Intelligent ventilation and purification method and system for constructional engineering

Abstract

The invention relates to the technical field of constructional engineering, and discloses an intelligent ventilation and purification method and system for constructional engineering, and the method comprises the steps: 1, determining the concentration of pollutants in a building based on real-time air quality data, and calculating the required ventilation quantity meeting a preset standard; secondly, the fresh air introduction proportion is adjusted according to the indoor and outdoor temperature and humidity difference value so as to optimize energy consumption and thermal comfort balance; 3, according to the personnel distribution density, the local area air supply intensity is set, and the air circulation uniformity is ensured; fourthly, the system operation mode is regulated and controlled based on the working state of the air purification equipment, so that energy consumption is reduced, and the service life of the equipment is prolonged; the fresh air introduction proportion is intelligently adjusted by comparing indoor and outdoor temperature and humidity differences. For example, when the outdoor temperature and humidity are close to the indoor comfortable interval, the fresh air volume is increased to utilize natural cold and heat sources; when the outdoor environment is extreme, the fresh air volume is reduced, the heat recovery device is started, the air conditioner load is reduced, and energy waste is reduced.

Description

Technical Field

[0001] The present application relates to the field of construction engineering technology, and in particular to an intelligent ventilation and purification method and system for construction engineering. Background Art

[0002] In the field of construction engineering, the development of an intelligent ventilation and purification system focuses on integrating real-time sensor data with an intelligent control system. This system is dedicated to improving the air quality inside buildings while achieving a dynamic balance between energy conservation and emission reduction and user thermal comfort. During the development and application of this system, several key technical challenges needed to be overcome:

[0003] First, in terms of air quality control, we need to solve the problem of how to accurately adjust the ventilation volume within the building based on real-time air quality data to effectively deal with situations where indoor pollutant concentrations exceed the standard. There are many types of indoor pollutants, such as formaldehyde, benzene, and PM2.5. Their concentrations will change continuously with factors such as time, human activities, and the release of decoration materials. This requires the system to be able to capture these subtle changes in real time and accurately adjust the ventilation volume accordingly, ensuring that pollutant concentrations are quickly reduced to a safe range while avoiding energy waste caused by excessive ventilation.

[0004] Secondly, in response to the temperature and humidity differences between indoors and outdoors, it is necessary to dynamically adjust the ratio of fresh air introduced to maintain thermal comfort while avoiding energy waste. Indoor and outdoor temperatures and humidity vary significantly in different seasons and time periods. For example, in summer, it is hot and humid outdoors, while in winter, it is cold and dry. If a large amount of untreated fresh air is directly introduced, the indoor air conditioning load will increase significantly, resulting in energy waste. Therefore, the system needs to intelligently adjust the ratio of fresh air to return air based on real-time temperature and humidity data. While ensuring the supply of fresh air, it can reduce energy loss through technologies such as heat recovery, maintain the stability of indoor temperature and humidity, and ensure the thermal comfort of users.

[0005] Third, in the case of uneven distribution of personnel, it is crucial to optimize the air supply intensity in local areas based on the population density to solve the problem of uneven air circulation. In some large public buildings, such as shopping malls, conference rooms, and gymnasiums, the distribution of personnel is often uneven. Some areas are densely populated, while other areas are relatively open. Crowded areas produce higher levels of carbon dioxide concentration, odor, and other pollutants, and have a higher demand for fresh air. If a unified air supply mode is adopted, the air quality in crowded areas will be poor, while open areas may experience excessive air supply. Therefore, the system needs to monitor the population density in real time through sensors, and dynamically adjust the air supply intensity according to the needs of different areas to achieve uniform air circulation and improve overall air quality.

[0006] Fourth, the working status of the air purification equipment directly affects the energy consumption of the system and the service life of the equipment. The system operation mode needs to be adjusted in time according to the working status of the air purification equipment to reduce energy consumption and extend the service life of the equipment. For example, after the air purification equipment has been running for a period of time, the filter will gradually become clogged, the purification efficiency will decrease, and the energy consumption will increase. At this time, the system needs to be able to monitor the working status of the equipment in real time, such as the degree of filter blockage and the operating power of the equipment, and promptly remind maintenance personnel to replace the filter or adjust the equipment operating parameters. At the same time, the system operation mode can be automatically adjusted according to the equipment status, such as reducing the wind speed, switching to backup equipment, etc., to reduce energy consumption and extend the service life of the equipment while ensuring the purification effect.

[0007] Finally, combined with the air quality prediction model, the ventilation time allocation can be reasonably adjusted to reduce the impact of sudden pollution sources on indoor environmental quality. Sudden pollution sources such as outdoor industrial waste gas emissions, exhaust gas generated by traffic congestion, indoor decoration construction, etc. are often uncertain and will have a serious impact on indoor air quality. By establishing an air quality prediction model, the system can predict the possible occurrence time and impact range of sudden pollution sources in advance, and adjust the ventilation time allocation accordingly. For example, when it is predicted that the outdoor air quality is poor, the fresh air inlet will be closed in advance to increase indoor air circulation purification; after the impact of the sudden pollution source, the fresh air ventilation will be turned on in time to introduce fresh air and reduce the concentration of indoor pollutants.

[0008] Properly solving the above problems is of vital importance to improving the functions of the intelligent ventilation and purification system and enhancing the user experience. By accurately controlling the ventilation volume, optimizing the proportion of fresh air introduction, reasonably allocating the air supply intensity, intelligently managing equipment status and scientifically predicting air quality, the system will be able to more efficiently improve the air quality inside the building, achieve a perfect balance between energy conservation and emission reduction and user thermal comfort, and create a healthier, more comfortable and energy-saving indoor building environment for people. Summary of the Invention

[0009] In order to solve the problems raised by the above background technology, the present application provides an intelligent ventilation and purification method and system for construction projects.

[0010] This application provides an intelligent ventilation and purification method for construction projects, which adopts the following technical solutions:

[0011] Step 1: Determine the pollutant concentration inside the building based on real-time air quality data and calculate the ventilation rate required to meet the preset standards;

[0012] Step 2: Adjust the fresh air introduction ratio according to the difference in indoor and outdoor temperature and humidity to optimize the balance between energy consumption and thermal comfort;

[0013] Step 3: Set the air supply intensity in the local area according to the population density to ensure uniform air circulation;

[0014] Step 4: Adjust the system operation mode based on the working status of the air purification equipment to reduce energy consumption and extend the service life of the equipment.

[0015] Preferably, the method further includes:

[0016] Get real-time concentrations of PM5, TVOC and CO2 inside buildings pm 、C tvoc 、C co2 ;

[0017] According to the formula Where a is the correction coefficient, C std_pm 、C std_tvoc 、C std_co2 are the standard concentrations of the corresponding pollutants;

[0018] When the calculated ventilation volume V vent Exceeds the threshold V max , then press V max Set; otherwise keep V vent constant;

[0019] Adjust the fan speed based on the final set ventilation volume.

[0020] Preferably, the method further includes:

[0021] Real-time acquisition (NO x ) and (SO2) concentration values and

[0022] Use the following criteria to assess whether ventilation needs to be increased: If ( or ), then the ventilation volume change value Where γ1 and γ2 are the preset critical concentrations, and k is the proportional factor;

[0023] By updating the total ventilation volume V vent =V vent0 +ΔV vent To optimize ventilation control;

[0024] Output the adjusted ventilation volume to the fan controller

[0025] Preferably, adjusting the fresh air introduction ratio based on the indoor and outdoor temperature and humidity difference further includes:

[0026] Determine the current outdoor air enthalpy value H ext and indoor air enthalpy H int ;

[0027] Calculate the difference between the two δH=|H ext -H int |, as a measure of energy flow;

[0028] When δH is less than the energy-saving mode threshold λ1 or greater than the comfort mode lower limit threshold λ2, the corresponding control rule is triggered: f(new_air_rate)-u(λ2 / δH), where μ is the scaling parameter;

[0029] Adjust the intake valve opening according to the calculation results to change the ratio new_air_rate

[0030] Preferably, the fresh air control is more refined after considering the temperature and humidity difference, and a prediction module is added for improvement. The specific steps are:

[0031] Predict the temperature difference change trajectory T in the next hour pred (t), t is the time series indicator;

[0032] According to the formula T weight =η*max(T pred ), if the maximum value exceeds the limit, the predicted weight is reduced. The function is defined as θ(f(x)) = 1-exp(η);

[0033] Use the modified weighting function to adjust f(new_air_rate) = (1-θ)*f(new_air_rate) + θ*f std (new_air_rate);

[0034] Output f is given to the actuator to achieve energy saving.

[0035] Preferably, the air supply intensity in the local area is further limited to:

[0036] Collect the crowd density matrix P in the activity area from the human body thermal sensor mat (i,j,k);

[0037] Define the baseline strength S base , for the element P(i,j,k) in the matrix, when P(i,j,k)>θ P When (θ P is the set number of people threshold), the enhanced wind force F is calculated as follows:

[0038]

[0039] Map the enhanced results to the corresponding regional ventilation duct distribution plan Z map ;

[0040] Smooth Z map Get Z finalAs output signal to control the air outlet opening.

[0041] Preferably, the regional air supply intensity distribution logic is improved in combination with the air mobility model, which further includes:

[0042] Calculate the effective circulation rate per unit space (U eff ); When there is a blind spot or weak area, adjust the weight g(i,j) ratio to meet g(i,j)=(g mean +c U *ΔU)*S knot / (c S +c W ), where c U 、c S 、c W All are weight-corrected values;

[0043] Add the above ratio back to P mat Update to (W dist =P mat *g(i,j)); reallocate resources to each air outlet based on the updated density.

[0044] Preferably, according to the working state of the air purification device, the method further comprises: monitoring the cumulative pressure drop ΔP-f of the filter membrane of the purification device to further refine the mode conversion rule;

[0045] Use the following conditions to determine whether to switch to low-frequency operation:

[0046] As ΔP f ≥φ, then enable E power_mod (eff=eff×(1-Δψ), psi represents the percentage of energy consumption reduction;

[0047] Otherwise, maintain the current status and record the number of days C count ;

[0048] Add air quality prediction module to solve environmental quality problems caused by sudden pollution sources;

[0049] Obtain air quality prediction probability p based on machine learning training air (t+Δ), Δ represents the future span of the window;

[0050] According to p air The emergency warning threshold Ψ is set as a result thresh Once touched, the ventilation intensity is immediately increased to max_mode_state=f(max{L total})×4;

[0051] Further supplement and accurately calculate the time allocation plan of the ventilation system;

[0052] Define the ventilation objective function for each cycle And Q air_dev,i Quantifying the indoor air deviation, λ env To adjust the sensitivity constant;

[0053] The optimal configuration scheme O is obtained by using constraint solving strategy time ={τ i |i=1,n cycle}Then the final execution timing is generated by smooth interpolation O smooth =

[0054] SmoothInterpolation(O time ).

[0055] Preferably, equipment life extension is optimized through adaptive load management:

[0056] Define the workload factors α and β, and their formulas are defined as follows: κ,T controls the exponential growth rate balance;

[0057] Determine the load rating table L according to f output tier , and dynamically switch the corresponding working level.

[0058] An intelligent ventilation and purification system for construction engineering, comprising:

[0059] Air quality monitoring and ventilation volume calculation module: real-time monitoring of pollutant concentrations inside the building, calculation and dynamic adjustment of ventilation volume;

[0060] Temperature and humidity balance and fresh air control module: Based on the difference in indoor and outdoor temperature and humidity, it optimizes the proportion of fresh air introduced to balance energy consumption and thermal comfort;

[0061] Personnel distribution and regional air supply control module: Dynamically adjusts the air supply intensity in local areas according to personnel density and air mobility to ensure uniform air circulation;

[0062] Equipment status management and energy consumption optimization module: regulates the system operation mode according to the working status of the purification equipment, reduces energy consumption and extends equipment life.

[0063] In summary, this application includes at least one of the following beneficial technical effects:

[0064] The present disclosure provides an intelligent ventilation and purification method and system for construction projects. By monitoring the concentration of pollutants (such as formaldehyde, PM2.5, and VOCs) inside the building in real time, the system can automatically calculate and match the ventilation volume required to meet national standards, avoiding the accumulation of pollutants caused by insufficient ventilation and ensuring indoor air quality and safety.

[0065] In addition, by comparing the difference in temperature and humidity between indoor and outdoor, the ratio of fresh air introduced is intelligently adjusted. For example, when the outdoor temperature and humidity are close to the indoor comfort zone, the fresh air volume is increased to utilize natural cooling and heating sources; when the outdoor environment is extreme, the fresh air volume is reduced and the heat recovery device is activated to reduce the air conditioning load and reduce energy waste.

[0066] Secondly, sensors or personnel positioning systems are used to identify the distribution of people indoors (such as conference rooms, office areas, corridors, etc.). Air supply intensity is increased in high-density areas to ensure that fresh air preferentially reaches areas where people are active, reducing "ineffective air supply" in low-density areas.

[0067] In terms of equipment status linkage control, the system automatically adjusts the operating mode based on the operating status (such as resistance, operating time, and purification efficiency degradation) of air purification equipment (such as filters, electrostatic dust removal devices, and activated carbon adsorption equipment). For example, when filter resistance is low, low power operation is adopted. When it approaches saturation, an alert is issued and the system switches to backup equipment or increases ventilation volume to avoid overloading the equipment.

[0068] In terms of prediction-driven ventilation strategies, it is possible to respond to sudden pollution risks, integrate air quality prediction models (such as prediction algorithms based on historical data, meteorological conditions, and surrounding pollution sources), and plan ventilation time windows in advance. For example, when it is predicted that outdoor haze will worsen or indoor activities (such as decoration and intensive printing) will cause pollution, the ventilation volume can be increased in advance or the high-efficiency purification mode can be enabled to reduce the impact of sudden pollution on the indoor environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A flow chart of the intelligent ventilation and purification method for the building project;

[0070] Figure 2 This is a flow chart of the intelligent ventilation and purification system for this building project. DETAILED DESCRIPTION

[0071] Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings.

[0072] Throughout this specification, reference to the terms "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0073] The present application discloses an intelligent ventilation and purification method and system for construction engineering. Figure 1-2 , describing an intelligent ventilation and purification method for construction engineering of the present invention: wherein claim 1 lays the basic framework of the intelligent ventilation and purification method:

[0074] Step 1: Determine the pollutant concentration inside the building based on real-time air quality data and calculate the ventilation rate required to meet the preset standards;

[0075] Step 2: Adjust the fresh air introduction ratio according to the difference in indoor and outdoor temperature and humidity to optimize the balance between energy consumption and thermal comfort;

[0076] Step 3: Set the air supply intensity in the local area according to the population density to ensure uniform air circulation;

[0077] Step 4: Adjust the system operation mode based on the working status of the air purification equipment to reduce energy consumption and extend the service life of the equipment.

[0078] The specific technical solution of the intelligent ventilation and purification method is as follows:

[0079] First, the system calculates the minimum ventilation required by real-time monitoring of the concentration of air pollutants inside the building. Air quality sensors distributed in key areas of the building will collect concentration data of pollutants such as PM2.5, VOC (volatile organic compounds), and CO2. When the concentration of a pollutant exceeds the national or international preset air quality standards, the system will initiate an adjustment strategy: based on the gap between the real-time air quality data and the preset safe concentration range, the system calculates the proportion of fresh air to be introduced, and increases the fan speed or opens more air supply valves accordingly to ensure that the indoor air quality remains within a safe range;

[0080] For example, if the carbon dioxide concentration in a conference room exceeds 1000ppm (exceeding the comfort range standard value of 900ppm), the model calculation requires a 30% increase in the proportion of fresh air introduced. The system will adjust the fan operation mode and enhance the fresh air ventilation function to reduce pollutant concentrations.

[0081] The system dynamically adjusts the proportion of fresh air introduced based on the temperature and humidity differences between indoor and outdoor areas, balancing efficient energy use with user thermal comfort. The system receives real-time data on outdoor climate conditions and analyzes the actual temperature and humidity levels within the building. If the temperature difference is large but the humidity difference is small, the system prioritizes pre-processing the cooling demand through waste heat recovery and other methods before delivering it to the room. If the humidity is too high, the introduction of wet, cool fresh air is reduced to prevent condensation and maintain a moderately dry atmosphere for energy conservation.

[0082] Winter example: When the outside temperature drops to -5°C and the room remains warm, the intelligent system closes a certain proportion of direct-exhaust windows and only uses the heat exchange mechanism to introduce heated air, saving fuel costs without affecting the thermal experience.

[0083] The system determines the distribution of air supply intensity in local areas based on the distribution of people within the building, addressing the problem of uneven ventilation caused by differences in passenger flow. The sensor array periodically scans the dynamic flow of people on each floor, and uses an artificial intelligence deep learning algorithm to infer the optimal air distribution parameters and output them to the controller. When a large number of visitors are detected in crowded places such as lobbies and public restaurants, the system automatically increases the air change rate per unit time to ensure oxygen supply and dilute the concentration of concentrated pollutants. In relatively private and less crowded personal offices, the ventilation intensity is reduced to save electricity and maintain a quiet office environment.

[0084] For example, during the peak operating hours of large shopping malls on weekends, the main entrance hall on the first floor is flooded with people. The air-conditioning units at the corresponding locations increase the blowing force to improve breathing quality, and return to normal mode during idle periods to reduce the risk of resource waste.

[0085] The system prioritizes the evaluation and implementation of optimization plans for the long-term stability of air purification equipment. It regularly tracks and checks the filter blockage level, and when it approaches critical warning levels, it prompts replacement of consumable parts according to pre-set preventive maintenance rules, extending equipment life. During sensitive periods like seasonal changes, when the risk of mold and allergen transmission increases, enhanced cleaning capacity options are implemented to effectively remove more fine particles and improve health protection. Based on daily monitoring records, machine-based predictive analysis methods are used to take action before potential failures occur, reducing the frequency of accidents and the maintenance burden.

[0086] According to statistical data analysis over the past three years, a certain brand of electron positive ionization device experiences a serious performance degradation due to dust accumulation in the filter element after every seven months of operation. Therefore, the system pops up a notification to prepare replacement components in advance two weeks before the sixth full month, so that replacement can be made calmly to ensure the quality stability of the equipment's continuous operation.

[0087] The present invention's intelligent ventilation and purification method and system for construction projects includes a comprehensive process combining real-time monitoring, intelligent analysis, and dynamic control, aiming to address the core technical challenge of optimizing the air environment within buildings. The following is a detailed description of the invention's key technical steps and how they address specific technical challenges.

[0088] First, regarding question one:

[0089] Adjust ventilation volume based on real-time air quality data to address excessive pollutant concentrations;

[0090] The system collects real-time data (such as PM2.5, VOC, and CO2 concentrations) from air quality sensors located throughout the building. This data is analyzed in real time to determine whether it exceeds preset standards. Advanced algorithms are then used to calculate the specific ventilation rate that meets air quality requirements. This ensures that pollutants are expelled promptly while also avoiding energy waste and increased system load caused by blindly increasing ventilation.

[0091] Secondly, regarding question 2:

[0092] Optimize the fresh air introduction ratio based on the difference in indoor and outdoor temperature and humidity;

[0093] This is a key energy-saving feature of the system. The system continuously collects indoor and outdoor temperature and humidity data and uses intelligent algorithms to assess the impact of these differences. It then appropriately reduces the proportion of fresh air introduced or adjusts its supply temperature without compromising thermal comfort, achieving a dynamic balance between energy consumption and user comfort. This significantly reduces unnecessary energy consumption caused by overcooling or overheating fresh air.

[0094] In addition, regarding question three:

[0095] The air supply intensity in local areas is adjusted according to the density of personnel distribution;

[0096] This invention incorporates an adaptive airflow distribution mechanism that uses infrared sensors, Wi-Fi signal tracking, or other positioning technologies to precisely detect the density of activity within each area of a building. For example, in high-density areas like conference rooms, airflow can be increased locally, while in unoccupied rooms, airflow can be reduced or even shut off. This ensures uniform airflow and maximizes overall space efficiency, while also reducing unnecessary power consumption.

[0097] In addition, regarding question 4:

[0098] Regulate energy consumption and extend lifespan of air purification equipment according to its operating status;

[0099] The system's built-in monitoring module continuously tracks key parameters of the purification equipment (such as filter blockage, motor current, etc.). When it detects abnormal equipment load or reduced efficiency, it promptly triggers energy-saving mode switching or fault warning prompts, effectively reducing the damage to equipment hardware caused by long-term full-load operation.

[0100] Finally, regarding question five:

[0101] That is, the risk of sudden changes in air quality caused by sudden pollution sources;

[0102] This invention builds an air quality prediction model to predict pollutant fluctuation trends in advance and proactively optimizes fan on / off times and power allocation strategies at different time stages. This can avoid the harm caused by untimely response to sudden pollution events and ensure a smooth and controllable process.

[0103] In summary, an intelligent ventilation and purification method and system for construction projects integrates multiple sensing, analysis and response functions, comprehensively solving the various complex challenges of air quality control in building indoor environments.

[0104] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An intelligent ventilation and purification method for construction engineering, characterized in that: include: Step 1: Determine the pollutant concentration inside the building based on real-time air quality data and calculate the ventilation rate required to meet the preset standards; Step 2: Adjust the fresh air introduction ratio according to the difference in indoor and outdoor temperature and humidity to optimize the balance between energy consumption and thermal comfort; Step 3: Set the air supply intensity in the local area according to the population density to ensure uniform air circulation; Step 4: Adjust the system operation mode based on the working status of the air purification equipment to reduce energy consumption and extend the service life of the equipment.

2. The intelligent ventilation and purification method for construction engineering according to claim 1, characterized in that: Based on real-time air quality data, it further includes: Get real-time concentrations of PM5, TVOC and CO2 inside buildings pm 、C tvoc 、C co2 ; According to the formula Where a is the correction coefficient, C std_pm 、C std_tvoc 、C std_co2 are the standard concentrations of the corresponding pollutants; When the calculated ventilation volume V vent Exceeds the threshold V max , then press V max Set; otherwise keep V vent constant; Adjust the fan speed based on the final set ventilation volume.

3. The intelligent ventilation and purification method for construction engineering according to claim 1, characterized in that: Based on real-time air quality data, it further includes: Real-time acquisition (NO x ) and (SO2) concentration values and Use the following criteria to assess whether ventilation needs to be increased: If ( or ), then the ventilation volume change value Where γ1 and γ2 are the preset critical concentrations, and k is the proportional factor; By updating the total ventilation volume V vent =V vent0 +ΔV vent To optimize ventilation control; Output the adjusted ventilation volume to the fan controller.

4. The intelligent ventilation and purification method for construction engineering according to claim 2, characterized in that: Adjusting the fresh air introduction ratio based on the indoor and outdoor temperature and humidity difference further includes: Determine the current outdoor air enthalpy value H ext and indoor air enthalpy H int ; Calculate the difference between the two δH=|H ext -H int |, as a measure of energy flow; When δH is less than the energy-saving mode threshold λ1 or greater than the comfort mode lower limit threshold λ2, the corresponding control rule is triggered: f(new_air_rate)-u(λ2 / δH), where μ is the scaling parameter; Adjust the intake valve opening according to the calculation results to change the ratio new_air_rate.

5. The intelligent ventilation and purification method for construction engineering according to claim 3, characterized in that: Taking temperature and humidity differences into account, fresh air control is more precise and improved by adding a prediction module. The specific steps are as follows: Predict the temperature difference change trajectory T in the next hour pred (t), t is the time series indicator; According to the formula T weight =η*max(T pred ), if the maximum value exceeds the limit, the predicted weight is reduced. The function is defined as θ(f(x)) = 1-exp(η); Use the modified weighting function to adjust f(new_air_rate) = (1-θ)*f(new_air_rate) + θ*f std (new_air_rate); Output f is given to the actuator to achieve energy saving.

6. The intelligent ventilation and purification method for construction engineering according to claim 1, characterized in that: The air supply intensity in local areas is further limited to the following according to the density of personnel distribution: Collect the crowd density matrix P in the activity area from the human body thermal sensor mat (i,j,k); Define the baseline strength S base , for the element P(i,j,k) in the matrix, when P(i,j,k)>θ P When (θ P is the set number of people threshold), the enhanced wind force F is calculated as follows: Map the enhanced results to the corresponding regional ventilation duct distribution plan Z map ; Smooth Z map Get Z final As output signal to control the air outlet opening.

7. The intelligent ventilation and purification method for construction engineering according to claim 5, characterized in that: The regional air supply intensity allocation logic is improved by combining the air mobility model, which further includes: Calculate the effective circulation rate per unit space (U eff ); When there is a blind spot or weak area, adjust the weight g(i,j) ratio to meet g(i,j)=(g mean +c U *ΔU)*S knot / (c S +c W ), where c U 、c S 、c W All are weight-corrected values; Add the above ratio back to P mat Update to (W dist =P mat *g(i,j)); reallocate resources to each air outlet based on the updated density.

8. The intelligent ventilation and purification method for construction engineering according to claim 6, characterized in that: According to the working status of the air purification equipment, it further includes: monitoring the cumulative pressure drop ΔP-f of the filter membrane of the purification device to further refine the mode conversion rules; Use the following conditions to determine whether to switch to low-frequency operation: As ΔP f ≥φ, then enable E power_mod (eff=eff×(1-Δψ), psi represents the percentage of energy consumption reduction; Otherwise, maintain the current status and record the number of days C count ; Add air quality prediction module to solve environmental quality problems caused by sudden pollution sources; Obtain air quality prediction probability p based on machine learning training air (t+Δ), Δ represents the future span of the window; According to p air The emergency warning threshold Ψ is set as a result thresh Once touched, the ventilation intensity is immediately increased to max_mode_state=f(max{L total })×4; Further supplement and accurately calculate the time allocation plan of the ventilation system; Define the ventilation objective function for each cycle And Q air_dev,i Quantifying the indoor air deviation, λ env To adjust the sensitivity constant; The optimal configuration scheme O is obtained by using constraint solving strategy time ={τ i |i=[1,n cycle ]}Then the final execution timing is generated by smooth interpolation. smooth =SmoothInterpolation( time ).

9. The intelligent ventilation and purification method for construction engineering according to claim 7, characterized in that: Equipment service life extension is optimized in part through adaptive load management: Define the workload factors α and β, and their formulas are defined as follows: κ,T controls the exponential growth rate balance; Determine the load rating table L according to f output tier , and dynamically switch the corresponding working level.

10. An intelligent ventilation and purification system for construction engineering, characterized in that: The system is implemented by the intelligent ventilation and purification method for construction engineering according to claims 1 to 9, and the system includes: Air quality monitoring and ventilation volume calculation module: real-time monitoring of pollutant concentrations inside the building, calculation and dynamic adjustment of ventilation volume; Temperature and humidity balance and fresh air control module: Based on the difference in indoor and outdoor temperature and humidity, it optimizes the proportion of fresh air introduced to balance energy consumption and thermal comfort; Personnel distribution and regional air supply control module: Dynamically adjusts the air supply intensity in local areas according to personnel density and air mobility to ensure uniform air circulation; Equipment status management and energy consumption optimization module: regulates the system operation mode according to the working status of the purification equipment, reduces energy consumption and extends equipment life.

Images