Machine room control method and system based on communication between air conditioner terminal and water chilling unit

Abstract

The invention provides a machine room control method and system based on communication between an air conditioner terminal and a water chilling unit, and the method comprises the steps: obtaining three-dimensional data and equipment attribute data of a machine room and accessory equipment of the machine room through a server, and building a digital twinborn model of the machine room; the server establishes a dynamic load prediction model and sends the dynamic load prediction model to a first controller of the water chilling unit; the intelligent sensor collects environment data and equipment data; a second controller of the air conditioner terminal obtains the environment data and sends the environment data to the first controller; the first controller predicts the cold load change according to the dynamic load prediction model and the historical data and adjusts the operation strategy of the air conditioning system; the server simulates a machine room environment and an equipment operation condition in real time by using a digital twin model, generates a control strategy in combination with a self-adaptive control strategy after evaluating a result, and sends the control strategy to the first controller; and the first controller dynamically adjusts operation parameters of the air conditioning system according to the strategy. By means of the scheme, efficient, intelligent and accurate real-time monitoring and control over the energy efficiency of the air conditioning system can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning control, and in particular to a method and system for controlling a machine room based on communication between an air conditioning terminal and a chiller. Background Art

[0002] The air conditioning equipment in the computer room usually adjusts the cooling capacity according to the set temperature. The choice of the set temperature depends on the type of air conditioning equipment. For example, for non-liquid cooling equipment, the cooling capacity is adjusted based on the supply air temperature; while for liquid cooling equipment, it is adjusted based on the supply liquid temperature. The current computer room control scheme has problems such as delayed air conditioning regulation, low efficiency, and inaccuracy. Summary of the invention

[0003] The present invention is based on the above-mentioned problems and proposes a computer room control method and system based on communication between air-conditioning terminals and chillers. The scheme of the present invention can realize efficient, intelligent and accurate real-time monitoring of the energy efficiency of the air-conditioning system, use data analysis technology to evaluate the energy efficiency performance of the system operation, and provide optimization suggestions based on the analysis results to form a closed-loop control system and continuously improve energy efficiency.

[0004] In view of this, one aspect of the present invention proposes a computer room control method based on communication between an air conditioning terminal and a chiller, comprising: The IoT server obtains the three-dimensional data and device attribute data of the computer room and its ancillary equipment, and establishes the first digital twin model of the computer room based on the three-dimensional data and device attribute data; The IoT server establishes a dynamic load prediction model through a machine learning algorithm and sends it to the first controller of the chiller; Deploy multiple smart sensors in the computer room according to the first digital twin model; The intelligent sensor collects first environment data and first equipment data in the computer room in real time; The second controller of the air conditioner terminal obtains the first environment data and the first device data, and sends the first environment data and the first device data to the first controller through the Internet of Things technology; The first controller predicts the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environmental data and the first equipment data, and automatically adjusts the operation strategy of the air conditioning system according to the prediction result; The IoT server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluates the simulation results to obtain a first evaluation result; The IoT server combines a preset adaptive control strategy with the first environment data, the first device data and the first evaluation result, and sends the first control strategy to the first controller; The first controller dynamically adjusts operating parameters of the air-conditioning system according to the first control strategy.

[0005] Optionally, the IoT server acquires three-dimensional data and device attribute data of the computer room and its ancillary equipment, and establishes a first digital twin model of the computer room according to the three-dimensional data and the device attribute data, comprising: The IoT server obtains the three-dimensional data and equipment attribute data of the computer room and its ancillary equipment; Processing and integrating the three-dimensional data with the device attribute data to ensure the accuracy and consistency of the data; Acquire point cloud data of the machine room from the three-dimensional data; Use modeling software to process point cloud data and convert it into a three-dimensional mesh model; Associate the 3D mesh model with the equipment attribute data to build a semantic 3D model of the computer room; Establish a device object model based on the unified modeling language to define the interaction relationship between devices; Use Web3D technology to realize the visualization of 3D models; Establish a physical model library containing multiple physical models; Integrate 3D models, object models, and physical models to build a complete digital twin model system; Verify and optimize the established digital twin model to ensure that it can accurately reflect the actual situation of the computer room, including: comparing with the actual computer room to check the accuracy of the model; adjusting and optimizing the model based on feedback to obtain the first digital twin model.

[0006] Optionally, the IoT server establishes a dynamic load prediction model through a machine learning algorithm and sends the model to the first controller of the chiller, comprising: The IoT server obtains historical environmental data and historical equipment operation data; After preprocessing the historical environmental data and historical equipment operation data, the first feature that affects the load forecast is extracted; Constructing a first feature vector according to the first feature, the first feature vector including the following parameters: time feature, environment feature, equipment feature, load feature, and business feature; A deep learning model is used to build a prediction framework, including: using long short-term memory network as the basic network structure, adding attention mechanism to capture key timing features, introducing residual connection to prevent gradient disappearance, and using batch normalization to accelerate the training process; Combining the first eigenvector and the prediction framework, model training and optimization are performed to obtain a first model; Establishing a model evaluation system to evaluate the first model; Regularly collect new operating data, use the new operating data to evaluate the prediction accuracy of the first model, perform incremental learning updates, automatically adjust model parameters, and obtain the final dynamic load forecasting model; The trained dynamic load forecasting model is converted into a format suitable for running on the controller and then deployed to the first controller.

[0007] Optionally, the step of deploying a plurality of smart sensors in the computer room according to the first digital twin model includes: According to the characteristics of the first digital twin model, analyze the key parameters that need to be monitored in the computer room and determine the type and number of smart sensors required; According to the results of the demand analysis, select suitable smart sensors and design the specific installation locations of the sensors based on the spatial layout information in the first digital twin model; Mark the installation points of sensors in the first digital twin model to ensure that all key areas in the machine room are covered; Install smart sensors in the computer room according to the design plan, and connect the installed sensors to the IoT server for data integration; Establish a monitoring mechanism for sensors and regularly check the working status and data quality of sensors.

[0008] Optionally, the first controller predicts the cooling load change data of the computer room based on the dynamic load prediction model, historical data, the first environment data and the first equipment data, and automatically adjusts the operation strategy of the air-conditioning system according to the prediction result, including: The first controller obtains historical cooling load data and historical equipment operation status data from the IoT server; Integrate the historical cooling load data, the historical equipment operating status data, the first environment data and the first equipment data to form a first data set for load forecasting; Inputting the integrated first data set into the dynamic load prediction model to obtain cold load change prediction data within a preset future period of time; Compare cooling load change forecast data with current system load capacity to identify potential load change trends; Automatically adjust the operation strategy of the air-conditioning system according to the load change trend.

[0009] Optionally, the IoT server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluates the simulation results to obtain a first evaluation result, including: Inputting the first environmental data into the first digital twin model to perform real-time simulation of the environmental state, specifically including: simulating environmental changes in the computer room according to the input first environmental data; predicting environmental state changes under different control strategies, and generating multiple possible operation scenarios; While simulating the environmental state, the IoT server also simulates the operation of the device according to the first device data, specifically including: simulating the energy consumption, load and operation efficiency of the device according to the current working parameters of the device extracted from the first device data and in combination with the current environmental state; and evaluating the performance of the device under different environmental conditions; The simulation results are evaluated to obtain the first evaluation results, including: setting evaluation indicators and conducting quantitative analysis on the simulation results; comparing the simulation results under different scenarios and identifying the best operation strategy and potential problems.

[0010] Optionally, the IoT server combines a preset adaptive control strategy and sends the first control strategy to the first controller according to the first environment data, the first device data and the first evaluation result, including: Load the preset adaptive control strategy library to obtain the adaptive control strategy, including: importing basic control rules, configuring parameter adjustment range, setting control priority, and defining constraints; Combined with the adaptive control strategy, a multi-objective optimization model is established, including: setting environmental comfort goals, configuring energy efficiency goals, defining equipment life goals, and establishing cost control goals; Executing strategy optimization calculation based on the multi-objective optimization model, including: inputting first environmental data and first evaluation results, calculating weight coefficients of each objective, running an optimization solution algorithm, and generating multiple candidate control schemes; Implement strategy evaluation and screening for multiple candidate control schemes to obtain the first control strategy, including: verifying control constraint satisfaction, analyzing strategy execution risk, evaluating regulation stability, and calculating expected benefits; Generate instructions for the first control strategy according to the first control strategy, including: encapsulating control parameters, setting execution timing, adding security verification codes, and configuring priority identifiers; The generated instruction of the first control strategy is sent to the first controller, including: establishing a communication connection, sending the instruction in batches, confirming the receiving status, and recording the sending log.

[0011] Optionally, the step of the first controller dynamically adjusting the operating parameters of the air-conditioning system according to the first control strategy includes: The first controller receives the instruction of the first control strategy through the network communication protocol, and confirms the integrity and validity of the received instruction of the first control strategy; The first controller parses the received instruction of the first control strategy, extracts parameters in the instruction of the first control strategy, and verifies whether the extracted parameters are within an acceptable range of the device; According to the analyzed control strategy, the first controller dynamically adjusts the operating parameters of the air-conditioning system.

[0012] Optionally, the step of integrating the three-dimensional model, the object model and the physical model to construct a complete digital twin model system includes: Establish a model integration framework between 3D models, object models and physical models, including: defining a unified data structure, establishing a model interaction interface, configuring model mapping relationships, and designing data conversion rules; Determine the geometric information association between the 3D model, object model and physical model, including: establishing the spatial coordinate correspondence, defining geometric feature attributes, associating equipment layout information, and building a topological structure model; Construct a dynamic link mechanism between 3D models, object models and physical models, including: establishing a real-time data interface, configuring state synchronization rules, setting update trigger conditions, and realizing bidirectional data flow; Establish the interaction mechanism between 3D models, object models and physical models, including: defining the communication protocol between models, setting the data exchange format, establishing the event trigger mechanism, and implementing the state synchronization strategy; Integrated physical computing module, including: embedded thermodynamic computing engine, integrated fluid dynamics model, associated energy transfer equations, and establishment of multi-physics field coupling; Based on the aforementioned model integration framework, geometric information association, dynamic link mechanism, interaction mechanism and physical calculation module, a visualization presentation layer is constructed to obtain a complete digital twin model system. Implement verification and optimization of the digital twin model system, including: performing consistency checks, conducting accuracy verification, evaluating performance indicators, and optimizing operational efficiency.

[0013] Another aspect of the present invention provides a computer room control system based on communication between an air conditioner terminal and a chiller, which is used to execute a computer room control method based on communication between an air conditioner terminal and a chiller, and is characterized in that it includes: an Internet of Things server, an intelligent sensor, a first controller of the chiller, and a second controller of the air conditioner terminal; wherein, The IoT server is used for: Obtain the three-dimensional data and equipment attribute data of the computer room and its ancillary equipment, and establish the first digital twin model of the computer room based on the three-dimensional data and equipment attribute data; Establishing a dynamic load prediction model through a machine learning algorithm and sending it to the first controller; Deploy a plurality of the smart sensors in the computer room according to the first digital twin model; The intelligent sensor is used to collect first environmental data and first equipment data in the computer room in real time; The second controller is used to obtain the first environment data and the first device data, and send the first environment data and the first device data to the first controller through the Internet of Things technology; The first controller is used to predict the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environmental data and the first equipment data, and automatically adjust the operation strategy of the air conditioning system according to the prediction result; The IoT server is also used for: Using the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluating the simulation results to obtain a first evaluation result; In combination with a preset adaptive control strategy, according to the first environmental data, the first device data and the first evaluation result, the first control strategy is sent to the first controller; The first controller is further used to dynamically adjust operating parameters of the air-conditioning system according to the first control strategy.

[0014] According to the technical solution of the present invention, a computer room control method based on communication between an air-conditioning terminal and a chiller includes: an Internet of Things server acquires three-dimensional data and device attribute data of a computer room and its ancillary equipment, and establishes a first digital twin model of the computer room according to the three-dimensional data and the device attribute data; the Internet of Things server establishes a dynamic load prediction model through a machine learning algorithm, and sends it to a first controller of the chiller; a plurality of smart sensors are deployed in the computer room according to the first digital twin model; the smart sensors collect first environmental data and first device data in the computer room in real time; a second controller of the air-conditioning terminal acquires the first environmental data and the first device data, and sends the first environmental data and the first device data to the first controller through the Internet of Things technology; The first controller predicts the cold load change data of the computer room based on the dynamic load prediction model, historical data, the first environmental data and the first equipment data, and automatically adjusts the operation strategy of the air-conditioning system according to the prediction result; the Internet of Things server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluates the simulation results to obtain a first evaluation result; the Internet of Things server combines the preset adaptive control strategy, according to the first environmental data, the first equipment data and the first evaluation result, and sends the first control strategy to the first controller; the first controller dynamically adjusts the operating parameters of the air-conditioning system according to the first control strategy. The scheme of the present invention can realize efficient, intelligent and accurate real-time monitoring of the energy efficiency of the air-conditioning system, use data analysis technology to evaluate the energy efficiency performance of the system operation, and provide optimization suggestions based on the analysis results to form a closed-loop control system and continuously improve energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a computer room control method based on communication between an air-conditioning terminal and a chiller provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of a computer room control system based on communication between an air-conditioning terminal and a chiller provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0017] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0018] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0019] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0020] Refer to the following Figure 1 to Figure 2 A computer room control method and system based on communication between an air-conditioning terminal and a chiller unit according to some embodiments of the present invention will be described.

[0021] like Figure 1 As shown, an embodiment of the present invention provides a computer room control method based on communication between an air conditioning terminal and a chiller, comprising: The IoT server obtains the three-dimensional data and device attribute data of the computer room and its ancillary equipment, and establishes the first digital twin model of the computer room based on the three-dimensional data and device attribute data; The IoT server establishes a dynamic load prediction model through a machine learning algorithm and sends it to the first controller of the chiller; Deploy multiple smart sensors in the computer room according to the first digital twin model; The intelligent sensor collects first environment data and first equipment data in the computer room in real time; In this step, the first environmental data may include: environmental parameters such as temperature, humidity, air flow rate, and also include data of environmental participants such as indoor personnel data (identified by video AI) and indoor movable objects data. The first device data includes device working data, device load, etc. The acquisition parameters of various sensors can be configured, such as: temperature sensor (sampling frequency 0.5Hz, accuracy ±0.1℃), humidity sensor (sampling frequency 0.5Hz, accuracy ±2%RH), airflow sensor (sampling frequency 1Hz, measurement range 0-10m / s), power sensor (sampling frequency 2Hz, accuracy ±0.5%), load sensor (sampling frequency 1Hz, accuracy ±1%), etc.; establish a data preprocessing mechanism, including: real-time filtering to remove high-frequency noise, data smoothing, outlier detection and elimination, data compensation and correction, etc.; realize multi-dimensional data fusion, including: synchronized sampling clock, data Data timestamp alignment, multi-source data correlation analysis, establishment of data fusion model, etc.; set data cache and transmission strategy, including: local data cache, hierarchical data storage, data compression encoding, bandwidth adaptive transmission, etc.; build a data quality assessment system, including: real-time calculation of signal-to-noise ratio, evaluation of data integrity, monitoring of data delay, analysis of data consistency, etc.; establish an emergency collection mechanism, including: setting up backup sampling strategy, defining emergency sampling frequency, configuring data supplementary collection rules, establishing data recovery plan, etc.; realize edge computing preprocessing, including: local data analysis, feature value extraction, preliminary fault diagnosis, emergency warning, etc. In this step, high-precision sensors can ensure the accuracy of basic data, preprocessing mechanisms can eliminate interference factors, and data fusion can improve information integrity.

[0022] The second controller of the air conditioner terminal obtains the first environment data and the first device data, and sends the first environment data and the first device data to the first controller through the Internet of Things technology; In this step, the second controller obtains the first environmental data and the first device data by connecting to the smart sensor (such as temperature, humidity sensor, etc.), specifically including: configuring the sampling frequency of the sensor to ensure that it can collect environmental data regularly; reading the sensor data through the data interface (such as serial port, I2C, etc.) and performing preliminary processing; formatting the obtained first environmental data for subsequent transmission, specifically including: converting the data into a unified format (such as JSON or XML) to ensure a clear data structure; adding necessary metadata (such as timestamp, sensor ID, etc.) to facilitate data tracking and management; sending the formatted first environmental data to the first controller through the Internet of Things technology, specifically including: selecting a suitable communication protocol (such as MQTT, HTTP, CoAP, etc.) to ensure the reliability and real-time nature of data transmission; establishing a network connection with the first controller and sending a data packet. In addition, the first controller receives the first environment data and the first device data from the second controller and processes them, including: verifying the integrity and validity of the received data; adjusting the operating state of the air-conditioning system (such as temperature setting, wind speed adjustment, etc.) according to the received environment data; establishing a feedback mechanism to ensure the validity of data transmission and the stability of the system, including: setting a data transmission confirmation mechanism in the second controller to ensure that the data is successfully sent; after receiving the data, the first controller sends a confirmation message to the second controller to ensure the reliability of two-way communication. In this step, through the first environment data and the first device data obtained by the second controller, the first controller can monitor the environment data and device data in real time, quickly respond to environmental changes, and optimize the operation of the air-conditioning system; accurate environment data transmission enables the air-conditioning system to be adjusted according to actual needs, thereby improving energy efficiency and reducing energy consumption.

[0023] The first controller predicts the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environmental data and the first equipment data, and automatically adjusts the operation strategy of the air conditioning system according to the prediction result; The IoT server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time (the model can simulate the effects of different control strategies to help managers optimize decisions), and evaluates the simulation results to obtain a first evaluation result; The IoT server combines a preset adaptive control strategy with the first environment data, the first device data and the first evaluation result, and sends the first control strategy to the first controller; The first controller dynamically adjusts operating parameters (such as cold water temperature, wind speed, etc.) of the air-conditioning system according to the first control strategy.

[0024] In the embodiments of the present invention, a multi-level communication architecture can also be used to ensure efficient communication between air-conditioning terminal equipment, chillers and other related equipment; through standardized communication protocols, interconnection between devices can be achieved, thereby improving the overall flexibility and scalability of the system.

[0025] By adopting the technical solution of this embodiment, it is possible to realize efficient, intelligent and accurate real-time monitoring of the energy efficiency of the air-conditioning system, use data analysis technology to evaluate the energy efficiency performance of the system operation, and provide optimization suggestions based on the analysis results, forming a closed-loop control system to continuously improve energy efficiency.

[0026] In some possible implementations of the present invention, the IoT server acquires three-dimensional data and device attribute data of a computer room and its ancillary equipment, and establishes a first digital twin model of the computer room according to the three-dimensional data and the device attribute data, including: The IoT server obtains the three-dimensional data and equipment attribute data of the computer room and its ancillary equipment; In this step, the spatial layout, equipment location, point cloud data, etc. of the computer room can be obtained by using a laser scanner or a three-dimensional camera. The radio frequency identification (RFID) technology can be used to identify the equipment in the computer room to obtain the equipment's attribute data (including: equipment model specifications, rated power, operating parameter range, equipment physical location coordinates, equipment connection relationship, etc.), and then store these data in a cloud server or database server. The IoT server can obtain it from the cloud server or database server according to the computer room identification.

[0027] Process and integrate the three-dimensional data with the equipment attribute data to ensure the accuracy and consistency of the data (e.g. data cleaning: removing redundant and erroneous data; data format conversion: converting data from different sources into a unified format for subsequent processing; etc.); Acquire point cloud data of the machine room from the three-dimensional data; Use modeling software to process point cloud data (including: point cloud registration, stitching point cloud data collected from multiple locations into a complete 3D model; point cloud denoising, removing noise points and outliers; point cloud segmentation, identifying and segmenting each device entity in the machine room; etc.), and convert point cloud data into a 3D mesh model; Associate the 3D mesh model with the equipment attribute data to build a semantic 3D model of the computer room; Establish a device object model based on the Unified Modeling Language (UML) to define the interaction relationship between devices; Use Web3D technology to realize the visualization of 3D models (support the following functions: model rotation, zooming and translation operations; real-time display of equipment status; real-time viewing of equipment parameters; dynamic update of equipment operation data); Establish a physical model library containing multiple physical models (including: thermodynamic model, airflow dynamics model, equipment energy consumption model, heat dissipation model); Integrate 3D models, object models, and physical models to build a complete digital twin model system; Verify and optimize the established digital twin model to ensure that it can accurately reflect the actual situation of the computer room, including: comparing with the actual computer room to check the accuracy of the model; adjusting and optimizing the model based on feedback to obtain the first digital twin model.

[0028] In this embodiment, by establishing a digital twin model, managers can monitor the equipment status and environmental changes in the computer room in real time, thereby improving management efficiency; the digital twin model provides managers with a visual decision support tool to help them make more accurate control decisions in complex environments; through real-time data analysis and model optimization, the operation and maintenance costs of the computer room can be effectively reduced, and the utilization efficiency and reliability of equipment can be improved; the application of the digital twin model enables the system to quickly adapt to environmental changes, thereby improving the flexibility and response speed of computer room management.

[0029] In some possible implementations of the present invention, the IoT server establishes a dynamic load prediction model through a machine learning algorithm and sends the model to the first controller of the chiller, including: The IoT server obtains historical environmental data (including but not limited to: environmental parameters such as temperature, humidity, air velocity, and historical indoor personnel data (identified by video AI), historical indoor movable objects data and other environmental participant data) and historical equipment operation data (including but not limited to: equipment working status, load conditions and energy consumption data, etc.); After preprocessing the historical environmental data and historical equipment operation data (including: data cleaning (removing missing values ​​and outliers), data standardization (converting data of different dimensions into a unified standard for subsequent analysis)), the first features that affect load forecasting (such as time features (hours, days, weeks, etc.), environmental features, etc.) are extracted; Construct a first feature vector based on the first feature. The first feature vector includes the following parameters: time features (time, date, season, etc.), environmental features (indoor and outdoor temperature and humidity, air pressure, etc.), equipment features (operating status, power, etc.), load features (historical load data), and business features (business volume, number of online devices, etc.); A deep learning model is used to build a prediction framework, including: using the long short-term memory network (LSTM) as the basic network structure, adding an attention mechanism to capture key timing features, introducing residual connections to prevent gradient disappearance, and using batch normalization to accelerate the training process; Combine the first eigenvector and the prediction framework, perform model training and optimization (adaptive learning rate optimizer; early stopping method to prevent overfitting; cross validation to evaluate model performance; dynamically adjust model parameters according to validation results) to obtain the first model; Establish a model evaluation system to evaluate the first model (calculate the root mean square error (RMSE); evaluate the mean absolute percentage error (MAPE); analyze the fit of the prediction curve; and visualize the prediction results); Regularly collect new operating data, use the new operating data to evaluate the prediction accuracy of the first model, perform incremental learning updates, automatically adjust model parameters, and obtain the final dynamic load forecasting model; The trained dynamic load forecasting model is converted into a format suitable for running on the controller (such as API or data packet) and then deployed to the first controller (including: model compression and optimization; interface protocol definition; data transmission encryption; operation status monitoring).

[0030] In this embodiment, the dynamic load prediction model established by the machine learning algorithm can provide more accurate load prediction based on historical data and real-time environmental data, helping to optimize the operation of the chiller; the real-time load prediction results enable the chiller to respond quickly to environmental changes and automatically adjust the operating strategy, thereby improving the flexibility and energy efficiency of the system; by optimizing the operating strategy of the chiller, it can effectively reduce energy consumption, reduce operating costs, and improve economic benefits; the dynamic load prediction model provides data support for managers, helping them make more accurate decisions in complex environments and improving the intelligence level of computer room management.

[0031] In some possible implementations of the present invention, the step of deploying a plurality of smart sensors in the computer room according to the first digital twin model includes: According to the characteristics of the first digital twin model, analyze the key parameters that need to be monitored in the computer room (such as temperature, humidity, air flow rate, equipment status, etc.), and determine the type and number of smart sensors required; In this step, the computer room environment analysis is performed based on the first digital twin model, including but not limited to: using computational fluid dynamics (CFD) to simulate the airflow distribution in the computer room, analyzing the heat dissipation characteristics of the heat source equipment, identifying the distribution law of the temperature field, and determining the airflow dead zone and hot spot area; According to the results of the demand analysis, select suitable smart sensors and design the specific installation locations of the sensors based on the spatial layout information in the first digital twin model; In this step, the factors to be considered in selecting suitable smart sensors include: the measurement accuracy and range of the sensor, the compatibility of the communication protocol (such as Zigbee, LoRa, Wi-Fi, etc.) with the IoT server, the power consumption of the sensor, and the ease of installation. A sensor deployment optimization model can be established first, including: defining coverage evaluation indicators, establishing measurement accuracy constraints, considering energy consumption balance factors, and setting cost constraints; then the sensor layout plan is determined based on the use of intelligent optimization algorithms, including: using genetic algorithms to optimize the number of sensors, using particle swarm algorithms to optimize sensor locations, establishing multi-objective optimization functions, and iteratively calculating the optimal solution; finally, combined with the spatial layout information in the first digital twin model, the specific installation location of the sensor is designed.

[0032] Mark the installation points of sensors in the first digital twin model to ensure that all key areas in the machine room are covered (consider the distance between sensors and signal interference to ensure the accuracy and reliability of data collection); Install smart sensors in the computer room according to the design plan, and connect the installed sensors to the IoT server for data integration (this step includes: testing the data acquisition function of the sensor to ensure that it can transmit data to the IoT server in real time; verifying the accuracy and completeness of the data to ensure that the sensor can work properly); In this step, after installing the smart sensor, each sensor node is configured (including: setting the sampling frequency, configuring communication parameters, defining alarm thresholds, establishing data filtering rules, etc.) to ensure that it can safely and effectively collect and transmit data with the IoT server; and build the sensor network topology, including: planning communication routes, setting backup links, optimizing network loads, and achieving fault self-healing, etc.; finally, perform sensor self-calibration settings, including: establishing a sensor error model, designing a self-calibration algorithm, defining a calibration cycle, and recording calibration data.

[0033] Establish a monitoring mechanism for sensors and regularly check the working status and data quality of sensors.

[0034] In this step, a sensor health monitoring mechanism is established, including monitoring signal strength, detecting data anomalies, evaluating battery life, diagnosing fault conditions, etc.; an alarm mechanism is set up to promptly detect and handle sensor failures or data anomalies; and sensors are calibrated and maintained regularly to ensure their long-term stable operation.

[0035] In this embodiment, after the deployment of smart sensors, real-time monitoring of the computer room environment and equipment status can be achieved, thereby improving management efficiency. Through real-time collected data, managers can make more accurate decisions based on data analysis and optimize the operation strategy of the computer room. Real-time monitoring of sensors can promptly detect potential problems, reduce equipment failure rates, and improve the overall reliability of the system. Combined with the first digital twin model, the deployment of smart sensors provides an intelligent foundation for computer room management, promoting the transformation of computer room management towards automation and intelligence.

[0036] In some possible implementations of the present invention, the first controller predicts the cooling load change data of the computer room based on the dynamic load prediction model, historical data, the first environment data, and the first equipment data, and automatically adjusts the operation strategy of the air-conditioning system according to the prediction result, including: The first controller obtains historical cooling load data and historical equipment operation status data from the IoT server; Integrate the historical cooling load data, the historical equipment operating status data, the first environment data and the first equipment data to form a first data set for load forecasting; In this step, in order to further improve the accuracy of load forecasting, outdoor environmental data can be collected in real time, and the outdoor environmental data can be integrated with the historical cooling load data, the historical equipment operating status data, the first environmental data, and the first equipment data to form a first data set for load forecasting.

[0037] Inputting the integrated first data set into the dynamic load prediction model to obtain cold load change prediction data within a preset future period of time; Compare cooling load change forecast data with current system load capacity to identify potential load change trends; Automatically adjust the operation strategy of the air-conditioning system according to the load change trend.

[0038] In this step, if the predicted cooling load is higher than the current system load capacity, the cooling capacity of the air-conditioning system is automatically increased (such as increasing the operating frequency of the compressor); if the predicted cooling load is lower than the current system load capacity, the cooling capacity of the air-conditioning system is automatically reduced to save energy.

[0039] In this embodiment, through the dynamic load prediction model, the cooling load changes of the computer room can be accurately predicted based on historical data and real-time environmental data, reducing energy waste caused by inaccurate load prediction; the operation strategy of the air-conditioning system is automatically adjusted so that it can be flexibly adjusted according to actual needs, thereby improving the energy efficiency and response speed of the system.

[0040] In some possible implementations of the present invention, the IoT server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluates the simulation results to obtain a first evaluation result, including: Input the first environmental data (including temperature, humidity, air flow rate, equipment status, etc.) into the first digital twin model, and perform real-time simulation of the environmental status (load the latest digital twin model, synchronize the first real-time parameters, set the simulation time step, and configure the boundary conditions), specifically including: simulating environmental changes in the computer room (such as temperature distribution, humidity changes, etc.) according to the input first environmental data; predicting environmental status changes under different control strategies, and generating multiple possible operation scenarios; In this step, multi-physics field coupling simulation is performed, including: calculating the heat transfer process; simulating the airflow distribution state; analyzing the humidity field change; calculating the equipment heat load; and so on.

[0041] While simulating the environmental state, the IoT server also simulates the operation of the device according to the first device data, specifically including: simulating the energy consumption, load and operation efficiency of the device according to the current working parameters of the device extracted from the first device data and in combination with the current environmental state; and evaluating the performance of the device under different environmental conditions; In this step, the simulation of equipment dynamic characteristics includes but is not limited to: simulating the operating status of the compressor; calculating the efficiency of the heat exchanger; analyzing the operating parameters of the fan; evaluating the system energy efficiency ratio; and so on.

[0042] Evaluate the simulation results to obtain the first evaluation results, including: setting evaluation indicators (such as energy consumption, temperature stability, equipment load, etc.), quantitatively analyzing the simulation results; comparing the simulation results under different scenarios, and identifying the best operation strategy and potential problems.

[0043] In this embodiment, by simulating the computer room environment status and equipment operation status in real time, managers can timely understand the operation status of the computer room and make quick responses; the evaluation of simulation results provides data support for managers, helping them to make more accurate decisions in complex environments and optimize the operation strategy of the computer room; through the simulation and evaluation of different control strategies, the energy consumption of the computer room can be effectively reduced, the operating costs can be reduced, and the economic benefits can be improved; the real-time simulation and evaluation mechanism enables the system to quickly adapt to environmental changes and improve the flexibility and reliability of computer room management.

[0044] In some possible implementations of the present invention, the IoT server combines a preset adaptive control strategy, according to the first environment data, the first device data and the first evaluation result, and sends the first control strategy to the first controller, comprising: Load the preset adaptive control strategy library to obtain the adaptive control strategy, including: importing basic control rules, configuring parameter adjustment range, setting control priority, and defining constraints; Combined with the adaptive control strategy, a multi-objective optimization model is established, including: setting environmental comfort goals, configuring energy efficiency goals, defining equipment life goals, and establishing cost control goals; Executing strategy optimization calculation based on the multi-objective optimization model, including: inputting first environmental data and first evaluation results, calculating weight coefficients of each objective, running an optimization solution algorithm, and generating multiple candidate control schemes; In this embodiment, the optimization algorithm can be one of: Genetic Algorithm (GA), which is a heuristic search algorithm based on natural selection and genetics principles, suitable for solving complex multi-objective optimization problems; Particle Swarm Optimization (PSO), which simulates the foraging behavior of bird flocks and finds the optimal solution through information sharing between individuals, and is suitable for dealing with multi-objective optimization problems in continuous space; Simulated Annealing (SA), a probabilistic search algorithm that simulates the cooling process of matter, is suitable for finding the global optimal solution, especially performing well in complex multi-objective problems; Multi-Objective Evolutionary Algorithms (MOEAs), including NSGA-II, MOEA / D, etc., which are specially designed to deal with multi-objective optimization problems and optimize multiple objectives simultaneously through the evolutionary process; Goal Programming, which finds solutions by setting goals and minimizing the deviations between goals, and is suitable for linear and nonlinear multi-objective problems; Weighted Sum Method (WSM) Method, which converts the multi-objective problem into a single-objective problem by assigning weights to each objective; Tchebycheff Method, which finds the Pareto optimal solution by maximizing the distance between the objective function and the reference point.

[0045] Implement strategy evaluation and screening for multiple candidate control schemes to obtain the first control strategy, including: verifying control constraint satisfaction, analyzing strategy execution risk, evaluating regulation stability, and calculating expected benefits; Generate instructions for the first control strategy according to the first control strategy, including: encapsulating control parameters, setting execution timing, adding security verification codes, and configuring priority identifiers; The generated instruction of the first control strategy is sent to the first controller, including: establishing a communication connection, sending the instruction in batches, confirming the receiving status, and recording the sending log.

[0046] In this embodiment, by real-time analysis of environmental data and evaluation results, the system can quickly generate and adjust control strategies to improve the response speed to environmental changes; the application of adaptive control strategies enables the system to dynamically adjust operating parameters according to actual needs, thereby optimizing energy utilization efficiency and reducing energy consumption; through continuous feedback and optimization mechanisms, the system can maintain stable operation under different environmental conditions and reduce the probability of failure.

[0047] In some possible implementations of the present invention, the step of dynamically adjusting the operating parameters of the air-conditioning system according to the first control strategy by the first controller includes: The first controller receives the instruction of the first control strategy through a network communication protocol (such as MQTT, HTTP, etc.), and confirms the integrity and validity of the received instruction of the first control strategy; The first controller parses the received instruction of the first control strategy, extracts parameters (such as temperature setting, wind speed, operation mode, etc.) in the instruction of the first control strategy, and verifies whether the extracted parameters are within an acceptable range of the device; According to the analyzed control strategy, the first controller dynamically adjusts the operating parameters of the air-conditioning system (specifically including: adjusting the temperature setting value of the air-conditioning to ensure that it meets the requirements of the first control strategy; adjusting the wind speed and operating mode (such as cooling, heating, dehumidification, etc.) as needed to adapt to the current environmental conditions; monitoring the real-time operating status of the air-conditioning system to ensure that the adjusted parameters can be effectively executed).

[0048] In this embodiment, dynamic adjustment of the operating parameters of the air-conditioning system can optimize energy consumption according to actual environmental requirements and reduce energy waste; by precisely controlling indoor environmental parameters, it can effectively improve user comfort and meet the needs of different users; by responding to environmental changes and user needs in real time, the air-conditioning system can flexibly adapt to various usage scenarios; through continuous monitoring and feedback mechanisms, potential problems can be discovered and resolved in a timely manner, reducing the incidence of equipment failures.

[0049] In some possible implementations of the present invention, it also includes: after adjusting the operating parameters, the first controller collects the latest real-time operating data of the air-conditioning system (such as indoor temperature, humidity, energy consumption, etc.), and feeds back the latest real-time operating data to the Internet of Things server for subsequent control strategy optimization; analyzes the gap between the feedback data and the preset target, evaluates the effectiveness of the current control strategy, and adjusts the control strategy or requests a new control strategy based on the evaluation results to improve the operating efficiency and comfort of the system.

[0050] In some possible implementations of the present invention, the step of integrating the three-dimensional model, the object model and the physical model to construct a complete digital twin model system includes: Establish a model integration framework between 3D models, object models and physical models, including: defining a unified data structure, establishing a model interaction interface, configuring model mapping relationships, and designing data conversion rules; Determine the geometric information association between the 3D model, object model and physical model, including: establishing the spatial coordinate correspondence, defining geometric feature attributes, associating equipment layout information, and building a topological structure model; Construct a dynamic link mechanism between 3D models, object models and physical models, including: establishing a real-time data interface, configuring state synchronization rules, setting update trigger conditions, and realizing bidirectional data flow; Establish the interaction mechanism between 3D models, object models and physical models, including: defining the communication protocol between models, setting the data exchange format, establishing the event trigger mechanism, and implementing the state synchronization strategy; Integrated physical computing module, including: embedded thermodynamic computing engine, integrated fluid dynamics model, associated energy transfer equations, and establishment of multi-physics field coupling; Based on the aforementioned model integration framework, geometric information association, dynamic link mechanism, interaction mechanism and physical calculation module, a visualization presentation layer is constructed (including: integrating a 3D rendering engine, configuring dynamic display rules, establishing an interactive control interface, and realizing multi-dimensional data presentation) to obtain a complete digital twin model system. Implement verification and optimization of the digital twin model system, including: performing consistency checks, conducting accuracy verification, evaluating performance indicators, and optimizing operational efficiency.

[0051] In this embodiment, a unified framework ensures data consistency, multi-model integration provides comprehensive expression, and dynamic links ensure real-time performance; physical computing supports deep analysis, interactive mechanisms achieve collaborative operation, and visualization provides intuitive display; optimized data structures improve performance, efficient interactive mechanisms reduce latency, and reasonable update strategies save resources; the complete model supports multi-scenario applications, precise calculations provide reliable decision-making basis, and intuitive display facilitates management and maintenance.

[0052] See also Figure 2 Another embodiment of the present invention provides a computer room control system based on communication between an air conditioner terminal and a chiller, which is used to execute a computer room control method based on communication between an air conditioner terminal and a chiller, characterized in that it includes: an Internet of Things server, an intelligent sensor, a first controller of the chiller, and a second controller of the air conditioner terminal; wherein, The IoT server is used for: Obtain the three-dimensional data and equipment attribute data of the computer room and its ancillary equipment, and establish the first digital twin model of the computer room based on the three-dimensional data and equipment attribute data; Establishing a dynamic load prediction model through a machine learning algorithm and sending it to the first controller; Deploy a plurality of the smart sensors in the computer room according to the first digital twin model; The intelligent sensor is used to collect first environmental data and first equipment data in the computer room in real time; The second controller is used to obtain the first environment data and the first device data, and send the first environment data and the first device data to the first controller through the Internet of Things technology; The first controller is used to predict the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environmental data and the first equipment data, and automatically adjust the operation strategy of the air conditioning system according to the prediction result; The IoT server is also used for: Using the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluating the simulation results to obtain a first evaluation result; In combination with a preset adaptive control strategy, according to the first environmental data, the first device data and the first evaluation result, the first control strategy is sent to the first controller; The first controller is further used to dynamically adjust operating parameters of the air-conditioning system according to the first control strategy.

[0053] It should be known that Figure 2 The block diagram of the computer room control system based on the communication between the air conditioner terminal and the chiller is shown for illustration only, and the number of modules shown does not limit the protection scope of the present invention. The computer room control system based on the communication between the air conditioner terminal and the chiller provided in this embodiment can be used to execute the corresponding computer room control method based on the communication between the air conditioner terminal and the chiller. For the specific implementation process, please refer to the description of each method embodiment, which will not be repeated here.

[0054] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0055] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0056] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0057] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0058] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0059] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0060] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0061] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0062] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A computer room control method based on communication between air conditioning terminals and chillers, characterized in that: include: The IoT server obtains the three-dimensional data and device attribute data of the computer room and its ancillary equipment, and establishes the first digital twin model of the computer room based on the three-dimensional data and device attribute data; The IoT server establishes a dynamic load prediction model through a machine learning algorithm and sends it to the first controller of the chiller; Deploy multiple smart sensors in the computer room according to the first digital twin model; The intelligent sensor collects first environment data and first equipment data in the computer room in real time; The second controller of the air conditioner terminal obtains the first environment data and the first device data, and sends the first environment data and the first device data to the first controller through the Internet of Things technology; The first controller predicts the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environmental data and the first equipment data, and automatically adjusts the operation strategy of the air conditioning system according to the prediction result; The IoT server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluates the simulation results to obtain a first evaluation result; The IoT server combines a preset adaptive control strategy with the first environment data, the first device data and the first evaluation result, and sends the first control strategy to the first controller; The first controller dynamically adjusts operating parameters of the air-conditioning system according to the first control strategy.

2. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 1 is characterized in that: The IoT server acquires three-dimensional data and device attribute data of the computer room and its ancillary equipment, and establishes a first digital twin model of the computer room according to the three-dimensional data and the device attribute data, including: The IoT server obtains the three-dimensional data and equipment attribute data of the computer room and its ancillary equipment; Processing and integrating the three-dimensional data with the device attribute data to ensure the accuracy and consistency of the data; Acquire point cloud data of the machine room from the three-dimensional data; Use modeling software to process point cloud data and convert it into a three-dimensional mesh model; Associate the 3D mesh model with the equipment attribute data to build a semantic 3D model of the computer room; Establish a device object model based on the unified modeling language to define the interaction relationship between devices; Use Web3D technology to realize the visualization of 3D models; Establish a physical model library containing multiple physical models; Integrate 3D models, object models, and physical models to build a complete digital twin model system; The established digital twin model is verified and optimized to ensure that it can accurately reflect the actual situation of the computer room, including: comparing with the actual computer room to check the accuracy of the model; adjusting and optimizing the model based on feedback to obtain the first digital twin model.

3. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 2 is characterized in that: The step of the Internet of Things server establishing a dynamic load prediction model through a machine learning algorithm and sending the model to the first controller of the chiller comprises: The IoT server obtains historical environmental data and historical equipment operation data; After preprocessing the historical environmental data and historical equipment operation data, the first feature that affects the load forecast is extracted; Constructing a first feature vector according to the first feature, the first feature vector including the following parameters: time feature, environment feature, equipment feature, load feature, and business feature; A deep learning model is used to build a prediction framework, including: using long short-term memory network as the basic network structure, adding attention mechanism to capture key timing features, introducing residual connection to prevent gradient disappearance, and using batch normalization to accelerate the training process; Combining the first eigenvector and the prediction framework, model training and optimization are performed to obtain a first model; Establishing a model evaluation system to evaluate the first model; Regularly collect new operating data, use the new operating data to evaluate the prediction accuracy of the first model, perform incremental learning updates, automatically adjust model parameters, and obtain the final dynamic load forecasting model; The trained dynamic load forecasting model is converted into a format suitable for running on the controller and then deployed to the first controller.

4. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 3 is characterized in that: The step of deploying a plurality of smart sensors in the computer room according to the first digital twin model includes: According to the characteristics of the first digital twin model, analyze the key parameters that need to be monitored in the computer room and determine the type and number of smart sensors required; According to the results of the demand analysis, select suitable smart sensors and design the specific installation locations of the sensors based on the spatial layout information in the first digital twin model; Mark the installation points of sensors in the first digital twin model to ensure that all key areas in the machine room are covered; Install smart sensors in the computer room according to the design plan, and connect the installed sensors to the IoT server for data integration; Establish a monitoring mechanism for sensors and regularly check the working status and data quality of sensors.

5. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 4 is characterized in that: The first controller predicts the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environment data and the first equipment data, and automatically adjusts the operation strategy of the air conditioning system according to the prediction result, including: The first controller obtains historical cooling load data and historical equipment operation status data from the IoT server; Integrate the historical cooling load data, the historical equipment operating status data, the first environment data and the first equipment data to form a first data set for load forecasting; Inputting the integrated first data set into the dynamic load prediction model to obtain cold load change prediction data within a preset future period of time; Compare cooling load change forecast data with current system load capacity to identify potential load change trends; Automatically adjust the operation strategy of the air-conditioning system according to the load change trend.

6. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 5, characterized in that: The IoT server uses the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluates the simulation results to obtain a first evaluation result, including: Inputting the first environmental data into the first digital twin model to perform real-time simulation of the environmental state, specifically including: simulating environmental changes in the computer room according to the input first environmental data; predicting environmental state changes under different control strategies, and generating multiple possible operation scenarios; While simulating the environmental state, the IoT server also simulates the operation of the device according to the first device data, specifically including: simulating the energy consumption, load and operation efficiency of the device according to the current working parameters of the device extracted from the first device data and in combination with the current environmental state; and evaluating the performance of the device under different environmental conditions; The simulation results are evaluated to obtain the first evaluation results, including: setting evaluation indicators and conducting quantitative analysis on the simulation results; comparing the simulation results under different scenarios and identifying the best operation strategy and potential problems.

7. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 6, characterized in that: The step of the IoT server combining a preset adaptive control strategy and sending the first control strategy to the first controller according to the first environment data, the first device data and the first evaluation result includes: Load the preset adaptive control strategy library to obtain the adaptive control strategy, including: importing basic control rules, configuring parameter adjustment range, setting control priority, and defining constraints; Combined with the adaptive control strategy, a multi-objective optimization model is established, including: setting environmental comfort goals, configuring energy efficiency goals, defining equipment life goals, and establishing cost control goals; Executing strategy optimization calculation based on the multi-objective optimization model, including: inputting first environmental data and first evaluation results, calculating weight coefficients of each objective, running an optimization solution algorithm, and generating multiple candidate control schemes; Implement strategy evaluation and screening for multiple candidate control schemes to obtain the first control strategy, including: verifying control constraint satisfaction, analyzing strategy execution risk, evaluating regulation stability, and calculating expected benefits; Generate instructions for the first control strategy according to the first control strategy, including: encapsulating control parameters, setting execution timing, adding security verification codes, and configuring priority identifiers; The generated instruction of the first control strategy is sent to the first controller, including: establishing a communication connection, sending the instruction in batches, confirming the receiving status, and recording the sending log.

8. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 7, characterized in that: The step of dynamically adjusting the operating parameters of the air-conditioning system according to the first control strategy by the first controller includes: The first controller receives the instruction of the first control strategy through the network communication protocol, and confirms the integrity and validity of the received instruction of the first control strategy; The first controller parses the received instruction of the first control strategy, extracts parameters in the instruction of the first control strategy, and verifies whether the extracted parameters are within an acceptable range of the device; According to the analyzed control strategy, the first controller dynamically adjusts the operating parameters of the air-conditioning system.

9. The computer room control method based on communication between the air conditioning terminal and the chiller according to claim 8, characterized in that: The steps of integrating the three-dimensional model, the object model and the physical model to build a complete digital twin model system include: Establish a model integration framework between 3D models, object models and physical models, including: defining a unified data structure, establishing a model interaction interface, configuring model mapping relationships, and designing data conversion rules; Determine the geometric information association between the 3D model, object model and physical model, including: establishing the spatial coordinate correspondence, defining geometric feature attributes, associating equipment layout information, and building a topological structure model; Construct a dynamic link mechanism between 3D models, object models and physical models, including: establishing a real-time data interface, configuring state synchronization rules, setting update trigger conditions, and realizing bidirectional data flow; Establish the interaction mechanism between 3D models, object models and physical models, including: defining the communication protocol between models, setting the data exchange format, establishing the event trigger mechanism, and implementing the state synchronization strategy; Integrated physical computing module, including: embedded thermodynamic computing engine, integrated fluid dynamics model, associated energy transfer equations, and establishment of multi-physics field coupling; Based on the aforementioned model integration framework, geometric information association, dynamic link mechanism, interaction mechanism and physical calculation module, a visualization presentation layer is constructed to obtain a complete digital twin model system. Implement verification and optimization of the digital twin model system, including: performing consistency checks, conducting accuracy verification, evaluating performance indicators, and optimizing operational efficiency.

10. A computer room control system based on communication between an air conditioner terminal and a chiller, used to execute the computer room control method based on communication between an air conditioner terminal and a chiller as claimed in any one of claims 1 to 9, characterized in that: include: Internet of Things server, smart sensor, first controller of chiller and second controller of air conditioner terminal; wherein, The IoT server is used for: Obtain the three-dimensional data and equipment attribute data of the computer room and its ancillary equipment, and establish the first digital twin model of the computer room based on the three-dimensional data and equipment attribute data; Establishing a dynamic load prediction model through a machine learning algorithm and sending it to the first controller; Deploy a plurality of the smart sensors in the computer room according to the first digital twin model; The intelligent sensor is used to collect first environmental data and first equipment data in the computer room in real time; The second controller is used to obtain the first environment data and the first device data, and send the first environment data and the first device data to the first controller through the Internet of Things technology; The first controller is used to predict the cooling load change data of the computer room according to the dynamic load prediction model, based on historical data, the first environmental data and the first equipment data, and automatically adjust the operation strategy of the air conditioning system according to the prediction result; The IoT server is also used for: Using the first digital twin model to simulate the environmental status and equipment operation status in the computer room in real time, and evaluating the simulation results to obtain a first evaluation result; In combination with a preset adaptive control strategy, according to the first environmental data, the first device data and the first evaluation result, the first control strategy is sent to the first controller; The first controller is further used to dynamically adjust operating parameters of the air-conditioning system according to the first control strategy.

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