Intelligent temperature control system based on Internet of Things and cloud computing

Abstract

The invention discloses an intelligent temperature control system based on Internet of Things and cloud computing, and relates to the technical field of intelligent temperature control. The method comprises the following steps: constructing a three-dimensional twinborn model in a building, deploying a data acquisition end, obtaining a sunshade data set and a ventilation data set, obtaining a photo-thermal influence quantity in the building according to the sunshade data set, and obtaining a sensible heat influence quantity and a latent heat influence quantity in the building according to the ventilation data set; temperature fluctuation values under different influence quantities are obtained in the three-dimensional twinborn model, a fluctuation evaluation model is constructed, a first-class fluctuation value and a second-class fluctuation value are obtained through the fluctuation evaluation model, first-class adjustment is conducted on equipment according to the first-class fluctuation value to obtain a first-class temperature control scheme, and a second-class temperature control scheme is obtained; performing second-class adjustment on the equipment according to the second-class fluctuation value to obtain a second-class temperature control scheme; the stability of the indoor temperature can be guaranteed, the energy consumption of the temperature control equipment can be effectively reduced, and an intelligent temperature control scheme can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent temperature control, and specifically to an intelligent temperature control system based on the Internet of Things and cloud computing. Background Art

[0002] Intelligent temperature control is an advanced technology that integrates the Internet of Things, cloud computing, sensors, and automatic control. Sensors distributed at each monitoring point are used to collect relevant data in real time, and the relevant data is transmitted through the network to a cloud computing platform for storage, analysis, and processing. According to the analysis results, intelligent control is performed on temperature control devices, enabling precise and efficient temperature control.

[0003] Most of the existing technical solutions only perform intelligent adjustment on temperature control devices, achieving temperature control by pre-lowering or raising their set temperatures. This adjustment method is too one-sided, fails to perform coordinated control on different devices inside the building, and ignores the influence of other devices on the indoor temperature. In view of the deficiencies of the existing technology, the present invention provides an intelligent temperature control system based on the Internet of Things and cloud computing. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent temperature control system based on the Internet of Things and cloud computing.

[0005] The purpose of the present invention can be achieved through the following technical solutions: An intelligent temperature control system based on the Internet of Things and cloud computing includes the following modules:

[0006] A data acquisition module, used to obtain the structural information and equipment information inside the building, construct a corresponding three-dimensional twin model, deploy different data acquisition terminals at different devices respectively, and obtain corresponding multimodal data sets respectively.

[0007] An influence analysis module, used to divide the multimodal data set into a shading data set and a ventilation data set, obtain the light and heat influence amount inside the building according to the shading data set, and obtain the sensible heat influence amount and latent heat influence amount inside the building according to the ventilation data set.

[0008] A fluctuation evaluation module, used to obtain the temperature fluctuation values under different influence amounts in the three-dimensional twin model, and construct a corresponding fluctuation evaluation model.

[0009] A temperature control module, used to respectively obtain the current type-one fluctuation value and type-two fluctuation value by using the fluctuation evaluation model, perform type-one adjustment on the devices according to the type-one fluctuation value to obtain a type-one temperature control plan, and perform type-two adjustment on the devices according to the type-two fluctuation value to obtain a type-two temperature control plan.

[0010] Furthermore, the process of obtaining the structural information and equipment information inside the building and constructing a corresponding three-dimensional twin model includes:

[0011] The structural information refers to the parameters of different structures inside a building, including the dimensions, positions, and shapes of walls, floors, beams, columns, pipes, and windows. The equipment information refers to the parameters of different equipment inside a building, including the dimensions, power, positions, efficiency, and status of temperature control equipment, sunshade equipment, and ventilation equipment.

[0012] Using BIM technology, a three-dimensional physical model of the interior of the building is constructed based on the structural information, and the equipment information is uploaded to the three-dimensional physical model for synchronization. The three-dimensional physical model containing the equipment information is imported into simulation software, and the simulation software is used to simulate the temperature change situation inside the building to obtain a three-dimensional twin model.

[0013] Furthermore, the process of separately deploying different data acquisition terminals at different equipment and separately obtaining the corresponding multi-modal data sets includes:

[0014] The data acquisition terminals include a temperature control acquisition unit, a sunshade acquisition unit, and a ventilation acquisition unit. The multi-modal data sets include temperature control data, sunshade data, and ventilation data.

[0015] A temperature control acquisition unit is set on the temperature control equipment, and temperature control data is obtained through the temperature control acquisition unit, including the mode, indoor temperature, outdoor temperature, cooling / heating capacity, energy efficiency ratio, and cooling / heating power.

[0016] A sunshade acquisition unit is set on the sunshade equipment, and sunshade data is obtained through the sunshade acquisition unit, including the light transmittance, sunshade area, light intensity, and ultraviolet intensity. A ventilation acquisition unit is set on the ventilation equipment, and ventilation data is obtained through the ventilation acquisition unit, including the ventilation volume, indoor humidity, and outdoor humidity.

[0017] Furthermore, the process of dividing the multi-modal data sets into a sunshade data set and a ventilation data set and obtaining the light and heat influence amount inside the building according to the sunshade data set includes:

[0018] All the temperature control data and ventilation data in the multi-modal data sets are classified into the ventilation data set, and all the sunshade data in the multi-modal data sets are classified into the sunshade data set.

[0019] The light transmittance, sunshade area, and light intensity in the sunshade data set are respectively marked as Z a 、Z b 、Z c , and the light and heat influence amount Q g of the sunshade equipment on the interior of the building is obtained;

[0020] Q g =S w Z c ·S h +(1-Z a )Zb ·Z c ;

[0021] where S w is the unshaded area of the window, which is equal to the window area minus the shaded area, and S h is the preset solar heat gain coefficient. If there are multiple shading devices, the total light and heat impact of each shading device is used as the total light and heat impact inside the building.

[0022] Furthermore, the process of obtaining the sensible heat impact and latent heat impact inside the building based on the ventilation dataset includes:

[0023] Mark the ventilation volume, indoor temperature, and outdoor temperature in the ventilation dataset as T f , T in , T out respectively, and obtain the sensible heat impact Q a of the ventilation equipment on the inside of the building;

[0024]

[0025] where ρ is the air density, c p is the specific heat capacity of air, V r is the internal volume of the building, and T f / V r represents the air exchange rate of the ventilation equipment;

[0026] Mark the indoor humidity and outdoor humidity in the ventilation dataset as W in , W out respectively, and obtain the latent heat impact Q b of the ventilation equipment on the inside of the building;

[0027]

[0028] where L v is the latent heat of vaporization coefficient of water. If there are multiple ventilation equipment, the total sensible heat impact and the total latent heat impact of each ventilation equipment are used as the total sensible heat impact and the total latent heat impact inside the building respectively.

[0029] Furthermore, the process of obtaining the temperature fluctuation values under different impact amounts in the 3D twin model and constructing the corresponding fluctuation evaluation model includes:

[0030] Upload the data in the current multi-modal dataset to the 3D twin model for synchronization, and keep the parameters of different devices unchanged in the 3D twin model;

[0031] Set an evaluation period, and obtain the temperature fluctuation value after one evaluation period under the combined action of the current total solar-thermal influence, total sensible heat influence, and total latent heat influence.

[0032] Generate a fluctuation evaluation set based on the total solar-thermal influence, total sensible heat influence, total latent heat influence, and their temperature fluctuation values under different multi-modal data sets, and divide it into a training set and a test set.

[0033] Construct a convolutional neural network. Use the total solar-thermal influence, total sensible heat influence, and total latent heat influence under different multi-modal data sets in the training set as the input data of the convolutional neural network, and use the corresponding temperature fluctuation values in the training set as the output data of the convolutional neural network.

[0034] Train the convolutional neural network to obtain an initial convolutional neural network, and use the test set to verify the model of the initial convolutional neural network. Output the initial convolutional neural network with a test error threshold less than or equal to the preset value as the fluctuation evaluation model.

[0035] Further, the process of using the fluctuation evaluation model to obtain the current type-one fluctuation value and type-two fluctuation value respectively, and performing type-one adjustment on the equipment according to the type-one fluctuation value to obtain the type-one temperature control scheme includes:

[0036] When the shading equipment and ventilation equipment inside the building change due to human factors, regard it as a type-one fluctuation situation, obtain the total solar-thermal influence, total sensible heat influence, and total latent heat influence under the current multi-modal data set, and input them into the fluctuation evaluation model to obtain the corresponding type-one fluctuation value.

[0037] When the shading equipment and ventilation equipment inside the building do not change, but the outdoor environmental conditions change over time, regard it as a type-two fluctuation situation, and input the current total solar-thermal influence, total sensible heat influence, and total latent heat influence into the fluctuation evaluation model to obtain the corresponding type-two fluctuation value.

[0038] When the type-one fluctuation value is positive, lower the set temperature of the temperature control equipment; when the type-one fluctuation value is negative, raise the set temperature of the temperature control equipment; when the type-one fluctuation value is 0, do not adjust the set temperature of the temperature control equipment. Take the above adjustment content as the type-one temperature control scheme.

[0039] Further, the process of performing type-two adjustment on the equipment according to the type-two fluctuation value to obtain the type-two temperature control scheme includes:

[0040] When the type-two fluctuation value is positive, adjust the shading equipment and ventilation equipment respectively in the 3D twin model, and synchronize the adjustment method that reduces the total solar-thermal influence, total sensible heat influence, and total latent heat influence to the actual application scenario.

[0041] When the secondary fluctuation value is negative, the shading device and the ventilation device are respectively adjusted in the three-dimensional twin model, and the adjustment method that increases the total amount of light and heat influence, the total amount of sensible heat influence, and the total amount of latent heat influence is synchronized to the actual application scenario;

[0042] When the secondary fluctuation value is 0, the shading device and the ventilation device are not adjusted, and the above adjustment content is used as the secondary temperature control scheme.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] By constructing a three-dimensional twin model of the building interior, the present invention can simulate the temperature change situation inside the building under different environmental conditions and equipment influences. By obtaining the light and heat influence amount of the shading device, it can evaluate the heat influence situation caused by the shading device on the building interior. By obtaining the sensible heat influence amount and the latent heat influence amount of the ventilation device, it can evaluate the heat influence situation caused by the ventilation device on the building interior;

[0045] By keeping the current operation of each device unchanged in the three-dimensional twin model and obtaining the temperature fluctuation value under various influence amounts, it can effectively obtain the influence degree of different devices on the indoor temperature. By constructing a fluctuation evaluation model to directly obtain the current temperature fluctuation value, and by adopting different temperature control schemes for the building interior, it can not only ensure the stability of the indoor temperature, but also effectively reduce the energy consumption of the temperature control equipment, which is beneficial to realizing an intelligent temperature control scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is the schematic diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] As Figure 1 shown, an intelligent temperature control system based on the Internet of Things and cloud computing includes the following modules:

[0048] A data acquisition module, which is used to obtain the structural information and equipment information of the building interior, construct a corresponding three-dimensional twin model, deploy different data acquisition terminals at different devices, and respectively obtain corresponding multi-modal data sets;

[0049] An influence analysis module, which is used to divide the multi-modal data set into a shading data set and a ventilation data set, obtain the light and heat influence amount of the building interior according to the shading data set, and obtain the sensible heat influence amount and the latent heat influence amount of the building interior according to the ventilation data set;

[0050] A fluctuation evaluation module, which is used to obtain the temperature fluctuation value under different influence amounts in the three-dimensional twin model and construct a corresponding fluctuation evaluation model;

[0051] A temperature control module is used to respectively obtain the current first - type fluctuation value and second - type fluctuation value by using a fluctuation evaluation model, perform first - type adjustment on the device according to the first - type fluctuation value to obtain a first - type temperature control scheme, and perform second - type adjustment on the device according to the second - type fluctuation value to obtain a second - type temperature control scheme.

[0052] It should be further noted that in the specific implementation process, the process of obtaining the structural information and equipment information inside the building and constructing the corresponding three - dimensional digital twin model includes:

[0053] The structural information refers to the parameters of different structures inside the building, including the dimensions, positions, shapes, etc. of structures such as walls, floors, beams, columns, pipes, windows, etc. The equipment information refers to the parameters of different equipment inside the building, including temperature control equipment, shading equipment, ventilation equipment, etc.

[0054] Taking the air conditioner as an example for the temperature control equipment, the sunshade curtain as an example for the shading equipment, and the ventilator as an example for the ventilation equipment, their parameters are used to describe the performance, specifications, characteristics, etc. of the equipment, including dimensions, power, position, efficiency, status, etc.

[0055] Use BIM technology to construct a three - dimensional physical model inside the building based on the collected structural information, upload the collected equipment information to the constructed three - dimensional physical model for synchronization, import the three - dimensional physical model containing equipment information into the simulation software, and use the simulation software to simulate the temperature change situation inside the building to obtain the corresponding three - dimensional digital twin model.

[0056] It should be further noted that in the specific implementation process, the process of respectively deploying different data acquisition terminals at different equipment and respectively obtaining the corresponding multi - modal data sets includes:

[0057] For the temperature control equipment, set a temperature control acquisition unit on the temperature control equipment, and obtain the corresponding temperature control data through the temperature control acquisition unit, including mode, indoor temperature, outdoor temperature, cooling / heating capacity, energy efficiency ratio, cooling / heating power, etc.

[0058] For the shading equipment, set a shading acquisition unit on the shading equipment, and obtain the corresponding shading data through the shading acquisition unit, including light transmittance, shading area, light intensity, ultraviolet intensity, etc.

[0059] For the ventilation equipment, set a ventilation acquisition unit on the ventilation equipment, and obtain the corresponding ventilation data through the ventilation acquisition unit, including ventilation volume, indoor humidity, outdoor humidity, etc.

[0060] The data acquisition terminals include a temperature control acquisition unit, a shading acquisition unit, and a ventilation acquisition unit, and the multi - modal data sets include the data in the temperature control data, shading data, and ventilation data.

[0061] It should be further noted that in the specific implementation process, dividing the multi-modal data set into a sunshade data set and a ventilation data set, the process of obtaining the light and heat impact amount inside the building according to the sunshade data set includes:

[0062] Dividing each temperature control data and ventilation data in the multi-modal data set into the ventilation data set, and dividing each sunshade data in the multi-modal data set into the sunshade data set;

[0063] Mark the light transmittance, sunshade area, and light intensity in the sunshade data set as Z a 、Z b 、Z c respectively, obtain the light and heat impact amount of the sunshade device on the interior of the building, and mark it as Q g ;

[0064] Q g =S w Z c ·S h +(1 - Z a )Z b ·Z c ;

[0065] Among them, S w is the unshaded area of the window, which is equal to the window area minus the sunshade area, S h is the solar heat gain coefficient of the glass, and its value range is 0.1 - 1.0. The above is the method for obtaining the light and heat impact amount of a single sunshade device inside the building. If there are multiple sunshade devices, the sum of their light and heat impact amounts is used as the total light and heat impact amount inside the building.

[0066] It should be further noted that in the specific implementation process, the process of obtaining the sensible heat impact amount and latent heat impact amount inside the building according to the ventilation data set includes:

[0067] Mark the ventilation volume, indoor temperature, and outdoor temperature in the ventilation data set as T f 、T in 、T out respectively, obtain the sensible heat impact amount of the ventilation device on the interior of the building, and mark it as Q a ;

[0068]

[0069] Among them, ρ is the air density, c p is the specific heat capacity of air, V r is the interior volume of the building, T f / V r represents the air exchange rate of the ventilation device;

[0070] Mark the indoor humidity and outdoor humidity in the ventilation dataset as W in and W out respectively, obtain the latent heat influence amount of the ventilation equipment on the interior of the building, and mark it as Q b ;

[0071]

[0072] Among them, L v is the latent heat of vaporization coefficient of water, with a value of 2.45×10 6 J / kg. The above is the way to obtain the sensible heat influence amount and latent heat influence amount of a single ventilation equipment inside the building. If there are multiple ventilation equipment, the sum of their sensible heat influence amounts and the sum of their latent heat influence amounts are respectively used as the total sensible heat influence amount and total latent heat influence amount inside the building.

[0073] It should be further noted that in the specific implementation process, the process of obtaining the temperature fluctuation value under different influence amounts in the 3D twin model and constructing the corresponding fluctuation evaluation model includes:

[0074] Upload the data in the current multi-modal data set to the 3D twin model for synchronization, and keep the parameters of different devices unchanged in the 3D twin model;

[0075] Set the evaluation period, and obtain the temperature fluctuation value after one evaluation period under the combined action of the current total photothermal influence amount, total sensible heat influence amount, and total latent heat influence amount. The temperature fluctuation value refers to the change of the indoor temperature after one evaluation period under the above combined action;

[0076] Generate a corresponding fluctuation evaluation set according to the total photothermal influence amount, total sensible heat influence amount, total latent heat influence amount, and their temperature fluctuation values under different multi-modal data sets, and divide the fluctuation evaluation set into a training set and a test set;

[0077] Construct a convolutional neural network, use the total photothermal influence amount, total sensible heat influence amount, and total latent heat influence amount under different multi-modal data sets in the training set as the input data of the convolutional neural network, and use the corresponding temperature fluctuation values in the training set as the output data of the convolutional neural network;

[0078] Train the convolutional neural network to obtain an initial convolutional neural network, use the test set to verify the model of the initial convolutional neural network, and output the initial convolutional neural network with a test error threshold less than or equal to the preset value as the corresponding fluctuation evaluation model.

[0079] It should be further noted that in the specific implementation process, the process of using the fluctuation evaluation model to respectively obtain the current type-one fluctuation value and type-two fluctuation value, and performing type-one adjustment on the equipment according to the type-one fluctuation value to obtain the type-one temperature control scheme includes:

[0080] When the shading equipment and ventilation equipment inside the building change due to human factors, it is analyzed as a type of fluctuation situation to obtain the total amount of light and heat influence, the total amount of sensible heat influence, and the total amount of latent heat influence under the current multimodal data set, and input them into the fluctuation evaluation model to obtain the corresponding temperature fluctuation value, which is recorded as the type I fluctuation value;

[0081] When the shading equipment and ventilation equipment inside the building remain unchanged while the outdoor environmental conditions change over time, it is analyzed as a type II fluctuation situation to obtain the total amount of light and heat influence, the total amount of sensible heat influence, and the total amount of latent heat influence under the current multimodal data set, and input them into the fluctuation evaluation model to obtain the corresponding temperature fluctuation value, which is recorded as the type II fluctuation value;

[0082] When the obtained type I fluctuation value is positive, the set temperature of the temperature control equipment is lowered; when the obtained type I fluctuation value is negative, the set temperature of the temperature control equipment is raised; when the obtained type I fluctuation value is 0, no adjustment is made to the set temperature of the temperature control equipment. The above is the method of type I adjustment, and its adjustment content is marked as the type I temperature control plan.

[0083] It should be further noted that in the specific implementation process, the process of performing type II adjustment on the equipment according to the type II fluctuation value to obtain the type II temperature control plan includes:

[0084] When the obtained type II fluctuation value is positive, the shading equipment and ventilation equipment are respectively adjusted in the 3D twin model, and the adjustment method that reduces the total amount of light and heat influence, the total amount of sensible heat influence, and the total amount of latent heat influence is synchronized to the actual application scenario;

[0085] When the obtained type II fluctuation value is negative, the shading equipment and ventilation equipment are respectively adjusted in the 3D twin model, and the adjustment method that increases the total amount of light and heat influence, the total amount of sensible heat influence, and the total amount of latent heat influence is synchronized to the actual application scenario;

[0086] When the obtained type II fluctuation value is 0, no adjustment is made to the shading equipment and ventilation equipment. The above is the method of type II adjustment, and its adjustment content is marked as the type II temperature control plan.

[0087] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An intelligent temperature control system based on the Internet of Things and cloud computing, characterized in that, It includes the following modules: A data acquisition module, which is used to obtain the structural information and equipment information inside the building, construct a corresponding three-dimensional twin model, deploy different data acquisition terminals at different devices respectively, and obtain corresponding multi-modal data sets respectively; An impact analysis module, which is used to divide the multi-modal data set into a sunshade data set and a ventilation data set, obtain the light and heat impact amount inside the building according to the sunshade data set, and obtain the sensible heat impact amount and latent heat impact amount inside the building according to the ventilation data set; A fluctuation evaluation module, which is used to obtain the temperature fluctuation values under different impact amounts in the three-dimensional twin model and construct a corresponding fluctuation evaluation model; A temperature control module, which is used to respectively obtain the current first-class fluctuation value and second-class fluctuation value by using the fluctuation evaluation model, perform first-class adjustment on the equipment according to the first-class fluctuation value to obtain a first-class temperature control scheme, and perform second-class adjustment on the equipment according to the second-class fluctuation value to obtain a second-class temperature control scheme.

2. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 1, characterized in that, The process of obtaining the structural information and equipment information and constructing a three-dimensional twin model includes: The structural information refers to the parameters of different structures inside the building, including the dimensions, positions, and shapes of walls, floors, beams, columns, pipes, and windows. The equipment information refers to the parameters of different equipment inside the building, including the dimensions, power, positions, efficiencies, and states of temperature control equipment, sunshade equipment, and ventilation equipment; Use BIM technology to construct a three-dimensional physical model inside the building according to the structural information, upload the equipment information to the three-dimensional physical model for synchronization, import the three-dimensional physical model containing the equipment information into simulation software, and use the simulation software to simulate the temperature change situation inside the building to obtain a three-dimensional twin model.

3. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 2, characterized in that, The process of deploying data acquisition terminals and obtaining multi-modal data sets includes: The data acquisition terminals include a temperature control acquisition unit, a sunshade acquisition unit, and a ventilation acquisition unit. The multi-modal data set includes temperature control data, sunshade data, and ventilation data; Set a temperature control acquisition unit on the temperature control equipment, and obtain temperature control data through the temperature control acquisition unit, including the mode, indoor temperature, outdoor temperature, cooling / heating capacity, energy efficiency ratio, and cooling / heating power; Set a sunshade acquisition unit on the sunshade equipment, and obtain sunshade data through the sunshade acquisition unit, including the light transmittance, sunshade area, light intensity, and ultraviolet intensity. Set a ventilation acquisition unit on the ventilation equipment, and obtain ventilation data through the ventilation acquisition unit, including the ventilation volume, indoor humidity, and outdoor humidity.

4. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 3, characterized in that, The process of obtaining the light and heat impact amount according to the sunshade data set includes: Divide each item of temperature control data and ventilation data in the multi-modal data set into the ventilation data set, and divide each item of sunshade data in the multi-modal data set into the sunshade data set; Mark the transmittance, shading area, and light intensity in the shading dataset as Z a , Z b , Z c , and obtain the amount of light and heat influence Q of the shading device on the interior of the building g ; Q g = S w Z c ·S h +(1 - Z a )Z b ·Z c ; Among them, S w is the unshaded area of the window, which is equal to the window area minus the shaded area. S h is the preset solar heat gain coefficient. If there are multiple shading devices, the total sum of the light and heat influence amounts of each shading device is used as the total light and heat influence amount inside the building.

5. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 4, characterized in that, The process of obtaining the sensible heat impact amount and latent heat impact amount according to the ventilation data set includes: Mark the ventilation volume, indoor temperature, and outdoor temperature in the ventilation dataset as T f , T in , T out , and obtain the sensible heat influence quantity Q a ; Among them, ρ is the air density, c p is the specific heat capacity of air, V r is the internal volume of the building, T f / V r represents the air exchange rate of the ventilation equipment; Mark the indoor humidity and outdoor humidity in the ventilation dataset as W in , W out , and obtain the latent heat influence quantity Q of the ventilation equipment on the interior of the building b ; where L v is the latent heat of vaporization coefficient of water. If there are multiple ventilation devices, the total sensible heat influence amount and the total latent heat influence amount of each ventilation device are respectively used as the total sensible heat influence amount and the total latent heat influence amount inside the building.

6. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 5, characterized in that, The process of obtaining the temperature fluctuation value and constructing a fluctuation evaluation model includes: Upload each item of data in the current multi-modal data set to the three-dimensional twin model for synchronization, and keep the parameters of different devices unchanged in the three-dimensional twin model; Set an evaluation period, and obtain the temperature fluctuation value after one evaluation period under the combined action of the current total light and heat impact amount, total sensible heat impact amount, and total latent heat impact amount; Generate a fluctuation evaluation set based on the total photothermal influence, total sensible heat influence, total latent heat influence, and their temperature fluctuation values under different multimodal data sets, and divide it into a training set and a test set; Construct a convolutional neural network. Use the total photothermal influence, total sensible heat influence, and total latent heat influence under different multimodal data sets in the training set as the input data of the convolutional neural network, and use the corresponding temperature fluctuation values in the training set as the output data of the convolutional neural network; Train the convolutional neural network to obtain an initial convolutional neural network, and use the test set to verify the model of the initial convolutional neural network. Output the initial convolutional neural network with a test error threshold less than or equal to the preset value as the fluctuation evaluation model.

7. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 6, characterized in that, Obtain a first-class fluctuation value and a second-class fluctuation value. The process of performing the first-class adjustment on the equipment includes: When the shading equipment and ventilation equipment inside the building change due to human factors, regard it as a first-class fluctuation situation, obtain the total photothermal influence, total sensible heat influence, and total latent heat influence under the current multimodal data set, and input them into the fluctuation evaluation model to obtain the corresponding first-class fluctuation value; When the shading equipment and ventilation equipment inside the building do not change, but the outdoor environmental conditions change over time, regard it as a second-class fluctuation situation, and input the current total photothermal influence, total sensible heat influence, and total latent heat influence into the fluctuation evaluation model to obtain the corresponding second-class fluctuation value; When the first-class fluctuation value is positive, lower the set temperature of the temperature control equipment; when the first-class fluctuation value is negative, raise the set temperature of the temperature control equipment; when the first-class fluctuation value is 0, do not adjust the set temperature of the temperature control equipment. Take the above adjustment content as the first-class temperature control plan.

8. An intelligent temperature control system based on the Internet of Things and cloud computing according to claim 7, characterized in that, The process of performing the second-class adjustment on the equipment includes: When the second-class fluctuation value is positive, adjust the shading equipment and ventilation equipment respectively in the 3D twin model, and synchronize the adjustment method that reduces the total photothermal influence, total sensible heat influence, and total latent heat influence to the actual application scenario; When the second-class fluctuation value is negative, adjust the shading equipment and ventilation equipment respectively in the 3D twin model, and synchronize the adjustment method that increases the total photothermal influence, total sensible heat influence, and total latent heat influence to the actual application scenario; When the second-class fluctuation value is 0, do not adjust the shading equipment and ventilation equipment. Take the above adjustment content as the second-class temperature control plan.

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