources, the system will continue to track data changes as the equipment workload fluctuates; for example, when the system determines that the regional status of different energy consumption areas cannot match the pre-set load resources, the system will consider that the current computer room cooling strategy cannot match the actual heat of each area. The system will obtain the real-time regional temperature requirements of the energy consumption areas, identify the energy consumption fluctuation information of the energy consumption areas, and dynamically adjust the fan output parameters in the computer room according to different real-time regional temperature requirements and energy consumption fluctuation information. The fan output parameters specifically include fan speed, fan direction, and fan temperature and humidity. The system dynamically adjusts the fan output parameters based on the real-time temperature requirements of each area, and can accurately match the heat requirements of different areas. This can avoid overheating of high heat density areas due to insufficient cooling, or energy waste due to excessive cooling in low temperature demand areas, such as increasing wind speed and air volume in server-intensive areas, and appropriately reducing them in areas with lighter loads. Cooling resources optimize the overall cooling effect. At the same time, by adjusting the fan output parameters according to the energy consumption fluctuation information, the system can respond quickly when the load fluctuates. For example, when the load in a certain area decreases, the system will automatically reduce the fan speed and air volume in that area, thereby reducing energy consumption and helping to avoid energy waste caused by fixed cooling strategies. Especially during load peaks or troughs, dynamic adjustment can effectively save energy and adjust the cooling strategy in time to prevent performance degradation or failure risks caused by overheating of the equipment, effectively improve the operating stability of the equipment, and continuously optimize the fan output parameters to avoid unnecessary pressure on the equipment due to excessive cooling, thereby extending the life of the equipment. The system can quickly adjust the fan output according to the real-time regional temperature requirements and energy consumption fluctuation information to adapt to the rapid changes in load in different areas. This flexible cooling adjustment enables the system to maintain efficient operation during load peaks and troughs without cooling lags or imbalanced resource allocation.

[0198] In this embodiment, it also includes:

[0199] An acquisition module is used to regularly collect the equipment operation energy consumption data in the computer room based on a preset period, compare the equipment operation energy consumption data with a pre-generated computer room energy consumption baseline, and obtain energy consumption change data;

[0200] A fourth judgment module is used to judge whether the energy consumption change data exceeds a preset change threshold;

[0201] The fourth execution module is used to obtain the computer room load energy consumption corresponding to the energy consumption change data, detect the load equipment in the computer room according to the computer room load energy consumption, and apply a preset load balancing to evenly distribute the computer room load energy consumption to the load equipment through the computer room server, wherein the load equipment specifically includes loaded equipment and non-load equipment.

[0202] In this embodiment, the system regularly collects the equipment operation energy consumption data in the computer room within a preset period of time, compares the equipment operation energy consumption data with the pre-generated computer room energy consumption baseline, obtains energy consumption change data, and then the system determines whether these energy consumption change data exceed the preset change threshold to execute the corresponding steps; for example, when the system determines that the energy consumption change data does not exceed the preset change threshold, the system will consider that the operation status of the equipment in the computer room is stable, the equipment load matches the preset baseline, and there is no abnormal energy consumption or load fluctuation, and the system will continue to maintain the current equipment operation parameters and energy saving. Strategy to ensure that the equipment operates in the best energy efficiency state. No immediate adjustment is required, such as continuing to maintain the current cooling wind speed, wind direction or air conditioning temperature setting, while continuing to regularly collect and monitor energy consumption data to prevent abnormal fluctuations in the future and maintain the sensitivity of the early warning mechanism. If the data in the future tends to exceed the threshold, the system can issue an early warning signal in time and maintain the current load distribution strategy. There is no need to immediately adjust the load balance or cooling resource allocation. The energy consumption data and operating status of this period are saved as a reference for future analysis or backtracking; for example, when the system determines that the energy consumption change data exceeds The system will consider the operation status of the equipment in the computer room to be unstable when the preset change threshold is reached. The system will obtain the total energy consumption of the computer room load corresponding to the energy consumption change data, detect the loaded equipment in the computer room according to the total energy consumption of the computer room load, and apply the preset load balancing to evenly distribute the total energy consumption of the computer room load to each unloaded equipment through the computer room server; by evenly distributing the total energy consumption of the computer room load to the unloaded equipment, the system can reduce the working pressure of the current loaded equipment and avoid overloading or a sharp increase in energy consumption due to excessive load of a single device. This can effectively balance the load distribution and make the energy consumption of the entire computer room more stable and controllable. , reducing the impact of energy consumption fluctuations on equipment. At the same time, when the energy consumption exceeds the threshold, the system actively detects and redistributes the load, avoiding overheating, overload or failure of some equipment due to long-term high-load operation. It is of great help to the operating stability of key equipment such as servers and refrigeration systems, extending the life of equipment, and improving the reliability of the computer room. In addition, unloaded equipment can fully participate in operating tasks to avoid idle and waste of resources. Through load balancing, the power and energy consumption of all equipment are reasonably allocated, which improves the overall resource utilization of equipment in the computer room, reduces unnecessary electricity consumption, and reduces operating costs.

[0203] In this embodiment, the second execution module further includes:

[0204] A construction unit, configured to construct a fault load scenario for testing the computer room based on a preset fault type, wherein the fault type specifically includes equipment overheating, network congestion, power failure, and cooling system failure;

[0205] A second judgment unit, used to judge whether the fault load scenario can consume the corresponding energy consumption of the computer room;

[0206] The second execution unit is used to generate a fault trigger condition for the fault load scenario, run the fault test content of the computer room according to the fault trigger condition, collect the energy consumption change process of the computer room equipment in the fault test content, and obtain the impact coefficient of the fault load scenario on cooling the computer room based on the energy consumption change process.

[0207] In this embodiment, the system constructs fault load scenarios for testing the computer room based on pre-set fault types, which specifically include equipment overheating, network congestion, power failure, and cooling system failure. The system then determines whether these fault load scenarios can consume the corresponding energy consumption of the computer room to execute corresponding steps. For example, when the system determines that the fault load scenario being tested cannot consume the corresponding energy consumption of the computer room, the system will consider that the current fault load scenario fails to effectively simulate the energy consumption status in actual operation. The system will check the current preset fault scenario to ensure that it is reasonable and close to reality. For example, when the test equipment is overheated, it should be simulated under specific temperature and load conditions to ensure The scenario setting can truly reflect the energy consumption characteristics under these fault scenarios, while increasing the pressure of the test to ensure that the relevant equipment can reach the expected load in the fault scenario. For example, the operating temperature of the equipment can be increased, the cooling efficiency can be reduced, or the network traffic can be increased in the network congestion test, so that the equipment is closer to the high energy consumption state under the actual operation. If it is found that the cooling system or other redundant protection mechanisms interfere with the load test, the sensitivity or startup threshold of these mechanisms can be temporarily adjusted to allow the test equipment to achieve a higher energy consumption level under preset conditions, including temporarily lowering the cooling response rate of the air conditioner or fan; for example, when the system determines that the fault load scenario being tested can consume the machine The corresponding energy consumption of the computer room. At this time, the system will think that the current fault load scenario can effectively simulate the energy consumption status in real operation. The system will generate the fault trigger conditions of the fault load scenario, and run the fault test content of the computer room according to different fault trigger conditions. In the fault test content, the energy consumption change process of the equipment in the computer room is collected, and the influence coefficient of the fault load scenario on the cooling of the computer room is obtained according to the energy consumption change process. The system can effectively simulate the real energy consumption status in the fault load scenario and generate specific fault trigger conditions to ensure that each simulation is close to the actual fault situation, making the energy consumption assessment of the computer room more accurate and able to truly reflect the energy consumption fluctuation when the fault occurs and its impact on the operation of the equipment. At the same time, by collecting the energy consumption change process in the fault test, the system can identify the cooling demand at the time of the fault and calculate the impact coefficient of the fault on the refrigeration system, which helps to dynamically adjust the refrigeration strategy according to different fault types, ensure that the cooling resources can be efficiently allocated, avoid wasting energy or causing equipment overheating due to excessive or insufficient cooling, and based on the simulation and analysis of different fault triggering conditions, the system can respond quickly and adjust the operating parameters when similar faults occur, improve the computer room's ability to respond to various abnormal conditions, not only shorten the system recovery time, but also minimize energy waste during the fault period and improve the overall operating efficiency.

[0208] In this embodiment, the judgment module further includes:

[0209] A marking unit, used for marking the load room equipment with the upper limit of the load detected by the computer room server based on the hierarchical control preset by the computer room on the equipment in the computer room, wherein the hierarchical control specifically includes local control and global control;

[0210] A third judgment unit is used to judge whether the load room equipment can respond to the room server;

[0211] The third execution unit is used to identify the load components in the load room equipment, temporarily hibernate the load components, activate preset backup components according to the load components, and use preset redundant sensors and the backup components to maintain the closed-loop operation of the load room equipment.

[0212] In this embodiment, the system is based on the hierarchical control pre-set by the computer room for the computer room equipment. The hierarchical control specifically includes local control and global control. The load computer room equipment with the upper limit of the load is detected by the computer room server mark, and then the system determines whether these load computer room equipment can respond to the computer room server to execute the corresponding steps; for example, when the system determines that the load computer room equipment with the upper limit of the load cannot respond to the computer room server, the system will think that the equipment with the upper limit of the load may be overloaded, resulting in its inability to communicate with the server normally. Overload will cause equipment performance degradation, processing delays or even downtime. The system will use fault detection tools to analyze the reasons why the equipment cannot respond, such as checking whether it is a communication failure, hardware failure, or a problem caused by equipment overload. At the same time, if it is detected that the temperature of the equipment rises and the response fails, the system should immediately increase the cooling intensity of the area, increase the fan speed, adjust the wind direction, etc., to ensure that the equipment cools down and returns to normal working temperature, and if the problem is in the communication link, the system can perform network diagnosis to detect whether there are problems such as network delay and data packet loss. If a network problem is detected, communication can be restored by switching to a backup link; for example, when the system determines that the load computer room equipment with the upper limit of the load can respond to the computer room server At this time, the system will think that the equipment with the load reaching the upper limit can still communicate normally. The system will identify the load components in the load room equipment, put the load components into short-term dormancy, activate the corresponding pre-set spare components according to different load components, and use the pre-set redundant sensors and spare components to maintain the closed-loop operation of the load room equipment; the system reduces the continuous load pressure of the equipment by short-term dormancy of the load components, prevents the equipment from overheating or damage due to long-term high load, and extends the service life of the equipment. At the same time, by enabling the pre-set spare components, the system can continue to maintain the closed-loop operation of the equipment without affecting the overall operation. This dynamic switching mechanism ensures that the equipment can maintain stable operation even under high load conditions, reducing system downtime or performance degradation caused by equipment overload, and using redundant sensors to monitor the status of the load room equipment in real time, and can provide accurate feedback and adjustment when the load changes. This feedback loop enables the system to respond quickly to load changes and ensure that the operating status of the equipment is always in the best state. Activating spare components and working with redundant sensors enhances the redundancy and fault tolerance of the system. When the equipment is overloaded, fails or has performance degradation, the spare components can take over in time to avoid system interruption.

[0213] In this embodiment, the generation module further includes:

[0214] A second construction unit is used to construct a load period corresponding to a functional area preset in the computer room, wherein the functional area specifically includes a high-performance computing area, a storage area, and a network equipment area;

[0215] A fourth judgment unit, used to judge whether the load period exceeds a preset period upper limit;

[0216] The fourth execution unit is used to dynamically adjust the cooling strategy of the functional area according to the load period, and adaptively optimize the cooling efficiency in the computer room through the cooling strategy, wherein the cooling strategy specifically includes regional temperature monitoring and feedback, zoned cooling and resource allocation, and wind speed and direction adjustment.

[0217] In this embodiment, the system constructs load time periods corresponding to the functional areas based on the pre-set functional areas in the computer room, which specifically include high-performance computing areas, storage areas, and network equipment areas. The system then determines whether these load time periods exceed the pre-set time period upper limit to execute the corresponding steps; for example, when the system determines that the load time period corresponding to the functional area does not exceed the pre-set time period upper limit, the system will consider that the operation status of the current functional area is normal, the load is within the expected range, and there is no overload or abnormal situation. The system will check the resource utilization of each area. If the load time period is not exceeded, a small optimization can be performed, such as by adjusting the resource allocation of each area, some unused resources are reallocated to areas with lighter loads, thereby improving the overall resource utilization rate. At the same time, real-time monitoring is continuously performed, especially in high-load functional areas, by monitoring the temperature, power, and efficiency of the equipment, to ensure that there is no sudden increase in load, and to issue a warning in time when the load approaches the upper limit, and regularly collect the energy consumption, load distribution and other data of the current functional areas, and compare them with the historical data. If the time period upper limit is not exceeded for multiple operations, the system can fine-tune the preset time period upper limit according to the long-term data trend to ensure Resource allocation will be more accurate in the future. For example, when the system determines that the load period corresponding to the functional area exceeds the preset period upper limit, the system will consider the current functional area to be abnormal in operation. The system will dynamically adjust the cooling strategy of the functional area according to the load period. The cooling strategy specifically includes regional temperature monitoring and feedback, partition cooling and resource allocation, and wind speed and wind direction adjustment. The cooling efficiency in the computer room is adaptively optimized through the cooling strategy. Through regional temperature monitoring and feedback, the system can accurately capture which areas have increased temperatures due to overload operation. By dynamically adjusting the wind speed and wind direction, these areas can be cooled in a targeted manner to avoid equipment performance degradation or damage due to local overheating. At the same time, partition cooling can flexibly allocate cooling resources according to the load requirements of each functional area. This adaptive cooling strategy not only avoids unnecessary cooling waste, but also ensures that high-load areas obtain sufficient cooling, thereby improving energy utilization efficiency as a whole. When the system adjusts the cooling strategy through real-time temperature feedback, it can quickly respond to the over-limit situation of the load period to prevent hardware damage or failure of the equipment due to long-term overload operation. In addition, real-time monitoring and adaptive adjustment can detect abnormal conditions in time, thereby reducing the occurrence of failures.

[0218] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for simulating energy consumption of a refrigeration room, characterized in that: The following steps are involved: Based on the real-time monitored energy consumption data of the computer room, generate the load period of the equipment in the computer room in the current quarter, wherein the load period specifically includes the peak load period and the low load period; Determining whether the predicted energy consumption value in the load period deviates from the preset energy consumption value; If so, then according to the pre-entered computer room equipment information, the environmental data in the computer room is identified, the energy consumption impact trend of the computer room equipment is obtained according to the environmental data, the overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend are input into the pre-trained prediction model, and the energy consumption variable information of the computer room equipment is generated, wherein the computer room equipment information specifically includes power, efficiency curve and operation mode, the environmental data specifically includes temperature change, humidity change and seasonal change, and the overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data; Determining whether at least two energy consumption variable events are detected in the energy consumption variable information; If detected, the load type of the equipment in the computer room is identified, and based on the load type, the cooling working value during different load scenario tests is limited, and the energy consumption variable events are subjected to a combined load test to obtain energy consumption influencing factors under different load scenarios, wherein the load type specifically includes servers, air conditioners, and lighting, the cooling working value specifically includes a load working range and a load peak and valley value, and the energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication; Wherein, the step of generating the energy consumption variable information of the equipment in the computer room further includes: Based on the pre-recorded thermal image data in the computer room, identifying the regional status of different energy consumption areas in the computer room, wherein the regional status specifically includes a high heat density area and a low temperature demand area; Determining whether the zone state matches the load resources preset in the energy consumption zone; If not, obtain the real-time regional temperature demand of the energy consumption area, identify the energy consumption fluctuation information of the energy consumption area, and dynamically adjust the fan output parameters in the computer room according to the real-time regional temperature demand and the energy consumption fluctuation information, wherein the fan output parameters specifically include fan speed, fan direction, and fan temperature and humidity.

2. The energy consumption simulation method of a refrigeration room according to claim 1, characterized in that: Before the step of identifying the environmental data in the computer room, the method further includes: Based on the preset equipment working areas in the computer room, identifying the heat generation data of each equipment in the different computer room from the equipment working areas, and dividing the heat dissipation areas of the different equipment in the computer room according to the heat generation data; Determining whether the heat dissipation area has at least two identified heat sources; If so, activate the preset sensor type according to the heat dissipation area, apply the preset multi-point sampling method, cross-validate the activated sensors, monitor the redundant data of the computer room equipment in real time, and dynamically adjust the sensor position parameters of the activated sensors based on the redundant data, wherein the sensor types specifically include temperature and humidity sensors, airflow sensors and redundant sensors, and the sensor position parameters specifically include sensor height and sensor angle.

3. The energy consumption simulation method of a refrigeration room according to claim 1, characterized in that: After the step of obtaining the energy consumption influencing factors under different load scenarios, the method further includes: Regularly collect the equipment operation energy consumption data in the computer room based on a preset period, compare the equipment operation energy consumption data with a pre-generated computer room energy consumption baseline, and obtain energy consumption change data; Determining whether the energy consumption change data exceeds a preset change threshold; If so, obtain the computer room load energy consumption corresponding to the energy consumption change data, detect the load equipment in the computer room based on the computer room load energy consumption, and apply the preset load balancing to evenly distribute the computer room load energy consumption to the load equipment through the computer room server, wherein the load equipment specifically includes loaded equipment and non-load equipment.

4. The energy consumption simulation method of a refrigeration room according to claim 1, characterized in that: The step of performing a combined load test on the energy consumption variable event further includes: Based on the preset fault types, construct a fault load scenario for testing the computer room, wherein the fault types specifically include equipment overheating, network congestion, power failure, and cooling system failure; Determine whether the fault load scenario can consume the corresponding energy consumption of the computer room; If possible, generate a fault trigger condition for the fault load scenario, run the fault test content of the computer room according to the fault trigger condition, collect the energy consumption change process of the computer room equipment in the fault test content, and obtain the impact coefficient of the fault load scenario on cooling the computer room based on the energy consumption change process.

5. The energy consumption simulation method of a refrigeration room according to claim 1, characterized in that: The step of determining whether the predicted energy consumption value in the load period deviates from the preset energy consumption value also includes: Based on the hierarchical control preset by the computer room for the equipment in the computer room, the load computer room equipment with the upper limit of the load is detected by marking the server in the computer room, wherein the hierarchical control specifically includes local control and global control; Determine whether the load room equipment can respond to the room server; If possible, the load components in the load room equipment are identified, the load components are temporarily dormant, preset backup components are activated according to the load components, and preset redundant sensors and the backup components are used to maintain the closed-loop operation of the load room equipment.

6. The energy consumption simulation method of a refrigeration room according to claim 1, characterized in that: The step of generating the load period of the equipment in the computer room in the current quarter based on the real-time monitored computer room energy consumption data also includes: Based on the preset functional areas in the computer room, construct the load time period corresponding to the functional areas, wherein the functional areas specifically include a high-performance computing area, a storage area, and a network equipment area; Determining whether the load period exceeds a preset period upper limit; If so, the cooling strategy of the functional area is dynamically adjusted according to the load period, and the cooling efficiency in the computer room is adaptively optimized through the cooling strategy.

7. An energy consumption simulation system for a refrigeration room, characterized in that: include: A generation module, used to generate the load period of the equipment in the computer room in the current quarter based on the real-time monitored energy consumption data of the computer room, wherein the load period specifically includes a load peak period and a load off-peak period; A judgment module, used to judge whether the predicted energy consumption value in the load period deviates from the preset energy consumption value; An execution module is used to identify environmental data in the computer room according to pre-entered computer room equipment information, obtain the energy consumption impact trend of the computer room equipment according to the environmental data, input the overall energy consumption data within a preset period, the environmental data and the energy consumption impact trend into a pre-trained prediction model, and generate energy consumption variable information of the computer room equipment, wherein the computer room equipment information specifically includes power, efficiency curve and operation mode, the environmental data specifically includes temperature change, humidity change and seasonal change, and the overall energy consumption data specifically includes predicted energy consumption data and actual energy consumption data; A second judgment module is used to judge whether at least two energy consumption variable events are detected in the energy consumption variable information; A second execution module is used to identify the load type of the equipment in the computer room, limit the cooling working value during different load scenario tests based on the load type, perform a combined load test on the energy consumption variable events, and obtain energy consumption influencing factors under different load scenarios, wherein the load type specifically includes servers, air conditioners, and lighting, the cooling working value specifically includes a load working range and a load peak and valley value, and the energy consumption influencing factors specifically include load changes, environmental factors, equipment efficiency, cooling system, power quality, and network and communication; Wherein, the execution module also includes: An identification unit, configured to identify the regional status of different energy consumption areas in the computer room based on the pre-recorded thermal image data in the computer room, wherein the regional status specifically includes a high heat density area and a low temperature demand area; A judging unit, used to judge whether the regional state matches the load resources preset in the energy consumption region; An execution unit is used to obtain the real-time regional temperature demand of the energy consumption area, identify the energy consumption fluctuation information of the energy consumption area, and dynamically adjust the fan output parameters in the computer room according to the real-time regional temperature demand and the energy consumption fluctuation information, wherein the fan output parameters specifically include fan speed, fan direction and fan temperature and humidity.

8. The energy consumption simulation system for a refrigeration room according to claim 7, characterized in that: Also includes: A division module, for identifying heat generation data of various equipment in different equipment rooms from the equipment working areas based on equipment working areas preset in the equipment room, and dividing heat dissipation areas of the different equipment rooms according to the heat generation data; A third judgment module is used to judge whether the heat dissipation area has at least two identified heat sources; The third execution module is used to activate the preset sensor type according to the heat dissipation area, apply the preset multi-point sampling method, cross-validate the activated sensors, monitor the redundant data of the computer room equipment in real time, and dynamically adjust the sensor position parameters of the activated sensors based on the redundant data, wherein the sensor types specifically include temperature and humidity sensors, airflow sensors and redundant sensors, and the sensor position parameters specifically include sensor height and sensor angle.

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