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

Abstract

The invention discloses an air volume control method and device for a building smoke exhaust system and electronic equipment. The method comprises the steps that an exhaust air volume prediction model of each floor is established based on smoke exhaust data of each floor in the building smoke exhaust system; the exhaust air rate initial value and the exhaust air rate optimization range of each floor in the building smoke exhaust system are determined according to the range hood on-off states and the range hood performance parameters of all the floors in the building smoke exhaust system; according to the on-off state of the range hood, the exhaust air rate initial value, the exhaust air rate optimization range and the exhaust air rate prediction model, the exhaust air rate of each floor is iteratively updated, so that the target exhaust air rate of each floor is obtained; and air volume adjustment is conducted on the range hood of the corresponding floor according to the target exhaust air volume. The exhaust air rate is initialized by combining the on-off state of the range hood of each floor, and the exhaust air rate of the corresponding floor is iteratively calculated by constructing the exhaust air rate prediction model of each floor, so that reasonable distribution of the exhaust air rate of the range hood of each floor under different working conditions is ensured, the floor smoke exhaust efficiency is improved, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of range hood control, and in particular to an air volume control method, a control device and electronic equipment for a building smoke exhaust system. Background Art

[0002] In modern high-rise buildings, the building exhaust system is an important facility that connects the kitchens of residents on each floor with the outdoor environment and is responsible for exhausting the fumes and harmful gases generated during cooking. The effective operation of the building exhaust system is directly related to the quality of life of residents and the indoor air quality.

[0003] Existing smoke exhaust systems in multi-story buildings usually use the same exhaust structure and a "one-size-fits-all" control strategy. For example, fixed power control strategy, timing control strategy or pressure sensing control strategy are used to control the operation of range hoods, without considering the actual needs of users on different floors. The existing smoke exhaust systems in multi-story buildings have the following problems: The existing exhaust system structure does not fully consider the pressure differences between floors, which may result in poor exhaust effects on lower floors in actual operation. In addition, since the start-up rates of range hoods on different floors may vary significantly during peak hours, the exhaust volume of the existing smoke exhaust system cannot be dynamically adjusted according to actual needs, resulting in insufficient exhaust volume on some floors and smoke retention, while excess exhaust volume on other floors causes energy waste, resulting in reduced energy efficiency and increased operating costs, and affecting the comfort of the building's living environment. Summary of the invention

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

[0005] According to one aspect of the present invention, a method for controlling air volume of a building smoke exhaust system is provided, comprising: establishing an exhaust volume prediction model for each floor based on 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; determining the exhaust volume initial value and exhaust volume optimization range for each floor in the building smoke exhaust system according to the switch status of the range hoods on all floors in the building smoke exhaust system and the performance parameters of the range hoods; iteratively updating the exhaust volume of each floor according to the switch status of the range hoods, the exhaust volume initial value, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor; and adjusting the air volume of the range hoods on the corresponding floors according to the target exhaust volume.

[0006] Optionally, the exhaust volume of each floor is iteratively updated according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor, including: when the switch state of the range hood on the i-th floor is on, the exhaust volume of all floors except the i-th floor is imported into the exhaust volume prediction model of the i-th floor; wherein i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors; the exhaust volume of the i-th floor is updated according to the output data of the exhaust volume prediction model.

[0007] Optionally, the exhaust volume of the i-th layer is updated according to the output data of the exhaust volume prediction model, including: determining a preset iteration step according to 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 an updated value of the exhaust volume of the i-th layer according to the exhaust volume obtained in the j-1-th iteration and the preset iteration step; when the updated value of the exhaust volume is within the exhaust volume optimization range, updating the exhaust volume of the i-th layer according to the updated value of the exhaust volume.

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

[0009] Optionally, the exhaust volume of each floor is iteratively updated according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor, and also includes: determining whether the iteration is completed according to 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; determining the target exhaust volume of the i-th floor according to the exhaust volume of the i-th floor when the iteration is completed.

[0010] Optionally, the initial value of the exhaust volume and the optimized range of the exhaust volume for each floor in the building smoke exhaust system are determined according to the switch status of the range hoods and the performance parameters of the range hoods on all floors in the building smoke exhaust system, including: when the switch status of the range hood is off, the initial value of the exhaust volume is set to 0; when the switch status of the range hood is on, 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.

[0011] Optionally, an exhaust volume prediction model for each floor is established based on the smoke exhaust data of each floor in the building smoke exhaust system, including: obtaining the smoke exhaust data of all floors in the building smoke exhaust system under different working conditions and at different times; taking any floor as the target floor; taking the smoke exhaust data of all floors except the target floor in the building smoke exhaust system at the same time and the same working conditions as input parameters, and taking the smoke exhaust data of the target floor at the same time and the same working conditions as output parameters, and training the exhaust volume prediction model.

[0012] Optionally, the building smoke exhaust system air volume control method further includes: acquiring environmental data of each floor in the building smoke exhaust system, and optimizing the exhaust volume prediction model according to the environmental data.

[0013] According to another aspect of the present invention, there is provided an air volume control device for a building smoke exhaust system, comprising: a model building module for establishing an exhaust volume prediction model for 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 for determining the exhaust volume initial value and exhaust volume optimization range of each floor in the building smoke exhaust system according to the switch state of the range hoods and the performance parameters of the range hoods on all floors in the building smoke exhaust system; an iterative calculation module for iteratively updating the exhaust volume of each floor according to the switch state of the range hoods, the exhaust volume initial value, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor; and an air volume adjustment module for adjusting the air volume of the range hoods on the corresponding floors according to the target exhaust volume.

[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, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned building smoke exhaust system air volume control method.

[0015] The technical solution of the embodiment of the present invention establishes an exhaust volume prediction model for each floor through the smoke exhaust data of each floor in the building smoke exhaust system; determines the exhaust volume initial value and exhaust volume optimization range of each floor in the building smoke exhaust system according to the smoke exhaust machine switch status and smoke machine performance parameters of all floors in the building smoke exhaust system; iteratively updates the exhaust volume of each floor according to the smoke exhaust machine switch status, the exhaust volume initial value, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor, and then adjusts the air volume of the smoke exhaust machine on the corresponding floor according to the target exhaust volume, which solves the problem that the existing building smoke exhaust system cannot be dynamically adjusted according to actual needs and floor structure, resulting in low smoke exhaust energy efficiency and high cost, and can adjust the exhaust volume of the smoke exhaust machines on each floor in real time to ensure that the exhaust volume of the smoke exhaust machines on each floor under different working conditions is reasonably distributed, improve the floor smoke exhaust efficiency, and reduce energy consumption.

[0016] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

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

[0019] Figure 2 A schematic diagram of the structure of a building smoke exhaust system provided by an embodiment of the present invention;

[0020] Figure 3 A flow chart of an exhaust volume updating method provided by an embodiment of the present invention;

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

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

[0023] Figure 6 A schematic structural diagram of an air volume control device for a building smoke exhaust system provided by an embodiment of the present invention;

[0024] Figure 7 It is a structural schematic diagram 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] Reference numerals:

[0026] 101. Model building module; 102. Initialization module; 103. Iterative calculation module; 104. Air volume adjustment module; 10. Electronic device; 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 DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatus.

[0029] Figure 1 A flow chart of a building smoke exhaust system air volume control method provided in an embodiment of the present invention. This embodiment is suitable for application scenarios in which the exhaust volume of range hoods in a public flue system in a high-rise building is adjusted. The method can be executed by a building smoke exhaust system air volume control device, which can be implemented in the form of hardware and / or software, and can be configured in a smoke exhaust system controller or an independent electronic device.

[0030] Figure 2 A structural schematic diagram of a building smoke exhaust system provided in an embodiment of the present invention takes 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 to exemplarily illustrate the flue structure of a high-rise building smoke exhaust system.

[0031] See also Figure 1 and Figure 2 As shown, the air volume control method of the building smoke exhaust system of the present application specifically includes:

[0032] S1: Establish an exhaust volume prediction model for 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 in the building smoke exhaust system and the smoke exhaust data of other floors.

[0034] In this embodiment, the smoke exhaust data can be understood as actual operating data directly or indirectly related to the exhaust volume of the smoke exhaust system when the building smoke exhaust system is in different working conditions (under different working conditions, the switch status and power of the smoke exhaust system on each floor are different). Typically, the smoke exhaust data includes but is not limited to: exhaust volume, wind pressure, smoke exhaust system power or environmental parameters (temperature, humidity or atmospheric pressure). In this embodiment, the smoke exhaust data can be updated regularly as the system runs for a period of time.

[0035] In this embodiment, the exhaust volume prediction model can be understood as a regression equation obtained by training based on the self-learning machine model and the actual operation 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 need to be established for a building with N floors. Exemplarily, taking the exhaust volume prediction model of the i-th floor as an example, the exhaust volume prediction model of the i-th floor is established based on the exhaust data of the i-th floor and the exhaust data of all floors except the i-th floor. The exhaust volume prediction model of the N-story building is shown in Formula 1:

[0036]

[0037] Among them, Q1 represents the smoke exhaust data of the range hood on the first floor; Q2 represents the smoke exhaust data of the range hood on the second floor; Q i represents the exhaust data of the smoke exhaust fan on the i-th floor; Q i-1 Indicates the smoke exhaust data of the smoke exhaust fan on the i-1th floor; Q i+1 represents the smoke exhaust data of the range hood on the i+1th floor; Q N represents the smoke exhaust data of the smoke exhaust fan on the Nth floor; f1(Q2,Q3,...,Q N ) represents the exhaust volume prediction model of the first layer, the input parameter of the exhaust volume prediction model is the smoke exhaust data of the second to Nth layers, and the output parameter of the exhaust volume prediction model is the smoke exhaust data of the first layer; f i (Q1,Q2,...,Q i-1 ,Q i+1 ,…,Q N ) represents the exhaust volume prediction model of the i-th layer, the input parameters of the exhaust volume prediction model are the smoke exhaust data of the 1st layer to the i-1th layer, and the i+1th layer to the Nth layer, and the output parameter of the exhaust volume prediction model is the smoke exhaust data of the i-th layer; f N (Q1,Q2,…,Q N-1 ) represents the exhaust volume prediction model of the Nth layer, the input parameter of the exhaust volume prediction model is the smoke exhaust data of the 1st to the N-1th layers, and the output parameter of the exhaust volume prediction model is the smoke exhaust data of the Nth layer.

[0038] S2: Determine the initial value and optimized range of exhaust volume for each floor in the building smoke exhaust system according to the switch status of the smoke hoods and the performance parameters of the smoke hoods on all floors in the building smoke exhaust system.

[0039] The range hood switch status can be understood as data indicating whether the range hood is in an on or off state. Typically, the range hood switch status is any of the following: on state or off state. In this embodiment, the range hood switch status of each floor can be obtained through communication interaction with the range hoods on each floor.

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

[0041] The exhaust volume initial value can be understood as the initialization assignment of the exhaust volume. In this embodiment, the exhaust volume initial value of the corresponding floor can be assigned based on the switch status of the smoke exhaust machine on each floor in the building 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 user-defined data configuration.

[0043] Optionally, the initial value of the exhaust volume and the optimized range of the exhaust volume for each floor in the building smoke exhaust system are determined according to the switch status of the range hoods and the performance parameters of the range hoods on all floors in the building smoke exhaust system, including: when the switch status of the range hood is off, the initial value of the exhaust volume is set to 0; when the switch status of the range hood is on, 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.

[0044] Specifically, when the switch state of the range hood on the i-th layer is the off state, the initial value of the exhaust volume of the i-th layer is set to 0. At this time, there is no need to optimize the exhaust volume of the i-th layer; when the switch state of the range hood on the i-th layer is the on state, at this time, the exhaust volume of the i-th layer needs to be optimized, and 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 static pressure-air volume curve (i.e., PQ curve) of the range hood, 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 according to the switch status of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor.

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

[0047] In this embodiment, iterative update means that in each iteration process, the exhaust volume of each floor is updated layer by layer according to the switch status of the range hoods 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 be stable, the next iteration is executed to update the exhaust volume of each floor layer by layer again.

[0048] Specifically, during the first iteration, the range hood exhaust volume is updated layer by layer from the 1st floor to the Nth floor according to the range hood switch state and the initial value of the exhaust volume on each floor. In the subsequent iterations, taking the i-th floor as an example, when the range hood switch state on the i-th floor is off, the exhaust volume update of the i-th floor is completed after the initial value of the exhaust volume on the i-th floor is set to 0; when the range hood switch state on the i-th floor is on, the exhaust volume of the i-th floor is updated based on the exhaust volume prediction model of the i-th floor and the exhaust volume of other floors obtained in the previous iteration, until all floors are updated, and the exhaust volume of each floor tends to be stable, and the target exhaust volume is obtained. During the iteration process, the target exhaust volume does not exceed the exhaust volume optimization range.

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

[0050] In this embodiment, remote control can be used to adjust the air volume of the range hoods on each floor to the corresponding target exhaust volume, or the target exhaust volume of each floor can be pushed to the terminal device, and the operator adjusts 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 the actual operation data of the range hoods in the building exhaust system. The exhaust volume prediction model can fit the mutual influence relationship between the range hoods on each floor in the building exhaust system. Furthermore, the exhaust volume of each floor is initialized based on the switch status of the range hoods on each floor (such as on or off) and the performance parameters of the range hoods (such as PQ curve, maximum air volume and static pressure, minimum air volume and static pressure, and rated air volume of the range hood), and the optimal range of exhaust volume for the powered-on floors is calculated. For the powered-on floors, the exhaust volume of the floor is updated based on the exhaust volume prediction model of the floor and the initial values ​​of the exhaust volume of other floors until the iteration is completed to obtain the target exhaust volume of the floor. The exhaust volume of the range hoods is updated layer by layer from the 1st floor to the Nth floor to obtain the target exhaust volume corresponding to each floor, and the range hoods on each floor are adjusted to the corresponding target exhaust volume. It can adjust the exhaust volume of the range hoods on each floor in real time to ensure that the exhaust volume of the range hoods on each floor is reasonably distributed under different working conditions, improve the smoke exhaust efficiency of each floor, reduce energy consumption, and improve the comfort of building occupants.

[0052] Figure 3 A flow chart of an exhaust volume updating method provided in an embodiment of the present invention exemplarily shows a specific implementation method of performing exhaust volume iterative updating based on an exhaust volume prediction model.

[0053] See also Figure 3 As shown, in the above step S3, the exhaust volume of each floor is iteratively updated according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor, which specifically includes the following steps:

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

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

[0056] In this embodiment, when the range hood switch state is on, the exhaust volume is updated based on the exhaust volume prediction model. In the first iteration, the input parameter of the exhaust volume prediction model is the exhaust volume initial value configured based on the range hood switch state. In subsequent iterations, the input parameter of the exhaust volume prediction model is the exhaust volume after the previous iteration update.

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

[0058] In this embodiment, the exhaust volume of the i-th layer is updated according to the output data of the exhaust volume prediction model, including: updating the exhaust volume of the i-th layer according to 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 according to the output data of the exhaust volume prediction model includes: determining a preset iteration step according to 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 exhaust volume update value of the i-th layer according to the exhaust volume obtained in the j-1-th iteration and the preset iteration step; when the exhaust volume update value is in the exhaust volume optimization range, updating the exhaust volume of the i-th layer according to the exhaust volume update value. Specifically, the preset iteration step 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 exhaust volume update value is equal to the sum of the exhaust volume obtained in the j-1-th iteration and the preset iteration step. When the exhaust volume update value is in the exhaust volume optimization range (for example, greater than the exhaust volume lower limit threshold and less than the exhaust volume upper limit threshold), the exhaust volume of the i-th layer is updated according to the exhaust volume update value.

[0060] Optionally, iteratively updating the exhaust volume of the i-th layer according to the output data of the exhaust volume prediction model also includes: when the exhaust volume update value is greater than or equal to the exhaust volume upper limit threshold of the exhaust volume optimization range, updating the exhaust volume of the i-th layer according to the exhaust volume upper limit threshold; and / or, when the exhaust volume update value is less than or equal to the exhaust volume lower limit threshold of the exhaust volume optimization range, updating the exhaust volume of the i-th layer according to the exhaust volume lower limit threshold. Specifically, if the exhaust volume update 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 process is greater than or equal to the exhaust volume upper limit threshold, the exhaust volume of the i-th layer is set to the exhaust volume upper limit threshold (i.e., the maximum value within the exhaust volume optimization range); if the exhaust volume update 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 process is less than or equal to the exhaust volume lower limit threshold, the exhaust volume of the i-th layer is set to the exhaust volume lower limit threshold (i.e., the minimum value within the exhaust volume optimization range). By establishing an optimal range of exhaust volume, the exhaust volume distribution is optimized to avoid the exhaust volume exceeding a specific range that affects the air volume and air pressure, thereby improving the air volume control effect.

[0061] See also Figure 3 As shown, in the above step S3, the exhaust volume of each floor is iteratively updated according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor, and the following steps are also included:

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

[0063] If the iteration is completed, execute step S304; if the iteration is not completed, execute step S302.

[0064] S304: Determine the target exhaust volume of the i-th layer according to 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 jth iteration and the output data of the exhaust volume prediction model in the j-1th iteration of any floor is greater than a preset difference threshold (for example, 0.1 m 3 / min), the iteration is determined to be incomplete, and the next iteration is started based on the exhaust volume obtained in the current iteration, and the exhaust volume of each floor is updated layer by layer (for example, in the order from the 1st floor to the Nth floor); if the difference between the output data of the exhaust volume prediction model in the jth iteration and the output data of the exhaust volume prediction model in the j-1th iteration of all floors is less than or equal to the preset difference threshold (for example, 0.1m 3 / min), it is determined that the range hood air volume of all floors is stable, and the iteration is completed. The updated exhaust volume of each floor at this time is determined as the target exhaust volume of the corresponding floor. Through the cyclic iteration of all floors, the exhaust volume of each floor under different working conditions is evenly distributed, thereby improving the exhaust efficiency.

[0066] Optionally, an exhaust volume prediction model for each floor is established based on the smoke exhaust data of each floor in the building smoke exhaust system, including: obtaining smoke exhaust data of all floors in the building smoke exhaust system under different working conditions (such as different smoke exhaust hood switch states and smoke exhaust hood powers on each floor) and at different times; taking any floor as the target floor (for example, the i-th floor); taking the smoke exhaust data of all floors except the target floor (for example, the i-th floor) in the building smoke exhaust system at the same time (such as time t1) and the same working condition (such as the smoke exhaust hoods on the 1st to 5th floors are turned on, and the smoke exhaust hoods on other floors are turned off) as input parameters, and taking the smoke exhaust data of the target floor at the same time (such as time t1) and the same working condition (such as the smoke exhaust hoods on the 1st to 5th floors are turned on, and the smoke exhaust hoods on other floors are turned off) as output parameters, and performing model training on the exhaust volume prediction model. Specifically, the exhaust data of all floors at the same time (such as time t1) and the same working condition (such as the smoke exhaust hoods on the 1st to 5th floors are turned on, and the smoke exhaust hoods on other floors are turned off) are taken as an array, and the exhaust volume prediction model of the same floor is trained respectively through multiple arrays at different times and different working conditions. Figure 2 As with the above formula 1, taking the target floor as the i-th floor as an example, when training the exhaust volume prediction model for the i-th floor, multiple arrays at different times and under different working conditions are obtained, and the smoke exhaust data of all floors except the i-th floor in the same array are used as input parameters, and the smoke exhaust data of the i-th floor in the same array are used as output data. The exhaust volume prediction model for the i-th floor is trained until the model output tends to be stable. For example, the difference between the output data of the exhaust volume prediction model in the two iterations is less than the preset difference threshold (for example, 0.1m 3 / min) to complete the model training.

[0067] Figure 4 This is a flow chart of another method for controlling the air volume of a building smoke exhaust system provided by an embodiment of the present invention. Figure 4 As shown, the air volume control method of the building smoke exhaust system of the present application specifically includes:

[0068] S401: Obtain the switch status and performance parameters of the range hoods on each floor.

[0069] S402: On the floor where the range hood switch is on, an optimized range of exhaust volume is set based on the range hood performance parameters.

[0070] S403: Set the initial value of the exhaust volume of each floor. The initial value of the exhaust volume of the power-on floor is set to the rated air volume, and the initial value of the exhaust volume of the power-off floor is set to 0.

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

[0072] S405: Fitting 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 iteration.

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

[0076] S409: Whether the switch status of the range hood on the i-th layer is on.

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

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

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

[0080] S411: Whether the exhaust volume update value of the i-th layer is within the exhaust volume optimization range.

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

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

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

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

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

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

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

[0088] Specifically, the above steps S401 to S403 record a specific implementation of exhaust volume initialization, the above steps S404 to S406 record a specific implementation of a modeling method of an exhaust volume prediction model, and the above steps S407 to S414 record a specific implementation of iterative optimization of the exhaust volume of each floor based on the exhaust volume prediction model and the initialization value. The machine learning model is trained with the actual operating data of the range hoods in the building exhaust system, and the exhaust volume of each floor is assigned and iteratively updated in combination with the actual operating conditions of the range hoods on each floor. The exhaust volume distribution of the range hoods on each floor under different operating conditions can be adjusted in real time, thereby improving the exhaust efficiency of each floor and reducing energy consumption.

[0089] Figure 5 A flowchart of another method for controlling the air volume of a building smoke exhaust system provided by an embodiment of the present invention exemplarily shows a specific implementation method for performing model training based on environmental data and smoke exhaust data. Figure 5 As shown, the air volume control method of the building smoke exhaust system of the present application also includes:

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

[0091] S502: Obtain environmental data of each floor in the building smoke exhaust system.

[0092] In this embodiment, the environmental data represents parameters that affect the smoke exhaust airflow and smoke exhaust resistance. Typically, the 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 according to the environmental data.

[0094] In this embodiment, the exhaust volume prediction model is optimized according to the environmental data, including: taking the exhaust volume and environmental parameters of each floor except the target floor as input parameters, taking the exhaust volume of the target floor as output parameters, and training and optimizing the exhaust volume prediction model.

[0095] Specifically, taking the target floor as the i-th floor as an example, the smoke exhaust data and environmental data of all floors except the i-th floor are used as model input parameters, and the smoke exhaust data and environmental data of the i-th floor are used as model output parameters to regress and fit the exhaust volume prediction model of the i-th floor. By introducing environmental parameters, the dimension of the data related to the actual operation of the flue during the model training process is expanded, the impact of environmental parameter changes on the exhaust volume relationship of each floor is reduced, the modeling accuracy is optimized, and the control accuracy of exhaust volume regulation based on the exhaust volume prediction model is improved.

[0096] Based on the same inventive concept as the above embodiments, an embodiment of the present invention further provides a building smoke exhaust system air volume control device, which can execute the building smoke exhaust system air volume control method provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

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

[0098] Among them, the model building module 101 is used to establish an exhaust volume prediction model for 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; the initialization module 102 is used to determine the exhaust volume initial value and exhaust volume optimization range of each floor in the building smoke exhaust system according to the range hood switch status and range hood performance parameters of all floors in the building smoke exhaust system; the iterative calculation module 103 is used to iteratively update the exhaust volume of each floor according to the range hood switch status, exhaust volume initial value, exhaust volume optimization range and 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 range hood on the corresponding floor according to the target exhaust volume.

[0099] Optionally, the iterative calculation module 103 is configured as follows: when the range hood switch state of the i-th floor is on, the exhaust volume of all floors except the i-th floor is imported into the exhaust volume prediction model of the i-th floor; wherein i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors; and the exhaust volume of the i-th floor is updated 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 according to 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 according to the exhaust volume obtained in the j-1-th iteration and the preset iteration step; when the exhaust volume update value is within the exhaust volume optimization range, update the exhaust volume of the i-th layer according to the exhaust volume update value.

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

[0102] Optionally, the iterative calculation module 103 is further configured to: determine whether the iteration is completed 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; 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.

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

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

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

[0106] Based on the above embodiments, an embodiment of the present invention further 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 so that the at least one processor can execute the above-mentioned building smoke exhaust system air volume control method.

[0107] Figure 7A 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. 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 may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present 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 connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

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

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

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

[0112] Various implementations 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 chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] Computer programs for implementing 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, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

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

[0115] To provide interaction with a user, the systems and techniques described herein may 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 a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0117] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0118] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0119] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for controlling the air volume of a building smoke exhaust system, characterized in that: include: Establishing an exhaust volume prediction model for each floor based on the 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; Determining the exhaust volume initial value and exhaust volume optimization range of each floor in the building smoke exhaust system according to the switch status of the smoke exhaust fans on all floors in the building smoke exhaust system and the performance parameters of the smoke exhaust fans; Iteratively updating the exhaust volume of each floor according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor; The air volume of the range hoods on the corresponding floors is adjusted according to the target exhaust volume.

2. The air volume control method of a building smoke exhaust system according to claim 1, characterized in that: The iterative updating of the exhaust volume of each floor according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor includes: When the switch state of the range hood on the i-th floor is on, the exhaust volume of all floors except the i-th floor is imported into the exhaust volume prediction model of the i-th floor; wherein i is a positive integer greater than or equal to 1 and less than or equal to the maximum number of floors; The exhaust volume of the i-th layer is updated according to the output data of the exhaust volume prediction model.

3. The air volume control method of a building smoke exhaust system according to claim 2, characterized in that: The updating of the exhaust volume of the i-th layer according to the output data of the exhaust volume prediction model comprises: Determining a preset iteration step length according to the output data of the exhaust air volume prediction model in the j-th iteration and the output data of the exhaust air volume prediction model in the j-1-th iteration; Determine the exhaust volume update value of the i-th layer according to the exhaust volume obtained in the j-1th iteration and the preset iteration step; 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.

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

5. The air volume control method of a building smoke exhaust system according to claim 2, characterized in that: The iterative updating of the exhaust volume of each floor according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range and the exhaust volume prediction model to obtain the target exhaust volume of each floor also includes: Determining whether the iteration is completed according to the difference between the output data of the exhaust air volume prediction model in the j-th iteration and the output data of the exhaust air volume prediction model in the j-1-th iteration; The target exhaust volume of the i-th layer is determined according to the exhaust volume of the i-th layer when iteration is completed.

6. The air volume control method of a building smoke exhaust system according to claim 1, characterized in that: The method of determining the exhaust volume initial value and exhaust volume optimization range of each floor in the building smoke exhaust system according to the switch status of the smoke exhaust machines on all floors in the building smoke exhaust system and the performance parameters of the smoke exhaust machines includes: When the range hood switch state is off, the exhaust air volume initial value is set to 0; When the range hood switch state is the on state, the initial value of the exhaust volume is assigned according to the range hood performance parameter, and the exhaust volume optimization range is determined according to the range hood performance parameter.

7. The air volume control method of a building smoke exhaust system according to claim 1, characterized in that: The exhaust volume prediction model for each floor is established based on the smoke exhaust data of each floor in the building smoke exhaust system, including: Obtaining smoke exhaust data of all floors in the building smoke exhaust system under different working conditions and at different times; Select any floor as the target floor; The exhaust volume prediction model is trained by taking the exhaust data of all floors except the target floor in the building smoke exhaust system at the same time and under the same working conditions as input parameters, and taking the exhaust data of the target floor at the same time and under the same working conditions as output parameters.

8. The air volume control method of a building smoke exhaust system according to any one of claims 1 to 7, characterized in that: Also includes: The environmental data of each floor in the building smoke exhaust system is obtained, and the exhaust volume prediction model is optimized according to the environmental data.

9. A building smoke exhaust system air volume control device, characterized in that: include: A model building module, used to establish an exhaust volume prediction model for each floor based on the 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, used to determine the exhaust volume initial value and exhaust volume optimization range of each floor in the building smoke exhaust system according to the switch status of the smoke exhaust fans and the performance parameters of the smoke exhaust fans on all floors in the building smoke exhaust system; An iterative calculation module, used for iteratively updating the exhaust volume of each floor according to the switch state of the range hood, the initial value of the exhaust volume, the exhaust volume optimization range 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 range hood on the corresponding floor according to the target exhaust volume.

10. An electronic device, characterized in that: The electronic device comprises: 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 so that the at least one processor can execute the air volume control method for a building smoke exhaust system according to any one of claims 1 to 8.

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