Energy-saving ventilation method of commercial residential fresh air and indoor air flow synergistic optimization

Abstract

The application discloses a kind of commercial residential fresh air and indoor airflow synergic optimization energy-saving ventilation method, belong to building ventilation energy-saving control technical field.The application includes simultaneously collecting commercial and residential area multiple environmental data, calculates partition basis and compensates fresh air volume and is superimposed to obtain target fresh air volume, controls fresh air host and air valve opening, maintains constant static pressure and 1-5 Pa positive pressure difference of residential area relative to commercial area, periodically adjusts fresh air volume to maximize integrated energy efficiency ratio, triggers emergency to start full load operation until pollutants meet standards.The method can solve the problem of ventilation demand contradiction, high energy consumption and cross influence of pollutants between regions in commercial and residential mixed buildings, realize ventilation, energy saving and airflow synergic optimization, improve indoor air quality and system operation efficiency.

Description

Technical Field

[0001] This invention relates to the field of building ventilation energy-saving control technology. More specifically, this invention relates to an energy-saving ventilation method for the coordinated optimization of fresh air intake and indoor airflow in commercial and residential buildings. Background Technology

[0002] In commercial and residential complexes, commercial and residential areas typically share a central fresh air system. The usage scenarios for the two types of areas differ significantly: commercial areas have frequent pedestrian traffic and large density fluctuations, and businesses such as catering and retail are prone to generating pollutants such as fumes and odors, resulting in drastic changes in pollution load and high requirements for the dynamic adaptability of fresh air volume; residential areas have relatively stable pedestrian activity and place greater emphasis on living comfort, air quality stability, and quietness, thus having more stringent requirements for the uniformity of fresh air supply and reasonable energy consumption.

[0003] In existing technologies, central fresh air systems generally adopt constant or variable air volume control methods based on a single parameter (such as carbon dioxide concentration) to provide fresh air with a uniform or simple proportional distribution for buildings. In addition, indoor airflow mostly relies on fixed air outlet layout and independently operating circulating fans, lacking coordinated control with the fresh air system, and cannot carry out fine control for the differentiated needs of the two types of areas.

[0004] This traditional control model has many drawbacks: First, a uniform or simple proportional distribution of fresh air makes it difficult to balance the high ventilation demands of commercial areas with the comfort needs of residential areas. This can easily lead to odors, residual heat, and high concentrations of pollutants from commercial areas entering residential areas through airflow or building gaps, damaging residents' health and comfort. Second, the independent operation of the fresh air system and indoor airflow organization can easily cause uneven distribution of fresh air, creating dead zones or short circuits, reducing the efficiency of fresh air utilization. To achieve the expected air quality, the fresh air volume needs to be increased, resulting in energy waste. Third, the lack of precise control methods and linkage mechanisms for the static pressure difference between residential and commercial areas makes it difficult to maintain a reasonable pressure difference, further exacerbating airflow crosstalk and imbalance in fresh air distribution. Fourth, the failure to achieve coordinated optimization of fresh air volume and average system ventilation efficiency ignores the comprehensive consideration of overall energy consumption and air quality, easily leading to a dilemma of excessive energy consumption but poor ventilation or wasting energy in pursuit of ventilation standards. Fifth, emergency pollution control often adopts a single start-stop mode, with unclear parameters and cycles for periodic adjustments, making it impossible to adapt to load fluctuations, resulting in insufficient system stability and adaptability.

[0005] It can be seen that this traditional control model does not carry out refined control on the differences between commercial and residential zones in commercial and residential complexes, and lacks effective means to integrate and optimize fresh air volume distribution, airflow organization and system energy efficiency. Summary of the Invention

[0006] This invention provides an energy-saving ventilation method that optimizes the synergistic effect of fresh air intake and indoor airflow in commercial and residential buildings. It can solve the problems of conflicting ventilation needs, high energy consumption, and cross-influence of pollutants between areas in mixed commercial and residential buildings, and achieve synergistic optimization of ventilation, energy saving and airflow, thereby improving indoor air quality and system operating efficiency.

[0007] To achieve these objectives and other advantages according to the present invention, an energy-saving ventilation method for synergistically optimizing fresh air intake and indoor airflow in commercial and residential buildings is provided, comprising the following steps: Environmental monitoring devices installed in commercial and residential areas are used to simultaneously collect data on carbon dioxide concentration, PM2.5 concentration, temperature, humidity, and total volatile organic compound concentration in each area. The central controller calculates the basic fresh air volume for each zone based on the carbon dioxide concentration data and the preset hygiene requirement thresholds. The real-time number of people required for calculating the basic fresh air volume for each zone is obtained by counting in real time by the passenger flow statistics device or wireless signal access point set up in each zone. At the same time, the compensation coefficient is calculated by weighted summation based on the deviation of PM2.5 concentration, total volatile organic compound concentration, temperature and humidity data of each zone from their corresponding thresholds. The compensation coefficient is multiplied by the basic fresh air volume for each zone to obtain the compensated fresh air volume for each zone. The basic fresh air volume for each zone and the compensated fresh air volume for each zone are superimposed to obtain the target fresh air volume for the commercial area and the target fresh air volume for the residential area. The central controller controls the operation of the fresh air unit, ensuring that its total air supply volume equals the sum of the target fresh air volume for the commercial area and the target fresh air volume for the residential area. It also adjusts the opening of the air supply branch valves for the commercial area and the residential area according to the ratio, and dynamically adjusts the fan speed of the fresh air unit based on feedback from the static pressure sensor on the main air supply duct to maintain a constant static pressure in the main duct. The central controller adjusts the air valves of the air supply branches in the residential area to control the static pressure value in the residential area to be 1-5 Pa higher than that in the commercial area. The central controller uses periodic intervals and the target fresh air volume of commercial areas and residential areas as related optimization variables. Under the premise of ensuring that the pollutant concentration in each area does not exceed the standard, it coordinates and adjusts the distribution ratio of the two to maximize the comprehensive energy efficiency ratio. The comprehensive energy efficiency ratio is defined as the average ventilation efficiency of the system divided by the total input power of the fresh air unit and all circulating fans. The larger the value, the better the ventilation effect per unit of energy consumption. The average ventilation efficiency of the system is the weighted average of the carbon dioxide concentration reduction rate in each area. When the PM2.5 concentration or total volatile organic compound concentration monitored in any area increases by more than 50% of its preset threshold within a predetermined time, the fresh air unit is controlled to operate at its maximum design air volume, and all circulating fans are controlled to operate at their highest speed until the pollutant concentration falls back below the corresponding threshold.

[0008] Preferably, the method further includes the following steps: the central controller calculates the uniformity of airflow velocity distribution based on data monitored by multiple airflow velocity sensors in each area, and adjusts the angle of the adjustable air outlet at the air supply end in that area and the rotation speed of the circulating fan in linkage to make the uniformity of airflow velocity distribution reach a preset standard.

[0009] Preferably, the overall energy efficiency ratio is maximized by using a model predictive control algorithm. The model predictive control algorithm has a built-in system dynamic model identified based on the system's historical operating data. The system dynamic model includes a fresh air unit energy consumption model, a circulating fan energy consumption model, a regional pollutant diffusion model, and an airflow organization prediction model. In each control cycle, the model predictive control algorithm takes the current monitoring data as the initial state, the next four control cycles as the prediction time domain, and the overall energy efficiency ratio as the optimization objective. Under the constraints of ensuring that the predicted pollutant concentration in each area does not exceed the threshold and the physical operating limits of each device, it performs rolling optimization and outputs the target fresh air volume setpoint for the commercial and residential areas in the current cycle. The central controller updates the control parameters based on the setpoint.

[0010] Preferably, the improved particle swarm optimization algorithm is used to maximize the overall energy efficiency ratio. The improved particle swarm optimization algorithm uses the target fresh air volume of the commercial area and the target fresh air volume of the residential area as optimization variables, takes the maximization of the overall energy efficiency ratio as the fitness function, and takes the pollutant concentration of each area not exceeding its threshold and the static pressure value in the residential area being 1-5 Pa higher than that in the commercial area as constraints. It adopts a linear decreasing inertial weight strategy and gradually transitions from global search to local search during the iteration process. When the fitness function converges or the number of iterations reaches 50-100, the optimal combination of fresh air volume control parameters is output and the process is updated.

[0011] Preferably, the positive pressure difference between the residential area and the commercial area is maintained by linking with the exhaust system of the commercial area. An independent variable frequency drive exhaust fan is installed in the commercial area. When the central controller detects that the static pressure difference between the two areas is less than 1 Pa, it adjusts the air valve of the air supply branch in the residential area and increases the speed of the variable frequency drive exhaust fan in the commercial area. When the static pressure difference is detected to be greater than 5 Pa, the speed of the variable frequency drive exhaust fan in the commercial area is reduced. The speed adjustment range of the variable frequency drive exhaust fan is 30-100% of its rated speed.

[0012] Preferably, the uniformity of airflow velocity distribution is quantified by the coefficient of variation of all airflow velocity sensor readings in the area. The preset standard is that the coefficient of variation is less than 0.35. The central controller performs closed-loop control by adjusting the adjustable air outlet angle and the circulating fan speed to minimize the coefficient of variation as a sub-objective.

[0013] Preferably, the uniformity of airflow velocity distribution is controlled by zonal fine-tuning: The commercial and residential areas are divided into multiple functional sub-areas, and each sub-area is equipped with an independent airflow velocity sensor group. The central controller sets differentiated thresholds for the uniformity of airflow velocity distribution for different functional sub-areas. The threshold for the coefficient of variation of the commercial sub-area is set to be less than 0.30, and the threshold for the coefficient of variation of the residential sub-area is set to be less than 0.25. Based on sensor data from each sub-region, the central controller uses a fuzzy control algorithm to independently adjust the angle of the adjustable air outlet at the air supply terminal and the operating status of the circulating fan in that sub-region.

[0014] Preferably, when the PM2.5 concentration or total volatile organic compound concentration increases by 50-80%, a Level 1 emergency response is initiated, increasing the total air volume of the fresh air unit to 70-80% of its design maximum and increasing the speed of all circulating fans to 70-80% of their rated speed. When the PM2.5 concentration or total volatile organic compound concentration increases by more than 80%, activate the Level II emergency response and set both the fresh air unit and the circulating fan to full load operation. In emergency mode, the central controller simultaneously activates the high-efficiency air filtration device located at the air supply terminal; When the pollutant concentration falls below the threshold, the central controller begins to gradually reduce the air volume of the fresh air unit and the speed of the circulating fan according to the preset gradient, and simultaneously starts the dynamic adjustment and optimization process until the system returns to the optimized operating state with the comprehensive energy efficiency ratio as the core.

[0015] Preferably, the control process for maintaining constant static pressure in the main pipe employs a parameter-adaptive PID algorithm. The central controller dynamically adjusts the proportional and integral coefficients of the PID controller based on the deviation between the static pressure setpoint and the feedback value from the static pressure sensor. When the absolute value of the static pressure deviation is greater than 1 Pa, increase the proportional coefficient and decrease the integral coefficient; When the absolute value of the deviation is less than or equal to 1 Pa, decrease the proportional coefficient and increase the integral coefficient; Meanwhile, the central controller combines the real-time operating power data of the fresh air unit's fan to perform online self-calibration of the PID controller parameters.

[0016] Preferably, the calculation of the zoned compensation fresh air volume is achieved by the central controller through the construction of a BP neural network. Based on the PM2.5 concentration, total volatile organic compound concentration, temperature and humidity data of each area in the past 24 hours, as well as the passenger flow data of commercial areas and the occupancy rate data of residential areas, the change trend of pollution load and heat and humidity load of each area in the next hour is predicted. The predicted trend is output in the form of a quantitative index. The central controller dynamically adjusts the weight coefficient used when calculating the zoned compensation fresh air volume according to the index. The adjustment rule is: adjusted weight = baseline weight × (1 + α × prediction index), where α is a preset adjustment amplitude coefficient, so that the compensation air volume matches the predicted load.

[0017] The present invention has at least the following beneficial effects: First, this invention calculates the basic fresh air volume for each zone using a steady-state dilution equation, calculates the compensated fresh air volume for each zone using weighted or logical judgment, and then superimposes the target fresh air volume to distribute the fresh air by adjusting the fan speed and the valve opening. It maintains a positive pressure difference of 1-5 Pa between residential and commercial areas to prevent airflow crosstalk, and achieves precise control of fresh air in residential and commercial zones. It balances ventilation effect and energy saving, and effectively solves the technical problems of contradictory ventilation demand, high energy consumption and cross-contamination of airflow between areas in the existing technology.

[0018] Secondly, this invention, by placing airflow velocity sensors at key locations in commercial and residential areas, collects wind speed data in real time and calculates the uniformity of airflow velocity distribution. It then links the adjustable air outlet angle with the circulating fan speed to form a closed-loop control, ensuring a balanced distribution of indoor airflow and avoiding localized airflow stagnation or a drafty feeling. This significantly improves the comfort of residential and commercial activities, enabling the system to not only control how much air is delivered but also actively optimize how the air is distributed, thereby improving the efficiency of fresh air utilization and the comfort experience of people at a micro level.

[0019] Third, this invention employs a model predictive control algorithm or an improved particle swarm optimization algorithm, combined with a dynamic model of the system to deduce operating conditions. Under the constraints of pollutant compliance and equipment safety, it optimizes the allocation of target fresh air volume in commercial and residential areas, maximizes the comprehensive energy efficiency ratio (efficiency / power), and adapts to load fluctuations. Compared with conventional control, it can predict changes in operating conditions in advance, thereby improving the stability of system operation and energy-saving effect.

[0020] Fourth, this invention uses bidirectional linkage between residential air supply valves and commercial variable frequency exhaust fans, combined with feedback from regional static pressure sensors, to dynamically fine-tune the valve opening and exhaust speed, counteracting interference from inside and outside the building, accurately and stably maintaining a positive pressure difference of 1-5 Pa, solving the problem of pressure difference fluctuations caused by single valve adjustment, expanding pressure difference control from single air supply adjustment to coordinated control, increasing control freedom, significantly improving anti-interference ability, and effectively preventing pollutants from commercial areas from spreading to residential areas.

[0021] Fifth, this invention adopts a parameter adaptive PID algorithm, which dynamically adjusts the PID parameters according to the static pressure deviation. Combined with the fan power-air volume-static pressure model, it realizes online self-correction of parameters, adapts to changes in characteristics such as fan wear and filter blockage, improves the accuracy and response speed of the main pipe static pressure control, avoids control lag or overshoot, ensures stable air volume distribution, and maintains long-term stable control quality.

[0022] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0023] The present invention will be further described in detail below with reference to examples, so that those skilled in the art can implement it based on the description.

[0024] It should be understood that terms such as “having,” “including,” and “comprising” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0025] It should be noted that, unless otherwise specified, the experimental methods described in the following embodiments are all conventional methods. Those skilled in the art can understand the specific meaning of the terms in this invention based on the specific circumstances, and therefore should not be construed as limiting the invention.

[0026] This invention provides an energy-saving ventilation method for synergistically optimizing fresh air intake and indoor airflow in commercial and residential buildings, comprising the following steps: Environmental monitoring devices are installed in commercial areas (such as shops and lobbies) and residential areas (living rooms and bedrooms of each suite). These devices are equipped with built-in carbon dioxide sensors, fine particulate matter (PM2.5) sensors, temperature sensors, humidity sensors, and total volatile organic compound (TVOC) sensors. Through these devices, data on carbon dioxide concentration, PM2.5 concentration, temperature, humidity, and TVOC concentration are collected simultaneously in each area. The collection cycle is preset, and all monitoring data is transmitted to the central controller in real time via wired or wireless network. The central controller stores preset thresholds for various environmental parameters in each area. Based on the carbon dioxide concentration data of each area and its preset hygiene requirement thresholds, the central controller calculates the basic fresh air volume of each zone to meet basic hygiene requirements, taking into account the fluctuation characteristics of personnel density in commercial and residential areas and incorporating the relationship between area volume and personnel density. The real-time number of people required for calculating the basic fresh air volume of each zone is obtained by real-time counting from the passenger flow statistics device or wireless signal access point set up in each area. Specifically, the steady-state dilution equation is used as the basic calculation model to calculate the basic fresh air volume of each zone; in, L b Indicates the basic fresh air volume of the zone (m³) 3 / h), which is the minimum fresh air volume required to meet carbon dioxide hygiene requirements. G CO2 This represents the amount of carbon dioxide produced per hour by a single adult (m³). 3 / (person·h)), the preset value is 0.02m under normal activity intensity. 3 / (person·h), adjusted to 0.03m when there is intense human activity in commercial areas. 3 / (person·h), N The real-time number of people in a given area is obtained from passenger flow statistics devices or wireless signal access points, and the relevant formula is: N = V × ρ p ,in V Represents the volume of the partition (m 3 ), ρ p This indicates the real-time population density of a given area (people / m²). 3 ), ρ CO2,out Indicates the preset health requirement threshold for carbon dioxide within the zone (m 3 / m 3 (ppm), the default setting for commercial areas is 1000ppm (0.001m). 3 / m 3 The preset concentration for residential areas is 800 ppm (0.0008 m). 3 / m 3 ), ρ CO2,in Indicates the carbon dioxide concentration in outdoor fresh air (m³) 3 / m 3 The default value is 350ppm (0.00035m). 3 / m 3 The data is collected in real time and dynamically corrected by environmental monitoring devices.

[0027] Meanwhile, based on the deviations of PM2.5 concentration, total volatile organic compound concentration, temperature and humidity data of each region from their corresponding thresholds, a compensation coefficient is calculated by weighted summation. This compensation coefficient is then multiplied by the basic fresh air volume of the zone to obtain the compensated fresh air volume of the zone. The basic fresh air volume of the zone and the compensated fresh air volume of the zone are then superimposed to obtain the target fresh air volume of the commercial area and the target fresh air volume of the residential area.

[0028] Specifically, the compensation coefficient is calculated using a weighted calculation method. C For example, in, k i Indicates the first i The weighting coefficients for the parameters are preset according to the priority of parameter importance: PM2.5, TVOC (pollutants) > temperature, humidity (comfort index), with specific values ​​as follows: PM2.5 ( k 1 = 0.4), TVOC ( k 2 = 0.3), temperature ( k 3=0.15), humidity ( k 4 = 0.15), Δ ρ i Indicates the first i The deviation value of this parameter is obtained by normalizing the difference between the measured value and the preset threshold. A negative deviation is taken as 0 (no compensation required). The preset threshold range can be: PM2.5 ≤ 35 μg / m³ 3 TVOC ≤ 0.6 mg / m³ 3 Temperature 22-26℃, humidity 40%-60%; Specifically, taking the weighted calculation method for calculating the compensation coefficient C as an example, the measured deviation values ​​of each parameter are first normalized to be dimensionless. The normalization method is as follows: divide the measured deviation value of each parameter by its corresponding reference deviation range (for example, the reference deviation range for PM2.5 can be taken as 0-100 μg / m³). 3 TVOC is taken as 0-1.0 mg / m³ 3 (Temperature range: 0-5℃, Humidity range: 0-20%), and the normalized deviation value Δ of all parameters. ρ i All are within the comparable dimension range of 0-1, ensuring that the weighted summation has physical meaning.

[0029] Taking the calculation of zoned compensation fresh air volume using logical judgment as an example, a priority-based judgment is adopted, prioritizing the needs of pollutant control, and then supplementing the needs of temperature and humidity comfort. The preset judgment rules and compensation gradients are as follows: Level 1 Judgment (Pollutants Prioritized): If PM2.5 or TVOC exceeds the standard, a compensation coefficient is set according to the extent of the exceedance. When the exceedance is 10%-50%, the compensation coefficient = 0.3; when the exceedance is more than 50%, the compensation coefficient = 0.6. Secondary judgment (temperature and humidity supplement): If the temperature / humidity exceeds the comfort range and the pollutants do not exceed the standard, the compensation coefficient = 0.15; if the pollutants exceed the standard, the superimposed compensation coefficient = 0.1. Level 3 judgment: When no parameters exceed the limit, the compensation coefficient = 0.05 (redundant compensation to ensure comfort). The central controller determines the total target fresh air volume based on the sum of the target fresh air volumes for the two types of areas, sends instructions to the fresh air unit, controls the operation of the fresh air unit, and adjusts the operating frequency or speed of the fresh air unit so that its total air supply volume is equal to the sum of the target fresh air volume for the commercial area and the target fresh air volume for the residential area. The central controller also adjusts the opening of the air supply branch valves for the commercial area and the residential area according to the ratio to achieve the initial allocation of fresh air volume. During the adjustment of the air valve opening, the static pressure sensor installed on the main air supply duct monitors the static pressure value of the main duct in real time and feeds it back to the central controller. The central controller compares the measured static pressure value with the set value and uses a conventional PID algorithm (proportional-integral-derivative control). By comparing the measured static pressure value of the main duct with the set value in real time, the central controller outputs a speed adjustment signal to dynamically adjust the fan speed of the fresh air unit, offsetting the changes in duct resistance, so as to maintain a constant static pressure in the main duct and ensure the stability and accuracy of air volume distribution. in, u ( t This indicates the PID output, which corresponds to the fresh air unit fan speed adjustment signal (0-10V analog quantity, corresponding to fan speed 0-rated speed). K p This represents the proportionality coefficient, with a preset initial value of 5.0. It is used to adjust the response speed; the larger the deviation, the stronger the adjustment. K i This represents the integral coefficient, with a preset initial value of 0.1. It is used to eliminate steady-state deviations and prevent static pressure from deviating from the set value for extended periods. K d This represents the differential coefficient, with a preset initial value of 0.5, used to suppress overshoot and prevent static pressure fluctuations caused by excessively rapid speed adjustments. e ( t )express t Static pressure deviation (Pa) at any given time. e ( t ) = P set - P meas ,in P set The static pressure setting value (Pa) for the main pipe. P meas This is the measured static pressure value (Pa). The integral term representing the deviation value accumulates the deviation over a period of time, eliminating steady-state error. The derivative term represents the deviation value, reflects the rate of change of the deviation, suppresses regulation overshoot, and presets the main pipe static pressure setpoint. P set =50 Pa, rated speed of the fan n max= 1480 r / min, speed and PID output u ( t ) shows linear correlation ; When the measured static pressure is higher than the set value (the deviation is negative), the PID output decreases, the fan speed decreases, and the total air volume is reduced to decrease the static pressure. When the measured value is lower than the set value (the deviation is positive), the output increases, the speed increases, and the air volume is increased to increase the static pressure, thus forming a closed-loop control. To prevent airflow interference, the central controller continuously receives static pressure monitoring data from commercial and residential areas. By adjusting the air supply branch valves in the residential area, it controls the static pressure value in the residential area to be 1-5 Pa higher than that in the commercial area, thus creating a tendency for air to permeate from the residential area to the commercial area and preventing the unorganized infiltration of air containing odors, fumes, etc. from the commercial area into the residential area. The central controller triggers a dynamic adjustment and optimization process at periodic intervals. Under the premise of ensuring that the pollutant concentration in each area does not exceed the standard, it uses the target fresh air volume in the commercial area and the target fresh air volume in the residential area as related optimization variables. By coordinating the adjustment of the allocation ratio between the two, the comprehensive energy efficiency ratio is maximized. The comprehensive energy efficiency ratio is defined as the average ventilation efficiency of the system divided by the total input power of the fresh air unit and all circulating fans. The larger the value, the better the ventilation effect per unit of energy consumption. The average ventilation efficiency of the system is the weighted average of the carbon dioxide concentration reduction rate in each area. In addition, an emergency purification mode is set up. The central controller continuously monitors the PM2.5 and total volatile organic compound (TVOC) concentrations in each area. When the PM2.5 concentration or TVOC concentration in any area increases by more than 50% of its preset threshold within a predetermined time, it is determined to be a sudden pollution event. The system immediately switches to emergency mode, controls the fresh air unit to operate at its maximum design air volume, and controls all circulating fans to operate at their highest speed to quickly dilute and remove pollutants until the pollutant concentration drops below the corresponding threshold. Then, the system automatically resumes the regular periodic optimized control process.

[0030] In the above technical solution, by precisely maintaining a positive pressure difference of 1-5 Pa between the two types of areas, airflow crosstalk is effectively controlled, preventing the spread of pollutants from commercial areas to residential areas. Combined with periodic dynamic optimization, the overall energy efficiency ratio is maximized while ensuring air quality meets standards, taking into account both ventilation effect and energy saving. In the event of sudden pollution, an emergency mode is quickly activated to ensure that pollutants quickly meet standards. The solution is adapted to the dual functional needs of commercial and residential areas, achieving precise control of fresh air volume in commercial and residential areas, improving the operational stability, practicality and engineering adaptability of the fresh air system, and effectively solving the problem of insufficient ventilation, energy saving and airflow coordination in existing technologies.

[0031] In another technical solution, a step of actively optimizing indoor airflow organization is further included: multiple airflow velocity sensors are distributed in typical human activity spaces in commercial and residential areas to ensure coverage of key locations throughout the area. In commercial areas (such as shops), sensors can be placed at entrances, centers, corners, etc., while in residential areas (such as living rooms and bedrooms), sensors can be placed in the main areas where people stay (sofa areas, dining areas, etc.). The airflow velocity sensors continuously collect airflow velocity data at their locations and transmit it to the central controller in real time via wired or wireless networks.

[0032] The central controller calculates the uniformity of airflow velocity distribution by statistically analyzing the dispersion of each reading based on data monitored by multiple airflow velocity sensors in each area. This indicator is used to measure the balance of indoor airflow distribution and reflect the differences in airflow velocity at different locations.

[0033] Several adjustable speed circulating fans are deployed in the area as the actuators for regulating airflow uniformity. At the same time, each area's air supply terminal is equipped with an adjustable air outlet (such as a swirl air outlet or a louvered air outlet), which can adjust the air outlet direction according to control commands (both horizontal and vertical directions can be deflected).

[0034] The central controller compares the real-time calculated airflow velocity distribution uniformity with preset standards. If the standard is not met, a closed-loop control process is immediately initiated: on the one hand, based on the differences in sensor readings, the deflection angle of the adjustable air vents in the corresponding area is adjusted to change the airflow diffusion direction and guide the airflow to diffuse towards the area with lower velocity; on the other hand, the start / stop and speed of the circulating fan in that area are adjusted in conjunction to accelerate indoor airflow circulation and reduce the airflow velocity difference between different locations.

[0035] After the control action is executed, a period of time is allowed for airflow to stabilize. The central controller then collects data again through the airflow velocity sensor, calculates the uniformity of airflow velocity distribution, and repeats the above control process until the uniformity of airflow velocity distribution reaches or exceeds the preset standard. Once the standard is met, continuous monitoring is maintained, and any fluctuations are immediately addressed with fine-tuning to maintain stable uniformity.

[0036] In the above technical solution, the airflow velocity sensor accurately monitors and the adjustable air outlet and circulating fan work together to ensure a balanced distribution of indoor airflow. This ensures the continuous and stable maintenance of airflow uniformity, avoids local airflow stagnation areas or a drafty feeling, and significantly improves the comfort of residential and commercial activities. This solution further improves the airflow collaborative optimization function of the fresh air system, enabling the system to not only control how much air is delivered, but also to actively optimize how the air is distributed, thereby improving the efficiency of fresh air utilization and the comfort experience of people at the micro level.

[0037] In another technical solution, maximizing the overall energy efficiency ratio is achieved using a model predictive control algorithm. This algorithm incorporates a dynamic system model identified from historical system operating data. This model includes a fresh air unit energy consumption model, a circulating fan energy consumption model, a regional pollutant diffusion model, and an airflow organization prediction model. The fresh air unit energy consumption model predicts the power consumption of the fresh air unit under different total airflow volumes and duct static pressures, establishing a correlation between the fresh air unit's rotational speed and input power. The circulating fan energy consumption model predicts the power consumption of circulating fans in each area at different rotational speeds, accurately calculating the total system energy consumption. The regional pollutant diffusion model predicts the levels of carbon dioxide, PM2.5, and total volatile organic compounds (TVOCs) in each area given a given fresh airflow volume, population density, and initial pollutant concentration. The dynamic trend of organic matter concentration is analyzed, and the airflow organization prediction model is used to qualitatively and semi-quantitatively evaluate the airflow distribution effect under different combinations of air outlet angles and fan speeds, thereby helping to improve the accuracy of comprehensive energy efficiency ratio evaluation. Specifically, under the normal operating conditions of the fresh air system, by collecting a sufficient number of input (such as fresh air volume setpoint, air valve opening, and circulating fan speed) and output (such as pollutant concentration in each area, fan power, and static pressure) data pairs, a state-space linear time-invariant model is established as the core prediction model using the subspace identification method or prediction error method. For links with strong nonlinearity (such as pollutant diffusion), piecewise linearization or the introduction of the Hammerstein-Wiener structure can be used for description. The model order is determined according to the Akaike information criterion.

[0038] In each control cycle, the model predictive control algorithm collects various real-time monitoring data as the initial state, including pollutant concentrations, temperature, humidity, and static pressure data for all areas, the current fresh air volume setpoints for commercial and residential areas, and the operating status data of the fresh air unit, circulating fan, and dampers. Using the current monitoring data as the initial state and the next four control cycles as the prediction time domain (e.g., 60 minutes as the prediction time domain with a 15-minute discrete time step), the algorithm simulates and extrapolates the system's operating state in the future time domain. The optimization objective is to maximize the overall energy efficiency ratio. Target fresh air volumes for commercial and residential areas are selected as core decision variables. For each combination of decision variables, the system dynamic model extrapolates the corresponding system state trajectory, including the predicted pollutant concentrations for each area, the total system power consumption (calculated collaboratively by the fresh air unit energy consumption model and the circulating fan energy consumption model), and the dynamic changes in the overall energy efficiency ratio. The optimization process must meet two constraints: firstly, environmental constraints, specifically the carbon dioxide and PM2.5 concentrations in each area within the prediction time domain. 2.5. The total volatile organic compound (TVOC) concentration must not exceed the preset safety threshold to ensure indoor air quality. Secondly, there are equipment constraints. The decision variables (fresh air volume) and the operating parameters of the fresh air unit, circulating fan, and dampers must all be within their respective physical allowable operating ranges to avoid equipment overload or abnormal operation. After optimization calculation, the algorithm outputs the optimal control sequence. This sequence contains the target fresh air volume setpoints for commercial and residential areas in multiple future control cycles. The central controller only executes the first control action in this sequence, that is, updates the target fresh air volume setpoints for commercial and residential areas in the current cycle, and synchronously adjusts the speed of the fresh air unit and the opening of the corresponding area air supply branch dampers to ensure that the total air supply volume matches the zonal fresh air volume, while maintaining the stability of the main duct static pressure. When the next control cycle arrives, the central controller uses the new real-time monitoring data as the initial state and repeats the process of data acquisition-state prediction-multi-constraint optimization-instruction execution to continuously adapt to the load fluctuations of commercial and residential areas and ensure that the comprehensive energy efficiency ratio always tends to be maximized.

[0039] In the above technical solution, model predictive control algorithms ensure that the optimization results closely match the real-time operating conditions and load fluctuation characteristics of commercial and residential buildings. This allows for the precise output of the optimal fresh air volume setpoint, balancing system energy efficiency and ventilation performance, while ensuring pollutant compliance and safe equipment operation. Compared to conventional control algorithms, model predictive control algorithms can anticipate changes in operating conditions, improving the operational stability and energy-saving effect of the fresh air system.

[0040] In another technical solution, an improved particle swarm optimization algorithm is used to maximize the overall energy efficiency ratio. This algorithm uses the target fresh air volume for commercial and residential areas as optimization variables, with the maximization of the overall energy efficiency ratio as the fitness function. Constraints include pollutant concentrations in each area not exceeding their preset thresholds, and a positive pressure difference of 1-5 Pa between the static pressure in residential areas and that in commercial areas. The algorithm employs a linearly decreasing inertial weight strategy, gradually transitioning from global search to local search during iteration. When the fitness function converges or the number of iterations reaches 50-100, the optimal combination of fresh air volume control parameters is output and the algorithm is updated.

[0041] Specifically, after the improved particle swarm optimization algorithm is started, it first presets a reasonable search range for the target fresh air volume of the commercial area and the target fresh air volume of the residential area based on the design air volume of the fresh air unit, the duct load capacity and the spatial characteristics of the commercial and residential areas. This ensures that the values ​​are close to the operating limits of the equipment and the actual ventilation needs. The algorithm randomly initializes a group of particles within the preset search range. The position of each particle is composed of a set of values ​​(target fresh air volume of the commercial area and target fresh air volume of the residential area), representing a potential fresh air volume allocation scheme. Then, it enters the iterative search process.

[0042] During the iteration process, the comprehensive energy efficiency ratio is used as the fitness function to evaluate the merits of the fresh air volume allocation scheme for each particle. Strict constraints are set: first, based on the current pollutant concentration and the corresponding fresh air volume scheme, the concentrations of carbon dioxide, PM2.5, and total volatile organic compounds in each area must not exceed preset thresholds to ensure indoor air quality; second, under the corresponding fresh air volume scheme and damper adjustment, the residential area must be able to stably maintain a positive pressure difference of 1-5 Pa over the commercial area to avoid airflow crosstalk. Fitness is calculated only for particles that meet all constraints; particles that do not meet the constraints are assigned a fitness value with a very poor range and are excluded.

[0043] The algorithm adopts a linear decreasing inertia weight strategy. The inertia weight decreases linearly from 0.9 to 0.4 in each optimization process. In the early stage of iteration, a larger inertia weight is used to enhance the global search capability, quickly scan the entire search space, and avoid missing potential optimal solutions. In the later stage of iteration, a smaller inertia weight is used to enhance the local search accuracy, refine the optimization near potential optimal solutions, and balance the comprehensiveness and accuracy of the optimization.

[0044] The optimization process stops when either of the following conditions is met: 1) the fitness function value shows no significant improvement in multiple iterations, indicating convergence; or 2) the number of iterations reaches a preset maximum value (50-100 times) to meet the real-time control requirements in actual engineering. After the optimization process terminates, the algorithm outputs the fresh air volume allocation scheme corresponding to the particle with the highest fitness function value, which is the optimal combination of target fresh air volume control parameters for commercial and residential areas. The central controller updates its own control parameters based on this optimal parameter combination, and synchronously adjusts the speed of the fresh air unit and the opening of the corresponding air supply branch valves to ensure accurate matching between the total air supply volume and the fresh air volume of each zone, while maintaining stable static pressure in the main pipe and a positive pressure difference of 1-5 Pa between the residential and commercial areas.

[0045] In the above technical solution, the improved particle swarm optimization algorithm can effectively handle nonlinear and multi-constrained fresh air volume optimization problems. The linear decreasing inertial weight strategy effectively balances optimization efficiency and accuracy, avoids the algorithm from getting trapped in local optima, and improves the stability of the optimal solution. This solution is suitable for the load fluctuation characteristics of commercial and residential buildings, complements the model predictive control algorithm, enriches the control scheme of fresh air system, and further improves the system's adaptability, operational stability and energy saving.

[0046] In another technical solution, the positive pressure differential between the residential area and the commercial area is maintained by linking with the commercial area's exhaust system. An independent variable frequency controlled exhaust fan is installed in the commercial area, and the exhaust outlet can be arranged on the top or side wall of the commercial area to systematically exhaust the polluted air in the commercial area. Static pressure sensors are installed in the commercial area and the residential area respectively to collect the static pressure data of the two areas in real time and transmit it to the central controller.

[0047] When the central controller detects a static pressure difference of less than 1 Pa between two areas, it determines that the positive pressure difference is weakening and there is a risk of airflow crosstalk. Simultaneously, it adjusts the air supply branch dampers in the residential area to increase the air supply volume and raise its static pressure, while increasing the speed of the variable frequency exhaust fan in the commercial area to increase the exhaust volume and create a more pronounced negative pressure effect. When the static pressure difference exceeds 5 Pa, it determines that an excessively large positive pressure difference could cause difficulty in opening residential doors or generate airflow noise. Therefore, it reduces the opening of the air supply branch dampers in the residential area to decrease the air supply volume, while simultaneously reducing the speed of the variable frequency exhaust fan in the commercial area to decrease the exhaust volume and weaken the negative pressure effect. The speed adjustment range of the variable frequency exhaust fan is 30% to 100% of its rated speed.

[0048] Throughout the entire control process, the regional static pressure sensor continuously feeds back data, and the central controller dynamically fine-tunes the opening of the air valve and the speed of the exhaust fan based on the feedback value, effectively counteracting interference from inside and outside the building such as opening doors and windows and the chimney effect, and ensuring that the static pressure difference is stably maintained within the target range of 1-5 Pa.

[0049] In the above technical solution, the problem of excessive static pressure difference fluctuation caused by adjusting only the air valve is solved by bidirectional linkage control between the supply air side and the exhaust air side. The pressure difference control is expanded from single supply air adjustment to coordinated control, increasing the degree of control freedom, significantly improving the system's anti-interference ability, and achieving accurate and stable maintenance of a positive pressure difference of 1-5 Pa in commercial and residential areas. This effectively blocks the diffusion of pollutants from commercial areas to residential areas and improves indoor air quality.

[0050] In another technical solution, the uniformity of airflow velocity distribution is quantified by the coefficient of variation (COP) of all airflow velocity sensor readings within the area. The COP is the ratio of the standard deviation to the mean, reflecting the dispersion of velocity at each point relative to the average velocity. The central controller periodically acquires real-time velocity readings from N airflow velocity sensors within the area. V 1. V 2…… V n First, the average value μ and standard deviation σ of all readings are calculated. Then, the coefficient of variation is calculated using the formula CV=σ / μ to complete the uniformity measurement. The preset standard is that the coefficient of variation is less than 0.35. The central controller performs closed-loop control by adjusting the adjustable air outlet angle and the circulating fan speed to minimize the coefficient of variation as a sub-objective.

[0051] In the above technical solution, the dimensionless nature of the coefficient of variation makes it suitable for uniformity assessment in different regions and under different wind speed conditions. The coefficient of variation is stable below 0.35, which significantly improves the comfort of indoor living and commercial activities, avoids discomfort caused by excessively high or low local airflow speeds, and provides clear and quantifiable evaluation indicators and control targets for airflow organization optimization, making the airflow uniformity control process more precise and predictable.

[0052] In another technical solution, the uniformity of airflow velocity distribution is controlled by fine-grained zoning: The commercial and residential areas are divided into multiple functional sub-areas, which are further broken down into independent functional sub-areas based on their intended use: the commercial area can be divided into an entrance display area, main shopping aisle, cashier area, and warehousing and logistics area, etc., while the residential area can be divided into a living room activity area, dining area, master bedroom, secondary bedroom, etc., to ensure that the division fits the actual usage scenario. Each sub-area is equipped with an independent airflow velocity sensor group, consisting of multiple airflow velocity sensors, which are evenly distributed in the main activity positions of people in the sub-area to ensure comprehensive collection of real-time airflow velocity data of the area.

[0053] The central controller, taking into account the usage scenarios and comfort requirements of each functional sub-area, sets differentiated thresholds for the uniformity of airflow speed distribution for different functional sub-areas. The coefficient of variation threshold for commercial sub-areas is set to be less than 0.30. For example, in sub-areas with high population density, such as the main shopping aisle, where pollutants need to be quickly diluted, the coefficient of variation needs to be controlled below 0.30. The coefficient of variation threshold for residential sub-areas is set to be less than 0.25. For example, in sub-areas requiring quiet and comfort, such as bedrooms, the coefficient of variation needs to be controlled below 0.25.

[0054] Based on sensor data from each sub-region, the central controller uses a fuzzy control algorithm to independently adjust the adjustable air outlet angle and circulating fan operation status of the air supply terminal in that sub-region. The airflow velocity sensor group in each sub-region collects airflow velocity data in real time and transmits it to the central controller. The central controller processes the data of each sub-region separately, calculates the current average wind speed and coefficient of variation for that region, and determines the average wind speed deviation (the difference between the current average wind speed and the target value). The fuzzy control algorithm uses the average wind speed deviation and coefficient of variation as input parameters, and completes the fuzzy inference and defuzzification process according to preset fuzzy rules, outputting independent control commands for that sub-region. For example, when the average wind speed of a certain sub-region is low and the coefficient of variation is high, the adjustable air outlet angle of the corresponding sub-region's air supply terminal is significantly adjusted, and the circulating fan speed in that region is increased by a moderate amount. The fuzzy controller uses the average wind speed deviation e and the coefficient of variation CV as input linguistic variables, and the air outlet angle adjustment amount Δθ and the fan speed adjustment amount Δn as output linguistic variables. The universes of discourse for both input and output variables are quantized into seven fuzzy subsets: {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}, with trigonometric functions used for membership. The fuzzy control rules follow these principles: if e is negative large and CV is positive large, then Δθ is positive large and Δn is positive medium; if e is zero and CV is positive medium, then Δθ is positive medium and Δn is zero; if e is positive small and CV is negative small, then Δθ is negative small and Δn is negative small, etc. A total of 49 fuzzy rules were designed, and the centroid method was used for defuzzification.

[0055] In the above technical solution, by dividing functional sub-regions and setting differentiated thresholds, the personalized comfort requirements of each sub-region are taken into account, effectively solving the problem of uneven local airflow caused by overall control. The independent application of fuzzy control algorithm improves the stability and accuracy of control under complex working conditions. The control of each sub-region does not interfere with each other, which can provide the most suitable airflow environment for the activities of people in different functional areas, significantly enhancing the adaptability and practicality of the system.

[0056] In another technical solution, the central controller pre-stores preset safety thresholds for PM2.5 and total volatile organic compound (TVOC) concentrations. It receives real-time data on these two pollutant concentrations from environmental monitoring devices, accurately calculates the concentration increase for a single statistical period, and compares this increase with 50% and 80% of the preset thresholds to determine the emergency response level. This avoids misjudging sudden pollution events. When the PM2.5 or TVOC concentration increases by 50-80%, it is classified as a moderate sudden pollution event, triggering a Level 1 emergency response. Balancing purification capacity and energy consumption control, the total airflow of the fresh air unit is increased to 70-80% of its design maximum, and the speed of all circulating fans is increased to 70-80% of their rated speed to accelerate indoor air circulation and improve pollutant dilution and removal efficiency. When PM2.5 or TVOC concentrations increase by 50-80%, the system is considered to be in a state of moderate sudden pollution, triggering a Level 1 emergency response. When the concentration of PM2.5 or total volatile organic compounds increases by more than 80%, it is judged as a severe sudden pollution event, and a level-two emergency response is activated. The fresh air unit and the circulating fan are both adjusted to full load operation, that is, the fresh air unit operates at 100% of the maximum design air volume and the circulating fan operates at 100% of the rated speed, to maximize the ventilation and dilution capacity. In emergency mode, the central controller simultaneously activates the high-efficiency air filtration device set at the air supply terminal, forming a dual purification of ventilation dilution and deep filtration, effectively intercepting pollutants such as smoke and strong odors.

[0057] The central controller continuously tracks the pollutant concentration that triggers the emergency. When the pollutant concentration falls below the threshold, the pollution threat is determined to be eliminated. The controller then begins to gradually reduce the air volume of the fresh air unit and the speed of the circulating fan according to a preset gradient. The adjustment interval for each gradient is tailored to the operating conditions to avoid system fluctuations caused by sudden changes in equipment operating parameters. During the reduction process, a dynamic adjustment and optimization process is simultaneously initiated. Starting from the current lower load, the system recalculates the periodic comprehensive energy efficiency ratio until the system returns to the optimized operating state with the comprehensive energy efficiency ratio as the core. This ensures that the system smoothly transitions to the optimal normal operating state, significantly improving the emergency pollution prevention and control capabilities, adaptability, and practicality of the fresh air system.

[0058] In the above technical solution, the graded emergency response mode can accurately match and adjust the intensity according to the severity of pollution, avoiding the problems of energy waste or insufficient treatment efficiency of severe pollution caused by a single emergency mode. It takes into account both emergency purification effect and energy saving. The dual purification of ventilation dilution and deep filtration significantly improves the speed of pollutant treatment and shortens the emergency response time compared with single ventilation dilution, and is suitable for sudden pollution scenarios of different intensities.

[0059] In another technical solution, the control process for maintaining constant static pressure in the main duct employs a parameter-adaptive PID algorithm. The central controller presets the static pressure setpoint for the main duct, while the static pressure sensor on the main air supply duct collects real-time measured static pressure data. Based on the deviation 'e' between the static pressure setpoint and the static pressure sensor feedback value, and calculating the absolute value of the deviation |e|, the proportional gain of the PID controller is dynamically adjusted.K p With integral coefficient K i : When the absolute value of the static pressure deviation |e| is greater than 1 Pa, increase the proportional coefficient. K p To enhance real-time adjustment capabilities, accelerate the speed adjustment of the fresh air unit's rotational speed, and reduce the integral coefficient. K i To prevent excessive accumulation of integral terms from causing system overshoot or oscillation; When the absolute value of the deviation |e| is less than or equal to 1 Pa, decrease the proportional coefficient. K p To avoid excessive proportional effects causing small oscillations, and to increase the integral coefficient. K i To enhance the ability to correct small, persistent deviations and accurately stabilize the static pressure at the set value; Meanwhile, the central controller combines the real-time operating power data of the fresh air unit's fan to perform online self-calibration of the PID controller parameters. The central controller has a built-in fan power-airflow-static pressure relationship model identified based on historical operating data. This model is established by collecting fan speed, airflow, static pressure, and power data under different operating conditions, through data cleaning and quadratic polynomial fitting, and verified by measured data to ensure prediction accuracy. The central controller periodically analyzes the difference between the actual required fan power and the model's predicted power under similar static pressure settings. If a trend deviation is found, it indicates that the fan efficiency may have changed due to factors such as wear and tear or filter blockage. At this time, the controller triggers the parameter self-calibration process, fine-tuning the proportional and integral coefficients of the PID controller based on the power difference data to ensure that the algorithm adapts to the dynamic characteristics of the controlled object and maintains stable control quality.

[0060] In the above technical solution, the parameter adaptive PID algorithm significantly improves the accuracy, response speed and anti-interference ability of the main pipe static pressure control through the dual optimization of dynamic parameter adjustment and online self-correction. Dynamic parameter adjustment can quickly adapt to changes in static pressure deviation, taking into account both deviation correction speed and steady-state stability, avoiding control lag or overshoot problems. Online self-correction can adapt to changes in the characteristics of the controlled object caused by fan wear, filter blockage and other factors, ensuring long-term stable control quality.

[0061] In another technical solution, the calculation of the zoned compensation fresh air volume is achieved by the central controller through the construction of a BP neural network. The input parameters of this model include PM2.5 concentration, total volatile organic compound concentration, temperature and humidity data collected in each area over the past 24 hours, passenger flow data collected by the commercial area passenger flow statistics device, and occupancy rate data collected by the residential area occupancy rate statistics device. The output parameters of the model are the pollution load and heat and humidity load change trends of each area in the next hour. The predicted trend is output in the form of a quantitative index and transmitted to the central controller.

[0062] The central controller dynamically adjusts the weighting coefficients used when calculating the zonal compensation fresh air volume based on the prediction index. The adjustment rule is: Adjusted weight = Base weight × (1 + α × Prediction index), where α is a preset adjustment range coefficient to match the compensation air volume with the predicted load. The prediction index is a dimensionless value output by the BP neural network, ranging from -1 to +1. A positive value indicates an upward trend in load, and a negative value indicates a downward trend in load. α is a preset adjustment range coefficient, ranging from 0.1 to 0.3. The specific value is determined through simulation or on-site debugging based on the system's requirements for prediction sensitivity. In this example, it is 0.2. Specifically, when the predicted load increases, the prediction index is positive, and the adjusted weight increases, resulting in a corresponding increase in the compensation fresh air volume to ensure ventilation. When the predicted load decreases, the prediction index is negative, and the adjusted weight decreases, resulting in a decrease in the compensation fresh air volume to reduce energy waste. Based on the adjusted weighting coefficients, the central controller calculates the compensation coefficient through weighted summation. The compensation coefficient is multiplied by the zonal base fresh air volume to obtain the zonal compensation fresh air volume, which is then superimposed with the zonal base fresh air volume to obtain the target fresh air volume.

[0063] In the above technical solution, the BP neural network is used to accurately predict future load trends, which solves the problem of lag caused by calculating the compensation fresh air volume based solely on real-time data. The dynamic adjustment of the weight coefficients enables the zoned compensation fresh air volume to be accurately matched with the actual load demand, which can respond to pollution and heat and humidity load fluctuations in advance, ensure ventilation effect while reducing energy waste, and improve the adaptability and practicality of the fresh air system.

[0064] The feasibility and synergistic effects of the present invention's ventilation method on energy saving and pollution control are verified in an example of a commercial residential building's fresh air and indoor airflow optimization method: 1. Building and area parameters Commercial area: Ground floor street-facing shops, 200 m² 3 According to the passenger flow statistics system, the real-time personnel density is 0.05 people / m². 3 There were 10 people present at the moment, and their activities were quite intense.

[0065] Residential area: Ten-story, two-bedroom building, total floor area 90 m² 3 (Living room 40 m)3 Master bedroom 30 m 3 Second bedroom 20 m 3 According to the smart home system, the real-time population density is 0.033 people / m². 3 The number of people in the household is 3, and the intensity of their home activities is normal.

[0066] 2. Preset environmental thresholds and system parameters Air quality threshold: Carbon dioxide (CO2): 1000 ppm for commercial areas, 800 ppm for residential areas, and 350 ppm for outdoor background concentration.

[0067] Fine particulate matter (PM2.5): Health threshold 35 μg / m³ 3 .

[0068] Total volatile organic compounds (TVOC): Health threshold 0.6 mg / m³ 3 .

[0069] Thermal comfort range: Temperature: 22-26℃, with a comfortable median of 24℃.

[0070] Humidity: 40%-60%, with a comfortable median of 50%.

[0071] System control target value: The static pressure setting value for the main air supply pipe is 50 Pa.

[0072] The target range for static pressure difference between residential and commercial areas is 1-5 Pa.

[0073] Standards for uniformity of airflow velocity distribution (coefficient of variation): <0.35 for the entire area; <0.30 for the commercial sub-area; <0.25 for the residential sub-area.

[0074] Equipment parameters: The maximum design air volume of the fresh air unit is 1500 m³ / h. 3 / h, the rated speed of the fan is 1480 r / min.

[0075] Independent variable frequency exhaust fans for commercial areas, with a speed adjustment range of 30%-100%.

[0076] Emergency response thresholds: A level 1 emergency response is initiated when the concentration increases by 50% within 1 minute, and a level 2 emergency response is initiated when the concentration increases by more than 80%.

[0077] II. Execution Steps and Calculation Process Step 1: Synchronous Collection of Environmental Data The central controller synchronously collects real-time data from each area through environmental monitoring devices (including CO2, PM2.5, TVOC, and temperature and humidity sensors) installed in commercial and residential areas, as shown in Table 1.

[0078] Table 1 area <![CDATA[CO2(ppm)]]> <![CDATA[PM 2.5(μg / m 3 )]]> <![CDATA[TVOC(mg / m 3 )]]> Temperature (°C) humidity(%) Business 850 55 0.8 27 65 Residential 600 40 0.7 25 55 Step 2: Feedforward prediction based on BP neural network The central controller calls the trained BP neural network model and inputs the historical data sequence of the past 24 hours (including pollutant concentration, temperature and humidity, commercial passenger flow, and residential occupancy rate). The model outputs the trend index of pollution load and heat and humidity load of each area in the next hour (range -1 to +1).

[0079] Forecast results: The pollution load trend index for commercial areas is +0.8 (strong increase), and the trend index for residential areas is +0.1 (stable).

[0080] Dynamic parameter adjustment: The central controller adjusts the weighting coefficients in the subsequent compensation fresh air volume calculation based on the predicted index. The adjustment rule is: Adjusted weight = Base weight × (1 + α × Predicted index), where α is the preset adjustment amplitude coefficient (0.2 in this example). To keep the total weights at 1, the adjusted weights are normalized. The final weights used for calculating the compensation coefficient in commercial areas are: PM2.5 weight 0.45, TVOC weight 0.35, temperature weight 0.10, humidity weight 0.10, and the residential areas maintain the base weights of 0.4, 0.3, 0.15, and 0.15, respectively.

[0081] Step 3: Calculate the target fresh air volume for each zone 3.1 Basic Fresh Air Volume for Each Zone (Based on CO2 Hygiene Requirements) The steady-state dilution equation was used for calculation, with the CO2 exhalation rate in the commercial area taken as 0.03 m³. 3 / (person·h), residential areas take 0.02 m 3 / (person·h).

[0082] Basic fresh air volume for commercial areas: L bc = 461.54 m 3 / h Basic fresh air volume for residential areas: L br = 133.33 m 3 / h 3.2 Zonal compensation of fresh air volume (using weighted ratio method) The deviations between the measured values ​​of each parameter and the threshold values ​​are normalized to obtain dimensionless deviation values: Commercial area: PM2.5 deviation 20, TVOC deviation 20, temperature deviation 3, humidity deviation 15.

[0083] Residential area: PM2.5 deviation 5, TVOC deviation 10, temperature deviation 1, humidity deviation 5.

[0084] Compensation coefficient C c It is obtained by weighting the normalized deviation and the corresponding weight and then dividing by 100 (to make the order of magnitude reasonable).

[0085] Commercial area compensation coefficient C c1 = (0.45×20+0.35×20+0.10×3+0.10×15) / 100 = 0.178 Compensation for fresh air volume L cc = L bc ×C c1 = 461.54 × 0.178 ≈ 82.15 m 3 / h Residential area compensation coefficient C c2 = (0.4×5+0.3×10+0.15×1+0.15×5) / 100 = 0.059 Compensation for fresh air volume L cr = L br ×C c2 = 133.33 × 0.059 ≈ 7.87 m 3 / h 3.3 Determination of Target Fresh Air Volume Target fresh air volume (L) for commercial areas c,target = 461.54 + 82.15 = 543.69 m 3 / h Target fresh air volume (L) for residential areas r,target = 133.33 + 7.87 = 141.20 m 3 / h Total target fresh air volume (L) for the system total = 543.69 + 141.20 = 684.89 m 3 / h Step 4: Airflow distribution and static pressure constant control 4.1 Distribution of damper opening Adjust the supply air branch dampers according to the target fresh air volume ratio: Commercial area damper opening degree = 543.69 / 684.89 ≈ 79.4% Residential area damper opening = 141.20 / 684.89 ≈ 20.6% 4.2 Parameter Adaptive PID Static Pressure Control and Online Self-Calibration The static pressure sensor in the main air supply duct provides real-time feedback of the static pressure value. The initial measured static pressure is 44 Pa, with a deviation e = 50 - 44 = 6 Pa (|e| > 1 Pa). The adaptive PID algorithm increases the proportional coefficient K. p Reduce the integral coefficient K to 6.0. i The speed of the fan is rapidly increased to 0.08, and the static pressure rises rapidly. When the static pressure reaches 49 Pa and the deviation e = 1 Pa (|e| ≤ 1 Pa), the algorithm reduces K. p Up to 4.0, increase K i When the pressure reaches 0.12, it enters the fine voltage stabilization mode, and finally stabilizes the static pressure at 50±0.5 Pa.

[0086] Online self-calibration: After the static pressure stabilizes, the controller calls the fan power-airflow-static pressure fitting model (obtained from historical data: P) pred = 0.00002L 2 +0.005L+0.2, R 2 =0.98), input the current total air volume 684.89 m³. 3 / h and static pressure 50Pa, model predicts fan power P pred = 0.00002 × 684.89 2 +0.005×684.89+0.2 ≈ 13.01 kW, the measured wind turbine power Pact = 13.20 kW, the deviation is 1.5% (<5%), the model matching is good, and the current PID parameters do not need to be adjusted. If the measured power is consistently higher than the predicted power for several consecutive cycles and the deviation exceeds 5%, the PID parameters should be adjusted proportionally to adapt to the changes in wind turbine efficiency.

[0087] Step 5: Static pressure difference linkage control between residential and commercial areas The area static pressure sensor shows: static pressure in the commercial area is 49.5 Pa, static pressure in the residential area is 53.0 Pa, and the pressure difference ΔP = 3.5 Pa, which is within the target range of 1-5 Pa.

[0088] Simulated disturbance: Opening the shop doors causes the static pressure in the commercial area to drop to 47.0 Pa, and the static pressure in the residential area to 51.0 Pa, increasing the pressure difference to 4.0 Pa (still within the range). The system will only continuously monitor this. If the continuous disturbance causes the pressure difference to drop to 0.8 Pa (<1 Pa), the controller will execute the following actions: The opening of the air supply branch damper in the residential area was slightly adjusted from 20.6% to 21.5% (an increase of approximately 4.4%) to improve the static pressure in the residential area. At the same time, the speed of the variable frequency exhaust fan in the commercial area will be increased from 30% to 55% (adjusted proportionally according to the pressure difference deviation) to enhance the negative pressure in the commercial area.

[0089] The two regulators worked together to quickly restore the pressure differential to 2.2 Pa.

[0090] Step 6: Controlling the uniformity of airflow velocity distribution 6.1 Monitoring and Quantification Three airflow velocity sensors were installed in the commercial entrance display area (sub-area A). The real-time readings were 0.32 m / s, 0.20 m / s, and 0.15 m / s, respectively. The calculated average value μ = 0.223 m / s, standard deviation σ ≈ 0.085 m / s, and coefficient of variation CV = σ / μ ≈ 0.381. The coefficient of variation threshold for this sub-area is less than 0.30, therefore it is judged as non-compliant.

[0091] 6.2 Partitioned Fine-grained Fuzzy Control The central controller initiates a fuzzy control algorithm for this sub-region, inputting the average wind speed deviation (-0.027 m / s) and the current coefficient of variation (0.381), and outputs control commands through fuzzy inference: The adjustable air vents in the entrance display area are deflected 18° to the left horizontally and 8° to the top vertically to guide the airflow towards the low-speed zone. The speed of the circulating fan in this sub-region was increased from 600 r / min to 780 r / min to accelerate local airflow circulation.

[0092] After the wind speed stabilized, the measured wind speeds were 0.26 m / s, 0.23 m / s, and 0.21 m / s. The calculated values ​​were μ = 0.233 m / s, σ ≈ 0.020 m / s, and CV ≈ 0.107, which are less than the threshold of 0.30 and meet the standard.

[0093] Step 7: Periodic optimization of overall energy efficiency ratio The central controller triggers the MPC optimization process every 15 minutes. The optimization objective is to maximize the overall energy efficiency ratio (EER), which is defined as the system's average ventilation efficiency η divided by the total input power P of the fresh air unit and all circulating fans. total That is, EER = η / P total (Unit: h) -1 / kW), the larger the value, the better the ventilation effect per unit of energy consumption.

[0094] Before optimization: Current total air volume 684.89 m³ 3 The system's total power is Ptotal = 14.70 kW, with a fresh air unit power of 13.20 kW and a total circulating fan power of 1.50 kW across all zones. The average system ventilation efficiency η is calculated as 0.65 h based on the weighted average of CO2 concentration decrease rates in each zone. -1 Overall Energy Efficiency Ratio (EER) current= 0.65 / 14.70 ≈ 0.0442.

[0095] MPC Optimization: The algorithm is based on a system dynamic model (including energy consumption model, pollutant diffusion model, etc.), with the next four control cycles (60 min) as the prediction time domain. Aiming to maximize EER, it performs rolling optimization under the constraints of ensuring that pollutant concentrations in each area do not exceed standards and equipment operation does not exceed limits. The output is the optimal fresh air volume setpoint for the next cycle: 530.00 m³ / h for commercial areas. 3 / h, residential area air volume 150.00 m³ / h 3 / h, total air volume 680.00 m³ 3 / h.

[0096] Optimized status: After implementing the new settings, the power of the fresh air unit decreased to 12.80 kW, the total power of the circulating fan decreased to 1.40 kW, and the total system power P... total = 14.20 kW. Average ventilation efficiency η increased to 0.72 h -1 Overall Energy Efficiency Ratio (EER) opt =0.72 / 14.20 ≈ 0.0507.

[0097] Energy efficiency improvement: The overall energy efficiency after optimization is approximately 14.7% higher than before optimization (0.0507-0.0442) / 0.0442, achieving a significant improvement in ventilation efficiency per unit energy consumption.

[0098] Step 8: Emergency Response to Sudden Pollution Emergency Trigger: PM2.5 concentration in commercial areas increases from 55 μg / m³ within 1 minute. 3 The concentration of the substance suddenly increased to 100 μg / m 3 The increase was 81.8% (>80%), and the central controller determined it to be a Level 2 emergency, immediately activating the emergency mode: The fresh air unit has a maximum designed air volume of 1500 m³ / h. 3 / h runs; All circulating fans are running at a maximum speed of 1000 r / min; The high-efficiency air filtration device installed at the air supply end is activated simultaneously, forming a dual purification process of ventilation dilution and physical filtration.

[0099] Emergency Recovery: After 20 minutes of continuous operation, the PM2.5 concentration decreased to 32 μg / m³. 3 35 μg / m 3 The system has determined that the pollution threat has been eliminated and has initiated a gradual recovery strategy. Reduce the fresh air supply volume of the main unit by 50 m³ every 3 minutes. 3 / h, reduce the speed of the circulating fan by 50 r / min; When the air volume approaches the MPC optimization value (680 m³ / h) 3 When / h), the periodic MPC optimization process in step 7 is restarted synchronously; After about 15 minutes, the system smoothly returned to an optimized operating state with the overall energy efficiency ratio as the core.

[0100] The above examples have the following beneficial effects: First, precise control of fresh air volume in different zones: The system calculates the commercial area of ​​543.69 m² by dynamically adjusting weights based on the differentiated needs of commercial and residential areas and using a backpropagation neural network for prediction. 3 / h, residential 141.20 m 3 The target fresh air volume is / h, achieving on-demand supply.

[0101] Second, effective regional isolation: Through the coordinated control of supply and exhaust ventilation, the static pressure difference between residential and commercial areas is maintained at 1-5 Pa and recovers quickly under disturbance, effectively preventing commercial pollutants from spreading to residential areas.

[0102] Third, continuous energy efficiency optimization: Through periodic optimization using MPC, the overall energy efficiency ratio improved from 0.0442 to 0.0507, and the unit energy consumption ventilation efficiency increased by 14.7%, verifying the energy-saving effect.

[0103] Fourth, proactive enhancement of comfort: By monitoring airflow uniformity and using fuzzy control, the coefficient of variation of substandard sub-regions was optimized from 0.381 to 0.107, significantly improving indoor airflow distribution.

[0104] Fifth, intelligent safety emergency response: The system can accurately determine the pollution level and respond in stages, smoothly returning to normal operation after the concentration reaches the standard, taking into account both safety and energy conservation.

[0105] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0106] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and examples shown and described herein.

Claims

1. An energy-saving ventilation method for synergistic optimization of fresh air intake and indoor airflow in commercial and residential buildings, characterized in that, Includes the following steps: Environmental monitoring devices installed in commercial and residential areas are used to simultaneously collect data on carbon dioxide concentration, PM2.5 concentration, temperature, humidity, and total volatile organic compound concentration in each area. The central controller calculates the basic fresh air volume for each zone based on the carbon dioxide concentration data and the preset hygiene requirement thresholds. The real-time number of people required for calculating the basic fresh air volume for each zone is obtained by counting in real time by the passenger flow statistics device or wireless signal access point set up in each zone. At the same time, the compensation coefficient is calculated by weighted summation based on the deviation of PM2.5 concentration, total volatile organic compound concentration, temperature and humidity data of each zone from their corresponding thresholds. The compensation coefficient is multiplied by the basic fresh air volume for each zone to obtain the compensated fresh air volume for each zone. The basic fresh air volume for each zone and the compensated fresh air volume for each zone are superimposed to obtain the target fresh air volume for the commercial area and the target fresh air volume for the residential area. The central controller controls the operation of the fresh air unit, ensuring that its total air supply volume equals the sum of the target fresh air volume for the commercial area and the target fresh air volume for the residential area. It also adjusts the opening of the air supply branch valves for the commercial area and the residential area according to the ratio, and dynamically adjusts the fan speed of the fresh air unit based on feedback from the static pressure sensor on the main air supply duct to maintain a constant static pressure in the main duct. The central controller adjusts the air valves of the air supply branches in the residential area to control the static pressure value in the residential area to be 1-5 Pa higher than that in the commercial area. The central controller uses periodic intervals and the target fresh air volume of commercial areas and residential areas as related optimization variables. Under the premise of ensuring that the pollutant concentration in each area does not exceed the standard, it coordinates and adjusts the distribution ratio of the two to maximize the comprehensive energy efficiency ratio. The comprehensive energy efficiency ratio is defined as the average ventilation efficiency of the system divided by the total input power of the fresh air unit and all circulating fans. The larger the value, the better the ventilation effect per unit of energy consumption. The average ventilation efficiency of the system is the weighted average of the carbon dioxide concentration reduction rate in each area. When the PM2.5 concentration or total volatile organic compound concentration monitored in any area increases by more than 50% of its preset threshold within a predetermined time, the fresh air unit is controlled to operate at its maximum design air volume, and all circulating fans are controlled to operate at their highest speed until the pollutant concentration falls back below the corresponding threshold.

2. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, The process also includes the following steps: The central controller calculates the uniformity of airflow velocity distribution based on data monitored by multiple airflow velocity sensors in each area, and adjusts the angle of the adjustable air outlet at the air supply end in that area and the speed of the circulating fan in linkage to make the uniformity of airflow velocity distribution reach the preset standard.

3. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, To maximize the overall energy efficiency ratio, a model predictive control algorithm is used. The model predictive control algorithm has a built-in system dynamic model identified based on the system's historical operating data. The system dynamic model includes the fresh air unit energy consumption model, the circulating fan energy consumption model, the regional pollutant diffusion model, and the airflow organization prediction model. In each control cycle, the model predictive control algorithm takes the current monitoring data as the initial state, the next four control cycles as the prediction time domain, and the overall energy efficiency ratio as the optimization objective. Under the constraints of ensuring that the predicted pollutant concentration in each area does not exceed the threshold and the physical operating limits of each device, it performs rolling optimization and outputs the target fresh air volume setpoint for the commercial and residential areas in the current cycle. The central controller updates the control parameters based on the setpoint.

4. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, To maximize the overall energy efficiency ratio, an improved particle swarm optimization algorithm is used. The improved particle swarm optimization algorithm uses the target fresh air volume of the commercial area and the target fresh air volume of the residential area as optimization variables, takes the maximization of the overall energy efficiency ratio as the fitness function, and takes the pollutant concentration of each area not exceeding its threshold and the static pressure value of the residential area being 1-5 Pa higher than that of the commercial area as constraints. It adopts a linear decreasing inertial weight strategy and gradually transitions from global search to local search during the iteration process. When the fitness function converges or the number of iterations reaches 50-100, the optimal combination of fresh air volume control parameters is output and the process is updated.

5. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, Maintaining the positive pressure difference between the residential area and the commercial area is achieved through linkage with the commercial area's exhaust system. An independent variable frequency drive (VFD) exhaust fan is installed in the commercial area. When the central controller detects that the static pressure difference between the two areas is less than 1 Pa, it adjusts the air supply branch valves in the residential area and increases the speed of the VFD exhaust fan in the commercial area. When the static pressure difference is greater than 5 Pa, it reduces the speed of the VFD exhaust fan in the commercial area. The speed adjustment range of the VFD exhaust fan is 30-100% of its rated speed.

6. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, The uniformity of airflow velocity distribution is quantified by the coefficient of variation of all airflow velocity sensor readings in the area. The preset standard is a coefficient of variation of less than 0.

35. The central controller performs closed-loop control by adjusting the adjustable air outlet angle and the circulating fan speed to minimize the coefficient of variation as a sub-objective.

7. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 6, characterized in that, The uniformity of airflow velocity distribution is controlled by zoning. The commercial and residential areas are divided into multiple functional sub-areas, and each sub-area is equipped with an independent airflow velocity sensor group. The central controller sets differentiated thresholds for the uniformity of airflow velocity distribution for different functional sub-areas. The threshold for the coefficient of variation of the commercial sub-area is set to be less than 0.30, and the threshold for the coefficient of variation of the residential sub-area is set to be less than 0.

25. Based on sensor data from each sub-region, the central controller uses a fuzzy control algorithm to independently adjust the angle of the adjustable air outlet at the air supply terminal and the operating status of the circulating fan in that sub-region.

8. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, When the concentration of PM2.5 or total volatile organic compounds increases by 50-80%, the Level 1 emergency response will be activated, increasing the total air volume of the fresh air unit to 70-80% of the design maximum value and increasing the speed of all circulating fans to 70-80% of the rated speed. When the PM2.5 concentration or total volatile organic compound concentration increases by more than 80%, a level-two emergency response will be activated, and both the fresh air unit and the circulating fan will be set to full load operation. In emergency mode, the central controller simultaneously activates the high-efficiency air filtration device located at the air supply terminal; When the pollutant concentration falls below the threshold, the central controller begins to gradually reduce the air volume of the fresh air unit and the speed of the circulating fan according to the preset gradient, and simultaneously starts the dynamic adjustment and optimization process until the system returns to the optimized operating state with the comprehensive energy efficiency ratio as the core.

9. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, The control process for maintaining constant static pressure in the main pipe employs a parameter-adaptive PID algorithm. The central controller dynamically adjusts the proportional and integral coefficients of the PID controller based on the deviation between the static pressure setpoint and the feedback value from the static pressure sensor. When the absolute value of the static pressure deviation is greater than 1 Pa, increase the proportional coefficient and decrease the integral coefficient; When the absolute value of the deviation is less than or equal to 1 Pa, decrease the proportional coefficient and increase the integral coefficient; Meanwhile, the central controller combines the real-time operating power data of the fresh air unit's fan to perform online self-calibration of the PID controller parameters.

10. The energy-saving ventilation method for synergistic optimization of fresh air and indoor airflow in commercial and residential buildings according to claim 1, characterized in that, The calculation of the zoned compensation fresh air volume is achieved by the central controller through the construction of a BP neural network. Based on the PM2.5 concentration, total volatile organic compound concentration, temperature and humidity data of each area in the past 24 hours, as well as the passenger flow data of commercial areas and the occupancy rate data of residential areas, the change trend of pollution load and heat and humidity load of each area in the next hour is predicted. The predicted trend is output in the form of a quantitative index. The central controller dynamically adjusts the weight coefficient used when calculating the zoned compensation fresh air volume according to the index. The adjustment rule is: adjusted weight = baseline weight × (1 + α × prediction index), where α is a preset adjustment amplitude coefficient, so that the compensation air volume matches the predicted load.

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