Energy-saving operation control method and system for central air conditioner

Abstract

The invention relates to the technical field of air conditioner energy-saving operation control, and discloses a central air conditioner energy-saving operation control method and system. The method comprises the following steps: acquiring and processing data through a temperature and humidity sensor to obtain a temperature and humidity distribution index; calculating load values according to the indexes and the personnel density to form a distribution diagram; regulating and controlling cold and heat source equipment according to the distribution map to obtain operation parameters; adjusting the water pump fan based on the parameters to form balance data; adjusting the end equipment according to the data to generate a control instruction; and the system state is adjusted in real time according to instructions and comfort feedback to realize energy-saving operation. The problem of energy waste caused by decentralized control of subsystems of a traditional central air conditioning system is solved. By constructing a complete closed-loop control system from environmental perception, load prediction to equipment regulation and control, the operation efficiency of the system under partial load conditions is improved. And meanwhile, refined control is achieved according to different requirements of different areas, and energy consumption is reduced to the maximum extent on the premise that the comfort level of the user is guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of energy-saving operation control of air conditioners, and particularly relates to a central air conditioner energy-saving operation control method and system. Background Art

[0002] As the proportion of building energy consumption in the global total energy consumption continues to increase, the central air-conditioning system, as a major component of building energy consumption, its energy-saving operation control method has received extensive attention. Traditional central air-conditioning control methods mainly adopt fixed-parameter control strategies, such as constant water supply temperature, constant flow control, and simple timed start and stop. With the development of technology, technologies such as variable-frequency control of single equipment, equipment start and stop control based on load, and simple temperature feedback regulation have been widely applied. Some advanced systems have begun to use a building automation system (BAS) for centralized monitoring and achieve 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 pump speed according to the demand of the terminal.

[0003] However, the existing technologies have obvious deficiencies. First, most control systems still adopt an independent control mode, lacking unified coordinated control of the cold and heat sources, water systems, and terminal equipment, resulting in mutual influence between subsystems and unable to reach the optimal operating state. Second, traditional control methods are mainly based on simple temperature feedback, lacking comprehensive consideration of multi-dimensional factors such as occupancy density, usage patterns, and thermal comfort, and it is difficult to achieve precise control. Third, the existing systems do not adequately consider the building's physical characteristics and dynamic load changes, and are unable to effectively handle complex situations in actual operation, such as uneven heating and cooling, and low part-load efficiency. In addition, most control systems lack self-learning and prediction functions, and can only respond passively rather than anticipate actively, resulting in control lag and energy waste. These problems are particularly prominent in modern buildings with variable loads and differentiated demands, and there is an urgent need for a central air conditioner energy-saving operation control method that is fully system coordinated, multi-dimensionally perceptive, and intelligently predictive. Summary of the Invention

[0004] This application provides a central air conditioner energy-saving operation control method and system, which solves the problem of energy waste caused by decentralized control of each subsystem in the traditional central air-conditioning system. 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 for the differentiated demands of different regions, and energy consumption is minimized to the greatest extent while ensuring user comfort.

[0005] In a first aspect, the present application provides a method for controlling the energy-saving operation of a central air conditioner. The method for controlling the energy-saving operation of the central air conditioner includes: collecting indoor and outdoor environmental temperature and humidity data through a temperature and humidity sensor, performing time series processing on the collected data to obtain a regional temperature and humidity distribution index; calculating the cooling and heating load values of each space according to the regional temperature and humidity distribution index and the personnel density data to obtain a regional load distribution map; regulating the start-stop quantity and operation frequency of the central air conditioner's cold and heat source equipment based on the regional load distribution map to obtain the operation parameters of the chiller; adjusting the rotation speeds of the water pump and the fan and the opening degree of the valve based on the operation parameters of the chiller to form water system balance control data; adjusting the supply air temperature and air volume of each terminal device according to the water system balance control data to generate a supply air control instruction; and adjusting the operation state of the air conditioning system in real time according to the supply air control instruction and the indoor thermal comfort feedback data to achieve the energy-saving operation control of the central air conditioning system.

[0006] In a second aspect, the present application provides a central air conditioner energy-saving operation control system, which includes: A processing module for collecting indoor and outdoor environmental temperature and humidity data through a temperature and humidity sensor, performing time series processing on the collected data to obtain a regional temperature and humidity distribution index; A calculation module for calculating the cooling and heating load values of each space according to the regional temperature and humidity distribution index and the personnel density data to obtain a regional load distribution map; A regulation module for regulating the start-stop quantity and operation frequency of the central air conditioner's cold and heat source equipment based on the regional load distribution map to obtain the operation parameters of the chiller; A control module for adjusting the rotation speeds of the water pump and the fan and the opening degree of the valve based on the operation parameters of the chiller to form water system balance control data; A generation module for adjusting the supply air temperature and air volume of each terminal device according to the water system balance control data to generate a supply air control instruction; An adjustment module for adjusting the operation state of the air conditioning system in real time according to the supply air control instruction and the indoor thermal comfort feedback data to achieve the energy-saving operation control of the central air conditioning system.

[0007] In a third aspect of the present invention, there is provided a computer device, including: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned central air conditioner energy-saving operation control method.

[0008] In a fourth aspect of the present invention, there is provided a computer-readable storage medium, in which instructions are stored, and when the instructions are run on a computer, the computer is made to execute the above-mentioned central air conditioner energy-saving operation control method.

[0009] In the technical solution provided by this application, the regional temperature and humidity distribution index obtained by collecting indoor and outdoor environmental temperature and humidity data through temperature and humidity sensors and performing time series processing provides the system with accurate environmental perception ability, enabling the control system to grasp the temperature and humidity conditions of each region in real time, thereby laying a data foundation for subsequent precise control. By combining the regional temperature and humidity distribution index and the personnel density data to calculate the cooling and heating load values of each space, the formed regional load distribution map realizes the accurate mapping from environmental data to load demand, greatly improving the accuracy of load prediction. The application of deep learning algorithm in load prediction is particularly worthy of emphasis. Through the autonomous learning of historical data, this algorithm can identify complex load change patterns, significantly enhancing the prediction ability of building physical characteristics, personnel activity rules, and meteorological impacts. The contribution of this algorithm feature to the solution is to transform passive response into active prediction, reducing the control lag problem. When regulating the central air-conditioning cold and heat source equipment according to the regional load distribution map and obtaining the operating parameters of the chiller, the introduction of the partial load efficiency gain algorithm solves the problem of low efficiency of traditional air-conditioning systems under partial load. By intelligently combining chillers with different capacities, the system always operates near the optimal efficiency point, significantly reducing energy consumption. The technical feature of adjusting the rotational speed of the water pump and the fan and the valve opening based on the chiller operating parameters to form water system balance control data solves the problems of energy waste and uneven comfort caused by hydraulic imbalance in traditional systems, enabling the system to maintain the best hydraulic balance state under different load conditions. The technical feature of adjusting the supply air temperature and volume of each terminal device according to the water system balance control data to generate supply air control instructions realizes the precise control of the system terminal and meets the differentiated requirements of different regions. Finally, the closed-loop control of real-time adjustment of the air-conditioning system based on the supply air control instructions and indoor thermal comfort feedback data not only ensures the control accuracy but also continuously optimizes the control strategy through the self-learning thermal comfort preference algorithm, achieving the dynamic balance of comfort and energy conservation. Description of the Drawings

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0011] Figure 1 It is a schematic diagram of an embodiment of the central air-conditioning energy-saving operation control method in the embodiments of this application; Figure 2 It is a schematic diagram of an embodiment of the central air-conditioning energy-saving operation control system in the embodiments of this application; Figure 3It is a schematic block diagram of a computer device in an embodiment of the present invention. Detailed implementation manners

[0012] The embodiments of the present application provide a central air-conditioning energy-saving operation control method and system. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims and the above-mentioned drawings of the present application are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0013] For ease of understanding, the specific process of the embodiments of the present application is described below. Please refer to Figure 1 , an embodiment of the central air-conditioning energy-saving operation control method in the embodiments of the present application includes: Step S101: 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; Step S102: Calculate the cooling and heating load values of each space according to the regional temperature and humidity distribution indicators and the personnel density data, and obtain a regional load distribution map; Step S103: According to the regional load distribution map, regulate the start-stop number and operating frequency of the central air-conditioning cold and heat source equipment to obtain the operating parameters of the chiller; Step S104: Based on the operating parameters of the chiller, adjust the rotation speeds of the water pump and the fan and the valve opening degree to form water system balance control data; Step S105: According to the water system balance control data, adjust the supply air temperature and air volume of each terminal device to generate a supply air control instruction; Step S106: According to the supply air control instruction and the indoor thermal comfort feedback data, adjust the operating state of the air-conditioning system in real time to achieve energy-saving operation control of the central air-conditioning system.

[0014] It can be understood that the execution subject of the present application can be a central air-conditioning energy-saving operation control system, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application are described by taking the server as the execution subject as an example.

[0015] Specifically, the temperature and humidity sensors collect indoor and outdoor environmental temperature and humidity data. These sensors are distributed in different areas of the building to form a distributed sensing network. The collected temperature and humidity data are processed in time series to remove outliers and noise, and then numerical estimation is performed on the non-sampled point positions through a spatial interpolation function to generate continuous temperature and humidity field data. These data are further used to calculate the temperature gradient and humidity gradient, identify the uneven heating and cooling areas, and finally generate the regional temperature and humidity distribution index. For example, in an office building, multiple temperature and humidity sensors collect data every 5 minutes. The temperature data collected in a certain area within one hour ranges from 22°C to 26°C, and the humidity data ranges from 45% to 55%. By interpolating and calculating the temperature and humidity distribution of the entire area, it is found that the temperature in the southeast corner is relatively high at 26°C, while the temperature in the northwest corner is only 22°C, forming an obvious temperature gradient. This distribution index directly reflects the actual operation effect of the air conditioning system.

[0016] Calculate the heating and cooling load values based on the regional temperature and humidity distribution index and the personnel density data. The system divides the building space into multiple thermal zones, calculates the temperature deviation coefficient of each zone, and combines the heat transfer coefficient of the building envelope and the outer surface area to calculate the conduction heat load. The personnel density sensors collect the real-time personnel distribution density in each area and convert it into the heat generation of the human body according to the standard of human metabolism rate. At the same time, considering the internal heat increment in the lighting equipment, the actual required heating and cooling load values of each zone are obtained through heat balance calculation, and finally the regional load distribution map is generated. For example, the personnel density detected in the meeting room area is 0.5 people per square meter. Combining the average heat generation of the human body of 100 W / person, the total heat generation of the human body is calculated, and then adding the heat generation of the lighting equipment and subtracting the building heat storage, it is obtained that the area requires about 20 kW of cooling capacity.

[0017] Perform spatio-temporal analysis on the load information, construct a three-dimensional load intensity matrix, judge the current system load rate, and determine the start-stop quantity of the cold and heat source equipment. Through load characteristic analysis, the system determines the optimal equipment start-stop combination plan, assigns the load sharing ratio to each started equipment, calculates the required operating frequency, and finally forms the operating parameters of the chiller. During actual operation, when the total cooling load is detected to be 500 kW, the system determines that two chillers need to be started, and distributes the load according to the energy efficiency curve. The first one undertakes 60% of the load and operates at a frequency of 45 Hz, and the second one undertakes 40% of the load and operates at a frequency of 35 Hz, so as to achieve efficient and energy-saving operation. The system extracts the supply and return water temperature, flow rate and pressure data from the chiller operating parameters, constructs a hydraulic relationship diagram, and determines the basic head and flow rate requirements of the water pump. Decompose the pressure requirements of each branch through hydraulic calculation methods, calculate the required speed of the water pump, and at the same time determine the opening degree of each regulating valve according to the flow balance calculation. These adjustment results comprehensively form the water system balance control data. For example, when the chiller supply water temperature is 7 °C and the flow rate is 80 m³ / h, the system calculates that the main water pump needs to operate at 38 Hz to provide an appropriate head, and sets the opening degrees of the branch valves on different floors to range from 60% to 85% to ensure hydraulic balance.

[0018] Adjust the terminal equipment according to the water system balance control data. The system extracts key parameters from the water system balance control data, establishes a cooling capacity distribution table for the terminal equipment, and distributes the cooling capacity to each terminal equipment according to the regional priority and actual demand. Through heat exchange calculation, convert the distributed cooling capacity into the supply air temperature parameter, and at the same time determine the required air volume and the fan speed, and form a supply air control instruction. When a certain area is allocated 25 kW of cooling capacity, the system calculates that the fan coil in this area needs a supply air temperature of 14 °C, an air volume of 3000 m³ / h, and the corresponding fan speed is set to 75%. Make real-time adjustments according to the supply air control instruction and the thermal comfort feedback data. The system collects the user's thermal comfort feedback and the measured temperature and humidity parameters, compares and analyzes them with the execution results of the supply air control instruction, and calculates the control deviation value. Divide the comfort priority area and the energy-saving priority area according to the deviation value, adopt different control strategies respectively, adjust the supply air parameters or expand the temperature allowable range, and finally feedback to the front-end equipment to form a closed-loop control. After implementing this method in the entire office building, it can intelligently adjust the air conditioning parameters according to the characteristics of different areas, ensure that crowded areas such as meeting rooms are maintained at a comfortable temperature of 24 ± 0.5 °C, while the sparsely populated corridor areas are allowed to fluctuate within the range of 22 - 26 °C, realizing regional precise control and significantly reducing energy consumption.

[0019] In the embodiment of the present application, the regional temperature and humidity distribution index obtained by collecting indoor and outdoor environmental temperature and humidity data and performing time series processing through temperature and humidity sensors provides the system with accurate environmental perception capabilities, enabling the control system to grasp the temperature and humidity conditions of each region in real time, thereby laying a data foundation for subsequent precise control. The regional temperature and humidity distribution index and personnel density data are combined to calculate the cold and hot load values ​​of each space, and the regional load distribution map formed realizes the accurate mapping from environmental data to load demand, greatly improving the accuracy of load prediction. The application of deep learning algorithms in load prediction is particularly worthy of emphasis. The algorithm can identify complex load change patterns through autonomous learning of historical data, significantly improving the prediction ability of building physical characteristics, personnel activity patterns and meteorological influences. The contribution of this algorithm feature to the solution is to transform passive response into active prediction and reduce the control lag problem. In the process of regulating the central air-conditioning cold and heat source equipment according to the regional load distribution map and obtaining the operating parameters of the chiller, the introduction of the partial load efficiency gain algorithm solves the problem of low efficiency of traditional air-conditioning systems under partial load. By intelligently combining chillers of different capacities, the system is always operated near the optimal efficiency point, significantly reducing energy consumption. The technical feature of adjusting the speed and valve opening of the water pump and fan based on the operating parameters of the chiller to form the water system balance control data solves the energy waste and uneven comfort caused by hydraulic imbalance in the traditional system, so that the system can maintain the best hydraulic balance under different load conditions. The technical feature of adjusting the air supply temperature and air volume of each terminal device according to the water system balance control data and generating air supply control instructions realizes precise control of the system terminal and meets the differentiated needs of different regions. Finally, the closed-loop control of the air conditioning system in real time based on the air supply control instructions and indoor thermal comfort feedback data not only ensures the control accuracy, but also continuously optimizes the control strategy through the self-learning thermal comfort preference algorithm, achieving a dynamic balance between comfort and energy saving.

[0020] In a specific embodiment, the process of executing step S101 may specifically include the following steps: (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; (2) Arrange the spatial temperature and humidity discrete sampling values ​​in the order of collection time to construct the temperature and humidity time series data stream; (3) Clean the temperature and humidity time series data stream, remove outliers and noise points, and form a valid temperature and humidity data set; (4) Numerical estimation of the effective temperature and humidity data set at non-sampling point locations to generate continuous temperature and humidity field data; (5) Calculate the temperature gradient and humidity gradient at each point in the continuous temperature and humidity field data, and identify the uneven hot and cold areas; (6) Compare and analyze the temperature gradient and humidity gradient with historical data to generate regional temperature and humidity distribution indicators.

[0021] Specifically, in the central air-conditioning energy-saving operation control method, intelligent acquisition and analysis of environmental parameters is the basic link of the entire system. First, deploy multiple sampling points in the air-conditioning coverage area through distributed temperature and humidity sensors. These sensors usually include PT100 platinum resistance temperature sensors and capacitive humidity sensors, and are reasonably arranged according to spatial geometric characteristics. Set one sampling point every 50 - 100 square meters in large commercial buildings, and increase the sampling density in key areas such as meeting rooms and crowded areas. Each sensor collects temperature and humidity data at a preset sampling frequency (usually 1 - 5 minutes / time), and sends the data to the central data processing unit through a wireless transmission network to form discrete temperature and humidity sampling values with timestamp and spatial location information.

[0022] These discrete sampling values are arranged in time sequence according to the acquisition time to construct a temperature and humidity time series data stream. The data stream contains the temperature values and humidity values of each sampling point at different time points, where the sensor number and timestamp are used as index keywords. Through time window sliding processing, the data of multiple consecutive time points are organized in matrix form for subsequent data analysis and processing. The time series data stream not only records the current environmental state, but also contains temperature and humidity change trend information, which is of great value for predicting short-term environmental changes.

[0023] Data cleaning of the temperature and humidity time series data stream is a key step to ensure data quality. The data cleaning adopts a multi-level filtering algorithm. First, identify obvious outliers through the threshold method, such as data outside the normal range (temperature -10°C to 45°C, humidity 0% to 100%). Then, remove short-term noise interference through the moving window median filtering method, and the window size is usually set to 5 - 7 sampling points. For data points with sudden changes, use the gradient detection method to calculate the change rate of adjacent time points. If the change rate exceeds the preset threshold (such as temperature change rate > 2°C / minute), it is marked as a suspicious point. Finally, through the consistency verification in the time domain and spatial domain, check the correlation between the suspicious point and the data of surrounding sensors to confirm whether it belongs to abnormal data. After the cleaning process, an effective temperature and humidity data set is formed, excluding unreliable data caused by factors such as sensor failures, communication interference, and environmental mutations.

[0024] The effective temperature and humidity data set only contains discrete data points at the locations of distributed sensors. To obtain the continuous distribution of the entire air-conditioning coverage area, numerical estimation is required for the positions of non-sampled points. Here, the Kriging interpolation method is used to estimate the temperature and humidity at unsampled points in space. This method is based on the variogram theory, takes into account the spatial autocorrelation, constructs a weight coefficient matrix, and performs weighted averaging on the surrounding known points to obtain the estimated value of any point. The interpolation calculation first needs to determine the spatial variogram to describe the correlation between data points at different distances; then construct a weight equation to solve the influence weights of each known point; finally, use the weight coefficients for weighted calculation to obtain the temperature and humidity values of the unknown points. After the interpolation calculation is completed, the discrete data is converted into continuous temperature and humidity field data, realizing the data expansion from points to surfaces and providing complete spatial information for subsequent analysis.

[0025] Based on the continuous temperature and humidity field data, calculate the temperature gradient and humidity gradient at each point in space. The temperature gradient represents the rate of change of temperature in space and is obtained by dividing the temperature difference between adjacent grid points by the distance. Specifically, when operating, the space is divided into regular grids, and for the temperature values at each grid node, calculate the rate of change in the horizontal and vertical directions respectively. The rates of change in the two directions form the temperature gradient vector, whose magnitude reflects the severity of temperature change, and the direction points to the direction where the temperature rises fastest. Similarly, calculate the humidity gradient. By the magnitude of the gradient, the severity of temperature and humidity changes in different regions can be quantified. Regions with large gradients indicate rapid temperature and humidity changes, and there may be uneven heating and cooling phenomena. Compare the magnitude of the gradient with a preset threshold to identify regions with uneven heating and cooling, and visually display them in the form of a heat map. Regions with uneven heating and cooling often mean uneven air supply distribution of the air-conditioning system or local heat source interference.

[0026] Finally, compare and analyze the currently calculated temperature gradient and humidity gradient with historical data. The historical data includes records of temperature and humidity distributions under similar time periods and similar meteorological conditions. By calculating the difference index between the current gradient data and the historical data, identify regions with abnormal changes. The comparative analysis uses the sliding time window method, selects historical data under past similar operating conditions as the reference baseline, and calculates the deviation of the current temperature and humidity gradients from the historical average. Regions with obvious deviations may indicate changes in the performance of the air-conditioning system or changes in the usage pattern. Combine the current temperature and humidity distribution, information on regions with uneven heating and cooling, and the comparison results with historical data to generate regional temperature and humidity distribution indicators, including multi-dimensional evaluation parameters such as mean indicators, gradient indicators, stability indicators, and anomaly indicators.

[0027] Taking the central air-conditioning system of a large commercial complex as an example, 25 temperature and humidity sensors are deployed in the office area on the first floor, and data is collected every 3 minutes. At 10 am on a summer weekday, the collected temperature data ranges from 23.4°C to 27.8°C, and the humidity data ranges from 45% to 62%. Through data cleaning, it is found that the temperature near sensor No. 8 suddenly jumps from 24.6°C to 21.2°C at 10:15, and the change rate exceeds the normal threshold, while the data of surrounding sensors shows no obvious change. It is judged as an outlier and removed. At the same time, similar anomalies are also found in sensors No. 17 and No. 22. After cleaning, an effective data set is formed. The Kriging interpolation method is used to generate a continuous temperature and humidity field for the entire office area, with a grid accuracy of 1 meter × 1 meter, and the temperature gradient is calculated for each grid point. The calculation results show that the temperature gradient in the area near the east window reaches 0.8°C / m, which is significantly higher than the average value of 0.3°C / m in other areas, and it is marked as an area with uneven heating and cooling. Compared with the historical data in the same period, it is found that the temperature gradient in this area has increased by 40% compared with usual. Based on this, the regional temperature and humidity distribution index is generated, and it is determined that the air-conditioning system needs to be adjusted directionally for the east area to enhance the cold supply to eliminate the problem of excessive temperature gradient. This process uses scientific data processing methods to convert scattered temperature and humidity data into effective information for guiding the precise operation of the air-conditioning system, providing data support for subsequent load calculation and control strategy formulation.

[0028] In a specific embodiment, the process of executing step S102 may specifically include the following steps: (1) Divide the building space into multiple independent thermal zones according to functional types, orientation characteristics, and usage periods, and establish a partition coding index table; (2) For the regional temperature and humidity distribution index, extract the difference between the average temperature value and the set temperature value of each thermal zone to obtain the temperature deviation coefficient; (3) According to the product of the temperature deviation coefficient and the heat transfer coefficient, outer surface area, and temperature difference of the envelope structure of each thermal zone, obtain the envelope conduction heat load; (4) Collect the real-time personnel distribution density data of each thermal zone through personnel density sensors, and convert the personnel density data into human heat production according to the standard of human metabolic rate; (5) Combine the lighting power density, equipment power density, and actual operation coefficient of each thermal zone to calculate the internal heat increment in lighting equipment; (6) Perform a heat balance calculation on the envelope conduction heat load, human heat production, and internal heat increment in lighting equipment. By making the total cooling and heating load equal to the envelope conduction heat load plus human heat production plus equipment heat production minus the building structure heat storage, the actual required cooling and heating load values of each zone are obtained; (7) Generate the time-varying cooling and heating load values of each thermal zone according to the heat balance calculation results; (8) Visualize the time-varying cooling and heating load values of each thermal zone in the form of thermal load density isothermal lines to generate a regional load distribution map that includes load intensity, distribution boundaries, and change trends.

[0029] Specifically, divide the building space according to functional types, orientation characteristics, and usage periods to create independent thermal zones. This division method comprehensively considers the nature of space usage (such as office areas, meeting rooms, rest areas, etc.), building orientations (east, west, south, north, and their combined directions), and usage patterns (such as operating throughout the day, intermittent use, etc.). By cross-analyzing the characteristics of these three dimensions, the building space is divided into regional units with similar thermal characteristics, and each regional unit is assigned a unique code to form a partition coding index table. This index table includes the location information, area data, usage function codes, orientation identifiers, and time period parameters of each partition.

[0030] For the obtained regional temperature and humidity distribution indicators, extract the average value of all temperature sampling points within each thermal zone, compare it with the preset temperature set value of this zone, and calculate the temperature difference. This difference is standardized 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 judging the air-conditioning regulation demand. The calculation of the temperature deviation coefficient takes into account the different differential requirements for temperature accuracy in different functional areas. For example, the deviation tolerance of an important meeting room is lower than that of a general corridor area, and weighted adjustment is used to make the deviation coefficient more meaningful in practice.

[0031] According to the principles of thermology, the conduction heat load of the building envelope is closely related to the temperature deviation coefficient, the heat transfer coefficient of the building envelope, the outer surface area, and the indoor-outdoor temperature difference. The heat transfer coefficient is a material characteristic parameter that reflects the heat insulation performance of the building envelope and is obtained by looking up tables or on-site testing. The outer 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 on-site measurements. The indoor-outdoor temperature difference is the difference between the average indoor temperature and the outdoor environmental temperature. Multiply these parameters to obtain the conduction heat load of the building envelope, and its calculation formula is:

[0032] Among them, represents the conduction heat load of the building envelope (W), represents the heat transfer coefficient of the i-th type of building envelope (W / m²·K), represents the outer surface area of the i-th type of building envelope (m²), represents the temperature difference between both sides of the i-th type of building envelope (K), represents the temperature deviation coefficient (dimensionless), and n represents the number of types of building envelopes.

[0033] Collecting real-time personnel distribution data of each thermal engineering zone through personnel density sensors is the basis for calculating human heat production. Personnel density sensors include infrared array sensors, CO2 concentration sensors, or image recognition devices, which detect the number of people and their distribution in the area through different principles. The obtained personnel density data is combined with the human metabolism rate standard to calculate the human heat production. The human metabolism rate varies depending on the activity state. For example, it is about 100 W / person in a sedentary state, 150 W / person in a lightly active state, and 200 W / person in a moderately active state. The formula for calculating human heat production is:

[0034] Among them, represents the human heat production (W), represents the personnel density (persons / m²), represents the area of the zone (m²), represents the human metabolism rate (W / person), represents the activity coefficient (dimensionless), which is adjusted according to different activity types. It is 1.0 for sedentary, 1.2 for standing work, 1.6 for light exercise, etc.

[0035] The lighting equipment and electronic equipment in each thermal engineering zone are also important heat sources. The lighting power density refers to the power of lighting equipment per unit area. Generally, it is 10 - 15 W / m² in an office area and 15 - 20 W / m² in a meeting room. The equipment power density includes the power per unit area of office equipment such as computers and printers, and the typical value is 20 - 30 W / m². The actual operation coefficient takes into account the actual utilization rate of the equipment and is usually determined based on historical data and usage patterns, with a range between 0.5 - 0.9. The formula for internal heat increment is:

[0036] Among them, represents the internal heat increment of lighting equipment (W), represents the lighting power density (W / m²), represents the lighting operation coefficient (dimensionless), represents the equipment power density (W / m²), represents the equipment operation coefficient (dimensionless), represents the area of the zone (m²).

[0037] Thermal balance calculation is the core step in determining the actual cooling and heating loads of each zone. According to the first law of thermodynamics, the energy conservation of the system requires that the total cooling and heating load is equal to the algebraic sum of all heat income and expenditure items. Considering that the building structure has heat storage capacity, the actual cooling and heating load needs to subtract the heat storage of the building structure. The heat storage of the building structure is related to the specific heat capacity, mass, and temperature change rate of the material, and is quantified through the thermal inertia coefficient. The formula for cooling and heating load is:

[0038] Among them, represents the total heating and cooling load (W), represents the heat transfer load of the building envelope (W), represents the heat generation of the human body (W), represents the internal heat increment in lighting equipment (W), represents the heat storage capacity of the building structure (W), calculated as:

[0039] Among them, represents the specific heat capacity of the j-th material (J / kg·K), represents the mass of the j-th material (kg), represents the temperature change rate (K / s), represents the heat inertia correction factor (dimensionless), represents the number of material types.

[0040] According to the results of the heat balance calculation, the heating and cooling load values changing with time for each thermal zone are generated, that is, the time-varying heating and cooling load values. These data are visualized in the form of heat load density isotherms to form a regional load distribution map. The regional load distribution map intuitively shows the load intensity, distribution boundary and change trend of each region, and is an important basis for the regulation decision of the air conditioning system.

[0041] Taking a commercial office building as an example, through the division of functions, orientations and usage periods, a 5000-square-meter space is divided into 25 thermal zones. The average measured temperature of an east-facing office area (code E-03-OF) is 26°C, and the set temperature is 24°C. The calculated temperature deviation coefficient is 1.08. The external 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. The outdoor temperature is 33°C, and the calculated heat transfer load of the building envelope is 4080 W. The personnel density sensor shows that there are 25 people in this area (density 0.1 person / m²). According to the metabolic rate of 125 W / person for office activities and an activity coefficient of 1.1, the calculated heat generation of the human body is 3438 W. The lighting power density of this area is 12 W / m², the operation coefficient is 0.85, the equipment power density is 25 W / m², and the operation coefficient is 0.7. The calculated internal heat increment is 7225 W. The heat storage capacity of the building structure is calculated as 1500 W according to the temperature change history and material properties. The comprehensive calculation shows that the total cooling load of this area is 13243 W. The cooling load data of the 25 thermal zones are interpolated to generate a heat load density isotherm map, clearly showing the distribution characteristics of higher loads in the east and south areas, moderate loads in the central area, and lower loads in the north area.

[0042] In a specific embodiment, the process of executing step S103 may specifically include the following steps: (1) Conduct spatio-temporal 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 of the cooling and heat source equipment; (2) Decompose the load characteristics according to the three-dimensional load intensity matrix, judge the current system load rate, and determine the start-stop quantity of the central air-conditioning cooling and heat source equipment; (3) Based on the cross-analysis of the system load rate and the energy efficiency curves of each equipment, calculate the optimal operating points of each central air-conditioning cooling and heat source equipment to form an equipment start-stop combination plan; (4) Perform load distribution on the started central air-conditioning cooling and heat source equipment through a load distribution algorithm to determine the load sharing ratio of each equipment; (5) Accurately calculate the required operating frequencies of each central air-conditioning cooling and heat source equipment according to the load sharing ratio and generate a variable frequency control instruction; (6) Integrate and process the start-stop quantity, start-stop combination plan, load sharing ratio and operating frequency of the central air-conditioning cooling and heat source equipment to form the operating parameters of the chiller.

[0043] Specifically, conduct spatio-temporal analysis on the cooling and heating load information of the regional load distribution map to construct a three-dimensional load intensity matrix. The regional load distribution map contains the cooling and heating demand data of each area of the building. By adding a time dimension, a three-dimensional matrix composed of spatial coordinates (x, y), time coordinates (t) and load values (L) is formed. The spatio-temporal analysis process uses a sliding time window method to divide 24 hours into multiple time windows, analyze the load distribution characteristics of each area within each window, and capture the load change law. The three-dimensional load intensity matrix reflects the relationship between the cooling and heating loads in the building with respect to spatial position and time, and the total demand of the cooling and heat source equipment is calculated through integration or weighted summation, providing a data basis for subsequent equipment regulation.

[0044] Decomposing the load characteristics according to the three-dimensional load intensity matrix is the key to judging the current system load rate and determining the start-stop quantity of central air-conditioning cold and heat source equipment. The load characteristics decomposition uses the principal component analysis method to decompose the load into three parts: basic load, periodic load, and random load. The basic load is the long-term stable minimum load demand, the periodic load reflects the part that changes regularly with time, and the random load represents the unpredictable fluctuation component. The system load rate is defined as the ratio of the current load to the system rated load and is the main basis for equipment start-stop decision-making. According to the empirical threshold rule, such as starting the minimum number of equipment at a low load rate (0 - 30%), appropriately increasing the number of equipment at a medium load rate (30% - 70%), and starting most or all of the equipment at a high load rate (70% - 100%). Considering that frequent start-stop of equipment will increase energy consumption and wear, a start-stop inertia factor is introduced to avoid frequent switching caused by short-term load fluctuations. Based on the cross-analysis of the system load rate and the energy efficiency curves of each equipment, the optimal operating point is calculated. The energy efficiency curve describes the variation characteristics of the energy efficiency coefficient (COP) of the equipment at different load rates and is usually obtained through tests or manufacturer data. The cross-analysis matches the system load rate with the energy efficiency curve of each equipment to find the equipment combination with the highest energy efficiency under the current load conditions. This process considers the type differences of equipment (such as centrifugal machines, screw machines, piston machines, etc.), capacity sizes, and energy efficiency characteristics differences, and uses the enumeration method or dynamic programming algorithm to calculate the total energy efficiency of all possible combinations and selects the combination scheme with the highest energy efficiency. The formed equipment start-stop combination scheme includes the list of equipment to be started and its preliminary operating parameters, laying a foundation for subsequent refined control.

[0045] Through the load distribution algorithm, the load of the started central air-conditioning cold and heat source equipment is distributed to determine the load sharing ratio of each equipment. The load distribution algorithm is based on the Lagrange multiplier method, with minimizing the total energy consumption as the objective function, considering the operating constraint conditions of each equipment, and solving the optimal load distribution ratio. The algorithm inputs include the energy efficiency curves of each equipment, 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 equipment is gradually adjusted until the balance point of minimum energy consumption is reached. The determination of the load sharing ratio not only considers the instantaneous energy efficiency but also takes into account factors such as the operating time balance of the equipment, start-stop switching cost, and maintenance cycle, realizing global optimal control.

[0046] Based on the load sharing ratio, accurately calculate the operating frequency required for each central air-conditioning cold and heat source equipment and generate a variable frequency control instruction. There is a non-linear correspondence between the equipment operating frequency and the load sharing ratio, which needs to be converted through the equipment characteristic equation. The variable frequency control system usually adopts the PID (Proportional-Integral-Derivative) control strategy to automatically adjust the frequency output according to the load deviation. The operating frequency calculation formula is:

[0047] Among them, represents the operating frequency of the device (Hz), represents the reference frequency (usually 50 Hz), represents the actual load borne by the device (kW), represents the rated load of the device (kW), represents the compressor characteristic coefficient (dimensionless), represents the system correction factor (dimensionless), represents the temperature difference adjustment coefficient (1 / °C), represents the deviation between the actual temperature and the target temperature (°C). Different types of compressors have different characteristic coefficients. For example, the centrifugal compressor is approximately 1.2, and the screw compressor is approximately 1.0. The system correction factor considers the influence of operating parameters such as the condensation temperature and evaporation temperature on the frequency, and is usually between 0.9 - 1.1. The temperature difference adjustment coefficient introduces a feedback control mechanism. When there is a deviation between the actual temperature and the target temperature, the frequency is automatically adjusted to accelerate convergence.

[0048] Finally, the start-stop quantity, start-stop combination scheme, load sharing ratio, and operating frequency of the central air-conditioning cold heat source equipment are integrated and processed to form the operating parameters of the chiller. The integration and processing adopt parameter encapsulation technology, which unifies the scattered 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 considers safety constraint conditions, such as maximum starting current limit, minimum operating time protection, etc., to ensure the safety and reliability of control instructions. The formed operating parameters of the chiller are sent to each device controller through the communication network to achieve precise control.

[0049] In a specific embodiment, the process of executing step S104 may specifically include the following steps: (1) Extract the supply water temperature, return water temperature, flow rate, and pressure data from the operating parameters of the chiller to establish a reference point for the water system operating conditions; (2) According to the reference point of the water system operating conditions, construct a hydraulic relationship diagram of the main pipeline and branch pipelines to determine the basic head and flow rate requirements of the water pump; (3) Through hydraulic calculation methods, decompose the pressure requirements of the main pipeline to each branch step by step, calculate the required speed of the water pump, and achieve the adjustment of the water pump speed; (4) For the proportional relationship between the flow rate requirements of each branch and the total water supply, determine the opening degree of each regulating valve through flow balance calculation to achieve the adjustment of the valve opening degree; (5) Combine the water pump speed and valve opening degree, calculate the pressure distribution and flow rate distribution at each point in the system, and generate the operating state parameters of the water system. Integrate the water pump speed, valve opening, and water system operating status parameters into water system balance control data.

[0050] Specifically, extract the supply water temperature, return water temperature, flow rate, and pressure data from the chiller operating parameters to establish a water system operating condition reference point. The chiller operating parameters are the control instruction set generated in the previous steps, which includes the chiller operating status and output parameters. Among them, the supply water temperature represents the chilled water temperature output by the chiller to the system, usually in the range of 5 - 7 °C; the return water temperature represents the water temperature returned from the user side 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, with the unit of cubic meters per hour; the pressure data represents the pressure value at the outlet of the chiller, with the unit of kilopascals. These four parameters constitute the basic operating conditions of the water system, which is called the water system operating condition reference point and is the starting point for subsequent water system regulation.

[0051] According to the water system operating condition reference point, construct a hydraulic relationship diagram of the main pipeline and branch pipelines to determine the basic head and flow rate requirements of the water pump. The hydraulic relationship diagram is a mathematical expression of the water system topology structure, described in the form of a directed graph, where the nodes represent the pipeline connection points, the edges represent the pipelines, and the weights represent the pipeline resistance coefficients. The construction process first draws the physical structure diagram of the system, and then calculates the resistance coefficient of each section of the pipeline according to parameters such as pipe diameter, length, and the number of elbows. The main pipeline is the main line from the chiller to the branch points on each floor, and the branch pipelines are the distribution pipelines from the branch points to each end user. Perform hydraulic calculations according to the hydraulic relationship diagram to determine the key control points of the water system. Usually, the end of the "most unfavorable loop" is selected as the control point, that is, the user end farthest from the water pump or with the greatest resistance. The basic head of the water pump refers to the pressure rise that the water pump needs to provide at the design flow rate to overcome the system resistance; the flow rate requirement is jointly determined by the cooling load and the supply - return water temperature difference.

[0052] Through the hydraulic calculation method, decompose the pressure requirements of the main pipeline to each branch step by step, calculate the required speed of the water pump, and realize 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 against the flow. The water pump speed calculation formula is:

[0053] where, represents the required speed of the water pump (rpm), represents the reference speed of the water pump (usually 1450 rpm), represents the current required head (m), represents the reference head (m), represents the system resistance correction coefficient (dimensionless), represents the pressure difference adjustment coefficient (dimensionless), Indicates the differential pressure deviation value (kPa). Indicates the differential pressure set value (kPa). The current required head Is the total resistance of the most unfavorable loop obtained through hydraulic calculation, including pipe resistance, equipment resistance, and elevation difference, with the unit of meters of water column. System resistance correction factor Considers the deviation between the actual pipe network condition and the design state, usually between 0.95 - 1.05. Differential pressure regulation coefficient Is a parameter for closed-loop control. When the detected differential pressure deviates from the set value, it dynamically adjusts the pump speed to ensure the stability of the system pressure. Generally, the value ranges from 0.1 - 0.3. For variable-frequency pumps, the adjustment of the speed is directly achieved through the frequency converter, and the calculated speed value is converted into a frequency command and sent to the variable-frequency equipment.

[0054] Regarding the proportional relationship between the flow demand of each branch and the total water supply, the opening degree of each regulating valve is determined through flow balance calculation to achieve the adjustment of the valve opening degree. The flow balance calculation is based on the corresponding relationship between equipment load and flow. First, the cooling and heating loads of each terminal equipment are converted into the required water flow, and then the proportion of each branch in the total flow is calculated as the basis for flow distribution. The valve opening calculation adopts the equal pressure drop method to ensure hydraulic balance when distributing flow in each parallel branch. For electric control valves, the opening degree is usually expressed as a percentage, from 0% (fully closed) to 100% (fully open). The valve characteristic curve describes the non-linear relationship between the opening degree and the flow. Common ones include linear characteristics, equal percentage characteristics, and quick opening characteristics. According to the valve characteristic curve, the required flow is converted into the corresponding valve opening command, and the valve position is adjusted through the actuator. Combining the pump speed and the valve opening degree, the pressure distribution and flow distribution at each point in the system are calculated to generate the operating state parameters of the water system. This calculation process uses pipe network hydraulic simulation technology to establish a mathematical model containing nodes and pipe segments, applies the principles of energy conservation and mass conservation, and solves the pressure at each node and the flow in each pipe segment under the balanced state of the system. The calculation process usually adopts the Hardy Cross iteration method or the node-loop method, starting from the initial estimated value and gradually correcting until the convergence condition is met. The generated operating state parameters of the water system include the pressure values at each key node, the flow values in each pipe segment, the differential pressure values at the differential pressure monitoring points, etc. These parameters comprehensively reflect the operating condition of the water system and are important indicators for evaluating the system stability and energy efficiency.

[0055] Finally, the water pump speed, valve opening, and water system operation 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 with different units into a standard format; parameter correlation analysis examines whether there are conflicts or mutual exclusions among control parameters; control instruction generation converts parameters into specific control signals according to the interface requirements of control devices. The structured design of water system balance control data facilitates the execution and monitoring of subsequent control instructions and also provides data support for system fault diagnosis and performance evaluation.

[0056] Taking the central air-conditioning water system of a commercial building as an example, this system is equipped with two variable-frequency water pumps and multiple electric control valves. The supply water temperature is extracted from the operating parameters of the chiller as 6°C, the return water temperature is 12°C, the designed flow rate is 120 cubic meters per hour, and the outlet pressure is 350 kPa. Based on these data, a benchmark point of the water system operating conditions is established, and combined with the water system layout diagram of the building, a hydraulic relationship diagram of the main pipeline and branch pipelines is constructed, including 1 main line and 15 branch pipelines. Through hydraulic calculation, it is determined that the most unfavorable loop is the end user in the northwest corner of the 8th floor, and the minimum pressure difference that needs to be maintained at this point is 80 kPa. According to the step-by-step pressure drop method, the pressure loss of the main pipeline is 120 kPa, the static pressure generated by the height difference is 784 kPa (the height of the 8th floor is about 80 meters), and considering the pressure drop of each device, the total head that the water pump needs to provide is 32 meters of water column. Substituting into the water pump speed calculation formula, the reference speed is 1450 rpm, the reference head is 40 meters, the system resistance correction coefficient is 1.02, the pressure difference adjustment coefficient is 0.15, and the currently detected pressure difference deviation is -5 kPa (the pressure difference set value is 80 kPa), and the calculated water pump required speed is 1280 rpm, corresponding to the inverter output frequency of 44.1 Hz. At the same time, according to the load distribution of each area, the flow demand ratios of the 15 branch pipelines are calculated, which are 8%, 7%, 9%, etc. According to the equal pressure drop method and the valve characteristic curve, the opening degrees of each control valve are determined to be 65%, 58%, 72%, etc. Combining the water pump speed and valve opening degrees, through network hydraulic simulation, the pressure distribution and flow allocation of key points in the system are calculated, such as the main pipe inlet pressure of 350 kPa, and the branch point pressures on each floor are 320 kPa, 290 kPa, etc. Finally, the water pump speed (1280 rpm), the opening degrees of each valve (65%, 58%, 72%, etc.), and the system operation status parameters (pressures and flow data at each point) are integrated into water system balance control data.

[0057] In a specific embodiment, the process of executing step S105 may specifically include the following steps: (1) Extract the supply water temperature, supply water pressure, and flow distribution ratio from the water system balance control data to establish a cooling capacity distribution table for the terminal equipment; (2) Encode and classify the terminal air-conditioning equipment in each area, form a terminal equipment hierarchical control architecture, and establish a terminal control response matrix; (3) According to 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; (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; (5) Based on the regional load variation characteristics, determine the air volume required for each terminal device and calculate the corresponding fan speed control value; (6) Integrate the supply air temperature parameters and fan speed control values ​​into control data packets for each terminal device to form supply air control instructions.

[0058] Specifically, key parameters such as water supply temperature, water supply pressure and flow distribution ratio are extracted from the water system balance control data to establish a cooling capacity distribution table for the terminal equipment. The water system balance control data is a set of control parameters output in the previous step, which contains the status information of each node in the water system. The water supply temperature refers to the temperature of the chilled water when it reaches the terminal equipment. Due to the heat loss of the pipeline, this temperature is slightly higher than the outlet water temperature of the chiller; the water supply pressure is the water pressure at the terminal inlet, which directly affects the flow of the terminal heat exchanger; the flow distribution ratio indicates the distribution of chilled water in each area. Through thermodynamic calculations, these water system parameters are converted into the available cooling capacity of the terminal equipment to form a cooling capacity distribution table. The calculation process takes into account the water flow, the temperature difference between the supply and return water, and the specific heat capacity of water, and directly converts the water volume and temperature difference in each area into available cooling capacity, providing data support for subsequent precise allocation. Encoding and classifying the terminal air-conditioning equipment in each area to form a hierarchical control architecture for the terminal equipment is the basic work to achieve refined control. The coding classification adopts a multi-level classification method, and is classified according to dimensions such as equipment 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 control signals and device responses. 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.

[0059] According to the hierarchical control architecture of terminal devices, the cooling capacity indicators in the cooling capacity distribution table are allocated to each terminal device according to the regional priority and actual demand. The allocation process uses a weighted allocation algorithm, considering multiple factors: the regional priority reflects the requirements for temperature control accuracy in different spaces. For example, the priority of a meeting room is usually higher than that of a corridor; the actual demand is provided by the regional load distribution map, indicating the current cooling load size of each region; the equipment capacity is the maximum cooling capacity that the terminal device can provide. The allocation algorithm first meets the basic needs of high-priority regions and then allocates the remaining cooling capacity proportionally. When the total cooling capacity is insufficient to meet all demands, a priority pruning strategy is adopted to ensure the service quality of high-priority regions. The allocation result determines the specific cooling capacity supply value for each terminal device, which is the basic data for subsequent parameter calculations.

[0060] Through the heat exchange calculation method, the cooling capacity allocated to each terminal device is converted into the supply air temperature parameter. The heat exchange calculation is based on the air enthalpy difference principle, considering the specific heat capacity of 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 according to the air volume. The supply air temperature also needs to meet the supply air temperature difference limit condition, that is, the difference between the supply air temperature and the indoor set temperature should not be too large to avoid cold air downdraft and discomfort caused by air flow. The supply air temperature difference limit is usually between 8 - 12 °C and is adjusted according to the space height and supply air method. If the calculated supply air temperature exceeds the limit condition, the air volume needs to be adjusted to ensure comfort. The heat exchange calculation also considers the influence of the latent load. The latent load will affect the air state through latent heat exchange. Especially in a high-humidity environment where the proportion of latent heat is relatively large, the sensible heat calculation result needs to be adjusted.

[0061] Combined with the characteristics of regional load changes, determine the required air volume of each terminal device and calculate the corresponding fan speed control value. The air volume control of terminal devices is the key means to achieve precise air conditioning regulation. The air volume demand calculation is based on the relationship between the cooling load and the supply air temperature difference. That is, under the same load, a smaller supply air temperature difference requires a larger air volume, and a larger supply air temperature difference allows a smaller air volume. The characteristics of regional load changes are obtained through a load prediction model, including parameters such as the load change rate, fluctuation range, and duration. The calculation of the fan speed control value considers the pressure-flow characteristic curve of the fan, the air volume and static pressure that the fan can provide at different speeds, and the change in the duct system resistance. The speed control adopts the quadratic flow law, that is, the air volume is linearly related to the speed, while the static pressure is quadratically related to the speed. To avoid energy waste and equipment wear caused by frequent speed regulation, the control algorithm introduces a start-stop hysteresis strategy, and the speed adjustment is only triggered when the load change exceeds the set threshold.

[0062] Finally, the supply air temperature parameter and the fan speed control value are integrated into the control data packets for each terminal device to form a supply air control instruction. The encapsulation of the control data packet follows the standard communication protocol and includes information such as device identification, control parameters, execution time, priority, etc. During the data integration process, consistency checks are performed to ensure that there are no conflicts between the control parameters, such as whether the combination of supply air temperature and air volume can meet the cooling demand. The supply air control instruction is sent to the terminal device controller through the communication network to achieve precise control. The timing arrangement of the control instruction 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 instruction also includes adaptive parameters, allowing the device to fine-tune the control parameters according to the actual operation effect.

[0063] Taking the central air-conditioning system of a comprehensive office building as an example, 12 fan coil terminal devices are configured in the office area of the east district on the third floor. The supply water temperature in this area is extracted from the water system balance control data as 7.5 °C, the supply water pressure is 240 kPa, the flow distribution ratio is 8.5%, and the total flow is 120 m³ / h. According to thermodynamic calculations, the total available cooling capacity in this area is approximately 100 kW. These 12 terminal devices are coded and classified, and are identified in the form of "floor - area - device type - serial number", such as "3 - E - FCU - 01" representing the first fan coil in the east district on the third floor. According to the usage function, they are divided into two categories: open office areas (8 units) and independent offices (4 units), establishing a two-level control architecture to form a terminal control response matrix. According to the actual cooling load demand and the area priority (the priority of independent offices is higher than that of open areas), the 100 kW cooling capacity is allocated as follows: 8 kW for each independent office, totaling 32 kW; 8.5 kW for each open office area, totaling 68 kW. Through heat exchange calculations, the supply air temperature of the fan coil in the independent office 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 the load prediction, the load fluctuation in this area is small, and the change rate is less than 5% per hour. Therefore, the air volume control adopts a steady-state mode. It is calculated that the air volume of the fan coil in the independent office is 1200 m³ / h, corresponding to a fan speed of 850 rpm; the air volume in the open office area is 1500 m³ / h, corresponding to a speed of 950 rpm. Finally, the supply air temperature (16 °C / 15 °C) and the fan speed control values (850 rpm / 950 rpm) are respectively encapsulated into 12 control data packets to form the supply air control instructions for each terminal device, achieving precise air-conditioning control and energy-saving operation.

[0064] In a specific embodiment, the process of executing step S106 may specifically include the following steps: (1) Collect the user's thermal comfort perception feedback signal through the distributed environment perception system, and combine the measured temperature and humidity parameters to generate indoor thermal comfort feedback data; (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; (3) Divide the comfort priority area and the energy-saving priority area according to the magnitude and change trend of the control deviation value to form a zoning control strategy table; (4) For the comfort priority area, adjust the combined parameters of the air supply temperature and the air supply volume to improve the temperature and humidity stability; (5) For the energy-saving priority area, expand the allowable temperature fluctuation range to reduce the start-stop frequency of equipment and energy consumption; (6) Feed back the adjustment results of the comfort priority area and the energy-saving priority area to the cold and heat source equipment and the distribution system, recalculate the operating parameters, and complete the energy-saving operation control of the central air-conditioning system.

[0065] Specifically, the distributed environment perception system collects the feedback signals of the user's thermal comfort feelings, and combines the measured temperature and humidity parameters to generate the indoor thermal comfort feedback data. The distributed environment perception system consists of a variety of sensing devices, including fixed temperature and humidity sensors, mobile measurement terminals, and user feedback interfaces. The feedback signals of the user's thermal comfort feelings are obtained through multiple channels, such as the thermal feeling scoring panel installed on the wall, the feedback of the mobile application, and the monitoring data of the smart wearable device. These signals are quantified according to the PMV-PPD (Predicted Mean Vote - Predicted Percentage of Dissatisfied People) standard, and the user's subjective feelings are converted into values from -3 to +3, where 0 represents thermal neutrality, the most comfortable state. The measured temperature and humidity parameters include dry bulb temperature, wet bulb temperature, globe temperature, and air velocity, which are collected in real time by sensors arranged at multiple points. The user feedback signals and the measured physical parameters are correlated and analyzed to generate the indoor thermal comfort feedback data, which contains both objective measurement values and subjective evaluation values, and comprehensively reflects the operation effect of the air conditioner.

[0066] 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. The air supply control instructions are the control parameter packets generated in the previous step, which contain the set values of the air supply temperature and the air volume. The actual execution results refer to the physical states actually achieved by these control instructions on the terminal equipment, which are collected by the outlet temperature sensor and the air volume measurement device. The comparison and analysis process adopts a multi-dimensional evaluation method, including four dimensions: temperature deviation evaluation, humidity deviation evaluation, comfort evaluation, and energy consumption evaluation. The temperature deviation is the difference between the actual temperature and the set temperature, and the humidity deviation is the difference between the actual humidity and the set humidity. The comfort evaluation compares the PMV value feedback by the user with the target range, and the energy consumption evaluation is obtained by comparing the actual energy consumption with the theoretical optimal energy consumption. The evaluation indicators of these four dimensions are weighted and comprehensively form the control deviation value, which not only reflects the accuracy of the physical parameters but also reflects the satisfaction of the user experience, and is the core basis for formulating the subsequent zoning strategy.

[0067] According to the magnitude and trend of the control deviation value, the comfort priority area and the energy-saving priority area are divided to form a partition control strategy table. The magnitude of the control deviation value directly reflects the degree of proximity between the current control effect and the target requirement, and the trend indicates the stability and response characteristics of the system. The division process uses an adaptive threshold method to dynamically determine the division boundary based on historical data and current working conditions. The comfort priority area is usually those areas with large deviation values and high user comfort requirements, such as important meeting rooms, reception areas, etc.; the energy-saving priority area is the area with small deviation values and allowing appropriate sacrifice of comfort in exchange for energy efficiency, such as corridors, equipment rooms, etc. The partition control strategy table is a multi-dimensional decision matrix. The rows represent different areas, the columns represent control parameters, and the matrix elements are specific control strategies, such as "strict control", "moderate control", "loose control", etc. The table also includes a time dimension, and different control strategies are adopted at different time periods. For example, comfort is emphasized during working hours, and energy conservation is given priority during non-working hours.

[0068] For the comfort priority area, the combined parameters of the supply air temperature and the supply air volume are adjusted to improve the temperature and humidity stability. The control objective of the comfort priority area is to maintain a strict temperature and humidity range. Usually, the allowable temperature deviation does not exceed ±0.5°C, and the allowable humidity deviation does not exceed ±5%. The adjustment process uses a combined parameter optimization method. By combining different supply air temperatures and supply air volumes, the parameter group that can both meet the load demand and provide the best comfort is found. This optimization process takes into account the airflow perception characteristics. The human body is more sensitive to cold airflows than to static cold air. Therefore, under the same load conditions, a combination with a moderate temperature and an appropriate air volume is preferred rather than a combination with a too low temperature and a too large air volume. At the same time, a feedforward control mechanism is introduced. According to the outdoor meteorological changes and the prediction of internal loads, the control parameters are adjusted in advance to reduce the impact of environmental disturbances on the indoor temperature and humidity. This refined control strategy ensures high-quality air conditioning services in the comfort priority area.

[0069] For the energy-saving priority area, expand the allowable temperature fluctuation range to reduce the frequency of equipment start-stop and energy consumption. The control feature of the energy-saving priority area is to appropriately relax the requirements for temperature and humidity control accuracy. Generally, the allowable temperature deviation can reach ±2°C, and the allowable humidity deviation can reach ±10%. The control strategy for expanding the temperature fluctuation range adopts the dead zone control method, which sets a dead zone that does not trigger regulation between the upper and lower limits of the set temperature. Only when the actual temperature exceeds the dead zone range will the adjustment be started. This method significantly reduces the frequency of equipment start-stop, avoiding the peak power consumption and equipment wear caused by frequent start-stop. At the same time, combining predictive control and hysteresis control, adjust the dead zone range according to the load change trend. Narrow the dead zone to strengthen control when the load changes violently, and expand the dead zone to reduce regulation when the load is stable. The energy-saving priority area also adopts a periodic temperature reset strategy, periodically adjusting the set temperature within the allowable range, making full use of the thermal inertia characteristics of the building itself to further reduce energy consumption.

[0070] Feed back the adjustment results of the comfort priority area and the energy-saving priority area to the cold heat source equipment and the distribution system, recalculate the operating parameters, and complete the energy-saving operation control of the central air-conditioning system. The adjustment results include the corrected load demands and control parameters of each area, and these data are transmitted back to the central controller through the system bus. The cold heat source equipment recalculates the equipment start-stop combination and the operating load rate according to the updated load demand, and adjusts the refrigeration and heating capacity output. The distribution system redistributes the water flow and adjusts the pump speed according to the actual demand changes in each area to ensure hydraulic balance. The feedback process adopts the cyclic iteration method, that is, the change in the end demand causes the adjustment of the intermediate system, the adjustment of the intermediate system affects the operation of the cold heat source, and the change in the operating state of the cold heat source in turn affects the end supply capacity. Through multiple iterations, the overall balance is finally achieved. This closed-loop feedback control mechanism ensures the coordinated operation of all parts of the system and realizes the dynamic balance between energy saving and comfort.

[0071] Taking an office building as an example, this building has 5 floors, and each floor is divided into 4 control areas. The data collected by the distributed environmental perception system shows that the PMV value feedback by users in the south area of the second floor is +1.2, exceeding the comfort range (±0.5). The measured temperature is 23.5°C, slightly lower than the set value of 24.5°C, and the humidity is 52%, close to the set value of 50%. Analysis reveals that although the temperature is controlled within a reasonable range, users still feel hot. The main reason is that there is a large area of glass curtain wall in this area, and solar radiation causes the perceived temperature to be higher than the measured temperature. Comparing the execution results of the air supply control instructions (air supply temperature 17°C, air volume 2000 m³ / h) with the thermal comfort feedback data, the temperature dimension deviation is calculated to be -1.0°C, and the comfort dimension deviation is +0.7. The comprehensive evaluation control deviation value is +0.5, and it shows an upward trend recently. According to the size and change trend of the control deviation value, the south area of the second floor is classified as a comfort priority area, and other areas with smaller deviation values such as stairwells and corridors are classified as energy-saving priority areas, forming a partition control strategy table. For the south area of the second floor, which is a comfort priority area, the air supply temperature is adjusted from 17°C to 16°C, and at the same time, the air volume is increased to 2500 m³ / h to offset the influence of solar radiation; for the corridor area classified as an energy-saving priority area, the allowable temperature fluctuation range is expanded from ±1°C to ±2°C, and a minimum start-stop interval time of 20 minutes is set. These adjustment results are fed back to the chiller and pump systems, resulting in an increase in the total cooling load of about 5%. The number of operating chillers changes from one to two, but their operating frequencies are all reduced, and the pump speed is slightly increased to meet the increased flow demand. Through this whole-system collaborative optimization, not only the comfort requirements of key areas are met, but also the overall energy is efficiently utilized through a differentiated treatment strategy.

[0072] The central air-conditioning energy-saving operation control method in the embodiments of the present application has been described above. Next, the central air-conditioning energy-saving operation control system in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the central air-conditioning energy-saving operation control system in the embodiments of the present application includes: A processing module, configured 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; A calculation module, configured to calculate the cooling and heating load values of each space according to the regional temperature and humidity distribution indicators and personnel density data, and obtain a regional load distribution map; A regulation module, configured to regulate the start-stop quantity and operation frequency of central air-conditioning cold and heat source equipment according to the regional load distribution map, and obtain chiller operation parameters; A control module, configured to adjust the rotation speeds of pumps and fans and the opening degrees of valves based on the chiller operation parameters, and form water system balance control data; A generation module, configured to adjust the supply air temperature and volume of each terminal device according to the water system balance control data, and generate a supply air control instruction; An adjustment module, configured to adjust the operating state of the air conditioning system in real time according to the supply air control instruction and the indoor thermal comfort feedback data, so as to achieve the energy-saving operation control of the central air conditioning system.

[0073] Through the collaborative cooperation of the above-mentioned various components, the regional temperature and humidity distribution index obtained by collecting the indoor and outdoor environmental temperature and humidity data through the temperature and humidity sensors and performing sequential processing provides the system with accurate environmental perception ability, enabling the control system to grasp the temperature and humidity conditions of each area in real time, thereby laying a data foundation for subsequent precise control. Calculating the cooling and heating load values of each space by combining the regional temperature and humidity distribution index and the personnel density data, the formed regional load distribution map realizes the accurate mapping from environmental data to load demand, greatly improving the accuracy of load prediction. The application of the deep learning algorithm in load prediction is particularly worth emphasizing. This algorithm can identify complex load change patterns through self-learning of historical data, significantly improving the prediction ability of building physical characteristics, personnel activity rules and meteorological impacts. The contribution of this algorithm feature to the solution is to transform passive response into active prediction, reducing the control lag problem. When regulating the central air conditioning cold and heat source equipment according to the regional load distribution map and obtaining the operating parameters of the chiller, the introduction of the partial load efficiency gain algorithm solves the problem of low efficiency of the traditional air conditioning system under partial load. By intelligently combining chillers with different capacities, the system always operates near the best efficiency point, significantly reducing energy consumption. The technical feature of adjusting the rotational speed of the water pump and the fan and the opening degree of the valve based on the operating parameters of the chiller to form the water system balance control data solves the problems of energy waste and uneven comfort caused by hydraulic imbalance in the traditional system, enabling the system to maintain the best hydraulic balance state under different load conditions. The technical feature of adjusting the supply air temperature and volume of each terminal device according to the water system balance control data and generating a supply air control instruction realizes the precise control of the system terminal and meets the differentiated requirements of different areas. Finally, the closed-loop control of adjusting the air conditioning system in real time according to the supply air control instruction and the indoor thermal comfort feedback data not only ensures the control accuracy, but also continuously optimizes the control strategy through the self-learning thermal comfort preference algorithm, achieving the dynamic balance of comfort and energy saving.

[0074] Refer to Figure 3 , in the embodiment of the present invention, a computer device is further provided. This computer device can be a server, and its internal structure can be as Figure 3As shown in the figure. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected via a system bus. Among them, 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 the 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.

[0075] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of a part 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.

[0076] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0077] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to memory, storage, database, or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or an external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0078] Those skilled in the art can 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 foregoing method embodiments, and will not be elaborated herein.

[0079] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

Claims

1. A central air conditioning energy-saving operation control method, characterized in that: The central air-conditioning energy-saving operation control method comprises: The temperature and humidity data of indoor and outdoor environments are collected through temperature and humidity sensors, and the collected data are processed in time series to obtain regional temperature and humidity distribution indicators; According to the regional temperature and humidity distribution index and personnel density data, the cooling and heating load values ​​of each space are calculated to obtain a regional load distribution diagram; According to the regional load distribution diagram, the start and stop quantity and operation frequency of the central air-conditioning cold and heat source equipment are regulated to obtain the chiller operation parameters; Based on the operating parameters of the chiller, the rotation speed and valve opening of the water pump and the 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 temperature and humidity sensors are used to collect the indoor and outdoor environmental temperature and humidity data, and the collected data are processed in time series to obtain the regional temperature and humidity distribution indicators, including: 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. Arrange the discrete sampling values ​​of the 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, removing outliers and noise points, and forming a valid temperature and humidity data set; Numerical estimation is performed on the effective temperature and humidity data set at non-sampling point locations to generate continuous temperature and humidity field data; Calculate the temperature gradient and humidity gradient of each point in the continuous temperature and humidity field data, and identify the hot and cold uneven areas; The temperature gradient and humidity gradient are compared and analyzed with historical data to generate regional temperature and humidity distribution indicators.

3. 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 according to 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 use period, and establish a zone coding index table; According to 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 according to the product of the temperature deviation coefficient and the heat transfer coefficient, the surface area and the temperature difference of the enclosure structure of each thermal zone; The real-time personnel distribution density data of each thermal zone is collected through the personnel density sensor, and the personnel density data is converted into human body heat production according to the human body metabolic rate standard; Calculate the heat gain in lighting equipment by combining lighting power density, equipment power density and actual operation coefficient of each thermal zone; The heat balance calculation is performed on the heat conduction load of the enclosure structure, the heat generated by the human body and the heat increment in the lighting equipment. The total heat and cold load is equal to the heat conduction 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 cold load value required for each partition; According to the heat balance calculation results, the time-varying cooling and heating load values ​​of each thermal zone are generated; 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.

4. The central air conditioning energy-saving operation control method according to claim 1, characterized in that: According to the regional load distribution diagram, the start and stop quantity and operation frequency of the central air-conditioning cold and heat source equipment are regulated to obtain the chiller operation parameters, including: Performing spatiotemporal analysis on the cold and hot load information of the regional load distribution diagram to construct a three-dimensional load intensity matrix for determining the total demand for cold and hot source equipment; Decomposing the load characteristics according to the three-dimensional load intensity matrix, judging the current system load rate, and determining the start and stop quantity of the central air conditioning cold and heat source equipment; Based on the cross analysis of system load rate and energy efficiency curves of each equipment, the optimal working point of each central air-conditioning cold and heat source equipment is calculated to form a combination plan for equipment start and stop; Use the load distribution algorithm to distribute the load to the activated central air-conditioning cold and heat source equipment and determine the load-bearing ratio of each equipment; According to the load bearing 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-stop quantity, start-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.

5. 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 the fan based on the operating parameters of the chiller to form water system balance control data includes: Extracting water supply temperature, return water temperature, flow rate and pressure data from the chiller operating parameters to establish a water system operating reference point; According to the water system working condition reference point, a hydraulic relationship diagram of the main pipeline and the 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; According to 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 the adjustment of the valve opening; Combined with the water pump speed and valve opening, the pressure distribution and flow distribution of each point in the system are calculated to generate water system operation status parameters; The water pump speed, valve opening and water system operating status parameters are integrated into water system balance control data.

6. The central air conditioning energy-saving operation control method according to claim 1, characterized in that: The method 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 restriction 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.

7. 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 according to the air supply control instruction and the indoor thermal comfort feedback data to realize the energy-saving operation control of the central air conditioning system, including: The distributed environmental sensing system collects user thermal comfort feedback signals and generates indoor thermal comfort feedback data based on the measured temperature and humidity parameters. 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; According to the size and change trend of the control deviation value, the comfort priority area and the energy saving priority area are divided to form a partition 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, reduce the frequency of equipment start-stop 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.

8. 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 7, characterized in that: The central air-conditioning energy-saving operation control system comprises: The processing module is used to collect the temperature and humidity data of the indoor and outdoor environments through the temperature and humidity sensors, perform time series processing on the collected data, and obtain the regional temperature and humidity distribution indicators; A calculation module, used to calculate the cooling and heating load value of each space according to the regional temperature and humidity distribution index and the personnel density data, and obtain a regional load distribution diagram; A control module is used to control the start and stop quantity and operation frequency of the central air-conditioning cold and heat source equipment according to the regional load distribution diagram to obtain the chiller operation parameters; A control module, used to adjust the rotation speed and valve opening of the water pump and the fan based on the operating parameters of the chiller to form water system balance control data; A generation module, used 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 instruction and indoor thermal comfort feedback data, so as to realize energy-saving operation control of the central air-conditioning system.

9. A computer device, characterized in that: It 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 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is enabled to execute the central air-conditioning energy-saving operation control method according to any one of claims 1 to 7.

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