Dynamic partition refrigeration regulation and control system based on floor height

Abstract

The invention belongs to the technical field of building refrigeration, and discloses a dynamic partition refrigeration regulation and control system based on floor height, regulation and control areas are divided in real time according to the floor height, heat flux density and the like through a dynamic density clustering partition algorithm, and the influence of high-altitude air pressure on the sensible temperature is corrected in combination with a height compensation module; an adaptation strategy is generated based on a'height-load 'bivariate model; the high-layer area fits the air convection characteristic through a linear function, and the low-layer area reflects the soil thermal buffer effect through an exponential function, so that refrigeration parameters are accurately matched with actual requirements of all floors, and overheating or insufficient refrigeration caused by traditional unified parameter regulation and control is avoided; data are collected in real time through a space-air-ground three-in-one sensing network; when the personnel density in a region is suddenly increased or functions are switched, the system can complete temporary partition merging and regulation and control parameter adjustment in a short time, and the intelligent operation and maintenance requirements of scenes such as a campus and a hospital for real-time monitoring and rapid optimization are met.

Description

Technical Field

[0001] This invention belongs to the field of building refrigeration technology, specifically a dynamic zoned refrigeration control system based on floor height. Background Technology

[0002] Current building cooling systems mostly adopt a broad management model with uniform parameter control or functional zoning, which makes it difficult to adapt to the differentiated cooling needs of different floors due to differences in physical environment. Existing cooling control systems have the following technical problems:

[0003] Low-rise buildings have a significantly higher thermal inertia than high-rise buildings due to factors such as soil heat storage and surrounding shading; while high-rise buildings have a significantly different heat exchange rate due to factors such as high-altitude airflow and solar radiation. However, existing systems use the same cooling parameters for different floors, resulting in overheating or energy waste in some areas, which contradicts the concept of "precise temperature control" in smart energy management.

[0004] In places like campuses and hospitals, there is frequent movement of people and the usage status of functional areas often changes, resulting in changes in cooling demand. However, traditional systems rely on manual adjustments, which have significant response delays and cannot meet the requirements of intelligent operation and maintenance such as "real-time monitoring and rapid optimization".

[0005] Existing systems mostly use single-point temperature sensors and lack a three-dimensional sensing network. They are not effectively linked to environmental parameters such as solar radiation and outdoor wind speed, resulting in a large deviation between the cooling strategy and the actual load, making it difficult to achieve the goal of energy saving and consumption reduction.

[0006] Due to the characteristics of the outdoor environment, the cooling efficiency of high-rise areas is theoretically different from that of low-rise areas. However, traditional systems do not utilize this characteristic for tiered energy utilization, which is inconsistent with the industry development direction of "multi-energy complementarity and maximum energy efficiency". Summary of the Invention

[0007] The purpose of this invention is to provide a dynamic zoned cooling control system based on floor height to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a dynamic zoned cooling control system based on floor height, the system comprising:

[0009] The overall system architecture design unit adopts a three-layer architecture of "cloud-edge-device". The cloud layer deploys a digital twin engine to predict the cooling effect, the edge layer sets up a floor-level intelligent gateway to solve the transmission delay, and the terminal layer sets up a micro-zone controller to realize cross-floor collaboration. A height compensation module is introduced to correct the influence of high-altitude air pressure, providing stable architectural support for the data acquisition and transmission unit.

[0010] Data Acquisition and Transmission Unit: Based on the overall system architecture design unit architecture, it constructs an integrated "space-air-ground" sensing network, collects data from multiple dimensions including space, air, and ground, and uses 5G slicing technology and "data gravity" routing algorithm to ensure transmission, providing comprehensive and accurate data support for floor zoning and demand analysis units;

[0011] Floor Zoning and Demand Analysis Unit: Based on the data from the data acquisition and transmission unit, a dynamic density clustering algorithm is introduced to divide the control area, establish a "height-load" model, fit a function according to the floor characteristics, accurately predict the cooling load, and output the analysis results;

[0012] Intelligent Algorithm and Strategy Generation Unit: Based on the analysis results of the floor zoning and demand analysis unit, the model is developed by integrating reinforcement and transfer learning. The optimal strategy is learned with floor height as a feature. The enthalpy priority logic and cooling potential energy index are introduced to generate intelligent control strategies to guide the refrigeration equipment control unit.

[0013] Refrigeration equipment control unit: Based on intelligent control strategy, compressor height adaptive control technology is introduced, heat pipe-air conditioning composite system is applied, fault propagation blocking mechanism is built-in, and the problem of uneven flow is solved by PWM control;

[0014] Communication and Remote Monitoring Unit: To ensure equipment control, a satellite-ground dual-backup communication network is constructed, an AR monitoring interface is designed, data is stored using blockchain technology, and a height tag retrieval system is set up to provide data support for user interaction and feedback units;

[0015] User interaction and feedback unit: Based on data from the communication and remote monitoring unit, a highly adaptable interactive interface is designed, EEG feedback is introduced, a closed-loop mechanism for user feedback is established, voice recognition is adapted to floor noise, and the entire system is optimized.

[0016] Preferably, the overall system architecture design unit includes:

[0017] (1) Innovative construction of a three-layer architecture: This unit breaks through the traditional centralized control mode and adopts a three-layer distributed architecture of "cloud-edge-terminal". The cloud layer deploys a digital twin engine, integrates data such as floor height, building materials, and meteorological parameters to build a dynamic model, which can simulate and predict the cooling effect 4 hours in advance; the edge layer sets up floor-level intelligent gateways, integrates FPGA chips to achieve fast real-time response, and effectively solves the problem of data transmission delay difference between high-rise and low-rise floors; the terminal layer introduces "micro-zone controllers", each controller covers 3-5 rooms, and realizes cross-floor collaborative control through power line carrier communication, improving the overall response efficiency of the system;

[0018] The edge layer smart gateway integrates a Xilinx Artix-7 series FPGA chip, which supports real-time data processing of ≥1000 data entries / second and ensures that the latency difference between parallel processing of high-level and low-level data is ≤5ms.

[0019] Data transmission priority calculation formula:

[0020]

[0021] In the formula: P 传输 This indicates the data transmission priority, with a value ranging from 0 to 1. A higher value indicates that the data will be transmitted with higher priority.

[0022] λ represents the delay weighting coefficient, which ranges from 0.6 to 0.8 and is dimensionless, emphasizing the high demand for real-time data in high-level areas;

[0023] t 延迟 This indicates the historical average data transmission latency (in milliseconds) for this floor, reflecting the time consumption characteristics of past data transmission.

[0024] Q 实时 This indicates the current cooling load (in W), reflecting the urgency of real-time cooling demand;

[0025] Q 平均 This represents the historical average cooling load (in W) for that floor, serving as a benchmark for determining whether the current load is abnormal.

[0026] Determine the transmission priority of data from different floors, and prioritize the real-time data of high-rise and high-load areas to avoid affecting the control effect due to data delays.

[0027] (2) Height Compensation and Architecture Advantages: The architecture introduces a "height compensation module" which uses a built-in air pressure sensor to determine the absolute height of the floor in real time and automatically corrects the cooling parameters. For example, in areas above the 30th floor, the temperature sensor value is automatically compensated by +0.8℃ to offset the effect of high air pressure on the perceived temperature. This architecture can support seamless expansion in super high-rise buildings, significantly reducing wiring costs compared to traditional solutions, and providing a stable and efficient infrastructure support for subsequent data acquisition and transmission units, ensuring efficient linkage of the entire system.

[0028] Height compensation temperature correction formula:

[0029] T 修正 =T 实测 +ΔT 高度

[0030] ΔT 高度 = 0.026 × (h - 30) × 10 -3 (When h ≥ 30 floors)

[0031] In the formula: T 修正 This represents the perceived temperature after high compensation (in °C), which is the temperature value ultimately used by the system for control decisions and can more accurately reflect the human body's perception of temperature.

[0032] T 实测 This represents the actual indoor temperature (in °C) directly collected by the temperature sensor, which is the raw detection data.

[0033] ΔT 高度 This indicates the temperature difference compensation at height (in °C), which only takes effect when the building height exceeds 30 stories. It increases linearly with the number of floors and is used to counteract the effect of high-altitude air pressure on the perceived temperature.

[0034] h represents the absolute height of the floor (in floors), which is calculated by combining the air pressure value collected by the air pressure sensor with the standard floor height of the building, reflecting the vertical height of the current area.

[0035] The effect of high-altitude air pressure on perceived temperature has been corrected, making the temperature readings more closely match the actual human experience and resolving the discrepancy between perceived temperature and measured temperature caused by air pressure changes above the 30th floor.

[0036] Preferably, the data acquisition and transmission unit includes:

[0037] (1) Space-Air-Ground Integrated Sensing Network: Based on the architectural sensing requirements of the overall system architecture design unit, a space-air-ground integrated sensing network is constructed. The space base receives data from the Fengyun-4 meteorological satellite and analyzes the solar radiation intensity and wind speed gradient at different altitudes. The air base deploys a UAV inspection formation, equipped with an infrared thermal imager to periodically scan the building facade and generate heat loss distribution maps for each floor. The ground base uses fiber optic grating array sensors, which are vertically deployed along the elevator shaft to achieve high-density temperature gradient measurement. The data density is significantly improved compared to traditional point sensors, providing the system with multi-dimensional and high-precision raw data.

[0038] In the integrated air-space-ground sensing network, the data received by the Fengyun-4 meteorological satellite from the space-based system needs to be preprocessed (data during periods of cloud cover is removed), the infrared thermal imager of the air-based UAV inspection formation has a resolution of ≥640×512, and the temperature measurement range of the ground-based fiber optic grating sensor is -30℃ to 80℃ with an accuracy of ±0.1℃.

[0039] (2) Differentiated transmission technology and algorithms: 5G slicing technology is used in the transmission link to allocate differentiated channels for data on different floors: ultra-low latency slicing is used in high-rise areas to ensure real-time control needs; large-connection slicing is used in low-rise areas to carry high-density sensor data; a "data gravity" routing algorithm is designed to simulate the gravity effect to realize the priority transmission of high-rise data to edge nodes, avoid low-rise data congestion, and significantly improve transmission reliability; through this collection and transmission mechanism, comprehensive, accurate and timely data support can be provided for floor zoning and demand analysis units.

[0040] Preferably, the floor zoning and demand analysis unit includes:

[0041] (1) Dynamic density clustering partitioning algorithm: Based on the multidimensional data provided by the data acquisition and transmission unit, this unit breaks through the fixed partitioning mode and introduces the "dynamic density clustering partitioning algorithm". The algorithm takes the floor height as the core dimension, integrates real-time heat flux density, personnel movement trajectory and other data, and periodically re-divides the control area. For example, when a large number of people are temporarily added to the conference room on the west side of the 15th floor, the system can automatically merge the three rooms on the west side of the 14th-16th floors into a temporary partition to ensure that the partitioning matches the actual cooling demand.

[0042] Formula for calculating cluster centers in dynamic partitioning:

[0043]

[0044] w i =α·h i +β·q i +γ·p i

[0045] In the formula: C k The cluster center of the kth dynamic partition is the virtual center coordinates (in meters) determined after comprehensively considering the characteristics of multiple rooms, which serves as the reference point for the control of this partition.

[0046] w i This represents the weight value of the i-th room, which has no unit and reflects the degree of influence of the room in the partitioning. The higher the weight, the greater the influence on the cluster center.

[0047] x i Represents the physical coordinates (in meters) of the i-th room, usually based on the architectural plane coordinate system;

[0048] α, β, γ represent weighting coefficients, all of which are dimensionless parameters and satisfy α+β+γ=1. They represent the influence weights of floor height, heat flux density, and personnel density in zoning, respectively, and can be dynamically adjusted according to building characteristics.

[0049] h iThis represents the floor height (in floors) of the i-th room, reflecting its vertical position characteristics.

[0050] q i This represents the real-time heat flux density of the i-th room (in W / m³). 2 This reflects the intensity of heat exchange in the region;

[0051] p i This represents the population density in the i-th room (unit: person / m²). 2 This reflects the heat generated by human activities;

[0052] The center position of the cooling control zone is dynamically determined, taking into account factors such as floor height, heat flux density and personnel density, to achieve precise division and real-time adjustment of the zone, ensuring that the zone matches the actual cooling demand;

[0053] (2) Construction of the height-load bivariate model: A height-load bivariate model is established, and a correction coefficient for the change of building thermal resistance with height is introduced: the bottom area (1-5 floors) adopts an exponential function to reflect the soil thermal buffering effect; the upper area (above 20 floors) adopts a linear function to reflect the heat exchange characteristics dominated by air convection; the model input parameters include real-time height, window-to-wall ratio, hourly solar altitude angle, etc., which can accurately output the cooling load prediction results and provide a reliable analytical basis for intelligent algorithms and strategy generation units;

[0054] Formula for the height-load bivariate model:

[0055] Bottom layer area (levels 1-5):

[0056] High-rise area (above 20 floors): Q=k3·h·A·Δt

[0057] In the formula: Q represents the predicted cooling load (in W), which is the core parameter for the system to formulate a cooling strategy and reflects the cooling capacity required to maintain the set temperature;

[0058] k1 and k2 represent the thermal resistance correction coefficients of the bottom area, both of which are dimensionless parameters; k1 reflects the influence of soil heat storage on the initial thermal resistance of the bottom area, and k2 reflects the rate of thermal resistance decay as the building height increases.

[0059] k3 represents the thermal resistance correction factor for the high-rise area. It is dimensionless and reflects the linear variation of thermal resistance with height under the dominance of air convection.

[0060] h represents the floor height (in floors), a key parameter that distinguishes between the ground floor and the high floor;

[0061] A represents the room's air-conditioned area (unit: m²). 2 This reflects the physical scale of the refrigeration area;

[0062] Δt represents the indoor-outdoor temperature difference (in °C), which is the difference between the indoor set temperature and the outdoor real-time temperature, and directly affects the size of the cooling load.

[0063] A cooling load prediction model is established based on the differences in floor height, adapting to the heat transfer characteristics of the ground floor (affected by soil heat storage) and the upper floor (affected by air convection) respectively, to achieve accurate prediction of cooling load.

[0064] Preferably, the intelligent algorithm and strategy generation unit includes:

[0065] (1) Development of a highly sensitive learning model: Based on the prediction results of floor zoning and demand analysis units, reinforcement learning and transfer learning algorithms are integrated to introduce a "highly sensitive Q-learning" model; taking floor height as the state feature, through continuous interaction with the digital twin environment, the model learns the optimal control strategy for different heights autonomously; for new buildings, some historical strategies of similar buildings can be inherited through transfer learning, which can significantly shorten the model training cycle and quickly adapt to the cooling needs of new buildings;

[0066] (2) Enthalpy priority strategy and cooling potential energy index: The strategy generation introduces the "enthalpy priority" control logic, which prioritizes the control of dew point temperature in high humidity floors (such as floors 1-5) and focuses on temperature regulation in dry high-rise areas; the "cooling potential energy" index is designed to quantify the cooling efficiency at different heights. For example, in the 25th floor area, due to the low outdoor temperature, the COP value can be significantly improved through strategy adjustment, achieving significant energy savings compared to traditional methods. These intelligent control strategies will directly guide the precise operation of the refrigeration equipment control unit.

[0067] Formula for converting refrigeration potential into energy:

[0068]

[0069] In the formula: P represents the cooling potential energy, a dimensionless parameter. The larger the value, the higher the cooling efficiency of the floor and the easier it is to achieve energy-saving operation.

[0070] T 室外 This indicates the outdoor temperature (in °C) corresponding to the current floor level. It varies with height, and higher floors are usually lower than lower floors.

[0071] T 设定 This indicates the indoor set temperature (in °C), the user's or system's preset comfort temperature target;

[0072] COP 基准 The coefficient of performance (COP) of a refrigeration equipment under standard operating conditions is dimensionless and typically ranges from 3.0 to 4.5, reflecting the inherent energy efficiency of the equipment. ρ represents the air density at the current floor height (in kg / m³), which decreases with increasing height and affects the heat exchange efficiency of the refrigeration equipment.

[0073] v represents the outdoor wind speed at the current floor level (in m / s). The wind speed at higher floors is usually higher than that at lower floors, which can enhance the outdoor heat dissipation effect.

[0074] Quantifying the differences in cooling efficiency at different floor heights provides a basis for developing differentiated energy-saving strategies. High-rise buildings can utilize lower outdoor temperatures and airflow characteristics to improve cooling efficiency.

[0075] Preferably, the refrigeration equipment control unit includes:

[0076] (1) Altitude adaptive control and energy cascade utilization: Based on the intelligent control strategy, the "altitude adaptive control" technology of the magnetic levitation variable frequency compressor is introduced. The compressor speed limit is automatically corrected according to the altitude (converted by air pressure). The operating frequency is reasonably adjusted in high-rise buildings to avoid the risk of surge caused by low air pressure at high altitudes. The heat pipe-air conditioning composite system is applied. The heat pipe technology is used in the high-rise area to transfer waste heat to the lower floor to preheat fresh air, realize cross-floor energy cascade utilization, and improve energy utilization efficiency.

[0077] Compressor height adaptive speed formula:

[0078]

[0079] In the formula: n represents the actual operating speed of the compressor (in r / min), which is the real-time control parameter of the system for the compressor;

[0080] n0 represents the compressor's rated speed (in r / min) under standard atmospheric pressure, which is the baseline operating parameter when the equipment leaves the factory.

[0081] P0 represents standard atmospheric pressure (value 101.325 kPa), which serves as the reference value for pressure correction.

[0082] P h The actual air pressure (in kPa) at the current floor height is collected in real time by an air pressure sensor and decreases as the height increases.

[0083] k h This represents a safety factor, which is dimensionless. It is set at 0.8-0.9 for high-rise areas (h≥20 floors) and 1.0 for low-rise areas, and is used to further reduce the operating risk of compressors in high-rise buildings.

[0084] Based on the air pressure difference at different floor heights, the compressor operating speed is dynamically adjusted to avoid the risk of compressor surge caused by low air pressure at high altitudes, while ensuring stable cooling output.

[0085] (2) Fault blocking and control mechanism: The refrigeration equipment control unit has a built-in "fault propagation blocking" mechanism. When an abnormality is detected in a chiller unit on a certain floor, the hydraulic connection between it and the adjacent floors is automatically cut off to prevent the fault from spreading. Through the pulse width modulation (PWM) control of the electronic expansion valve, the cooling capacity is finely adjusted to solve the problem of uneven flow caused by static pressure difference between high-rise and low-rise floors. The stable operation of this unit requires reliable guarantee from the communication and remote monitoring unit to ensure the accurate execution of control commands and real-time feedback of equipment status.

[0086] The pulse width modulation (PWM) control accuracy of the electronic expansion valve is ±0.1%. By fine-tuning every 100ms, the flow deviation between the upper and lower layers is controlled within ≤5%.

[0087] Preferably, the communication and remote monitoring unit includes:

[0088] (1) Dual backup network and AR monitoring interface: To ensure the efficient operation of the refrigeration equipment control unit, this unit constructs a "satellite-ground" dual backup communication network. A phased array antenna is deployed on the top of the super high-rise building to receive Beidou short messages to realize disaster recovery communication and ensure smooth communication in extreme situations. An AR digital twin monitoring interface is designed so that maintenance personnel can intuitively view the pressure field distribution of the refrigeration pipes on each floor through AR glasses. By clicking on the virtual model, they can retrieve the real-time operating data of that floor, improving the intuitiveness and convenience of monitoring.

[0089] In the dual-backup network, satellite communication uses BeiDou-3 short message service, and the ground network uses industrial Ethernet (transmission rate ≥100Mbps). Network switching is automatically determined by the edge layer intelligent gateway (switching is triggered when the ground network packet loss rate is >5%).

[0090] (2) Blockchain notarization and height tag retrieval: Blockchain technology is used to notarize data, and the hash value of cooling parameters is generated regularly and stored on the chain to ensure that the data cannot be tampered with, thus ensuring the authenticity and security of the data; a "height tag" retrieval system is introduced, which can query historical operating data by floor height range, making it easy to trace the cooling effect and equipment status of different floors, and providing comprehensive data support for user interaction and feedback units.

[0091] Preferably, the user interaction and feedback unit includes:

[0092] (1) Highly Adaptive Interface and EEG Feedback: Based on the data provided by the communication and remote monitoring unit, a "highly adaptable" interactive interface is designed to automatically adjust the display parameters according to the user's floor: the high-floor user interface highlights the wind speed adjustment option, while the low-floor user interface strengthens the humidity control function, thereby improving the targeting of user operations; EEG feedback technology is introduced to collect the user's alpha waves (relaxed state) and beta waves (tense state) through wearable devices, and automatically correct the temperature setting value to achieve "unconscious comfort adjustment" and improve the user experience;

[0093] (2) Feedback loop and floor dialect recognition: Establish a "user feedback-strategy evolution" mechanism, use the user's evaluation of the cooling effect (1-5 stars) as the reward value for reinforcement learning, so that the system can adapt to the comfort preferences of the user group in the short term; develop the "floor dialect" recognition function of voice control, which can distinguish the environmental noise characteristics of different floors, significantly improve the voice command recognition effect, form an optimization feedback for the entire system, and continuously improve the cooling control strategy.

[0094] User feedback on temperature correction formula:

[0095] T 新设定 =T 原设定 +δ·(f-3)·θ h

[0096] In the formula: T 新设定 This indicates the temperature setpoint (in °C) after correction based on user feedback, which is the updated control target of the system.

[0097] T 原设定 This represents the initial system temperature setpoint (in °C), the baseline value before correction.

[0098] δ represents the correction factor (unit: °C / star), with a value of 0.5-1.0, reflecting the degree of influence of user feedback on temperature adjustment. The larger the value, the more sensitive the feedback.

[0099] f represents the user rating star rating, ranging from 1 to 5 stars, with 3 stars as the baseline (indicating that the current temperature is suitable). Higher than 3 stars require a lower temperature, and lower than 3 stars require a higher temperature.

[0100] θ h This represents the height correction factor, which is dimensionless. The value is 1.2 for high-rise buildings (h≥15 floors) and 0.8 for low-rise buildings. It is used to correct for differences in the sensitivity of the human body to temperature changes at different heights.

[0101] Based on user feedback on cooling performance, the temperature setpoint is dynamically adjusted, while also taking into account the impact of floor height on perceived temperature, making the temperature adjustment more closely match the actual needs of users.

[0102] The beneficial effects of this invention are as follows:

[0103] 1. This invention uses a "dynamic density clustering partitioning algorithm" to divide control zones in real time according to factors such as floor height and heat flux density. Combined with a "height compensation module," it corrects the impact of high-altitude air pressure on perceived temperature and generates an adaptation strategy based on a "height-load" bivariate model. In high-rise areas, the air convection characteristics are fitted using a linear function, while in low-rise areas, the soil thermal buffering effect is reflected using an exponential function. This ensures that the cooling parameters are precisely matched with the actual needs of each floor, avoiding overheating or insufficient cooling caused by traditional uniform parameter control, and significantly improving energy efficiency and environmental comfort.

[0104] 2. This invention relies on a three-dimensional sensing network of "space, air, and ground" to collect data in real time, achieves rapid response through edge layer FPGA chips, and combines a "highly sensitive Q-learning" model to autonomously optimize strategies. When the population density in a region increases sharply or functions are switched, the system can complete temporary partition merging and adjustment of control parameters in a short time, replacing the traditional manual adjustment mode, solving the problem of response lag, and meeting the intelligent operation and maintenance needs of "real-time monitoring and rapid optimization" in scenarios such as campuses and hospitals.

[0105] 3. This invention acquires multi-dimensional environmental data through an "air-ground-space" sensing network, combines it with the "cooling potential energy" index to quantify the differences in cooling efficiency between floors, and guides strategy generation; at the same time, it innovatively applies a heat pipe-air conditioning composite system to transfer waste heat from high floors to preheat fresh air in lower floors; this not only improves the matching degree between cooling strategy and actual load, but also realizes cross-floor energy cascade utilization, perfectly matching the industry development direction of "multi-energy complementarity and energy efficiency maximization", and significantly reducing overall energy consumption. Attached Figure Description

[0106] Figure 1 This is a flowchart of the dynamic zoned cooling control system based on floor height according to the present invention. Detailed Implementation

[0107] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0108] like Figure 1 As shown, this embodiment of the invention provides a dynamic zoned cooling control system based on floor height, the system comprising:

[0109] The overall system architecture design unit adopts a three-layer architecture of "cloud-edge-device". The cloud layer deploys a digital twin engine to predict the cooling effect, the edge layer sets up a floor-level intelligent gateway to solve the transmission delay, and the terminal layer sets up a micro-zone controller to realize cross-floor collaboration. A height compensation module is introduced to correct the influence of high-altitude air pressure, providing stable architectural support for the data acquisition and transmission unit.

[0110] Data Acquisition and Transmission Unit: Based on the overall system architecture design unit architecture, it constructs an integrated "space-air-ground" sensing network, collects data from multiple dimensions including space, air, and ground, and uses 5G slicing technology and "data gravity" routing algorithm to ensure transmission, providing comprehensive and accurate data support for floor zoning and demand analysis units;

[0111] Floor Zoning and Demand Analysis Unit: Based on the data from the data acquisition and transmission unit, a dynamic density clustering algorithm is introduced to divide the control area, establish a "height-load" model, fit a function according to the floor characteristics, accurately predict the cooling load, and output the analysis results;

[0112] Intelligent Algorithm and Strategy Generation Unit: Based on the analysis results of the floor zoning and demand analysis unit, the model is developed by integrating reinforcement and transfer learning. The optimal strategy is learned with floor height as a feature. The enthalpy priority logic and cooling potential energy index are introduced to generate intelligent control strategies to guide the refrigeration equipment control unit.

[0113] Refrigeration equipment control unit: Based on intelligent control strategy, compressor height adaptive control technology is introduced, heat pipe-air conditioning composite system is applied, fault propagation blocking mechanism is built-in, and the problem of uneven flow is solved by PWM control;

[0114] Communication and Remote Monitoring Unit: To ensure equipment control, a satellite-ground dual-backup communication network is constructed, an AR monitoring interface is designed, data is stored using blockchain technology, and a height tag retrieval system is set up to provide data support for user interaction and feedback units;

[0115] User interaction and feedback unit: Based on data from the communication and remote monitoring unit, a highly adaptable interactive interface is designed, EEG feedback is introduced, a closed-loop mechanism for user feedback is established, voice recognition is adapted to floor noise, and the entire system is optimized.

[0116] The overall system architecture design unit includes:

[0117] (1) Innovative construction of a three-layer architecture: This unit breaks through the traditional centralized control mode and adopts a three-layer distributed architecture of "cloud-edge-terminal". The cloud layer deploys a digital twin engine, integrates data such as floor height, building materials, and meteorological parameters to build a dynamic model, which can simulate and predict the cooling effect 4 hours in advance; the edge layer sets up floor-level intelligent gateways, integrates FPGA chips to achieve fast real-time response, and effectively solves the problem of data transmission delay difference between high-rise and low-rise floors; the terminal layer introduces "micro-zone controllers", each controller covers 3-5 rooms, and realizes cross-floor collaborative control through power line carrier communication, improving the overall response efficiency of the system;

[0118] In addition to floor height, building materials, and meteorological parameters, the dynamic model input data of the digital twin engine also includes real-time personnel density (infrared counting data from ground-based sensors) and equipment operating status (such as compressor speed and valve opening). The model is updated every 15 minutes to ensure that the deviation between simulation prediction and actual working conditions is within an acceptable range.

[0119] The micro-zone controller uses the PRIME protocol in power line carrier communication (PLC) with a communication rate of 100kbps. It supports priority scheduling during cross-floor collaboration. When a controller detects that its own load exceeds a threshold (such as 80% of the rated load), it automatically sends a collaboration request to the controllers on adjacent floors. The requested controllers respond to the support needs of the higher floors first (response priority: higher floors request > lower floors request).

[0120] Data transmission priority calculation formula:

[0121]

[0122] In the formula: P 传输 This indicates the data transmission priority, with a value ranging from 0 to 1. A higher value indicates that the data will be transmitted with higher priority.

[0123] λ represents the delay weighting coefficient, ranging from 0.6 to 0.8, dimensionless, emphasizing the high demand for real-time data in high-rise areas; the value of λ needs to be dynamically adjusted according to the building type: λ is 0.7 for office buildings (focusing on real-time performance in high-rise buildings), λ is 0.6 for hospital buildings (balancing the needs of high and low floors), and λ is 0.8 for commercial buildings (higher real-time requirements in high-rise densely populated areas).

[0124] t 延迟 This indicates the historical average data transmission latency (in milliseconds) for this floor, reflecting the time consumption characteristics of past data transmission.

[0125] Q 实时 This indicates the current cooling load (in W), reflecting the urgency of real-time cooling demand;

[0126] Q 平均This represents the historical average cooling load (in W) for that floor, serving as a benchmark for determining whether the current load is abnormal.

[0127] Determine the transmission priority of data from different floors, and prioritize the real-time data of high-rise and high-load areas to avoid affecting the control effect due to data delays.

[0128] (2) Height Compensation and Architecture Advantages: The architecture introduces a "height compensation module" which uses a built-in air pressure sensor to determine the absolute height of the floor in real time and automatically corrects the cooling parameters. For example, in areas above the 30th floor, the temperature sensor value is automatically compensated by +0.8℃ to offset the effect of high air pressure on the perceived temperature. This architecture can support seamless expansion in super high-rise buildings, significantly reducing wiring costs compared to traditional solutions, and providing a stable and efficient infrastructure support for subsequent data acquisition and transmission units, ensuring efficient linkage of the entire system.

[0129] In the height compensation module, the conversion formula for the absolute floor height h is: h = (P D -P h ) / 0.12+1 (unit: layer), where P D The pressure (kPa) of the first floor of the building. h The current floor pressure (kPa) is given, and 0.12 is the standard atmospheric pressure reduction factor (kPa / floor) as the floor level increases. This conversion is based on measured data at an altitude of ≤500m where the building is located. For high-altitude areas, the conversion can be adjusted according to the local air pressure gradient correction factor.

[0130] Height compensation temperature correction formula:

[0131] T 修正 =T 实测 +ΔT 高度

[0132] ΔT 高度 = 0.026 × (h - 30) × 10 -3 (When h ≥ 30 floors)

[0133] In the formula: T 修正 This represents the perceived temperature after high compensation (in °C), which is the temperature value ultimately used by the system for control decisions and can more accurately reflect the human body's perception of temperature.

[0134] T 实测 This represents the actual indoor temperature (in °C) directly collected by the temperature sensor, which is the raw detection data.

[0135] ΔT 高度 This indicates the temperature difference compensation at height (in °C), which only takes effect when the building height exceeds 30 stories. It increases linearly with the number of floors and is used to counteract the effect of high-altitude air pressure on the perceived temperature.

[0136] h represents the absolute height of the floor (in floors), which is calculated by combining the air pressure value collected by the air pressure sensor with the standard floor height of the building, reflecting the vertical height of the current area.

[0137] The effect of high-altitude air pressure on perceived temperature has been corrected, making the temperature readings more closely match the actual human experience and resolving the discrepancy between perceived temperature and measured temperature caused by air pressure changes above the 30th floor.

[0138] The data acquisition and transmission unit includes:

[0139] (1) Space-Air-Ground Integrated Sensing Network: Based on the architectural sensing requirements of the overall system architecture design unit, a space-air-ground integrated sensing network is constructed. The space base receives data from the Fengyun-4 meteorological satellite and analyzes the solar radiation intensity and wind speed gradient at different altitudes. The air base deploys a UAV inspection formation, equipped with an infrared thermal imager to periodically scan the building facade and generate heat loss distribution maps for each floor. The ground base uses fiber optic grating array sensors, which are vertically deployed along the elevator shaft to achieve high-density temperature gradient measurement. The data density is significantly improved compared to traditional point sensors, providing the system with multi-dimensional and high-precision raw data.

[0140] The fiber optic grating array sensor deployment density is as follows: when the standard floor height is 3m, one group is deployed every two floors, and each group contains three sensors (corresponding to the center of the room, the window side, and the corner of the wall, respectively) to realize three-dimensional measurement of temperature gradient; the scanning path of the UAV inspection formation is set parallel to the building facade, the scanning range covers all floors, and the single scanning time does not exceed 10 minutes to ensure the timeliness of the heat loss spectrum;

[0141] (2) Differentiated transmission technology and algorithms: 5G slicing technology is used in the transmission link to allocate differentiated channels for data from different floors: ultra-low latency slicing is used in high-rise areas to ensure real-time control needs; large-connection slicing is used in low-rise areas to carry high-density sensor data; a "data gravity" routing algorithm is designed to simulate the gravity effect to achieve priority transmission of high-rise data to edge nodes, avoid low-rise data congestion, and significantly improve transmission reliability; through this collection and transmission mechanism, comprehensive, accurate and timely data support can be provided for floor zoning and demand analysis units;

[0142] The specific implementation of "prioritizing the transmission of higher-level data" in the data gravity routing algorithm is as follows: a routing weight coefficient of 1.2 is assigned to higher-level data (≥20 layers), 0.8 to lower-level data (≤10 layers), and 1.0 to lower-level data (11-19 layers); when bandwidth is insufficient, lower-level non-critical data (such as historical temperature curves) is discarded first, while higher-level real-time control commands (such as compressor speed control signals) are retained.

[0143] 5G slice dynamic switching logic: When the system detects that the terminal device moves across floors (such as a temperature control panel carried by a person moving from the 10th floor to the 20th floor), the system triggers slice switching through the floor height sensor - floors 10 and below automatically switch to the large connection slice, and floors 20 and above switch to the ultra-low latency slice. The switching process takes ≤200ms to ensure the continuity of data transmission.

[0144] Channel allocation parameters for 5G slicing technology: High-rise areas (above 20 floors) use URLLC slicing with a bandwidth of ≥20MHz, end-to-end latency of ≤10ms, and support for the transmission of ≥50 real-time control commands per floor; Low-rise areas (floors 1-19) use mMTC slicing with a bandwidth of ≥50MHz, support for concurrent transmission of ≥1000 sensor nodes per floor, and packet loss rate of ≤0.1%.

[0145] The floor zoning and demand analysis unit includes:

[0146] (1) Dynamic density clustering partitioning algorithm: Based on the multidimensional data provided by the data acquisition and transmission unit, this unit breaks through the fixed partitioning mode and introduces the "dynamic density clustering partitioning algorithm". The algorithm takes the floor height as the core dimension, integrates real-time heat flux density, personnel movement trajectory and other data, and periodically re-divides the control area. For example, when a large number of people are temporarily added to the conference room on the west side of the 15th floor, the system can automatically merge the three rooms on the west side of the 14th-16th floors into a temporary partition to ensure that the partitioning matches the actual cooling demand.

[0147] The cycle of re-dividing the control area is dynamically adjusted according to the building function: office buildings are clustered every 1 hour (to adapt to changes in commuting traffic), hospitals are clustered every 30 minutes (to deal with frequent patient flow in wards), and commercial buildings are clustered every 2 hours (balancing energy consumption and real-time performance). Clustering trigger conditions also include 'single area load fluctuation exceeding 20%' (such as a meeting room suddenly becoming full), in which case temporary clustering is immediately initiated.

[0148] Formula for calculating cluster centers in dynamic partitioning:

[0149]

[0150] w i =α·h i +β·q i +γ·p i

[0151] In the formula: C k The cluster center of the kth dynamic partition is the virtual center coordinates (in meters) determined after comprehensively considering the characteristics of multiple rooms, which serves as the reference point for the control of this partition.

[0152] w iThis represents the weight value of the i-th room, which has no unit and reflects the degree of influence of the room in the partitioning. The higher the weight, the greater the influence on the cluster center.

[0153] x i Represents the physical coordinates (in meters) of the i-th room, usually based on the architectural plane coordinate system;

[0154] α, β, and γ represent weighting coefficients, all dimensionless parameters, satisfying α + β + γ = 1. They represent the influence weights of floor height, heat flux density, and personnel density in zoning, respectively, and can be dynamically adjusted according to building characteristics. The rules for the values ​​of weighting coefficients α, β, and γ are as follows: Office buildings: α = 0.5 (floor height has the highest weight), β = 0.3 (heat flux density), γ = 0.2 (personnel density); Hospital buildings: α = 0.4, β = 0.2, γ = 0.4 (personnel density has a higher weight); Commercial buildings: α = 0.5, β = 0.2, γ = 0.3. The adjustment is based on the differences in the sensitivity of building functions to heat flux and personnel flow.

[0155] h i This represents the floor height (in floors) of the i-th room, reflecting its vertical position characteristics.

[0156] q i This represents the real-time heat flux density of the i-th room (in W / m³). 2 This reflects the intensity of heat exchange in the region;

[0157] p i This represents the population density in the i-th room (unit: person / m²). 2 This reflects the heat generated by human activities;

[0158] The center position of the cooling control zone is dynamically determined, taking into account factors such as floor height, heat flux density and personnel density, to achieve precise division and real-time adjustment of the zone, ensuring that the zone matches the actual cooling demand;

[0159] (2) Construction of the height-load bivariate model: A height-load bivariate model is established, and a correction coefficient for the change of building thermal resistance with height is introduced: the bottom area (1-5 floors) adopts an exponential function to reflect the soil thermal buffering effect; the upper area (above 20 floors) adopts a linear function to reflect the heat exchange characteristics dominated by air convection; the model input parameters include real-time height, window-to-wall ratio, hourly solar altitude angle, etc., which can accurately output the cooling load prediction results and provide a reliable analytical basis for intelligent algorithms and strategy generation units;

[0160] The model input parameters are obtained as follows: real-time height is derived from barometric pressure sensor data, window-to-wall ratio is a building design parameter (pre-entered into the system), hourly solar altitude angle is analyzed from space-based meteorological satellite data (updated every 10 minutes), and indoor-outdoor temperature difference is calculated from the difference between ground-based temperature sensor data and space-based outdoor temperature data.

[0161] Formula for the height-load bivariate model:

[0162] Bottom layer area (levels 1-5):

[0163] High-rise area (above 20 floors): Q=k3·h·A·Δt

[0164] In the formula: Q represents the predicted cooling load (in W), which is the core parameter for the system to formulate a cooling strategy and reflects the cooling capacity required to maintain the set temperature;

[0165] k1 and k2 represent the thermal resistance correction coefficients for the ground floor area, both being dimensionless parameters. k1 reflects the influence of soil heat storage on the initial thermal resistance of the ground floor, while k2 reflects the rate of thermal resistance decay as the building height increases. The determination methods for k1 and k2 in the ground floor area are as follows: k1 is obtained based on experiments on the thermal conductivity of the ground floor soil (k1 is 1.0 for clay soil and 1.2 for sandy soil); k2 reflects the decay characteristics of thermal resistance as the building height increases, and is determined based on the thermal insulation performance of the building envelope (k2 is -0.02 for high insulation levels and -0.05 for low insulation levels); k3 in the high-rise area is determined in conjunction with the local annual average wind speed (k3 is 0.05 when the wind speed is ≥3m / s and 0.02 when the wind speed is ≤1.5m / s).

[0166] k3 represents the thermal resistance correction factor for the high-rise area. It is dimensionless and reflects the linear variation of thermal resistance with height under the dominance of air convection.

[0167] h represents the floor height (in floors), a key parameter that distinguishes between the ground floor and the high floor;

[0168] A represents the room's air-conditioned area (unit: m²). 2 This reflects the physical scale of the refrigeration area;

[0169] Δt represents the indoor-outdoor temperature difference (in °C), which is the difference between the indoor set temperature and the outdoor real-time temperature, and directly affects the size of the cooling load.

[0170] A cooling load prediction model is established based on the differences in floor height, adapting to the heat transfer characteristics of the ground floor (affected by soil heat storage) and the upper floor (affected by air convection) respectively, to achieve accurate prediction of cooling load.

[0171] The intelligent algorithm and strategy generation unit includes:

[0172] (1) Development of a highly sensitive learning model: Based on the prediction results of floor zoning and demand analysis units, reinforcement learning and transfer learning algorithms are integrated to introduce a "highly sensitive Q-learning" model; taking floor height as the state feature, through continuous interaction with the digital twin environment, the model learns the optimal control strategy for different heights autonomously; for new buildings, some historical strategies of similar buildings can be inherited through transfer learning, which can significantly shorten the model training cycle and quickly adapt to the cooling needs of new buildings;

[0173] During the training of the highly sensitive Q-learning model, the interaction frequency with the digital twin environment is once every 5 minutes, and each interaction generates 100 sets of state-action samples (states include floor height and real-time load, and actions include compressor speed and valve opening). During transfer learning, the reuse ratio of historical strategies for similar buildings is as follows: 70% of high-rise strategies are reused (due to small differences in the high-altitude environment), and 50% of low-rise strategies are reused (due to large differences in soil and shading). New buildings are put into use after training until the strategy converges (error < 5% for 100 consecutive iterations).

[0174] In transfer learning, the rules for reusing historical strategies of similar buildings in new buildings are as follows: retain 70% of the control parameters of high-rise buildings (≥20 floors), such as compressor speed correction coefficient and heat pipe start-stop threshold, and adjust 30% of the parameters of low-rise buildings (≤19 floors) - among which the parameters of densely populated areas (such as classrooms and wards) are corrected by 1.2 times the actual population density, and the parameters of non-densely populated areas (such as corridors) are corrected by 0.8 times the actual population density, to ensure that they are adapted to the functional layout of the new building.

[0175] (2) Enthalpy priority strategy and cooling potential energy index: The strategy generation introduces the "enthalpy priority" control logic, which prioritizes the control of dew point temperature in high humidity floors (such as floors 1-5) and focuses on temperature regulation in dry high-rise areas; the "cooling potential energy" index is designed to quantify the cooling efficiency at different heights. For example, in the 25th floor area, due to the low outdoor temperature, the COP value can be significantly improved through strategy adjustment, achieving significant energy savings compared to traditional methods. These intelligent control strategies will directly guide the precise operation of the refrigeration equipment control unit.

[0176] Criteria for determining high humidity and dry areas: For lower floors (floors 1-5), dew point temperature control is triggered when relative humidity > 60% (preferably controlling the dew point at 12-14℃); for higher floors (floors 20 and above), temperature regulation is emphasized when relative humidity < 40% (target temperature 24-26℃); humidity data comes from ground temperature and humidity sensors (3 sensors are deployed on each floor, and the average value is taken).

[0177] Formula for converting refrigeration potential into energy:

[0178]

[0179] In the formula: P represents the cooling potential energy, a dimensionless parameter. The larger the value, the higher the cooling efficiency of the floor and the easier it is to achieve energy-saving operation.

[0180] T 室外 This indicates the outdoor temperature (in °C) corresponding to the current floor level. It varies with height, and higher floors are usually lower than lower floors.

[0181] T 设定 This indicates the indoor set temperature (in °C), the user's or system's preset comfort temperature target;

[0182] COP 基准 The coefficient of performance (COP) represents the cooling equipment under standard operating conditions. It is dimensionless and typically ranges from 3.0 to 4.5, reflecting the inherent energy efficiency of the equipment.

[0183] ρ represents the air density at the current floor height (in kg / m³), which decreases as the height increases, affecting the heat exchange efficiency of the refrigeration equipment;

[0184] v represents the outdoor wind speed at the current floor level (in m / s). The wind speed at higher floors is usually higher than that at lower floors, which can enhance the outdoor heat dissipation effect.

[0185] The baseline COP value is determined based on the standard operating condition COP indicated on the refrigeration equipment nameplate: 3.8-4.5 for magnetic levitation compressors and 3.0-3.5 for screw compressors; the air density ρ is calculated based on the ideal gas law: ρ = 1.225 × (P h / P D )×(273 / (273+T D )), where P h P represents the current floor air pressure. D For standard atmospheres, T D Outdoor temperature;

[0186] Quantifying the differences in cooling efficiency at different floor heights provides a basis for developing differentiated energy-saving strategies. High-rise buildings can utilize lower outdoor temperatures and airflow characteristics to improve cooling efficiency.

[0187] The refrigeration equipment control unit includes:

[0188] (1) Altitude adaptive control and energy cascade utilization: Based on the intelligent control strategy, the "altitude adaptive control" technology of the magnetic levitation variable frequency compressor is introduced. The compressor speed limit is automatically corrected according to the altitude (converted by air pressure). The operating frequency is reasonably adjusted in high-rise buildings to avoid the risk of surge caused by low air pressure at high altitudes. The heat pipe-air conditioning composite system is applied. The heat pipe technology is used in the high-rise area to transfer waste heat to the lower floor to preheat fresh air, realize cross-floor energy cascade utilization, and improve energy utilization efficiency.

[0189] The specific path for transferring waste heat from high-rise buildings via heat pipes is as follows: the heat pipe evaporator is located at the condenser end of the high-rise air conditioning unit, and the condenser is located at the inlet of the fresh air duct on the lower floor. The working fluid of the heat pipe is R134a (suitable for operating conditions from -20℃ to 50℃). When the high-rise condensing temperature is >35℃, the heat pipe automatically starts (controlled by a solenoid valve), and the temperature increase of the waste heat preheating fresh air is 5-8℃ (dynamically adjusted according to the high-rise waste heat power).

[0190] Compressor height adaptive speed formula:

[0191]

[0192] In the formula: n represents the actual operating speed of the compressor (in r / min), which is the real-time control parameter of the system for the compressor;

[0193] n0 represents the compressor's rated speed (in r / min) under standard atmospheric pressure, which is the baseline operating parameter when the equipment leaves the factory.

[0194] P0 represents standard atmospheric pressure (value 101.325 kPa), which serves as the reference value for pressure correction.

[0195] P h The actual air pressure (in kPa) at the current floor height is collected in real time by an air pressure sensor and decreases as the height increases.

[0196] k h The height safety factor, dimensionless, is 0.8-0.9 for high-rise areas (h≥20 stories) and 1.0 for low-rise areas, used to further reduce the operational risks of compressors in high-rise buildings; the height safety factor k h The specific values ​​are: 0.9 for floors 20-25, 0.85 for floors 26-30, and 0.8 for floors 31 and above; the values ​​are based on the surge test data of the magnetic levitation compressor at different heights—when the air pressure is lower in higher floors, the upper limit of the speed is reduced to avoid impeller stall;

[0197] Based on the air pressure difference at different floor heights, the compressor operating speed is dynamically adjusted to avoid the risk of compressor surge caused by low air pressure at high altitudes, while ensuring stable cooling output.

[0198] (2) Fault blocking and control mechanism: The refrigeration equipment control unit has a built-in "fault propagation blocking" mechanism. When an abnormality is detected in a chiller unit on a certain floor, the hydraulic connection between it and the adjacent floors is automatically cut off to prevent the fault from spreading. Through the pulse width modulation (PWM) control of the electronic expansion valve, the cooling capacity is finely adjusted to solve the problem of uneven flow caused by static pressure difference between high-rise and low-rise floors. The stable operation of this unit requires reliable guarantee from the communication and remote monitoring unit to ensure the accurate execution of control commands and real-time feedback of equipment status.

[0199] The pulse frequency of PWM control is 10Hz, and the duty cycle adjustment range is 5%-95%. In response to the difference in static pressure between high-rise and low-rise buildings (the static pressure of high-rise buildings is 0.2-0.5MPa higher than that of low-rise buildings), the initial opening of the electronic expansion valve of the high-rise buildings is 10% larger than that of the low-rise buildings. Through feedback adjustment every 100ms (based on the data of the flow sensor), the flow deviation is controlled within ±5%.

[0200] The threshold for judging abnormalities of the chiller unit is: the outlet temperature deviates from the set value by ±5℃ for more than 30 seconds, or the pressure fluctuation exceeds ±0.1MPa; the operation steps for hydraulic connection disconnection are as follows: ① Close the electric butterfly valves of the abnormal floor and adjacent floors (response time ≤5s); ② Turn on the standby unit (capacity of 50% of the abnormal unit); ③ Send the fault alarm to the AR monitoring interface through the communication and remote monitoring unit.

[0201] The communication and remote monitoring unit includes:

[0202] (1) Dual backup network and AR monitoring interface: To ensure the efficient operation of the refrigeration equipment control unit, this unit constructs a "satellite-ground" dual backup communication network. A phased array antenna is deployed on the top of the super high-rise building to receive Beidou short messages to realize disaster recovery communication and ensure smooth communication in extreme situations. An AR digital twin monitoring interface is designed so that maintenance personnel can intuitively view the pressure field distribution of the refrigeration pipes on each floor through AR glasses. By clicking on the virtual model, they can retrieve the real-time operating data of that floor, improving the intuitiveness and convenience of monitoring.

[0203] The response time for clicking on the virtual model to retrieve data is ≤1s. The displayed content includes: the temperature curve of the floor in the past hour, the current device status (running / standby / fault), and the real-time energy consumption value; it supports gesture operation (such as swiping to view data of adjacent floors, zooming to view details), and is compatible with AR glasses devices on Android and iOS systems.

[0204] The switching logic of the satellite-ground dual backup network is as follows: When the ground network is normal, fiber optic transmission (bandwidth ≥ 100Mbps) is used first; when the ground network is interrupted (such as fiber optic failure), it automatically switches to Beidou short message communication, sending key data (including temperature of each floor and equipment status) every 15 minutes, with a single message data size ≤ 140 bytes, to ensure monitoring continuity under extreme conditions.

[0205] (2) Blockchain notarization and height tag retrieval: Blockchain technology is used to notarize data, and the hash value of cooling parameters is generated regularly and stored on the chain to ensure that the data cannot be tampered with, thus ensuring the authenticity and security of the data; a "height tag" retrieval system is introduced, which can query historical operating data by floor height range, making it easy to trace the cooling effect and equipment status of different floors, and providing comprehensive data support for user interaction and feedback units;

[0206] The data stored on the blockchain includes: real-time temperature of each floor (after correction), cooling load, compressor operating parameters (speed, pressure), and energy consumption data (kWh). A hash value is generated and uploaded to the blockchain every 30 minutes. The blockchain adopts a consortium blockchain architecture (nodes include property management servers and equipment manufacturer servers) to ensure that the data is traceable and tamper-proof, and the validity period of the evidence is 5 years.

[0207] The user interaction and feedback unit includes:

[0208] (1) Highly Adaptive Interface and EEG Feedback: Based on the data provided by the communication and remote monitoring unit, a "highly adaptable" interactive interface is designed to automatically adjust the display parameters according to the user's floor: the high-floor user interface highlights the wind speed adjustment option, while the low-floor user interface strengthens the humidity control function, thereby improving the targeting of user operations; EEG feedback technology is introduced to collect the user's alpha waves (relaxed state) and beta waves (tense state) through wearable devices, and automatically correct the temperature setting value to achieve "unconscious comfort adjustment" and improve the user experience;

[0209] The interface for high-rise buildings (≥15 floors) highlights the wind speed adjustment option (the default display shows a wind speed slider of level 1-3), because outdoor wind speed at high-rise buildings affects the perceived temperature; the interface for low-rise buildings (≤5 floors) enhances the humidity control function (displays the difference between the current humidity and the target humidity), because low-rise buildings are easily affected by soil moisture; the interface parameters are refreshed every 30 seconds, and the data comes from real-time pushes from the communication and remote monitoring unit.

[0210] The correction logic for EEG feedback is as follows: When the wearable device detects an alpha wave intensity > 60 μV (relaxed state), the current temperature setting is maintained; when the beta wave intensity > 50 μV (stress / discomfort state), the temperature is corrected by an amplitude of 0.5℃ / 5 μV (the higher the beta wave intensity, the larger the correction amplitude, with a maximum correction amount ≤ 2℃); after correction, EEG is re-acquired after an interval of 5 minutes until the alpha wave proportion is ≥ 60%;

[0211] (2) Feedback loop and floor dialect recognition: Establish a "user feedback-strategy evolution" mechanism, use the user's evaluation of the cooling effect (1-5 stars) as the reward value for reinforcement learning, so that the system can adapt to the comfort preferences of the user group in the short term; develop the "floor dialect" recognition function of voice control, which can distinguish the environmental noise characteristics of different floors, significantly improve the voice command recognition effect, form an optimization feedback for the entire system, and continuously improve the cooling control strategy.

[0212] The system pre-trains a noise feature library for each floor: lower floors (1-5 floors) include traffic noise (50-200Hz low frequency) and conversations among people; upper floors (≥20 floors) include wind noise (2000-5000Hz high frequency) and equipment operation noise. During speech recognition, background noise is first filtered through the noise feature library (improving the signal-to-noise ratio by more than 20dB) before command recognition is performed. It supports floor adaptation for Mandarin and 8 dialects (such as Cantonese and Sichuanese).

[0213] User feedback on temperature correction formula:

[0214] T 新设定 =T 原设定 +δ·(f-3)·θ h

[0215] In the formula: T 新设定 This indicates the temperature setpoint (in °C) after correction based on user feedback, which is the updated control target of the system.

[0216] T 原设定 This represents the initial system temperature setpoint (in °C), the baseline value before correction.

[0217] δ represents the correction factor (unit: °C / day), ranging from 0.5 to 1.0, reflecting the degree of influence of user feedback on temperature adjustment; a larger value indicates more sensitive feedback. The rules for determining the correction factor δ are: summer δ = 0.5 °C / day (to avoid excessive cooling), winter δ = 1.0 °C / day (for rapid response to heating needs); height correction factor θ h The adjustment criteria are as follows: 1.2 for floors 15-25, 1.3 for floors above 25 (high floors are more sensitive to temperature changes), and 0.8 for floors 1-14 (greatly affected by humidity).

[0218] f represents the user rating star rating, ranging from 1 to 5 stars, with 3 stars as the baseline (indicating that the current temperature is suitable). Higher than 3 stars require a lower temperature, and lower than 3 stars require a higher temperature.

[0219] θ h This represents the height correction factor, which is dimensionless. The value is 1.2 for high-rise buildings (h≥15 floors) and 0.8 for low-rise buildings. It is used to correct for differences in the sensitivity of the human body to temperature changes at different heights.

[0220] Based on user feedback on cooling performance, the temperature setpoint is dynamically adjusted, while also taking into account the impact of floor height on perceived temperature, making the temperature adjustment more closely match the actual needs of users.

[0221] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0222] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic zoned cooling control system based on floor height, characterized in that: The system includes: The overall system architecture design unit adopts a three-layer architecture of cloud-edge-device. The cloud layer deploys a digital twin engine to predict the cooling effect, the edge layer sets up a floor-level smart gateway to solve the transmission delay, and the device layer sets up a micro-zone controller to realize cross-floor collaboration. A height compensation module is introduced to correct the influence of high-altitude air pressure. Data acquisition and transmission unit: Constructs an integrated space-air-ground sensing network, collects data from multiple dimensions including space, air, and ground, and uses 5G slicing technology and data gravity routing algorithm to achieve priority transmission; Floor Zoning and Demand Analysis Unit: A dynamic density clustering zoning algorithm is introduced to divide the control area, a height-load model is established, a function is fitted according to the floor characteristics, the cooling load is predicted, and the analysis results are output. Intelligent Algorithm and Strategy Generation Unit: Based on the analysis results of the floor zoning and demand analysis unit, the model is developed by integrating reinforcement and transfer learning. The optimal strategy is learned with floor height as a feature. The enthalpy priority logic and cooling potential energy index are introduced to generate intelligent control strategies. Refrigeration equipment control unit: Based on the intelligent control strategy, the compressor's height adaptive control technology is introduced, a heat pipe-air conditioning composite system is applied, a built-in fault propagation blocking mechanism is incorporated, and the problem of uneven flow is solved through PWM control; Communication and Remote Monitoring Unit: Construct a satellite-ground dual-backup communication network, design an AR monitoring interface, use blockchain technology to store data, design a high-level tag retrieval system, and provide data support for user interaction and feedback units; User interaction and feedback unit: Design a highly adaptable interactive interface, introduce EEG feedback technology, establish a closed-loop mechanism for user feedback, and use voice recognition to distinguish floor noise characteristics.

2. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The overall system architecture design unit includes: (1) Innovative construction of a three-layer architecture: A three-layer distributed architecture of cloud-edge-terminal is adopted. The cloud layer deploys a digital twin engine to integrate data including floor height, building materials and meteorological parameters to build a dynamic model and simulate and predict the cooling effect. The edge layer sets up a floor-level smart gateway and integrates FPGA chips to solve the problem of data transmission delay difference between high-rise and low-rise floors. The terminal layer introduces a micro-zone controller to achieve cross-floor collaborative control through power line carrier communication. (2) Height compensation and architectural advantages: The architecture introduces a height compensation module, which determines the absolute height of the floor in real time through the built-in air pressure sensor, automatically corrects the cooling parameters, supports seamless expansion of super high-rise buildings, and provides basic architectural support for data acquisition and transmission units.

3. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The data acquisition and transmission unit includes: (1) Three-dimensional sensing network: A three-dimensional sensing network is constructed, in which the space base receives meteorological satellite data and analyzes the solar radiation intensity and wind speed gradient at different altitudes; the air base deploys UAV inspection formations, equipped with infrared thermal imagers to regularly scan the building facades and generate heat loss distribution maps for each floor; the ground base uses fiber optic grating array sensors to realize temperature gradient measurement. (2) Differentiated transmission technology and algorithm: 5G slicing technology is used in the transmission link to allocate differentiated channels for data on different floors: ultra-low latency slicing is enabled in high-rise areas and large connection slicing is used in low-rise areas; a data gravity routing algorithm is designed to realize the priority transmission of high-rise data to edge nodes.

4. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The floor zoning and demand analysis unit includes: (1) Dynamic density clustering partitioning algorithm: Based on the multidimensional data of the data acquisition and transmission unit, the dynamic density clustering partitioning algorithm is introduced to integrate data including heat flux density and personnel movement trajectory, and the control area is periodically re-divided; (2) Construction of the height-load bivariate model: A height-load bivariate model is established, and a correction coefficient for the change of building thermal resistance with height is introduced: the bottom area is fitted with an exponential function; the top area is fitted with a linear function; the model input parameters include real-time height, window-to-wall ratio, and hourly solar altitude angle, and the output cooling load prediction results are provided.

5. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The intelligent algorithm and strategy generation unit includes: (1) Development of a highly sensitive learning model: Based on the prediction results of floor zoning and demand analysis units, reinforcement learning and transfer learning algorithms are integrated to introduce a highly sensitive Q-learning model; taking floor height as the state feature, through continuous interaction with the digital twin environment, the model learns the optimal control strategy for different heights autonomously. (2) Enthalpy priority strategy and cooling potential energy index: The strategy generation introduces enthalpy priority control logic, prioritizes control of dew point temperature in high humidity and low floors, and focuses on temperature regulation in dry high-rise areas; the cooling potential energy index is designed to quantify the cooling efficiency at different heights and generate intelligent control strategy.

6. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The refrigeration equipment control unit includes: (1) Altitude adaptive control and energy cascade utilization: Based on the intelligent control strategy, the altitude adaptive control technology of the magnetic levitation variable frequency compressor is introduced to automatically correct the upper limit of the compressor speed according to the altitude; the heat pipe-air conditioning composite system is applied to transfer waste heat to the lower floor for preheating fresh air in the high-rise area using heat pipe technology; (2) Fault blocking and control mechanism: The refrigeration equipment control unit has a built-in fault propagation blocking mechanism. When an abnormality is detected in a chiller unit on a certain floor, the hydraulic connection between it and the adjacent floor is automatically cut off. The problem of uneven flow caused by static pressure difference between high-rise and low-rise floors is solved by pulse width modulation control of electronic expansion valve.

7. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The communication and remote monitoring unit includes: (1) Dual backup network and AR monitoring interface: Construct a satellite-ground dual backup communication network, deploy phased array antennas on the top of the super high-rise building, design an AR digital twin monitoring interface, and click on the virtual model to retrieve the real-time operation data of the floor; (2) Blockchain notarization and height tag retrieval: Blockchain technology is used to notarize data, and the hash value of cooling parameters is generated regularly and stored on the chain to ensure that the data cannot be tampered with. A height tag retrieval system is introduced to query historical operating data by floor height range, providing data support for user interaction and feedback units.

8. The dynamic zoned cooling control system based on floor height according to claim 1, characterized in that: The user interaction and feedback unit includes: (1) Highly Adaptive Interface and EEG Feedback: Based on the data provided by the communication and remote monitoring unit, a highly adaptable interactive interface is designed. The display parameters are automatically adjusted according to the user's floor. EEG feedback technology is introduced. The user's alpha and beta waves are collected through wearable devices to automatically correct the temperature setting. (2) Feedback loop and floor dialect recognition: Establish a user feedback-policy evolution mechanism, use the user's evaluation of the cooling effect as the reward value for reinforcement learning, and distinguish the environmental noise characteristics of different floors through the voice-controlled floor dialect recognition function.

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