Building smoke exhaust system air volume control method, control device and electronic equipment

Abstract

The application discloses a kind of building exhaust system air volume control method, control device and electronic equipment, the method includes establishing the exhaust capacity prediction model of each floor based on the exhaust data of each floor in building exhaust system;According to the switch state and the performance parameters of range hood in all floors in building exhaust system, the initial value and the optimization range of exhaust capacity of each floor in building exhaust system are determined;According to the switch state, the initial value of exhaust capacity, the optimization range of exhaust capacity and exhaust capacity prediction model, the exhaust capacity of each floor is iteratively updated, to obtain the target exhaust capacity of each floor;According to target exhaust capacity, the air volume of the range hood of corresponding floor is adjusted.The application initializes exhaust capacity in combination with the switch state of range hood of each floor, iteratively calculates the exhaust capacity of corresponding floor by constructing the exhaust capacity prediction model of each floor, ensures that the exhaust capacity of range hood of each floor is reasonably distributed under different working conditions, improves floor exhaust efficiency, and reduces energy consumption.

Description

Technical Field

[0001] This invention relates to the field of range hood control technology, and in particular to a method, control device and electronic equipment for controlling the air volume of a building exhaust system. Background Technology

[0002] In modern high-rise buildings, the building exhaust system is a crucial facility connecting the kitchens of residents on each floor to the outdoor environment, responsible for removing cooking fumes and harmful gases generated during cooking. The effective operation of the building exhaust system directly affects residents' quality of life and indoor air quality.

[0003] Existing multi-story building smoke extraction systems typically employ a uniform exhaust structure and a "one-size-fits-all" control strategy, such as fixed power control, timed control, or pressure-sensor control, without considering the actual needs of users on different floors. These existing systems suffer from the following problems: Firstly, the current exhaust system structure does not adequately account for pressure differences between floors, potentially leading to poor smoke extraction on lower floors. Secondly, because the operating rates of range hoods on different floors can vary significantly during peak hours, the current system's exhaust volume cannot be dynamically adjusted according to actual demand. This results in insufficient exhaust volume on some floors, leading to smoke retention, while other floors experience excessive exhaust volume, resulting in energy waste, reduced energy efficiency, increased operating costs, and negatively impacting the comfort of the building's living environment. Summary of the Invention

[0004] This invention provides a method, control device, and electronic equipment for controlling the air volume of a building smoke exhaust system, in order to solve the problem that existing building smoke exhaust systems cannot be dynamically adjusted according to actual needs and floor structure, resulting in low smoke exhaust efficiency and high cost, thereby improving floor smoke exhaust efficiency and reducing energy consumption.

[0005] According to one aspect of the present invention, a method for controlling the air volume of a building smoke exhaust system is provided, comprising: establishing a predictive model for the air volume of each floor based on smoke exhaust data of each floor within the building smoke exhaust system; wherein the predictive model is used to fit the relationship between the air volume of each floor and the air volume of other floors within the building smoke exhaust system; determining the initial value and optimization range of the air volume of each floor within the building smoke exhaust system based on the on / off status and performance parameters of the smoke hoods on all floors within the building smoke exhaust system; iteratively updating the air volume of each floor based on the on / off status of the smoke hoods, the initial value of the air volume, the optimization range of the air volume, and the predictive model of the air volume to obtain the target air volume of each floor; and adjusting the air volume of the smoke hoods on the corresponding floors according to the target air volume.

[0006] Optionally, the exhaust volume of each floor is iteratively updated based on the range hood's on / off status, the initial exhaust volume, the exhaust volume optimization range, and the exhaust volume prediction model to obtain the target exhaust volume for each floor. This includes: when the range hood on the i-th floor is in the on / off state, importing the exhaust volume of all floors except the i-th floor into the exhaust volume prediction model of the i-th floor; where i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors; and updating the exhaust volume of the i-th floor based on the output data of the exhaust volume prediction model.

[0007] Optionally, updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model includes: determining a preset iteration step size based on the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration; determining the updated exhaust volume value of the i-th layer based on the exhaust volume obtained in the (j-1)-th iteration and the preset iteration step size; and updating the exhaust volume of the i-th layer based on the updated exhaust volume value when the updated exhaust volume value is within the exhaust volume optimization range.

[0008] Optionally, updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model further includes: updating the exhaust volume of the i-th layer according to the upper limit threshold of the exhaust volume when the updated exhaust volume value is greater than or equal to the upper limit threshold of the exhaust volume optimization range; and / or updating the exhaust volume of the i-th layer according to the lower limit threshold of the exhaust volume when the updated exhaust volume value is less than or equal to the lower limit threshold of the exhaust volume optimization range.

[0009] Optionally, the exhaust volume of each floor is iteratively updated based on the range hood's on / off status, the initial exhaust volume value, the exhaust volume optimization range, and the exhaust volume prediction model to obtain the target exhaust volume of each floor. This also includes: determining whether the iteration is complete based on the difference between the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration; and determining the target exhaust volume of the i-th floor based on the exhaust volume of the i-th floor when the iteration is complete.

[0010] Optionally, the initial value and optimization range of exhaust volume for each floor in the building's smoke exhaust system are determined based on the on / off status and performance parameters of the smoke exhaust fans on all floors. This includes: setting the initial exhaust volume to 0 when the smoke exhaust fan is in the off state; and assigning a value to the initial exhaust volume based on the smoke exhaust fan performance parameters when the smoke exhaust fan is in the on state, and determining the optimization range of exhaust volume based on the smoke exhaust fan performance parameters.

[0011] Optionally, an exhaust volume prediction model for each floor is established based on the exhaust data of each floor in the building exhaust system. This includes: acquiring exhaust data of all floors in the building exhaust system under different operating conditions and at different times; taking any floor as the target floor; using the exhaust data of all floors in the building exhaust system other than the target floor at the same time and under the same operating conditions as input parameters, and using the exhaust data of the target floor at the same time and under the same operating conditions as output parameters, to train the exhaust volume prediction model.

[0012] Optionally, the air volume control method for building smoke exhaust systems also includes: acquiring environmental data of each floor within the building smoke exhaust system, and optimizing the air volume prediction model based on the environmental data.

[0013] According to another aspect of the present invention, a building smoke exhaust system airflow control device is provided, comprising: a model building module, used to establish a predictive model of the exhaust airflow for each floor based on smoke exhaust data of each floor in the building smoke exhaust system; wherein the predictive model of the exhaust airflow is used to fit the relationship between the exhaust airflow of each floor in the building smoke exhaust system and the exhaust airflow of other floors; an initialization module, used to determine the initial value of the exhaust airflow and the optimized range of the exhaust airflow for each floor in the building smoke exhaust system according to the on / off status of the smoke hoods and the performance parameters of the smoke hoods on all floors in the building smoke exhaust system; an iterative calculation module, used to iteratively update the exhaust airflow of each floor according to the on / off status of the smoke hoods, the initial value of the exhaust airflow, the optimized range of the exhaust airflow, and the predictive model of the exhaust airflow to obtain the target exhaust airflow for each floor; and an airflow adjustment module, used to adjust the airflow of the smoke hoods on the corresponding floors according to the target exhaust airflow.

[0014] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the above-described building smoke exhaust system airflow control method.

[0015] The technical solution of this invention establishes a predictive model for the exhaust volume of each floor using exhaust data from each floor within a building exhaust system. Based on the on / off status and performance parameters of the exhaust fans on all floors within the system, the initial value and optimized range of the exhaust volume for each floor are determined. The exhaust volume of each floor is iteratively updated based on the exhaust fan on / off status, initial exhaust volume value, optimized range, and predictive model to obtain the target exhaust volume for each floor. Then, the airflow of the exhaust fans on the corresponding floors is adjusted according to the target exhaust volume. This solves the problem that existing building exhaust systems cannot dynamically adjust according to actual needs and floor structure, resulting in low exhaust efficiency and high costs. It enables real-time adjustment of the exhaust volume of exhaust fans on each floor, ensuring a reasonable allocation of exhaust volume under different operating conditions, improving floor exhaust efficiency, and reducing energy consumption.

[0016] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a method for controlling the air volume of a building smoke exhaust system provided in an embodiment of the present invention;

[0019] Figure 2 This is a schematic diagram of a building smoke exhaust system provided in an embodiment of the present invention;

[0020] Figure 3 A flowchart of an exhaust volume update method provided in an embodiment of the present invention;

[0021] Figure 4 A flowchart of another method for controlling the air volume of a building smoke exhaust system provided in an embodiment of the present invention;

[0022] Figure 5 A flowchart of another method for controlling the air volume of a building smoke exhaust system provided in an embodiment of the present invention;

[0023] Figure 6 This is a schematic diagram of the structure of a building smoke exhaust system air volume control device provided in an embodiment of the present invention;

[0024] Figure 7 This is a schematic diagram of the structure of an electronic device for implementing the air volume control method of a building smoke exhaust system according to an embodiment of the present invention.

[0025] Figure label:

[0026] 101. Model building module; 102. Initialization module; 103. Iterative calculation module; 104. Air volume adjustment module; 10. Electronic equipment; 11. Processor; 12. ROM (Read-Only Memory); 13. RAM (Random Access Memory); 14. Bus; 15. I / O interface; 16. Input unit; 17. Output unit; 18. Storage unit; 19. Communication unit. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "comprising" and "having" and any variations thereof in the specification, claims and accompanying drawings of this invention are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such process, method, product or device.

[0029] Figure 1 This is a flowchart of a method for controlling the air volume of a building smoke exhaust system according to an embodiment of the present invention. This embodiment is applicable to the application scenario of adjusting the exhaust volume of a range hood in a public smoke duct system in a high-rise building. The method can be executed by a building smoke exhaust system air volume control device, which can be implemented in hardware and / or software. The building smoke exhaust system air volume control device can be configured in a smoke exhaust system controller or an independent electronic device.

[0030] Figure 2 This is a schematic diagram of a building smoke exhaust system provided in an embodiment of the present invention. Taking an N-story building (such as the 1st floor, ..., the i-th floor, the i+1th floor, ..., the Nth floor from bottom to top) as an example, the smoke duct structure of a high-rise building smoke exhaust system is illustrated.

[0031] See Figure 1 and Figure 2 As shown, the air volume control method for building smoke exhaust systems in this application specifically includes:

[0032] S1: Establish a predictive model for the exhaust volume of each floor based on the smoke exhaust data of each floor in the building smoke exhaust system.

[0033] Among them, the exhaust volume prediction model is used to fit the relationship between the smoke exhaust data of each floor and the smoke exhaust data of other floors in the building smoke exhaust system.

[0034] In this embodiment, smoke exhaust data can be understood as the actual operating data of the building's smoke exhaust system, which is directly or indirectly related to the exhaust volume of the smoke hoods, when the system is under different operating conditions (the on / off status and power of the smoke hoods on each floor are different under different operating conditions). Typically, smoke exhaust data includes, but is not limited to: exhaust volume, air pressure, smoke hood power, or environmental parameters (temperature, humidity, or atmospheric pressure). In this embodiment, the smoke exhaust data can be updated periodically as the system operates over time.

[0035] In this embodiment, the exhaust volume prediction model can be understood as a regression equation trained based on a self-learning machine model and the actual operating data of the range hood. Typically, the self-learning machine model can be an LSTM neural network model. In this embodiment, an exhaust volume prediction model is established for each floor. N exhaust volume prediction models are needed for an N-story building. For example, taking the exhaust volume prediction model for the i-th floor as an example, the exhaust volume prediction model for the i-th floor is established by fitting the smoke exhaust data of the i-th floor with the smoke exhaust data of all floors except the i-th floor. The exhaust volume prediction model for an N-story building is shown in Formula 1:

[0036]

[0037] Where Q1 represents the smoke exhaust data of the first-layer range hood; Q2 represents the smoke exhaust data of the second-layer range hood; Q i Q represents the smoke exhaust data of the i-th layer smoke hood; i-1 Q represents the smoke exhaust data of the (i-1)th layer smoke machine; i+1 Q represents the smoke exhaust data of the (i+1)th floor smoke hood; N This represents the smoke exhaust data of the Nth layer of the smoke exhaust system; f1(Q2,Q3,...,Q N () represents the exhaust volume prediction model for the first floor. The input parameters of this exhaust volume prediction model are the smoke exhaust data from the second to the Nth floor, and the output parameters are the smoke exhaust data for the first floor; f i (Q1,Q2,...,Q i-1 Q i+1 ,…,Q N ) represents the exhaust volume prediction model for the i-th floor. The input parameters of this exhaust volume prediction model are the smoke exhaust data from the 1st to the (i-1th)th floor and from the (i+1th)th to the Nth floor. The output parameter of this exhaust volume prediction model is the smoke exhaust data for the i-th floor. N (Q1,Q2,…,Q N-1 ) represents the exhaust volume prediction model for the Nth floor. The input parameters of this exhaust volume prediction model are the smoke exhaust data from the 1st to the (N-1)th floor, and the output parameters are the smoke exhaust data for the Nth floor.

[0038] S2: Determine the initial value and optimization range of exhaust volume for each floor in the building's smoke exhaust system based on the on / off status and performance parameters of the smoke exhaust fans on all floors.

[0039] The range hood's on / off status can be understood as data indicating whether the range hood is on or off. Typically, the range hood's on / off status is either on or off. In this embodiment, the on / off status of the range hoods on each floor can be obtained through communication interaction with the range hoods on each floor.

[0040] Range hood performance parameters can be understood as data characterizing the operating performance of the range hood. Typically, range hood performance parameters include, but are not limited to: the range hood static pressure-airflow curve (i.e., the PQ curve), maximum airflow and static pressure, minimum airflow and static pressure, and the range hood's rated airflow. In this embodiment, the performance parameters of the range hoods on each floor can be obtained through communication interaction with the range hoods on each floor, or the performance parameters of the range hoods on each floor can be pre-calibrated and stored.

[0041] The initial value of the exhaust volume can be understood as the initialization assignment of the exhaust volume. In this embodiment, the initial value of the exhaust volume for each floor can be assigned based on the on / off status of the smoke hoods on each floor within the building's smoke exhaust system.

[0042] The exhaust volume optimization range can be understood as the fluctuation range of the exhaust volume during the exhaust volume update process. In this embodiment, the exhaust volume optimization range can be based on the air volume and static pressure effect of the building smoke exhaust system, or it can be configured by user-defined data.

[0043] Optionally, the initial value and optimization range of exhaust volume for each floor in the building's smoke exhaust system are determined based on the on / off status and performance parameters of the smoke exhaust fans on all floors. This includes: setting the initial exhaust volume to 0 when the smoke exhaust fan is in the off state; and assigning a value to the initial exhaust volume based on the smoke exhaust fan performance parameters when the smoke exhaust fan is in the on state, and determining the optimization range of exhaust volume based on the smoke exhaust fan performance parameters.

[0044] Specifically, when the range hood on the i-th floor is in the off state, the initial value of the exhaust volume of the i-th floor is set to 0, and no optimization of the exhaust volume of the i-th floor is required. When the range hood on the i-th floor is in the on state, the exhaust volume of the i-th floor needs to be optimized. The initial value of the exhaust volume is assigned according to the performance parameters of the range hood, for example, the initial value of the exhaust volume is set to the rated air volume. At the same time, the optimization range of the exhaust volume is determined based on the range hood static pressure-air volume curve (i.e., PQ curve), the maximum air volume and static pressure, and the minimum air volume and static pressure.

[0045] S3: Iteratively update the exhaust volume of each floor based on the range hood's on / off status, initial exhaust volume value, exhaust volume optimization range, and exhaust volume prediction model to obtain the target exhaust volume for each floor.

[0046] The target exhaust volume can be understood as the exhaust volume with the best smoke extraction effect, determined through iterative updates.

[0047] In this embodiment, iterative update means that during each iteration, the exhaust volume of each floor is updated layer by layer according to the on / off status of the range hood on each floor and the exhaust volume of each floor obtained in the previous iteration; if the exhaust volume of any floor does not tend to stabilize, the next iteration is executed, and the exhaust volume of each floor is updated layer by layer again.

[0048] Specifically, in the first iteration, the exhaust volume of the range hoods is updated layer by layer from floor 1 to floor N based on the on / off status of the range hoods and the initial exhaust volume value for each floor. In subsequent iterations, taking floor i as an example, when the range hood on floor i is in the off state, the initial exhaust volume value for floor i is set to 0, thus completing the exhaust volume update for floor i; when the range hood on floor i is in the on state, the exhaust volume for floor i is updated based on the exhaust volume prediction model for floor i and the exhaust volumes of other floors obtained in the previous iteration, until all floors have been updated and the exhaust volume of each floor tends to stabilize, yielding the target exhaust volume. During the iteration process, the target exhaust volume does not exceed the exhaust volume optimization range.

[0049] S4: Adjust the airflow of the range hood on the corresponding floor according to the target exhaust volume.

[0050] In this embodiment, the air volume of the range hoods on each floor can be adjusted to the corresponding target exhaust volume using remote control, or the target exhaust volume of each floor can be pushed to the terminal device, and the operator can adjust the air volume according to the pushed target exhaust volume.

[0051] Specifically, firstly, an exhaust volume prediction model is established based on machine learning technology and actual operating data of range hoods within the building's smoke extraction system. This model can fit the mutual influence relationships between range hoods on different floors within the system. Further, the exhaust volume for each floor is initialized by combining the range hood's on / off status (e.g., on or off) and performance parameters (e.g., PQ curve, maximum airflow and static pressure, minimum airflow and static pressure, rated airflow) on each floor, and the optimization range for the exhaust volume of the floors with the range hoods on is calculated. For each floor with the range hoods on, the exhaust volume is updated based on the floor's exhaust volume prediction model and the initial exhaust volume values ​​of other floors until the iteration is complete, yielding the target exhaust volume for that floor. The exhaust volume of the range hoods is updated layer by layer from floor 1 to floor N to obtain the target exhaust volume for each floor, and the range hoods on each floor are adjusted to the corresponding target exhaust volume. It can adjust the exhaust volume of each floor's smoke hood in real time, ensuring a reasonable distribution of exhaust volume for each floor under different operating conditions, improving the efficiency of smoke extraction, reducing energy consumption, and enhancing the comfort of building residents.

[0052] Figure 3 The flowchart of an exhaust volume update method provided in an embodiment of the present invention exemplifies a specific implementation of an exhaust volume iterative update based on an exhaust volume prediction model.

[0053] See Figure 3 As shown, in step S3 above, the exhaust volume of each floor is iteratively updated based on the range hood's on / off status, the initial exhaust volume value, the exhaust volume optimization range, and the exhaust volume prediction model to obtain the target exhaust volume for each floor. This specifically includes the following steps:

[0054] S301: When the range hood on the i-th floor is in the on state, import the exhaust volume of all floors except the i-th floor into the exhaust volume prediction model of the i-th floor.

[0055] Where i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors (e.g., N).

[0056] In this embodiment, when the range hood is in the "on" state, the exhaust volume is updated based on the exhaust volume prediction model. During the first iteration, the input parameter of the exhaust volume prediction model is the initial exhaust volume value configured based on the range hood's on / off state. In subsequent iterations, the input parameter of the exhaust volume prediction model is the exhaust volume updated in the previous iteration.

[0057] S302: Update the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model.

[0058] In this embodiment, updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model includes: updating the exhaust volume of the i-th layer based on the exhaust volume obtained in the previous iteration and the output data of the exhaust volume prediction model in the current iteration.

[0059] Optionally, updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model includes: determining a preset iteration step size based on the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration; determining the updated exhaust volume value of the i-th layer based on the exhaust volume obtained in the (j-1)-th iteration and the preset iteration step size; and updating the exhaust volume of the i-th layer based on the updated exhaust volume value when the updated exhaust volume value is within the exhaust volume optimization range. Specifically, the preset iteration step size can be set to one-thousandth of the difference between the output data of the exhaust volume prediction model of the i-th layer in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration. The updated exhaust volume value is equal to the sum of the exhaust volume obtained in the (j-1)-th iteration and the preset iteration step size. When the updated exhaust volume value is within the exhaust volume optimization range (e.g., greater than the lower limit threshold and less than the upper limit threshold), the exhaust volume of the i-th layer is updated based on the updated exhaust volume value.

[0060] Optionally, iteratively updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model further includes: updating the exhaust volume of the i-th layer according to the upper limit threshold of the exhaust volume when the updated exhaust volume value is greater than or equal to the upper limit threshold of the exhaust volume optimization range; and / or, updating the exhaust volume of the i-th layer according to the lower limit threshold of the exhaust volume optimization range when the updated exhaust volume value is less than or equal to the lower limit threshold of the exhaust volume optimization range. Specifically, if the updated exhaust volume value calculated based on the exhaust volume obtained in the previous iteration and the output data of the exhaust volume prediction model in the current iteration is greater than or equal to the upper limit threshold of the exhaust volume, then the exhaust volume of the i-th layer is set to the upper limit threshold of the exhaust volume (i.e., the maximum value within the exhaust volume optimization range); if the updated exhaust volume value calculated based on the exhaust volume obtained in the previous iteration and the output data of the exhaust volume prediction model in the current iteration is less than or equal to the lower limit threshold of the exhaust volume, then the exhaust volume of the i-th layer is set to the lower limit threshold of the exhaust volume (i.e., the minimum value within the exhaust volume optimization range). By establishing an optimized range for exhaust volume, the allocation of exhaust volume can be optimized to avoid the exhaust volume exceeding a specific range, which would affect air volume and air pressure, and improve the air volume control effect.

[0061] See Figure 3 As shown, in step S3 above, the exhaust volume of each floor is iteratively updated based on the range hood's on / off status, the initial exhaust volume value, the exhaust volume optimization range, and the exhaust volume prediction model to obtain the target exhaust volume for each floor. The step also includes the following steps:

[0062] S303: Determine whether the iteration is complete based on the difference between the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration.

[0063] If the iteration is complete, proceed to step S304; if the iteration is not complete, proceed to step S302.

[0064] S304: Determine the target exhaust volume of the i-th layer based on the exhaust volume of the i-th layer when the iteration is completed.

[0065] Specifically, if the difference between the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration of any floor is greater than a preset difference threshold (e.g., 0.1m), then... 3 If the exhaust volume is less than or equal to the output data of the exhaust volume prediction model in the j-th iteration (e.g., in the order from the 1st to the Nth floor), then the iteration is considered incomplete. The next iteration is then initiated based on the exhaust volume obtained in the current iteration, updating the exhaust volume of each floor layer by layer (e.g., in the order from the 1st to the Nth floor). If the difference between the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1th)-th iteration is less than or equal to a preset difference threshold (e.g., 0.1m), then the iteration is considered incomplete. 3If the exhaust volume of the range hoods on all floors reaches a certain value (e.g., / min), then the iteration is considered complete, and the updated exhaust volume for each floor is determined as the target exhaust volume for that floor. Through iterative iteration across all floors, a balanced distribution of exhaust volume is achieved for each floor under different operating conditions, thereby improving smoke extraction efficiency.

[0066] Optionally, an exhaust volume prediction model for each floor is established based on the exhaust data of each floor within the building exhaust system. This includes: acquiring exhaust data of all floors within the building exhaust system under different operating conditions (such as different on / off states and power of the exhaust fans on each floor) and at different times; designating any floor as the target floor (e.g., floor i); using the exhaust data of all floors within the building exhaust system other than the target floor (e.g., floor i) under the same operating conditions (e.g., exhaust fans on floors 1 to 5 are on, and exhaust fans on other floors are off) at the same time (e.g., time t1) as input parameters, and using the exhaust data of the target floor under the same operating conditions (e.g., exhaust fans on floors 1 to 5 are on, and exhaust fans on other floors are off) as output parameters to train the exhaust volume prediction model. Specifically, the smoke exhaust data of all floors under the same time (e.g., time t1) and the same operating condition (e.g., the smoke hoods on floors 1 to 5 are on, and the smoke hoods on other floors are off) is collected as an array. Multiple arrays under different times and operating conditions are used to train the air volume prediction model for the same floor. For example, see... Figure 2 Similar to Formula 1 above, taking the i-th floor as the target floor as an example, when training the exhaust volume prediction model for the i-th floor, multiple arrays are obtained under different times and operating conditions. The smoke exhaust data of all floors except the i-th floor within the same array are used as input parameters, and the smoke exhaust data of the i-th floor within the same array is used as output data. The exhaust volume prediction model for the i-th floor is trained until the model output stabilizes. For example, the difference between the output data of the exhaust volume prediction model in two iterations is less than a preset difference threshold (e.g., 0.1m). 3 ( / min) to complete model training.

[0067] Figure 4 A flowchart illustrating another method for controlling the air volume of a building smoke exhaust system, provided as an embodiment of the present invention. See also... Figure 4 As shown, the air volume control method for building smoke exhaust systems in this application specifically includes:

[0068] S401: Obtain the on / off status of the range hood and the performance parameters of the range hood on each floor.

[0069] S402: On floors where the range hood is in the "on" state, set the exhaust volume optimization range based on the range hood performance parameters.

[0070] S403: Set the initial exhaust volume for each floor. The initial exhaust volume for floors that are powered on is set to the rated air volume, and the initial exhaust volume for floors that are powered off is set to 0.

[0071] S404: Obtain the exhaust volume for each floor.

[0072] S405: Fit the relationship between the exhaust volume of the i-th floor and the exhaust volume of other floors based on the regression equation.

[0073] S406: Establish N exhaust volume prediction models.

[0074] S407: Start executing the iteration.

[0075] S408: Update the exhaust volume of the i-th floor based on the exhaust volume prediction model of the i-th floor and the exhaust volume of other floors.

[0076] S409: Is the switch status of the range hood on the i-th floor "on"?

[0077] If the device is powered on, proceed to step S410; if the device is not powered on, proceed to step S410'.

[0078] S410: The updated exhaust volume of the i-th layer is equal to the sum of one-thousandth of the exhaust volume of the i-th layer in the previous iteration and the difference in exhaust volume between the two iterations.

[0079] S410': The exhaust volume is set to 0.

[0080] S411: Is the updated exhaust volume value of the i-th layer within the range of exhaust volume optimization?

[0081] If the updated exhaust volume value of the i-th floor is within the exhaust volume optimization range, then proceed to step S412; if the updated exhaust volume value of the i-th floor is not within the exhaust volume optimization range, then proceed to step S412'.

[0082] S412: The exhaust volume of the i-th floor has been updated.

[0083] S412': When the updated exhaust volume value is greater than or equal to the upper limit threshold of the exhaust volume, the exhaust volume of the i-th floor is set to the upper limit threshold of the exhaust volume; when the updated exhaust volume value is less than or equal to the lower limit threshold of the exhaust volume, the exhaust volume of the i-th floor is set to the lower limit threshold of the exhaust volume.

[0084] S413: The exhaust volume of all floors has been updated and is now stable.

[0085] If the exhaust volume of all floors has been updated and the exhaust volume tends to be stable, then proceed to step S414; if the exhaust volume of any floor has not been updated, or the exhaust volume of any floor is unstable, then return to proceed to step S408.

[0086] S414: Obtain the target exhaust volume for each floor.

[0087] S415: Adjust the airflow of the range hood on the corresponding floor according to the target exhaust volume.

[0088] Specifically, steps S401 to S403 describe a specific implementation method for initializing exhaust volume; steps S404 to S406 describe a specific implementation method for modeling an exhaust volume prediction model; and steps S407 to S414 describe a specific implementation method for iteratively optimizing the exhaust volume of each floor based on the exhaust volume prediction model and initial values. By training a machine learning model with actual operating data of the range hoods in the building's smoke extraction system, and combining this with the actual operating conditions of the range hoods on each floor, the exhaust volume of each floor can be assigned and iteratively updated. This allows for real-time adjustment of the exhaust volume distribution of range hoods on each floor under different operating conditions, improving floor smoke extraction efficiency and reducing energy consumption.

[0089] Figure 5 The flowchart of another method for controlling the air volume of a building smoke exhaust system provided in this embodiment of the invention exemplifies a specific implementation method for model training based on environmental data and smoke exhaust data. See also... Figure 5 As shown, the air volume control method for building smoke exhaust systems in this application further includes:

[0090] S501: Establish a predictive model for the exhaust volume of each floor based on the smoke exhaust data of each floor in the building smoke exhaust system.

[0091] S502: Obtain environmental data for each floor within the building's smoke exhaust system.

[0092] In this embodiment, environmental data represents parameters that affect the exhaust airflow and exhaust resistance. Typically, environmental data includes, but is not limited to, at least one of the following: atmospheric pressure, temperature, and humidity.

[0093] S503: Optimize the exhaust volume prediction model based on environmental data.

[0094] In this embodiment, the exhaust volume prediction model is optimized based on environmental data, including: using the exhaust volume and environmental parameters of each floor other than the target floor as input parameters, and the exhaust volume of the target floor as the output parameter, to train and optimize the exhaust volume prediction model.

[0095] Specifically, taking the target floor as the i-th floor as an example, smoke exhaust data and environmental data from all floors except the i-th floor are used as model input parameters, and smoke exhaust data and environmental data from the i-th floor are used as model output parameters. A regression model for predicting the exhaust volume of the i-th floor is then fitted. By introducing environmental parameters, the dimensionality of data related to the actual operation of the flue during model training is expanded, reducing the impact of environmental parameter changes on the exhaust volume relationship between floors, optimizing modeling accuracy, and improving the control accuracy of exhaust volume regulation based on the exhaust volume prediction model.

[0096] Based on the same inventive concept as the above embodiments, this invention also provides a building smoke exhaust system air volume control device. This device can execute the building smoke exhaust system air volume control method provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method.

[0097] Figure 6 This is a schematic diagram of a building smoke exhaust system airflow control device provided in an embodiment of the present invention. See also... Figure 6 As shown, the building smoke exhaust system air volume control device of this application includes: a model building module 101, an initialization module 102, an iterative calculation module 103, and an air volume adjustment module 104.

[0098] The system includes a model building module 101, which is used to establish a prediction model for the exhaust volume of each floor based on the exhaust data of each floor in the building smoke exhaust system. The exhaust volume prediction model is used to fit the relationship between the exhaust volume of each floor and the exhaust volume of other floors in the building smoke exhaust system. The initialization module 102 is used to determine the initial value and optimization range of the exhaust volume of each floor in the building smoke exhaust system based on the on / off status and performance parameters of the smoke hoods on all floors. The iterative calculation module 103 is used to iteratively update the exhaust volume of each floor based on the on / off status of the smoke hoods, the initial value of the exhaust volume, the optimization range of the exhaust volume, and the exhaust volume prediction model to obtain the target exhaust volume of each floor. The air volume adjustment module 104 is used to adjust the air volume of the smoke hoods on the corresponding floors according to the target exhaust volume.

[0099] Optionally, the iterative calculation module 103 is configured to: when the range hood on the i-th floor is in the on state, import the exhaust volume of all floors except the i-th floor into the exhaust volume prediction model of the i-th floor; where i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors; and update the exhaust volume of the i-th floor according to the output data of the exhaust volume prediction model.

[0100] Optionally, the iterative calculation module 103 is further configured to: determine a preset iteration step size based on the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration; determine the exhaust volume update value of the i-th layer based on the exhaust volume obtained in the (j-1)-th iteration and the preset iteration step size; and update the exhaust volume of the i-th layer based on the exhaust volume update value when the exhaust volume update value is within the exhaust volume optimization range.

[0101] Optionally, the iterative calculation module 103 is further configured to: update the exhaust volume of the i-th layer according to the upper limit threshold of the exhaust volume when the exhaust volume update value is greater than or equal to the upper limit threshold of the exhaust volume optimization range; and / or update the exhaust volume of the i-th layer according to the lower limit threshold of the exhaust volume when the exhaust volume update value is less than or equal to the lower limit threshold of the exhaust volume optimization range.

[0102] Optionally, the iterative calculation module 103 is further configured to: determine whether the iteration is complete based on the difference between the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration; and determine the target exhaust volume of the i-th layer based on the exhaust volume of the i-th layer when the iteration is complete.

[0103] Optionally, the initialization module 102 is configured to: set the initial value of the exhaust volume to 0 when the range hood is in the off state; and assign a value to the initial value of the exhaust volume according to the performance parameters of the range hood when the range hood is in the on state, and determine the optimization range of the exhaust volume according to the performance parameters of the range hood.

[0104] Optionally, the model building module 101 is configured to: acquire smoke exhaust data of all floors in the building smoke exhaust system under different operating conditions and at different times; take any floor as the target floor; take the smoke exhaust data of all floors in the building smoke exhaust system except the target floor at the same time and under the same operating conditions as input parameters, and take the smoke exhaust data of the target floor at the same time and under the same operating conditions as output parameters, and train the exhaust volume prediction model.

[0105] Optionally, the model building module 101 is also configured to: acquire environmental data of each floor in the building smoke exhaust system, and optimize the exhaust volume prediction model based on the environmental data.

[0106] Based on the above embodiments, the present invention also provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the above-described building smoke exhaust system airflow control method.

[0107] Figure 7This is a schematic diagram of the structure of an electronic device for implementing the airflow control method of a building smoke exhaust system according to an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0108] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0109] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0110] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the airflow control method for the building smoke exhaust system described above.

[0111] In some embodiments, the above-described building smoke exhaust system airflow control method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the building smoke exhaust system airflow control method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the above-described building smoke exhaust system airflow control method by any other suitable means (e.g., by means of firmware).

[0112] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0115] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0116] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0117] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through a communication network. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0118] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0119] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for controlling the air volume of a building smoke exhaust system, characterized in that, include: Based on the smoke exhaust data of each floor in the building smoke exhaust system, an exhaust volume prediction model for each floor is established; wherein, the exhaust volume prediction model is used to fit the relationship between the exhaust volume of each floor in the building smoke exhaust system and the exhaust volume of other floors. The initial value and optimization range of exhaust volume for each floor in the building smoke exhaust system are determined based on the on / off status and performance parameters of the smoke hoods on all floors within the building smoke exhaust system. The exhaust volume of each floor is iteratively updated based on the range hood's on / off status, the initial exhaust volume value, the optimized exhaust volume range, and the exhaust volume prediction model to obtain the target exhaust volume for each floor. Adjust the airflow of the range hoods on the corresponding floors according to the target exhaust volume; The step of iteratively updating the exhaust volume of each floor based on the range hood's on / off status, the initial exhaust volume value, the optimized exhaust volume range, and the exhaust volume prediction model to obtain the target exhaust volume for each floor includes: When the range hood on the i-th floor is in the on state, the exhaust volume of all floors except the i-th floor is imported into the exhaust volume prediction model of the i-th floor; where i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors; Update the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model; The step of establishing a predictive model for the exhaust volume of each floor based on the smoke exhaust data of each floor within the building's smoke exhaust system includes: Obtain smoke exhaust data for all floors within the building's smoke exhaust system under different operating conditions and at different times; Set any floor as the target floor; The smoke exhaust data of all floors in the building's smoke exhaust system, excluding the target floor, under the same time and operating conditions, are used as input parameters, and the smoke exhaust data of the target floor under the same time and operating conditions are used as output parameters to train the exhaust volume prediction model.

2. The method for controlling the air volume of a building smoke exhaust system according to claim 1, characterized in that, Updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model includes: The preset iteration step size is determined based on the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration. The updated value of the exhaust volume of the i-th layer is determined based on the exhaust volume obtained in the (j-1)-th iteration and the preset iteration step size. When the exhaust volume update value is within the exhaust volume optimization range, the exhaust volume of the i-th layer is updated according to the exhaust volume update value.

3. The method for controlling the air volume of a building smoke exhaust system according to claim 2, characterized in that, The step of updating the exhaust volume of the i-th layer based on the output data of the exhaust volume prediction model further includes: When the updated exhaust volume value is greater than or equal to the upper limit threshold of the exhaust volume optimization range, the exhaust volume of the i-th layer is updated according to the upper limit threshold; and / or, When the updated exhaust volume value is less than or equal to the lower limit threshold of the exhaust volume optimization range, the exhaust volume of the i-th layer is updated according to the lower limit threshold.

4. The method for controlling the air volume of a building smoke exhaust system according to claim 1, characterized in that, The step of iteratively updating the exhaust volume of each floor based on the range hood's on / off status, the initial exhaust volume value, the optimized exhaust volume range, and the exhaust volume prediction model to obtain the target exhaust volume for each floor also includes: Whether the iteration is complete is determined by the difference between the output data of the exhaust volume prediction model in the j-th iteration and the output data of the exhaust volume prediction model in the (j-1)-th iteration. The target exhaust volume of the i-th layer is determined based on the exhaust volume of the i-th layer when the iteration is completed.

5. The method for controlling the air volume of a building smoke exhaust system according to claim 1, characterized in that, The process of determining the initial exhaust volume and optimized range for each floor in the building's smoke exhaust system based on the on / off status and performance parameters of the smoke exhaust fans on all floors includes: When the range hood is in the off state, the initial value of the exhaust volume is set to 0; When the range hood is in the on state, the initial value of the exhaust volume is assigned according to the performance parameters of the range hood, and the optimized range of the exhaust volume is determined according to the performance parameters of the range hood.

6. The method for controlling the air volume of a building smoke exhaust system according to any one of claims 1 to 5, characterized in that, Also includes: The environmental data of each floor in the building's smoke exhaust system is obtained, and the exhaust volume prediction model is optimized based on the environmental data.

7. A building smoke exhaust system airflow control device, characterized in that, The building smoke exhaust system airflow control device is used to perform the building smoke exhaust system airflow control method according to any one of claims 1 to 6, wherein the building smoke exhaust system airflow control device comprises: The model building module is used to establish a prediction model for the exhaust volume of each floor based on the smoke exhaust data of each floor in the building smoke exhaust system; wherein, the exhaust volume prediction model is used to fit the relationship between the exhaust volume of each floor in the building smoke exhaust system and the exhaust volume of other floors. An initialization module is used to determine the initial value and optimization range of the exhaust volume for each floor in the building smoke exhaust system based on the on / off status and performance parameters of the smoke exhaust fans on all floors in the building smoke exhaust system. The iterative calculation module is used to iteratively update the exhaust volume of each floor based on the switch status of the range hood, the initial value of the exhaust volume, the optimization range of the exhaust volume, and the exhaust volume prediction model, so as to obtain the target exhaust volume of each floor. The air volume adjustment module is used to adjust the air volume of the smoke hood on the corresponding floor according to the target exhaust volume.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the building smoke exhaust system airflow control method according to any one of claims 1 to 6.

Images