Distributed intelligent air conditioner terminal system

Abstract

The application provides a kind of distributed intelligent air conditioner terminal system, belongs to distributed air conditioner terminal technical field, including: air handling unit, intelligent control cabinet, information screen, line sound source, camera and electric nozzle;Air handling unit includes air return grille, filter, surface cooler, air return sound attenuation section, fan section, air supply sound attenuation section and static pressure tank connected in turn;Electric nozzle is connected with the outlet of static pressure tank;Camera is personnel detection camera, used to capture surrounding personnel intensive situation, and electrically connected with intelligent control cabinet;Control chip is arranged in intelligent control cabinet, air handling unit adjustment module is arranged in control chip, used to adjust the parameters of air handling unit according to the data collected by air return temperature and humidity sensor and personnel detection camera, including the air supply angle, air speed, air supply temperature of electric nozzle and unit operation parameter;Solve the problem that prior art cannot use environmental data and personnel density information for intelligent control.

Description

Technical Field

[0001] This invention belongs to the field of distributed air conditioning terminal technology, and more specifically, relates to a distributed intelligent air conditioning terminal system. Background Technology

[0002] Existing centralized air conditioning systems are widely used in large office buildings, shopping malls, hospitals, and other similar locations, providing cooling, heating, and ventilation to the entire building through a single centralized air conditioning unit. While these systems offer advantages such as centralized equipment and convenient management, they also present several challenges. First, due to the large building area, the air conditioning system needs to transport heat and cold over long distances, and heat and airflow are easily lost during transmission through pipes, resulting in low energy efficiency. Second, a single centralized design cannot provide precise control based on the actual needs of different areas, making it difficult to meet personalized temperature and humidity requirements. Furthermore, troubleshooting centralized systems is also more difficult; once a problem occurs, the entire building's air conditioning system will be affected.

[0003] To address the problems of centralized air conditioning systems, distributed air conditioning systems have emerged. Distributed air conditioning systems disperse air conditioning equipment throughout various areas of a building, with each area equipped with independent air conditioning terminal units. This approach effectively shortens the distance for transporting heat and cold, improving energy efficiency. Simultaneously, decentralized air conditioning equipment allows for more precise control based on the needs of different areas, meeting personalized temperature and humidity requirements. Furthermore, distributed systems offer greater flexibility in fault handling; localized failures do not affect the normal operation of the entire system.

[0004] However, existing distributed air conditioning terminal equipment generally suffers from a single control strategy and cannot fully utilize environmental data and population density information for intelligent optimization, making it difficult for existing distributed air conditioning systems to truly achieve precise control and energy efficiency improvement. Summary of the Invention

[0005] In view of this, the present invention provides a distributed intelligent air conditioning terminal system that can solve the problem that existing distributed air conditioning terminal devices generally have a single control strategy and cannot use environmental data and personnel density information for intelligent optimization.

[0006] This invention is implemented as follows:

[0007] This invention provides a distributed intelligent air conditioning terminal system, comprising:

[0008] Air handling units, intelligent control cabinets, information screens, line sound sources, cameras, and electric nozzles;

[0009] The air handling unit includes a return air grille, an air filter, a surface cooler, a return air silencer, a supply air fan, a supply air silencer, and a supply air static box connected in sequence; the electric nozzle is connected to the outlet of the static pressure box.

[0010] The camera is a people detection camera, used to capture dense crowds in the surrounding area, and is electrically connected to the intelligent control cabinet;

[0011] The intelligent control cabinet is equipped with a control chip, which contains an air handling unit adjustment module. This module is used to adjust the parameters of the air handling unit based on data collected by the return air temperature and humidity sensor and the personnel detection camera. These parameters include the air delivery angle, wind speed, air delivery temperature, and unit operating parameters of the electric nozzles.

[0012] The information screen is electrically connected to the intelligent control cabinet and is used to display the operating status of indoor temperature, humidity, personnel density, air supply parameters and energy consumption data in real time.

[0013] Based on the above technical solution, the distributed intelligent air conditioning terminal system of the present invention can be further improved as follows:

[0014] The system includes a fire extinguisher box, a fire monitor, and an alarm. When a high-temperature fire is detected, the alarm sounds. The fire monitor is electrically connected to the intelligent control cabinet to receive and execute commands from the cabinet. The fire extinguisher box is embedded in the chassis and can be quickly activated in the event of a high-temperature fire.

[0015] The airflow direction of the air handling unit is as follows: air enters the system from the front and rear bottom through the return air grille, passes through the air filter and surface cooler, enters the air handling unit, then passes through the return air silencer, the supply air fan and the supply air silencer, and finally enters the static pressure box and is delivered to the indoor space through the electric nozzle; the return air grille is located at the bottom of the air handling unit, and the electric nozzle is located at the top of the air handling unit.

[0016] It also includes a sound-absorbing / sound-insulating shell, which covers the outside of the air handling unit; the sound-absorbing / sound-insulating shell is made of sound-insulating material and is used to reduce the noise generated by the air handling unit during operation.

[0017] It also includes skirting boards and floor water inlet and outlet pipes, with the floor water inlet and outlet pipes installed inside the skirting boards; the floor water inlet and outlet pipes are connected to the surface cooler to provide hot and cold water to the surface cooler.

[0018] It also includes an access door, which is installed on the air handling unit for maintenance and repair; the access door is connected to the air handling unit via hinges.

[0019] This also includes a unit foundation, on which the air handling unit is installed; the unit foundation provides stable support for the air handling unit and has vibration damping function.

[0020] The air handling unit adjustment module is used to perform the following steps:

[0021] S10. Collect temperature and humidity data from the return air temperature and humidity sensor and personnel density data from the personnel detection camera to form a temperature and humidity-personnel density signal group;

[0022] S20. Preprocess the collected temperature, humidity and personnel density signal group, including noise reduction, filtering and signal alignment, to improve data quality;

[0023] S30. Based on each signal in the aligned temperature, humidity and personnel density signal group, use time-frequency analysis to determine the optimal time window length in order to capture the characteristics of indoor environmental changes.

[0024] S40. Using the determined optimal time window length, extract the time-frequency features of each signal, including the rate of change of temperature, the rate of change of humidity, and the rate of change of personnel density, and record the time-frequency features of all signals as the feature group to be measured.

[0025] S50. Establish a set of indoor environmental dynamic equations, including heat balance equation, humidity balance equation, air flow equation, human thermal comfort empirical equation, and air conditioning system response equation.

[0026] S60. Divide the indoor space into several grid units, perform differential discretization on the heat balance equation, humidity balance equation, and air flow equation, and transform the human thermal comfort empirical equation and the air conditioning system response equation into discrete form.

[0027] S70. Obtain the initial and boundary conditions of the indoor environmental dynamics equations. Use the Gauss-Seidel iterative method to solve the discretized heat balance equation, humidity balance equation and air flow equation. Iterate until convergence to obtain a numerical solution, including the temperature, humidity and airflow velocity fields of each grid point in the room. Calculate the comfort index of each grid point based on the human thermal comfort empirical equation.

[0028] S80. Define an adjustment objective function, including an average comfort index and an energy consumption index. The constraints of the adjustment objective function include at least the physical limitations of the air conditioning system and the allowable range of indoor temperature and humidity. Using the numerical solution as the initial population and the human thermal comfort empirical equation as the fitness function, a genetic algorithm is used to optimize the optimal adjustment parameters, which are then output to the air handling unit for adjustment.

[0029] Specifically, step S10 includes: collecting return air temperature and humidity data, as well as personnel density data, to form a temperature-humidity-personnel density signal set. First, a return air temperature and humidity sensor is used to measure indoor temperature and humidity in real time, obtaining two time-series signals for temperature and humidity. Simultaneously, a personnel detection camera captures the dense distribution of people indoors, providing a personnel density time-series signal. These three time-series signals are integrated into a temperature-humidity-personnel density signal set, providing basic data for subsequent data processing and analysis.

[0030] Specifically, step S20 includes preprocessing the temperature, humidity, and personnel density signal group. First, a digital filtering algorithm is used to denoise the original signal, improving signal quality. Second, the three signals are time-aligned to ensure consistent timestamps. Finally, the preprocessed temperature, humidity, and personnel density signals are combined into a temperature, humidity, and personnel density signal group. This preprocessing effectively improves the accuracy and reliability of signal analysis in subsequent steps.

[0031] Specifically, step S30 includes determining the optimal time window length using a time-frequency analysis method. First, a short-time Fourier transform (STFT) analysis is performed on each signal in the temperature, humidity, and personnel density signal group to obtain a time-frequency domain representation. Observing the STFT results reveals significant changes in indoor environmental parameters across different time scales. To better capture these changing characteristics, a suitable time window length needs to be selected. Here, the method of minimizing information entropy is used to determine the optimal time window length, i.e., calculating the information entropy of the STFT results in the time-frequency plane and finding the time window length that minimizes the information entropy. This time window length can extract the time-frequency characteristics of environmental changes to the maximum extent.

[0032] Specifically, step S40 includes: extracting the time-frequency features of the temperature, humidity, and personnel density signal group using a determined optimal time window length. First, each signal is subjected to STFT analysis again to obtain a time-frequency domain representation. Then, three feature quantities are extracted from the STFT results: temperature change rate, humidity change rate, and personnel density change rate. The temperature change rate is represented by the derivative of the temperature signal in the time domain, the humidity change rate by the derivative of the humidity signal in the time domain, and the personnel density change rate by the derivative of the personnel density signal in the time domain. These three feature quantities are combined into a feature group to be measured, providing basic data for subsequent dynamic modeling.

[0033] Specifically, step S50 includes establishing a mathematical model describing the dynamic processes of the indoor environment. This model comprises five parts: a heat balance equation, a humidity balance equation, an airflow equation, an empirical equation for human thermal comfort, and an air conditioning system response equation. These equations describe the dynamic changes in key environmental parameters such as indoor temperature, humidity, airflow velocity, and human thermal comfort, covering aspects such as heat transfer, humidity changes, airflow, and human thermal comfort. This mathematical model provides the foundation for subsequent numerical solutions and parameter optimization.

[0034] Specifically, step S60 includes: dividing the indoor space into several grid cells, performing differential discretization on the heat balance equation, humidity balance equation, and air flow equation, and simultaneously transforming the empirical equation for human thermal comfort and the air conditioning system response equation into discrete forms. In this way, the original set of partial differential equations is transformed into a discretized set of equations composed of multiple algebraic equations and difference equations, which can be solved using numerical methods. This discretization process lays the foundation for subsequent numerical calculations.

[0035] Specifically, step S70 includes: obtaining the initial and boundary conditions of the indoor environmental dynamics equations, and numerically solving the heat balance equation, humidity balance equation, and airflow equation using the iterative Gauss-Seidel method. During the iterative calculation, the temperature, humidity, and airflow velocity at each grid point need to be continuously updated. Finally, the PMV value at each grid point is calculated based on the empirical equation for human thermal comfort, reflecting the degree of influence of the indoor environment on human thermal comfort. This step lays the foundation for subsequent parameter optimization.

[0036] Specifically, step S80 includes: defining an adjustment objective function and solving it using a genetic algorithm. The adjustment objective function includes an average comfort index and an energy consumption index. The average comfort index is represented by the average absolute value of the PMV values ​​of each grid point, and the energy consumption index is represented by the ratio of actual energy consumption to maximum energy consumption. These two indices are combined to form the adjustment objective function. To solve this multi-objective optimization problem, a genetic algorithm is used for optimization. The final optimal individual is the best adjustment parameter, including the air delivery angle, wind speed, air delivery temperature, and other operating parameters of the unit. These parameters are output to the air handling unit for adjustment.

[0037] Furthermore, the criteria for judging convergence of iterative calculations are that the number of iterations reaches 2000 steps or the similarity between two consecutive iterations is greater than 99.75%.

[0038] Furthermore, in the step of dividing the indoor space into several grid units, the size of each grid unit is 0.2 meters to 1 meter.

[0039] The equations or functions involved in the calculations in the present invention will be described in detail below:

[0040] 1. Heat balance equation:

[0041] The heat balance equation describes the dynamic process of temperature change in an indoor space, taking into account various factors of heat transfer and generation. Specifically, it is expressed as follows:

[0042]

[0043] In the formula, ρ is the air density, kg / m³ 3 c p t is the specific heat capacity of air, J / (kg·K); T is the indoor temperature, K; t is the time, s; k is the thermal conductivity of air, W / (m·K); Q h Heat provided to the air conditioning system, W / m 3 Q s For solar radiation thermal gain, W / m 3 Q e For the thermal gain of the equipment, W / m 3 Q v For ventilation and heat exchange, W / m 3 Q p Heat dissipation for the human body, W / m 3 ;ε T This is the temperature error term.

[0044] Parameter acquisition method:

[0045] ρ, c p The values ​​for k and k can be found in the standard air property tables.

[0046] T is obtained through real-time measurement using a temperature sensor.

[0047] Q h Calculated based on the operating parameters of the air conditioning system: in For air supply mass flow rate, kg / s; T s For supply air temperature, K; T r V is the return air temperature, K; V is the room volume, m. 3 .

[0048] Q s The external radiation intensity is measured by a solar radiometer, and then calculated based on the heat transfer characteristics of the window.

[0049] Q e Estimate based on equipment power and usage.

[0050] Q v The calculation formula is: in For fresh air volume, m 3 / s;T o The outdoor temperature is in Kelvin.

[0051] Q p Calculated based on population density and average heat dissipation of the human body: Where n is the number of people indoors, q p Let W be the average heat dissipation per person.

[0052] 2. Humidity balance equation:

[0053] The humidity balance equation describes the change process of indoor air humidity, taking into account various sources and removal of moisture. It is expressed as follows:

[0054]

[0055] In the formula, ω is the air moisture content, kg / kg (dry air); D is the water vapor diffusion coefficient, m. 2 / s;W h Dehumidification / humidification capacity of air conditioning system, kg / s; W v Moisture exchange rate due to ventilation, kg / s; W p Human body moisture excretion, kg / s; W m For other water sources (such as plant transpiration), kg / s; V is the room volume, m³. 3 ;ε ω This is the humidity error term.

[0056] Parameter acquisition method:

[0057] ω is obtained by measuring relative humidity using a humidity sensor and then calculating it.

[0058] D can be found in the standard air property table.

[0059] W h Calculated based on the operating parameters of the air conditioning system: Where ω s and ω r These are the moisture contents of the supply air and return air, respectively.

[0060] W v The calculation formula is: Where ω o This refers to the outdoor air humidity.

[0061] W p Calculated based on population density and average human body moisture excretion: W p =nq w , where q w The average moisture excretion per person is expressed in kg / s.

[0062] Wm Obtained through experimental measurement or empirical estimation.

[0063] 3. Airflow equations:

[0064] The airflow equation describes the motion of indoor air and is simplified from the Navier-Stokes equation. Specifically, it is expressed as follows:

[0065]

[0066]

[0067] In the formula, u is the air velocity vector, m / s; p is the pressure, Pa; and v is the kinematic viscosity of air, m³ / s. 2 / s; g is the gravitational acceleration vector, m / s². 2 β is the coefficient of thermal expansion of air, 1 / K; T0 is the reference temperature, K; F is other volume forces (such as fan driving force), N / m 3 ;ε u This is the speed error term.

[0068] Parameter acquisition method:

[0069] u was obtained by measuring at multiple points using a hot-wire anemometer.

[0070] p is measured by a pressure sensor.

[0071] v and β can be found in the standard air property tables.

[0072] F is calculated based on the wind turbine characteristic curve and operating parameters.

[0073] 4. Empirical equation for human thermal comfort:

[0074] The PMV (Predicted Average Votes) index is used to represent human thermal comfort, as shown below:

[0075] PMV = (0.303e -0.036M +0.028)[(MW)-3.05×10 -3 (5733-6.99(MW)-p a )-0.42((MW)-58.15)-1.7×10 -5 M(5867-p a -0.0014M(34-T) a -3.96×10 -8 f cl ((T cl +273) 4 -(T r +273) 4 )-fcl h c (T cl -T a )]+ε PMV ;

[0076] In the formula, PMV is the predicted average number of votes; M is the metabolic rate, W / m 2 W represents effective mechanical power, W / m 2 ;p a T represents the partial pressure of water vapor, in Pa; a Air temperature, °C; T r f is the average radiation temperature, in °C; cl For clothing area factor; T cl The surface temperature of the garment, in °C; h c The convective heat transfer coefficient is W / (m²). 2 ·K); ε PMV This is the PMV error term.

[0077] Parameter acquisition method:

[0078] M and W are retrieved from the standard table based on the type of personnel activity.

[0079] p a It is obtained by measuring relative humidity and temperature using a psychological calculation formula.

[0080] T a and T r The measurements were obtained using a temperature sensor and a black ball thermometer, respectively.

[0081] f cl and T cl The calculation formula is obtained through iterative calculation:

[0082]

[0083] T cl =35.7-0.028(MW)-I cl [3.96×10 -8 f cl ((T cl +273) 4 -(T r +273) 4 )+f cl h c (T cl -T a )];

[0084] Among them, I cl For the thermal resistance of clothing, m 2 ·K / W.

[0085] h c The calculation formula is: Where v a ρ is the relative air velocity, in m / s.

[0086] 5. Air conditioning system response equation:

[0087] The air conditioning system response equation describes the relationship between the system output and the control input, using a first-order time-lag model. Specifically, it is expressed as follows:

[0088]

[0089] In the formula, Y is the system output (such as supply air temperature, air volume, etc.); U is the control input; K is the system gain; τ is the system time constant, s; and εY is the system response error term.

[0090] Parameter acquisition method:

[0091] K and τ were obtained through a system identification experiment, the specific steps of which are as follows:

[0092] 1. Apply a step input signal when the system is running stably.

[0093] 2. Record the system output response curve.

[0094] 3. Calculate the system gain based on the response curve.

[0095] 4. Determine the time required for the system output to reach 63.2% of the final value, which is the time constant τ.

[0096] 6. Adjust the objective function:

[0097] The adjustment objective function takes into account both the average comfort index and the energy consumption index, and is expressed as follows:

[0098]

[0099] In the formula, J is the objective function value; w1 and w2 are weight coefficients, and w1 + W2 = 1; N is the number of room grids; PMV i Let E be the PMV value of the i-th grid point; E is the system energy consumption, kWh; E max The maximum energy consumption of the system is kWh; ε J This is the error term of the objective function.

[0100] Constraints:

[0101] 1. Temperature constraint: T min ≤T≤T max ;

[0102] 2. Humidity constraint: RH min≤RH≤RH max ;

[0103] 3. Wind speed constraint: 0 ≤ v ≤ v max

[0104] 4. Supply air temperature constraint: T s，min ≤T s ≤T s，max ;

[0105] 5. Air supply volume constraints:

[0106] In the formula, T min and T max These are the minimum and maximum permissible indoor temperatures, in K and RH, respectively. min and RH max These are the minimum and maximum permissible relative humidity, respectively; v max Maximum permissible indoor wind speed, m / s; T s，min and T s，max These are the minimum and maximum permissible supply air temperatures, respectively, in K; and These are the minimum and maximum allowable air supply volumes, respectively, in m. 3 / s.

[0107] Parameter acquisition method:

[0108] w1 and w2 are set based on user preferences and can be determined through questionnaires or expert experience.

[0109] PMV i It was calculated using the empirical equation for human thermal comfort.

[0110] E is obtained through real-time measurement using an energy consumption metering device.

[0111] E max Calculated based on the rated power and operating time of the air conditioning system.

[0112] The parameters in the constraints are determined based on the performance parameters of the air conditioning system and the requirements of the indoor environment.

[0113] Compared with existing technologies, the beneficial effects of the distributed intelligent air conditioning terminal system provided by this invention are:

[0114] 1. Employing multi-sensor fusion technology, it monitors key environmental parameters such as indoor temperature, humidity, and personnel density in real time, providing a reliable data foundation for intelligent control.

[0115] 2. A comprehensive dynamic model including heat, humidity, airflow, and human thermal comfort has been established, which can accurately predict the trend of indoor environmental changes and provide a basis for proactive regulation.

[0116] 3. Employing a model-based intelligent optimization algorithm, it can balance the accuracy of temperature and humidity regulation, human thermal comfort, and energy consumption indicators, achieving multi-objective optimization.

[0117] 4. The system adopts a distributed design, enabling personalized adjustments based on the actual needs of different areas, thus improving energy efficiency. Furthermore, localized failures will not affect the normal operation of the entire system.

[0118] In summary, the distributed intelligent air conditioning terminal system proposed in this invention integrates advanced sensing technology, dynamic modeling, and intelligent optimization algorithms, enabling precise control of the indoor environment and improved energy efficiency, thus providing strong support for building comfortable and energy-saving intelligent buildings. It solves the problem that existing distributed air conditioning terminal devices generally have a single control strategy and cannot utilize environmental data and personnel density information for intelligent optimization. Attached Figure Description

[0119] Figure 1 This is a schematic diagram of the structure of a distributed intelligent air conditioning terminal system provided by the present invention;

[0120] Figure 2 This is a schematic diagram of the left side of a distributed intelligent air conditioning terminal system provided by the present invention;

[0121] Figure 3 This is a schematic diagram on the right side of a distributed intelligent air conditioning terminal system provided by the present invention;

[0122] Figure 4 A top view of a distributed intelligent air conditioning terminal system provided by the present invention;

[0123] Figure 5 A flowchart illustrating the steps performed by the air handling unit's conditioning module;

[0124] Figure 6 This shows the changes in temperature, humidity, and PMV values ​​in a certain area over time in Example 2.

[0125] In the attached diagram: 1. Electric nozzle; 2. Personnel detection camera; 3. Linear sound source; 4. Fire box; 5. Return air grille; 6. Skirting board; 7. Ground inlet and outlet water pipes; 8. Supply air static pressure box; 9. Supply air silencer; 10. Information screen; 11. Supply air fan; 12. Return air silencer; 13. Intelligent control cabinet; 14. Inspection door; 15. Temperature and humidity sensor; 16. Air filter; 17. Surface cooler; 18. Unit foundation; 19. Sound-absorbing / sound-insulating shell; 20. Fire monitor. Detailed Implementation

[0126] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0127] like Figure 1-4 The diagram shown is a structural schematic of a distributed intelligent air conditioning terminal system provided by the present invention, comprising:

[0128] Air handling unit, intelligent control cabinet 13, information screen, line sound source 3, personnel detection camera 2 and electric nozzle 1;

[0129] The air handling unit includes a return air grille 5, an air filter 16, a surface cooler 17, a return air silencer 12, a supply air fan 11, a supply air silencer 9, and a supply air static pressure box 8 connected in sequence; the electric nozzle is connected to the outlet of the static pressure box.

[0130] The camera is a people detection camera, used to capture dense crowds in the surrounding area, and is electrically connected to the intelligent control cabinet;

[0131] The intelligent control cabinet is equipped with a control chip, which contains an air handling unit adjustment module. This module is used to adjust the parameters of the air handling unit based on data collected by the return air temperature and humidity sensor 15 and the personnel detection camera. These parameters include the air delivery angle, wind speed, air delivery temperature, and unit operating parameters of the electric nozzles.

[0132] The information screen is electrically connected to the intelligent control cabinet and is used to display the operating status of indoor temperature, humidity, personnel density, air supply parameters and energy consumption data in real time.

[0133] Based on the above technical solution, the distributed intelligent air conditioning terminal system of the present invention can be further improved as follows:

[0134] It also includes a fire box 4 containing a fire hydrant and an alarm, which issues an alarm when a high-temperature fire is detected; the fire hydrant is electrically connected to the intelligent control cabinet and is used to receive and execute control commands issued by the intelligent control cabinet.

[0135] The airflow direction of the air handling unit is as follows: air enters the system from the front and rear bottom through the return air grille, passes through the filter and surface cooler, enters the air handling unit, then passes through the return air silencer section, the fan section and the supply air silencer section, and finally enters the static pressure box and is delivered to the indoor space through the electric nozzle; the return air grille is located at the bottom of the air handling unit, and the electric nozzle is located at the top of the air handling unit.

[0136] It also includes a sound-absorbing / sound-insulating shell 19, which covers the outside of the air handling unit; the sound-absorbing / sound-insulating shell is made of sound-insulating material and is used to reduce the noise generated by the air handling unit during operation.

[0137] It also includes a skirting board 6 and a ground water inlet / outlet pipe 7, the ground water inlet / outlet pipe being installed inside the skirting board; the ground water inlet / outlet pipe is connected to the surface cooler and is used to provide hot and cold water to the surface cooler.

[0138] It also includes an inspection door 14, which is installed on the air handling unit for maintenance and repair; the inspection door is connected to the air handling unit via a hinge.

[0139] It also includes a unit foundation 18, on which the air handling unit is mounted; the unit foundation provides stable support for the air handling unit and has a vibration damping function.

[0140] like Figure 5 As shown, the air handling unit adjustment module is used to perform the following steps:

[0141] S10. Collect temperature and humidity data from the return air temperature and humidity sensor and personnel density data from the personnel detection camera to form a temperature and humidity-personnel density signal group;

[0142] S20. Preprocess the collected temperature, humidity and personnel density signal group, including noise reduction, filtering and signal alignment, to improve data quality;

[0143] S30. Based on each signal in the aligned temperature, humidity and personnel density signal group, use time-frequency analysis to determine the optimal time window length in order to capture the characteristics of indoor environmental changes.

[0144] S40. Using the determined optimal time window length, extract the time-frequency features of each signal, including the rate of change of temperature, the rate of change of humidity, and the rate of change of personnel density, and record the time-frequency features of all signals as the feature group to be measured.

[0145] S50. Establish a set of indoor environmental dynamic equations, including heat balance equation, humidity balance equation, air flow equation, human thermal comfort empirical equation, and air conditioning system response equation.

[0146] S60. Divide the indoor space into several grid units, perform differential discretization on the heat balance equation, humidity balance equation, and air flow equation, and transform the human thermal comfort empirical equation and the air conditioning system response equation into discrete form.

[0147] S70. Obtain the initial and boundary conditions of the indoor environmental dynamics equations. Use the Gauss-Seidel iterative method to solve the discretized heat balance equation, humidity balance equation and air flow equation. Iterate until convergence to obtain a numerical solution, including the temperature, humidity and airflow velocity fields of each grid point in the room. Calculate the comfort index of each grid point based on the human thermal comfort empirical equation.

[0148] S80. Define an adjustment objective function, including an average comfort index and an energy consumption index. The constraints of the adjustment objective function include at least the physical limitations of the air conditioning system and the allowable range of indoor temperature and humidity. Using the numerical solution as the initial population and the human thermal comfort empirical equation as the fitness function, a genetic algorithm is used to optimize the optimal adjustment parameters, which are then output to the air handling unit for adjustment.

[0149] The specific implementation methods of the above steps are described in detail below:

[0150] The specific implementation of step S10 involves collecting indoor environmental signals using a return air temperature and humidity sensor and a people detection camera. First, the return air temperature and humidity sensor measures indoor temperature and humidity data in real time, generating two time-series signals for temperature and humidity. Simultaneously, the people detection camera captures the density distribution of people indoors, providing a people density time-series signal. These three time-series signals are then integrated into a temperature / humidity-people density signal group.

[0151] The specific implementation of step S20 involves preprocessing the acquired temperature, humidity, and personnel density signal group. First, a digital filtering algorithm is used to denoise the original signal, improving signal quality. A Butterworth filter is used, with its passband and stopband frequencies set to 0.1Hz and 0.5Hz respectively, effectively filtering out high-frequency noise. Second, the three signals are time-aligned to ensure consistent timestamps. This step is achieved through linear interpolation. Finally, the preprocessed temperature, humidity, and personnel density signals are combined into a temperature, humidity, and personnel density signal group.

[0152] The specific implementation of step S30 involves determining the optimal time window length using a time-frequency analysis method. First, a short-time Fourier transform (STFT) is used to perform time-frequency analysis on each signal in the temperature, humidity, and personnel density signal group. STFT converts the time-domain signal into a time-frequency domain representation, reflecting the energy changes of the signal at different frequencies over time. By observing the STFT results, significant changes in indoor environmental parameters can be observed at different time scales. To better capture these changing characteristics, a suitable time window length needs to be selected. Here, the method of minimizing information entropy is used to determine the optimal time window length. Specifically, the information entropy of the STFT results in the time-frequency plane is calculated, and the time window length that minimizes the information entropy is found. This time window length can extract the time-frequency characteristics of environmental changes to the maximum extent.

[0153] The specific implementation of step S40 is as follows: using the determined optimal time window length, the time-frequency features of the temperature, humidity, and personnel density signal group are extracted. First, STFT analysis is performed again on each signal to obtain a time-frequency domain representation. Then, the following three feature quantities are extracted from the STFT results: temperature change rate, humidity change rate, and personnel density change rate. The temperature change rate is represented by the derivative of the temperature signal in the time domain; the humidity change rate is represented by the derivative of the humidity signal in the time domain; and the personnel density change rate is represented by the derivative of the personnel density signal in the time domain. These three feature quantities are combined into a feature group to be measured.

[0154] The specific implementation of step S50 involves establishing a mathematical model describing the dynamic processes of the indoor environment. This model includes the following parts:

[0155] 1. Heat Balance Equation: This equation describes the dynamic process of indoor temperature change, considering various factors of heat transfer and generation, such as heat provided by the air conditioning system, solar radiation heat gain, equipment heat gain, ventilation heat exchange, and heat dissipation from the human body. The equation can be expressed as:

[0156]

[0157] 2. Humidity Balance Equation: This equation describes the dynamic process of indoor air humidity changes, considering various moisture sources and removal processes, such as dehumidification / humidification by air conditioning systems, moisture exchange through ventilation, moisture excretion from the human body, and other moisture sources. The equation can be expressed as:

[0158]

[0159] 3. Airflow Equation: Describes the indoor airflow state, derived from the simplified Navier-Stokes equations. This equation includes:

[0160]

[0161]

[0162] 4. Empirical Equation for Human Thermal Comfort: Human thermal comfort is represented by the PMV (Predicted Average Votes) index, taking into account factors such as metabolic rate, clothing thermal resistance, and convective and radiative heat transfer. The equation can be expressed as:

[0163] PMV = (0.303e -0.036M +0.028)[(MW)-3.05×10 -3 (5733-6.99(MW)-p a )-0.42((MW)-58.15)-1.7×10 -5 M(5867-p a -0.0014M(34-T) a -3.96×10 -8 f cl ((T cl +273) 4 -(T r +273) 4 )-f cl h c (T cl -T a )]+ε PMV ;

[0164] 5. Air conditioning system response equation: Describes the relationship between system output and control input, using a first-order time lag model, which can be expressed as:

[0165]

[0166] The above five equations constitute a mathematical model describing the dynamic processes of the indoor environment, covering aspects such as heat transfer, humidity changes, airflow, and human thermal comfort. These equations contain multiple physical and environmental parameters, which need to be obtained through measurement or empirical methods.

[0167] The specific implementation of step S60 is to divide the indoor space into several grid units, perform differential discretization on the heat balance equation, humidity balance equation and air flow equation, and at the same time transform the human thermal comfort empirical equation and the air conditioning system response equation into discrete form.

[0168] First, the three-dimensional continuous space is divided into a three-dimensional grid, with each grid point representing a discrete volume element. The partial derivative terms in the heat balance and humidity balance equations are approximated using finite difference, transforming the partial differential equations into linear algebraic equations. The convection and diffusion terms in the airflow equations are also treated using differential discretization.

[0169] Secondly, continuous variables in the empirical equations for human thermal comfort, such as air temperature, relative humidity, and air velocity, are represented by discrete values ​​at grid points. Simultaneously, the response equations of air conditioning systems can also be transformed into discrete forms, thus discretizing the continuous-time system.

[0170] In this way, the original partial differential equation system is transformed into a discretized equation system consisting of multiple algebraic equations and difference equations, which can be solved using numerical computation methods.

[0171] The specific implementation of step S70 is to obtain the initial conditions and boundary conditions of the indoor environmental dynamics equations, and to use the iterative Gauss-Seidel method to numerically solve the heat balance equation, humidity balance equation, and air flow equation.

[0172] First, set the initial conditions according to the actual situation. For example, the initial temperature, humidity, and airflow velocity field can be obtained through measurement. Boundary conditions may include: ambient temperature and humidity, ventilation volume, and equipment thermal power.

[0173] Then, the Gauss-Seidel iterative method is used to solve the discretized system of equations. The Gauss-Seidel method is a commonly used iterative method for solving linear equation systems. It converges to the solution of the system of equations by updating the unknowns at each grid point. During the iterative calculation, the temperature, humidity, and airflow velocity at each grid point need to be continuously updated.

[0174] Finally, after obtaining the numerical solution, the PMV value of each grid point, i.e., the thermal comfort index, is calculated according to the empirical equation for human thermal comfort. These PMV values ​​can reflect the degree of influence of the indoor environment on human thermal comfort.

[0175] The specific implementation of step S80 is to define an adjustment objective function and use a genetic algorithm to optimize and solve it.

[0176] The adjustment objective function consists of two parts: an average comfort index and an energy consumption index. The average comfort index is represented by the average absolute value of the PMV values ​​at each grid point, and the energy consumption index is represented by the ratio of actual energy consumption to maximum energy consumption. Combining these two indices forms the adjustment objective function:

[0177]

[0178] Here, w1 and w2 are weighting coefficients, satisfying w1 + w2 = 1.

[0179] The constraints on the objective function include: indoor temperature, humidity, wind speed, and upper and lower limits of supply air temperature and air volume. These constraints ensure that the adjustment results meet the indoor environmental requirements.

[0180] To solve this multi-objective optimization problem, a genetic algorithm is used. The genetic algorithm is a stochastic search algorithm based on the principles of biological evolution, adept at handling complex nonlinear optimization problems. The algorithm first randomly generates an initial population, with each individual representing a set of adjustment parameters. Then, through operations such as selection, crossover, and mutation, the population is continuously updated, gradually optimizing the objective function until convergence. The final optimal individual represents the best adjustment parameters, including the air delivery angle, air velocity, air delivery temperature, and other unit operating parameters of the electric nozzle. These parameters are then output to the air handling unit for adjustment.

[0181] To better understand and implement the present invention, a specific embodiment 1 of the air handling unit adjustment module of the present invention is provided below. The steps of this embodiment 1 are described in detail as follows: The specific implementation of step S10 is to collect return air temperature and humidity data and personnel density data to form a temperature, humidity and personnel density signal group.

[0182] First, the indoor temperature T and humidity ω are measured in real time using a return air temperature and humidity sensor to obtain temperature signal T(t) and humidity signal ω(t). The temperature signal T(t) describes the change of indoor temperature over time t, and the humidity signal ω(t) describes the change of indoor humidity over time t.

[0183] Meanwhile, the personnel detection camera captures the dense distribution of people indoors and provides a personnel density signal n(t). The personnel density signal n(t) describes the change in the number of people indoors over time t.

[0184] The three time series signals T(t), ω(t), and n(t) are integrated into a temperature, humidity, and population density signal group x(t) = [T(t), ω(t), n(t)], providing basic data for subsequent data processing and analysis.

[0185] The specific implementation of step S20 is to preprocess the temperature, humidity and personnel density signal group.

[0186] First, a digital filtering algorithm is used to denoise the original signal and improve its quality. A Butterworth low-pass filter is used here, and its transfer function is:

[0187]

[0188] Where s is the complex frequency, ω c Here, n is the cutoff frequency, and n is the filter order. By adjusting the filter parameters, high-frequency noise can be effectively filtered out while preserving the low-frequency characteristics of environmental variables.

[0189] Secondly, time alignment is performed on the three signals T(t), ω(t), and n(t) to ensure that the timestamps of each signal are consistent. Linear interpolation is used here to unify signals with different sampling frequencies onto the same time grid. Specifically, let the sampling time of the original signal be t. i After linear interpolation, the signal value can be expressed as:

[0190]

[0191] Among them, t j These are the interpolated time grid points.

[0192] Finally, the preprocessed temperature signal humidity signal and personnel density signal Combined into temperature, humidity and personnel density signal groups This preprocessing can effectively improve the accuracy and reliability of signal analysis in subsequent steps.

[0193] The specific implementation of step S30 is to determine the optimal time window length using a time-frequency analysis method.

[0194] First, the temperature, humidity, and personnel density signal group Each signal in Performing short-time Fourier transform (STFT) analysis yields the time-frequency domain representation:

[0195]

[0196] Where w(t) is the time window function, such as the Hamming window. STFT result X i (t, f) reflects the energy distribution of the signal at different times and frequencies, and it can be found that the indoor environmental parameters have significant changes at different time scales.

[0197] To better capture these changing characteristics, a suitable time window length W needs to be selected. Here, the method of minimizing information entropy is used to determine the optimal time window length. Specifically, the information entropy of the STFT results in the time-frequency plane is calculated:

[0198]

[0199] And find the time window length W that minimizes the information entropy H(t,f). * This is considered the optimal value. This optimal time window length can extract the time-frequency characteristics of environmental changes to the greatest extent.

[0200] The specific implementation of step S40 is to utilize the determined optimal time window length W. * Extract the time-frequency features of the temperature, humidity and personnel density signal group.

[0201] First, for each signal Perform STFT analysis again to obtain the time-frequency domain representation X. i (t, f).

[0202] Then, the following three features are extracted from the STFT results:

[0203] 1. Rate of temperature change

[0204] 2. Humidity change rate

[0205] 3. Population density change rate

[0206] Among them, the rate of temperature change From temperature signal The derivative in the time domain represents the rate of change of humidity. From humidity signal The derivative in the time domain represents the rate of change of population density. From personnel density signal The derivative representation in the time domain.

[0207] These three features are combined to form the feature set to be tested. This provides foundational data for subsequent dynamic modeling.

[0208] The specific implementation of step S50 is to establish a mathematical model describing the dynamic process of the indoor environment.

[0209] The model consists of the following 5 parts:

[0210] 1. Heat balance equation:

[0211]

[0212] Where ρ is the air density, c p Q is the specific heat capacity of air, k is the thermal conductivity of air, and Q is the specific heat capacity of air. h The heat provided by the air conditioning system, Q s For solar radiation thermal gain, Q e For the thermal gain of the device, Q v For ventilation and heat exchange, Q p To dissipate heat from the human body, ε T This represents the temperature error term. The equation describes the dynamic process of indoor temperature changes, taking into account various factors of heat transfer and generation.

[0213] 2. Humidity balance equation:

[0214]

[0215] Where ω is the air humidity, D is the water vapor diffusion coefficient, and W h For the dehumidification / humidification capacity of the air conditioning system, W v For the amount of moisture exchanged due to ventilation, W p W is the amount of moisture excreted by the human body. m For other water sources, V is the room volume, ε ω This represents the humidity error term. The equation describes the dynamic process of indoor humidity changes, taking into account various moisture sources and removal processes.

[0216] 3. Airflow equations:

[0217]

[0218]

[0219] Where u is the air velocity vector, p is the pressure, v is the air kinematic viscosity, g is the gravitational acceleration vector, β is the air thermal expansion coefficient, T0 is the reference temperature, F is other volume forces, and ε u This represents the velocity error term. This equation describes the indoor air motion state and is derived from the simplified Navier-Stokes equations.

[0220] 4. Empirical equation for human thermal comfort:

[0221] PMV = (0.303e -0.036M +0.028)[(MW)-3.05×10 -3 (5733-6.99(MW)-p a )-0.42((MW)-58.15)-1.7×10 -5 M(5867-p a -0.0014M(34-T) a -3.96×10 -8 f cl ((T cl +273) 4 -(T r +273) 4 )-f cl h c (T cl -T a )]+ε PMV ;

[0222] Where PMV is the predicted average number of votes, M is the metabolic rate, W is the effective mechanical power, and p a T is the partial pressure of water vapor. a For air temperature, T r f is the average radiation temperature. cl For clothing area factor, Tcl h represents the surface temperature of the garment. c ε is the convective heat transfer coefficient. PMV This represents the PMV error term. The equation uses the PMV index to represent human thermal comfort, taking into account various factors.

[0223] 5. Air conditioning system response equation:

[0224]

[0225] Where Y is the system output, U is the control input, K is the system gain, τ is the system time constant, and ε is the system output. Y This represents the system response error term. The equation describes the relationship between the system output and the control input, employing a first-order time lag model.

[0226] The five equations above constitute a mathematical model describing the dynamic processes of the indoor environment. These equations can describe the dynamic changes of key environmental parameters such as indoor temperature, humidity, airflow velocity, and human thermal comfort, covering aspects such as heat transfer, humidity changes, airflow, and human thermal comfort. This mathematical model provides the foundation for subsequent numerical solutions and parameter optimization.

[0227] The specific implementation of step S60 is to divide the indoor space into several grid units, perform differential discretization on the heat balance equation, humidity balance equation and air flow equation, and at the same time transform the human thermal comfort empirical equation and the air conditioning system response equation into discrete form.

[0228] First, the continuous three-dimensional space is divided into a three-dimensional grid, with each grid point representing a discrete volume element. The partial derivative terms in the heat balance and humidity balance equations are approximated using finite difference finite difference approximations, transforming the partial differential equations into linear algebraic equations. For example, the diffusion term in the heat balance equation... Discretization can be performed using the central difference scheme:

[0229]

[0230] Where i, j, and k are grid coordinates. This yields the discretized heat balance equation.

[0231] For the convection term in the airflow equation and diffusion terms A similar differential discretization process is also used.

[0232] Secondly, continuous variables in the empirical equations for human thermal comfort and the response equations for air conditioning systems, such as temperature T, humidity ω, and airflow velocity u, are represented by discrete values ​​at grid points. Furthermore, the response equations for air conditioning systems can also be transformed into discrete forms, thus discretizing the continuous-time system.

[0233] In this way, the original system of partial differential equations is transformed into a discretized system of equations consisting of multiple algebraic equations and difference equations, which can be solved using numerical methods. This discretization process lays the foundation for subsequent numerical calculations.

[0234] The specific implementation of step S70 is to obtain the initial conditions and boundary conditions of the indoor environmental dynamics equations, and to use the iterative Gauss-Seidel method to numerically solve the heat balance equation, humidity balance equation, and air flow equation.

[0235] First, set the initial conditions according to the actual situation. For example, the initial temperature T(0), humidity ω(0), and airflow velocity u(0) can be obtained through measurement. Boundary conditions may include: the ambient temperature T. o Humidity ω o Ventilation volume Equipment thermal power Qe, etc.

[0236] Then, the Gauss-Seidel iterative method is used to solve the discretized equations. The Gauss-Seidel method is a commonly used iterative method for solving linear equations; it converges to a solution by updating the unknowns at each grid point. During the iterative calculation, the temperature T, humidity ω, and airflow velocity u at each grid point need to be continuously updated. Specifically, for the discretized form of the heat balance equations:

[0237]

[0238] In the (k+1)th step of the iteration, first update the temperature. Then update the humidity. and airflow speed This iterative calculation process continues until the system of equations converges.

[0239] Finally, after obtaining the numerical solution, the PMV value of each grid point, i.e., the thermal comfort index, is calculated according to the empirical equation for human thermal comfort. These PMV values ​​can reflect the degree of influence of the indoor environment on human thermal comfort.

[0240] The specific implementation of step S80 is to define an adjustment objective function and use a genetic algorithm to optimize and solve it.

[0241] The adjustment objective function consists of two parts: an average comfort index and an energy consumption index. The average comfort index is represented by the average absolute value of the PMV values ​​at each grid point, and the energy consumption index is represented by the sum of the actual energy consumption E and the maximum energy consumption E. max The ratio is expressed as [value]. Combining these two indicators forms the adjustment objective function:

[0242]

[0243] Here, w1 and w2 are weighting coefficients, satisfying W1+w2=1.

[0244] The constraints for adjusting the objective function include:

[0245] 1. Temperature constraint: T min ≤T≤T max ;

[0246] 2. Humidity constraint: RH min ≤RH≤RH max ;

[0247] 3. Wind speed constraint: 0 ≤ v ≤ v max ;

[0248] 4. Supply air temperature constraint: T s，min ≤T s ≤T s，max ;

[0249] 5. Air supply volume constraints:

[0250] These constraints ensure that the adjustment results meet the indoor environmental requirements.

[0251] To solve this multi-objective optimization problem, a genetic algorithm is used. The genetic algorithm is a stochastic search algorithm based on the principles of biological evolution, adept at handling complex nonlinear optimization problems. The algorithm first randomly generates an initial population, with each individual representing a set of adjustment parameters. Then, through operations such as selection, crossover, and mutation, the population is continuously updated, gradually optimizing the objective function until convergence. The final optimal individual represents the best adjustment parameters, including the air delivery angle, air velocity, air delivery temperature, and other unit operating parameters of the electric nozzle. These parameters are then output to the air handling unit for adjustment.

[0252] Specifically, the principle of this invention is:

[0253] First, key environmental parameters such as indoor temperature, humidity, and personnel density are collected in real time using temperature and humidity sensors and personnel detection cameras, providing basic data for subsequent data processing and analysis. To improve signal quality, the raw data undergoes preprocessing such as digital filtering and time alignment.

[0254] Secondly, using time-frequency analysis, three time-frequency features—temperature change rate, humidity change rate, and population density change rate—were extracted from the preprocessed signal. These features can reflect the dynamic changes of indoor environmental parameters at different time scales.

[0255] Then, based on five physical sub-models—heat balance, humidity balance, air flow, human thermal comfort, and system response—a mathematical model describing the dynamic processes of the indoor environment was established. These sub-models cover various aspects such as heat transfer, humidity change, air flow, and human thermal sensation, and can accurately describe the dynamic changes in indoor environmental parameters.

[0256] Next, the continuous-time dynamic equations are transformed into discrete form, and numerical methods are used to solve the system of equations. This discretization process transforms the original partial differential equations into computable algebraic equations and difference equations, laying the foundation for subsequent parameter optimization.

[0257] Finally, a multi-objective optimization function, including average comfort and energy consumption indices, is defined and optimized using a genetic algorithm. The optimization constraints cover the allowable ranges of various environmental parameters such as temperature, humidity, and wind speed. Through this model-predictive intelligent optimization control, multi-objective optimization of indoor temperature and humidity, human thermal comfort, and energy consumption can be achieved, outputting optimal air conditioning control parameters.

[0258] In summary, the technical solution of this invention fully integrates sensing technology, dynamic modeling, and intelligent optimization algorithms to construct a precise control framework for distributed air conditioning systems. This method can perceive indoor environmental conditions in real time, establish accurate mathematical models, and optimize the best control strategy based on the models, thereby improving the accuracy of temperature and humidity regulation, human thermal comfort, and energy efficiency.

[0259] The following is a specific application scenario of the present invention, Example 2: A high-rise office building adopts the distributed intelligent air conditioning terminal system proposed in this invention. The system is equipped with independent air conditioning terminal devices on each floor and in each area, including air handling units, intelligent control cabinets, information screens, etc.

[0260] like Figure 1-4 As shown, the main components and functions of the system are as follows:

[0261] 1. The air handling unit includes components such as return air grille (5), filter (16), surface cooler (17), return air silencer section (12), fan section (11), supply air silencer section (9) and static pressure box (8); it is responsible for filtering, cooling / heating the return air entering the system, and sending it to the static pressure box through the supply fan; the outlet of the static pressure box is connected to an electric nozzle (1) to deliver the regulated air to the room;

[0262] 2. Intelligent control cabinet (13): Built-in control chip, used to adjust the operating parameters of the air handling unit according to environmental parameters; connected to temperature and humidity sensor (15), personnel detection camera (2) and other equipment to obtain indoor temperature, humidity and personnel density data in real time;

[0263] 3. Information screen (10): Electrically connected to the intelligent control cabinet, displaying indoor temperature and humidity, personnel density, air supply parameters and energy consumption data;

[0264] 4. Linear sound source (3): used for active noise reduction of air conditioner;

[0265] 5. Camera (2): A personnel detection camera used to capture dense crowds in the surrounding area.

[0266] 6. Electric nozzle (1): Connected to the outlet of the static pressure box, it can adjust the air delivery angle and wind speed;

[0267] 7. Other components

[0268] The fire box (4) and fire monitor (20) are used for fire fighting;

[0269] The skirting board (6) and the floor inlet and outlet water pipes (7) are used to connect the hot and cold water sources;

[0270] The access door (14) facilitates maintenance and repair;

[0271] The unit foundation (18) provides stable support and vibration damping;

[0272] Sound-absorbing / sound-insulating housing (19) is used for noise reduction;

[0273] Table 1. Main parameters of the distributed intelligent air conditioning terminal system

[0274] parameter numerical values Return air grille size 1.2m × 0.6m Filter efficiency 95％ Surface cooler rated cooling capacity 40kW Rated air volume of blower <![CDATA[8000m 3 / h]]> Static pressure box outlet size 0.8m × 0.6m Electric nozzle outlet air speed adjustment range 4-12m / s Electric nozzle outlet temperature adjustment range 16-35℃ Electric nozzle air delivery angle adjustment range ±15° Personnel detection camera resolution 1080p Information screen size 42 inches Unit base dimensions 1.5m × 1m × 0.3m Sound-absorbing / sound-insulating shell noise reduction effect 15-20 dB(A)

[0275] The features of this distributed intelligent air conditioning terminal system are as follows:

[0276] 1. It adopts a distributed design, with each area equipped with independent air conditioning terminal equipment, which can be finely controlled according to the actual needs of different areas.

[0277] 2. It integrates multiple sensing technologies to monitor key environmental parameters such as indoor temperature, humidity, and personnel density in real time, providing basic data for intelligent control.

[0278] 3. The electric nozzle can adjust the air delivery angle and wind speed according to real-time environmental changes, improving the accuracy of temperature and humidity regulation.

[0279] 4. Linear sound sources and sound-absorbing / sound-insulating shells can effectively reduce air conditioning noise and improve the indoor acoustic environment.

[0280] 5. The information screen displays the system's operating status in real time, making it convenient for managers to monitor and adjust the system.

[0281] 6. The use of unit foundations provides stable support and vibration reduction, improving equipment reliability.

[0282] In summary, this distributed intelligent air conditioning terminal system integrates advanced sensing technology, intelligent control algorithms, and noise control measures, enabling precise regulation and optimization of the indoor environment.

[0283] Example 2 – Air Handling Unit Regulation Module

[0284] The following example, using a floor of an office building, illustrates the specific workflow of the air handling unit's regulating module.

[0285] The floor area is approximately 1500 square meters. 2 It is equipped with 6 distributed air handling units, each serving a 250m² area. 2 The system is divided into left and right zones. Each zone is equipped with temperature and humidity sensors and personnel detection cameras, which are connected to the corresponding intelligent control cabinet. The intelligent control cabinet has a built-in air handling unit adjustment module, used to adjust the unit in real time according to environmental parameters.

[0286] 1. Data Collection

[0287] The return air temperature and humidity sensor (15) measures the temperature T and humidity ω of the area in real time, with a sampling frequency of 1Hz.

[0288] The personnel detection camera (2) captures the dense distribution of people and gives the personnel density n, with a sampling frequency of 0.5Hz;

[0289] The collected temperature, humidity, and personnel density data form a temperature-humidity-personnel density signal group x(t) = [T(t), ω(t), n(t)];

[0290] 2. Data Preprocessing

[0291] A Butterworth filter (cutoff frequency 0.1Hz, order 4) is used to filter the original signal to remove high-frequency noise;

[0292] The three signals were time-aligned using a linear interpolation method to unify them to a sampling frequency of 0.5 Hz;

[0293] Obtain the preprocessed temperature, humidity and personnel density signal group

[0294] 3. Feature Extraction

[0295] right Perform a short-time Fourier transform (STFT) to obtain the time-frequency domain representation X. i (t, f), i = 1, 2, 3;

[0296] Calculate the information entropy H(t, f) of the STFT results and determine the optimal time window length W. * =60s;

[0297] Using W * Extracting the rate of temperature change Humidity change rate and population density change rate Form the feature set y(t) to be tested;

[0298] 4. Dynamic Modeling

[0299] Establish a mathematical model to describe the heat balance, humidity balance, air flow, human thermal comfort, and air conditioning system response in this area;

[0300] The heat balance equation, humidity balance equation, and air flow equation are discretized by finite difference and transformed into a system of algebraic equations.

[0301] The empirical equations for human thermal comfort and the response equations for air conditioning systems are directly presented in discrete form.

[0302] The initial and boundary conditions are obtained, and the discrete equations are solved using the Gauss-Seidel iterative method.

[0303] Calculate the temperature T, humidity ω, airflow velocity u, and PMV value for each grid point;

[0304] 5. Parameter optimization:

[0305] Define the adjustment objective function Where w1 = 0.6, w2 = 0.4;

[0306] The constraints include: temperature range 20-26℃, relative humidity range 40-60%, wind speed ≤0.5m / s, supply air temperature 18-26℃, and supply air volume 3000-8000m³ / h. 3 / h;

[0307] A genetic algorithm is used to optimize the objective function to obtain the optimal adjustment parameters such as the electric nozzle air delivery angle, wind speed, and air delivery temperature.

[0308] The optimization results are output to the air handling unit for real-time adjustment;

[0309] like Figure 6 As shown, under optimized control, the temperature in this area is maintained between 22-24℃, the humidity is stable at around 50%, and the PMV value under typical seasonal clothing and activity levels is basically between -0.5 and +0.5, fully meeting the requirements for human thermal comfort. At the same time, the energy consumption of the system is also controlled within a reasonable range, with the total daily power consumption of the six units under typical operating hours being approximately 300 kWh.

[0310] In summary, this air handling unit's control module fully utilizes sensor data, dynamic modeling, and intelligent optimization technology to achieve multi-objective optimized control of temperature and humidity, human thermal comfort, and energy consumption. Compared to traditional simple temperature and humidity control methods, this solution can significantly improve indoor environmental comfort and system energy efficiency.

[0311] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed intelligent air conditioning terminal system, characterized in that, include: Air handling units, intelligent control cabinets, information screens, line sound sources, cameras, and electric nozzles; The air handling unit includes a return air grille, an air filter, a surface cooler, a return air silencer, a supply air fan, a supply air silencer, and a supply air static box connected in sequence; the electric nozzle is connected to the outlet of the static pressure box. The camera is a people detection camera, used to capture dense crowds in the surrounding area, and is electrically connected to the intelligent control cabinet; The intelligent control cabinet is equipped with a control chip, which contains an air handling unit adjustment module. This module is used to adjust the parameters of the air handling unit based on data collected by the return air temperature and humidity sensor and the personnel detection camera. These parameters include the air delivery angle, wind speed, air delivery temperature, and unit operating parameters of the electric nozzles. The information screen is electrically connected to the intelligent control cabinet and is used to display the operating status of indoor temperature, humidity, personnel density, air supply parameters and energy consumption data in real time. The air handling unit regulation module is used to perform the following steps: S10. Collect temperature and humidity data from the return air temperature and humidity sensor and personnel density data from the personnel detection camera to form a temperature and humidity-personnel density signal group; S20. Preprocess the collected temperature, humidity and personnel density signal group, including noise reduction, filtering and signal alignment, to improve data quality; S30. Based on each signal in the aligned temperature, humidity and personnel density signal group, use time-frequency analysis to determine the optimal time window length in order to capture the characteristics of indoor environmental changes. S40. Using the determined optimal time window length, extract the time-frequency features of each signal, including the rate of change of temperature, the rate of change of humidity, and the rate of change of personnel density, and record the time-frequency features of all signals as the feature group to be measured. S50. Establish a set of indoor environmental dynamic equations, including heat balance equation, humidity balance equation, air flow equation, human thermal comfort empirical equation, and air conditioning system response equation. S60. Divide the indoor space into several grid units, perform differential discretization on the heat balance equation, humidity balance equation, and air flow equation, and transform the human thermal comfort empirical equation and the air conditioning system response equation into discrete form. S70. Obtain the initial and boundary conditions of the indoor environmental dynamics equations. Use the Gauss-Seidel iterative method to solve the discretized heat balance equation, humidity balance equation and air flow equation. Iterate until convergence to obtain a numerical solution, including the temperature, humidity and airflow velocity fields of each grid point in the room. Calculate the comfort index of each grid point based on the human thermal comfort empirical equation. S80. Define an adjustment objective function, including an average comfort index and an energy consumption index. The constraints of the adjustment objective function include at least the physical limitations of the air conditioning system and the allowable range of indoor temperature and humidity. Using the numerical solution as the initial population and the human thermal comfort empirical equation as the fitness function, a genetic algorithm is used to optimize the optimal adjustment parameters, which are then output to the air handling unit for adjustment. The empirical equation for human thermal comfort uses the PMV index to represent human thermal comfort, as detailed below: ; In the formula, To predict the average number of votes; Metabolic rate; For effective mechanical power; It is the partial pressure of water vapor; Air temperature; The mean radiation temperature; For clothing area factor; The surface temperature of the garment; The convective heat transfer coefficient; This is the PMV error term; and The calculation formula is obtained through iterative calculation: ; ; in, For the thermal resistance of clothing; The calculation formula is: ,in Relative air velocity; The adjustment objective function takes into account both the average comfort index and the energy consumption index, and is expressed as follows: ; In the formula, To adjust the objective function value; and These are the weighting coefficients, and ; Determine the number of room grids; For the first PMV value of each grid point; The system energy consumption is in kWh. The maximum energy consumption of the system is in kWh. The error term of the objective function; The constraints are as follows: Temperature constraints: ; Humidity constraints: ; Wind speed constraints: ; Supply air temperature constraints: ; Air volume constraints: ; In the formula, and These are the minimum and maximum permissible indoor temperatures, in K; and These are the minimum and maximum permissible relative humidity, respectively. The maximum permissible indoor wind speed; and These are the minimum and maximum permissible supply air temperatures, respectively. and These are the minimum and maximum permissible air supply volumes, respectively.

2. The distributed intelligent air conditioning terminal system according to claim 1, characterized in that, It also includes a fire box, a fire monitor, and an alarm. When a high-temperature fire is detected, the alarm will issue an alarm message. The fire monitor is electrically connected to the intelligent control cabinet and is used to receive and execute instructions issued by the intelligent control cabinet.

3. A distributed intelligent air conditioning terminal system according to claim 1, characterized in that, The airflow direction of the air handling unit is as follows: air enters the system from the front and rear bottom through the return air grille, passes through the air filter and surface cooler, enters the air handling unit, then passes through the return air silencer, the supply air fan and the supply air silencer, and finally enters the static pressure box and is delivered to the indoor space through the electric nozzle; the return air grille is located at the bottom of the air handling unit, and the electric nozzle is located at the top of the air handling unit.

4. A distributed intelligent air conditioning terminal system according to claim 1, characterized in that, It also includes a sound-absorbing / sound-insulating housing that covers the outside of the air handling unit; the sound-absorbing / sound-insulating housing is made of sound-insulating material and is used to reduce the noise generated by the air handling unit during operation.

5. A distributed intelligent air conditioning terminal system according to claim 1, characterized in that, It also includes skirting boards and floor water inlet / outlet pipes, with the floor water inlet / outlet pipes installed inside the skirting boards; the floor water inlet / outlet pipes are connected to the surface cooler to provide hot and cold water to the surface cooler.

6. A distributed intelligent air conditioning terminal system according to claim 1, characterized in that, It also includes an access door, which is installed on the air handling unit for maintenance and repair; the access door is connected to the air handling unit via hinges.

7. A distributed intelligent air conditioning terminal system according to claim 1, characterized in that, It also includes a unit foundation, on which the air handling unit is mounted; the unit foundation provides stable support for the air handling unit and has vibration damping function.

8. A distributed intelligent air conditioning terminal system according to claim 7, characterized in that, The criteria for judging convergence of iterative calculations are that the number of iterations reaches 2000 or the similarity between two consecutive iterations is greater than 99.75%.

9. A distributed intelligent air conditioning terminal system according to claim 8, characterized in that, In the step of dividing the indoor space into several grid units, the size of each grid unit is 0.2 meters to 1 meter.

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