Method and system for predicting and controlling the flow of indoor air quality in buildings

Abstract

The application provides a building indoor air quality prediction and circulation control method. The method obtains environmental data and passenger flow of a target area, sets a reference ventilation working condition, and dynamically corrects through multiple levels of parameters: generates a first ventilation parameter based on the current passenger flow to realize on-demand ventilation; generates a second ventilation parameter based on historical passenger flow to pre-compensate using inertia; generates a third ventilation parameter according to the deviation of the actual change rate of pollutants from the preset change rate to respond to sudden pollution; and simultaneously introduces a fourth ventilation parameter and a height correction parameter in combination with the area of the enclosure and the height of the top to adapt to the geometric characteristics of the space. Each parameter is limited between 1 and 2 to ensure stable and energy-saving regulation and control. The application integrates real-time, historical, abnormal and spatial multi-dimensional information to realize accurate, efficient and self-adaptive intelligent regulation and control of indoor air quality.

Description

Technical Field

[0001] This application relates to the field of building ventilation technology, specifically to methods and systems for predicting and controlling indoor air quality in buildings. Background Technology

[0002] With the increasing demand for healthy living environments, indoor air quality (IAQ) control has become a key technology in the fields of smart buildings and green buildings. Traditional ventilation control systems often employ fixed schedules, simple threshold switches, or static settings based on per capita fresh air volume. For example, they may set uniform fresh air volume or air exchange rate according to the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" (GB 50736) based on the area's purpose. However, such methods have significant limitations: on the one hand, they cannot respond to real-time changes in occupant flow, leading to excessive air supply and energy waste when no one is present, while when people are present, the delayed response results in excessive concentrations of pollutants such as CO2 and TVOC. On the other hand, they ignore the influence of the physical characteristics of the building space itself (such as area, enclosure degree, and ceiling height) on airflow organization and pollutant diffusion, making it difficult to achieve refined and differentiated control.

[0003] In recent years, some systems have attempted to incorporate people flow sensors or historical data for ventilation adjustment, but these are mostly limited to single-dimensional compensation, lacking a synergistic integration of multiple factors such as "real-time load, historical trends, sudden disturbances, and spatial morphology." For example, while some solutions can adjust airflow based on current people flow, they fail to consider recent usage inertia, easily leading to premature reductions in ventilation intensity due to brief periods of absence. Other systems monitor pollutant concentrations but only trigger actions based on absolute thresholds, failing to identify sudden pollution events reflected by abnormal concentration change rates. Furthermore, for non-standard layouts such as open-plan office areas and high-ceilinged loft spaces, existing control strategies often use parameters common in ordinary rooms, resulting in uneven ventilation coverage in large open areas and excessive ventilation in smaller cubicles, making it difficult to balance overall energy efficiency and comfort.

[0004] Therefore, there is an urgent need for a method that can comprehensively analyze environmental parameters, dynamic pedestrian flow, and historical data to make intelligent air quality predictions and control air circulation, thereby enabling proactive prediction and precise correction. This will not only ensure healthy indoor air quality but also significantly improve the energy efficiency, adaptability, and robustness of ventilation systems. Summary of the Invention

[0005] In view of this, this application provides a method and system for predicting and controlling indoor air quality in buildings. It can integrate environmental parameters, dynamic human flow, and historical data to carry out intelligent air quality prediction and circulation control, realize proactive prediction and accurate correction, and significantly improve the energy efficiency, adaptability and robustness of the ventilation system while ensuring the health level of indoor air.

[0006] In a first aspect, this application provides a method for predicting and controlling indoor air quality in buildings, comprising: acquiring environmental data of multiple areas within a building; acquiring current pedestrian traffic and historical pedestrian traffic for a preset historical period in a target area; obtaining a first ventilation parameter based on the current pedestrian traffic and a second ventilation parameter based on the historical pedestrian traffic; the first ventilation parameter being greater than or equal to 1 and proportional to the current pedestrian traffic, and the second ventilation parameter being greater than or equal to 1 and proportional to the historical pedestrian traffic; setting a baseline ventilation condition for the target area based on the environmental parameters; adjusting the baseline ventilation condition in real time based on the first ventilation parameter and the second ventilation parameter to obtain a first-corrected ventilation condition; obtaining a third ventilation parameter based on the difference between the actual rate of change of the environmental parameters based on the first-corrected ventilation condition and a preset rate of change; the third ventilation parameter being greater than or equal to 1 and proportional to the difference; and adjusting the first-corrected ventilation condition based on the third ventilation parameter to obtain a second-corrected ventilation condition.

[0007] In conjunction with the first aspect, in one possible implementation, the preset change rate is set by the following steps: obtaining multiple change rates of the target area when the same modified ventilation condition is triggered in a historical database; and calculating the average of the multiple change rates to obtain the preset change rate.

[0008] In conjunction with the first aspect, in one possible implementation, the preset rate of change is set by the following steps: calculating the theoretical rate of change of the target area under the same environmental parameters based on the first-corrected ventilation condition, and obtaining the preset rate of change.

[0009] In conjunction with the first aspect, one possible implementation further includes: setting the value range of the first ventilation parameter to be greater than or equal to 1 and less than or equal to 2; setting the first ventilation parameter to be 1 when the current pedestrian flow in the target area is 0; and setting the first ventilation parameter to be 2 when the current pedestrian flow in the target area is greater than or equal to a first preset upper limit pedestrian flow.

[0010] In conjunction with the first aspect, one possible implementation further includes: setting the value range of the second ventilation parameter to be greater than or equal to 1 and less than or equal to 2; setting the second ventilation parameter to 1 when the historical pedestrian flow in the target area is 0 for a preset duration backward from the current moment; and setting the second ventilation parameter to 2 when the historical pedestrian flow in the target area is greater than or equal to a second preset upper limit pedestrian flow for the preset duration backward from the current moment.

[0011] In conjunction with the first aspect, one possible implementation further includes: setting the third ventilation parameter to 1 when the difference is 0; and setting the third ventilation parameter to 2 when the absolute value of the difference is equal to the preset rate of change.

[0012] In conjunction with the first aspect, one possible implementation further includes: if the target area is a walled enclosure area, obtaining the enclosure area within the walled enclosure area; obtaining a fourth ventilation parameter based on the enclosure area; and correcting the baseline ventilation condition based on the fourth ventilation parameter; the fourth ventilation parameter is proportional to the enclosure area; when the enclosure area is equal to the baseline area, the fourth ventilation parameter is 1; when the enclosure area is less than the baseline area, the fourth ventilation parameter is greater than 0 and less than 1; when the enclosure area is greater than or equal to twice the baseline area, the fourth ventilation parameter is 2.

[0013] In conjunction with the first aspect, one possible implementation further includes: if the ceiling height of the target area is greater than or equal to a preset ceiling height, generating a ceiling height correction parameter; the ceiling height correction parameter is proportional to the ceiling height, and the ceiling height correction parameter is greater than or equal to 1 and less than or equal to 2; and correcting the baseline ventilation condition based on the ceiling height correction parameter.

[0014] In conjunction with the first aspect, one possible implementation further includes: obtaining the building parameters of the building; and obtaining the corresponding baseline ventilation conditions based on the building parameters.

[0015] Secondly, this application provides a building indoor air quality prediction and circulation control system, including: a data acquisition module configured to: acquire environmental data of multiple areas within a building; acquire current pedestrian traffic and historical pedestrian traffic for a preset historical period in a target area; and a ventilation parameter setting module communicatively connected to the data acquisition module, wherein the ventilation parameter setting module is configured to: obtain a corresponding first ventilation parameter based on the current pedestrian traffic and obtain a corresponding second ventilation parameter based on the historical pedestrian traffic. In conjunction with the first aspect, in one possible implementation, the first ventilation parameter is greater than or equal to 1 and is proportional to the current pedestrian traffic, and the second ventilation parameter is greater than or equal to 1 and is proportional to the historical pedestrian traffic. The historical pedestrian flow is directly proportional; a baseline ventilation condition for the target area is set according to the environmental parameters; and a ventilation compensation module is communicatively connected to the ventilation parameter setting module. The ventilation compensation module is configured to: adjust the baseline ventilation condition in real time based on the first ventilation parameter and the second ventilation parameter to obtain a first-corrected ventilation condition; obtain a third ventilation parameter based on the difference between the actual rate of change of the environmental parameters based on the first-corrected ventilation condition and the preset rate of change; the third ventilation parameter is greater than or equal to 1, and the third ventilation parameter is directly proportional to the difference; and adjust the first-corrected ventilation condition based on the third ventilation parameter to obtain a second-corrected ventilation condition.

[0016] In this embodiment, environmental data from multiple areas within the building and the current and historical pedestrian traffic in the target area are acquired to generate a first ventilation parameter and a second ventilation parameter, each not less than 1. The first ventilation parameter reflects the real-time air pollution load, while the second reflects the past air pollution load. A correction is made based on a set baseline ventilation condition to achieve on-demand ventilation adjustment with pre-compensation capabilities. Furthermore, by comparing the difference between the actual rate of change of environmental parameters under the first correction condition and the preset rate of change, a third ventilation parameter, not less than 1, is generated, and a second correction is performed accordingly. This avoids the first correction ventilation condition failing to achieve reasonable or ideal ventilation efficiency, thus enabling rapid compensation for sudden pollution events or model deviations. The overall method balances energy efficiency, predictability, and robustness, ensuring indoor air quality meets standards while effectively avoiding energy waste or decreased comfort caused by response lag or excessive conservatism in traditional control methods. Attached Figure Description

[0017] Figure 1 The diagram shows the steps of a method for predicting and controlling indoor air quality in a building, according to an embodiment of this application.

[0018] Figure 2 The diagram shows the steps for setting a preset rate of change.

[0019] Figure 3The diagram shows another method for setting a preset rate of change.

[0020] Figure 4 The diagram shows the steps for setting the first ventilation parameter.

[0021] Figure 5 The diagram shows the steps for setting the second ventilation parameter.

[0022] Figure 6 The diagram shows the steps for setting the third ventilation parameter.

[0023] Figure 7 The diagram shows the steps of a method for defining baseline ventilation conditions based on the fenced area.

[0024] Figure 8 The diagram shows the steps of a ventilation system based on the ceiling height limit.

[0025] Figure 9 The diagram shows the steps for setting the baseline ventilation conditions.

[0026] Figure 10 The diagram shown is a schematic diagram of the system structure of a building indoor air quality prediction and circulation control system provided in an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0028] An exemplary method for predicting indoor air quality and controlling air circulation in buildings is as follows: Figure 1 The diagram shown is a schematic representation of the method steps for predicting and controlling indoor air quality in a building according to an embodiment of this application. This embodiment provides a method for predicting and controlling indoor air quality in a building. In one embodiment, as shown... Figure 1 As shown, the method includes: Step 110: Obtain environmental data for multiple areas within the building.

[0029] In this step, environmental data includes, but is not limited to, PM2.5 concentration, CO2 concentration, TVOC content, temperature and humidity, and population density information.

[0030] Step 120: Obtain the current pedestrian traffic and the historical pedestrian traffic for the preset historical time period in the target area.

[0031] Step 130: Obtain the corresponding first ventilation parameter based on the current pedestrian flow, and obtain the corresponding second ventilation parameter based on the historical pedestrian flow.

[0032] In this step, the first ventilation parameter is greater than or equal to 1 and is directly proportional to the current pedestrian flow, while the second ventilation parameter is greater than or equal to 1 and is directly proportional to the historical pedestrian flow. The second ventilation parameter, obtained based on historical data, can, on the one hand, predict the future pedestrian flow trend of the target area based on historical pedestrian flow, thus achieving a pre-compensation effect for ventilation; on the other hand, it can match the corresponding air exchange efficiency based on historical pedestrian flow, thereby providing appropriate ventilation efficiency for the target area.

[0033] Step 140: Set the baseline ventilation conditions for the target area based on environmental parameters.

[0034] Step 150: Based on the first ventilation parameter and the second ventilation parameter, adjust the baseline ventilation condition in real time to obtain the first corrected ventilation condition.

[0035] In this step, the baseline ventilation condition is multiplied sequentially by the first ventilation parameter and the second ventilation parameter to obtain the first corrected ventilation condition. For example, the wind speed, swing speed, and humidity adjustment speed in the baseline ventilation condition are multiplied by the first ventilation parameter and the second ventilation parameter, respectively, to obtain the first corrected wind speed, swing speed, and humidity adjustment speed.

[0036] Step 160: Obtain the third ventilation parameter based on the difference between the actual rate of change of the environmental parameters under the first-corrected ventilation condition and the preset rate of change.

[0037] In this step, the third ventilation parameter is greater than or equal to 1, and the third ventilation parameter is directly proportional to the difference.

[0038] Step 170: Based on the third ventilation parameter, adjust the first corrected ventilation condition to obtain the second corrected ventilation condition.

[0039] In this step, the first corrected ventilation condition is multiplied by the third ventilation parameter to obtain the second corrected ventilation condition.

[0040] In this embodiment, environmental data from multiple areas within the building and current and historical pedestrian traffic in the target area are acquired to generate a first ventilation parameter (reflecting real-time air pollution load) and a second ventilation parameter (reflecting past air pollution load) of not less than 1. A first correction is then made based on a set baseline ventilation condition, achieving on-demand ventilation adjustment with pre-compensation capabilities. Furthermore, by comparing the difference between the actual rate of change of the environmental parameters under the first correction condition and the preset rate of change, a third ventilation parameter of not less than 1 is generated, and a second correction is performed accordingly. This avoids the first correction ventilation condition failing to achieve reasonable or ideal ventilation efficiency, thus enabling rapid compensation for sudden pollution events or model deviations. The overall method balances energy efficiency, predictability, and robustness, ensuring indoor air quality meets standards while effectively avoiding energy waste or decreased comfort caused by response lag or excessive conservatism in traditional control methods.

[0041] Figure 2 The diagram illustrates the steps of a method for setting a preset rate of change. In one embodiment, as shown... Figure 2 As shown, the preset rate of change is set through the following steps: Step 210: Obtain multiple change rates of the target area when the same modified ventilation condition is triggered in the historical database.

[0042] Step 220: Calculate the average of multiple rates of change to obtain the preset rate of change.

[0043] In this embodiment, the setting benchmark for the third ventilation parameter is based on historical operating data consistent with the current environmental parameters. Specifically, in step 210, the system does not arbitrarily collect historical change rates, but rather limits them to a unified standard judgment for a single parameter under the same corrected ventilation condition: for example, when the CO2 concentration is in the same range, and a corrected ventilation condition is obtained after triggering the same value of the first and second ventilation parameters, the actual CO2 change rate detected in these historical scenarios is extracted. This ensures that the collected change rate is a referable actual CO2 response under similar pollution source intensities and similar ventilation intervention levels. It should be noted that the multiple change rates calculated in this step are calculated for a specific single parameter, rather than several CO2 change rates, several temperature change rates, or several humidity change rates.

[0044] Subsequently, in step 220, the average of these comparable historical change rates is taken as the preset change rate for the current moment. This average essentially represents how quickly the pollutant concentration changes on average under the current environmental and ventilation conditions. If the current actual change rate is significantly slower than this preset value (e.g., CO2 concentration decreases too slowly), it indicates that there may be a persistent pollution source not covered by the model or insufficient ventilation efficiency; conversely, if the change is too fast, it may be sensor noise or a brief air disturbance. Specifically, in the historical database, 10 change rates of the same first-correction ventilation condition are selected, and then the average of the 10 change rates is calculated. This embodiment can mitigate the impact of abnormal fluctuations on the overall data referenceability, thereby improving the reliability of the third ventilation parameter. Furthermore, when comparing the actual change rate and the preset change rate, it is also necessary to compare the change rates corresponding to the same environmental parameter. For example, if the preset change rate of CO2 is calculated in steps 210 and 220, then in step 160, the actual change rate of CO2 is monitored in real time, and the two are compared to obtain the difference.

[0045] This embodiment extracts the actual observed pollutant concentration change rates of the target area under the same environmental parameters and based on a primary corrected ventilation condition across multiple historical time periods, and uses the average value of these rates as a preset change rate. This establishes a dynamic benchmark that matches the current control strategy (i.e., the primary corrected condition). This method ensures that the "preset change rate" is no longer a fixed empirical value, but rather reflects the typical response behavior of the system under similar operating and control conditions. This significantly improves the accuracy of identifying abnormal changes: when the actual change rate deviates significantly from this historical average, sudden pollution or equipment malfunction can be more reliably identified, thereby triggering precise secondary ventilation correction, avoiding erroneous actions or missed responses, and enhancing the stability and adaptability of the entire air quality control system.

[0046] Figure 3 The diagram illustrates another method for setting a preset rate of change. In another embodiment, as shown... Figure 3 As shown, the preset rate of change is set through the following steps: Step 310: Calculate the theoretical rate of change of the target area under the same environmental parameters based on a single modified ventilation condition, and obtain the preset rate of change.

[0047] In this embodiment, by using a first-corrected ventilation condition, the air velocity in the target area can be theoretically calculated to obtain the theoretical rate of change of environmental parameters, which is then used as the preset rate of change. Based on the physical model and the current ventilation control state, the theoretical rate of change of pollutant concentration under ideal conditions is directly derived and used as a benchmark (i.e., the preset rate of change) to determine whether the actual operation is abnormal. Specifically, in step 310, the system uses the determined "first-corrected ventilation condition" (including fresh air volume, supply and exhaust mode, fan speed, etc.) combined with the spatial volume of the target area, air flow path, and mixing assumptions to calculate the theoretical air exchange efficiency or air velocity of indoor air through a fluid dynamics or mass balance model (such as a fully mixed chamber model). Then, based on this velocity and the current pollution source intensity (which can be indirectly reflected by environmental parameters such as CO2 and TVOC), the attenuation rate or change trend of pollutant concentration under ideal ventilation is derived, i.e., the "theoretical rate of change," which is then directly used as the preset rate of change. This embodiment eliminates the reliance on historical data and instead adopts a mechanism-driven modeling approach, giving the preset rate of change a clear physical meaning and real-time adaptability—as long as the ventilation conditions are updated once, the theoretical rate of change can be recalculated synchronously, always remaining consistent with the current control strategy.

[0048] This embodiment can improve the system's adaptability in scenarios where there is a lack of sufficient historical data (such as in newly built buildings or newly opened rooms) or in cases of sudden changes in operating conditions. At the same time, since the theoretical model can accurately reflect the quantitative relationship between ventilation volume and pollutant dilution, it is more sensitive and reliable in detecting deviations from the actual rate of change. It can identify abnormal situations such as ventilation failure, sudden increase in pollution sources, or sensor failure earlier, thereby triggering precise secondary ventilation correction and achieving intelligent air quality control with high responsiveness and low false alarm rate.

[0049] Figure 4 The diagram illustrates the steps of setting the first ventilation parameter. In one embodiment, as shown... Figure 4 As shown, the building's indoor air quality prediction and circulation control method also includes: Step 410: Set the range of the first ventilation parameter to be greater than or equal to 1 and less than or equal to 2.

[0050] Step 420: When the current pedestrian flow in the target area is 0, the corresponding first ventilation parameter is 1.

[0051] Step 430: When the current pedestrian flow in the target area is greater than or equal to the first preset upper limit pedestrian flow, the corresponding first ventilation parameter is 2.

[0052] In this embodiment, the first ventilation parameter is limited to the interval [1, 2] and a linear mapping relationship with the current flow of people is established: when the current flow of people in the target area is 0, the first ventilation parameter is 1; when the flow of people reaches or exceeds the preset upper limit (i.e., the first preset upper limit flow of people, such as the maximum capacity of the target area), the first ventilation parameter is 2; the flow of people in the middle is interpolated proportionally. The first ventilation parameter is designed as a bounded, monotonic, and interpretable adjustment coefficient, the physical meaning of which is "to increase the fresh air volume by up to 2 times on the basis of the baseline ventilation conditions". The upper limit (2 times) is set based on the actual capacity of the building HVAC system, energy consumption constraints and marginal benefits of pollutant dilution efficiency, to avoid fan overload or energy waste due to flow of people estimation errors or extreme values; the lower limit (1) ensures that when there is no one, the basic ventilation is returned to and the minimum hygiene standard is maintained.

[0053] In this embodiment, by strictly limiting the parameter range, control commands are prevented from exceeding the physical limits of the equipment or causing system oscillations. Unrestrained increases in airflow are avoided, and energy consumption is controlled within a reasonable range while meeting the needs for diluting personnel pollution loads. A clear proportional relationship exists between personnel flow and the degree of ventilation enhancement, facilitating debugging, verification, and user understanding, thereby improving system deployability and robustness.

[0054] Figure 5 The diagram illustrates the steps of setting the second ventilation parameter. In one embodiment, as shown... Figure 5 As shown, the building's indoor air quality prediction and circulation control method also includes: Step 510: Set the range of the second ventilation parameter to be greater than or equal to 1 and less than or equal to 2.

[0055] Step 520: In the target area, when the historical pedestrian flow is 0 for a preset time backward from the current moment, the corresponding second ventilation parameter is 1.

[0056] Step 530: In the target area, when the historical pedestrian flow over a preset time period is greater than or equal to the second preset upper limit pedestrian flow, the corresponding second ventilation parameter is 2.

[0057] In this embodiment, the second ventilation parameter is limited to a value range of [1, 2] and is set based on the historical pedestrian flow over a first preset time period (e.g., 10 to 30 minutes) prior to the current moment: when the historical pedestrian flow is 0 during this time period, the second ventilation parameter is set to 1; when the historical pedestrian flow reaches or exceeds the design maximum capacity of the target area (i.e., the second preset upper limit pedestrian flow), the second ventilation parameter is set to 2, and the intermediate values ​​are mapped linearly. Short-term (10–30 minutes) historical pedestrian flow is used to reflect the recent usage intensity of the space, and the "design maximum capacity" defined in building codes is used as a standardized upper limit benchmark to construct a feedforward adjustment mechanism with clear physical meaning and engineering feasibility. Since human activities have short-term continuity (e.g., meetings and gatherings do not disappear instantly), even if the current instantaneous pedestrian flow decreases, if the space was recently used at near full capacity, a high ventilation level still needs to be maintained to remove residual pollutants. This parameter is essentially a quantitative compensation for the inertia of space use, and its upper limit is anchored to building design standards, ensuring that the control logic is consistent with the building function.

[0058] This embodiment prevents premature ventilation attenuation during use. During brief periods of absence or sensor sampling intervals, it maintains adequate fresh air based on recent high-traffic records, preventing CO2 or TVOC levels from rebounding and exceeding limits due to a sudden drop in ventilation. It can be standardized and adapted to different spaces, using the maximum designed capacity of each area as a benchmark, allowing the same control strategy to be adaptively applied to spaces of different sizes and uses, such as conference rooms, classrooms, and exhibition halls. Furthermore, it can enhance ventilation when there is evidence of recent use and revert to the baseline when there is no one present and no recent activity, avoiding energy waste caused by prolonged high-volume operation. In summary, this embodiment combines short-term historical traffic flow (10–30 minutes) with the area's design capacity to achieve reasonable inference and robust feedforward control of space usage status, improving air quality stability while ensuring system versatility, safety, and energy efficiency.

[0059] Figure 6 The diagram illustrates the steps for setting the third ventilation parameter. In one embodiment, as shown... Figure 6 As shown, the building's indoor air quality prediction and circulation control method also includes: Step 610: When the difference is set to 0, the third ventilation parameter is set to 1.

[0060] Step 620: When the absolute value of the difference is equal to the preset rate of change, the third ventilation parameter is 2.

[0061] In this embodiment, the third ventilation parameter is used to make a secondary dynamic adjustment to the first corrected ventilation condition. Its magnitude reflects the system's judgment strength on whether the current air quality change "deviates from expectations." In step 610, when the actual rate of change is exactly the same as the preset rate of change (i.e., the difference is 0), it indicates that the pollutant concentration is decreasing or stabilizing according to the expected trend. At this time, there is no need to increase ventilation, so the third ventilation parameter is set to 1 to keep the current condition unchanged. In step 620, when the absolute value of the difference reaches the preset rate of change itself (i.e., the actual rate of change is 0 or twice the preset value, etc., extreme deviations), it indicates that the system response is seriously lagging or there is a sudden pollution source. At this time, the third ventilation parameter is set to the upper limit value of 2, that is, the ventilation intensity is increased to twice the current condition to quickly suppress the risk. The intermediate difference can be interpolated by a linear or nonlinear function to achieve smooth adjustment. This embodiment establishes a relative deviation perception mechanism with the preset rate of change as the scale unit, so that the control response is proportional to the degree of deviation, and at the same time, the parameter range of [1, 2] is limited to prevent overreaction.

[0062] In this embodiment, ventilation is only enhanced when air quality changes significantly deviate from expectations, avoiding frequent adjustments due to minor fluctuations. Using a "preset rate of change" as the benchmark unit, the system can respond reasonably to anomalies in different pollutants (e.g., a slow rise in CO2 versus a sudden increase in TVOC). Regarding ventilation operation, the maximum increase is only double the airflow, ensuring effective dilution of sudden pollution while preventing fan overload or energy consumption surges. The correspondence between parameters and deviations is clear, facilitating engineering deployment and maintenance. In summary, this embodiment, through a structured difference-parameter mapping rule, enables the third-level ventilation correction to possess responsiveness, control stability, and engineering practicality, effectively improving the overall intelligence level of the air quality control system.

[0063] Figure 7 The diagram illustrates the steps of a method for defining baseline ventilation conditions based on a fenced area. In one embodiment, as... Figure 7 As shown, the building's indoor air quality prediction and circulation control method also includes: Step 710: If the target area is a walled area, obtain the area of ​​the walled area.

[0064] Step 720: Obtain the fourth ventilation parameter based on the area of ​​the enclosure.

[0065] Step 730: Correct the baseline ventilation condition based on the fourth ventilation parameter.

[0066] In this embodiment, the fourth ventilation parameter is directly proportional to the enclosure area. When the enclosure area equals the baseline area, the fourth ventilation parameter is 1; when the enclosure area is less than the baseline area, the fourth ventilation parameter is greater than 0 and less than 1; when the enclosure area is greater than or equal to twice the baseline area, the fourth ventilation parameter is 2. The baseline area is set to 100 square meters. An enclosure area greater than the baseline area means that the area is too open and requires increased ventilation (i.e., ventilation condition). When the enclosure area is between one and two times the baseline area, the fourth ventilation parameter increases proportionally.

[0067] This embodiment introduces the enclosure area as a quantitative indicator of the physical form of building space, and constructs a ventilation compensation mechanism based on the enclosure or openness of a region to more accurately match the actual needs of different spatial structures for air circulation efficiency. In step 710, the system first identifies whether the target area is a relatively independent space enclosed by walls (i.e., "wall-enclosed area"), and obtains its enclosure area (usually referring to the usable ground area of ​​the area). In step 720, the area of ​​the enclosure is compared with a preset benchmark area (e.g., 100 square meters), and a fourth ventilation parameter is generated accordingly: when the enclosure area is equal to 100 square meters, the fourth ventilation parameter is 1, indicating that the operation is performed according to the standard benchmark ventilation conditions; when the enclosure area is less than 100 square meters (e.g., small independent offices, cubicles), it indicates that the space is compact, the air mixing efficiency is high, and pollutants are easy to accumulate, but the required total amount of fresh air is small, so the fourth ventilation parameter is between (0, 1), and the ventilation intensity is appropriately reduced to save energy; when the enclosure area is greater than 100 square meters (e.g., large open office areas, large open spaces without partitions), the space is too open, the airflow organization is prone to unevenness, and ventilation dead zones may appear in some areas, so it is necessary to enhance the overall air supply to ensure the uniformity of air exchange. At this time, the fourth ventilation parameter is between [1, 2], and the benchmark conditions are positively corrected. This parameter is directly proportional to the enclosure area, reflecting the control logic that the larger the area, the stronger the required ventilation compensation.

[0068] This embodiment adapts to differences in spatial form during application: breaking through the limitations of traditional ventilation settings based solely on the number of people or volume, it incorporates the building's physical layout (open / closed) into the control logic, improving the rationality of regulation. It proactively enhances ventilation in large open areas, alleviating air quality stratification or localized exceedances caused by insufficient air supply coverage. It avoids excessive ventilation in small spaces, appropriately reducing airflow in small cubicles to minimize ineffective energy consumption while ensuring ventilation. Parameters are standardized and scalable: a unified benchmark of 100 square meters is provided, facilitating rapid deployment and adjustment in different projects. In summary, this embodiment, by introducing a fourth ventilation parameter driven by the enclosure area, achieves intelligent perception of building space geometry and refined adaptation of ventilation strategies, further improving the accuracy, energy efficiency, and engineering applicability of indoor air quality control.

[0069] Figure 8 The diagram illustrates the steps of a method for ventilation based on a ceiling height limit. In one embodiment, as... Figure 8 As shown, the building's indoor air quality prediction and circulation control method also includes: Step 810: If the ceiling height of the target area is greater than or equal to the preset ceiling height, then generate a ceiling height correction parameter. In this step, the ceiling height correction parameter is proportional to the ceiling height, and the ceiling height correction parameter is greater than or equal to 1 and less than or equal to 2.

[0070] Step 820: Correct the baseline ventilation conditions based on the height correction parameters.

[0071] In this embodiment, a ventilation compensation mechanism for tall spaces is constructed by introducing the ceiling height as a key feature of the vertical scale of the building space to solve the problems of decreased air mixing efficiency and pollutant retention caused by increased floor height. In step 810, the system first determines whether the ceiling height (i.e., the net indoor height) of the target area reaches or exceeds a preset ceiling height threshold (e.g., 4.5 meters or 5 meters, typical tall spaces such as LOFT office areas, atrium-style halls, and industrial-style open office areas). If the condition is met, a ceiling height correction parameter is generated. This parameter is proportional to the actual ceiling height and is limited to the interval [1, 2]: when the ceiling height is equal to the preset ceiling height, the correction parameter is 1; as the ceiling height further increases (e.g., 6 meters, 8 meters), the correction parameter increases linearly or piecewise to a maximum value of 2. In step 820, this parameter is used to amplify and correct the baseline ventilation conditions, that is, to increase the fresh air volume or the fan supply intensity to compensate for the weakening of airflow dilution capacity caused by the increase in space height. With the same floor area, a higher ceiling significantly increases the volume of the space. Conventional air supply modes (such as side supply or bottom supply with top return) tend to create a stagnant zone of hot / stale air at the top, leading to reduced ventilation efficiency in the area where people are active (usually from the ground to 1.8 meters). Therefore, it is necessary to maintain effective ventilation by increasing the ventilation volume or optimizing the airflow organization. In some embodiments, when the ceiling height is greater than or equal to twice the preset ceiling height, the ceiling height correction parameter is 2, thereby avoiding an excessively large value for the ceiling height correction parameter.

[0072] This embodiment, when applied, can precisely adapt to the needs of high-ceilinged spaces, avoiding the application of ventilation standards for ordinary office floor heights to high-ceilinged areas and preventing the accumulation of CO2 or particulate matter in lower zones due to insufficient ventilation. It mitigates the dual risks of energy waste and insufficient ventilation by setting the upper limit of the correction parameter to 2, ensuring necessary enhancement while preventing unlimited increases in airflow that could overload fans or cause a surge in energy consumption. This allows the same ventilation control strategy to be seamlessly applied to modern office or commercial buildings with a mix of standard floor heights and high ceilings. It is particularly suitable for high-opening spaces employing natural ventilation or simplified mechanical systems, ensuring that air quality in the breathing zone meets standards. In summary, this embodiment, through the correction parameter driven by ceiling height, incorporates the building's vertical dimension into the intelligent ventilation decision-making system, achieving comprehensive perception and efficient response to the three-dimensional form of the space, significantly improving the scientific rigor and reliability of indoor air quality control in high-ceilinged spaces.

[0073] Figure 9 The diagram illustrates the steps of setting a baseline ventilation condition. In one embodiment, as shown... Figure 9 As shown, the building's indoor air quality prediction and circulation control method also includes: Step 910: Obtain the building parameters.

[0074] Step 920: Obtain the corresponding baseline ventilation conditions based on the building parameters.

[0075] In this embodiment, the baseline ventilation conditions are not set uniformly based on the building's parameters (such as shopping malls, office buildings, and public service halls) and specific uses. Instead, they should be configured differently based on national / local regulations, spatial functional characteristics, intensity of human activity, and types of pollutants. The following are examples illustrating different building types: Example 1: The building type corresponding to the building parameters is an office building; the characteristics of this use are: moderate personnel density but long-term stay (more than 8 hours), and the main pollutants are CO2 produced by human metabolism and trace amounts of TVOC (from office equipment and furniture); the baseline ventilation condition setting is based on the "Office" category in the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" (GB 50736), usually set at a fresh air volume per person ≥30 m³ / (h·person) or an air exchange rate ≥1.5 times / h. Target areas include open-plan office areas and private offices. For open-plan office areas, the minimum fresh air volume can be calculated based on the area and the maximum number of permanent residents as the baseline ventilation condition; for private offices, it is dynamically adjusted according to the room volume and the number of users. This can ensure cognitive performance and comfort in long-term working environments and avoid fatigue caused by CO2 accumulation. In existing technologies, the setting of baseline ventilation conditions for office spaces distinguishes between open-plan office areas and private offices: For open-plan office areas, the maximum number of permanent residents that the area can accommodate is estimated based on its usable area and the recommended per capita usable area in office building codes (e.g., 5-6 square meters / person); then, the total amount of fresh air required to meet the needs of all personnel is calculated based on the minimum fresh air volume per person for office spaces specified in HVAC design standards (e.g., GB 50736) (generally 30 cubic meters / hour / person); at the same time, the fresh air volume required to maintain the overall air renewal of the space is also calculated based on the volume of the area (area multiplied by floor height) and the minimum air exchange rate required by the code (generally 1.0-1.5 times / hour); finally, the larger of the two values ​​is taken as the baseline ventilation condition for the open-plan office area. For independent offices, given the fixed number of occupants and limited space, the standard ventilation requirement is typically calculated by multiplying the maximum number of users (e.g., 1-4 people) within the room by the average fresh air volume per person (generally 30 cubic meters per hour per person). This is then compared to the ventilation requirement calculated based on room volume, and the larger value is taken to determine the baseline ventilation conditions. This method ensures that both types of office spaces maintain basic ventilation when unoccupied or under low load, and guarantees air quality when fully occupied, providing a reasonable starting point for subsequent dynamic adjustments.

[0076] Example 2: The building parameters correspond to a large shopping mall; the characteristics of this use are: high population mobility and drastic density fluctuations (reaching 5-10 people / ㎡ during peak hours), and complex pollution sources (TVOC in the catering area, volatile organic compounds from cosmetics, particulate matter carried by people, etc.). The baseline ventilation conditions are set differently according to functional zones: Public atrium / corridor: Based on an air exchange rate of ≥2 times / hour, while taking into account smoke extraction and dilution; Food and beverage area: In addition to basic fresh air, forced local exhaust ventilation is implemented, and the benchmark fresh air volume is increased to 40–50 m³ / (h·person); Retail stores: Based on the store area and maximum capacity, set at 30 m³ / (h·person); This baseline ventilation setting can maintain basic air cleanliness under high traffic flow, suppress cross-contamination of odors, and increase customers' willingness to stay.

[0077] Example 3: The building parameters correspond to the type of public service hall; the characteristics of this use are: short-term gathering of people, high density of waiting areas but short stay time for each person (15-60 minutes), intermittent peaks (such as 9-11 am), and high requirements for public health and safety; Baseline ventilation settings: Prioritize air quality in waiting areas and window areas. Calculate the baseline fresh air volume based on the maximum instantaneous passenger flow (determined by the hall area and evacuation regulations), typically ≥40 m³ / (h·person) or air changes ≥2.5 times / h. Considering the openness of the space, the uniformity of air supply can be appropriately increased. This can reduce the risk of cross-infection (such as during flu season) and improve the hygiene image of the public service environment and public satisfaction.

[0078] An example building indoor air quality prediction and circulation control system is as follows: Figure 10 The diagram shown is a schematic representation of a building indoor air quality prediction and circulation control system according to an embodiment of this application. This application also provides a building indoor air quality prediction and circulation control system, such as... Figure 10 As shown, the system includes: a data acquisition module 1001, a ventilation parameter setting module 1002, and a ventilation compensation module 1003. The data acquisition module 1001 is configured to: acquire environmental data of multiple areas within the building; acquire the current pedestrian flow and the historical pedestrian flow for a preset historical time period in the target area.

[0079] The ventilation parameter setting module 1002 is communicatively connected to the data acquisition module 1001. The ventilation parameter setting module 1002 is configured to: obtain the corresponding first ventilation parameter based on the current flow of people, and obtain the corresponding second ventilation parameter based on the historical flow of people (historical data can play a pre-compensation role); the first ventilation parameter is greater than or equal to 1 and is proportional to the current flow of people, and the second ventilation parameter is greater than or equal to 1 and is proportional to the historical flow of people; and set the benchmark ventilation conditions of the target area based on environmental parameters.

[0080] The ventilation compensation module 1003 is communicatively connected to the ventilation parameter setting module 1002. The ventilation compensation module 1003 is configured to: adjust the baseline ventilation condition in real time based on the first ventilation parameter and the second ventilation parameter to obtain a first corrected ventilation condition; obtain a third ventilation parameter based on the difference between the actual rate of change of the environmental parameter based on the first corrected ventilation condition and the preset rate of change; the third ventilation parameter is greater than or equal to 1 and is proportional to the difference; and adjust the first corrected ventilation condition based on the third ventilation parameter to obtain a second corrected ventilation condition.

[0081] In this embodiment, environmental data from multiple areas within the building and current and historical pedestrian traffic in the target area are acquired to generate a first ventilation parameter (reflecting real-time air pollution load) and a second ventilation parameter (reflecting past air pollution load) of not less than 1. A first correction is then made based on a set baseline ventilation condition, achieving on-demand ventilation adjustment with pre-compensation capabilities. Furthermore, by comparing the difference between the actual rate of change of the environmental parameters under the first correction condition and the preset rate of change, a third ventilation parameter of not less than 1 is generated, and a second correction is performed accordingly. This avoids the first correction ventilation condition failing to achieve reasonable or ideal ventilation efficiency, thus enabling rapid compensation for sudden pollution events or model deviations. The overall method balances energy efficiency, predictability, and robustness, ensuring indoor air quality meets standards while effectively avoiding energy waste or decreased comfort caused by response lag or excessive conservatism in traditional control methods.

[0082] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0083] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0084] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0085] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features of the invention herein.

[0086] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for predicting and controlling indoor air quality in buildings, characterized in that, include: Acquire environmental data from multiple areas within the building; Obtain the current pedestrian traffic in the target area and the historical pedestrian traffic for a preset historical time period; A first ventilation parameter is obtained based on the current pedestrian flow, and a second ventilation parameter is obtained based on the historical pedestrian flow; the first ventilation parameter is greater than or equal to 1 and is proportional to the current pedestrian flow, and the second ventilation parameter is greater than or equal to 1 and is proportional to the historical pedestrian flow. The baseline ventilation conditions for the target area are set based on the environmental parameters. Based on the first ventilation parameter and the second ventilation parameter, the baseline ventilation condition is adjusted in real time to obtain a corrected ventilation condition; A third ventilation parameter is obtained based on the difference between the actual rate of change and the preset rate of change of the environmental parameters under the first-corrected ventilation condition; the third ventilation parameter is greater than or equal to 1, and the third ventilation parameter is proportional to the difference; and Based on the third ventilation parameter, the first corrected ventilation condition is adjusted to obtain the second corrected ventilation condition.

2. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, The preset rate of change is set through the following steps: Obtain multiple change rates of the target area in the historical database when the same modified ventilation condition is triggered; and The average of the multiple rates of change is calculated to obtain the preset rate of change.

3. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, The preset rate of change is set through the following steps: The theoretical rate of change of the target area under the same environmental parameters based on the first-corrected ventilation condition is calculated to obtain the preset rate of change.

4. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, Also includes: The value range of the first ventilation parameter is set to be greater than or equal to 1 and less than or equal to 2; When the current pedestrian flow in the target area is set to 0, the first ventilation parameter is set to 1. as well as When the current pedestrian flow in the target area is greater than or equal to the first preset upper limit pedestrian flow, the first ventilation parameter is set to 2.

5. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, Also includes: The value range of the second ventilation parameter is set to be greater than or equal to 1 and less than or equal to 2; When the historical pedestrian flow in the target area is set to 0 for a preset time backward from the current moment, the second ventilation parameter is set to 1. as well as In the target area, when the historical pedestrian flow over a preset time period is greater than or equal to the second preset upper limit pedestrian flow, the second ventilation parameter is set to 2.

6. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, Also includes: When the difference is set to 0, the third ventilation parameter is set to 1. as well as When the absolute value of the difference is set to be equal to the preset rate of change, the third ventilation parameter is 2.

7. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, Also includes: If the target area is a walled area, obtain the area of ​​the walled area; The fourth ventilation parameter is obtained based on the area of ​​the enclosure. as well as The baseline ventilation condition is corrected based on the fourth ventilation parameter; the fourth ventilation parameter is proportional to the area of ​​the enclosure; when the area of ​​the enclosure is equal to the baseline area, the fourth ventilation parameter is 1; when the area of ​​the enclosure is less than the baseline area, the fourth ventilation parameter is greater than 0 and less than 1; when the area of ​​the enclosure is greater than or equal to twice the baseline area, the fourth ventilation parameter is 2.

8. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, Also includes: If the top height of the target area is greater than or equal to the preset height, then a height correction parameter is generated; The height correction parameter is directly proportional to the top height, and the height correction parameter is greater than or equal to 1 and less than or equal to 2. as well as The baseline ventilation conditions are corrected based on the height correction parameters.

9. The method for predicting and controlling indoor air quality in buildings as described in claim 1, characterized in that, Also includes: Obtain the building parameters of the building; as well as The corresponding baseline ventilation conditions are obtained based on the building parameters.

10. A building indoor air quality prediction and circulation control system, characterized in that, include: The data acquisition module is configured to: acquire environmental data for multiple areas within the building; acquire the current pedestrian traffic and historical pedestrian traffic for a preset historical time period for the target area; A ventilation parameter setting module is communicatively connected to the data acquisition module. The ventilation parameter setting module is configured to: obtain a corresponding first ventilation parameter based on the current pedestrian flow, and obtain a corresponding second ventilation parameter based on the historical pedestrian flow; the first ventilation parameter is greater than or equal to 1 and is proportional to the current pedestrian flow, and the second ventilation parameter is greater than or equal to 1 and is proportional to the historical pedestrian flow; and set the baseline ventilation conditions of the target area based on the environmental parameters. as well as A ventilation compensation module is communicatively connected to the ventilation parameter setting module. The ventilation compensation module is configured to: adjust the baseline ventilation condition in real time based on the first ventilation parameter and the second ventilation parameter to obtain a first-corrected ventilation condition; obtain a third ventilation parameter based on the difference between the actual rate of change of the environmental parameter based on the first-corrected ventilation condition and the preset rate of change; the third ventilation parameter is greater than or equal to 1 and is proportional to the difference; and adjust the first-corrected ventilation condition based on the third ventilation parameter to obtain a second-corrected ventilation condition.

Images