Dynamic Control Method and System for Exhaust Fans Based on Ventilation and Exhaust Regulation

Abstract

This invention discloses a dynamic control method and system for exhaust fans based on ventilation and exhaust adjustment, relating to the field of data processing technology. The method includes: acquiring exhaust fan nodes, acquiring the main triggering area and secondary triggering area of ​​the i-th exhaust fan node; acquiring a preset time period, acquiring a processing window, acquiring a specified concentration data sequence of the i-th exhaust fan node, and acquiring the current specified concentration data of the i-th exhaust fan node; acquiring an increasing trend value, acquiring a secondary demand value, and acquiring a primary demand value; if the increasing trend value exceeds a first preset threshold, acquiring a first control strategy for the i-th exhaust fan node based on the primary demand value of the i-th exhaust fan node; if the increasing trend value does not exceed the first preset threshold and the correction value exceeds a second preset threshold, acquiring a second control strategy for the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node. This invention has the advantages of progressive pre-control, coordinated dynamic control, and reduced energy consumption.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a dynamic control method and system for exhaust fans based on ventilation and exhaust adjustment. Background Technology

[0002] In ventilation and exhaust duct systems, when the concentration of a specified gas (such as CO2, VOCs, etc.) in the main trigger area directly controlled by a certain exhaust fan node has not yet reached the preset start-up threshold, the existing technology lacks an effective dynamic prediction and coordination mechanism, resulting in multiple defects in the system.

[0003] First, existing methods only monitor the instantaneous concentration in the main triggering area and cannot identify potential risks of a continuous increase in the concentration of a specified gas even if it does not exceed the standard (such as a gradual increase in CO2 due to excessive personnel). If the fan is started only after the concentration exceeds the standard, it will result in the inability to quickly reduce the concentration of the specified gas. Not only will the sudden high-intensity ventilation bring energy consumption peaks, but the specified gas may have already spread to adjacent areas.

[0004] Secondly, the areas controlled by each exhaust fan node in the exhaust branch are interconnected (such as multiple sub-workshops sharing an air duct). However, the existing technology treats each node as an independent unit. When a certain area does not exceed the standard but an adjacent secondary trigger area (such as a sub-workshop or warehouse located on the same branch) suddenly becomes polluted, the exhaust fan node in that area remains closed. It is impossible to contain the spread of pollution through coordinated ventilation, resulting in local environmental deterioration.

[0005] Finally, when the pollution level is within acceptable limits, the exhaust fan nodes remain completely inactive, failing to suppress pollution trends through gradual ventilation or respond to hidden risks in the branch network as a whole (such as multiple secondary triggering areas simultaneously approaching the threshold), effectively wasting the adjustment capacity of ventilation resources. Summary of the Invention

[0006] To address the technical problem of passively waiting for a specified gas to accumulate to a critical point within a safe range where the concentration is below the activation threshold, which neither prevents sudden pollution events nor avoids missing the low-energy intervention window, resulting in low ventilation efficiency, energy waste, and the risk of local environmental loss of control, this invention provides a dynamic control method and system for exhaust fans based on ventilation and exhaust adjustment.

[0007] A dynamic control method for exhaust fans based on ventilation and exhaust adjustment includes: acquiring a ventilation and exhaust duct network and multiple exhaust fan nodes set on the ventilation and exhaust duct network; defining the control area directly connected to the i-th exhaust fan node as the main trigger area of ​​the i-th exhaust fan node; and defining the control areas directly connected to multiple exhaust fan nodes along the exhaust branch where the i-th exhaust fan node is located as multiple secondary trigger areas of the i-th exhaust fan node; acquiring a preset time period; acquiring the preset time period preceding the current time of the i-th exhaust fan node as a processing window; acquiring the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node within the processing window; and acquiring the data sequence of each secondary trigger area of ​​the i-th exhaust fan node within the processing window. The current specified concentration data at the current moment; the increasing trend value is obtained based on the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node, and the secondary demand value is obtained based on the current specified concentration data of each secondary trigger area of ​​the i-th exhaust fan node, and the main demand value of the i-th exhaust fan node is obtained based on the increasing trend value and the secondary demand value; if the increasing trend value exceeds the first preset threshold, the first control strategy of the i-th exhaust fan node is obtained based on the main demand value of the i-th exhaust fan node; if the increasing trend value does not exceed the first preset threshold and the correction value exceeds the second preset threshold, the second control strategy of the i-th exhaust fan node is obtained based on the secondary demand value of the i-th exhaust fan node.

[0008] Optionally, obtaining the increasing trend value based on the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node includes: dividing the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node into multiple consecutive sub-processing segments in chronological order; obtaining the average specified concentration data of each sub-processing segment, and obtaining an increasing mark based on the positive or negative difference between the average specified concentration data of adjacent sub-processing segments; obtaining the increasing percentage based on the number of increasing marks and the number of sub-processing segments, and using it as the increasing trend value of the i-th exhaust fan node.

[0009] Optionally, obtaining the secondary demand value based on the current specified concentration data of each secondary triggering area of ​​the i-th exhaust fan node includes: obtaining the specified concentration threshold of each secondary triggering area of ​​the i-th exhaust fan node, and obtaining the excess ratio based on the current specified concentration data and specified concentration threshold of each secondary triggering area of ​​the i-th exhaust fan node; accumulating the excess ratio of all secondary triggering areas of the i-th exhaust fan node and obtaining the secondary demand value.

[0010] Optionally, obtaining the primary demand value of the i-th exhaust fan node based on the increasing trend value and secondary demand value of the i-th exhaust fan node includes: obtaining a correction ratio based on the increasing trend value; multiplying the correction ratio by the secondary demand value to obtain the primary demand value.

[0011] Optionally, obtaining the first control strategy for the i-th exhaust fan node based on the primary demand value of the i-th exhaust fan node includes: comparing the primary demand value of the i-th exhaust fan node with a plurality of preset first numerical ranges, and determining the target first numerical range in which the primary demand value of the i-th exhaust fan node is located; and obtaining the control command corresponding to the target first numerical range as the first control strategy based on the preset mapping relationship between the first numerical range and the control command.

[0012] Optionally, obtaining the second control strategy for the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node includes: comparing the secondary demand value of the i-th exhaust fan node with a plurality of preset second numerical ranges, and determining the target second numerical range in which the secondary demand value of the i-th exhaust fan node is located; and obtaining the control command corresponding to the target second numerical range as the second control strategy based on the preset mapping relationship between the second numerical range and the control command.

[0013] A dynamic control system for exhaust fans based on ventilation and exhaust adjustment is also provided. The system includes: a data association module, used to acquire the ventilation and exhaust duct network and multiple exhaust fan nodes set on the ventilation and exhaust duct network, and to take the control area directly connected to the i-th exhaust fan node as the main trigger area of ​​the i-th exhaust fan node, and to take the control areas directly connected to multiple exhaust fan nodes along the exhaust branch where the i-th exhaust fan node is located as multiple secondary trigger areas of the i-th exhaust fan node; and a data acquisition module, used to acquire a preset time period, and to acquire the preset time period before the current time of the i-th exhaust fan node as a processing window, and to acquire the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node within the processing window, and to acquire the data of each secondary trigger area of ​​the i-th exhaust fan node at the current time. The system includes: a current specified concentration data; a data processing module, used to obtain an increasing trend value based on the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node, and to obtain secondary demand values ​​based on the current specified concentration data of each secondary trigger area of ​​the i-th exhaust fan node, and to obtain the main demand value of the i-th exhaust fan node based on the increasing trend value and secondary demand values; a first control adjustment module, used to obtain a first control strategy for the i-th exhaust fan node based on the main demand value of the i-th exhaust fan node if the increasing trend value exceeds a first preset threshold; and a second control adjustment module, used to obtain a second control strategy for the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node if the increasing trend value does not exceed the first preset threshold and the correction value exceeds the second preset threshold.

[0014] Optionally, the data processing module is further configured to: divide the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node into multiple consecutive sub-processing segments in chronological order; obtain the average specified concentration data of each sub-processing segment, and obtain an incrementing marker based on the sign of the difference between the average specified concentration data of adjacent sub-processing segments; obtain the incrementing percentage based on the number of incrementing markers and the number of sub-processing segments, and use it as the incrementing trend value of the i-th exhaust fan node.

[0015] Optionally, the data processing module is further configured to: obtain the specified concentration threshold of each sub-triggering area of ​​the i-th exhaust fan node, and obtain the excess ratio based on the current specified concentration data and specified concentration threshold of each sub-triggering area of ​​the i-th exhaust fan node; accumulate the excess ratio of all sub-triggering areas of the i-th exhaust fan node and obtain the sub-demand value.

[0016] Optionally, the data processing module is also used to: obtain the correction ratio based on the increasing trend value; multiply the correction ratio by the secondary demand value to obtain the primary demand value.

[0017] The beneficial effects of this invention are reflected in:

[0018] In the dynamic control method of exhaust fans based on ventilation and exhaust adjustment, firstly, by quantifying the concentration increase trend in the main triggering area in real time, the risk of gradual deformation can be identified in the early stage of the accumulation of the specified gas. Gradual ventilation (such as continuous low air volume operation) is initiated when the concentration is far from the critical point. This effectively suppresses the potential deterioration path of the continuous rise of the specified gas, avoids the energy consumption peak caused by the passive waiting of existing technologies until the standard is exceeded and then suddenly exhausting at full power. It also compresses the diffusion window of the specified gas by early intervention, reducing the probability of its spread to adjacent areas. Secondly, a collaborative response mechanism for secondary triggering areas based on pipeline connectivity is constructed. When the main area is stable but a secondary area sharing the air duct suddenly becomes polluted (such as a chemical warehouse leak), the exhaust fan node is no longer regarded as an independent unit, but its low-speed operation is activated to establish a negative pressure barrier in the pipeline. At the same time, a joint defense command is sent to the associated nodes in the same branch. By increasing the air volume of multiple nodes synchronously, a collaborative interception network is formed. This not only blocks the path of backflow or diffusion of the specified gas through the air duct, but also significantly reduces the energy consumption for clearing cross-regional pollution through the distributed low-speed operation mode (rather than single-node full power). Finally, by fully utilizing the adjustment potential of exhaust fans in non-exceeding ranges, and through the dynamic needs of risk coupling between the main and secondary areas, it can both respond to the gradual ventilation needs of the main area and quickly capture sudden pollution signals in the secondary area. Attached Figure Description

[0019] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0020] Figure 1 This is a partial flowchart of the dynamic control method for exhaust fans based on ventilation and exhaust adjustment according to the present invention.

[0021] Figure 2 This is a schematic diagram of another part of the dynamic control method for exhaust fans based on ventilation and exhaust adjustment of the present invention;

[0022] Figure 3 This is a schematic diagram of the steps in one embodiment of the dynamic control method for exhaust fans based on ventilation and exhaust adjustment of the present invention.

[0023] Figure 4 This is a schematic diagram of part of step S3 in the dynamic control method of exhaust fan based on ventilation and exhaust adjustment of the present invention;

[0024] Figure 5 This is a schematic diagram of another part of step S3 in the dynamic control method of exhaust fan based on ventilation and exhaust adjustment of the present invention;

[0025] Figure 6 This is a schematic diagram of another part of step S3 in the dynamic control method of exhaust fan based on ventilation and exhaust adjustment of the present invention;

[0026] Figure 7 This is a schematic diagram of part of step S4 in the dynamic control method for exhaust fans based on ventilation and exhaust adjustment of the present invention;

[0027] Figure 8 This is a schematic diagram of part of step S5 in the dynamic control method of exhaust fan based on ventilation and exhaust adjustment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0029] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0030] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0031] like Figure 1 , Figure 2 and Figure 3 As shown, a dynamic control method for an exhaust fan based on ventilation regulation is provided. In one embodiment, the method includes:

[0032] S1. Obtain the ventilation and exhaust duct network and multiple exhaust fan nodes set on the ventilation and exhaust duct network, and take the control area directly connected to the i-th exhaust fan node as the main trigger area of ​​the i-th exhaust fan node, and take the control areas directly connected to multiple exhaust fan nodes along the exhaust branch where the i-th exhaust fan node is located as multiple secondary trigger areas of the i-th exhaust fan node.

[0033] S2. Obtain a preset time period, and obtain the preset time period before the current time of the i-th exhaust fan node as the processing window, and obtain the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node within the processing window, and obtain the current specified concentration data of each secondary trigger area of ​​the i-th exhaust fan node at the current time.

[0034] S3. Obtain the increasing trend value based on the specified concentration data sequence of the main triggering area of ​​the i-th exhaust fan node, obtain the secondary demand value based on the current specified concentration data of each secondary triggering area of ​​the i-th exhaust fan node, and obtain the main demand value of the i-th exhaust fan node based on the increasing trend value and secondary demand value of the i-th exhaust fan node.

[0035] S4. If the increasing trend value exceeds the first preset threshold, then obtain the first control strategy of the i-th exhaust fan node based on the main demand value of the i-th exhaust fan node.

[0036] S5. If the increasing trend value does not exceed the first preset threshold and the correction value exceeds the second preset threshold, then the second control strategy of the i-th exhaust fan node is obtained according to the secondary demand value of the i-th exhaust fan node.

[0037] In this embodiment, it should be noted that in S1, the relationship between each exhaust fan node and its physical space control range is clearly defined, and the mutual influence between areas caused by the connectivity of the pipeline network is particularly emphasized. Specifically, this is divided into two key definitions: First, the specific physical space in which the i-th exhaust fan node (such as the exhaust fan numbered i) is directly connected to and responsible for forcibly exhausting air in the pipeline network is defined as the "main triggering area" of this node. For example, in a large open-plan office design scenario, the workstation area covered by fan A (node ​​i) installed at a specific position on the ceiling and the surrounding area of ​​a few meters directly affected by its suction are the main triggering area of ​​fan A—this area mainly monitors the concentration of a specified gas and is its main responsibility.

[0038] Furthermore, S1 identifies other areas that are aerodynamically associated with the primary triggering area of ​​the node due to shared exhaust branches. These areas are defined as "secondary triggering areas" of the i-th exhaust fan node. This is based on the fact that the exhaust duct network is not entirely independent branches; the exhaust outlets of multiple exhaust fans often converge on the branch ducts of the same main trunk. Therefore, for fan A (node ​​i), the primary triggering area (such as the adjacent meeting room, rest area, or document storage room) of other exhaust fans (such as fan B, fan C, etc.) connected to the same physical branch duct connected to its exhaust outlet automatically becomes the secondary triggering area of ​​fan A. Although these secondary triggering areas are not directly controlled by fan A, the air they exhaust (which may carry specific gases) will affect each other within the duct, and may even backflow or diffuse within the branch. Essentially, S1 constructs a control domain for each exhaust fan node through the definition of primary / secondary triggering areas. This domain includes not only the primary triggering area directly under its jurisdiction but also the secondary triggering areas that have "joint responsibility" due to the connection of shared branches in the duct network, laying the physical foundation for regional correlation for subsequent collaborative early warning and control.

[0039] In S2, key, time-sensitive environmental data inputs are provided for each exhaust fan node (taking the i-th node as an example). This step first obtains a preset time period, and then obtains a processing window based on the preset time period. The endpoint is the current time, and the starting point is the time elapsed backward from the current time, representing the length of the preset time period. This processing window (e.g., the past 30 minutes) focuses on analyzing the historical change trajectory of the specified gas concentration in the main triggering area directly under the responsibility of the i-th exhaust fan node. For this main triggering area, S2 extracts the set of specified gas concentration measurements arranged chronologically within the entire processing window, forming a complete time series data. This contains detailed information on how the concentration evolves over time, serving as the foundational data source for subsequent trend analysis. Specifically, the preset time period (i.e., the length of the processing window) can be obtained by a fixed value pre-set by a person skilled in the art based on the business scenario; specifically, it depends on the diffusion characteristics of the specified gas. For example, the concentration of CO2 rises slowly in densely populated spaces, so the processing window needs to cover 30 to 60 minutes to capture the trend, while VOCs may surge during chemical leaks, so the window needs to be shortened to 5 to 10 minutes to achieve a rapid response; it also depends on the physical space scale. Large factories have low air circulation and slow mixing of specified gases, so the window needs to be extended (e.g., 45 minutes), while small cubicles, due to rapid changes, can be shortened to 15 minutes.

[0040] Furthermore, in addition to historical trend analysis of the main triggering area, S2 also focuses on the current real-time status of the secondary triggering areas associated with the shared pipeline network. Unlike the main triggering area, which acquires a sequence of changes over a time period, for each secondary triggering area of ​​the i-th exhaust fan node (such as adjacent workshops, warehouses, or functional zones sharing the same ventilation branch), S2 only acquires the real-time measurement of a specified gas concentration at the current specific moment (i.e., the moment the processing window ends). This is to avoid the impact of instantaneous high concentrations in the secondary triggering areas on the main triggering area. Essentially, through its data acquisition strategy, S2 provides the main triggering area with dynamic change clues in the time dimension (the sequence within the processing window) and provides the secondary triggering areas with instantaneous risk snapshots in the spatial dimension (concentrations at various points at the current moment), together forming a complete decision dataset for the i-th node that includes environmental evolution and neighborhood dynamics. For example, in a factory workshop, in addition to analyzing whether VOCs in the core processing area (primary triggering area) have been quietly rising over a period of time, it is also necessary to know whether there is a risk of a surge in the concentration of a specific gas in the spray booth (secondary triggering area) upstream or the material storage room (another secondary triggering area) downstream of the same ventilation duct.

[0041] In S3, a dynamic demand assessment is constructed. By quantifying environmental risks in two dimensions (the potential deterioration tendency of the main triggering area and the immediate threat level of the secondary triggering area), a decision-making basis is provided for proactive intervention of exhaust fans when the main triggering area is not exceeding the standard. Specifically, the potential risk of the main triggering area is quantified (increasing trend value). For the main triggering area of ​​the i-th exhaust fan node, the change pattern of the specified gas concentration within the processing window is analyzed. First, the time concentration sequence of the main triggering area is divided into multiple equal-length time periods (e.g., every 5 minutes), and the average concentration value is calculated in each time period. Further, the average concentration values ​​of adjacent time periods are compared in chronological order. If the average value of the later time period is higher than that of the previous time period, an "increasing mark" is recorded (representing an upward trend in that time period). Further, the percentage of increasing marks in all adjacent time periods is counted (e.g., 10 time periods form 9 sets of comparisons. If 7 sets show an increase, the trend value is 7 / 9≈0.78). The closer this value is to 1, the higher the certainty of the continuous increase of the specified gas in the main triggering area.

[0042] Furthermore, assess the current designated gas conditions in the secondary triggering areas that share the duct with node i. First, for each secondary triggering area, calculate the percentage by which its current concentration exceeds its own safety threshold (0 if not exceeding the limit). Second, sum the exceedance percentages of all secondary triggering areas (e.g., if secondary triggering area 1 exceeds the limit by 20% and secondary triggering area 2 exceeds the limit by 50%, then the secondary demand value = 0.7). The larger the value, the more urgent the overall pollution pressure of the secondary triggering area group. For example, the secondary triggering areas of a certain exhaust fan node in the factory include the spray painting room (currently exceeding the VOCs limit by 30%) and the raw material warehouse (exceeding the dust limit by 15%); the secondary demand value = 0.45, reflecting a significant risk of pollution diffusion in the adjacent area.

[0043] Furthermore, the potential risks of the primary triggering area are coupled with the urgency of the secondary triggering areas. First, the secondary demand value is weighted and amplified using the trend value of the primary triggering area. If the primary triggering area itself has a strong trend (trend value → 1), even if the secondary demand value is low (e.g., 0.3), the merged primary demand value will still be significantly increased (e.g., 0.3 × (1 + 0.9) = 0.57); conversely, if the primary triggering area is stable (trend value → 0), the primary demand value will approach the secondary demand value. Through a three-stage assessment mechanism, within a safe range where the specified gas concentration is below the existing activation threshold, the mechanism actively captures the slowly changing threat of continuously rising concentration in the primary triggering area, while simultaneously sensing sudden pulse-type pollution in the secondary triggering area to prevent pollution from backflowing through shared ducts. Ultimately, this achieves the activation of gradual ventilation during low-energy consumption windows, avoiding energy consumption peaks and the risk of uncontrolled diffusion during sudden high-intensity exhaust.

[0044] In S4, this step begins when the concentration of the specified gas in the main trigger area of ​​the i-th exhaust fan node shows a significant upward trend (i.e., the increasing trend value exceeds the first preset threshold). The first preset threshold is determined through the cumulative dynamics of the specified gas within a safe range, i.e., obtained through a limited number of experiments. Specifically, a concentration-time second derivative curve is fitted using a large amount of data, and the increasing trend value corresponding to the inflection point of the curve is taken as the first preset threshold. Its core lies in dynamically generating a graded first control strategy using the main demand value (integrating the potential deterioration risk of the main area and the urgency level of the sub-area), achieving gradual intervention in the stage where the risk is not exceeded but is evident. Specifically, the process begins by matching demand value ranges. Several non-overlapping value ranges (such as low / medium / high ranges) are preset, each corresponding to a different pollution threat level. The calculated main demand value (reflecting the overall risk level) is then matched with these ranges to determine its corresponding threat level. Next, based on the differentiated control instructions corresponding to each value range, these instructions include the fan speed settings (e.g., low speed corresponds to continuous ventilation at a low air volume, medium speed increases to 50% of the standard air volume). Additional instructions can also be added to control the opening of associated air valves or activate sensors in adjacent areas to enhance monitoring.

[0045] In S5, this step begins when the specified gas in the main triggering area of ​​the i-th exhaust fan node does not show a significant upward trend (i.e., the increasing trend value does not exceed the first preset threshold), but the sudden pollution pressure in the secondary triggering area reaches an emergency level (the secondary demand value exceeds the second preset threshold). The second preset threshold is determined based on the detection of abrupt changes in the diffusion of specified gases in the pipeline network, i.e., through a limited number of experiments. Specifically, through a large number of historical specified gas diffusion events, the secondary demand value at the time of the diffusion event is taken and averaged, with 90% of the average value used as the first preset threshold. Its core lies in dynamically generating a cross-regional collaborative defense strategy based on the secondary demand value, achieving pollution interception and enhanced exhaust intensity when the main area is safe but the secondary area is in crisis. Specifically, firstly, demand value interval matching is performed. Several numerical intervals corresponding to secondary demands are preset (such as warning / emergency / high-risk intervals). The real-time calculated secondary demand value (reflecting the cumulative degree of pollution exceeding the standard in the secondary area group) is matched with these intervals to determine the corresponding interval. Secondly, based on the cross-node collaborative control command corresponding to each numerical interval, the control command corresponding to the corresponding interval is found, and a second control strategy is formed.

[0046] In summary, the dynamic control method for exhaust fans based on ventilation regulation achieves the following: First, by quantifying the concentration increase trend in the main triggering area in real time, it can identify the risk of gradual deformation in the early stage of gas accumulation. Gradual ventilation (such as continuous low-volume operation) is initiated before the concentration reaches the critical point, effectively suppressing the potential deterioration path of the continuously rising gas and avoiding the energy consumption peak caused by the passive waiting of existing technologies until the concentration exceeds the limit and then suddenly exhausting at full power. Furthermore, early intervention compresses the diffusion window of the gas, reducing the probability of its spread to adjacent areas. Second, a collaborative response mechanism for secondary triggering areas based on pipeline connectivity is constructed. When a secondary area sharing a ductwork experiences sudden pollution (such as a chemical warehouse leak), the exhaust fan node is no longer considered an independent unit but is activated to operate at low speed to establish a negative pressure barrier within the ductwork. Simultaneously, a joint defense command is sent to related nodes on the same branch, forming a collaborative interception network by synchronously increasing the airflow of multiple nodes. This not only blocks the path of backflow or diffusion of the gas through the ductwork but also significantly reduces the energy consumption for clearing cross-regional pollution through a distributed low-speed operation mode (rather than single-node full power). Finally, by fully utilizing the adjustment potential of exhaust fans in non-exceeding ranges and leveraging the dynamic demand of risk coupling between the main and secondary areas, the system can respond to the gradual ventilation needs of the main area while quickly capturing sudden pollution signals in the secondary area. This transforms ventilation resources into a gradient-scheduling environmental regulation tool, significantly reducing overall energy consumption while ensuring local environmental stability.

[0047] like Figure 1 and Figure 4 As shown, in one embodiment, obtaining the increasing trend value in S3 based on the specified concentration data sequence of the main triggering region of the i-th exhaust fan node includes:

[0048] S31. Divide the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node into multiple consecutive sub-processing segments according to the time sequence;

[0049] S32. Obtain the average specified concentration data of each sub-processing segment, and obtain an incrementing flag based on the sign of the difference between the average specified concentration data of adjacent sub-processing segments;

[0050] S33. Obtain the increment ratio based on the number of incrementing markers and the number of sub-processing segments, and use it as the incrementing trend value of the i-th exhaust fan node.

[0051] In this embodiment, it should be noted that in S31, the specified concentration history sequence of the main triggering region is divided into multiple continuous and equally long time period units along the time axis. The length of the time period is determined by the total duration of the processing window and the preset division granularity. For example, a 30-minute processing window is divided into 6 segments with a granularity of 5 minutes, and each segment contains several concentration sampling points. This discretizes the continuous time series, providing a structured data foundation for trend analysis.

[0052] Example: In the main area of ​​the data center server room, the data from the past 30 minutes C The concentration sequence was divided into 6 segments with 5-minute intervals, and each segment contained 12 sampling points (assuming one sampling per minute).

[0053] In S32, the average concentration of all sampling points within each time period is calculated, and the average values ​​of adjacent time periods are compared sequentially. If the average value of the later time period is strictly greater than that of the previous time period (i.e., the difference is positive), a binary incrementing flag (denoted as 1) is generated; otherwise, it is denoted as 0. This design filters out instantaneous fluctuation noise through the mean, capturing only continuously rising signals.

[0054] Example: The CO2 mean values ​​for six time periods in the computer room are [420ppm, 428ppm, 425ppm, 432ppm, 440ppm, 445ppm]. By comparing adjacent values, the labeled sequence [1, 0, 1, 1, 1] is obtained (because 428>420→1, 425<428→0, 432>425→1...).

[0055] In S33, the percentage of times the increment marker is 1 is calculated in all comparisons of adjacent time periods (i.e., number of increment markers / total number of comparisons). This percentage is defined as the increasing trend value, with a value range of [0, 1]. The larger the value, the more monotonically the concentration of the specified gas increases.

[0056] Example: The above data center marking sequence has 4 increases (marked 1) and 1 decrease (marked 0), with an increasing trend value of 4 / 5 = 0.8, reflecting an extremely high risk of continuous CO2 increase.

[0057] It should also be noted that the increasing trend value obtained in S3 based on the specified concentration data sequence of the main triggering region of the i-th exhaust fan node is represented as follows:

[0058]

[0059] in, Let be the increasing trend value of the i-th exhaust fan node. The number of sub-processing segments in the specified concentration data sequence of the main trigger region of the i-th exhaust fan node. The average specified concentration data of the (j+1)th sub-processing segment in the specified concentration data sequence of the main trigger region of the i-th exhaust fan node. The average specified concentration data of the j-th sub-processing segment in the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node.

[0060] It should also be noted that the entire expression employs a three-level quantization mechanism to address the current inability to identify slowly evolving risks. Specifically, To discretize the average concentration over time periods, the continuous concentration sequence within the main processing window is divided into m equal-length sub-segments, and the average concentration of each segment is calculated. ;at the same time, The sign function represents the quantification of trend direction, expressed as the difference between the mean values ​​of adjacent time periods. When performing symbolic function operations, hour, This indicates an increase in concentration; when hour, This indicates that the concentration is stable or decreasing, and this treatment effectively distinguishes between stable fluctuations and continuous deterioration.

[0061] Furthermore, This represents non-negative truncation quantization, which directly eliminates negative perturbations and forces them to be truncated. The result is set to zero, retaining only the strictly rising signal and ignoring the interference signals during the falling / stable period. This avoids the situation where existing slope calculation methods (such as linear regression) would count a short-term decline as a negative contribution, weakening the weight of the true upward trend and thus leading to misjudgment.

[0062] Furthermore, To accumulate and normalize, representing the quantification of trend persistence, the binary results of all adjacent time period pairs are summed and then divided by the total number of comparisons (m-1) to obtain the proportion of rising periods. To address the issue of "misjudging occasional increases as persistent risks": when An increase occurs 30% of the time (an occasional risk requiring minor intervention); when The concentration of a gas increased 90% of the time (continuously worsening, requiring a stronger response). For example, a fault caused the concentration of a specific gas to increase 9 times out of 10 time periods within 30 minutes. This triggers ventilation and cooling in advance to prevent the server from overheating.

[0063] like Figure 1 and Figure 5 As shown, in one embodiment, obtaining the secondary demand value in S3 based on the current specified concentration data of each secondary triggering region of the i-th exhaust fan node includes:

[0064] S34. Obtain the specified concentration threshold of each sub-triggering area of ​​the i-th exhaust fan node, and obtain the excess ratio based on the current specified concentration data and specified concentration threshold of each sub-triggering area of ​​the i-th exhaust fan node.

[0065] S35. Accumulate the excess ratio of all secondary trigger areas of the i-th exhaust fan node and obtain the secondary demand value.

[0066] In this embodiment, it should be noted that in S34, for each sub-triggered region, a preset designated gas safety threshold is obtained, the absolute difference between the current real-time concentration value of that region and the safety threshold is calculated, and the difference is divided by the safety threshold to obtain the excess ratio. If the current concentration does not exceed the standard, the ratio is recorded as 0. This design quantifies the instantaneous pollution pressure of a single sub-region.

[0067] Example: A factory's exhaust fan sub-area includes a hazardous chemical storage room (threshold 100 ppm) and a raw material pretreatment room (threshold 80 ppm). The current measured concentrations are 112 ppm and 75 ppm respectively. Therefore, the exceedance ratio is (112-100) / 100=0.12, (75-80) / 80=0 (0 is taken as not exceeding the standard).

[0068] In S35, the sum of the excess percentages (non-negative values) of all secondary triggering areas is used as the secondary demand value. This value reflects the overall pollution diffusion pressure of the secondary area group associated with the shared duct and is the core basis for the coordinated response of the pipeline network.

[0069] Example: In the example above, the sum of the excess rates of the two sub-areas is 0.12 + 0 = 0.12. If the excess rate of the third sub-area (packaging room) is 0.2, then the total sub-demand value = 0.32.

[0070] It should also be noted that in S3, the secondary demand value obtained based on the current specified concentration data of each secondary triggering region of the i-th exhaust fan node is represented as follows:

[0071] ;in,

[0072] Let be the secondary demand value for the i-th exhaust fan node. Let be the number of secondary triggering regions for the i-th exhaust fan node. The percentage of exceeding the limit in the k-th secondary triggering region of the i-th exhaust fan node. The specified concentration threshold is the k-th sub-triggering region of the i-th exhaust fan node.

[0073] It should also be noted that the entire expression achieves network-level risk aggregation. Specifically, To quantify relative exceedances, a unified risk scale is established for multiple specified gases. For the k-th secondary triggering region, its current concentration is calculated. Exceeding the safety threshold The relative proportion (not the absolute difference) is used. Thresholds for different specified gases (e.g., CO2 / VOCs) or areas (warehouse / workshop) can vary significantly: using only absolute differences (e.g., CO2 exceeding the standard by 100 ppm vs. VOCs exceeding the standard by 5 ppm) makes it impossible to directly compare risk levels. Relative proportions eliminate dimensional differences: 200 ppm CO2 (threshold 1000 ppm) → exceedance rate 0.2; 15 ppm VOCs (threshold 10 ppm) → exceedance rate 0.5; thus, VOCs pollution is more urgent.

[0074] Furthermore, Non-negative truncation is achieved, eliminating interference from the safe region.

[0075] when When (not exceeding the standard), a forced output of 0 is applied. This solves the problem that existing technologies misjudge the overall safety of a branch when there are uncontaminated sub-areas in a shared duct, due to ignoring the uncontaminated sub-areas. The entire expression ensures that only the threat value of the actual exceeding area is accumulated, avoiding the dilution of the overall risk value by safe areas (e.g., when 2 out of 3 sub-areas are not exceeding the standard, the accumulated value still reflects the true risk).

[0076] Furthermore, To achieve cross-regional accumulation, the system dynamically senses pollution pressure at the pipeline level. It sums the exceedance ratios of all P secondary triggering areas. When multiple areas simultaneously approach or exceed the threshold, single-area exceedances may be ignored (e.g., a workshop's VOCs exceedance by 0.1%), but the cumulative effect amplifies hidden risks (if each of the three areas exceeds the threshold by 0.1, the secondary demand value is 0.3), thus addressing the loophole of unresponsive critical pollution in multiple areas.

[0077] like Figure 1 and Figure 6 As shown, in one embodiment, obtaining the primary demand value of the i-th exhaust fan node in S3 based on the increasing trend value and secondary demand value of the i-th exhaust fan node includes:

[0078] S36. Obtain the correction ratio based on the increasing trend value;

[0079] S37. Multiply the correction ratio by the secondary demand value to obtain the primary demand value.

[0080] In this embodiment, it should be noted that in S36, the increasing trend value of the main region is directly used as the correction factor. This factor characterizes the potential risk intensity of the main region itself: when the trend value is 0 (no increase), the factor is ineffective; when the trend value is close to 1, the factor has a strong corrective effect on secondary demand.

[0081] In S37 (Demand Fusion), the secondary demand value is multiplied by (1 + correction factor) to obtain the primary demand value. When the primary region trend is strong (factor → 1): primary demand value ≈ 2 × secondary demand value, and even if the secondary region pollution is slight, it is still responded to as high-risk. When the primary region trend is stable (factor → 0): primary demand value ≈ secondary demand value, and the response depends entirely on the pressure in the secondary region.

[0082] Example: If the main demand value of a laboratory is 0.9 (strong upward trend) and the secondary demand value is 0.2 (low pressure), then the main demand value = (1 + 0.9) × 0.2 = 0.38, triggering a moderate response; if the trend value is only 0.1, the main demand value = 1.1 × 0.2 = 0.22, triggering a low response.

[0083] It should also be noted that, in S3, the primary demand value of the i-th exhaust fan node, obtained based on the increasing trend value and secondary demand value, is expressed as follows:

[0084] ;in,

[0085] Let be the secondary demand value for the i-th exhaust fan node. Let be the increasing trend value of the i-th exhaust fan node. This represents the primary demand value for the i-th exhaust fan node.

[0086] It should also be noted that throughout the entire expression, As a correction ratio, the baseline coefficient 1 guarantees the secondary demand value. The original risk is not diluted, and the trend amplifier Based on the increasing trend value of the main region Risk weighting is applied. When the specified gas in the main region continues to rise but does not exceed the limit (e.g., CO2 increases by 0.5 ppm / min), existing technologies, due to independent judgment between the main and secondary regions, will not respond at all if the secondary region does not exceed the limit, but will instead use (1+ The structure needs to be modified, even if Security, primary requirement value It may also reach the control limits. At the same time, The larger the value, the stronger the response, translating the gradual ventilation needs of the main area into effective control commands. Simultaneously, the trend value... Strictly bound to [0, 1], making This avoids the distortion of control commands caused by risk values ​​in a single area.

[0087] like Figure 2 and Figure 7 As shown, in one embodiment, obtaining the first control strategy for the i-th exhaust fan node based on the primary demand value of the i-th exhaust fan node in S4 includes:

[0088] S41. Compare the main demand value of the i-th exhaust fan node with a plurality of preset first value ranges, and determine the target first value range in which the main demand value of the i-th exhaust fan node is located.

[0089] S42. Based on the preset mapping relationship between the first numerical range and the control command, obtain the control command corresponding to the target first numerical range and use it as the first control strategy.

[0090] In this embodiment, it should be noted that the primary demand value is precisely located using preset non-overlapping continuous intervals (such as low risk [0, 0.3], medium risk [0.3, 0.6], and high risk [0.6, 1.0]). Each interval corresponds to a critical inflection point for the diffusion of a specified gas: based on fluid dynamics simulation and historical accident data analysis; for example, in a duct, when the primary demand value is ≥0.3, the probability of the specified gas diffusing to adjacent areas exceeds 40%, and when the primary demand value is ≥0.6, the risk of branch air pressure imbalance increases sharply.

[0091] In S42, a tiered response strategy is used to bind control commands. The low-risk zone means that the commanded fan runs at a low speed (e.g., 30% air volume) and only maintains the basic negative pressure; the medium-risk zone means that the commanded medium air volume (60%) is added with associated air valves to suppress diffusion; the high-risk zone means that the commanded full-load air volume (100%) is added, the air supply valves in high-risk areas are closed, and the joint defense of adjacent nodes is activated.

[0092] It should also be noted that multiple first numerical ranges can be obtained through a limited number of experiments and gas diffusion coefficients (such as CO2 0.16 cm² / s, VOCs 0.08 cm² / s). The success rate of ventilation intervention can also be tracked in real time (number of successful pollution suppressions / total number of triggers); when the success rate of a certain interval is <85%, the interval is automatically narrowed by 0.05 and moved to an adjacent higher-level interval; for example, the initial high-risk range [0.6, 1.0] has a success rate of only 80%, but after adjusting to [0.65, 1.0], the success rate rises to 89%.

[0093] like Figure 2 and Figure 8 As shown, in one embodiment, the second control strategy for obtaining the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node in S5 includes:

[0094] S51. Compare the secondary demand value of the i-th exhaust fan node with a plurality of preset second numerical ranges, and determine the target second numerical range in which the secondary demand value of the i-th exhaust fan node is located.

[0095] S52. Based on the preset mapping relationship between the second numerical range and the control command, obtain the control command corresponding to the target second numerical range and use it as the second control strategy.

[0096] In this embodiment, it should be noted that in S51, a preset coordinated response interval (such as a warning interval [0.1, 0.4], an emergency interval [0.4, 0.7], and a high-risk interval above 0.7) is matched according to the secondary demand value (reflecting the instantaneous pollution superposition pressure of multiple secondary triggering areas in the shared air duct). The interval boundaries are set based on the specified gas diffusion rate threshold in the air duct, or can be obtained through a limited number of experiments and gas diffusion coefficients; the warning interval specifies the time window in which the specified gas may enter the adjacent air duct (such as 5 minutes for CO2 and 90 seconds for VOCs); the high-risk interval specifies the critical point where the specified gas has diffused to the main air duct (which requires immediate physical blocking).

[0097] Example: When the secondary demand value reaches the emergency range, it indicates that at least two secondary areas (such as the hazardous chemical warehouse and the spraying room) exceed the standard at the same time, and cross-node ventilation linkage needs to be activated.

[0098] In S52, a network-level collaborative strategy is used to bind commands. In the alert zone, the exhaust fan at this node is activated at its lowest setting (20% airflow) and a warning signal is sent to nodes on the same branch. In the emergency zone, the airflow at this node is increased to medium speed (50%), and the airflow at adjacent nodes is simultaneously increased by 30%, while the opening of branch dampers is adjusted. In the high-risk zone, the airflow at this node is increased to full capacity (100%), valves in the pollution source area are forcibly closed, and the emergency exhaust protocol for the entire branch is activated.

[0099] A dynamic control system for exhaust fans based on ventilation and exhaust adjustment is also provided, the system comprising:

[0100] The data association module is used to obtain the ventilation and exhaust duct network and multiple exhaust fan nodes set on the ventilation and exhaust duct network, and to take the control area directly connected to the i-th exhaust fan node as the main trigger area of ​​the i-th exhaust fan node, and to take the control areas directly connected to multiple exhaust fan nodes along the exhaust branch where the i-th exhaust fan node is located as multiple secondary trigger areas of the i-th exhaust fan node.

[0101] The data acquisition module is used to acquire a preset time period, acquire the preset time period before the current time of the i-th exhaust fan node as the processing window, acquire the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node within the processing window, and acquire the current specified concentration data of each sub-trigger area of ​​the i-th exhaust fan node at the current time.

[0102] The data processing module is used to obtain the increasing trend value based on the specified concentration data sequence of the main triggering area of ​​the i-th exhaust fan node, obtain the secondary demand value based on the current specified concentration data of each secondary triggering area of ​​the i-th exhaust fan node, and obtain the main demand value of the i-th exhaust fan node based on the increasing trend value and secondary demand value of the i-th exhaust fan node.

[0103] The first control adjustment module is used to obtain the first control strategy of the i-th exhaust fan node based on the main demand value of the i-th exhaust fan node if the increasing trend value exceeds the first preset threshold.

[0104] The second control adjustment module is used to obtain the second control strategy of the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node if the increasing trend value does not exceed the first preset threshold and the correction value exceeds the second preset threshold.

[0105] In one implementation, the data processing module is further configured to: divide the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node into multiple consecutive sub-processing segments in chronological order; obtain the average specified concentration data of each sub-processing segment, and obtain an incrementing marker based on the sign of the difference between the average specified concentration data of adjacent sub-processing segments; obtain the incrementing percentage based on the number of incrementing markers and the number of sub-processing segments, and use it as the incrementing trend value of the i-th exhaust fan node.

[0106] In one implementation, the data processing module is further configured to: obtain the specified concentration threshold of each sub-triggering region of the i-th exhaust fan node, and obtain the excess ratio based on the current specified concentration data and specified concentration threshold of each sub-triggering region of the i-th exhaust fan node; accumulate the excess ratio of all sub-triggering regions of the i-th exhaust fan node and obtain the sub-demand value.

[0107] In one implementation, the data processing module is further configured to: obtain a correction ratio based on the increasing trend value; multiply the correction ratio by the secondary demand value to obtain the primary demand value.

[0108] In this embodiment, it should be noted that the specific method of performing the operation of the above-mentioned exhaust fan dynamic control system based on ventilation and exhaust regulation has been described in detail in the embodiments of the exhaust fan dynamic control method based on ventilation and exhaust regulation, and will not be elaborated here.

[0109] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0110] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0111] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A dynamic control method for an exhaust fan based on ventilation and exhaust adjustment, characterized in that, include: Obtain the ventilation and exhaust duct network and multiple exhaust fan nodes set on the ventilation and exhaust duct network, and take the control area directly connected to the i-th exhaust fan node as the main trigger area of ​​the i-th exhaust fan node, and take the control areas directly connected to multiple exhaust fan nodes along the exhaust branch where the i-th exhaust fan node is located as multiple secondary trigger areas of the i-th exhaust fan node. Get a preset time period, and get the preset time period before the current time of the i-th exhaust fan node as the processing window, and get the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node within the processing window, and get the current specified concentration data of each sub-trigger area of ​​the i-th exhaust fan node at the current time. The increasing trend value is obtained based on the specified concentration data sequence of the main triggering area of ​​the i-th exhaust fan node, and the secondary demand value is obtained based on the current specified concentration data of each secondary triggering area of ​​the i-th exhaust fan node. The main demand value of the i-th exhaust fan node is obtained based on the increasing trend value and the secondary demand value of the i-th exhaust fan node. The process of obtaining the increasing trend value includes: dividing the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node into multiple consecutive sub-processing segments in chronological order; obtaining the average specified concentration data of each sub-processing segment, and obtaining an increasing marker based on the sign of the difference between the average specified concentration data of adjacent sub-processing segments; obtaining the increasing percentage based on the number of increasing markers and the number of sub-processing segments, and using it as the increasing trend value of the i-th exhaust fan node. The process of obtaining the secondary demand value includes: obtaining the specified concentration threshold of each secondary triggering area of ​​the i-th exhaust fan node, and obtaining the excess ratio based on the current specified concentration data and specified concentration threshold of each secondary triggering area of ​​the i-th exhaust fan node; accumulating the excess ratio of all secondary triggering areas of the i-th exhaust fan node and obtaining the secondary demand value. The process of obtaining the primary demand value for the i-th exhaust fan node includes: obtaining a correction ratio based on the increasing trend value; multiplying the correction ratio by the secondary demand value to obtain the primary demand value; If the increasing trend value exceeds the first preset threshold, then the first control strategy of the i-th exhaust fan node is obtained based on the main demand value of the i-th exhaust fan node. If the increasing trend value does not exceed the first preset threshold and the correction value exceeds the second preset threshold, then the second control strategy of the i-th exhaust fan node is obtained based on the secondary demand value of the i-th exhaust fan node.

2. The dynamic control method for exhaust fans based on ventilation and exhaust adjustment according to claim 1, characterized in that, The first control strategy for obtaining the i-th exhaust fan node based on the primary demand value of the i-th exhaust fan node includes: The main demand value of the i-th exhaust fan node is compared with a number of preset first value ranges, and the target first value range in which the main demand value of the i-th exhaust fan node is located is determined. Based on the preset mapping relationship between the first numerical range and the control command, the control command corresponding to the first numerical range of the target is obtained and used as the first control strategy.

3. The dynamic control method for exhaust fans based on ventilation and exhaust adjustment according to claim 1, characterized in that, The second control strategy for obtaining the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node includes: The secondary demand value of the i-th exhaust fan node is compared with a plurality of preset second numerical ranges, and the target second numerical range in which the secondary demand value of the i-th exhaust fan node is located is determined. Based on the preset mapping relationship between the second numerical range and the control command, the control command corresponding to the target second numerical range is obtained and used as the second control strategy.

4. A dynamic control system for an exhaust fan based on ventilation and exhaust adjustment, characterized in that, The system includes: The data association module is used to obtain the ventilation and exhaust duct network and multiple exhaust fan nodes set on the ventilation and exhaust duct network, and to take the control area directly connected to the i-th exhaust fan node as the main trigger area of ​​the i-th exhaust fan node, and to take the control areas directly connected to multiple exhaust fan nodes along the exhaust branch where the i-th exhaust fan node is located as multiple secondary trigger areas of the i-th exhaust fan node. The data acquisition module is used to acquire a preset time period, acquire the preset time period before the current time of the i-th exhaust fan node as the processing window, acquire the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node within the processing window, and acquire the current specified concentration data of each sub-trigger area of ​​the i-th exhaust fan node at the current time. The data processing module is used to obtain the increasing trend value based on the specified concentration data sequence of the main triggering area of ​​the i-th exhaust fan node, obtain the secondary demand value based on the current specified concentration data of each secondary triggering area of ​​the i-th exhaust fan node, and obtain the main demand value of the i-th exhaust fan node based on the increasing trend value and secondary demand value of the i-th exhaust fan node. The data processing module is also used to: divide the specified concentration data sequence of the main trigger area of ​​the i-th exhaust fan node into multiple consecutive sub-processing segments in chronological order; obtain the average specified concentration data of each sub-processing segment, and obtain an incrementing mark based on the positive or negative difference between the average specified concentration data of adjacent sub-processing segments; obtain the incrementing percentage based on the number of incrementing marks and the number of sub-processing segments, and use it as the incrementing trend value of the i-th exhaust fan node. The data processing module is also used to: obtain the specified concentration threshold of each sub-triggering area of ​​the i-th exhaust fan node, and obtain the excess ratio based on the current specified concentration data and specified concentration threshold of each sub-triggering area of ​​the i-th exhaust fan node; accumulate the excess ratio of all sub-triggering areas of the i-th exhaust fan node and obtain the sub-demand value; The data processing module is also used to: obtain the correction ratio based on the increasing trend value; multiply the correction ratio by the secondary demand value to obtain the primary demand value; The first control adjustment module is used to obtain the first control strategy of the i-th exhaust fan node based on the main demand value of the i-th exhaust fan node if the increasing trend value exceeds the first preset threshold. The second control adjustment module is used to obtain the second control strategy of the i-th exhaust fan node based on the secondary demand value of the i-th exhaust fan node if the increasing trend value does not exceed the first preset threshold and the correction value exceeds the second preset threshold.

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