Building air conditioner control system and method based on combined driving of Internet of Things

Abstract

The invention discloses a building air conditioner control system and method based on combined driving of the Internet of Things, and relates to the technical field of air conditioner control. The control method specifically comprises the following steps that installation position information of all indoor units is obtained; an airflow adjusting area corresponding to each indoor unit is divided; judging whether the wind speed of the auxiliary node needs to be adjusted; and inputting the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed coordinated regulation model, and outputting a target regulation wind speed for the auxiliary node. According to the method, the airflow adjusting area is dynamically divided through the wind field diffusion model, the coupling relation between the target node and the auxiliary node is accurately recognized, the multi-target constrained wind speed cooperative adjusting model is combined, on the premise that the comfort degree is guaranteed, energy consumption can be reduced, the air outlet speeds of the two adjacent indoor units are automatically adjusted according to temperature changes, and the air quality is improved. Therefore, information islands among the devices are broken, and a real synergistic effect is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of air - conditioner control, and specifically to a building air - conditioner control system and method based on Internet of Things (IoT) joint drive. Background Technique

[0002] In some buildings, a central air - conditioning system is installed on each floor to adjust the temperature. The central air - conditioning system usually includes an outdoor unit and multiple indoor units, and one outdoor unit drives multiple indoor units to work.

[0003] Each indoor unit is allocated to different areas and jointly conducts regulation work. Although the indoor area is divided into several regions, the air between these regions is mutually circulating. Since

[0004] For the air outlets of each indoor unit, generally, they will not be easily changed after being set. However, the indoor units belonging to the public area cooperate with each other to adjust the temperature of the public area to an appropriate temperature. Since the air volume of each indoor unit is independently set, there is an information island problem. Summary of the Invention

[0005] The purpose of the present invention is to provide a building air - conditioner control system and method based on IoT joint drive to solve the problems raised in the prior art.

[0006] To achieve the above - mentioned purpose, the present invention provides the following technical solution: A building air - conditioner control method based on IoT joint drive, the control method specifically includes the following steps:

[0007] S1. Obtain the floor plan information of each floor of the building and the installation position information of each indoor unit on this floor;

[0008] S2. Based on the floor plan information and the indoor unit installation position information, construct a wind field diffusion model for simulating the diffusion range of the air flow from each indoor unit, and divide the air - flow adjustment area corresponding to each indoor unit according to the simulation result;

[0009] S3. Select a certain indoor unit as the target node, mark at least one other indoor unit whose air - flow adjustment area is adjacent to or overlaps with that of the target node as the auxiliary node, and judge whether it is necessary to adjust the wind speed of the auxiliary node; until it is judged that adjustment is needed, then proceed to step S4;

[0010] S4. Through the IoT sensor network, real - time obtain the current wind speed of the target node and the current wind speed of the auxiliary node;

[0011] S5. Establish a wind speed collaborative adjustment model, input the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed collaborative adjustment model, and output the target adjustment wind speed for the auxiliary node;

[0012] S6. Generate a control command according to the target adjustment wind speed and send it to the indoor unit actuator of the auxiliary node to adjust the actual air supply wind speed of the auxiliary node.

[0013] Furthermore, the wind field diffusion model is constructed based on computational fluid dynamics simulation. By simulating the air flow movement trajectories under different indoor unit air supply parameters, the air flow adjustment areas corresponding to each indoor unit are divided. The division process is as follows:

[0014] Take the indoor unit air outlet as the center and preset a circular diffusion range;

[0015] Simulate the gas flow trajectories of the indoor unit under different air supply parameters to form an air flow diffusion path;

[0016] Obtain the wind speed of gas diffusion at different radii;

[0017] Mark the boundary range where the wind speed decays to the set threshold as the simulated coverage range to form the air flow adjustment area of the indoor unit.

[0018] Furthermore, in step S3,

[0019] Obtain the current air supply parameters of the indoor unit, and obtain the air flow adjustment area of the indoor unit corresponding to the current air supply parameters. Set several temperature sensors to extend outward from the center in the direction of the radius of the air flow adjustment area, set an interval distance, and measure the temperature in the air flow adjustment area at different radii;

[0020] Obtain the set air supply temperature of each indoor unit. If the set air supply temperatures of two adjacent or overlapping indoor units in the air flow adjustment area are different, mark the indoor unit with the higher set air supply temperature as the target node and the other indoor unit with the lower set air supply temperature as the auxiliary node;

[0021] Take the target node and the auxiliary node as two endpoints, connect the auxiliary line, mark the temperatures corresponding to different radii on the auxiliary line, start from the target node, arrange the temperature values in sequence in the direction of the auxiliary node, and the arranged temperature values gradually decrease. Obtain the radius corresponding to the last temperature data in the arrangement as the temperature radius, and take the radius of the air flow adjustment area of the target node as the adjustment radius. If the temperature radius is greater than the adjustment radius, it is determined that the wind speed of the auxiliary node needs to be adjusted.

[0022] Further, the overlapping area is defined as follows: if the overlapping area of the air flow adjustment areas of two indoor units exceeds a set proportion of the area of any one area, it is determined that there is an overlapping area.

[0023] Further, the establishment process of the wind speed collaborative adjustment model includes:

[0024] Collect historical operation data and construct a training set with the wind speed of the target node and the wind speed of the auxiliary node as inputs and the comfort level after adjustment of the target node as the output.

[0025] Fit the mapping relationship between the input and the output through a machine learning algorithm to generate a wind speed collaborative adjustment model.

[0026] Input the supply air temperature of the auxiliary node into the wind speed collaborative adjustment model additionally, and output the target adjustment wind speed and the recommended supply air temperature of the auxiliary node.

[0027] Further, the generation of the control instruction includes:

[0028] Input the difference between the target adjustment wind speed and the current wind speed of the auxiliary node into a PID controller.

[0029] Output a control signal to the indoor unit fan frequency converter.

[0030] A building air conditioning control system based on Internet of Things joint drive, the control system includes an information collection module, a simulation diffusion module, a region division module, a data collection module and a collaborative adjustment module;

[0031] The information collection module is used to obtain the floor plan information of each floor of the building and the installation position information of each indoor unit on that floor;

[0032] The simulation diffusion module constructs a wind field diffusion model for simulating the diffusion range of the air flow discharged from each indoor unit based on the floor plan information and the indoor unit installation position information;

[0033] The region division module is used to divide the air flow adjustment region corresponding to each indoor unit according to the simulation result;

[0034] The data collection module is used to collect the temperature and wind speed of the air flow adjustment regions corresponding to the target node and the auxiliary node; where a certain indoor unit is selected as the target node, and at least one other indoor unit adjacent to or having an overlapping area with the air flow adjustment region of the target node is marked as the auxiliary node;

[0035] The collaborative adjustment module is used to input the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed collaborative adjustment model, and output the target adjustment wind speed for the auxiliary node.

[0036] Further, the data acquisition module includes a sensor unit and a marking unit;

[0037] The sensor unit is used to collect the temperature and wind speed in the air flow regulation area;

[0038] The marking unit is used to divide the target node and the auxiliary node. The process is as follows: obtain the current air supply parameter of the indoor unit, and obtain the air flow regulation area of the indoor unit corresponding to the current air supply parameter. Set several temperature sensors extending outward from the center with the radius of the air flow regulation area as the direction, set an interval distance, and measure the temperature in the air flow regulation area at different radii;

[0039] Obtain the set air supply temperature of each indoor unit. If the set air supply temperatures of two indoor units with adjacent or overlapping air flow regulation areas are different, mark the indoor unit with the higher set air supply temperature as the target node, and mark the other indoor unit with the lower set air supply temperature as the auxiliary node.

[0040] Further, the collaborative regulation module includes judging whether it is necessary to adjust the wind speed of the auxiliary node. The judging process is as follows: take the target node and the auxiliary node as two endpoints, connect the auxiliary line, mark the temperatures corresponding to different radii on the auxiliary line, take the target node as the starting point, arrange the temperature values in sequence in the direction of the auxiliary node, and the temperature values arranged in sequence gradually decrease. Obtain the radius corresponding to the last temperature data in the arrangement as the temperature radius, and take the radius of the air flow regulation area of the target node as the regulation radius. If the temperature radius is greater than the regulation radius, it is judged that it is necessary to adjust the wind speed of the auxiliary node.

[0041] Compared with the prior art, the beneficial effects of the present invention are:

[0042] 1. Dynamically divide the air flow regulation area through the wind field diffusion model, accurately identify the coupling relationship between the target node and the auxiliary node, and combine the wind speed collaborative regulation model with multi-objective constraints. On the premise of ensuring comfort, the energy consumption can be reduced, and the air outlet wind speeds of two adjacent indoor units can be automatically adjusted according to the temperature change, so that the information island between devices can be broken and the real collaborative effect can be achieved. Brief Description of the Drawings

[0043] Figure 1 It is a schematic flow chart of a building air conditioner control method based on Internet of Things joint drive of the present invention. Detailed Embodiment

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment: As Figure 1 shown, the present invention provides a building air-conditioning control method based on Internet of Things joint drive. The control method specifically includes the following steps:

[0046] S1. Obtain the floor plan information of each floor building and the installation position information of each indoor unit on that floor;

[0047] S2. Based on the floor plan information and the indoor unit installation position information, construct a wind field diffusion model for simulating the air flow diffusion range of each indoor unit, and divide the air flow adjustment area corresponding to each indoor unit according to the simulation results;

[0048] The wind field diffusion model is constructed based on computational fluid dynamics simulation. By simulating the air flow movement trajectories under different indoor unit air supply parameters, the air flow adjustment areas corresponding to each indoor unit are divided. The division process is as follows:

[0049] Take the indoor unit air outlet as the center of the circle and preset a circular diffusion range;

[0050] Simulate the gas flow trajectories of the indoor unit under different air supply parameters to form an air flow diffusion path;

[0051] Obtain the wind speed of gas diffusion at different radii;

[0052] Mark the boundary range where the wind speed decays to the set threshold as the simulated coverage range to form the air flow adjustment area of the indoor unit.

[0053] S3. Select a certain indoor unit as the target node, mark at least one other indoor unit adjacent to or having an overlapping area with the air flow adjustment area of the target node as the auxiliary node, and determine whether it is necessary to adjust the wind speed of the auxiliary node; until it is determined that adjustment is required, then proceed to step S4;

[0054] In the step S3,

[0055] Obtain the current air supply parameters of the indoor unit, and obtain the air flow adjustment area of the indoor unit corresponding to the current air supply parameters. Set a number of temperature sensors to extend outward from the center of the circle in the direction of the radius of the air flow adjustment area, set an interval distance, and measure the temperature of the air flow adjustment area at different radii;

[0056] Obtain the set supply air temperature of each indoor unit. If the set supply air temperatures of two indoor units with adjacent or overlapping air flow adjustment areas are different, mark the indoor unit with the higher set supply air temperature as the target node, and mark the other indoor unit with the lower set supply air temperature as the auxiliary node;

[0057] Take the target node and the auxiliary node as two endpoints, connect the auxiliary line, mark the temperatures corresponding to different radii on the auxiliary line, starting from the target node, arrange the temperature values in sequence in the direction of the auxiliary node, and the temperature values arranged in sequence gradually decrease. Obtain the radius corresponding to the last temperature data in the arrangement as the temperature radius, and take the radius of the air flow adjustment area of the target node as the adjustment radius. If the temperature radius is greater than the adjustment radius, it is determined that the wind speed of the auxiliary node needs to be adjusted.

[0058] The overlapping area is defined as follows: If the overlapping area of the air flow adjustment areas of two indoor units exceeds the set proportion of any area, it is determined that there is an overlapping area.

[0059] S4. Through the Internet of Things sensor network, real-time obtain the current wind speed of the target node and the current wind speed of the auxiliary node;

[0060] S5. Establish a wind speed collaborative adjustment model, input the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed collaborative adjustment model, and output the target adjustment wind speed for the auxiliary node; The objective function of the wind speed collaborative adjustment model in S5 is: minimize the wind speed fluctuation variance between the target node and the auxiliary node, while restricting the increase in energy consumption not to exceed 10%.

[0061] The establishment process of the wind speed collaborative adjustment model includes:

[0062] Collect historical operation data, and construct a training set with the wind speed of the target node and the wind speed of the auxiliary node as inputs and the comfort level after adjustment of the target node as the output;

[0063] Generate a wind speed collaborative adjustment model by fitting the mapping relationship between the input and the output through a machine learning algorithm;

[0064] Extra input the supply air temperature of the auxiliary node into the wind speed collaborative adjustment model, and output the target adjustment wind speed and the recommended supply air temperature of the auxiliary node.

[0065] The generation of the control instruction includes:

[0066] Input the difference between the target adjustment wind speed and the current wind speed of the auxiliary node into the PID controller;

[0067] Output a control signal to the indoor unit fan frequency converter.

[0068] S6. Adjust the wind speed according to the target, generate a control instruction and send it to the indoor unit actuator of the auxiliary node to adjust the actual air supply wind speed of the auxiliary node.

[0069] Embodiment 2: A building air-conditioning control system based on Internet of Things joint drive. The control system includes an information collection module, a simulation diffusion module, a region division module, a data collection module, and a cooperative adjustment module.

[0070] The information collection module is used to obtain the floor plan information of each floor building and the installation position information of each indoor unit on that floor.

[0071] The simulation diffusion module constructs a wind field diffusion model for simulating the air flow diffusion range of each indoor unit based on the floor plan information and the indoor unit installation position information.

[0072] The region division module is used to divide the air flow adjustment region corresponding to each indoor unit according to the simulation result.

[0073] The data collection module is used to collect the temperature and wind speed of the air flow adjustment regions corresponding to the target node and the auxiliary node. Among them, a certain indoor unit is selected as the target node, and at least one other indoor unit adjacent to or having an overlapping region with the air flow adjustment region of the target node is marked as the auxiliary node.

[0074] The cooperative adjustment module is used to input the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed cooperative adjustment model and output the target adjustment wind speed for the auxiliary node.

[0075] The data collection module includes a sensor unit and a marking unit.

[0076] The sensor unit is used to collect the temperature and wind speed of the air flow adjustment region.

[0077] The marking unit is used to divide the target node and the auxiliary node. The process is as follows: obtain the current air supply parameters of the indoor unit, and obtain the air flow adjustment region of the indoor unit corresponding to the current air supply parameters. Set several temperature sensors to extend outward from the center of the circle in the direction of the radius of the air flow adjustment region, set an interval distance, and measure the temperature of the air flow adjustment region at different radii.

[0078] Obtain the set air supply temperature of each indoor unit. If the set air supply temperatures of two indoor units with adjacent or overlapping air flow adjustment regions are different, mark the indoor unit with the higher set air supply temperature as the target node and the other indoor unit with the lower set air supply temperature as the auxiliary node.

[0079] The collaborative regulation module includes determining whether it is necessary to adjust the wind speed of the auxiliary node. The determination process is as follows: Taking the target node and the auxiliary node as two endpoints, connecting an auxiliary line, marking the temperatures corresponding to different radii on the auxiliary line, starting from the target node, arranging the temperature values in sequence in the direction of the auxiliary node, and the arranged temperature values gradually decrease. Obtain the radius corresponding to the last temperature data in the arrangement as the temperature radius, and take the radius of the air flow regulation area of the target node as the regulation radius. If the temperature radius is greater than the regulation radius, it is determined that it is necessary to adjust the wind speed of the auxiliary node.

[0080] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above-described exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms. Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be embraced within the present invention. Any reference signs in the claims should not be construed as limiting the claimed invention.

Claims

1. A building air-conditioning control method based on the combined drive of the Internet of Things, characterized in that: The control method specifically includes the following steps: S1. Obtain the floor plan information of each floor building and the installation position information of each indoor unit on that floor; S2. Based on the floor plan information and the indoor unit installation position information, construct a wind field diffusion model for simulating the air flow diffusion range of each indoor unit, and divide the air flow adjustment area corresponding to each indoor unit according to the simulation results; S3. Select a certain indoor unit as the target node, mark at least one other indoor unit adjacent to or having an overlapping area with the air flow adjustment area of the target node as the auxiliary node, and determine whether it is necessary to adjust the wind speed of the auxiliary node; until it is determined that adjustment is required, then proceed to step S4; S4. Through the Internet of Things sensor network, obtain the current wind speed of the target node and the current wind speed of the auxiliary node in real time; S5. Establish a wind speed collaborative adjustment model, input the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed collaborative adjustment model, and output the target adjustment wind speed for the auxiliary node; S6. Generate a control command according to the target adjustment wind speed and send it to the indoor unit actuator of the auxiliary node to adjust the actual air supply wind speed of the auxiliary node.

2. The building air-conditioning control method based on Internet of Things joint drive according to claim 1, characterized in that: The wind field diffusion model is constructed based on computational fluid dynamics simulation. By simulating the air flow movement trajectories under different air supply parameters of indoor units, the air flow adjustment areas corresponding to each indoor unit are divided. The division process is as follows: Take the air outlet of the indoor unit as the center of the circle and preset a circular diffusion range; Simulate the gas flow trajectories of the indoor unit under different air supply parameters to form an air flow diffusion path; Obtain the wind speed of gas diffusion at different radii; Mark the boundary range where the wind speed decays to the set threshold as the simulated coverage range to form the air flow adjustment area of the indoor unit.

3. The building air-conditioning control method based on Internet of Things combined drive according to claim 1, characterized in that: In step S3, Obtain the current air supply parameters of the indoor unit, and obtain the air flow adjustment area of the indoor unit corresponding to the current air supply parameters. Extend several temperature sensors outward from the center of the circle in the direction of the radius of the air flow adjustment area, set an interval distance, and measure the temperature at different radii in the air flow adjustment area; Obtain the set air supply temperature of each indoor unit. If the set air supply temperatures of two indoor units with adjacent or overlapping air flow adjustment areas are different, then mark the indoor unit with the higher set air supply temperature as the target node, and mark the other indoor unit with the lower set air supply temperature as the auxiliary node; Take the target node and the auxiliary node as two endpoints, connect the auxiliary line, mark the temperatures corresponding to different radii on the auxiliary line, start from the target node, arrange the temperature values in sequence in the direction of the auxiliary node, and the temperature values arranged in sequence gradually decrease. Obtain the radius corresponding to the last temperature data in the arrangement as the temperature radius, and take the radius of the air flow adjustment area of the target node as the adjustment radius. If the temperature radius is greater than the adjustment radius, then determine that it is necessary to adjust the wind speed of the auxiliary node.

4. The building air-conditioning control method based on Internet of Things combined drive according to claim 1, wherein: The overlapping area is: If the overlapping area of the air flow adjustment areas of two indoor units exceeds the set proportion of any area, it is determined that there is an overlapping area.

5. The building air-conditioning control method based on Internet of Things combined drive according to claim 1, characterized in that: The establishment process of the wind speed collaborative adjustment model includes: Collect historical operation data to construct a training set with the wind speed of the target node and the wind speed of the auxiliary node as inputs and the comfort level after adjustment of the target node as the output; Fit the mapping relationship between the input and the output through a machine learning algorithm to generate a wind speed collaborative adjustment model; Input the supply air temperature of the auxiliary node into the wind speed collaborative adjustment model additionally to output the target adjustment wind speed and the recommended supply air temperature of the auxiliary node.

6. The building air-conditioning control method based on Internet of Things combined drive according to claim 1, characterized in that: The generation of the control instruction includes: Input the difference between the target adjustment wind speed and the current wind speed of the auxiliary node into the PID controller; Output a control signal to the indoor unit fan frequency converter.

7. A building air-conditioning control system based on Internet of Things joint drive, which is applied to the building air-conditioning control method based on Internet of Things joint drive described in claims 1-6, and is characterized in that: The control system includes an information collection module, a simulation diffusion module, a region division module, a data collection module, and a collaborative adjustment module; The information collection module is used to obtain the floor plan information of each floor building and the installation position information of each indoor unit on that floor; The simulation diffusion module constructs a wind field diffusion model for simulating the diffusion range of the air flow discharged from each indoor unit based on the floor plan information and the indoor unit installation position information; The region division module is used to divide the air flow adjustment region corresponding to each indoor unit according to the simulation result; The data collection module is used to collect the temperature and wind speed of the air flow adjustment regions corresponding to the target node and the auxiliary node; Wherein, select a certain indoor unit as the target node, and mark at least one other indoor unit whose air flow adjustment region is adjacent to or overlaps with the air flow adjustment region of the target node as the auxiliary node; The collaborative adjustment module is used to input the current wind speed of the target node and the current wind speed of the auxiliary node into the wind speed collaborative adjustment model and output the target adjustment wind speed for the auxiliary node.

8. The building air-conditioning control system based on the combined drive of the Internet of Things according to claim 7, characterized in that: The data collection module includes a sensor unit and a marking unit; The sensor unit is used to collect the temperature and wind speed of the air flow adjustment region; The marking unit is used to divide the target node and the auxiliary node. The process is as follows: obtain the current supply air parameters of the indoor unit, and obtain the air flow adjustment region of the indoor unit corresponding to the current supply air parameters. Set a number of temperature sensors to extend outward from the center of the circle in the direction of the radius of the air flow adjustment region, set an interval distance, and measure the temperature of the air flow adjustment region at different radii; Obtain the set supply air temperature of each indoor unit. If the set supply air temperatures of two indoor units whose air flow adjustment regions are adjacent to or overlap with each other are different, mark the indoor unit with the higher set supply air temperature as the target node, and mark the other indoor unit with the lower set supply air temperature as the auxiliary node.

9. The building air-conditioning control system based on the combined drive of the Internet of Things according to claim 7, characterized in that: The collaborative adjustment module includes judging whether it is necessary to adjust the wind speed of the auxiliary node. The judging process is as follows: take the target node and the auxiliary node as two endpoints, connect the auxiliary line, mark the temperatures corresponding to different radii on the auxiliary line, start from the target node, arrange the temperature values in sequence in the direction of the auxiliary node, and the arranged temperature values gradually decrease. Obtain the radius corresponding to the last temperature data in the arrangement as the temperature radius, and take the radius of the air flow adjustment region of the target node as the adjustment radius. If the temperature radius is greater than the adjustment radius, it is judged that it is necessary to adjust the wind speed of the auxiliary node.

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