Intelligent control method based on Internet of Things and ecological building energy-saving air conditioning system

Abstract

The invention discloses an intelligent control method based on the Internet of Things and an ecological building energy-saving air conditioner system, and relates to the technical field of air conditioner regulation and control. The method comprises the steps that the disturbance relation between air conditioner air supply conditions and ecological building environment factors is analyzed based on a historical temperature data set, and the initial air conditioner air supply amount of an ecological building is set; a radiant heat gain compensation technology is used for constructing first-stage air volume response between the heat gain effect and the initial air conditioner air supply volume; the flow group density of the ecological building in the future time period is predicted, the Shapley values of different flow groups are calculated, and the secondary air volume response of the flow groups and the initial air conditioner air supply volume is constructed based on the Shapley values; and combining the first-stage air volume response with the second-stage air volume response to generate the optimal air conditioner air supply volume, comparing the optimal air conditioner air supply volume with the initial air conditioner air supply volume, and regulating and controlling the air supply mode of the air conditioner according to the comparison result. According to the method, more accurate air conditioner adjustment can be achieved, and it is ensured that the comfort requirement is met under different environment conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioning control, and in particular to an intelligent control method based on the Internet of Things and an ecological building energy-saving air conditioning system. Background Art

[0002] Eco-buildings are structures designed and constructed through the integration of ecological architectural concepts. Their core focus is to maximize the use of natural resources and high-efficiency systems to achieve a balance between energy conservation, emission reduction, environmental protection, and indoor comfort. Eco-building energy-saving air conditioning is a key system for regulating the indoor thermal environment in eco-buildings. It features low energy consumption, high efficiency, and intelligent control. It typically combines natural ventilation, ground-source heat pumps, air conditioning load prediction, and adaptive air volume control technologies to achieve on-demand air, cooling, and heating delivery, thereby achieving maximum energy conservation.

[0003] Existing technologies for air conditioning control rely primarily on indoor temperature prediction when light radiation is stable or changes in a relatively gentle pattern, which can accurately reflect temperature changes. However, when light radiation experiences sudden changes (such as clouds suddenly blocking the sun or strong light suddenly weakening), existing technologies have significant drawbacks, mainly manifested in the following aspects: It is insensitive to mutation responses and has difficulty identifying the occurrence and intensity of radiation mutations in a timely manner, resulting in delayed predictions. The nonlinear response of the indoor thermal environment caused by sudden changes is relatively complex, and existing models lack the ability to describe such high-order disturbances. Most existing technologies are static or semi-dynamic control mechanisms, which lack the real-time perception and active adaptation capabilities to sudden changes in signals. It is difficult to quickly adjust the air-conditioning operation strategy to cope with the load changes after the sudden change, which ultimately affects the indoor environmental comfort and the energy efficiency of the air-conditioning system.

[0004] Currently, no effective solutions have been proposed for the problems in related technologies. Summary of the Invention

[0005] In response to the problems in the related art, the present invention proposes an intelligent control method based on the Internet of Things and an ecological building energy-saving air-conditioning system to overcome the above-mentioned technical problems existing in the existing related art.

[0006] To this end, the specific technical solutions adopted in the present invention are as follows: According to one aspect of the present invention, there is provided an intelligent control method based on the Internet of Things, the method comprising: Obtain the historical temperature dataset of the eco-building from the IoT platform. Based on this dataset, analyze the disturbance relationship between the air conditioning supply conditions and the environmental factors of the eco-building, and set the initial air conditioning supply volume of the eco-building. Analyze the heat gain effect of light radiation on the ecological building envelope, and use radiation heat gain compensation technology to construct a first-level air volume response of the heat gain effect and the initial air conditioning supply volume; Predict the density of traffic groups in the future time period of the ecological building and calculate the Shapley value of different traffic groups. Based on the Shapley value, construct the secondary air volume response of traffic groups and initial air conditioning supply volume; The optimal air supply volume of the air conditioner is generated by combining the first-level air volume response and the second-level air volume response. The optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted according to the comparison result.

[0007] Furthermore, the heat gain effect of light radiation on the ecological building envelope is analyzed, and the radiation heat gain compensation technology is used to construct the first-level air volume response of the heat gain effect and the initial air conditioning supply volume, including: Finite element analysis technology was used to establish a finite element model of the ecological building's enclosure structure. Excessive light radiation intervals were marked in the finite element model, and the hourly dynamic penetration process of light radiation through wall components and window components was simulated within the excessive light radiation intervals. The temperature gradient change of the enclosure structure during the dynamic infiltration process is analyzed and used as the input of a pre-built Monte Carlo model. The Monte Carlo model is used to generate a light radiation mutation sequence, and the critical conditions for compensation failure are identified from the light radiation mutation sequence. Based on the critical condition of compensation failure, the heat gain of the building envelope caused by the sudden change of light radiation is quantified, and the impact intensity of the sudden change of light radiation on the air inside the ecological building is identified using a sparse identification algorithm. The first-order air volume response when the sudden change of light radiation contacts the air-conditioning wind under the initial air-conditioning supply volume is analyzed, and the first-order air volume response is converted into a first-order matrix through the state space model.

[0008] Furthermore, the temperature gradient change of the enclosure structure during the dynamic infiltration process is analyzed and used as the input of a pre-built Monte Carlo model. The Monte Carlo model generates a light radiation mutation sequence. The critical conditions for compensation failure are identified from the light radiation mutation sequence, including: The material properties of the ecological building envelope, the convection heat transfer coefficient between the outer surface and the air, and the heat flux density on the inner surface are introduced into the predefined unsteady-state heat conduction equation to obtain a three-dimensional transient heat transfer equation. The three-dimensional transient heat transfer equation is solved using the correlation analysis algorithm to obtain the temperature gradient change of the enclosure structure. Construct a Monte Carlo model, input the temperature gradient change into the Monte Carlo model to identify the statistical characteristics of the light radiation mutation, and synthesize the statistical characteristics of the light radiation mutation into a light radiation mutation sequence; Batch simulation is used to count the failure events and their corresponding failure radiation amplitudes in the illumination radiation mutation sequence, and the statistical characteristics corresponding to the failure radiation amplitude within a preset range are selected as the critical conditions for compensation failure.

[0009] Furthermore, the sparse identification algorithm is used to identify the impact intensity of sudden changes in light radiation on the air inside the ecological building, including: Set the initial number of iterations, error tolerance, and sparse failure radiation amplitude of the sparse identification algorithm; Extract the initial light radiation in contact with the enclosure from the IoT platform, calculate the transfer matrix of the enclosure's heat gain based on the initial light radiation, and analyze the changing trend of the heat gain within each time step based on the transfer matrix; The inflection point of the thermal gain change trend is selected as the abnormal mutation point of light radiation, and a new transfer matrix is ​​constructed based on the transfer matrix and the abnormal mutation point. The impact intensity of sudden changes in light radiation on the air inside the eco-building is iteratively calculated in the new transfer matrix. The inflation intensity generated in each iteration is compared with the error tolerance, and the convergence condition is determined based on the comparison results. If the convergence condition is met, it means that the sparse identification algorithm has obtained a stable solution, the iteration stops and the impact intensity and its corresponding air environment parameters are output. If the convergence condition is not met, the number of iterations is updated and a new iterative calculation process is repeated.

[0010] Furthermore, the impact intensity of the sudden change in light radiation on the air inside the eco-building is iteratively calculated in the new transfer matrix, including: A multi-layer feedforward neural network is established using a local recurrent neural network, and feedback neurons are added to the multi-layer feedforward neural network, while the input and output of the multi-layer feedforward neural network are updated; Based on the calculation results of the multi-layer feedforward neural network at the previous moment, the impact intensity at the next moment is recursively calculated. The new transfer matrix is ​​used as a state update mechanism in each iteration to transmit the impact of sudden changes in light radiation on the air inside the ecological building through the multi-layer feedforward neural network. Train a multi-layer feedforward neural network and adjust the weights of the new transfer matrix in real time through the gradient descent algorithm during the training process; In the trained multi-layer feedforward neural network, the weight guidance based on the new transfer matrix outputs the impact intensity of the sudden change of light radiation on the air inside the ecological building at each moment.

[0011] Furthermore, the optimal air supply volume of the air conditioner is generated by combining the first-level air volume response and the second-level air volume response, and the optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner. The air supply mode of the air conditioner is adjusted according to the comparison result, including: The contribution of the first-level air volume response and the second-level air volume response to the air inside the ecological building were calculated respectively, and a multi-objective optimization model was constructed based on the contribution; The order of the first-level and second-level air volume responses was evaluated based on the superior-inferior solution distance method. The optimal air volume under each evaluation criterion, comfort deviation, energy consumption, and response time, was calculated using a multi-objective optimization model. The optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted based on the comparison result. The air supply mode instruction is converted into a device control signal and sent to the building control system through the instruction transmission protocol to realize the adjustment of the air supply mode of the air conditioner.

[0012] Furthermore, the order of evaluating the first-level air volume response and the second-level air volume response based on the superiority-inferior solution distance method includes: Setting a multi-attribute decision matrix, standardizing the multi-attribute decision matrix to obtain a standard matrix, and weighting the standard matrix to obtain a weighted matrix; The first-level air volume and the second-level air volume are introduced into the weighted matrix as the positive ideal solution and the negative ideal solution, respectively, and the distance from the predefined air supply volume configuration scheme to the positive ideal solution and the negative ideal solution is calculated respectively; The calculated distances are sorted, and the order of priority of the first-level air volume response and the second-level air volume response is determined according to the sorting order.

[0013] Furthermore, the optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted based on the comparison result, including: If the optimal air supply volume is greater than the initial air supply volume of the air conditioner, the air supply intensity of the air conditioner is increased to increase the air supply volume of the air conditioner; If the optimal air supply volume is less than the initial air supply volume of the air conditioner, the air supply intensity of the air conditioner is reduced to reduce the air supply volume of the air conditioner; If the optimal air supply volume is equal to the initial air supply volume of the air conditioner, the system will operate stably according to the initial air supply volume of the air conditioner.

[0014] Furthermore, the first-level air volume and the second-level air volume are introduced into the weighted matrix as the positive ideal solution and the negative ideal solution, respectively, and the distance from the predefined air supply volume configuration scheme to the positive ideal solution and the negative ideal solution is calculated respectively, including: Normalize the air supply volume configuration plan according to the target parameters to obtain a weighted matrix. Define the positive ideal solution and negative ideal solution in the weighted matrix based on the primary air volume and the secondary air volume. Calculate the optimal and worst values ​​of each target parameter in the weighted matrix respectively; Based on the optimal value of each objective parameter, the Euclidean distance from the air supply volume configuration scheme to the positive ideal solution is calculated; Based on the worst value of each objective parameter, the Euclidean distance between the air supply volume configuration scheme and the negative ideal solution is calculated.

[0015] According to another aspect of the present invention, there is also provided an ecological building energy-saving air-conditioning system, the system comprising: The initial air volume setting module is used to obtain the historical temperature data set of the ecological building from the Internet of Things platform, analyze the disturbance relationship between the air conditioning supply conditions and the environmental factors of the ecological building based on the historical temperature data set, and set the initial air conditioning supply volume of the ecological building; Thermal effect analysis module, used to analyze the heat gain effect of light radiation on the ecological building envelope, and use radiation heat gain compensation technology to construct the first-level air volume response of the heat gain effect and the initial air conditioning supply volume; The flow group analysis module is used to predict the flow group density of the ecological building in the future time period and calculate the Shapley value of different flow groups. Based on the Shapley value, it constructs a secondary air volume response between the flow group and the initial air conditioning supply volume; The air supply mode control module is used to generate the optimal air supply volume of the air conditioner by combining the first-level air volume response and the second-level air volume response, and compare the optimal air supply volume with the initial air supply volume, and adjust the air supply mode of the air conditioner according to the comparison result.

[0016] The beneficial effects of the present invention are: 1. The present invention realizes efficient regulation of the air-conditioning system of the ecological building by integrating Internet of Things data, environmental analysis and intelligent control algorithms. By analyzing the disturbance relationship between air-conditioning air supply and environmental factors through historical temperature data sets, the initial air supply volume can be accurately set to ensure a comfortable indoor environment. The heat gain effect analysis of light radiation and radiation heat gain compensation technology can accurately adjust the first-level air volume response of the air-conditioning to optimize the heat load response. At the same time, by predicting the flow group density and calculating the Shapley value, the second-level response of the air-conditioning air supply volume is further optimized. The first-level and second-level responses are combined to generate the optimal air supply volume, which is compared and regulated with the initial air supply volume to ensure that the air-conditioning can meet the comfort requirements while reducing energy consumption during operation, thereby improving the system's adaptability and energy efficiency performance.

[0017] 2. By combining the analysis of the heat gain effect of light radiation on the enclosing structure with the radiation heat gain compensation technology, the present invention can optimize the air volume response of the air conditioner, thereby improving the energy efficiency and comfort of the air conditioning system; at the same time, based on the flow group density prediction and Shapley value calculation, it can accurately identify the impact of different groups on the air conditioning load, thereby dynamically adjusting the air supply volume of the air conditioner, thereby improving the adaptability of the air conditioning system, reducing energy waste, ensuring a comfortable indoor environment and optimizing energy efficiency.

[0018] 3. The present invention generates the optimal air supply volume for air conditioning by combining the primary and secondary air volume responses, thereby achieving more precise air conditioning adjustment and ensuring that comfort requirements are met under different environmental conditions. After comparing the optimal air supply volume with the initial air supply volume, the air supply mode is automatically adjusted according to real-time demand to avoid excessive operation or insufficient air supply, thereby reducing energy consumption, improving the operating efficiency and response speed of the air conditioning system, and ensuring that the indoor environment is always in the best condition. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 is a flow chart of an intelligent control method based on the Internet of Things according to an embodiment of the present invention; Figure 2 The present invention is a block diagram of an energy-saving air-conditioning system for an ecological building.

[0021] In the picture: 1. Initial air volume setting module; 2. Thermal effect analysis module; 3. Flow group analysis module; 4. Air supply mode control module. DETAILED DESCRIPTION

[0022] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention. They are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. By referring to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and the advantages of the present invention.

[0023] According to an embodiment of the present invention, an intelligent control method based on the Internet of Things and an ecological building energy-saving air-conditioning system are provided.

[0024] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, the intelligent control method based on the Internet of Things includes: S1. Obtain the historical temperature dataset of the eco-building from the IoT platform. Based on the historical temperature dataset, analyze the disturbance relationship between the air-conditioning supply conditions and the environmental factors of the eco-building, and set the initial air-conditioning supply volume of the eco-building.

[0025] It should be noted that the historical temperature dataset of the eco-building was obtained from the IoT platform. Based on the historical temperature dataset, the relationship between the air conditioning supply conditions and the disturbance of the eco-building environmental factors was analyzed, and the initial air conditioning supply volume of the eco-building was set as follows: The historical temperature data set of the ecological building is obtained by using the Internet of Things platform or database. The historical temperature data set includes indoor and outdoor temperature, air conditioning air volume, air supply temperature, humidity, CO2 concentration, light intensity and equipment operation log.

[0026] S2. Analyze the heat gain effect of light radiation on the ecological building envelope structure, and use the radiation heat gain compensation technology to construct the first-level air volume response of the heat gain effect and the initial air conditioning supply volume.

[0027] Among them, the heat gain effect of light radiation on the ecological building envelope is analyzed, and the radiation heat gain compensation technology is used to construct the first-level air volume response of the heat gain effect and the initial air conditioning supply volume, including: Finite element analysis technology is used to establish a finite element model of the ecological building envelope structure. The excessive light radiation interval is marked in the finite element model, and the hourly dynamic penetration process of light radiation through wall components and window components is simulated in the excessive light radiation interval.

[0028] It should be noted that the finite element model of the ecological building envelope structure is established using finite element analysis technology. The excessive light radiation interval is marked in the finite element model. The hourly dynamic penetration process of light radiation through wall components and window components in the excessive light radiation interval is simulated, including: A 3D geometric model was constructed in finite element analysis software, and wall components (thickness, thermal conductivity, density, specific heat capacity) and window components (thickness, thermal conductivity, transmittance, etc.) were defined. Unstructured meshing was performed on the geometric bodies in the 3D geometric model using hexahedral elements, and the mesh was refined in areas with high light radiation density. Based on the results of the full-year solar trajectory simulation, an excess radiation interval was defined, and a dynamic radiation heat source was loaded into the 3D geometric model using a time-stepping function. The expression is: ; Where q solar (t) represents the time step function loading dynamic radiation heat source; G solar represents solar irradiance; α wall represents the wall absorption rate; τ win represents the window absorption rate; t represents the timestamp.

[0029] In the radiation excess range, the total penetration heat is calculated by extracting the heat flux density of the wall and window hourly, and the expression is: ; Where Q totalrepresents the total penetration heat; t1 represents the start time of the radiation excess interval; t2 represents the end time of the radiation excess interval; Indicates the heat flux density on the wall surface, that is, the amount of heat passing through the wall per unit time and per unit area; represents the heat flux density on the window surface; dA represents the area element.

[0030] The temperature gradient change of the enclosure structure during the dynamic infiltration process is analyzed and used as the input of a pre-built Monte Carlo model. The Monte Carlo model is used to generate a light radiation mutation sequence, and the critical conditions for compensation failure are identified from the light radiation mutation sequence.

[0031] The temperature gradient change of the enclosure structure during the dynamic infiltration process is analyzed and used as the input of a pre-built Monte Carlo model. The Monte Carlo model is used to generate a light radiation mutation sequence. The critical conditions for compensation failure are identified from the light radiation mutation sequence, including: The material properties of the ecological building envelope, the convection heat transfer coefficient between the outer surface and the air, and the heat flux density on the inner surface are introduced into the predefined unsteady-state heat conduction equation to obtain a three-dimensional transient heat transfer equation.

[0032] The three-dimensional transient heat transfer equation is solved using the correlation analysis algorithm to obtain the temperature gradient change of the enclosure structure. A Monte Carlo model is constructed, and the temperature gradient change is input into the Monte Carlo model to identify the statistical characteristics of the light radiation mutation, and the statistical characteristics of the light radiation mutation are synthesized into a light radiation mutation sequence.

[0033] It should be noted that the construction of a Monte Carlo model to simulate and identify the statistical laws of light radiation mutation phenomena in the ecological building environment includes: obtaining the temperature gradient change data inside and outside the ecological building as the input variable of the Monte Carlo model; setting the corresponding probability distribution function based on the fitting relationship between the temperature gradient and the light mutation; performing random sampling based on the fitting relationship between the temperature gradient and the light mutation, and simulating a light mutation event each time, and integrating the light mutation events to obtain a set of light mutation samples, and statistically analyzing the probability density of mutation occurrence, time interval distribution, change amplitude and other characteristic information from the light mutation samples.

[0034] Batch simulation is used to count the failure events and their corresponding failure radiation amplitudes in the illumination radiation mutation sequence, and the statistical characteristics corresponding to the failure radiation amplitude within a preset range are selected as the critical conditions for compensation failure.

[0035] The heat gain of the building envelope caused by sudden changes in light radiation is quantified based on the critical condition of compensation failure, and the impact intensity of sudden changes in light radiation on the air inside the ecological building is identified using a sparse identification algorithm.

[0036] Among them, the sparse identification algorithm is used to identify the impact intensity of sudden changes in light radiation on the air inside the ecological building, including: Set the initial number of iterations, error tolerance, and sparse failure radiation amplitude of the sparse identification algorithm; Extract the initial light radiation in contact with the enclosure from the IoT platform, calculate the transfer matrix of the enclosure's heat gain based on the initial light radiation, and analyze the changing trend of the heat gain within each time step based on the transfer matrix; The inflection point of the thermal gain change trend is selected as the abnormal mutation point of light radiation, and a new transfer matrix is ​​constructed based on the transfer matrix and the abnormal mutation point. The impact intensity of the sudden change in light radiation on the air inside the ecological building is iteratively calculated in the new transfer matrix. The inflation intensity generated in each iteration is compared with the error tolerance, and whether the convergence condition is met is judged based on the comparison result.

[0037] The iterative calculation of the impact intensity of the sudden change in light radiation on the air inside the ecological building in the new transfer matrix includes: A multi-layer feedforward neural network is established using a local recurrent neural network, and feedback neurons are added to the multi-layer feedforward neural network, while the input and output of the multi-layer feedforward neural network are updated; The impact intensity at future moments is recursively calculated based on the calculation results of the multi-layer feedforward neural network at the previous moment. The new transfer matrix is ​​used as the state update mechanism in each iteration to transmit the impact of sudden changes in light radiation on the air inside the eco-building through the multi-layer feedforward neural network.

[0038] It should be noted that a multi-layer feedforward neural network consists of multiple layers of neurons. Each layer takes the output of the previous layer as input and generates output through weights and activation functions, which specifically include: Step 1: Construct a multi-layer feedforward neural network, and dynamically introduce the impact intensity value output by the network at the previous moment into the multi-layer feedforward neural network to form a recursive structure.

[0039] Step 2: Embed a learnable transfer matrix in the recursive multi-layer feedforward neural network. During each forward propagation of the recursive multi-layer feedforward neural network, multiply the hidden layer state at the previous moment by the transfer matrix to generate the initial state at the current moment, which is then combined with the current input data to form a dynamically updated network state.

[0040] Step 3: Monitor sudden changes in light radiation (such as a sudden increase or decrease in intensity) in real time, extract the duration of the sudden change as a feature through a sliding window, and concatenate the sudden change features with conventional environmental parameters as the input vector of a multi-layer feedforward neural network to ensure that the sudden change signal directly affects the activation pattern of the hidden layer.

[0041] Train a multi-layer feedforward neural network and adjust the weights of the new transfer matrix in real time through the gradient descent algorithm during the training process; In the trained multi-layer feedforward neural network, the weight guidance based on the new transfer matrix outputs the impact intensity of the sudden change of light radiation on the air inside the ecological building at each moment.

[0042] If the convergence condition is met, it means that the sparse identification algorithm has obtained a stable solution, the iteration stops and the impact intensity and its corresponding air environment parameters are output. If the convergence condition is not met, the number of iterations is updated and a new iterative calculation process is repeated.

[0043] The first-order air volume response when the sudden change of light radiation contacts the air-conditioning wind under the initial air-conditioning supply volume is analyzed, and the first-order air volume response is converted into a first-order matrix through the state space model.

[0044] S3. Predict the flow group density in the future time period of the ecological building and calculate the Shapley value of different flow groups. Based on the Shapley value, construct a secondary air volume response between the flow group and the initial air conditioning supply volume.

[0045] It should be noted that predicting the density of traffic groups in the future time period of the ecological building and calculating the Shapley value of different traffic groups include: The historical pedestrian flow density in each area of ​​the ecological building is collected, and the spatial topological relationship of the historical pedestrian flow density is extracted through the building information model. Based on the spatial topological relationship, a spatiotemporal graph convolutional network is constructed, and the feature tensor of the historical preset time period is used as the input of the spatiotemporal graph convolutional network. The flow group density in the future time period of the ecological building is predicted based on the spatiotemporal graph convolutional network. A characteristic function is constructed according to the flow group density in the future time period of the ecological building, and the characteristic function is solved based on the Monte Carlo technique to obtain the Shapley value.

[0046] S4. Combining the primary air volume response and the secondary air volume response to generate the optimal air supply volume of the air conditioner, and comparing the optimal air supply volume with the initial air supply volume of the air conditioner, and adjusting the air supply mode of the air conditioner according to the comparison result.

[0047] The optimal air supply volume is generated by combining the primary air volume response and the secondary air volume response, and the optimal air supply volume is compared with the initial air supply volume. The air supply mode of the air conditioner is controlled according to the comparison result, including: The contribution of the first-level air volume response and the second-level air volume response to the internal air of the ecological building are calculated respectively, and a multi-objective optimization model is constructed based on the contribution.

[0048] It should be noted that the multi-objective optimization model includes: constructing an evaluation function, for example, expressing the temperature optimization objective function as the contribution of the air-conditioning air supply volume to temperature control, and the humidity control objective function represents the air-conditioning response's ability to control humidity changes; using the pre-collected environmental factors of the ecological building as input variables of the multi-objective optimization model, and calculating the impact of the primary air volume response and the secondary air volume response on these environmental factors; using a genetic algorithm to perform multi-objective optimization solutions, and finding the balance point between multiple objectives through several iterative evolutions to obtain the optimal air volume adjustment solution.

[0049] The order of the first-level and second-level air volume responses is evaluated based on the superior-inferior solution distance method. The comfort deviation, energy consumption and response time of the air-conditioning supply mode are used as evaluation indicators. The multi-objective optimization model is used to calculate the optimal air volume under each evaluation indicator in the order of superiority and inferiority.

[0050] Among them, the order of evaluation of the first-level air volume response and the second-level air volume response based on the superiority and inferiority solution distance method includes: Setting a multi-attribute decision matrix, standardizing the multi-attribute decision matrix to obtain a standard matrix, and weighting the standard matrix to obtain a weighted matrix; The first-level air volume and the second-level air volume are introduced into the weighted matrix as the positive ideal solution and the negative ideal solution, respectively, and the distances from the predefined air supply volume configuration scheme to the positive ideal solution and the negative ideal solution are calculated respectively.

[0051] It should be noted that the first-level air volume and the second-level air volume are introduced into the weighted matrix as the positive ideal solution and the negative ideal solution, respectively. The distances from the predefined air supply volume configuration scheme to the positive ideal solution and the negative ideal solution are calculated respectively, including: Different air supply volume configuration schemes are normalized according to target parameters (such as temperature, humidity, energy consumption, etc.) to obtain a weighted matrix; the positive ideal solution and negative ideal solution in the weighted matrix are defined; the Euclidean distance is used to calculate the distance from each air supply volume configuration scheme to the positive ideal solution and the negative ideal solution respectively; based on the distance from each air supply volume configuration scheme to the positive ideal solution and the negative ideal solution, the relative distance of each air supply volume configuration scheme is calculated, and the scheme with the shortest distance to the positive ideal solution and the farthest distance to the negative ideal solution is selected as the optimal solution.

[0052] The calculated distances are sorted, and the order of priority of the first-level air volume response and the second-level air volume response is determined according to the sorting order.

[0053] The optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted based on the comparison result. The air supply mode instruction is converted into a device control signal and sent to the building control system through the instruction transmission protocol to realize the adjustment of the air supply mode of the air conditioner.

[0054] The optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted based on the comparison result, including: If the optimal air supply volume is greater than the initial air supply volume of the air conditioner, the air supply intensity of the air conditioner is increased to increase the air supply volume of the air conditioner; If the optimal air supply volume is less than the initial air supply volume of the air conditioner, the air supply intensity of the air conditioner is reduced to reduce the air supply volume of the air conditioner; If the optimal air supply volume is equal to the initial air supply volume of the air conditioner, the system will operate stably according to the initial air supply volume of the air conditioner.

[0055] According to another embodiment of the present invention, Figure 2 As shown, an ecological building energy-saving air-conditioning system is also provided, which includes: Initial air volume setting module 1 is used to obtain the historical temperature data set of the ecological building from the Internet of Things platform, analyze the disturbance relationship between the air conditioning supply conditions and the environmental factors of the ecological building based on the historical temperature data set, and set the initial air conditioning supply volume of the ecological building; Thermal effect analysis module 2 is used to analyze the heat gain effect of light radiation on the ecological building envelope structure and use radiation heat gain compensation technology to construct a first-level air volume response of the heat gain effect and the initial air conditioning supply volume; Flow group analysis module 3 is used to predict the flow group density of the ecological building in the future time period and calculate the Shapley value of different flow groups. Based on the Shapley value, it constructs a secondary air volume response between the flow group and the initial air conditioning supply volume; The air supply mode control module 4 is used to generate the optimal air supply volume of the air conditioner by combining the first-level air volume response and the second-level air volume response, and compare the optimal air supply volume with the initial air supply volume, and control the air supply mode of the air conditioner according to the comparison result.

[0056] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent control method based on the Internet of Things, characterized in that: The method includes: Obtain the historical temperature dataset of the eco-building from the IoT platform. Based on this dataset, analyze the disturbance relationship between the air conditioning supply conditions and the environmental factors of the eco-building, and set the initial air conditioning supply volume of the eco-building. Analyze the heat gain effect of light radiation on the ecological building envelope, and use radiation heat gain compensation technology to construct a first-level air volume response of the heat gain effect and the initial air conditioning supply volume; Predict the flow group density in the future time period of the ecological building and calculate the Shapley value of different flow groups. Based on the Shapley value, construct the secondary air volume response of the flow group and the initial air conditioning supply volume; The optimal air supply volume of the air conditioner is generated by combining the first-level air volume response and the second-level air volume response. The optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted according to the comparison result.

2. The intelligent control method based on the Internet of Things according to claim 1, characterized in that: The analysis of the heat gain effect of light radiation on the ecological building envelope and the use of radiation heat gain compensation technology to construct a first-level air volume response of the heat gain effect and the initial air conditioning supply volume include: Finite element analysis technology was used to establish a finite element model of the ecological building's enclosure structure. Excessive light radiation intervals were marked in the finite element model, and the hourly dynamic penetration process of light radiation through wall components and window components was simulated within the excessive light radiation intervals. The temperature gradient change of the enclosure structure during the dynamic infiltration process is analyzed and used as the input of a pre-built Monte Carlo model. The Monte Carlo model is used to generate a light radiation mutation sequence, and the critical conditions for compensation failure are identified from the light radiation mutation sequence. Based on the critical condition of compensation failure, the heat gain of the building envelope caused by the sudden change of light radiation is quantified, and the impact intensity of the sudden change of light radiation on the air inside the ecological building is identified using a sparse identification algorithm. The first-order air volume response when the sudden change of light radiation contacts the air-conditioning wind under the initial air-conditioning supply volume is analyzed, and the first-order air volume response is converted into a first-order matrix through the state space model.

3. The intelligent control method based on the Internet of Things according to claim 2, characterized in that: The analysis of the temperature gradient change of the enclosure structure during the dynamic infiltration process is performed, the temperature gradient change is used as the input of a pre-built Monte Carlo model, a light radiation mutation sequence is generated through the Monte Carlo model, and the critical conditions for compensation failure are identified from the light radiation mutation sequence. The material properties of the ecological building envelope, the convection heat transfer coefficient between the outer surface and the air, and the heat flux density on the inner surface are introduced into the predefined unsteady-state heat conduction equation to obtain a three-dimensional transient heat transfer equation. The three-dimensional transient heat transfer equation is solved using the correlation analysis algorithm to obtain the temperature gradient change of the enclosure structure. Construct a Monte Carlo model, input the temperature gradient change into the Monte Carlo model to identify the statistical characteristics of the light radiation mutation, and synthesize the statistical characteristics of the light radiation mutation into a light radiation mutation sequence; Batch simulation is used to count the failure events and their corresponding failure radiation amplitudes in the illumination radiation mutation sequence, and the statistical characteristics corresponding to the failure radiation amplitude within a preset range are selected as the critical conditions for compensation failure.

4. The intelligent control method based on the Internet of Things according to claim 3, characterized in that: The method of using a sparse identification algorithm to identify the impact intensity of sudden changes in light radiation on the air inside the ecological building includes: Set the initial number of iterations, error tolerance, and sparse failure radiation amplitude of the sparse identification algorithm; Extract the initial light radiation in contact with the enclosure from the IoT platform, calculate the transfer matrix of the enclosure's heat gain based on the initial light radiation, and analyze the changing trend of the heat gain within each time step based on the transfer matrix; The inflection point of the thermal gain change trend is selected as the abnormal mutation point of light radiation, and a new transfer matrix is ​​constructed based on the transfer matrix and the abnormal mutation point. The impact intensity of sudden changes in light radiation on the air inside the eco-building is iteratively calculated in the new transfer matrix. The inflation intensity generated in each iteration is compared with the error tolerance, and the convergence condition is determined based on the comparison results. If the convergence condition is met, it means that the sparse identification algorithm has obtained a stable solution, the iteration stops and the impact intensity and its corresponding air environment parameters are output. If the convergence condition is not met, the number of iterations is updated and a new iterative calculation process is repeated.

5. The intelligent control method based on the Internet of Things according to claim 4, characterized in that: The iterative calculation of the impact intensity of the sudden change in light radiation on the air inside the ecological building in the new transfer matrix includes: A multi-layer feedforward neural network is established using a local recurrent neural network, and feedback neurons are added to the multi-layer feedforward neural network, while the input and output of the multi-layer feedforward neural network are updated; Based on the calculation results of the multi-layer feedforward neural network at the previous moment, the impact intensity at the next moment is recursively calculated. The new transfer matrix is ​​used as a state update mechanism in each iteration to transmit the impact of sudden changes in light radiation on the air inside the ecological building through the multi-layer feedforward neural network. Train a multi-layer feedforward neural network and adjust the weights of the new transfer matrix in real time through the gradient descent algorithm during the training process; In the trained multi-layer feedforward neural network, the weight guidance based on the new transfer matrix outputs the impact intensity of the sudden change of light radiation on the air inside the ecological building at each moment.

6. The intelligent control method based on the Internet of Things according to claim 1, characterized in that: The step of combining the primary air volume response and the secondary air volume response to generate the optimal air supply volume for the air conditioner, comparing the optimal air supply volume with the initial air supply volume for the air conditioner, and regulating the air supply mode of the air conditioner according to the comparison result includes: The contribution of the first-level air volume response and the second-level air volume response to the air inside the ecological building were calculated respectively, and a multi-objective optimization model was constructed based on the contribution; The order of the first-level and second-level air volume responses was evaluated based on the superior-inferior solution distance method. The optimal air volume under each evaluation criterion, comfort deviation, energy consumption, and response time, was calculated using a multi-objective optimization model. The optimal air supply volume of the air conditioner is compared with the initial air supply volume of the air conditioner, and the air supply mode of the air conditioner is adjusted based on the comparison result. The air supply mode instruction is converted into a device control signal and sent to the building control system through the instruction transmission protocol to realize the adjustment of the air supply mode of the air conditioner.

7. The intelligent control method based on the Internet of Things according to claim 6, characterized in that: The order of evaluating the first-level air volume response and the second-level air volume response based on the superiority and inferiority solution distance method includes: Setting a multi-attribute decision matrix, standardizing the multi-attribute decision matrix to obtain a standard matrix, and weighting the standard matrix to obtain a weighted matrix; The first-level air volume and the second-level air volume are introduced into the weighted matrix as the positive ideal solution and the negative ideal solution, respectively, and the distance from the predefined air supply volume configuration scheme to the positive ideal solution and the negative ideal solution is calculated respectively; The calculated distances are sorted, and the order of priority of the first-level air volume response and the second-level air volume response is determined according to the sorting order.

8. The intelligent control method based on the Internet of Things according to claim 7, characterized in that: The step of comparing the optimal air supply volume of the air conditioner with the initial air supply volume of the air conditioner and adjusting the air supply mode of the air conditioner based on the comparison result includes: If the optimal air supply volume is greater than the initial air supply volume of the air conditioner, the air supply intensity of the air conditioner is increased to increase the air supply volume of the air conditioner; If the optimal air supply volume is less than the initial air supply volume of the air conditioner, the air supply intensity of the air conditioner is reduced to reduce the air supply volume of the air conditioner; If the optimal air supply volume is equal to the initial air supply volume of the air conditioner, the system will operate stably according to the initial air supply volume of the air conditioner.

9. The intelligent control method based on the Internet of Things according to claim 7, characterized in that: The method of introducing the primary air volume and the secondary air volume into the weighted matrix as a positive ideal solution and a negative ideal solution, and calculating the distance from the predefined air supply volume configuration scheme to the positive ideal solution and the negative ideal solution respectively includes: Normalize the air supply volume configuration plan according to the target parameters to obtain a weighted matrix. Define the positive ideal solution and negative ideal solution in the weighted matrix based on the primary air volume and the secondary air volume. Calculate the optimal and worst values ​​of each target parameter in the weighted matrix respectively; Based on the optimal value of each objective parameter, the Euclidean distance from the air supply volume configuration scheme to the positive ideal solution is calculated; Based on the worst value of each objective parameter, the Euclidean distance between the air supply volume configuration scheme and the negative ideal solution is calculated.

10. An ecological building energy-saving air conditioning system, controlled by the intelligent control method based on the Internet of Things according to any one of claims 1 to 9, characterized in that: The system includes: The initial air volume setting module is used to obtain the historical temperature data set of the ecological building from the Internet of Things platform, analyze the disturbance relationship between the air conditioning supply conditions and the environmental factors of the ecological building based on the historical temperature data set, and set the initial air conditioning supply volume of the ecological building; Thermal effect analysis module, used to analyze the heat gain effect of light radiation on the ecological building envelope, and use radiation heat gain compensation technology to construct the first-level air volume response of the heat gain effect and the initial air conditioning supply volume; The flow group analysis module is used to predict the flow group density of the ecological building in the future time period and calculate the Shapley value of different flow groups. Based on the Shapley value, it constructs a secondary air volume response between the flow group and the initial air conditioning supply volume; The air supply mode control module is used to generate the optimal air supply volume of the air conditioner by combining the first-level air volume response and the second-level air volume response, and compare the optimal air supply volume with the initial air supply volume, and adjust the air supply mode of the air conditioner according to the comparison result.

Images