Dynamic zoning refrigeration control system based on floor height

Abstract

The application belongs to the technical field of building refrigeration, and discloses a dynamic zoning refrigeration regulation system based on floor height, which divides the regulation area in real time according to floor height and heat flow density through a dynamic density clustering zoning algorithm, corrects the influence of high-altitude air pressure on the apparent temperature in combination with a height compensation module, and generates an adaptive strategy based on a height-load bivariate model; the air convection characteristics of high-rise areas are fitted through a linear function, and the soil heat buffering effect of low-rise areas is reflected through an exponential function, so that the refrigeration parameters are accurately matched with the actual demand of each floor, and overheating or insufficient refrigeration caused by traditional unified parameter regulation is avoided; data is collected in real time through a three-in-one space-air-ground sensing network; when the regional personnel density suddenly increases or the function is switched, the system can complete temporary zoning merging and regulation parameter adjustment in a short time, and meet the intelligent operation and maintenance demand of real-time monitoring and rapid optimization in scenes such as campuses and hospitals.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of building refrigeration, and specifically relates to a dynamic zoning refrigeration regulation system based on floor height. BACKGROUND

[0002] Current building refrigeration systems mostly adopt unified parameter regulation or extensive management mode according to functional zoning, and are difficult to adapt to the differentiation of refrigeration demand formed by the physical environment difference of different floors. The existing refrigeration regulation system has the following technical problems:

[0003] Low-rise buildings have significantly higher thermal inertia than high-rise buildings due to factors such as soil heat storage and surrounding shielding. The heat exchange rate of high-rise buildings is obviously different from that of low-rise buildings due to high air flow and solar radiation. However, the existing system uses the same refrigeration parameters for different floors, resulting in overheating or energy waste in some areas, which is contrary to the concept of "precise temperature control" in smart energy management.

[0004] Places such as campuses and hospitals have frequent personnel flow, and the use state of functional areas often changes, resulting in changes in refrigeration demand. However, the traditional system relies on manual adjustment, and the response lag is obvious, which cannot meet the requirements of "real-time monitoring and rapid optimization" for smart operation and maintenance.

[0005] The existing system mostly uses single-point temperature sensors, lacks a three-dimensional perception network, and does not effectively associate environmental parameters such as solar radiation and outdoor wind speed, resulting in a large deviation between the refrigeration strategy and the actual load, making it difficult to achieve the goal of energy saving and consumption reduction.

[0006] The refrigeration efficiency of high-rise areas theoretically differs from that of low-rise areas due to outdoor environmental characteristics, but the traditional system does not utilize this feature for hierarchical energy utilization, which is inconsistent with the industry development direction of "multi-energy complementation and maximum energy efficiency". SUMMARY

[0007] The purpose of the present application is to provide a dynamic zoning refrigeration regulation system based on floor height to solve the problems raised in the background.

[0008] In order to achieve the above purpose, the present application provides the following technical solution: a dynamic zoning refrigeration regulation system based on floor height, which comprises:

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

[0010] Data acquisition and transmission unit: based on the overall architecture design unit architecture, build a "space-ground" integrated sensing network, space-based, air-based, ground-based multi-dimensional data acquisition, using 5G slicing technology and "data gravity" routing algorithm to ensure transmission, provide comprehensive and accurate data support for floor partitioning and demand analysis unit;

[0011] Floor partitioning and demand analysis unit: based on the data of the data acquisition and transmission unit, introduce dynamic density clustering algorithm to divide the control area, establish a "height-load" model, and accurately predict the cooling load according to the floor characteristics fitting function, and output the analysis results;

[0012] Intelligent algorithm and strategy generation unit: based on the analysis results of the floor partitioning and demand analysis unit, integrate reinforcement and transfer learning to develop a model, learn the optimal strategy based on the floor height, introduce enthalpy priority logic and refrigeration potential index, and generate intelligent control strategy to guide the refrigeration equipment control unit;

[0013] Refrigeration equipment control unit: according to the intelligent control strategy, introduce compressor height adaptive control technology, apply heat pipe-air conditioning composite system, built-in fault propagation blocking mechanism, solve the problem of uneven flow through PWM control;

[0014] Communication and remote monitoring unit: to ensure equipment control, build a satellite-ground dual backup communication network, design an AR monitoring interface, use blockchain technology to store data, set up a height label retrieval system, and provide data support for the user interaction and feedback unit;

[0015] User interaction and feedback unit: based on the data of the communication and remote monitoring unit, design a height adaptive interaction interface, introduce brain wave feedback, establish a user feedback closed loop mechanism, voice recognition adapts to floor noise, and optimize the entire system.

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

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

[0018] The FPGA chip integrated in the edge layer intelligent gateway is Xilinx Artix-7 series, which supports real-time data processing of ≥1000 pieces / second, and ensures that the delay difference of parallel processing of high layer and low layer data is ≤5ms;

[0019] Data transmission priority calculation formula:

[0020]

[0021] In the formula, P 传输 represents the data transmission priority, and the value range is 0-1. The higher the value, the more priority the data has in transmission;

[0022] λ represents the delay weight coefficient, and the value is 0.6-0.8, which is dimensionless, and emphasizes the high demand of high layer area for data real-time performance;

[0023] t 延迟 represents the historical average data transmission delay of the floor (unit: ms), which reflects the time-consuming characteristics of past data transmission;

[0024] Q 实时 represents the current refrigeration load (unit: W), which reflects the urgency of real-time refrigeration demand;

[0025] Q 平均 represents the historical average refrigeration load of the floor (unit: W), which is used as a reference to judge whether the current load is abnormal;

[0026] The transmission priority of data of different floors is determined to ensure the data real-time performance of high layer area and high load area, and to avoid the influence of data delay on the control effect;

[0027] (2) Height compensation and architecture advantage: 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, and automatically corrects the refrigeration parameters. For example, in the area above 30 floors, the temperature sensor value is automatically compensated by +0.8℃ to offset the influence of high air pressure on the apparent temperature. This architecture can support seamless expansion of super high-rise buildings, significantly reduce the wiring cost compared with traditional solutions, and provide 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 represents the highly compensated sensible temperature (unit: ℃), which is the temperature value used for the final control decision of the system, and can more accurately reflect the human body's perception of temperature;

[0032] T 实测 represents the actual indoor temperature directly collected by the temperature sensor (unit: ℃), which is the original detection data;

[0033] ΔT 高度 represents the highly compensated temperature difference (unit: ℃), which only takes effect when the floor height exceeds 30 floors, and increases linearly with the increase of the floor, and is used to offset the influence of high air pressure on the sensible temperature;

[0034] h represents the absolute height of the floor (unit: floor), which is converted from the air pressure value collected by the air pressure sensor combined with the standard floor height of the building, and reflects the vertical height of the current area;

[0035] The influence of high air pressure on the sensible temperature is corrected to make the temperature detection value more consistent with the actual human perception, and to solve the problem of deviation between the sensible temperature and the measured temperature caused by air pressure changes above 30 floors.

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

[0037] (1) Air-space-ground three-in-one perception network: based on the architecture perception demand of the system overall architecture design unit, construct the "air-space-ground" three-in-one perception network, receive the Fengyun-4 meteorological satellite data from the space-based, analyze the solar radiation intensity and wind speed gradient of different height layers; deploy unmanned aerial vehicle inspection formation on the space-based, carry infrared thermal imager to regularly scan the building facade, generate the heat loss distribution atlas of each floor; use fiber grating array sensor on the ground, vertically arrange along the elevator shaft, realize high-density temperature gradient measurement, the data density is greatly improved compared with traditional point type sensor, provide multi-dimensional and high-precision original data for the system;

[0038] In the air-space-ground three-in-one perception network, the Fengyun-4 meteorological satellite data received from the space-based needs to be preprocessed (data in cloud layer shielding period is removed), the resolution of infrared thermal imager of unmanned aerial vehicle inspection formation on the space-based is ≥640×512, the temperature measurement range of ground fiber grating sensor is-30℃ to 80℃, and the accuracy is ±0.1℃;

[0039] (2) Differentiated transmission technology and algorithm: 5G slicing technology is adopted in the transmission link to allocate differentiated channels for data of different floors: ultra-low latency slicing is enabled in high-level areas to meet real-time regulation requirements; high-density sensor data is carried in low-level areas using large connection slicing; a "data gravity" routing algorithm is designed to simulate the effect of gravity to realize the preferential transmission of high-level data to edge nodes, avoid congestion of low-level data, and significantly improve transmission reliability; through this set of collection and transmission mechanism, comprehensive, accurate and timely data support can be provided for floor partitioning and demand analysis unit.

[0040] Preferably, the floor partitioning and demand analysis unit comprises:

[0041] (1) Dynamic density clustering partitioning algorithm: based on the multi-dimensional data provided by the data collection and transmission unit, this unit breaks through the fixed partitioning mode and introduces a "dynamic density clustering partitioning algorithm"; this algorithm takes floor height as the core dimension, integrates real-time heat flux density, personnel movement trajectory and other data, and re-divides the regulation area regularly; for example, when a large number of personnel 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 floor into a temporary partition, ensuring that the partition matches the actual cooling demand;

[0042] Dynamic partition clustering center calculation formula:

[0043]

[0044] w i = a h i + b q i + g p i

[0045] In the formula: C k represents the clustering center of the kth dynamic partition, which is a virtual center coordinate (unit: m) determined by comprehensively considering the characteristics of multiple rooms, serving as the reference point for the regulation of the partition;

[0046] w i represents the weight value of the ith room, which is dimensionless and reflects the influence degree of the room in partitioning, the higher the weight, the greater the influence on the clustering center;

[0047] x i represents the physical coordinates (unit: m) of the ith room, which is usually based on the building plane coordinate system;

[0048] a, b, g represent weight coefficients, which are all dimensionless parameters and satisfy a+b+g=1, representing the influence weight of floor height, heat flux density and personnel density in partitioning, which can be dynamically adjusted according to the characteristics of the building;

[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) is fitted with an exponential function to reflect the soil thermal buffering effect; the upper area (above 20 floors) is fitted with 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 (unit: ℃), i.e. the difference between the indoor set temperature and the outdoor real-time temperature, which directly affects the size of the refrigeration load;

[0063] According to the height difference of each floor, a refrigeration load prediction model is established to adapt to the heat transfer characteristics of the bottom floor (affected by soil heat storage) and the high floor (affected by air convection), and to realize accurate prediction of the refrigeration load.

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

[0065] (1) High-sensitivity learning model development: based on the prediction results of the floor partitioning and demand analysis unit, the reinforcement learning and transfer learning algorithms are fused, and the "high-sensitivity Q-learning" model is introduced; taking the floor height as the state feature, through continuous interaction with the digital twin environment, the optimal control strategy for different heights is learned autonomously; for new buildings, part of the historical strategies of the same type of building can be inherited through transfer learning, greatly shortening the model training period and quickly adapting to the refrigeration demand of new buildings;

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

[0067] Refrigeration potential quantization formula:

[0068]

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

[0070] T 室外 represents the outdoor temperature corresponding to the current floor height (unit: ℃), which varies with height, and is usually lower in high-rise buildings than in low-rise buildings;

[0071] T 设定 represents the indoor set temperature (unit: ℃), which is the comfort temperature target preset by the user or the system;

[0072] COP 基准 represents the refrigeration coefficient of the refrigeration equipment under standard working conditions, which is dimensionless, usually takes a value of 3.0-4.5, and reflects the inherent energy efficiency of the equipment ρ: the air density at the current floor height (unit: kg / m’), 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 height (unit: m / s), which is usually higher in high-rise buildings than in low-rise buildings, and can enhance the outdoor heat dissipation effect;

[0074] Quantify the differences in refrigeration efficiency at different floor heights to provide a basis for developing differentiated energy-saving strategies. High-rise buildings can use lower outdoor temperatures and air flow characteristics to improve refrigeration efficiency.

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

[0076] (1) Height adaptive control and energy cascade utilization: According to the intelligent control strategy, the "height adaptive control" technology of magnetic suspension variable frequency compressor is introduced, and the upper limit of compressor speed is automatically corrected according to altitude (converted by air pressure), the running frequency is reasonably adjusted in high-rise buildings to avoid the risk of surge caused by low air pressure at high altitude; The heat pipe-air conditioning composite system is applied, and the heat pipe technology is used to transfer waste heat to the low floor to preheat fresh air, realizing cross-floor energy cascade utilization and improving energy utilization efficiency;

[0077] Compressor height adaptive speed formula:

[0078]

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

[0080] n0 represents the rated speed of the compressor at standard atmospheric pressure (unit: r / min), which is the baseline operating parameter when the equipment is shipped;

[0081] P0 represents the standard atmospheric pressure (value 101.325 kPa), which is used as the baseline value for air pressure correction;

[0082] P h represents the actual air pressure at the current floor height (unit: kPa), which is collected by the air pressure sensor in real time and decreases with the increase of height;

[0083] k h represents the height safety factor, which is dimensionless, and the value is 0.8-0.9 in high-rise areas (h≥20 floors) and 1.0 in low-rise areas, which is used to further reduce the operation risk of high-rise compressors;

[0084] According to the air pressure difference at different floor heights, the running speed of the compressor is dynamically adjusted to avoid the risk of compressor surge caused by low air pressure at high altitude, while ensuring stable refrigeration output;

[0085] (2) Fault blocking and control mechanism: The refrigeration equipment control unit is built-in with a "fault propagation blocking" mechanism. When an abnormality of a water chiller on a certain floor is detected, the hydraulic connection with adjacent floors is automatically cut off to prevent the spread of the fault. Through pulse width modulation (PWM) control of the electronic expansion valve, the refrigeration capacity is fine-tuned to solve the problem of uneven flow caused by static pressure differences between high and low floors. The stable operation of this unit requires reliable protection from the communication and remote monitoring unit to ensure 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%. Through fine-tuning every 100ms, the flow deviation between high and low floors 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, a "satellite-ground" dual backup communication network is built. A phased array antenna is deployed at the top of the super high-rise building to receive Beidou short messages for disaster recovery communication, ensuring smooth communication in extreme situations. An AR digital twin monitoring interface is designed. Through AR glasses, operation and maintenance personnel can directly view the pressure field distribution of the refrigeration pipeline on each floor, click on the virtual model to retrieve real-time operation data of the floor, and improve the intuitiveness and convenience of monitoring.

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

[0090] (2) Blockchain storage and height label retrieval: Blockchain technology is used to achieve data storage. Refrigeration parameter hash values are generated regularly and stored on the chain to ensure data cannot be tampered with, ensuring data authenticity and security. The "height label" retrieval system is introduced, which can query historical operation data according to floor height intervals, making it easy to trace the refrigeration effect and equipment status of different height floors, and providing comprehensive data support for the user interaction and feedback unit.

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

[0092] (1)Highly adaptive interface and brain wave feedback: Based on the data provided by the communication and remote monitoring unit, a "highly adaptive" interactive interface is designed, which automatically adjusts the display parameters according to the user's floor: the high-rise user interface highlights the wind speed adjustment options, and the low-rise user interface enhances the humidity control function, improving the user's operation specificity; The brain wave feedback technology is introduced, and the wearable device collects the user's alpha wave (relaxation state) and beta wave (tension state), automatically corrects the temperature setting value, realizes "unconscious comfortable adjustment", and improves 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 refrigeration effect (1-5 stars) as the reward value of reinforcement learning, so that the system can adapt to the user's comfort preference in the short term; Develop a "floor dialect" recognition function for voice control, which can distinguish the environmental noise characteristics of different floors, significantly improve the voice command recognition effect, form an optimized feedback to the whole system, and continuously improve the refrigeration control strategy;

[0094] User feedback 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 represents the temperature setting value corrected by the user feedback (unit: ℃), which is the updated control target of the system;

[0097] T 原设定 represents the initial temperature setting value of the system (unit: ℃), the reference value before correction;

[0098] δ represents the correction coefficient (unit: ℃ / star), taking the value of 0.5-1.0, reflecting the influence of user feedback on temperature adjustment, the larger the value, the more sensitive the feedback;

[0099] f represents the user evaluation star level, taking the value of 1-5 stars, 3 stars as the reference value (indicating that the current temperature is appropriate), higher than 3 stars needs to lower the temperature, lower than 3 stars needs to raise the temperature;

[0100] θ h represents the height correction coefficient, dimensionless, taking the value of 1.2 for high-rise (h≥15 floors) and 0.8 for low-rise, used to correct the sensitivity difference of human body to temperature change at different heights;

[0101] Combined with the user's evaluation of the refrigeration effect, the temperature setting value is dynamically corrected, and the influence of floor height on body sensation is considered, so that the temperature adjustment is more in line with the user's actual needs.

[0102] The beneficial effects of the present application are as follows:

[0103] 1、The application divides the control area in real time according to floor height, heat flow density, etc. by the "dynamic density clustering partition algorithm", combines the "height compensation module" to correct the influence of high altitude air pressure on the apparent temperature, and generates adaptive strategies based on the "height-load" bivariate model; the air convection characteristics of high-rise areas are fitted by a linear function, and the soil heat buffering effect of low-rise areas is reflected by an exponential function, so that the cooling parameters are accurately matched with the actual demand of each floor, avoiding overheating or insufficient cooling caused by traditional unified parameter control, and significantly improving energy utilization efficiency and environmental comfort.

[0104] 2、The application relies on the "space-air-ground" three-in-one perception network to collect data in real time, realizes fast response through the edge layer FPGA chip, and combines the "height-sensitive Q-learning" model to optimize the strategy autonomously; when the regional population density increases suddenly or the function is switched, the system can complete temporary partition merging and control parameter adjustment in a short time, replacing the traditional manual adjustment mode, solving the response lag problem, and meeting the intelligent operation and maintenance demand of "real-time monitoring and rapid optimization" in campus, hospital and other scenes.

[0105] 3、The application obtains multi-dimensional environmental data through the "space-air-ground" perception network, combines the "cooling potential energy" index to quantify the differences in floor cooling efficiency, and guides strategy generation; at the same time, the heat pipe-air conditioner composite system is innovatively applied to transfer high-rise waste heat to low-rise preheated fresh air; both the matching degree of cooling strategy and actual load are improved, and cross-floor energy cascade utilization is realized, perfectly fitting the industry development direction of "multi-energy complementation and energy efficiency maximization", and significantly reducing comprehensive energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0106] Figure 1 The application is a dynamic partition cooling control system based on floor height. DETAILED DESCRIPTION

[0107] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0108] As shown in Figure 1 The application provides a dynamic partition cooling control system based on floor height, which comprises:

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

[0110] The data acquisition and transmission unit is based on the architecture of the system overall architecture design unit. It constructs an "air-space-ground" integrated perception network, collects data from space-based, air-based, and ground-based dimensions, uses 5G slicing technology and "data gravity" routing algorithm to ensure transmission, and provides comprehensive and accurate data support for the floor partitioning and demand analysis unit.

[0111] The floor partitioning and demand analysis unit is based on the data from the data acquisition and transmission unit. It introduces a dynamic density clustering algorithm to divide the control area, establishes a "height-load" model, and accurately predicts the refrigeration load according to the floor characteristics fitting function. The analysis results are output.

[0112] The intelligent algorithm and strategy generation unit is based on the analysis results of the floor partitioning and demand analysis unit. It integrates reinforcement and transfer learning to develop a model, learns the optimal strategy based on the floor height, introduces enthalpy priority logic and refrigeration potential index, and generates intelligent control strategies to guide the refrigeration equipment control unit.

[0113] The refrigeration equipment control unit is based on the intelligent control strategy. It introduces compressor height adaptive control technology, applies heat pipe-air conditioning composite system, and has a built-in fault propagation blocking mechanism. It solves the problem of uneven flow through PWM control.

[0114] The communication and remote monitoring unit is designed to ensure equipment control. It constructs a satellite-ground dual backup communication network, designs an AR monitoring interface, uses blockchain technology to store data, and sets up a height label retrieval system. It provides data support for the user interaction and feedback unit.

[0115] The user interaction and feedback unit is based on the data from the communication and remote monitoring unit. It designs a height-adaptive interaction interface, introduces brain wave feedback, establishes a user feedback closed-loop mechanism, and optimizes the entire system through voice recognition and floor noise adaptation.

[0116] The system overall architecture design unit includes:

[0117] (1) Three-tier architecture innovation: This unit breaks through the traditional centralized control mode, adopts a "cloud-edge-end" three-tier distributed architecture, deploys a digital twin engine in the cloud layer, integrates floor height, building materials, meteorological parameters, and other data to build a dynamic model that can simulate and predict cooling effects up to 4 hours in advance; The edge layer sets up a floor-level intelligent gateway, integrates FPGA chips to achieve fast real-time response, effectively solving the problem of transmission delay difference between high and low layers; The terminal layer introduces a "micro-partition controller", 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] The dynamic model of the digital twin engine inputs data in addition to floor height, building materials, and meteorological parameters, including real-time personnel density (infrared count data from ground sensors), equipment operating status (such as compressor speed, valve opening), and 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-partition controller uses the PRIME protocol in power line carrier communication (PLC), with a communication rate of 100kbps, supporting priority scheduling for collaborative control across floors - when a controller detects that its load exceeds the threshold (such as 80% of the rated load), it automatically sends a collaborative request to adjacent floor controllers, and the requested controller prioritizes responding to the support needs of high-rise areas (response priority: high-rise request > low-rise request);

[0120] Data transmission priority calculation formula:

[0121]

[0122] In the formula: P 传输 represents the data transmission priority, with a value range of 0-1, and the higher the value, the more priority the data has for transmission;

[0123] λ represents the delay weight coefficient, with a value of 0.6-0.8, 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 takes 0.7 for office buildings (emphasizing high-rise real-time performance), λ takes 0.6 for hospital buildings (balancing high and low demand), and λ takes 0.8 for commercial buildings (high demand for real-time performance in high-rise people-intensive areas);

[0124] t 延迟 represents the historical average data transmission delay of the floor (in ms), reflecting the time-consuming characteristics of past data transmission;

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

[0126] Q 平均Indicates the average cooling load of the floor (in W) as a reference for determining whether the current load is abnormal;

[0127] Determine the transmission priority of different floor data, prioritize real-time data in high-rise areas and high-load areas to avoid data delay affecting regulation effect;

[0128] (2) Height compensation and architecture advantage: The architecture introduces a "height compensation module" that automatically corrects cooling parameters by using an internal air pressure sensor to determine the absolute height of the floor in real time. For example, in areas above 30 floors, the temperature sensor value is automatically compensated by +0.8℃ to offset the impact of high air pressure on perceived temperature. This architecture supports seamless expansion of super high-rise buildings, significantly reduces wiring costs compared to traditional solutions, and provides a stable and efficient infrastructure for future data collection and transmission units, ensuring efficient system linkage.

[0129] In the height compensation module, the conversion formula for the absolute height of the floor h is: h = (P D -P h ) / 0.12 + 1 (unit: floor), where P D is the air pressure at the first floor of the building (kPa), P h is the current floor air pressure (kPa), and 0.12 is the standard atmospheric pressure decrement coefficient (kPa / floor). The conversion is based on actual data from buildings located at an altitude of ≤500m, and the adjustment coefficient can be adjusted according to the local air pressure gradient in high-altitude areas.

[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 represents the perceived temperature after height compensation (in ℃), which is the final temperature value used for regulation and decision-making, and can more accurately reflect human perception of temperature.

[0134] T 实测 represents the actual indoor temperature directly collected by the temperature sensor (in ℃), which is the original detection data.

[0135] ΔT 高度 represents the height compensation temperature difference (in ℃), which only takes effect when the floor height exceeds 30 floors, and increases linearly with the increase in floor height, used to offset the impact of high air pressure on perceived temperature.

[0136] h represents the absolute height of the floor (unit: floor), which is converted from the air pressure value collected by the air pressure sensor in combination with the standard floor height of the building, and reflects the vertical height of the current area;

[0137] The influence of high-altitude air pressure on the sensible temperature is corrected, so that the temperature detection value is more consistent with the actual feeling of the human body, and the deviation problem between the sensible temperature and the measured temperature caused by air pressure changes above the 30th floor is solved.

[0138] Among them, the data acquisition and transmission unit comprises:

[0139] (1) Air-space-ground three-dimensional perception network: based on the architecture perception demand of the system overall architecture design unit, construct the "air-space-ground" three-dimensional perception network, receive Fengyun-4 meteorological satellite data from space-based, analyze the solar radiation intensity and wind speed gradient at different height layers; deploy unmanned aerial vehicle patrol formation on space-based, carry infrared thermal imager to regularly scan building facade, generate each floor heat loss distribution atlas; use fiber grating array sensor on ground-based, vertically arrange along elevator shaft, realize high-density temperature gradient measurement, data density is greatly improved compared with traditional point sensor, provide multi-dimensional and high-precision raw data for the system;

[0140] The arrangement density of the fiber grating array sensor is: when the standard floor height is 3m, arrange 1 group every 2 floors, each group contains 3 sensors (corresponding to the middle of the room, the window side and the corner of the wall respectively), realize three-dimensional measurement of temperature gradient; the scanning path of the unmanned aerial vehicle patrol formation is arranged parallel to the building facade, the scanning range covers all floors, the single scanning time is not more than 10 minutes, ensuring the timeliness of the heat loss atlas;

[0141] (2) Differentiated transmission technology and algorithm: 5G slicing technology is used in the transmission link, different channels are allocated for data of different floors: ultra-low latency slicing is enabled in high-rise areas to meet real-time control requirements; large connection slicing is used in low-rise areas to carry high-density sensor data; design "data gravity" routing algorithm to simulate the effect of gravity to realize the preferential transmission of high-rise data to edge nodes, avoid congestion of low-rise data, and significantly improve the transmission reliability; through this set of collection and transmission mechanism, comprehensive, accurate and timely data support can be provided for the floor partitioning and demand analysis unit;

[0142] The specific implementation of "preferential transmission of high-rise data" in the data gravity routing algorithm: allocate a routing weight coefficient of 1.2 for high-rise data (≥20 floors), 0.8 for low-rise data (≤10 floors), and 1.0 for 11-19 floors; when the bandwidth is insufficient, preferentially discard low-rise non-critical data (such as historical temperature curve), and retain high-rise real-time control instructions (such as compressor speed signal);

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

[0144] Channel allocation parameters of 5G slice technology: high-level area (20 floors and above) uses URLLC slice, bandwidth ≥20MHz, end-to-end latency ≤10ms, supporting ≥50 real-time control instruction transmissions per floor; low-level area (1-19 floors) uses mMTC slice, bandwidth ≥50MHz, supporting ≥1000 sensor nodes concurrent transmission per floor, packet loss rate ≤0.1%.

[0145] Among them, the floor partitioning and demand analysis unit includes:

[0146] (1) Dynamic density clustering partitioning algorithm: Based on the multi-dimensional 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". This algorithm takes floor height as the core dimension, integrates real-time heat flux density, personnel movement trajectory and other data, and re-divides the control area regularly. For example, when a large number of people are temporarily added to the west conference room on the 15th floor, the system can automatically merge the three rooms on the west side of the 14th-16th floor into a temporary partition, ensuring that the partition matches the actual cooling demand;

[0147] The clustering period of the control area redivision is dynamically adjusted according to the building function: office buildings cluster every 1 hour (to adapt to the change of up and down personnel flow), hospitals cluster every 30 minutes (to cope with the frequent personnel flow in the ward), and commercial buildings cluster every 2 hours (to balance energy consumption and real-time performance); The clustering trigger condition also includes'single area load fluctuation exceeding 20%' (such as a conference room suddenly full of people), which immediately starts temporary clustering;

[0148] Dynamic partition clustering center calculation formula:

[0149]

[0150] w i = α · h i + β · q i + γ · p i

[0151] In the formula: C k represents the clustering center of the kth dynamic partition, which is a virtual center coordinate (unit: m) determined by considering the characteristics of multiple rooms as the reference point for the control of this partition;

[0152] w iWeight value of the i-th room, unitless, reflecting the influence degree of the room in the zoning division, the higher the weight, the greater the influence on the cluster center;

[0153] x i Physical coordinates of the i-th room (unit: m), usually based on the building plan coordinate system;

[0154] α, β, γ represent weight coefficients, all dimensionless parameters, and satisfy α+β+γ=1, respectively representing the influence weight of floor height, heat flux density, and personnel density in zoning, which can be dynamically adjusted according to building characteristics; the value rules of weight coefficients α, β, γ: office building α=0.5 (floor height weight is the highest), β=0.3 (heat flux density), γ=0.2 (personnel density); hospital building α=0.4, β=0.2, γ=0.4 (personnel density weight is higher); commercial building α=0.5, β=0.2, γ=0.3. The adjustment basis is the sensitivity difference of building function to heat flow and personnel flow;

[0155] h i Floor height of the i-th room (unit: floor), reflecting its vertical position characteristics;

[0156] q i Real-time heat flux density of the i-th room (unit: W / m 2 ), reflecting the heat exchange intensity of the area;

[0157] p i Personnel density of the i-th room (unit: person / m 2 ), reflecting the heat influence generated by personnel activities;

[0158] Dynamically determine the center position of the refrigeration control zoning, integrate factors such as floor height, heat flux density, and personnel density, realize accurate division and real-time adjustment of zoning, and ensure that the zoning matches the actual refrigeration demand;

[0159] (2) Height-load dual variable model construction: a "height-load" dual variable model is established, and a correction coefficient of building thermal resistance changing with height is introduced: an exponential function is used to fit the bottom area (1-5 floors), reflecting the soil heat buffering effect; a linear function is used for high-rise areas (above 20 floors), reflecting the heat exchange characteristics dominated by air convection; the model input parameters include real-time height, window-wall ratio, hourly solar altitude angle, etc., which can accurately output the refrigeration load prediction results, providing reliable analysis basis for the intelligent algorithm and strategy generation unit;

[0160] The acquisition path of the model input parameters: the real-time height comes from the conversion data of the air pressure sensor, the window-wall ratio is the architectural design parameter (pre-entered into the system), the hourly solar altitude angle is analyzed from the data of the meteorological satellite (updated every 10 minutes), and the indoor-outdoor temperature difference is calculated by the difference between the ground temperature sensor and the outdoor temperature data of the satellite;

[0161] The height-load bivariate model formula is:

[0162] The bottom area (1-5 floors):

[0163] The high-rise area (more than 20 floors): Q = k3 h A At

[0164] In the formula, Q represents the predicted value of the refrigeration load (unit: W), which is the core parameter for formulating the refrigeration strategy of the system and reflects the refrigeration amount required to maintain the set temperature;

[0165] k1 and k2 represent the thermal resistance correction coefficients of the bottom area, which are both dimensionless parameters; k1 reflects the influence of soil heat storage on the initial thermal resistance of the bottom area, and k2 reflects the attenuation rate of the thermal resistance with the increase of the floor height; the determination method of k1 and k2 of the bottom area is as follows: k1 is obtained based on the experiment of the thermal conductivity of the soil at the bottom of the building (k1 is 1.0 for clay soil and k1 is 1.2 for sandy soil); k2 reflects the attenuation characteristics of the thermal resistance with the increase of the floor height, and is determined according to the thermal insulation performance of the building envelope structure (k2 is -0.02 when the thermal insulation grade is high, and k2 is -0.05 when the thermal insulation grade is low); k3 of the high-rise area is determined in combination with the local average wind speed (k3 is 0.05 when the wind speed is greater than or equal to 3 m / s, and k3 is 0.02 when the wind speed is less than or equal to 1.5 m / s);

[0166] k3 represents the thermal resistance correction coefficient of the high-rise area, which is dimensionless and represents the linear change characteristics of the thermal resistance with the height under the dominance of air convection;

[0167] h represents the floor height (unit: floor), which is the key parameter for distinguishing between the bottom and the high-rise;

[0168] A represents the air conditioning area of the room (unit: m 2 ), which reflects the physical scale of the refrigeration area;

[0169] At represents the indoor-outdoor temperature difference (unit: ℃), which is the difference between the indoor set temperature and the outdoor real-time temperature, and directly affects the size of the refrigeration load;

[0170] The refrigeration load prediction model is established according to the difference in floor height, which adapts to the heat transfer characteristics of the bottom area (affected by soil heat storage) and the high-rise area (affected by air convection), respectively, to realize the accurate prediction of the refrigeration load.

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

[0172] (1) Highly sensitive learning model development: Based on the prediction results of the floor zoning and demand analysis unit, the reinforcement learning and transfer learning algorithm are integrated, and the "highly sensitive Q-learning" model is introduced. Taking the floor height as the state feature, through continuous interaction with the digital twin environment, the optimal control strategy for different heights is learned autonomously. For new buildings, part of the historical strategies of similar buildings can be inherited through transfer learning, greatly shortening the model training period and quickly adapting to the cooling demand 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 100 groups of state-action samples are generated each time (state includes floor height, real-time load, and action includes compressor speed, valve opening). The reuse ratio of historical strategies of similar buildings in transfer learning is: 70% for high-rise strategies (due to small differences in high-altitude environment) and 50% for low-rise strategies (due to large differences in soil and shading);

[0174] In transfer learning, the reuse rules of historical strategies of similar buildings for new buildings are: 70% of high-rise (≥20 floors) control parameters such as compressor speed correction coefficient and heat pipe start-stop threshold are retained, and 30% of low-rise (≤19 floors) parameters are adjusted - among them, the parameters of personnel-intensive areas (such as classrooms and wards) are corrected by actual personnel density × 1.2, and the parameters of non-intensive areas (such as corridors) are corrected by × 0.8, to ensure adaptation to the functional layout of new buildings;

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

[0176] Determination criteria for high humidity and dry areas: When the relative humidity is >60% in the low-rise area (1-5 floors), the dew point temperature control is triggered (preferably controlling the dew point at 12-14°C), and when the relative humidity is <40% in the high-rise area (above 20 floors), the temperature regulation is focused (target temperature 24-26°C). Humidity data comes from the ground temperature and humidity sensor (3 installed on each floor, taking the average value);

[0177] Quantitative formula of refrigeration potential:

[0178]

[0179] P represents the refrigeration potential energy, a dimensionless parameter, the larger the value, the higher the refrigeration efficiency of the floor, and the easier to achieve energy-saving operation;

[0180] T 室外 represents the outdoor temperature corresponding to the current floor height (unit: ℃), which varies with height, and is usually lower in high-rise buildings than in low-rise buildings;

[0181] T 设定 represents the indoor set temperature (unit: ℃), which is the user or system preset comfort temperature target;

[0182] COP 基准 represents the refrigeration coefficient of the refrigeration equipment under standard working conditions, which is dimensionless, usually 3.0-4.5, reflecting the inherent energy efficiency of the equipment;

[0183] ρ represents the air density of the current floor height (unit: kg / m3), which decreases with the increase of height, affecting the heat exchange efficiency of the refrigeration equipment;

[0184] v represents the outdoor wind speed of the current floor height (unit: m / s), which is usually higher in high-rise buildings than in low-rise buildings, and can enhance the outdoor heat dissipation effect;

[0185] The reference COP value is determined according to the standard working condition COP marked on the nameplate of the refrigeration equipment: 3.8-4.5 for magnetic suspension compressor, and 3.0-3.5 for screw compressor; The calculation of air density ρ is based on the ideal gas state equation: ρ = 1.225 × (P h / P D ) × (273 / (273 + T D )), where P h is the current floor pressure, P D is the standard atmospheric pressure, and T D is the outdoor temperature;

[0186] Quantify the refrigeration efficiency difference of different floor heights to provide basis for formulating differentiated energy-saving strategies. High-rise buildings can use lower outdoor temperature and air flow characteristics to improve refrigeration efficiency.

[0187] The refrigeration equipment control unit comprises:

[0188] (1) Height adaptive control and energy cascade utilization: According to the intelligent control strategy, the "height adaptive control" technology of magnetic suspension variable frequency compressor is introduced, the compressor speed upper limit is automatically corrected according to the altitude (converted by air pressure), the running frequency is reasonably adjusted in high-rise buildings to avoid the risk of surge caused by low air pressure at high altitude; The heat pipe-air conditioning composite system is applied, the heat pipe technology is used in high-rise areas to transfer waste heat to low-rise buildings to preheat fresh air, realizing cross-floor energy cascade utilization and improving energy utilization efficiency;

[0189] The specific path of high-level waste heat transfer through the heat pipe: the heat pipe evaporator is arranged at the condensing end of the high-level air conditioning unit, the condenser is arranged at the inlet of the low-level fresh air duct, and the working medium of the heat pipe is R134a (adapted to -20℃ to 50℃ working condition); when the high-level condensing temperature is greater than 35℃, the heat pipe is automatically started (controlled by a solenoid valve), and the temperature of the waste heat preheating fresh air is increased by 5-8℃ (adjusted dynamically according to the high-level waste heat power);

[0190] Compressor height adaptive speed formula:

[0191]

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

[0193] n0 represents the rated speed of the compressor under standard atmospheric pressure (unit: r / min), which is the reference running parameter when the equipment is shipped;

[0194] P0 represents the standard atmospheric pressure (value: 101.325kPa), which is used as the reference value for air pressure correction;

[0195] P h represents the actual air pressure of the current floor height (unit: kPa), which is collected by an air pressure sensor in real time and decreases with the increase of height;

[0196] k h represents the height safety factor, which is dimensionless, and the value is 0.8-0.9 in high-level areas (h≥20 floors) and 1.0 in low-level areas, which is used to further reduce the running risk of the high-level compressor; the specific value of the height safety factor k h : 0.9 for 20-25 floors, 0.85 for 26-30 floors, and 0.8 for more than 31 floors; the value is based on the surge test data of the magnetic suspension compressor at different heights—when the air pressure is lower at high floors, the upper limit of the speed is reduced to avoid impeller stall;

[0197] According to the air pressure difference of different floor heights, the running speed of the compressor is dynamically adjusted to avoid the risk of compressor surge caused by low air pressure at high altitudes, while ensuring stable refrigerating capacity output;

[0198] (2) Fault blocking and control mechanism: the refrigeration equipment control unit is built-in with a "fault propagation blocking" mechanism, which automatically cuts off the hydraulic connection between the water chiller of a floor and the adjacent floors when an abnormality is detected, preventing the spread of faults; through the pulse width modulation (PWM) control of the electronic expansion valve, the refrigerating capacity is fine-tuned to solve the problem of uneven flow caused by static pressure difference between high-level and low-level; the stable operation of this unit requires reliable protection from the communication and remote monitoring unit to ensure accurate execution of control commands and real-time feedback of equipment status;

[0199] PWM control pulse frequency is 10 Hz, duty cycle adjustment range 5%-95%; for high and low static pressure difference (high static pressure is 0.2-0.5 MPa higher than low static pressure), the initial opening of the high layer electronic expansion valve is 10% larger than that of the low layer, and the flow deviation is controlled within ±5% through feedback adjustment (according to flow sensor data) every 100 ms;

[0200] The judgment threshold of the chiller abnormality is that the outlet temperature deviates from the set value by ±5℃ and lasts for more than 30s, or the pressure fluctuation exceeds ±0.1 MPa; the operation steps of the water connection cut-off are: ① close the electric butterfly valve of the abnormal floor and the adjacent floor (response time ≤5s); ② start the standby unit (capacity is 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 comprises:

[0202] (1) Double backup network and AR monitoring interface: In order to ensure the efficient operation of the refrigeration equipment control unit, this unit builds a "satellite-ground" double backup communication network, deploys a phased array antenna at the top of the super high-rise building, receives Beidou short message to realize disaster recovery communication, and ensures smooth communication in extreme conditions; design AR digital twin monitoring interface, operation and maintenance personnel can directly view the pressure field distribution of each floor refrigeration pipeline through AR glasses, click the virtual model to retrieve the real-time running data of the floor, and improve the intuitiveness and convenience of monitoring;

[0203] The response time of clicking the virtual model to retrieve data is ≤1s, and the display content includes: the temperature curve of the floor in the past 1 hour, the current equipment state (running / standby / fault), the real-time value of energy consumption; support gesture operation (such as sliding to view adjacent floor data, zooming to view details), adapt to AR glasses devices of Android and iOS systems;

[0204] Switching logic of satellite-ground double backup network: when the ground network is normal, optical fiber transmission (bandwidth ≥100Mbps) is preferred; when the ground network is interrupted (such as optical fiber failure), automatically switch to Beidou short message communication, send key data (including temperature of each floor, equipment state) every 15 minutes, single message data volume ≤140 bytes, ensure the continuity of monitoring in extreme conditions;

[0205] (2) Blockchain storage and height label retrieval: use blockchain technology to realize data storage, regularly generate refrigeration parameter hash value, and store it on the chain to ensure data cannot be tampered with, and guarantee the authenticity and security of data; introduce "height label" retrieval system, which can query historical operation data according to floor height interval, facilitate tracing of refrigeration effect and equipment state of different height floors, and provide comprehensive data support for user interaction and feedback unit;

[0206] The data stored on the chain includes: real-time temperature (corrected) of each floor, refrigeration load, compressor operating parameters (speed, pressure), energy consumption data (kWh), and hash value generated every 30 minutes; the blockchain adopts a consortium chain architecture (nodes including property server, equipment manufacturer server), ensuring that data is traceable and tamper-proof, and the storage period is 5 years;

[0207] The user interaction and feedback unit includes:

[0208] (1) Highly adaptive interface and brain wave feedback: Based on the data provided by the communication and remote monitoring unit, a "highly adaptive" interaction interface is designed, which automatically adjusts the display parameters according to the floor where the user is located: the high-rise user interface highlights the wind speed adjustment options, and the low-rise user interface enhances the humidity control function, improving the relevance of user operation; Brain wave feedback technology is introduced, and wearable devices are used to collect alpha waves (relaxed state) and beta waves (tense state), automatically correcting the temperature setting value, achieving "unconscious comfort adjustment" and improving user experience;

[0209] The high-rise (≥15 floors) interface highlights the wind speed adjustment options (default display wind speed 1-3 level sliding bar), as high-rise outdoor wind speed affects the body sensation; the low-rise (≤5 floors) interface enhances the humidity control function (displays the difference between current humidity and target humidity), as low-rise is easily affected by soil moisture; the interface parameters are refreshed every 30 seconds, and the data comes from real-time push from the communication and remote monitoring unit;

[0210] Correction logic of brain wave feedback: When the wearable device collects alpha wave intensity > 60 μV (relaxed state), maintain the current temperature setting; when beta wave intensity > 50 μV (tense / inconvenient state), correct the temperature by 0.5℃ / 5μV (the higher the beta wave intensity, the greater the correction amplitude, the maximum correction amount ≤2℃); After correction, re-collect brain waves every 5 minutes until alpha wave proportion ≥60%;

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

[0212] System pre-train each floor noise feature library: low floor (1-5 floors) contains traffic noise (50-200Hz low frequency), crowd conversation, high floor (≥20 floors) contains wind noise (2000-5000Hz high frequency), equipment running sound; When speech recognition, first filter background noise through noise feature library (SNR improved by more than 20dB), then perform command recognition, support mandarin and 8 dialects (such as Cantonese, Sichuanese) floor adaptation;

[0213] User feedback 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 represents the temperature set value corrected by user feedback (unit: ℃), which is the updated control target of the system;

[0216] T 原设定 represents the initial temperature set value of the system (unit: ℃), the reference value before correction;

[0217] δ represents the correction coefficient (unit: ℃ / star), taking value 0.5-1.0, reflecting the influence degree of user feedback on temperature adjustment, the larger the value, the more sensitive the feedback; The value rule of correction coefficient δ: δ = 0.5 ℃ / star in summer (to avoid excessive cooling), δ = 1.0 ℃ / star in winter (to quickly respond to the demand for heating); The adjustment basis of height correction coefficient θ h : 1.2 for 15-25 floors, 1.3 for more than 25 floors (high floors are more sensitive to temperature changes), 0.8 for 1-14 floors (largely affected by humidity);

[0218] f represents the user evaluation star level, taking value 1-5 stars, 3 stars as the reference value (indicating that the current temperature is suitable), higher than 3 stars need to lower the temperature, lower than 3 stars need to raise the temperature;

[0219] θ h represents the height correction coefficient, dimensionless, taking value 1.2 for high floors (h≥15 floors) and 0.8 for low floors, used to correct the sensitivity difference of human body to temperature changes at different heights;

[0220] Combined with the user's evaluation of the refrigeration effect, the temperature set value is dynamically corrected, considering the influence of floor height on body sensation, so that the temperature adjustment is more in line with the actual needs of users.

[0221] It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting; it is not intended to exclude myriad other embodiments of the present application that other inventors can develop based on the same general inventive concepts embodied by the described embodiments. That is, although the present application is described in terms of particular embodiments and illustrative figures, it should be apparent that the scope of the present application is not limited to these specific embodiments.

[0222] While the embodiments of the application have been shown and described herein, it will be understood by those skilled in the art that many changes, modifications, substitutions and alterations to these embodiments can be made without departing from the principles and spirits of the application, and it is intended that the scope of the application be limited solely by the scope of the appended claims and the equivalents thereof.

Claims

1. A dynamic zoning refrigeration control system based on floor height, characterized in that: The system comprises: The system overall architecture design unit: adopts the cloud-edge-terminal three-layer architecture composed of cloud, edge and terminal, deploys a digital twin engine in the cloud layer for predicting refrigeration effect, sets a floor-level intelligent gateway in the edge layer for solving transmission delay, sets a micro-partition controller in the terminal layer for realizing cross-floor coordination, and introduces a height compensation module for correcting the influence of high-altitude air pressure; The data acquisition and transmission unit: constructs a space-air-ground three-in-one perception network, in which the space-based receives meteorological satellite data and analyzes the solar radiation intensity and wind speed gradient at different altitudes, the air-based deploys a UAV patrol formation and carries an infrared thermal imager to regularly scan the building facade to generate a heat loss distribution map of each floor, and the ground-based uses a fiber grating array sensor to measure the temperature gradient; in the data transmission link, the fifth generation mobile communication network slicing technology is adopted to allocate differentiated channels for different floor data, enabling the high-level area to use ultra-low latency slicing and the low-level area to use large connection slicing, and the data gravity routing algorithm is used to realize the preferential transmission of high-level data to the edge node; The floor partitioning and demand analysis unit: based on the multi-dimensional data provided by the data acquisition and transmission unit, a dynamic density clustering partitioning algorithm is introduced, which fuses data including heat flux density and personnel movement trajectory, re-divides the control area regularly, establishes a height-load bivariate model, introduces a correction coefficient of building thermal resistance varying with height, uses an exponential function to fit in the bottom area and a linear function in the high-level area, the model input parameters include real-time height, window-wall ratio and hourly solar altitude angle, and the output is the refrigeration load prediction result and the analysis result; The intelligent algorithm and strategy generation unit: based on the analysis result of the floor partitioning and demand analysis unit, the model is developed by fusing reinforcement learning and transfer learning, the optimal strategy is learned with floor height as the feature, the enthalpy priority logic and refrigeration potential index are introduced, and the intelligent control strategy is generated; The refrigeration equipment control unit: according to the intelligent control strategy, the height adaptive control technology of compressor is introduced, the heat pipe-air conditioner composite system is applied, the fault propagation blocking mechanism is built in, and the flow unevenness problem is solved by pulse width modulation control; The communication and remote monitoring unit: a satellite-ground dual backup communication network is constructed, an augmented reality monitoring interface is designed, blockchain technology is used to store data, and a height label retrieval system is designed to provide data support for the user interaction and feedback unit; The user interaction and feedback unit: a height adaptive interaction interface is designed, electroencephalogram feedback technology is introduced, a user feedback closed-loop mechanism is established, and voice recognition is used to distinguish the noise characteristics of different floors.

2. The dynamic zoning system based on floor height for refrigeration control according to claim 1, wherein: The system overall architecture design unit comprises: (1) Innovative construction of three-layer architecture: a cloud-edge-terminal three-layer distributed architecture is adopted, a digital twin engine is deployed in the cloud layer, data including floor height, building materials and meteorological parameters are integrated to build a dynamic model, and the refrigeration effect is simulated and predicted; a floor-level intelligent gateway is set in the edge layer, an FPGA chip is integrated, and the difference in data transmission delay between high-level and low-level areas is solved; a micro-partition controller is introduced in the terminal layer, and cross-floor collaborative control is realized through power line carrier communication; (2) Height compensation and architecture advantage: The architecture introduces a height compensation module, which determines the absolute height of the floor in real time through a built-in air pressure sensor, automatically corrects the refrigeration parameters, supports seamless expansion of super high-rise buildings, and provides basic architecture support for the data acquisition and transmission unit.

3. The dynamic zoning system based on floor height for refrigeration control according to claim 1, wherein: The intelligent algorithm and strategy generation unit includes: (1) Height-sensitive learning model development: Based on the prediction results of the floor partitioning and demand analysis unit, the height-sensitive Q learning model is introduced by combining reinforcement learning and transfer learning algorithms; taking the floor height as the state feature, through continuous interaction with the digital twin environment, the optimal control strategy for different heights is learned autonomously; (2) Enthalpy priority strategy and refrigeration potential index: The enthalpy priority control logic is introduced in the strategy generation, which prioritizes dew point temperature control in high humidity low floors and focuses on temperature regulation in dry high-rise areas; a refrigeration potential index is designed to quantify the refrigeration efficiency at different heights and generate intelligent control strategies.

4. The dynamic zoning system based on floor height for refrigeration control according to claim 1, wherein: The refrigeration equipment control unit includes: (1) Height adaptive control and energy cascade utilization: According to the intelligent control strategy, the height adaptive control technology of magnetic suspension variable frequency compressor is introduced, and the compressor speed upper limit is automatically corrected according to the altitude; the heat pipe-air conditioning composite system is applied, and the heat pipe technology is used to transfer waste heat to the low floor to preheat fresh air in the high floor area; (2) Fault blocking and control mechanism: The refrigeration equipment control unit has a fault propagation blocking mechanism, which automatically cuts off the hydraulic connection between the water chiller and the adjacent floor when an abnormality is detected, and solves the problem of uneven flow caused by static pressure difference between high and low floors through pulse width modulation control of electronic expansion valves.

5. The dynamic zoning system based on floor height for refrigeration control according to claim 1, wherein: The communication and remote monitoring unit includes: (1) Dual backup network and AR monitoring interface: A satellite-ground dual backup communication network is constructed, a phased array antenna is deployed on the top of the super high-rise building, and an AR digital twin monitoring interface is designed, which can access real-time operation data of the floor by clicking the virtual model; (2) Blockchain storage and height label retrieval: Blockchain technology is used to achieve data storage, and refrigeration parameter hash values are generated regularly to ensure data integrity; a height label retrieval system is introduced to query historical operation data by floor height interval, providing data support for the user interaction and feedback unit.

6. The dynamic zoning system based on floor height for refrigeration control according to claim 1, wherein: The user interaction and feedback unit includes: (1) Height adaptive interface and brain wave feedback: Based on the data provided by the communication and remote monitoring unit, a height adaptive interface is designed to automatically adjust the display parameters according to the user's floor, and brain wave feedback technology is introduced to collect user alpha and beta waves through wearable devices and automatically correct the temperature set value; (2) Feedback loop and floor dialect recognition: A user feedback-strategy evolution mechanism is established, which takes user evaluation of refrigeration effect as the reward value of reinforcement learning, and distinguishes environmental noise characteristics of different floors through voice-controlled floor dialect recognition function.

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