Air conditioner operation sensing control service system and method based on edge computing

Abstract

The application provides an air conditioner operation perception control service system and method, comprising: a model management unit creating a cold and heat load prediction model of a building and performance models of each execution mechanism in the air conditioner, the performance models comprising a relationship between the state of the execution mechanism and generated energy; an edge computing device deploying the cold and heat load prediction model and the performance models, inputting received supply demand and environmental information of the building into the cold and heat load prediction model to obtain a load prediction result of the building; simulating the load prediction result according to a preset scheduling strategy and transmitting a simulation result to a main server; the main server collecting a new supply demand when detecting that the supply demand of the building changes and sending the new supply demand to the edge computing device; and receiving the simulation result and controlling the action of each execution mechanism in the air conditioner according to the simulation result. The system can enable the entire air conditioner system to save energy to the maximum extent under the premise of meeting the load demand of the building and achieve the low-carbon goal of energy supply on demand.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) technology, specifically relating to an air conditioning operation sensing and control service system and method based on edge computing. Background Technology

[0002] With the emergence of the low-carbon concept, low-carbon emissions from building air conditioning systems have become a key concern. Most buildings (including public buildings) regulate their air conditioning based on people's perception of ambient temperature, relying on whether people find the temperature comfortable. However, this method is rather crude and cannot maximize energy savings. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides an air conditioning operation sensing and control service system and method based on edge computing, which enables refined management of air conditioning operation and maximizes energy saving.

[0004] The first aspect is an air conditioning operation sensing and control service system based on edge computing, including:

[0005] Model Management Unit: Used to create a building’s heating and cooling load prediction model, as well as a performance model of each actuator in the air conditioning system. The performance model includes the relationship between the state of the actuator and the energy generated.

[0006] Edge computing devices are used to deploy heating and cooling load forecasting models and performance models; they are also used to input the received building supply demand and environmental information into the heating and cooling load forecasting models to obtain the building load forecasting results; they simulate the load forecasting results according to the preset scheduling strategy and transmit the simulation results to the main server.

[0007] The main server is used to collect new supply demands when a change in the building's supply demand is detected, and send them to the edge computing device; it is also used to receive simulation results and control the actions of various actuators in the air conditioner based on the simulation results.

[0008] Furthermore, the simulation process includes:

[0009] The state of each actuator is determined according to the scheduling strategy, and the energy generated by each actuator is obtained according to the corresponding performance model. When the sum of all energy matches the load prediction result, the state of each actuator is output as the simulation result.

[0010] Furthermore, the simulation process also includes:

[0011] When the sum of all energy does not match the load forecast, the status of each actuator is adjusted according to the scheduling strategy until the sum of all energy matches the load forecast, at which point the adjustment stops.

[0012] Furthermore, the main server is also used for:

[0013] Real-time calculation of the total electrical energy consumed by all air conditioners in the building;

[0014] An energy consumption warning will be issued when the total electrical energy reaches the upper limit.

[0015] Furthermore, the heating and cooling load prediction model is determined based on the building's structure, shape, materials, and heat transfer method.

[0016] Secondly, the air conditioning operation sensing and control service method based on edge computing includes:

[0017] The model management unit creates a building’s heating and cooling load prediction model, as well as a performance model of each actuator in the air conditioning system. The performance model includes the relationship between the state of the actuator and the energy generated.

[0018] Deploy heating and cooling load prediction models and performance models on edge computing devices;

[0019] When the main server detects a change in the supply demand of a building, it collects the new supply demand and sends it to the edge computing device;

[0020] The edge computing device inputs the received building supply demand and environmental information into the heating and cooling load prediction model to obtain the building load prediction results; it then simulates the load prediction results according to the preset scheduling strategy and transmits the simulation results to the main server.

[0021] The main server receives the simulation results and controls the actions of each actuator in the air conditioner based on the simulation results.

[0022] Furthermore, the simulation process includes:

[0023] The state of each actuator is determined according to the scheduling strategy, and the energy generated by each actuator is obtained according to the corresponding performance model. When the sum of all energy matches the load prediction result, the state of each actuator is output as the simulation result.

[0024] Furthermore, the simulation process also includes:

[0025] When the sum of all energy does not match the load forecast, the status of each actuator is adjusted according to the scheduling strategy until the sum of all energy matches the load forecast, at which point the adjustment stops.

[0026] Furthermore, after controlling the actions of each actuator in the air conditioner based on the simulation results, the process also includes:

[0027] The main server calculates the total electricity consumed by all air conditioners in the building in real time, and issues an energy consumption warning when the total electricity consumption reaches the upper limit.

[0028] Furthermore, the heating and cooling load prediction model is determined based on the building's structure, shape, materials, and heat transfer method.

[0029] As can be seen from the above technical solution, the air conditioning operation sensing and control service system and method based on edge computing provided by the present invention can create a cooling and heating load prediction model according to the characteristics of the building and predict its load demand. In this way, the accurate load demand can be obtained, and the state of each actuator in the air conditioner can be controlled according to the load demand, so as to carry out refined management of the operation of the air conditioner. This enables the entire air conditioning system to maximize energy saving while meeting the building's load demand, and achieve the low-carbon goal of energy supply on demand. Attached Figure Description

[0030] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0031] Figure 1 A block diagram of an air conditioning operation sensing and control service system provided in an embodiment. Detailed Implementation

[0032] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore merely examples and should not be used to limit the scope of protection of the present invention. It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art to which this invention pertains.

[0033] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0034] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0035] As used in this specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrases "if determined" or "if [described condition or event] is detected" may be interpreted, depending on the context, as "once determined," "in response to determination," "once [described condition or event] is detected," or "in response to detection of [described condition or event]."

[0036] Example:

[0037] An air conditioning operation sensing and control service system based on edge computing, see [link / reference]. Figure 1 ,include:

[0038] Model Management Unit: Used to create a building’s heating and cooling load prediction model, as well as a performance model of each actuator in the air conditioning system. The performance model includes the relationship between the state of the actuator and the energy generated.

[0039] Edge computing devices are used to deploy heating and cooling load forecasting models and performance models; they are also used to input the received building supply demand and environmental information into the heating and cooling load forecasting models to obtain the building load forecasting results; they simulate the load forecasting results according to the preset scheduling strategy and transmit the simulation results to the main server.

[0040] The main server is used to collect new supply demands when a change in the building's supply demand is detected, and send them to the edge computing device; it is also used to receive simulation results and control the actions of various actuators in the air conditioner based on the simulation results.

[0041] In this embodiment, since different buildings have different sources of heating and cooling loads, the system constructs different heating and cooling load prediction models for each building, thus enabling precise control of each building. The heating and cooling load prediction model can predict the total energy demand of the building's heating and cooling load sources under various environmental conditions. The system also creates performance models for each actuator in the air conditioning system, including heat exchangers, valves, pumps, switches, etc. The performance models reflect how much energy is generated when the actuators are activated. The system deploys the created heating and cooling load prediction models and performance models on edge computing devices, which perform load prediction and simulation of the prediction results, reducing the computational load on the main server and ensuring that business processing on the main server is not affected.

[0042] In this embodiment, the edge computing device inputs the received building supply demand and environmental information into the heating and cooling load prediction model to obtain the building load prediction results. Changes in building supply demand include whether lighting in a certain area needs to be turned on, whether the start-up and maintenance time has changed, and whether certain production equipment needs to be shut down, etc. When the heating and cooling load sources in the building change, the building's energy requirements will increase or decrease. To maintain the comfort of personnel or users, the system first predicts the building's load demand based on the building's real-time supply demand and environmental information. Then, the edge computing device simulates the load prediction results according to a preset scheduling strategy to determine how to determine the state of each actuator in the air conditioning system while meeting the building's real-time load demand. Finally, the edge computing device sends the simulation results to the main server, which controls the actions of each actuator in the air conditioning system based on the simulation results.

[0043] This system can create a heating and cooling load prediction model based on the characteristics of the building to predict its load demand. This allows for accurate load demand forecasting, and the system can control the status of each actuator in the air conditioning system according to the load demand, enabling refined management of the air conditioning operation. This allows the entire air conditioning system to maximize energy saving while meeting the building's load demand, achieving the low-carbon goal of energy supply on demand.

[0044] Furthermore, in some embodiments, the simulation process includes:

[0045] The state of each actuator is determined according to the scheduling strategy, and the energy generated by each actuator is obtained according to the corresponding performance model. When the sum of all energy matches the load prediction result, the state of each actuator is output as the simulation result.

[0046] In this embodiment, when performing simulation, the edge computing device determines the state of each execution mechanism according to a scheduling strategy. This scheduling strategy may include the execution priority of each execution mechanism. During simulation, based on the load forecast results, the execution mechanisms with higher priority are executed first. The energy generated by each executed mechanism is obtained from its performance model, and the sum of the energy generated by all executed mechanisms is matched with the load forecast results. If the sum of all energy matches the load forecast results, it indicates that the currently executed mechanisms are able to meet the building's requirements for heating and cooling loads. At this point, the state of each execution mechanism is output as the simulation result.

[0047] Furthermore, in some embodiments, the simulation process further includes:

[0048] When the sum of all energy does not match the load forecast, the status of each actuator is adjusted according to the scheduling strategy until the sum of all energy matches the load forecast, at which point the adjustment stops.

[0049] In this embodiment, when the total energy is greater than or less than the load forecast result, it indicates that the sum of the total energy does not match the load forecast result. At this time, it is necessary to adjust the state of each actuator according to the scheduling strategy until the sum of the total energy matches the load forecast result, and then stop the adjustment.

[0050] Furthermore, in some embodiments, the master server is also used for:

[0051] Real-time calculation of the total electrical energy consumed by all air conditioners in the building;

[0052] An energy consumption warning will be issued when the total electrical energy reaches the upper limit.

[0053] In this embodiment, the system also has an energy consumption early warning function. The main server calculates the total electrical energy consumed by all air conditioners in the building in real time; when the total electrical energy reaches the upper limit, it indicates that the energy consumption of the air conditioning system in the building is too high. At this time, an energy consumption early warning is issued, and relevant personnel investigate the situation and make adjustments.

[0054] Furthermore, in some embodiments, the heating and cooling load prediction model is determined based on the building's structure, shape, materials, and heat transfer method.

[0055] In this embodiment, when constructing the heating and cooling load prediction model, the system needs to consider various factors such as the building's structure, geometry, material parameters (including thermal conductivity, heat capacity, etc.), heat transfer methods between indoor and outdoor environments, and heat transfer methods between different areas. The system can select the main heating and cooling load sources for analysis when constructing the heating and cooling load prediction model; for example, the main heating and cooling load sources include lighting, production equipment, and equipment ensuring normal personnel activities.

[0056] An edge computing-based air conditioning operation sensing and control service method includes:

[0057] The model management unit creates a building’s heating and cooling load prediction model, as well as a performance model of each actuator in the air conditioning system. The performance model includes the relationship between the state of the actuator and the energy generated.

[0058] Deploy heating and cooling load prediction models and performance models on edge computing devices;

[0059] When the main server detects a change in the supply demand of a building, it collects the new supply demand and sends it to the edge computing device;

[0060] The edge computing device inputs the received building supply demand and environmental information into the heating and cooling load prediction model to obtain the building load prediction results; it then simulates the load prediction results according to the preset scheduling strategy and transmits the simulation results to the main server.

[0061] The main server receives the simulation results and controls the actions of each actuator in the air conditioner based on the simulation results.

[0062] Furthermore, in some embodiments, the simulation process includes:

[0063] The state of each actuator is determined according to the scheduling strategy, and the energy generated by each actuator is obtained according to the corresponding performance model. When the sum of all energy matches the load prediction result, the state of each actuator is output as the simulation result.

[0064] Furthermore, in some embodiments, the simulation process further includes:

[0065] When the sum of all energy does not match the load forecast, the status of each actuator is adjusted according to the scheduling strategy until the sum of all energy matches the load forecast, at which point the adjustment stops.

[0066] Furthermore, in some embodiments, after controlling the actions of each actuator in the air conditioner based on the simulation results, the method further includes:

[0067] The main server calculates the total electricity consumed by all air conditioners in the building in real time, and issues an energy consumption warning when the total electricity consumption reaches the upper limit.

[0068] Furthermore, in some embodiments, the heating and cooling load prediction model is determined based on the building's structure, shape, materials, and heat transfer method.

[0069] The methods provided in the embodiments of the present invention are for brief description. For any parts not mentioned in the embodiments, please refer to the corresponding content in the foregoing embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. An air conditioning operation sensing and control service system based on edge computing, characterized in that, include: Model Management Unit: Used to create a building’s heating and cooling load prediction model, as well as a performance model of each actuator in the air conditioning system. The performance model includes the relationship between the state of the actuator and the energy generated. Edge computing device: used to deploy the heating and cooling load prediction model and the performance model; also used to input the received building supply demand and environmental information into the heating and cooling load prediction model to obtain the building load prediction results; simulate the load prediction results according to the preset scheduling strategy, and transmit the simulation results to the main server; Main server: Used to collect new supply requirements when a change in the supply demand of a building is detected, and send them to the edge computing device; It is also used to receive the simulation results and control the actions of each actuator in the air conditioner based on the simulation results; The simulation process includes: The state of each actuator is determined according to the scheduling strategy, and the energy generated by each actuator is obtained according to the corresponding performance model. When the sum of all energy matches the load prediction result, the state of each actuator is output as the simulation result. When the sum of all energy does not match the load forecast result, the state of each actuator is adjusted according to the scheduling strategy until the sum of all energy matches the load forecast result, and then the adjustment stops. The main server is also used for: Real-time calculation of the total electrical energy consumed by all air conditioners in the building; When the total electrical energy reaches the upper limit, an energy consumption warning will be issued; The heating and cooling load prediction model is determined based on the building's structure, shape, materials, and heat transfer method.

2. An air conditioning operation sensing and control service method based on edge computing, characterized in that, include: The model management unit creates a building’s heating and cooling load prediction model and a performance model of each actuator in the air conditioning system. The performance model includes the relationship between the state of the actuator and the energy generated. Deploy the heating and cooling load prediction model and the performance model on edge computing devices; When the main server detects a change in the supply demand of a building, it collects the new supply demand and sends it to the edge computing device; The edge computing device inputs the received building supply demand and environmental information into the heating and cooling load prediction model to obtain the building load prediction results; The load forecast results are simulated according to the preset scheduling strategy, and the simulation results are transmitted to the main server. The main server receives the simulation results and controls the actions of each actuator in the air conditioner based on the simulation results; The simulation process includes: The state of each actuator is determined according to the scheduling strategy, and the energy generated by each actuator is obtained according to the corresponding performance model. When the sum of all energy matches the load prediction result, the state of each actuator is output as the simulation result. When the sum of all energy does not match the load forecast result, the state of each actuator is adjusted according to the scheduling strategy until the sum of all energy matches the load forecast result, and then the adjustment stops. After controlling the actions of each actuator in the air conditioner based on the simulation results, the process also includes: The main server calculates the total electrical energy consumed by all air conditioners in the building in real time, and issues an energy consumption warning when the total electrical energy reaches the upper limit. The heating and cooling load prediction model is determined based on the building's structure, shape, materials, and heat transfer method.

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