Efficient machine room control method and management system based on Internet of Things

Abstract

The invention belongs to the technical field of intelligent management of air conditioner rooms, and particularly relates to an efficient machine room control method and management system based on the Internet of Things. Comprising an equipment type selection module, a system pipeline module, a data acquisition module, a data preprocessing module, a digital twinning and three-dimensional visualization module, a big data analysis and intelligent management and control module, an energy fine management module, an alarm management and linkage module and an efficient machine room management and control platform in communication connection with the modules. Energy-saving intelligent control and informatization management of cooling and heating facilities such as a water chilling unit, a heat pump unit and a heat exchange station are achieved. Through BIM design, comprehensive pipelines are optimized and arranged in advance, the disadvantage of'on-site installation and on-site design 'of traditional construction is abandoned, efficient installation is achieved, and the overhaul space is larger. And in combination with digital production, model data are input into digital production equipment, and constructed cutting and welding are rapidly completed. And the pain point that a traditional heating and ventilation machine room depends on manpower is broken through.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent management of air-conditioning machine rooms, and particularly relates to an efficient machine room control method and management system based on the Internet of Things. Background Art

[0002] With the rapid economic development, the improvement of the level of urbanization development, and the continuous improvement of people's living quality, problems such as resource shortage, climate change, and environmental pollution have become increasingly prominent. In order to achieve sustainable development, it is necessary to accelerate the promotion and implementation of energy conservation and emission reduction measures. In recent years, building energy consumption accounts for 25% - 30% of the total social energy consumption. Among them, the energy consumption of the central air-conditioning system in large public buildings accounts for 40% - 60% of the building energy consumption, and the energy consumption of the refrigeration machine room accounts for the largest proportion in the total energy consumption of the central air-conditioning system - up to 60%. After investigation, the energy efficiency of the air-conditioning cold source system is generally low. Therefore, building an efficient air-conditioning refrigeration machine room and reducing the system energy consumption of the air-conditioning refrigeration machine room have become an important task faced by the current industry.

[0003] The existing traditional HVAC machine room system uses fixed temperature threshold control and cannot adapt to dynamic heat load changes. The operating parameters of the machine room equipment can rarely be linked with real-time environmental data, and the unreasonable layout of the cold and hot channels in the machine room easily leads to frequent local hot spots. The traditional HVAC machine room relies on manual inspections, with a very high response delay and cannot achieve real-time dynamic adjustment. Excessive refrigeration and heating result in energy waste. The equipment has weak fault prediction ability and high later maintenance costs. How to provide an effective way to solve the current situation in the HVAC industry is particularly important. The concept of an efficient machine room has long been established in China. However, most refrigeration machine rooms only focus on functional requirements and do not consider operating efficiency, and often cannot meet the standards of an efficient machine room. Summary of the Invention

[0004] The present invention provides an efficient machine room control method and management system based on the Internet of Things to solve the problems pointed out in the background art.

[0005] An efficient machine room control method and management system based on the Internet of Things includes an equipment selection module, a system pipeline module, a data acquisition module, a data preprocessing module, a digital twin and three-dimensional visualization module, a big data analysis and intelligent control module, an energy refined management module, an alarm management and linkage module, and an efficient machine room control platform communicatively connected to the above modules;

[0006] The equipment selection module selects energy-consuming equipment for the air-conditioning machine room based on the principles of dynamic matching and energy efficiency priority;

[0007] The system pipeline module optimizes the reduction of pipeline resistance and designs the system of the chilled water pipeline;

[0008] The data acquisition module includes various sensors and actuators, which collect various data in the computer room in real time, providing basic data for the data preprocessing module, digital twin and 3D visualization module, big data analysis and intelligent control module, refined energy management module, alarm management and linkage module, and the efficient computer room control platform communicatively connected to the above modules, ensuring the authenticity of the data of the efficient computer room management system;

[0009] The data preprocessing module is a refined load calculation designed for the air conditioning system of the efficient computer room;

[0010] The specific calculation and analysis process is as follows: Set a certain device as the target device, use the data acquisition module to collect data of each point of the target device within the monitoring period, and based on the provided data, use the first formula

[0011]

[0012] Where:

[0013] p1 is the power consumption of the device; p2 is the power consumption of the cooling tower device; p3 is the power consumption of the auxiliary device; P e is the power consumption of the unit device within time t; μ e is the efficiency coefficient of the unit device within time t; α is the aging coefficient of the unit device; t is the time (in years); P c is the power consumption of the unit cooling device within time t; μ c is the efficiency coefficient of the unit cooling device within time t; β is the load sensitivity coefficient of the unit cooling device; ΔT is the temperature difference between inside and outside the computer room; T is the reference optimal temperature; P e is the power consumption of the unit auxiliary device within time t; μ e is the efficiency coefficient of the unit auxiliary device within time t; γ is the load sensitivity coefficient of the unit auxiliary device; W is the usage rate of the unit auxiliary device within time t; W max is the maximum usage rate of the unit auxiliary device; δ is the air flow efficiency coefficient; S is the area of the computer room; μ a is the air density; C a is the specific heat capacity of air; t a is the cooling time constant; p l is the power transmission loss; is the grid efficiency coefficient; μ g is the weight coefficient of the power loss;

[0014] The digital twin and 3D visualization module uses a 3D model to display the host model. The entire model can be moved, zoomed in and out, rotated, and inspected from a free perspective, and the current real-time data information of the host is displayed;

[0015] The big data analysis and intelligent control module processes the data from the data preprocessing module; automatically adjusts the device parameters and operation modes to keep the system running efficiently; the big data analysis and intelligent control module includes a device real-time monitoring and remote control unit, a partition management and intelligent strategy unit, and an intelligent optimization and automation rule unit;

[0016] The refined energy management module provides energy-saving suggestions by analyzing the energy consumption trends of each device, generates an energy consumption report to provide data support for the enterprise's energy management decision-making, and realizes real-time data collection and analysis, real-time monitoring of energy usage, remote control, and optimized operation strategies. The refined energy management module includes an energy consumption trend analysis and prediction unit, an intelligent energy-saving strategy unit, and an energy consumption statistics report unit;

[0017] The alarm management and linkage module combines real-time monitoring, intelligent analysis, and automatic linkage to analyze the device operation data in real time, detect potential problems, and issue corresponding alarms.

[0018] Preferably, the device selection module, the system pipeline module, the data acquisition module, the data preprocessing module, the digital twin and 3D visualization module, the big data analysis and intelligent control module, the refined energy management module, the alarm management and linkage module, and the high-efficiency computer room control platform are all communicatively connected to the mobile terminal.

[0019] Advantages: The present invention provides an efficient computer room control method and management system based on the Internet of Things; it has the following advantages:

[0020] 1. The present invention upgrades and transforms both the device selection and the system pipeline. In combination with the distributed sensor network, it jointly solves the high energy consumption pain points of traditional HVAC computer rooms.

[0021] 2. The data preprocessing module of the present invention performs refined load calculation for the air conditioning system of the high-efficiency computer room, using refined calculation; it calculates the computer room load in detail from multiple angles such as device power consumption, cooling device power consumption, auxiliary device power consumption, air flow and heat exchange, and power loss. It creates an intelligent HVAC high-efficiency computer room that dynamically reflects the computer room load. It realizes the efficient operation and refined management of the HVAC high-efficiency computer room system.

[0022] 3. The digital twin and 3D visualization module of the present invention uses a 3D model to display the host model. The entire model can be moved, zoomed in and out, and rotated, and the current real-time data information of the host is displayed. It can remotely monitor the operation status of the system, the distribution of the terminal status, and the outdoor environmental temperature and humidity in real time, so as to provide a basis for improving the scheduling, management, and operation levels of the system, optimize the operation mode of the system, and thus achieve the purpose of energy conservation and emission reduction.

[0023] 4. The big data analysis and intelligent control module of the present invention realizes the intelligent linkage control and management of terminal devices through the Internet of Things technology, not only improving the comfort of the indoor environment, but also effectively reducing energy consumption. The intelligent linkage realizes precise control of the operation mode and startup time of terminal devices, and supports multiple intelligent control strategies according to holidays and peak periods; it avoids unnecessary energy waste, thereby improving the overall energy efficiency.

[0024] 5. The energy refined management, alarm management and linkage module of the present invention can realize real-time data collection and analysis, real-time monitoring of energy usage, generate energy consumption reports, fault early warning and diagnosis, remote control, and optimize operation strategies, etc. When the system detects abnormal situations or potential faults, it automatically pushes alarm information and supports scenario linkage control to help management personnel quickly locate and solve problems.

[0025] 6. The corresponding intelligent HVAC high-efficiency machine room control platform and the matching mobile terminal of the present invention can use the mobile phone app and the small program terminal to monitor and manage the device status in real time, view the device operation status, and realize the interconnection, intercommunication and intercontrol of intelligent devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a logical schematic diagram of the present invention,

[0027] Description of the Reference Numerals in the Drawings:

[0028] Reference numerals in the figure: equipment selection module 1; system pipeline module 2; data acquisition module 3; data preprocessing module 4; digital twin and three-dimensional visualization module 5; big data analysis and intelligent control module 6; energy refined management module 7; alarm management and linkage module 8; high-efficiency machine room control platform 9; mobile terminal 10. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The following will describe in detail a specific embodiment of the present invention with reference to the drawings, but it should be understood that the protection scope of the present invention is not limited by the specific embodiment.

[0030] Embodiment:

[0031] As Figure 1 shown, an efficient machine room control method and management system based on the Internet of Things provided by an embodiment of the present invention includes an equipment selection module 1, a system pipeline module 2, a data acquisition module 3, a data preprocessing module 4, a digital twin and three-dimensional visualization module 5, a big data analysis and intelligent control module 6, an energy refined management module 7, an alarm management and linkage module 8, and a high-efficiency machine room control platform 9 communicatively connected to the above modules;

[0032] The equipment selection module 1 is for the selection of energy-consuming equipment in the air-conditioning room, based on the principles of dynamic matching and energy efficiency priority; it is mainly aimed at the selection of major energy-consuming equipment in the air-conditioning room, such as chillers, water pumps, cooling towers, etc.; the chiller can use a magnetic suspension chiller, which has the characteristics of first suspension and then rotation, and the magnetic suspension bearing shaft system is contactless, frictionless, and lubrication-free, with high efficiency and low noise, and is equipped with a high-efficiency inverter and a self-fall protection system. It is directly connected to the three-dimensional flow turbine, uses an electronic expansion valve, and adopts a PID algorithm to ensure accurate and automatic adjustment of superheat. The electronic expansion valve adopts a time-sharing control strategy, and the superheat is combined with the liquid level to control the opening of the electronic expansion valve and adjust the unit load; the chilled water pump and the cooling water pump both use comprehensive frequency conversion control technology to achieve low-energy operation of the water pump. The vertical multi-stage variable frequency centrifugal pump with integrated variable frequency control technology is used. It runs relatively smoothly and has low noise. Considering the attenuation of flow, a 5% to 9% margin is added. The number of parallel pumps is controlled within 3. For medium and large projects with large supply and return water temperature differences and large flow, cold and hot water circulation pumps are set separately. The cooling tower body structure adopts a countercurrent glass fiber reinforced plastic cooling tower, and uses high-efficiency PVC oblique wave filler. The heat exchange area is ≥200m 2 / m 3 Through the linkage optimization of the chiller, water pump and cooling tower in the equipment selection module, the overall energy efficiency of the system is improved by 23% to 35%;

[0033] The system pipeline module 2 optimizes for reducing pipeline resistance and designs the chilled water pipeline system; specifically, it adopts the principle of low resistance, reduces unnecessary local resistance components such as elbows, uses straight-in and straight-out connections, the joints adopt 45° bends, the water pump and the main pipe are arranged obliquely to reduce the elbow angle. The main chilled water pipe is DN400, uses spiral finned heat transfer enhancement pipes, and uses low resistance valve fittings. The resistance coefficient of a single elbow / valve is ≤0.3, and sharp turns are avoided. In actual construction, obtuse elbows are used instead of right-angled elbows, and acute-angle tees are used instead of right-angled tees to increase the curvature radius of the elbows. Elbows with a curvature radius of more than 1.5 times are used, and parallel flow tees are adopted. The diameter of the main pipe is increased, the flow velocity of the main pipe is reduced by 30%, and the specific friction loss of the main pipe is reduced by 80%; the specific friction loss of the air-conditioning water system should be controlled at 120 - 300 Pa / m, the maximum flow velocity at the air-conditioning terminal in the room should not exceed 1.5 m / s, and the maximum flow velocity of the main pipe should not exceed 2 m / s. Multiple measures simultaneously contribute to low-resistance pipelines. A dynamic balance electric valve can also be installed, a buffer water tank can be set up, and a pressure fluctuation suppressor can be adopted. Low-temperature anti-freezing protection is achieved by using a pipe electric heating tape + intelligent temperature control system. A hydraulic pulsation damper is installed, and a rubber soft joint is used for the flexible connection section. The chiller and the water pump adopt a header connection method. During the start-up process of the water pump, the chiller does not need to be shut down. If the water pump has an accidental failure, the standby water pump can be automatically started quickly. For the problems of pipeline corrosion and scale removal, an automatic dosing device is adopted to automatically put the slow-release agent on time to solve the failures caused by system oxidation corrosion. Seamless steel pipes (GB / T8163) + polyurethane insulation layer are selected for the chilled water pipes. Fiberglass reinforced plastic composite pipes with strong corrosion resistance are selected for the cooling water pipes. TIG welding process is used for pipeline welding. Through the above optimized design, the energy consumption of the pipeline system can be reduced by 20%, while improving the system stability and maintainability. At the same time, combined with the "BMMI" system and the digital twin and three-dimensional visualization module 5, BIM modeling of the machine room is carried out using Revit. The model design accuracy is L0D400, and all equipment and valve components are modeled at a scale of 1:1. The actual construction error is controlled within the range of ±3 mm of the BIM model.

[0034] The data acquisition module 3 includes various sensors and actuators, which collect various data of the computer room in real time, providing basic data for the data preprocessing module 4, digital twin and 3D visualization module 5, big data analysis and intelligent control module 6, refined energy management module 7, alarm management and linkage module 8, and the efficient computer room control platform 9 that is communicatively connected to the above modules, ensuring the authenticity of the data of the efficient computer room management system; the intelligent devices or subsystems mainly monitored include: intelligent power distribution cabinet monitoring, ordinary electric meter monitoring, distribution switch monitoring, UPS monitoring, battery monitoring, precision air conditioner monitoring, ordinary air conditioner monitoring, water leakage monitoring, fresh air fan monitoring, temperature and humidity monitoring, fire monitoring, access control monitoring, infrared monitoring, video monitoring, server monitoring, network monitoring, etc. It mainly includes: temperature and humidity sensor unit, smoke detector unit, UPS power supply monitoring module unit, fresh air fan detection unit, battery unit, electricity meter unit, infrared detector unit, network dome camera and bullet camera unit, water leakage detector unit, access control controller unit to collect the internal environment parameters and equipment status of the computer room in real time. A 5G industrial-grade high-performance router is deployed on-site and connected in cooperation with the above monitoring units. It is connected to the industrial intelligent gateway WG583 through the RS485 serial port, and the gateway collects device data in real time and uploads it to the cloud platform of the efficient computer room management system through 4G / WIFI / Ethernet, etc. The data acquisition module 3 transmits the collected data to the data preprocessing module 4 and the big data analysis and intelligent control module 6 for data analysis, processing, and presentation.

[0035] The data preprocessing module 4 is a refined load calculation designed for the air conditioning system of the efficient computer room;

[0036] The specific calculation and analysis process is as follows: Set a certain device as the target device, use the data acquisition module 3 to collect the data of each point of the target device within the monitoring period, and based on the provided data, use the first formula

[0037]

[0038] where:

[0039] p1 is the power consumption of the device; p2 is the power consumption of the cooling tower device; p3 is the power consumption of the auxiliary device; P e is the power consumption of the unit device within time t; μ e is the efficiency coefficient of the unit device within time t; α is the aging coefficient of the unit device; t is the time (in years); P c is the power consumption of the unit cooling device within time t; μ c is the efficiency coefficient of the unit cooling device within time t; β is the load sensitivity coefficient of the unit cooling device; △T is the temperature difference between inside and outside the computer room; T is the reference optimal temperature; P e is the power consumption of the unit auxiliary device within time t; μ eis the efficiency coefficient of the unit auxiliary equipment at time t; γ is the load sensitivity coefficient of the unit auxiliary equipment; W is the utilization rate of the unit auxiliary equipment at time t; W max is the maximum utilization rate of the unit auxiliary equipment; δ is the air flow efficiency coefficient; S is the computer room area; μ a is the air density; C a is the specific heat capacity of air; t a is the cooling time constant; p l is the power transmission loss; is the power grid efficiency coefficient; μ g is the weight coefficient of power loss; According to the data collected by the data acquisition module, the present invention gives the following examples: P e The power consumption of the unit equipment within time t is 10 kw; μ e The efficiency coefficient of the unit equipment at time t is 0.9; α The aging coefficient of the unit equipment is 0.02; t The time is 2 years; P c The power consumption of the unit cooling equipment at time t is 5 kw; μ c The efficiency coefficient of the unit cooling equipment at time t is 0.8; β The load sensitivity coefficient of the unit cooling equipment is 0.1; △T The temperature difference inside and outside the computer room is 10°C; T The reference optimal temperature is 25°C; P e The power consumption of the unit auxiliary equipment at time t is 2 kw; μ e The efficiency coefficient of the unit auxiliary equipment at time t is 0.85; γ The load sensitivity coefficient of the unit auxiliary equipment is 0.05; W The utilization rate of the unit auxiliary equipment at time t is 0.8; W max The maximum utilization rate of the unit auxiliary equipment is 1; δ The air flow efficiency coefficient is 0.2; S The computer room area is 100 m 2 ; μ a The air density is 1.225 kg / m 3 ; C a The specific heat capacity of air is 1005 J / kg·°C; t a The cooling time constant is 3600 s; p l The power transmission loss is 1 kw; The power grid efficiency coefficient is 0.95; μ g The weight coefficient of power loss is 0.05; Calculate p 总 is 84.594 kw. Combining the digital twin and 3D visualization module 5, big data analysis and intelligent control module 6, energy refined management module 7, alarm management and linkage module 8, to build an intelligent HVAC high-efficiency computer room that dynamically reflects the computer room load. Realize the efficient operation and refined management of the HVAC high-efficiency computer room system.

[0040] The digital twin and 3D visualization module 5 uses a 3D model to display the host model. The entire model can be moved, zoomed in and out, rotated, and inspected from a free perspective, and the current real-time data information of the host is displayed. Simulation data of additional stress fields for key components (such as compressor impellers), thermo-field simulation based on CFD, and a pipeline fluid dynamics model are created. The sensor data in the data acquisition module 3 is associated with the attributes of the 3D model. Multi-dimensional visualization is presented. By using the thermal field distribution and color mapping method to identify local hot spots, combined with the alarm management and linkage module 8, automatic early warning of abnormal and dangerous areas can be achieved. Specifically, an infrared thermal imager is arranged, and the high-temperature area flashes red. The fault location time is shortened from 1 hour to 10 minutes. The operating status of the system, the distribution of the end status, the outdoor environmental temperature and humidity, etc. can be remotely monitored in real time, so as to provide a basis for improving the scheduling, management, and operation levels of the system, optimizing the operation mode of the system, and achieving the purpose of energy conservation and emission reduction. The "BMMI" system is adopted, that is, a BIM technology engineering system integrating "BIM detailed design, mechanized prefabricated production, modular assembly, and intelligent control system". Detailed drawings are derived from the BIM 3D model, including specific drawings such as prefabricated pipe processing drawings, foundation drawings, equipment layout drawings, support and hanger positioning and processing drawings, module assembly drawings, and mechanical and electrical integration drawings. Using BIM technology, the loading and transportation simulation of prefabricated assembly units and prefabricated pipe groups is carried out, the prefabricated finished components are placed reasonably, and the space of the transport vehicle is fully utilized to maximize the transport efficiency. During shipment, product protection is done well, and the vulnerable parts of the components are packaged with stretch film and straw ropes. Using the BIM model + Trimble layout instrument, the equipment, modules, support and hangers, etc. are laid out and positioned, the equipment and pump group modules are transported and installed in place as a whole, and the row of pipe groups is hoisted as a whole. Relying on the BIM model, the assembly sequence, method, etc. of all prefabricated components are carefully planned, an assembly implementation plan is compiled, and a three-dimensional technical disclosure of the assembly plan is given to the assembly workers. Make a plan for the construction period in advance to improve the installation efficiency, maximize labor savings, and ensure the construction quality.

[0041] The big data analysis and intelligent control module 6 processes the data of the data preprocessing module 4; automatically adjusts the device parameters and operation modes to keep the system running efficiently; the big data analysis and intelligent control module 6 includes a device real-time monitoring and remote control unit, a zoning management and intelligent strategy unit, and an intelligent optimization and automation rule unit; the device real-time monitoring and remote control unit mainly supports the access of all standard protocol and private protocol devices. Upload data changes, and monitor the operation status of devices in real time (such as temperature, humidity, pressure, energy consumption, etc.). Remotely control the device switch and parameter adjustment. Keep historical records and queries of device operation data. The zoning management and intelligent strategy unit manages by region (such as office area, production area, public area, etc.). Set independent temperature and humidity strategies for different regions. Preset multiple operation modes (such as energy-saving mode, comfort mode, holiday mode, night mode). Automatically switch operation modes to adapt to different time periods or scenario requirements. The intelligent optimization and automation rule unit formulates optimization rules based on AI algorithm suggestions and combines with a rule engine. It can automatically adjust the device operation according to historical data, environmental changes and user habits. Support custom rules (such as adjusting the temperature during specific time periods, turning off devices in unoccupied areas). Record rule execution and analyze the effects. This module generates optimization strategies based on the big data analysis results to achieve remote management of each device in the computer room.

[0042] The refined energy management module 7 provides energy-saving suggestions by analyzing the energy consumption trends of each device, generates energy consumption reports to provide data support for the enterprise's energy management decision-making, and realizes real-time data collection and analysis, real-time monitoring of energy usage, remote control, and optimized operation strategies. The refined energy management module 7 includes an energy consumption trend analysis and prediction unit, an intelligent energy-saving strategy unit, and an energy consumption statistics report unit; the energy consumption trend analysis and prediction unit analyzes the energy efficiency change trend through historical energy consumption data. Multidimensional analysis (such as time dimension, regional dimension, device dimension). Visually display the energy consumption peaks and valleys. Energy consumption prediction and trend warning. The intelligent energy-saving strategy unit provides multiple energy-saving strategies (such as load regulation, device rotation operation). Support manual or automatic execution of energy-saving strategies. Real-time monitoring and analysis of energy-saving effects. Suggestions for optimizing energy-saving strategies. The energy consumption statistics report unit regularly generates energy consumption reports (such as daily, weekly, monthly, yearly). The report content includes total energy consumption, itemized energy consumption (such as air conditioning, heating) and energy-saving effect analysis. Support exporting in PDF or Excel format. Customize the report content and format. It is also characterized in that it combines big data visualization to intuitively display the energy consumption trend. By analyzing the energy consumption of each device, optimize energy usage and reduce operating costs.

[0043] The alarm management and linkage module 8 combines real-time monitoring, intelligent analysis, and automated linkage to analyze the device operation data in real time, detect potential problems, and issue corresponding alarms. Fault early warning and alarm notification (such as device anomalies, sudden increase in energy consumption). Fault cause analysis and recommended solutions. Fault handling records and statistics. Quickly detect anomalies and take corresponding measures to avoid equipment failures, environmental anomalies, or safety accidents. It mainly includes an alarm rule configuration unit, a real-time alarm unit, and a linkage control and automated processing unit. The alarm rule configuration unit is characterized in that: it adopts a composite alarm mode that triggers alarms when multiple parameters are abnormal simultaneously, and can independently configure the corresponding alarm rules for the rule engine module. Its content is to set rules for conditions, thresholds, conversion rates, delays, alarm levels, whether to automatically alarm, and recovery methods for points. The alarm level can be custom-set. The real-time alarm unit automatically processes alarms for devices and intuitively displays the alarm situation, displays alarm information in real time on the visualization interface, and marks the alarm devices on the computer room topology map. According to the preset threshold, it automatically judges abnormal situations and triggers the early warning or alarm mechanism, and immediately notifies the management personnel through multiple methods such as text messages, emails, APPs, and mini-program pushes to achieve rapid response. Once anomalies are detected, such as potential risks like too high temperature, excessive humidity, voltage fluctuations, etc., the early warning mechanism will be immediately triggered to effectively avoid major failures caused by delays. The linkage control and automated processing unit manages linkages of devices, environments, and notifications. For example: when the detected temperature is too high, automatically start the standby cooling equipment. When an emergency alarm is triggered, automatically notify relevant personnel and generate a work order. It has emergency measures and automatically executes emergency measures in case of emergencies. Improve the safety of the computer room and reduce manual inspections.

[0044] Specifically, the device selection module 1, the system pipeline module 2, the data acquisition module 3, the data preprocessing module 4, the digital twin and 3D visualization module 5, the big data analysis and intelligent control module 6, the energy refined management module 7, the alarm management and linkage module 8, and the high-efficiency computer room control platform 9 are all communicatively connected to the mobile terminal 10.

[0045] The above discloses only several specific embodiments of the present invention. However, the embodiments of the present invention are not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. An efficient computer room control method and management system based on the Internet of Things, characterized in that: It includes an equipment selection module (1), a system pipeline module (2), a data acquisition module (3), a data preprocessing module (4), a digital twin and 3D visualization module (5), a big data analysis and intelligent control module (6), an energy refined management module (7), an alarm management and linkage module (8), and an efficient computer room control platform (9) communicatively connected to the above modules; The equipment selection module (1) selects energy-consuming equipment for the air-conditioning computer room based on the principles of dynamic matching and energy efficiency priority; The system pipeline module (2) optimizes the reduction of pipeline resistance and designs the system of chilled water pipelines; The data acquisition module (3) includes various sensors and actuators, and collects various data of the computer room in real time, providing basic data for the data preprocessing module (4), the digital twin and 3D visualization module (5), the big data analysis and intelligent control module (6), the energy refined management module (7), the alarm management and linkage module (8), and the efficient computer room control platform (9) communicatively connected to the above modules, ensuring the authenticity of the data of the efficient computer room management system; The data preprocessing module (4) performs refined load calculation for the air-conditioning system of the efficient computer room; The specific calculation and analysis process is as follows: Set a certain equipment as the target equipment, and use the data acquisition module (3) to collect data at each point of the target equipment within the monitoring period. Based on the supplied data, use the first formula Where: p1 is the power consumption of the device; p2 is the power consumption of the cooling tower device; p3 is the power consumption of the auxiliary device; P e is the power consumption of the unit device within time t; μ e is the efficiency coefficient of the unit device within time t; α is the aging coefficient of the unit device; t is the time (in years); P c is the power consumption of the unit cooling device within time t; μ c is the efficiency coefficient of the unit cooling device within time t; β is the load sensitivity coefficient of the unit cooling device; △T is the temperature difference between inside and outside the computer room; T is the reference optimal temperature; P e is the power consumption of the unit auxiliary device within time t; μ e is the efficiency coefficient of the unit auxiliary device within time t; γ is the load sensitivity coefficient of the unit auxiliary device; W is the usage rate of the unit auxiliary device within time t; W max is the maximum usage rate of the unit auxiliary device; δ is the air flow efficiency coefficient; S is the area of the computer room; μ a is the air density; C a is the specific heat capacity of air; t a is the cooling time constant; p l is the power transmission loss; is the grid efficiency coefficient; μ g is the weight coefficient of the power loss; The digital twin and 3D visualization module (5) uses a 3D model to display the host model. The entire model can be moved, zoomed in and out, rotated, and inspected from a free perspective, and the current real-time data information of the host is displayed; The big data analysis and intelligent control module (6) processes the data of the data preprocessing module (4); automatically adjusts the equipment parameters and operation modes to keep the system running efficiently; the big data analysis and intelligent control module (6) includes an equipment real-time monitoring and remote control unit, a zoning management and intelligent strategy unit, and an intelligent optimization and automation rule unit; The energy refined management module (7) provides energy-saving suggestions by analyzing the energy consumption trends of each equipment, generates an energy consumption report to provide data support for the enterprise's energy management decision-making, realizes real-time data collection and analysis, real-time monitoring of energy usage, remote control, and optimized operation strategies. The energy refined management module (7) includes an energy consumption trend analysis and prediction unit, an intelligent energy-saving strategy unit, and an energy consumption statistical report unit; The alarm management and linkage module (8) combines real-time monitoring, intelligent analysis, and automated linkage, analyzes the equipment operation data in real time, discovers potential problems, and issues corresponding alarms.

2. An efficient computer room control method and management system based on the Internet of Things according to claim 1, characterized in that: The equipment selection module (1), the system pipeline module (2), the data acquisition module (3), the data preprocessing module (4), the digital twin and 3D visualization module (5), the big data analysis and intelligent control module (6), the energy refined management module (7), the alarm management and linkage module (8), and the efficient computer room control platform (9) are all communicatively connected to the mobile terminal (10).

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