Central air conditioning energy-saving operation control method and system

Abstract

The present application relates to the technical field of energy-saving operation control of air conditioners, and discloses a method and system for energy-saving operation control of central air conditioners. The method comprises: collecting data through temperature and humidity sensors and processing to obtain temperature and humidity distribution indicators; calculating the load value according to the indicator and the density of people to form a distribution map; regulating the cold and heat source equipment according to the distribution map to obtain operating parameters; adjusting the water pump and fan based on the parameters to form balance data; adjusting the terminal equipment according to the data to generate control instructions; adjusting the system status in real time according to the instructions and comfort feedback to achieve energy-saving operation. The present application solves the problem of energy waste caused by the decentralized control of the subsystems of traditional central air-conditioning systems. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation, the operating efficiency of the system under partial load conditions is improved. At the same time, refined control is achieved according to the differentiated needs of different regions, minimizing energy consumption while ensuring user comfort.

Description

Technical Field

[0001] The present application relates to the technical field of air-conditioning energy-saving operation control, and in particular to a central air-conditioning energy-saving operation control method and system. Background Art

[0002] As building energy consumption continues to increase as a proportion of global total energy consumption, central air conditioning systems, as a major component of building energy consumption, have attracted widespread attention for their energy-saving operation control methods. Traditional central air conditioning control methods mainly use fixed parameter control strategies, such as constant water supply temperature, constant flow control, and simple timed start and stop. With technological development, technologies such as variable frequency control of single equipment, load-based equipment start and stop control, and simple temperature feedback regulation have become widely used. Some advanced systems have begun to use building automation systems (BAS) for centralized monitoring and implement simple linkage control based on preset rules, such as adjusting the water supply temperature of the chiller according to the indoor temperature, or adjusting the water pump speed according to terminal demand.

[0003] However, existing technologies have obvious shortcomings. First, most control systems still use independent control modes and lack unified and coordinated control of cold and heat sources, water systems, and terminal equipment, resulting in mutual influence between subsystems and failure to achieve optimal operating conditions. Second, traditional control methods are mainly based on simple temperature feedback and lack comprehensive consideration of multi-dimensional factors such as occupant density, usage patterns, and thermal comfort, making it difficult to achieve precise control. Third, existing systems do not adequately consider the physical characteristics of buildings and dynamic changes in loads, and are unable to effectively deal with complex situations in actual operation, such as uneven cooling and heating, low efficiency of partial loads, and other problems. In addition, most control systems lack self-learning and prediction functions and can only respond passively rather than actively foresee, resulting in control lag and energy waste. These problems are particularly prominent in modern buildings with variable loads and differentiated demands. There is an urgent need for a central air-conditioning energy-saving operation control method that features full system coordination, multi-dimensional perception, and intelligent prediction. Summary of the Invention

[0004] This application provides a method and system for controlling the energy-saving operation of a central air conditioner, addressing the energy waste caused by decentralized control of the subsystems of traditional central air conditioning systems. By constructing a complete closed-loop control system from environmental perception and load forecasting to equipment regulation, the system's operating efficiency under partial load conditions is improved. Furthermore, refined control is implemented to meet the differentiated needs of different regions, minimizing energy consumption while ensuring user comfort.

[0005] In the first aspect, the present application provides a central air-conditioning energy-saving operation control method, which includes: collecting indoor and outdoor environmental temperature and humidity data through temperature and humidity sensors, and performing time series processing on the collected data to obtain regional temperature and humidity distribution indicators; calculating the cooling and heating load values ​​of each space based on the regional temperature and humidity distribution indicators and personnel density data to obtain a regional load distribution diagram; according to the regional load distribution diagram, regulating the start and stop number and operation frequency of the central air-conditioning cooling and heating source equipment to obtain the chiller operating parameters; based on the chiller operating parameters, adjusting the speed and valve opening of the water pump and fan to form water system balance control data; according to the water system balance control data, adjusting the supply air temperature and air volume of each terminal device to generate an air supply control instruction; according to the air supply control instruction and indoor thermal comfort feedback data, adjusting the operating status of the air-conditioning system in real time to achieve energy-saving operation control of the central air-conditioning system.

[0006] In a second aspect, the present application provides a central air-conditioning energy-saving operation control system, the central air-conditioning energy-saving operation control system comprising:

[0007] The processing module is used to collect indoor and outdoor environmental temperature and humidity data through temperature and humidity sensors, perform time series processing on the collected data, and obtain regional temperature and humidity distribution indicators;

[0008] A calculation module is used to calculate the cooling and heating load value of each space based on the regional temperature and humidity distribution index and the personnel density data to obtain a regional load distribution map;

[0009] A control module is used to control the start and stop quantity and operating frequency of the central air-conditioning cold and heat source equipment according to the regional load distribution map to obtain the operating parameters of the chiller;

[0010] A control module, configured to adjust the speed and valve opening of the water pump and fan based on the operating parameters of the chiller to form water system balance control data;

[0011] A generation module, configured to adjust the air supply temperature and air volume of each terminal device according to the water system balance control data and generate an air supply control instruction;

[0012] The adjustment module is used to adjust the operating state of the air-conditioning system in real time according to the air supply control instructions and indoor thermal comfort feedback data, so as to realize energy-saving operation control of the central air-conditioning system.

[0013] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned central air-conditioning energy-saving operation control method.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when executed on a computer, enable the computer to execute the above-mentioned central air-conditioning energy-saving operation control method.

[0015] In the technical solution provided by this application, temperature and humidity sensors collect indoor and outdoor ambient temperature and humidity data and perform time-series processing to generate regional temperature and humidity distribution indicators. This provides the system with precise environmental perception capabilities, enabling the control system to grasp the temperature and humidity conditions of each area in real time, laying the data foundation for subsequent precise control. Combining regional temperature and humidity distribution indicators with occupant density data to calculate the cooling and heating load values ​​for each space, the resulting regional load distribution map achieves a precise mapping from environmental data to load demand, significantly improving the accuracy of load forecasting. The application of deep learning algorithms in load forecasting is particularly noteworthy. By autonomously learning from historical data, the algorithm can identify complex load variation patterns, significantly improving the ability to predict building physical properties, occupant activity patterns, and meteorological influences. This algorithmic feature contributes to the solution by transforming passive response into active prediction, reducing control lag. In the process of controlling the central air conditioning cooling and heating source equipment based on the regional load distribution map and obtaining chiller operating parameters, the introduction of a part-load efficiency gain algorithm addresses the low efficiency of traditional air conditioning systems at part load. By intelligently combining chillers of different capacities, the system consistently operates near the optimal efficiency point, significantly reducing energy consumption. The technical feature of generating water system balance control data by adjusting the speed and valve opening of the water pump and fan based on the operating parameters of the chiller solves the energy waste and uneven comfort caused by hydraulic imbalance in traditional systems, allowing the system to maintain optimal hydraulic balance under different load conditions. The technical feature of generating air supply control instructions by adjusting the air supply temperature and air volume of each terminal device based on the water system balance control data achieves precise control of the system terminal and meets the differentiated needs of different regions. Finally, the closed-loop control that adjusts the air conditioning system in real time based on the air supply control instructions and indoor thermal comfort feedback data not only ensures control accuracy, but also continuously optimizes the control strategy through a self-learning thermal comfort preference algorithm, achieving a dynamic balance between comfort and energy saving. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a schematic diagram of an embodiment of a central air-conditioning energy-saving operation control method in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of a central air-conditioning energy-saving operation control system in an embodiment of the present application;

[0019] Figure 3 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide a method and system for controlling energy-saving operation of a central air conditioner. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, a method for controlling energy-saving operation of a central air conditioner includes:

[0022] Step S101: Collecting indoor and outdoor environmental temperature and humidity data through temperature and humidity sensors, performing time series processing on the collected data, and obtaining regional temperature and humidity distribution indicators;

[0023] Step S102: Calculate the cooling and heating load values ​​of each space based on the regional temperature and humidity distribution index and the occupant density data to obtain a regional load distribution map;

[0024] Step S103: According to the regional load distribution map, the start and stop quantity and operating frequency of the central air-conditioning cold and heat source equipment are regulated to obtain the chiller operating parameters;

[0025] Step S104: Based on the chiller operating parameters, adjust the speed and valve opening of the water pump and fan to form water system balance control data;

[0026] Step S105: Adjust the air supply temperature and air volume of each terminal device according to the water system balance control data, and generate an air supply control instruction;

[0027] Step S106: Based on the air supply control instruction and the indoor thermal comfort feedback data, the operating state of the air conditioning system is adjusted in real time to achieve energy-saving operation control of the central air conditioning system.

[0028] It is understandable that the execution subject of this application can be a central air-conditioning energy-saving operation control system, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, temperature and humidity sensors collect indoor and outdoor temperature and humidity data. These sensors are distributed across different areas of a building, forming a distributed sensor network. This collected temperature and humidity data undergoes time series processing to remove outliers and noise. Spatial interpolation functions are then used to estimate values ​​at non-sampling locations, generating continuous temperature and humidity field data. Temperature and humidity gradients are then calculated from this data to identify areas of uneven temperature distribution, ultimately generating a regional temperature and humidity distribution index. For example, in an office building, multiple temperature and humidity sensors collect data every five minutes. Within an hour, the temperature data collected in one area ranged from 22°C to 26°C, and the humidity ranged from 45% to 55%. Interpolation was used to calculate the temperature and humidity distribution for the entire area, revealing a high temperature of 26°C in the southeast corner, while only 22°C in the northwest corner, creating a significant temperature gradient. This distribution index directly reflects the actual performance of the air conditioning system.

[0030] The cooling and heating load values ​​are calculated based on the regional temperature and humidity distribution indicators and the population density data. The system divides the building space into multiple thermal zones, calculates the temperature deviation coefficient of each zone, and calculates the conductive heat load in combination with the heat transfer coefficient and surface area of ​​the enclosure structure. The population density sensor collects the real-time population distribution density in each area and converts it into human heat production based on the human metabolic rate standard. At the same time, the heat gain in the lighting equipment is taken into account, and the actual cooling and heating load values ​​required for each zone are calculated through heat balance calculation, and finally a regional load distribution map is generated. For example, the population density detected in the conference room area is 0.5 people per square meter. Combined with the average human heat production of 100W / person, the total human heat production is calculated, and then the heat production of the lighting equipment is added, and the building heat storage is subtracted to obtain a cooling capacity of approximately 20kW for this area.

[0031] Load information is analyzed spatiotemporally to construct a three-dimensional load intensity matrix, which determines the current system load rate and determines the number of cooling and heating source equipment to start and stop. Based on load characteristics analysis, the system determines the optimal equipment start-up and shutdown combination, assigns a load-sharing ratio to each activated device, calculates the required operating frequency, and ultimately generates chiller operating parameters. In actual operation, when a total cooling load of 500kW is detected, the system determines the need to start two chillers. Based on the energy efficiency curve, the system allocates the load: the first chiller carries 60% of the load and operates at a frequency of 45Hz, while the second chiller carries 40% of the load and operates at a frequency of 35Hz, achieving efficient and energy-saving operation. The system extracts supply and return water temperature, flow, and pressure data from the chiller operating parameters, constructs a hydraulic relationship diagram, and determines the basic head and flow requirements of the pumps. Using hydraulic calculation methods, the pressure requirements of each branch are decomposed to calculate the required pump speed. Furthermore, the opening of each regulating valve is determined based on flow balance calculations. These control results are combined to form water system balance control data. For example, when the chiller water supply temperature is 7°C and the flow rate is 80m³ / h, the system calculates that the main water pump needs to run at 38Hz to provide appropriate head, and sets the opening of branch valves on different floors to 60% to 85% to ensure hydraulic balance.

[0032] The system adjusts terminal equipment based on water system balance control data. The system extracts key parameters from the water system balance control data, creates a cooling capacity allocation table for terminal equipment, and allocates cooling capacity to each terminal equipment based on regional priority and actual demand. Through heat exchange calculations, the allocated cooling capacity is converted into supply air temperature parameters, and the required air volume and fan speed are determined, generating air supply control instructions. When a zone is allocated 25kW of cooling capacity, the system calculates the required supply air temperature for the fan coil units in that zone to be 14°C, with an air volume of 3000 m³ / h and a corresponding fan speed of 75%. Real-time adjustments are made based on air supply control instructions and thermal comfort feedback data. The system collects user thermal comfort feedback and measured temperature and humidity parameters, compares and analyzes them with the execution results of the air supply control instructions, and calculates the control deviation. Based on this deviation, the system divides the zones into comfort-priority and energy-saving-priority zones, adopting different control strategies to adjust air supply parameters or expand the allowable temperature range. Feedback is ultimately provided to the front-end equipment to form a closed-loop control system. After implementing this method throughout the entire office building, the air-conditioning parameters can be intelligently adjusted according to the characteristics of different areas, ensuring that crowded areas such as conference rooms maintain a comfortable temperature of 24±0.5℃, while sparsely populated corridor areas are allowed to fluctuate within the range of 22-26℃, achieving regional precise control and significantly reducing energy consumption.

[0033] In the embodiments of the present application, temperature and humidity sensors collect indoor and outdoor ambient temperature and humidity data and perform time-series processing to generate regional temperature and humidity distribution indicators, providing the system with precise environmental perception capabilities. This allows the control system to understand the temperature and humidity conditions of each area in real time, laying the data foundation for subsequent precise control. Combining the regional temperature and humidity distribution indicators with occupant density data to calculate the cooling and heating load values ​​for each space, the resulting regional load distribution map achieves a precise mapping from environmental data to load demand, significantly improving the accuracy of load forecasting. The application of deep learning algorithms in load forecasting is particularly noteworthy. By autonomously learning from historical data, the algorithm can identify complex load variation patterns, significantly improving the ability to predict building physical properties, occupant activity patterns, and meteorological influences. This algorithmic feature contributes to the solution by transforming passive response into active prediction, reducing control lag. In the process of controlling the central air conditioning cooling and heating source equipment based on the regional load distribution map and obtaining the chiller operating parameters, the introduction of a partial load efficiency gain algorithm addresses the low efficiency of traditional air conditioning systems at partial load. By intelligently combining chillers of different capacities, the system consistently operates near the optimal efficiency point, significantly reducing energy consumption. The technical feature of generating water system balance control data by adjusting the speed and valve opening of the water pump and fan based on the operating parameters of the chiller solves the energy waste and uneven comfort caused by hydraulic imbalance in traditional systems, allowing the system to maintain optimal hydraulic balance under different load conditions. The technical feature of generating air supply control instructions by adjusting the air supply temperature and air volume of each terminal device based on the water system balance control data achieves precise control of the system terminal and meets the differentiated needs of different regions. Finally, the closed-loop control that adjusts the air conditioning system in real time based on the air supply control instructions and indoor thermal comfort feedback data not only ensures control accuracy, but also continuously optimizes the control strategy through a self-learning thermal comfort preference algorithm, achieving a dynamic balance between comfort and energy saving.

[0034] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0035] (1) Distributed temperature and humidity sensors are used to collect temperature and humidity data at multiple locations in the air-conditioning coverage area to obtain discrete sampling values ​​of spatial temperature and humidity;

[0036] (2) Arrange the spatial temperature and humidity discrete sampling values ​​in the order of acquisition time to construct the temperature and humidity time series data stream;

[0037] (3) Clean the temperature and humidity time series data stream, remove outliers and noise points, and form a valid temperature and humidity data set;

[0038] (4) Numerical estimation of the effective temperature and humidity data set at non-sampling point locations to generate continuous temperature and humidity field data;

[0039] (5) Calculate the temperature gradient and humidity gradient at each point in the continuous temperature and humidity field data, and identify the hot and cold uneven areas;

[0040] (6) Compare and analyze the temperature gradient and humidity gradient with historical data to generate regional temperature and humidity distribution indicators.

[0041] Specifically, in the energy-saving operation control method for central air conditioning, the intelligent collection and analysis of environmental parameters is a fundamental component of the entire system. Distributed temperature and humidity sensors are first deployed at multiple sampling points throughout the air conditioning coverage area. These sensors typically include PT100 platinum resistance temperature sensors and capacitive humidity sensors, arranged according to the spatial geometry. In large commercial buildings, a sampling point is set up every 50-100 square meters, with increased sampling density in key areas such as conference rooms and crowded areas. Each sensor collects temperature and humidity data at a preset sampling frequency (typically 1-5 minutes per time) and transmits the data to a central data processing unit via a wireless transmission network, generating discrete temperature and humidity sampling values ​​with timestamps and spatial location information.

[0042] These discrete sample values ​​are arranged in chronological order according to their acquisition time to construct a temperature and humidity time series data stream. This data stream contains the temperature and humidity values ​​for each sampling point at different times, with the sensor number and timestamp serving as index keys. Using a sliding time window, data from multiple consecutive time points is organized into a matrix format, facilitating subsequent data analysis and processing. This time series data stream not only records the current environmental status but also includes information on temperature and humidity trends, making it valuable for predicting short-term environmental changes.

[0043] Data cleaning of temperature and humidity time series data streams is a critical step in ensuring data quality. Data cleaning utilizes a multi-stage filtering algorithm. First, a thresholding method is used to identify obvious outliers, such as data outside the normal range (temperature -10°C to 45°C, humidity 0% to 100%). Moving window median filtering is then used to remove short-term noise interference, with the window size typically set to 5-7 sampling points. For data points with sudden changes, a gradient detection method is used to calculate the rate of change between adjacent time points. Points that exceed a preset threshold (e.g., a temperature rate of change >2°C / minute) are flagged as suspicious. Finally, consistency verification is performed in both the temporal and spatial domains to check the correlation between suspicious points and surrounding sensor data to confirm whether they are anomalous data. After cleaning, a valid temperature and humidity dataset is generated, eliminating unreliable data caused by factors such as sensor failure, communication interference, and environmental changes.

[0044] The valid temperature and humidity dataset contains only discrete data points at the locations of distributed sensors. To obtain a continuous distribution across the entire air conditioning coverage area, numerical estimates are required for non-sampled locations. Kriging interpolation is used here to estimate the temperature and humidity at these unsampled points. This method, based on variogram theory and taking spatial autocorrelation into account, constructs a weight coefficient matrix and performs a weighted average of surrounding known points to obtain an estimated value for any point. The interpolation calculation first requires determining the spatial variogram, which describes the correlation between data points at different distances. Next, a weight equation is constructed to solve for the influence weight of each known point. Finally, a weighted calculation is performed using the weight coefficients to obtain the temperature and humidity values ​​at the unknown point. After the interpolation calculation is complete, the discrete data is converted into continuous temperature and humidity field data, achieving data expansion from point to surface, providing complete spatial information for subsequent analysis.

[0045] Based on continuous temperature and humidity field data, the temperature gradient and humidity gradient of each point in the space are calculated. The temperature gradient represents the rate of change of temperature in space, which is obtained by calculating the temperature difference between adjacent grid points and dividing it by the distance. In specific operations, the space is divided into a regular grid, and the temperature value at each grid node is calculated for its rate of change in the horizontal and vertical directions. The rate of change in the two directions constitutes the temperature gradient vector, whose magnitude reflects the severity of the temperature change, and its direction points to the direction of the fastest temperature rise. Similarly, the humidity gradient is calculated. The magnitude of the gradient can be used to quantify the severity of the temperature and humidity changes in different areas. Areas with large gradients indicate rapid changes in temperature and humidity, and there may be uneven heating and cooling. The gradient size is compared with the preset threshold to identify uneven heating and cooling areas, and they are intuitively displayed in the form of a heat map. Uneven heating and cooling areas often mean that the air supply of the air conditioning system is unevenly distributed or there is interference from local heat sources.

[0046] Finally, the currently calculated temperature and humidity gradients are compared and analyzed with historical data. Historical data includes records of temperature and humidity distribution under similar time periods and similar meteorological conditions. By calculating the difference index between the current gradient data and the historical data, areas of abnormal changes are identified. The comparative analysis uses a sliding time window method, selecting historical data under similar working conditions in the past as a reference baseline, and calculating the deviation of the current temperature and humidity gradient from the historical average. Areas with significant deviations may indicate changes in air-conditioning system performance or usage patterns. By combining the current temperature and humidity distribution, information on uneven hot and cold areas, and comparison results with historical data, regional temperature and humidity distribution indicators are generated, including multi-dimensional evaluation parameters such as mean index, gradient index, stability index, and anomaly index.

[0047] For example, the central air conditioning system of a large commercial complex had 25 temperature and humidity sensors deployed in the first-floor office area, collecting data every three minutes. At 10:00 AM on a summer weekday, the collected temperature data ranged from 23.4°C to 27.8°C, and the humidity data ranged from 45% to 62%. Data cleaning revealed that the temperature near sensor 8 suddenly jumped from 24.6°C to 21.2°C at 10:15 AM, exceeding the normal threshold. The data from surrounding sensors showed no significant change, so it was identified as an outlier and removed. Similar anomalies were also found in sensors 17 and 22. After cleaning, a valid data set was generated. Kriging interpolation was used to generate a continuous temperature and humidity field for the entire office area, with a grid resolution of 1 meter by 1 meter. The temperature gradient was calculated for each grid point. The results showed that the temperature gradient near the east window reached 0.8°C / meter, significantly higher than the average of 0.3°C / meter for other areas, indicating an area of ​​uneven heating and cooling. Comparing this with historical data from the same period revealed a 40% increase in the temperature gradient in this area. Based on this, a regional temperature and humidity distribution index was generated, confirming the need for targeted adjustments to the air conditioning system in the eastern area, increasing cooling capacity to eliminate the excessive temperature gradient. This process, through scientific data processing methods, transforms dispersed temperature and humidity data into effective information to guide the precise operation of the air conditioning system, providing data support for subsequent load calculations and control strategy development.

[0048] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0049] (1) Divide the building space into multiple independent thermal zones according to functional type, orientation characteristics and usage period, and establish a zone coding index table;

[0050] (2) Based on the regional temperature and humidity distribution indicators, the difference between the average temperature value and the set temperature value of each thermal zone is extracted to obtain the temperature deviation coefficient;

[0051] (3) The heat conduction load of the enclosure structure is obtained by multiplying the temperature deviation coefficient with the heat transfer coefficient, surface area, and temperature difference of the enclosure structure in each thermal zone;

[0052] (4) Collect the real-time personnel distribution density data of each thermal zone through the personnel density sensor, and convert the personnel density data into human body heat production according to the human metabolic rate standard;

[0053] (5) Calculate the heat gain in the lighting equipment by combining the lighting power density, equipment power density and actual operation coefficient of each thermal zone;

[0054] (6) The heat balance calculation is performed on the heat load of the envelope structure, the heat generated by the human body and the heat gain in the lighting equipment. The total heat and cooling load is equal to the heat load of the envelope structure plus the heat generated by the human body plus the heat generated by the equipment minus the heat storage of the building structure, thereby obtaining the actual heat and cooling load value required for each zone;

[0055] (7) Based on the heat balance calculation results, generate the time-varying cooling and heating load values ​​of each thermal zone;

[0056] (8) The time-varying cooling and heating load values ​​of each thermal zone are visualized by heat load density contour lines to generate a regional load distribution map including load intensity, distribution boundaries and change trends.

[0057] Specifically, the building space is divided according to function, orientation, and time of use, creating independent thermal zones. This division method comprehensively considers the nature of the space's use (e.g., office, conference room, rest area, etc.), the building's orientation (east, west, north, south, and any combination thereof), and usage patterns (e.g., full-day operation, intermittent use, etc.). By cross-analyzing the characteristics of these three dimensions, the building space is divided into regional units with similar thermal characteristics. Each regional unit is assigned a unique code, forming a regional code index table. This index table contains each zone's location information, area data, usage code, orientation identifier, and time of use parameters.

[0058] Based on the acquired regional temperature and humidity distribution indicators, the average value of all temperature sampling points within each thermal zone is extracted and compared with the preset temperature setpoint for that zone to calculate the temperature difference. This difference is normalized to form a temperature deviation coefficient, which reflects the degree of deviation between the current actual temperature and the target temperature and is an important basis for determining air conditioning control needs. The calculation of the temperature deviation coefficient takes into account the different temperature accuracy requirements of different functional areas. For example, the deviation tolerance in important meeting rooms is lower than that in general corridors. Through weighted adjustment, the deviation coefficient is made more meaningful.

[0059] According to thermal engineering principles, the heat conduction load of the building envelope is closely related to the temperature deviation coefficient, the heat transfer coefficient of the building envelope, the surface area, and the indoor-outdoor temperature difference. The heat transfer coefficient is a material characteristic parameter that reflects the thermal insulation performance of the building envelope and is obtained through table lookup or on-site testing. The surface area refers to the area of ​​components such as walls, windows, and roofs that are in direct contact with the outdoor environment and is determined through architectural drawings or field measurements. The indoor-outdoor temperature difference is the difference between the average indoor temperature and the outdoor ambient temperature. Multiplying these parameters together yields the heat conduction load of the building envelope, which is calculated as follows:

[0060]

[0061] in, represents the conductive heat load of the enclosure structure (W), represents the heat transfer coefficient of the i-th enclosure structure (W / m²·K), represents the surface area of ​​the i-th enclosure structure (m²), represents the temperature difference between the two sides of the i-th enclosure structure (K), represents the temperature deviation coefficient (dimensionless), and n represents the number of types of enclosure structures.

[0062] Collecting real-time occupant distribution data for each thermal zone using occupant density sensors is the basis for calculating human heat production. Occupant density sensors include infrared array sensors, CO2 concentration sensors, or image recognition devices, which use different principles to detect the number of people and their distribution within an area. The acquired occupant density data is combined with the human metabolic rate standard to calculate human heat production. The human metabolic rate varies depending on activity status, such as approximately 100W / person in a sedentary state, 150W / person for light activity, and 200W / person for moderate activity. The formula for calculating human heat production is:

[0063]

[0064] in, Indicates the heat production of the human body (W), Indicates the population density (people / m²), Indicates the area of ​​the region (m²), Indicates the human metabolic rate (W / person), It represents the activity coefficient (dimensionless), which is adjusted according to different activity types, such as 1.0 for sitting, 1.2 for standing work, 1.6 for light exercise, etc.

[0065] Lighting and electronic equipment in each thermal zone are also significant heat sources. Lighting power density refers to the power of lighting equipment per unit area, typically 10-15W / m² for office areas and 15-20W / m² for conference rooms. Equipment power density includes the power per unit area of ​​office equipment such as computers and printers, with a typical value of 20-30W / m². The actual operating coefficient takes into account the actual utilization rate of the equipment and is usually determined based on historical data and usage patterns, ranging from 0.5-0.9. The formula for calculating internal heat gain is:

[0066]

[0067] in, Indicates the heat gain in the lighting equipment (W), Indicates lighting power density (W / m²), represents the lighting operation coefficient (dimensionless), Indicates the power density of the equipment (W / m²), represents the equipment operating coefficient (dimensionless), Indicates the area of ​​the region (m²).

[0068] Heat balance calculations are a key step in determining the actual heating and cooling loads for each zone. According to the first law of thermodynamics, the conservation of energy in a system requires that the total heating and cooling loads be equal to the algebraic sum of all heat balance terms. Considering the building structure's heat storage capacity, the actual heating and cooling loads need to be reduced by the building's heat storage capacity. This heat storage capacity is related to the material's specific heat capacity, mass, and temperature change rate, and is quantified using the thermal inertia coefficient. The heating and cooling load calculation formula is:

[0069]

[0070] in, Indicates the total cooling and heating load (W), represents the conductive heat load of the enclosure structure (W), Indicates the heat production of the human body (W), Indicates the heat gain in the lighting equipment (W), represents the heat storage capacity of the building structure (W), which is calculated as:

[0071]

[0072] in, represents the specific heat capacity of the jth material (J / kg·K), represents the mass of the jth material (kg), Indicates the temperature change rate (K / s), represents the thermal inertia correction factor (dimensionless), Indicates the number of material types.

[0073] Based on the heat balance calculation results, the cooling and heating load values ​​for each thermal zone are generated over time, representing real-time cooling and heating load values. This data is visualized using heat load density contours to form a regional load distribution map. The regional load distribution map intuitively displays the load intensity, distribution boundaries, and changing trends of each zone, providing a crucial basis for air conditioning system control decisions.

[0074] For example, a commercial office building with 5,000 square meters of space was divided into 25 thermal zones based on function, orientation, and occupancy time. The average measured temperature in an east-facing office area (coded E-03-OF) was 26°C, with a set temperature of 24°C, resulting in a calculated temperature deviation coefficient of 1.08. The exterior wall area of ​​this area is 100 square meters, and the window area is 40 square meters, with heat transfer coefficients of 0.5 W / m²·K and 2.8 W / m²·K, respectively. With an outdoor temperature of 33°C, the calculated heat load on the building envelope was 4,080 W. The occupancy sensor indicated 25 people in the area (density 0.1 people / m²). Based on an office activity metabolic rate of 125 W / person and an activity coefficient of 1.1, the calculated human heat generation was 3,438 W. The lighting power density in this area was 12 W / m², with an operating coefficient of 0.85, and the equipment power density was 25 W / m², with an operating coefficient of 0.7, resulting in a calculated internal heat gain of 7,225 W. The building's heat storage capacity was calculated to be 1500W based on the temperature history and material properties. The total cooling load for the area was calculated to be 13243W. Interpolating the cooling load data for the 25 thermal zones generated a heat load density contour map, clearly showing a distribution pattern characterized by high loads in the east and south-facing areas, moderate loads in the central area, and low loads in the north-facing area.

[0075] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0076] (1) Perform spatiotemporal analysis on the cooling and heating load information of the regional load distribution map to construct a three-dimensional load intensity matrix to determine the total demand for cooling and heating source equipment;

[0077] (2) Decompose the load characteristics according to the three-dimensional load intensity matrix, judge the current system load rate, and determine the number of starts and stops of the central air-conditioning cooling and heating source equipment;

[0078] (3) Based on the cross analysis of the system load rate and the energy efficiency curves of each device, the optimal operating point of each central air-conditioning cooling and heating source device is calculated to form a combination plan for starting and stopping the equipment;

[0079] (4) Use the load distribution algorithm to distribute the load to the central air-conditioning cooling and heating source equipment that has been started, and determine the load-bearing ratio of each equipment;

[0080] (5) According to the load-bearing ratio, accurately calculate the operating frequency required by each central air-conditioning cooling and heating source equipment, and generate frequency conversion control instructions;

[0081] (6) Integrate the start-stop quantity, start-stop combination scheme, load bearing ratio and operation frequency of the central air-conditioning cold and heat source equipment to form the chiller operation parameters.

[0082] Specifically, the cooling and heating load information of the regional load distribution map is subjected to spatiotemporal analysis to construct a three-dimensional load intensity matrix. The regional load distribution map contains the cooling and heating demand data for each area of ​​the building. By adding the time dimension, a three-dimensional matrix consisting of spatial coordinates (x, y), time coordinates (t), and load values ​​(L) is formed. The spatiotemporal analysis process uses a sliding time window method to divide the 24-hour period into multiple time windows, analyze the load distribution characteristics of each area within each window, and capture the load variation patterns. The three-dimensional load intensity matrix reflects the relationship between the cooling and heating loads in the building and the spatial position and time. The total demand for cooling and heating source equipment is calculated through integration or weighted summation, providing a data basis for subsequent equipment control.

[0083] Decomposing load characteristics based on a three-dimensional load intensity matrix is ​​key to determining the current system load rate and the number of central air conditioning cooling and heating source equipment to start and stop. This load characteristic decomposition utilizes principal component analysis to break down the load into three components: base load, cyclical load, and random load. Base load represents the long-term, stable minimum load demand, cyclical load reflects the component that varies regularly over time, and random load represents the unpredictable, fluctuating component. The system load rate, defined as the ratio of the current load to the system's rated load, serves as the primary basis for equipment start-up and shutdown decisions. Based on empirical threshold rules, for example, at low load rates (0-30%), the minimum number of equipment is started; at medium load rates (30%-70%), the number of equipment is increased moderately; and at high load rates (70%-100%), most or all equipment is started. Considering that frequent equipment starts and stops increase energy consumption and wear, an inertia factor is introduced to avoid frequent switching caused by short-term load fluctuations. The optimal operating point is calculated by cross-analyzing the system load rate with the energy efficiency curves of each device. Energy efficiency curves describe the variation in the coefficient of performance (COP) of a device at different load rates and are typically obtained through testing or manufacturer data. Cross-analysis matches the system load rate with the energy efficiency curve of each device to identify the most energy-efficient equipment combination under the current load conditions. This process considers differences in equipment type (e.g., centrifuge, screw compressor, piston compressor, etc.), capacity, and energy efficiency characteristics. It uses enumeration or dynamic programming algorithms to calculate the total energy efficiency of all possible combinations and selects the most efficient combination. The resulting equipment start-stop combination includes a list of devices to be started and their preliminary operating parameters, laying the foundation for subsequent refined regulation.

[0084] The load distribution algorithm is used to distribute the load to the activated central air-conditioning cold and heat source equipment, and determine the load-bearing ratio of each device. The load distribution algorithm is based on the Lagrange multiplier method, with minimizing total energy consumption as the objective function, taking into account the operating constraints of each device, and solving the optimal load distribution ratio. The algorithm input includes the energy efficiency curve of each device, operating boundary conditions (such as minimum load rate, maximum load rate) and the current system total load demand. Through iterative calculation, the load distribution of each device is gradually adjusted until the balance point with minimum energy consumption is reached. The determination of the load-bearing ratio not only takes into account the instantaneous energy efficiency, but also takes into account factors such as the equipment's operating time balance, start-stop switching costs, and maintenance cycle, thereby achieving global optimal control.

[0085] Based on the load ratio, the required operating frequency of each central air conditioning cooling and heating source device is accurately calculated to generate variable frequency control instructions. The nonlinear relationship between the device operating frequency and the load ratio requires conversion using the device characteristic equation. Variable frequency control systems typically use a PID (proportional-integral-differential) control strategy to automatically adjust the frequency output based on load deviations. The operating frequency calculation formula is:

[0086]

[0087] in, Indicates the operating frequency of the device (Hz). Indicates the base frequency (usually 50Hz), Indicates the actual load borne by the equipment (kW). Indicates the rated load of the equipment (kW), represents the compressor characteristic coefficient (dimensionless), represents the system correction factor (dimensionless), Indicates the temperature difference adjustment coefficient (1 / ℃), Indicates the deviation between the actual temperature and the target temperature (°C). Different types of compressors have different characteristic coefficients, such as centrifugal compressors. About 1.2, screw compressor is about 1.0. System correction factor Taking into account the influence of operating parameters such as condensing temperature and evaporating temperature on the frequency, it is usually between 0.9-1.1. A feedback control mechanism is introduced to automatically adjust the frequency to accelerate convergence when there is a deviation between the actual temperature and the target temperature.

[0088] Finally, the start-stop quantity, start-stop combination scheme, load ratio, and operating frequency of the central air-conditioning cold and heat source equipment are integrated and processed to form the chiller operating parameters. The integration process uses parameter encapsulation technology to unify the decentralized control decisions into a parameter package in a standard format, including static parameters (such as device ID, type, capacity, etc.) and dynamic parameters (such as start-stop status, load ratio, operating frequency, etc.). Parameter encapsulation also takes into account safety constraints, such as maximum starting current limit and minimum operating time protection, to ensure the safety and reliability of control instructions. The resulting chiller operating parameters are sent to each device controller via the communication network to achieve precise control.

[0089] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0090] (1) Extract the supply water temperature, return water temperature, flow rate and pressure data from the chiller operating parameters to establish the water system operating condition benchmark;

[0091] (2) Based on the water system operating condition benchmark, construct a hydraulic relationship diagram of the main pipeline and branch pipeline to determine the basic head and flow requirements of the water pump;

[0092] (3) Through the hydraulic calculation method, the pressure demand of the main line is decomposed step by step into each branch line, the required speed of the water pump is calculated, and the speed of the water pump is adjusted;

[0093] (4) Based on the proportional relationship between the flow demand of each branch and the total water supply, the opening of each regulating valve is determined through flow balance calculation to achieve valve opening adjustment;

[0094] (5) Combined with the pump speed and valve opening, calculate the pressure distribution and flow distribution at each point in the system to generate the water system operating status parameters;

[0095] (6) Integrate the water pump speed, valve opening and water system operating status parameters into water system balance control data.

[0096] Specifically, the supply water temperature, return water temperature, flow rate, and pressure data are extracted from the chiller operating parameters to establish a water system operating condition benchmark. The chiller operating parameters are a set of control instructions generated in the previous step, which include the chiller operating status and output parameters. The supply water temperature represents the temperature of the chilled water output by the chiller to the system, usually in the range of 5-7°C; the return water temperature represents the temperature of the water returning from the user end to the chiller, usually in the range of 12-15°C; the flow rate data represents the water volume of the chilled water circulation system, in cubic meters per hour; and the pressure data represents the pressure value at the chiller outlet, in kilopascals. These four parameters constitute the basic operating conditions of the water system, called the water system operating condition benchmark, which is the starting point for subsequent water system regulation.

[0097] Based on the water system's operating benchmark, a hydraulic diagram of the main and branch lines is constructed to determine the pump's base head and flow requirements. A hydraulic diagram is a mathematical representation of the water system's topology, depicted as a directed graph. Nodes represent network connections, edges represent pipes, and weights represent the pipe resistance coefficient. The construction process begins with a physical diagram of the system. The resistance coefficient for each pipe segment is then calculated based on parameters such as pipe diameter, length, and number of elbows. The main line is the trunk line from the chiller to each branch point, while the branch lines are the distribution lines from the branch points to each end user. Hydraulic calculations based on the hydraulic diagram are performed to determine the critical control points of the water system. The control point is typically the end of the "worst loop," meaning the end of the user farthest from the pump or with the greatest resistance. The pump's base head refers to the pressure rise required by the pump to overcome system resistance at the design flow rate. The flow requirement is determined by both the cooling load and the supply and return water temperature difference.

[0098] Through hydraulic calculation methods, the pressure demand of the main line is decomposed step by step to each branch, and the required speed of the water pump is calculated to achieve the adjustment of the water pump speed. The hydraulic calculation adopts the step-by-step pressure drop method, starting from the control point and calculating the required pressure at each node upstream. The formula for calculating the water pump speed is:

[0099]

[0100] in, Indicates the required speed of the pump (rpm), Indicates the base speed of the water pump (usually 1450rpm). Indicates the current required lift (m), Indicates the reference head (m), represents the system resistance correction factor (dimensionless), represents the pressure difference adjustment coefficient (dimensionless), Indicates the pressure difference deviation value (kPa), Indicates the pressure difference setting value (kPa). The current required head It is the most unfavorable total loop resistance obtained through hydraulic calculation, including pipeline resistance, equipment resistance and height difference, in meters of water column. System resistance correction factor Taking into account the deviation between the actual pipe network conditions and the design conditions, it is usually between 0.95-1.05. This parameter is used for closed-loop control. When the detected differential pressure deviates from the set value, the pump speed is dynamically adjusted to ensure system pressure stability. A value typically ranges from 0.1 to 0.3. For variable-frequency pumps, speed adjustment is achieved directly through the inverter, which converts the calculated speed value into a frequency command and sends it to the inverter.

[0101] Based on the proportional relationship between the flow demand of each branch and the total water supply, a flow balance calculation is performed to determine the opening of each regulating valve, enabling valve opening adjustment. This flow balance calculation is based on the correspondence between equipment load and flow. The cooling and heating loads of each terminal device are first converted into required water flow. Each branch's contribution to the total flow is then calculated, serving as the basis for flow distribution. The valve opening calculation utilizes the equal pressure drop method to ensure hydraulic balance among parallel branches when distributing flow. For electric regulating valves, opening is typically expressed as a percentage, ranging from 0% (fully closed) to 100% (fully open). The valve characteristic curve describes the nonlinear relationship between opening and flow. Common examples include linear, equal percentage, and quick-opening characteristics. Based on the valve characteristic curve, the required flow is converted into a corresponding valve opening command, and the valve position is adjusted by the actuator. Combining the pump speed and valve opening, the pressure distribution and flow distribution at each point in the system are calculated to generate water system operating parameters. This calculation process uses pipe network hydraulic simulation technology to establish a mathematical model consisting of nodes and pipe sections. The principles of conservation of energy and mass are then applied to determine the pressure at each node and the flow rate at each pipe section in the system's equilibrium state. The calculation process typically employs the Hardy Cross iteration method or the node-ring method, starting with an initial estimate and gradually revising it until convergence conditions are met. The generated water system operating status parameters include pressure values ​​at key nodes, flow values ​​at each pipe section, and differential pressure values ​​at pressure differential monitoring points. These parameters comprehensively reflect the operating status of the water system and serve as important indicators for evaluating system stability and energy efficiency.

[0102] Finally, the pump speed, valve opening, and water system operating status parameters are integrated into water system balance control data. The integration process includes data formatting, parameter correlation analysis, and control instruction generation. Data formatting unifies parameters from different sources and units into a standard format; parameter correlation analysis verifies whether there are conflicts or mutual exclusions between control parameters; and control instruction generation converts parameters into specific control signals based on the interface requirements of the control device. The structured design of water system balance control data facilitates the subsequent execution and monitoring of control instructions, and also provides data support for system fault diagnosis and performance evaluation.

[0103] Take the central air conditioning water system of a commercial building as an example. The system is equipped with two variable-frequency pumps and multiple electric regulating valves. The chiller operating parameters were extracted as follows: a supply water temperature of 6°C, a return water temperature of 12°C, a design flow rate of 120 cubic meters per hour, and a discharge water pressure of 350 kPa. Based on this data, a water system operating benchmark was established. Combined with the building's water system layout, a hydraulic diagram of the main and branch lines was constructed, encompassing one main line and 15 branches. Hydraulic calculations determined that the most unfavorable loop was the end user at the northwest corner of the 8th floor, where the minimum pressure differential required was 80 kPa. Using the step-by-step pressure drop method, the main line pressure loss was 120 kPa, and the static pressure due to the height difference was 784 kPa (the 8th floor is approximately 80 meters high). After accounting for the pressure drops of various devices, the total head required from the pumps was 32 meters of water column. Substituting the pump speed calculation formula into the following parameters: a base speed of 1450 rpm, a base head of 40 meters, a system resistance correction factor of 1.02, a pressure differential adjustment factor of 0.15, and a currently detected pressure differential deviation of -5 kPa (the pressure differential setpoint is 80 kPa), the required pump speed is calculated to be 1280 rpm, corresponding to an inverter output frequency of 44.1 Hz. Simultaneously, based on the load distribution in each area, the flow demand ratios for the 15 branches are calculated as 8%, 7%, 9%, and so on. Using the equal pressure drop method and valve characteristic curves, the openings of the regulating valves are determined to be 65%, 58%, 72%, and so on. Combining the pump speed and valve openings, a hydraulic simulation of the pipe network was used to calculate the pressure and flow distribution at key points in the system. For example, if the main inlet pressure is 350 kPa, the pressures at the branch points on each floor are 320 kPa and 290 kPa, respectively. Finally, the water pump speed (1280rpm), valve openings (65%, 58%, 72%, etc.) and system operating status parameters (pressure and flow data at each point) are integrated into water system balance control data.

[0104] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0105] (1) Extract the water supply temperature, water supply pressure and flow distribution ratio from the water system balance control data and establish the cooling capacity distribution table for the terminal equipment;

[0106] (2) Code and classify the terminal air-conditioning equipment in each area, form a terminal equipment hierarchical control architecture, and establish a terminal control response matrix;

[0107] (3) Based on the terminal equipment hierarchical control architecture, the cooling capacity indicators in the cooling capacity allocation table are allocated to each terminal equipment according to regional priority and actual demand;

[0108] (4) The cooling capacity allocated to each terminal device is converted into supply air temperature parameters through heat exchange calculation method, while considering the supply air temperature difference constraint conditions;

[0109] (5) Determine the required air volume for each terminal device based on the regional load variation characteristics and calculate the corresponding fan speed control value;

[0110] (6) Integrate the air supply temperature parameters and fan speed control values ​​into the control data packets of each terminal device to form air supply control instructions.

[0111] Specifically, key parameters such as supply water temperature, supply water pressure, and flow distribution ratio are extracted from the water system balance control data to create a cooling capacity allocation table for the terminal equipment. The water system balance control data is a set of control parameters output from the previous step and contains status information for each node in the water system. Supply water temperature refers to the temperature of the chilled water when it reaches the terminal equipment. Due to pipeline heat loss, this temperature is slightly higher than the chiller outlet temperature. Supply water pressure is the water pressure at the terminal inlet, which directly affects the flow rate of the terminal heat exchanger. The flow distribution ratio indicates the chilled water distribution status among different zones. Through thermodynamic calculations, these water system parameters are converted into available cooling capacity for the terminal equipment, forming a cooling capacity allocation table. This calculation takes into account water flow rate, supply and return water temperature difference, and the specific heat capacity of water. The water volume and temperature difference in each zone are directly converted into available cooling capacity, providing data support for subsequent precise allocation. Coding and classifying the terminal air conditioning equipment in each zone to form a hierarchical control architecture for the terminal equipment is the foundation for achieving refined control. The coding classification adopts a multi-level classification method, and is classified according to dimensions such as device type (fan coil units, fresh air units, variable air volume terminals, etc.), area, service object and control mode, and a unique identification code is assigned to each terminal device. The hierarchical control architecture organizes the terminal devices into a tree structure according to the control priority. The upper nodes are usually area controllers or floor controllers, and the lower nodes are specific terminal devices. The terminal control response matrix is ​​a mathematical model that describes the relationship between the control signal and the device response. Each element in the matrix represents the device response strength corresponding to a specific control input, which is determined by measured data or device characteristic curves. This hierarchical and distributed control architecture improves the flexibility and robustness of the system, allowing control instructions to be accurately transmitted to each terminal device.

[0112] Based on the terminal device hierarchical control architecture, the cooling capacity indicators in the cooling capacity allocation table are allocated to each terminal device based on regional priority and actual demand. This allocation process uses a weighted allocation algorithm, taking into account multiple factors: regional priority reflects the temperature control accuracy requirements of different spaces; for example, conference rooms generally have a higher priority than corridors; actual demand is provided by the regional load distribution map, which indicates the current cooling load in each zone; and device capacity is the maximum cooling capacity that the terminal device can provide. The allocation algorithm first meets the basic needs of high-priority zones and then distributes the remaining cooling capacity proportionally. If the total cooling capacity is insufficient to meet all demands, a priority-based tailoring strategy is implemented to ensure service quality in high-priority zones. The allocation results determine the specific cooling capacity supply value for each terminal device, which serves as the basis for subsequent parameter calculations.

[0113] The cooling capacity allocated to each terminal device is converted into supply air temperature parameters through the heat exchange calculation method. The heat exchange calculation is based on the principle of air enthalpy difference, taking into account the specific heat capacity of the air, air volume, ambient air state and target air state. The calculation process first determines the required total heat transfer rate, and then calculates the required supply air temperature based on the air volume. The supply air temperature must also meet the supply air temperature difference limit, that is, the difference between the supply air temperature and the indoor set temperature should not be too large to avoid cold air falling and draft discomfort. The supply air temperature difference limit is usually between 8-12°C and is adjusted according to the space height and air supply method. If the calculated supply air temperature exceeds the limit, the air volume needs to be adjusted to ensure comfort. The heat exchange calculation also takes into account the influence of the wet load, which will affect the air state through latent heat exchange. Especially in a high humidity environment, when the latent heat accounts for a large proportion, the sensible heat calculation results need to be adjusted.

[0114] Based on the regional load variation characteristics, the required air volume for each terminal device is determined, and the corresponding fan speed control value is calculated. Air volume control for terminal devices is a key means of achieving precise air conditioning. Air volume demand calculation is based on the relationship between cooling load and supply air temperature difference. Specifically, for the same load, a smaller supply air temperature difference requires a higher air volume, while a larger supply air temperature difference can reduce the air volume. Regional load variation characteristics are captured by a load forecasting model, including parameters such as load change rate, fluctuation range, and duration. The calculation of the fan speed control value takes into account the fan's pressure-flow characteristic curve, the air volume and static pressure that the fan can provide at different speeds, and variations in duct system resistance. Speed ​​control utilizes the quadratic flow law, meaning that air volume is linearly related to speed, while static pressure is quadratically related to speed. To avoid energy waste and equipment wear caused by frequent speed adjustments, the control algorithm incorporates a start-stop strategy, triggering speed adjustment only when the load change exceeds a set threshold.

[0115] Finally, the supply air temperature parameters and fan speed control values ​​are integrated into the control data packets of each terminal device to form the supply air control instructions. The encapsulation of the control data packet follows the standard communication protocol and contains information such as device identification, control parameters, execution time, priority, etc. A consistency check is performed during the data integration process to ensure that there is no conflict between the various control parameters, such as whether the combination of supply air temperature and air volume can meet the cooling demand. The supply air control instructions are sent to the terminal device controller through the communication network to achieve precise control. The timing arrangement of the control instructions takes into account the system response characteristics. Usually, the supply air temperature is adjusted first, and then the air volume is adjusted to reduce the risk of system oscillation. For intelligent terminals with self-learning functions, the control instructions also include adaptive parameters, allowing the device to fine-tune the control parameters according to the actual operating results.

[0116] For example, the central air conditioning system of a complex office building features 12 fan coil unit terminals in the east office area on the third floor. Water system balance control data reveals a water supply temperature of 7.5°C, a pressure of 240 kPa, an 8.5% flow rate allocation, and a total flow rate of 120 m³ / h. Based on thermodynamic calculations, the total available cooling capacity in this area is approximately 100 kW. These 12 terminal units are coded and classified using the "floor-area-unit-unit-unit type-number" format, such as "3-E-FCU-01" for the first fan coil unit in the east office area on the third floor. These units are divided into two functional categories: open office areas (8 units) and private offices (4 units). A two-level control architecture is established, forming a terminal control response matrix. Based on actual cooling load demand and zone priority (private offices have higher priority than open areas), the 100 kW cooling capacity is allocated as follows: 8 kW per unit for the private offices, for a total of 32 kW; and 8.5 kW per unit for the open office area, for a total of 68 kW. Through heat exchange calculations, the supply air temperature of the fan coil units in the independent offices is set to 16°C (room temperature 24°C, supply air temperature difference 8°C); the supply air temperature in the open office area is set to 15°C (room temperature 25°C, supply air temperature difference 10°C). According to load forecasts, the load fluctuation in this area is small, with a rate of change of less than 5% / hour, so the air volume control adopts a steady-state mode. The calculation shows that the air volume of the fan coil units in the independent offices is 1200m³ / h, corresponding to a fan speed of 850rpm; the air volume in the open office area is 1500m³ / h, corresponding to a speed of 950rpm. Finally, the supply air temperature (16°C / 15°C) and fan speed control values ​​(850rpm / 950rpm) are respectively encapsulated into 12 control data packets to form air supply control instructions for each terminal device, realizing precise air conditioning control and energy-saving operation.

[0117] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0118] (1) Collect user thermal comfort feedback signals through a distributed environmental sensing system and generate indoor thermal comfort feedback data by combining them with measured temperature and humidity parameters;

[0119] (2) Compare and analyze the actual execution results of the air supply control instructions with the indoor thermal comfort feedback data to obtain the control deviation value;

[0120] (3) According to the size and change trend of the control deviation value, the comfort priority zone and the energy saving priority zone are divided to form a partition control strategy table;

[0121] (4) For the comfort priority area, adjust the combined parameters of air supply temperature and air supply volume to improve the temperature and humidity stability;

[0122] (5) For energy-saving priority areas, expand the allowable temperature fluctuation range, reduce the frequency of equipment start-up and shutdown and energy loss;

[0123] (6) Feedback the adjustment results of the comfort priority zone and energy-saving priority zone to the cold and heat source equipment and distribution system, recalculate the operating parameters, and complete the energy-saving operation control of the central air-conditioning system.

[0124] Specifically, a distributed environmental perception system collects user thermal comfort feedback signals and combines them with measured temperature and humidity parameters to generate indoor thermal comfort feedback data. The distributed environmental perception system consists of multiple sensing devices, including fixed temperature and humidity sensors, mobile measurement terminals, and a user feedback interface. User thermal comfort feedback signals are obtained through various channels, such as wall-mounted thermal perception scoring panels, mobile application feedback, and smart wearable device monitoring data. These signals are quantified using the PMV-PPD (Predicted Mean Vote - Predicted Percentage of Dissatisfied) standard, converting user subjective perceptions into a numerical value ranging from -3 to +3, where 0 represents thermal neutrality, the most comfortable state. Measured temperature and humidity parameters, including dry-bulb temperature, wet-bulb temperature, globe temperature, and air velocity, are collected in real time by sensors deployed at multiple locations. User feedback signals are correlated and analyzed with measured physical parameters to generate indoor thermal comfort feedback data. This data combines both objective measurements and subjective evaluations, comprehensively reflecting the effectiveness of air conditioning operation.

[0125] The actual execution results of the air supply control instructions are compared and analyzed with indoor thermal comfort feedback data to determine the control deviation value. The air supply control instructions are the control parameter packages generated in the previous step, containing the supply air temperature and air volume setpoints. The actual execution results refer to the physical state of these control instructions implemented on the terminal equipment, collected through the outlet temperature sensor and air volume measurement device. The comparative analysis process utilizes a multi-dimensional evaluation method, including temperature deviation evaluation, humidity deviation evaluation, comfort evaluation, and energy consumption evaluation. Temperature deviation is the difference between the actual temperature and the set temperature, and humidity deviation is the difference between the actual humidity and the set humidity. Comfort evaluation compares the PMV value reported by users with the target range, while energy consumption evaluation compares actual energy consumption with the theoretical optimal energy consumption. The evaluation indicators of these four dimensions are weighted to form a comprehensive control deviation value. This value reflects both the accuracy of the physical parameters and the user satisfaction, and serves as the core basis for the subsequent zoning strategy formulation.

[0126] Based on the magnitude and trend of control deviations, comfort-priority zones and energy-saving zones are divided into zones, creating a zoning control strategy table. The magnitude of the control deviation directly reflects how closely the current control effect meets the target, while the trend indicates the system's stability and responsiveness. This division process uses an adaptive threshold method, dynamically determining the boundaries based on historical data and current operating conditions. Comfort-priority zones typically represent areas with large deviations and high user comfort requirements, such as important meeting rooms and reception areas. Energy-saving zones are areas with small deviations and where a reasonable trade-off in comfort for energy efficiency is acceptable, such as corridors and equipment rooms. The zoning control strategy table is a multi-dimensional decision matrix, with rows representing different zones, columns representing control parameters, and matrix elements representing specific control strategies, such as "strict control," "moderate control," and "loose control." The table also includes a time dimension, allowing different control strategies to be applied during different time periods, such as prioritizing comfort during work hours and energy conservation during non-work hours.

[0127] For the comfort-priority zone, the combined parameters of supply air temperature and air volume are adjusted to improve temperature and humidity stability. The control objective for the comfort-priority zone is to maintain a strict temperature and humidity range, typically with a tolerance of ±0.5°C for temperature and ±5% for humidity. The adjustment process utilizes a combined parameter optimization method, using different combinations of supply air temperature and air volume to identify the parameter set that both meets load requirements and provides optimal comfort. This optimization process takes into account airflow perception. The human body is more sensitive to cold air than to still, cold air. Therefore, under the same load conditions, a combination of moderate temperature and appropriate air volume is preferred over a combination of excessively low temperature and excessive air volume. Furthermore, a feedforward control mechanism is introduced to proactively adjust control parameters based on outdoor weather changes and internal load forecasts, minimizing the impact of environmental disturbances on indoor temperature and humidity. This refined control strategy ensures high-quality air conditioning service in the comfort-priority zone.

[0128] For energy-saving priority zones, the allowable temperature fluctuation range is expanded to reduce equipment startup and shutdown frequency and energy loss. The control characteristics of energy-saving priority zones are appropriately relaxed temperature and humidity control accuracy requirements. Typically, the allowable temperature deviation is up to ±2°C, and the allowable humidity deviation is up to ±10%. The control strategy for expanding the temperature fluctuation range utilizes a deadband control method. A deadband is set between the upper and lower limits of the set temperature, preventing regulation. Regulation is initiated only when the actual temperature exceeds the deadband. This method significantly reduces the frequency of equipment startups and shutdowns, avoiding the peak power consumption and equipment wear caused by frequent startups and shutdowns. Furthermore, by combining predictive control with hysteresis control, the deadband range is adjusted based on load trends. During periods of drastic load fluctuations, the deadband is reduced to strengthen control, while during periods of stable load, the deadband is expanded to reduce regulation. Energy-saving priority zones also utilize a periodic temperature reset strategy, periodically adjusting the set temperature within the allowable range. This strategy leverages the building's inherent thermal inertia to further reduce energy consumption.

[0129] The adjustment results for the comfort and energy-saving priority zones are fed back to the cooling and heating source equipment and the distribution system, recalculating operating parameters and completing energy-saving operation control for the central air conditioning system. The adjustment results, including the revised load demand and control parameters for each zone, are transmitted back to the central controller via the system bus. Based on the updated load demand, the cooling and heating source equipment recalculates the equipment start / stop combinations and operating load rates, adjusting the cooling and heating output. The distribution system redistributes water flow and adjusts pump speeds based on actual demand changes in each zone to ensure hydraulic balance. The feedback process utilizes an iterative loop: changes in terminal demand trigger adjustments in the intermediate system, which in turn affect the operation of the cooling and heating sources. Changes in the operating status of the cooling and heating sources, in turn, affect the terminal supply capacity. Ultimately, overall balance is achieved through multiple iterations. This closed-loop feedback control mechanism ensures coordinated operation of all system components, achieving a dynamic balance between energy conservation and comfort.

[0130] For example, a five-story office building has four control zones on each floor. Data collected by a distributed environmental sensing system showed that users in the south section of the second floor reported a PMV value of +1.2, exceeding the comfort range (±0.5). The actual temperature was 23.5°C, slightly below the setpoint of 24.5°C, and the humidity was 52%, close to the setpoint of 50%. Analysis revealed that although the temperature was within a reasonable range, users still felt warm. This was primarily due to the large glass curtain wall area, which caused solar radiation to cause the perceived temperature to be higher than the measured temperature. Comparing the execution results of the air supply control command (supply air temperature 17°C, air volume 2000 m³ / h) with the thermal comfort feedback data, the calculated deviation in the temperature dimension was -1.0°C, and the deviation in the comfort dimension was +0.7. The overall assessment of the control deviation value was +0.5, and it has recently shown an upward trend. Based on the size and trend of the control deviation values, the south section of the second floor was designated as a comfort priority zone, while other areas with smaller deviation values, such as stairwells and corridors, were designated as energy-saving priority zones. This resulted in a zoning control strategy table. For the comfort-priority zone on the second floor, the south area, the supply air temperature was adjusted from 17°C to 16°C, while the air volume was increased to 2,500 m³ / h to offset the effects of solar radiation. For the corridor area, designated as an energy-saving priority zone, the allowable temperature fluctuation range was expanded from ±1°C to ±2°C, and a minimum start-stop interval of 20 minutes was set. These adjustments were fed back to the chiller and pump systems, resulting in an approximately 5% increase in the total cooling load. The number of chillers in operation was increased from one to two, but the operating frequency was reduced, and the pump speed was slightly increased to meet the increased flow demand. This coordinated optimization of the entire system not only met the comfort needs of key areas, but also achieved efficient overall energy utilization through a differentiated treatment strategy.

[0131] The above describes the central air-conditioning energy-saving operation control method in the embodiment of the present application. The following describes the central air-conditioning energy-saving operation control system in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the central air-conditioning energy-saving operation control system includes:

[0132] The processing module is used to collect indoor and outdoor environmental temperature and humidity data through temperature and humidity sensors, perform time series processing on the collected data, and obtain regional temperature and humidity distribution indicators;

[0133] A calculation module is used to calculate the cooling and heating load value of each space based on the regional temperature and humidity distribution index and the personnel density data to obtain a regional load distribution map;

[0134] A control module is used to control the start and stop quantity and operating frequency of the central air-conditioning cold and heat source equipment according to the regional load distribution map to obtain the operating parameters of the chiller;

[0135] A control module, configured to adjust the speed and valve opening of the water pump and fan based on the operating parameters of the chiller to form water system balance control data;

[0136] A generation module, configured to adjust the air supply temperature and air volume of each terminal device according to the water system balance control data and generate an air supply control instruction;

[0137] The adjustment module is used to adjust the operating state of the air-conditioning system in real time according to the air supply control instructions and indoor thermal comfort feedback data, so as to realize energy-saving operation control of the central air-conditioning system.

[0138] Through the collaborative efforts of these components, the system acquires indoor and outdoor temperature and humidity data through temperature and humidity sensors and performs time-series processing to generate regional temperature and humidity distribution indicators. This provides the system with precise environmental awareness, enabling the control system to understand the temperature and humidity conditions in each area in real time, laying the data foundation for subsequent precise control. Combining regional temperature and humidity distribution indicators with occupant density data to calculate the cooling and heating load values ​​for each space, the resulting regional load distribution map accurately maps environmental data to load demand, significantly improving load forecasting accuracy. The application of deep learning algorithms in load forecasting deserves special attention. By autonomously learning from historical data, the algorithm can identify complex load variation patterns, significantly improving its ability to predict building physical properties, occupant activity patterns, and meteorological influences. This algorithmic feature contributes to the solution by transforming passive response into active prediction, reducing control lag. In the process of controlling the central air conditioning cooling and heating source equipment based on the regional load distribution map and obtaining chiller operating parameters, the introduction of a part-load efficiency gain algorithm addresses the inefficiency of traditional air conditioning systems at part load. By intelligently combining chillers of varying capacities, the system consistently operates near its optimal efficiency point, significantly reducing energy consumption. The technical feature of generating water system balance control data by adjusting the speed and valve opening of the water pump and fan based on the operating parameters of the chiller solves the energy waste and uneven comfort caused by hydraulic imbalance in traditional systems, allowing the system to maintain optimal hydraulic balance under different load conditions. The technical feature of generating air supply control instructions by adjusting the air supply temperature and air volume of each terminal device based on the water system balance control data achieves precise control of the system terminal and meets the differentiated needs of different regions. Finally, the closed-loop control that adjusts the air conditioning system in real time based on the air supply control instructions and indoor thermal comfort feedback data not only ensures control accuracy, but also continuously optimizes the control strategy through a self-learning thermal comfort preference algorithm, achieving a dynamic balance between comfort and energy saving.

[0139] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, memory, display screen, input device, network interface and database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the above method is implemented.

[0140] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0141] An embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which implements the above-described method when executed by a processor. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0142] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware using a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media provided herein and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM.

[0143] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0144] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

Claims

1. A central air conditioning energy-saving operation control method, characterized in that: The central air-conditioning energy-saving operation control method includes: The temperature and humidity data of the indoor and outdoor environments are collected by temperature and humidity sensors, and the collected data are time-series processed to obtain regional temperature and humidity distribution indicators, including: collecting temperature data and humidity data at multiple locations in the air-conditioning coverage area by distributed temperature and humidity sensors to obtain discrete sampling values ​​of spatial temperature and humidity; arranging the discrete sampling values ​​of spatial temperature and humidity in the order of collection time to construct a temperature and humidity time series data stream; performing data cleaning on the temperature and humidity time series data stream to remove outliers and noise points to form a valid temperature and humidity data set; performing numerical estimation on the valid temperature and humidity data set at non-sampling point locations to generate continuous temperature and humidity field data; calculating the temperature gradient and humidity gradient of each point in the continuous temperature and humidity field data to identify uneven hot and cold areas; comparing and analyzing the temperature gradient and humidity gradient with historical data in combination with the uneven hot and cold areas to generate regional temperature and humidity distribution indicators; Calculate the cooling and heating load values ​​of each space based on the regional temperature and humidity distribution index and personnel density data to obtain a regional load distribution map; According to the regional load distribution diagram, the start and stop quantity and operating frequency of the central air-conditioning cold and heat source equipment are regulated to obtain the operating parameters of the chiller; Based on the operating parameters of the chiller, the speed and valve opening of the water pump and fan are adjusted to form water system balance control data; According to the water system balance control data, the air supply temperature and air volume of each terminal device are adjusted to generate an air supply control instruction; According to the air supply control instructions and indoor thermal comfort feedback data, the operating state of the air conditioning system is adjusted in real time to achieve energy-saving operation control of the central air conditioning system.

2. The central air-conditioning energy-saving operation control method according to claim 1, characterized in that: The method of calculating the cooling and heating load values ​​of each space based on the regional temperature and humidity distribution index and the personnel density data to obtain a regional load distribution diagram includes: Divide the building space into multiple independent thermal zones according to functional type, orientation characteristics and usage period, and establish a zone coding index table; Based on the regional temperature and humidity distribution index, the difference between the average temperature value and the set temperature value of each thermal zone is extracted to obtain the temperature deviation coefficient; The heat conduction load of the enclosure structure is obtained by multiplying the temperature deviation coefficient by the heat transfer coefficient, the surface area and the temperature difference of the enclosure structure of each thermal zone; The real-time personnel density data of each thermal zone is collected through personnel density sensors, and the personnel density data is converted into human heat production according to the human metabolic rate standard; Calculate the heat gain in lighting equipment by combining the lighting power density, equipment power density and actual operation coefficient of each thermal zone; The heat balance calculation is performed on the heat load of the enclosure structure, the heat generated by the human body, and the heat gain in the lighting equipment. The total heat and cooling load is equal to the heat load of the enclosure structure plus the heat generated by the human body plus the heat generated by the equipment minus the heat storage of the building structure, thereby obtaining the actual heat and cooling load value required for each zone; Based on the heat balance calculation results, generate the time-varying cooling and heating load values ​​of each thermal zone; The time-varying cooling and heating load values ​​of each thermal zone are visualized using heat load density contour lines to generate a regional load distribution map including load intensity, distribution boundaries, and change trends.

3. The central air-conditioning energy-saving operation control method according to claim 1, characterized in that: According to the regional load distribution map, the start and stop quantity and operating frequency of the central air-conditioning cold and heat source equipment are regulated to obtain the chiller operating parameters, including: Performing spatiotemporal analysis on the cooling and heating load information of the regional load distribution map to construct a three-dimensional load intensity matrix for determining the total demand for cooling and heating source equipment; Decomposing the load characteristics according to the three-dimensional load intensity matrix, judging the current system load rate, and determining the number of starts and stops of the central air conditioning cooling and heating source equipment; Based on the cross-analysis of system load rate and energy efficiency curves of each device, the optimal operating point of each central air-conditioning cooling and heating source device is calculated to form a combination plan for starting and stopping the equipment; Use the load distribution algorithm to distribute the load to the activated central air-conditioning cooling and heating source equipment and determine the load-bearing ratio of each equipment; According to the load sharing ratio, the operating frequency required by each central air-conditioning cold and heat source equipment is accurately calculated and a frequency conversion control instruction is generated; The start and stop quantity, start and stop combination scheme, load bearing ratio and operation frequency of the central air-conditioning cold and heat source equipment are integrated and processed to form the chiller operation parameters.

4. The central air-conditioning energy-saving operation control method according to claim 1, characterized in that: The method of adjusting the rotation speed and valve opening of the water pump and fan based on the operating parameters of the chiller to form water system balance control data includes: Extracting the supply water temperature, return water temperature, flow rate and pressure data from the chiller operating parameters to establish a water system operating condition benchmark; Based on the water system operating condition benchmark point, a hydraulic relationship diagram of the main pipeline and branch pipeline is constructed to determine the basic head and flow requirements of the water pump; Through hydraulic calculation method, the pressure demand of the main line is decomposed step by step to each branch line, and the required speed of the water pump is calculated to achieve the adjustment of the water pump speed; Based on the proportional relationship between the flow demand of each branch and the total water supply, the opening of each regulating valve is determined through flow balance calculation to achieve valve opening adjustment; Combined with the water pump speed and valve opening, the pressure distribution and flow distribution at each point in the system are calculated to generate the water system operating status parameters; The water pump speed, valve opening and water system operating status parameters are integrated into water system balance control data.

5. The central air-conditioning energy-saving operation control method according to claim 1, characterized in that: The step of adjusting the air supply temperature and air volume of each terminal device according to the water system balance control data and generating an air supply control instruction includes: Extracting water supply temperature, water supply pressure and flow distribution ratio from the water system balance control data, and establishing a cooling capacity distribution table for terminal equipment; Code and classify the terminal air-conditioning equipment in each area, form a terminal equipment hierarchical control architecture, and establish a terminal control response matrix; According to the terminal device hierarchical control architecture, the cooling capacity indicators in the cooling capacity allocation table are allocated to each terminal device according to regional priority and actual demand; Through the heat exchange calculation method, the cooling capacity allocated to each terminal device is converted into the supply air temperature parameter, while considering the supply air temperature difference constraint condition; Combined with the regional load variation characteristics, determine the air volume required by each terminal device and calculate the corresponding fan speed control value; The air supply temperature parameter and the fan speed control value are integrated into a control data packet of each terminal device to form an air supply control instruction.

6. The central air-conditioning energy-saving operation control method according to claim 1, characterized in that: The operation state of the air conditioning system is adjusted in real time based on the air supply control instruction and the indoor thermal comfort feedback data to achieve energy-saving operation control of the central air conditioning system, including: The distributed environmental sensing system collects user thermal comfort feedback signals and combines them with measured temperature and humidity parameters to generate indoor thermal comfort feedback data. Comparing and analyzing the actual execution result of the air supply control instruction with the indoor thermal comfort feedback data to obtain a control deviation value, wherein the control deviation value includes a temperature deviation value, a humidity deviation value, a comfort deviation value, and an energy consumption deviation value; According to the magnitudes of the temperature deviation value, the humidity deviation value, the comfort deviation value, and the energy consumption deviation value and the temperature variation trend, the humidity variation trend, the comfort variation trend, and the energy consumption variation trend, the comfort priority zone and the energy saving priority zone are divided to form a zoning control strategy table; For the comfort priority zone, the combined parameters of air supply temperature and air supply volume are adjusted to improve the temperature and humidity stability; For the energy-saving priority areas, expand the allowable temperature fluctuation range to reduce the frequency of equipment start-up and shutdown and energy loss; The adjustment results of the comfort priority zone and the energy-saving priority zone are fed back to the cold and heat source equipment and the distribution system, the operating parameters are recalculated, and the energy-saving operation control of the central air-conditioning system is completed.

7. A central air-conditioning energy-saving operation control system, used to implement the central air-conditioning energy-saving operation control method according to any one of claims 1 to 6, characterized in that: The central air-conditioning energy-saving operation control system includes: A processing module is used to collect indoor and outdoor environmental temperature and humidity data through temperature and humidity sensors, perform time series processing on the collected data, and obtain regional temperature and humidity distribution indicators, including: collecting temperature data and humidity data at multiple locations in the air-conditioning coverage area through distributed temperature and humidity sensors to obtain spatial temperature and humidity discrete sampling values; arranging the spatial temperature and humidity discrete sampling values ​​in the order of collection time to construct a temperature and humidity time series data stream; performing data cleaning on the temperature and humidity time series data stream to eliminate outliers and noise points to form a valid temperature and humidity data set; performing numerical estimation on the valid temperature and humidity data set at non-sampling point locations to generate continuous temperature and humidity field data; calculating the temperature gradient and humidity gradient of each point in the continuous temperature and humidity field data to identify uneven hot and cold areas; comparing and analyzing the temperature gradient and humidity gradient with historical data in combination with the uneven hot and cold areas to generate regional temperature and humidity distribution indicators; A calculation module is used to calculate the cooling and heating load value of each space based on the regional temperature and humidity distribution index and the personnel density data to obtain a regional load distribution map; A control module is used to control the start and stop quantity and operating frequency of the central air-conditioning cold and heat source equipment according to the regional load distribution map to obtain the operating parameters of the chiller; A control module, configured to adjust the speed and valve opening of the water pump and fan based on the operating parameters of the chiller to form water system balance control data; A generation module, configured to adjust the air supply temperature and air volume of each terminal device according to the water system balance control data and generate an air supply control instruction; The adjustment module is used to adjust the operating state of the air-conditioning system in real time according to the air supply control instructions and indoor thermal comfort feedback data, so as to realize energy-saving operation control of the central air-conditioning system.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the central air-conditioning energy-saving operation control method described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is caused to execute the central air-conditioning energy-saving operation control method according to any one of claims 1 to 6.

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