Intelligent temperature control method and device for outdoor building

Abstract

The invention discloses an intelligent temperature control method and device for an outdoor building. The method comprises the steps that a meteorological parameter value set and an environment temperature information set are acquired; the environment temperature information set comprises a building surface temperature sequence and a ground temperature sequence; processing the meteorological parameter value set and the environment temperature information set by using a preset temperature difference control model to obtain temperature control voltage information; inputting the temperature control voltage information into a heating module, and heating an outdoor building by using the heating module; and the heating module is arranged on the surface of an outdoor building or inside the outdoor building and is used for adjusting the output heat value according to the input temperature control voltage information.

Description

Technical Field

[0001] The present invention relates to the fields of infrared technology and artificial intelligence technology, and particularly relates to an intelligent temperature control method and device for outdoor buildings. Background Art

[0002] Currently, the commonly used target infrared characteristic control method mainly conducts temperature control, that is, measures are taken to heat the thermal characteristic parts of the false target to simulate the infrared radiation characteristics of the real target. The heating method generally is: selecting components such as heating films, heating rods, and heating tubes to be deployed in the heating area, designing corresponding electric heating circuits, and performing switching operations on the circuits in combination with the task requirements.

[0003] The existing infrared characteristic control methods have deficiencies such as low control accuracy, slow response speed, and poor environmental adaptability. In complex and changeable climate and geographical environments, the degradation of their various performances is even more serious. Therefore, how to conduct infrared temperature control of temperature for complex and changeable climate and geographical environments, and study an intelligent temperature control method for outdoor buildings with high control accuracy and strong environmental adaptability is a problem that needs to be solved currently. Summary of the Invention

[0004] The present invention mainly solves the problem of infrared temperature control of temperature for complex and changeable climate and geographical environments, and discloses an intelligent temperature control method and device for outdoor buildings.

[0005] In the first aspect of the embodiment of the present invention, an intelligent temperature control method for outdoor buildings is disclosed, including:

[0006] S1, collecting a set of meteorological parameter values and a set of environmental temperature information; the set of meteorological parameter values includes a visibility sequence, a wind speed sequence, an environmental temperature sequence, and a solar irradiance parameter sequence; the set of environmental temperature information includes a building surface temperature sequence and a ground temperature sequence;

[0007] S2, using a preset temperature difference control model to process the set of meteorological parameter values and the set of environmental temperature information to obtain temperature control voltage information;

[0008] S3, inputting the temperature control voltage information into a heating module to heat the outdoor building by using a heating model; the heating module is arranged on the surface or inside of the outdoor building and is used for adjusting the output heat value according to the input temperature control voltage information.

[0009] The temperature difference control model includes:

[0010] Obtain a set of standard meteorological value information and a target temperature difference value; the set of standard meteorological value information includes a visibility standard value, a wind speed standard value, an ambient temperature standard value, and a solar irradiance parameter standard value;

[0011] Use the first feedback sub-model to perform calculation processing on the set of standard meteorological value information and the set of meteorological parameter values to obtain a sequence of meteorological control factors;

[0012] Use the second feedback sub-module to perform calculation processing on the target temperature difference value, the building surface temperature sequence, and the ground temperature sequence to obtain a sequence of temperature difference control factors;

[0013] Use the sequence of meteorological control factors and the sequence of temperature difference control factors to construct a temperature difference feedback control sub-model;

[0014] Use the temperature difference feedback control sub-model to calculate the temperature control voltage information.

[0015] The expression of the first feedback sub-model is:

[0016]

[0017] where k 1 (0) is a preset feedback factor, and α 1 is a preset proportionality factor, are the differences between the values of the visibility sequence, wind speed sequence, ambient temperature sequence, and solar irradiance parameter sequence at time t and the corresponding standard values respectively, and L 2 () is a quadratic Laguerre polynomial, and k 1 (t) is the value of the meteorological control factor sequence at time t;

[0018] The expression of the second feedback sub-model is:

[0019]

[0020] where α 2 and α 3 are preset proportionality factors respectively, T 1 (t) and T 2 (t) represent the values of the building surface temperature sequence and the ground temperature sequence at time t respectively, Δt represents the target temperature difference value, and k 2 (t) is the value of the temperature difference control factor sequence at time t;

[0021] The expression of the temperature difference feedback control sub-model is:

[0022]

[0023] Among them, T is the length of the wind speed sequence, and u(t) is the temperature control voltage information at time t.

[0024] The temperature difference control model includes an environmental processing sub-model and a temperature difference processing sub-model;

[0025] The input end of the environmental processing sub-model is used to receive and obtain a set of meteorological parameter values;

[0026] The first input end of the temperature difference processing sub-model is used to receive and obtain a set of environmental temperature information and a target temperature difference value; the second input end of the temperature difference processing sub-model is connected to the output end of the environmental processing sub-model;

[0027] The output end of the temperature difference processing sub-model is used to output a predicted value of the temperature control voltage information;

[0028] The environmental processing sub-model includes: a first residual multi-head self-attention module, a second residual multi-head self-attention module, a third residual multi-head self-attention module, a fourth residual multi-head self-attention module, and a multi-head mutual attention module;

[0029] The first residual multi-head self-attention module, the second residual multi-head self-attention module, the third residual multi-head self-attention module, and the fourth residual multi-head self-attention module are respectively connected to the multi-head mutual attention module, and are used to respectively receive and obtain a visibility sequence, a wind speed sequence, an environmental temperature sequence, and a solar irradiance parameter sequence, and perform self-attention feature extraction on the received sequences to obtain corresponding feature information;

[0030] The multi-head mutual attention module is used to perform multi-head mutual attention feature extraction on the feature information corresponding to each sequence to obtain mutual attention features;

[0031] The output end of the multi-head mutual attention module is connected to the input module of the temperature difference processing sub-model.

[0032] The temperature difference processing sub-model includes: an input module, a first convolution module, a depthwise separable convolution module, a first upsampling convolution module, a second upsampling convolution module, a third upsampling convolution module, a fourth upsampling convolution module, a second convolution module, a first pooling module, a third convolution module, and a first fully connected module;

[0033] The input end of the input module of the temperature difference processing sub-model is used to receive the environmental temperature information set, the target temperature difference value, and the output value of the environmental processing sub-model; the output end of the input module of the temperature difference processing sub-model is connected to the input end of the first convolutional module of the temperature difference processing sub-model; the output end of the first convolutional module of the temperature difference processing sub-model is connected to the input end of the depthwise separable convolutional module of the temperature difference processing sub-model; the output end of the depthwise separable convolutional module of the temperature difference processing sub-model is connected to the input end of the first upsampling convolutional module of the temperature difference processing sub-model; the output end of the first upsampling convolutional module of the temperature difference processing sub-model is connected to the input end of the second upsampling convolutional module of the temperature difference processing sub-model; the output end of the second upsampling convolutional module of the temperature difference processing sub-model is connected to the input end of the third upsampling convolutional module of the temperature difference processing sub-model; the output end of the third upsampling convolutional module of the temperature difference processing sub-model is connected to the input end of the fourth upsampling convolutional module of the temperature difference processing sub-model; the output end of the fourth upsampling convolutional module of the temperature difference processing sub-model is connected to the input end of the second convolutional module of the temperature difference processing sub-model; the output end of the second convolutional module of the temperature difference processing sub-model is connected to the input end of the first pooling module of the temperature difference processing sub-model; the output end of the first pooling module of the temperature difference processing sub-model is connected to the input end of the third convolutional module of the temperature difference processing sub-model; the output end of the third convolutional module of the temperature difference processing sub-model is connected to the input end of the first fully connected module of the temperature difference processing sub-model; the output end of the first fully connected module of the temperature difference processing sub-model is used to output the predicted value of the temperature control voltage information.

[0034] Before the temperature difference control model is used, it is first subjected to training processing;

[0035] The training process of the temperature difference control model includes:

[0036] Initialize the training iteration number value; obtain the training data set; the training data set includes training data and corresponding label information; the training data includes visibility sequence, wind speed sequence, environmental temperature sequence, solar irradiance parameter sequence, building surface temperature sequence, ground temperature sequence, and target temperature difference value; the label information includes the temperature control voltage information corresponding to the training data;

[0037] Use the training data in the training data set as input data and input it into the temperature difference control model;

[0038] Use the temperature difference control model to process the input data to obtain a predicted value;

[0039] Perform a difference calculation process on the obtained predicted value and the label information corresponding to the input data to obtain a difference value;

[0040] Determine whether the difference value satisfies the convergence condition to obtain a first determination result;

[0041] When the first determination result is no, determine whether the training iteration number value is equal to the training number threshold to obtain a second determination result;

[0042] When the second determination result is no, determine that the model training state does not meet the termination training condition;

[0043] When the second determination result is yes, determine that the model training state meets the termination training condition;

[0044] When the first determination result is yes, determine that the model training state meets the termination training condition;

[0045] When the model training state does not meet the termination training condition, use the parameter update model to update the parameters of the temperature difference control model, increase the training iteration number value by 1, and trigger the execution of using the training data in the training dataset as input data and inputting it into the temperature difference control model;

[0046] When the model training state meets the termination training condition, complete the training process of the temperature difference control model to obtain a trained temperature difference control model.

[0047] In the second aspect of the embodiments of the present invention, an intelligent temperature control device for an outdoor building is disclosed, which is used to implement the intelligent temperature control method for the outdoor building, and includes: a meteorological parameter acquisition module, a temperature acquisition module, a control module, and a heating module;

[0048] The meteorological parameter acquisition module includes a visibility acquisition sub-module, a wind speed acquisition sub-module, an ambient temperature acquisition sub-module, and a solar irradiance acquisition sub-module; the visibility acquisition sub-module is used to acquire a visibility sequence around the outdoor building;

[0049] The wind speed acquisition sub-module is used to acquire a wind speed sequence around the outdoor building;

[0050] The ambient temperature acquisition sub-module is used to acquire an ambient temperature sequence around the outdoor building;

[0051] The solar irradiance acquisition sub-module is used to acquire a solar irradiance parameter sequence around the outdoor building;

[0052] The temperature acquisition module includes a building temperature acquisition sub-module and a ground temperature acquisition sub-module; the building temperature acquisition sub-module is arranged on the surface of the outdoor building and is used to acquire a building surface temperature sequence;

[0053] The ground temperature acquisition sub-module is arranged on the ground around the outdoor building and is used to acquire a ground temperature sequence;

[0054] The control module is respectively connected to the meteorological parameter acquisition module and the temperature acquisition module, and is used to process the meteorological parameter value set and the ambient temperature information set by using a preset temperature difference control model to obtain temperature control voltage information, and send the temperature control voltage information to the heating module;

[0055] The heating module is connected to the control module and is used to heat the outdoor building and adjust the output heat value according to the input temperature control voltage information.

[0056] In the third aspect of the implementation of the present invention, an intelligent temperature control device for an outdoor building is disclosed. The device includes:

[0057] A memory storing executable program code;

[0058] A processor coupled to the memory;

[0059] The processor calls the executable program code stored in the memory and executes the intelligent temperature control method for the outdoor building described above.

[0060] In the fourth aspect of the implementation of the present invention, a computer-readable storage medium is disclosed. The computer-readable storage medium stores computer instructions, and when the computer instructions are called by the computer, they are used to execute the intelligent temperature control method for the outdoor building described above.

[0061] In the fifth aspect of the implementation of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the intelligent temperature control method for the outdoor building described above.

[0062] The beneficial effects of the present invention are as follows:

[0063] Aiming at the problem of infrared temperature control of temperature in complex and changeable climate and geographical environments, the present invention provides an intelligent temperature control method for outdoor buildings with high control accuracy and strong environmental adaptability.

[0064] In view of the actual situation of complex and changeable climate and geographical environments, the present invention collects and processes multi-modal meteorological data and environmental data, constructs a corresponding temperature difference control model by using the collected data, and processes the real-time collected data through the temperature difference control model to obtain temperature control voltage. The temperature difference control model can be adaptively adjusted according to environmental factor variables, greatly expanding the application scope of the present method.

[0065] In view of the diversified characteristics of the collected meteorological data and temperature data, the present invention specifically constructs a corresponding multi-modal deep learning model. The temperature difference processing sub-model of this model can process multi-source meteorological data, extract feature information in the model by using a residual multi-head self-attention module and a multi-head mutual attention module, and use a convolutional model to process the environmental temperature difference and the target temperature, thereby improving the processing efficiency. By establishing this temperature difference control model, the accuracy of temperature control is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flowchart of the implementation of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] To better understand the content of the present invention, an embodiment is given here.

[0068] Figure 1 It is a flowchart of the implementation of the method of the present invention.

[0069] In the first aspect of the embodiment of the present invention, an intelligent temperature control method for an outdoor building is disclosed, including:

[0070] S1, collecting a set of meteorological parameter values and a set of environmental temperature information; the set of meteorological parameter values includes a visibility sequence, a wind speed sequence, an environmental temperature sequence, and a solar irradiance parameter sequence; the set of environmental temperature information includes a building surface temperature sequence and a ground temperature sequence;

[0071] S2, using a preset temperature difference control model to process the set of meteorological parameter values and the set of environmental temperature information to obtain temperature control voltage information;

[0072] S3, inputting the temperature control voltage information into a heating module, and using a heating model to heat the outdoor building; the heating module is arranged on the surface or inside of the outdoor building and is used to adjust the output heat value according to the input temperature control voltage information;

[0073] The temperature difference control model includes:

[0074] Obtaining a set of standard meteorological value information and a target temperature difference value; the set of standard meteorological value information includes a visibility standard value, a wind speed standard value, an environmental temperature standard value, and a solar irradiance parameter standard value;

[0075] Using a first feedback sub-model to perform calculation processing on the set of standard meteorological value information and the set of meteorological parameter values to obtain a sequence of meteorological control factors;

[0076] Using a second feedback sub-module to perform calculation processing on the target temperature difference value, the building surface temperature sequence, and the ground temperature sequence to obtain a sequence of temperature difference control factors;

[0077] Construct a temperature difference feedback control sub-model by using the meteorological control factor sequence and the temperature difference control factor sequence;

[0078] Calculate the temperature control voltage information by using the temperature difference feedback control sub-model;

[0079] The expression of the first feedback sub-model is:

[0080]

[0081] where k 1 (0) is a preset feedback factor, and α 1 is a preset proportionality factor, are the differences between the values of the visibility sequence, the wind speed sequence, the ambient temperature sequence, and the solar irradiance parameter sequence at time t and the corresponding standard values respectively, and L 2 () is a quadratic Laguerre polynomial, and k 1 (t) is the value of the meteorological control factor sequence at time t;

[0082] The expression of the second feedback sub-model is:

[0083]

[0084] where α 2 and α 3 are preset proportionality factors respectively, T 1 (t) and T 2 (t) represent the values of the building surface temperature sequence and the ground temperature sequence at time t respectively, Δt represents the target temperature difference value, and k 2 (t) is the value of the temperature difference control factor sequence at time t;

[0085] The expression of the temperature difference feedback control sub-model is:

[0086]

[0087] where T is the length of the wind speed sequence, and u(t) is the temperature control voltage information at time t;

[0088] In view of the actual complex and changeable climate environment and geographical environment, the embodiments of the present invention collect multi-modal meteorological data and environmental data for processing, construct a corresponding temperature difference control model by using the collected data, process the real-time collected data through the temperature difference control model, and obtain the temperature control voltage. The temperature difference control model can be adaptively adjusted according to environmental factor variables, greatly expanding the application scope of the present method.

[0089] The temperature difference control model includes an environmental processing sub-model and a temperature difference processing sub-model;

[0090] The input end of the environmental processing sub-model is used to receive and obtain a set of meteorological parameter values;

[0091] The first input end of the temperature difference processing sub-model is used to receive and obtain a set of environmental temperature information and a target temperature difference value; the second input end of the temperature difference processing sub-model is connected to the output end of the environmental processing sub-model;

[0092] The output end of the temperature difference processing sub-model is used to output a predicted value of temperature control voltage information;

[0093] The environmental processing sub-model includes: a first residual multi-head self-attention module, a second residual multi-head self-attention module, a third residual multi-head self-attention module, a fourth residual multi-head self-attention module, and a multi-head mutual attention module;

[0094] The first residual multi-head self-attention module, the second residual multi-head self-attention module, the third residual multi-head self-attention module, and the fourth residual multi-head self-attention module are respectively connected to the multi-head mutual attention module, and are used to respectively receive and obtain a visibility sequence, a wind speed sequence, an environmental temperature sequence, and a solar irradiance parameter sequence, and perform self-attention feature extraction on the received sequences to obtain corresponding feature information;

[0095] The multi-head mutual attention module is used to perform multi-head mutual attention feature extraction on the feature information corresponding to each signal sequence to obtain mutual attention features;

[0096] The output end of the multi-head mutual attention module is connected to the input module of the temperature difference processing sub-model;

[0097] The first residual multi-head self-attention module, the second residual multi-head self-attention module, the third residual multi-head self-attention module, and the fourth residual multi-head self-attention module can be Transformer models.

[0098] The multi-head mutual attention module is implemented by using a Mutli Self-Attention module.

[0099] The temperature difference processing sub-model includes: an input module, a first convolution module, a depthwise separable convolution module, a first upsampling convolution module, a second upsampling convolution module, a third upsampling convolution module, a fourth upsampling convolution module, a second convolution module, a first pooling module, a third convolution module, and a first fully connected module;

[0100] The input end of the input module of the temperature difference processing sub-model is used to receive the environmental temperature information set, the target temperature difference value, and the output value of the environmental processing sub-model; the output end of the input module of the temperature difference processing sub-model is connected to the input end of the first convolution module of the temperature difference processing sub-model; the output end of the first convolution module of the temperature difference processing sub-model is connected to the input end of the depthwise separable convolution module of the temperature difference processing sub-model; the output end of the depthwise separable convolution module of the temperature difference processing sub-model is connected to the input end of the first dimension-increasing convolution module of the temperature difference processing sub-model; the output end of the first dimension-increasing convolution module of the temperature difference processing sub-model is connected to the input end of the second dimension-increasing convolution module of the temperature difference processing sub-model; the output end of the second dimension-increasing convolution module of the temperature difference processing sub-model is connected to the input end of the third dimension-increasing convolution module of the temperature difference processing sub-model; the output end of the third dimension-increasing convolution module of the temperature difference processing sub-model is connected to the input end of the fourth dimension-increasing convolution module of the temperature difference processing sub-model; the output end of the fourth dimension-increasing convolution module of the temperature difference processing sub-model is connected to the input end of the second convolution module of the temperature difference processing sub-model; the output end of the second convolution module of the temperature difference processing sub-model is connected to the input end of the first pooling module of the temperature difference processing sub-model; the output end of the first pooling module of the temperature difference processing sub-model is connected to the input end of the third convolution module of the temperature difference processing sub-model; the output end of the third convolution module of the temperature difference processing sub-model is connected to the input end of the first fully-connected module of the temperature difference processing sub-model; the output end of the first fully-connected module of the temperature difference processing sub-model is used to output the predicted value of the temperature control voltage information.

[0101] The convolution module can be implemented by using 3D multi-channel convolution;

[0102] The depthwise separable convolution module can be implemented by connecting a channel splitting sub-module and a single-channel convolution sub-module. Specifically, it can be implemented by using the depthwise separable convolution module in the MobileNet network.

[0103] The dimension-increasing convolution module can be implemented by using the dimension-increasing convolution module in the ResNet network;

[0104] The pooling module can be implemented by using a maximum pooling operation.

[0105] In the embodiment of the present invention, a corresponding multi-modal deep learning model is specifically constructed for the diversified characteristics of the collected meteorological data and temperature data. The temperature difference processing sub-model of this model can process multi-source meteorological data, extract feature information in the model by using a residual multi-head self-attention module and a multi-head mutual attention module, and use a convolutional model to process the environmental temperature difference and the target temperature, improving the processing efficiency. By establishing this temperature difference control model, the accuracy of temperature control is greatly improved.

[0106] Before the temperature difference control model is used, it is first subjected to training processing;

[0107] The training process of the temperature difference control model includes:

[0108] Initialize the training iteration number value; obtain the training data set; the training data set includes training data and corresponding label information; the training data includes visibility sequence, wind speed sequence, environmental temperature sequence, solar irradiance parameter sequence, building surface temperature sequence, ground temperature sequence and target temperature difference value; the label information includes temperature control voltage information corresponding to the training data.

[0109] Use the training data in the training data set as input data and input it into the temperature difference control model;

[0110] Use the temperature difference control model to process the input data to obtain a predicted value;

[0111] Perform a difference calculation process on the obtained predicted value and the label information corresponding to the input data to obtain a difference value;

[0112] Judge whether the difference value meets the convergence condition to obtain a first judgment result;

[0113] When the first judgment result is no, judge whether the training iteration number value is equal to the training number threshold to obtain a second judgment result;

[0114] When the second judgment result is no, determine that the model training state does not meet the termination training condition;

[0115] When the second judgment result is yes, determine that the model training state meets the termination training condition;

[0116] When the first judgment result is yes, determine that the model training state meets the termination training condition;

[0117] When the model training state does not meet the termination training condition, use a parameter update model to update the parameters of the temperature difference control model, increase the training iteration number value by 1, and trigger the execution of using the training data in the training data set as input data and inputting it into the temperature difference control model;

[0118] When the model training state meets the termination training condition, the training process of the temperature difference control model is completed, and the trained temperature difference control model is obtained.

[0119] The difference value satisfying the convergence condition means that the difference value is less than a preset convergence threshold; the difference value not satisfying the convergence condition means that the difference value is not less than the preset convergence threshold.

[0120] The difference calculation process can be implemented using a loss function.

[0121] The loss function can adopt a cross-entropy loss function.

[0122] The parameter update model is:

[0123]

[0124] θ←θ+v;

[0125] In the formula, is the difference value calculated for the i-th training data in the training dataset, v is the parameter update value, θ is the parameter of the temperature difference control model, η is the initial parameter learning rate, α is the momentum angle parameter, 0≤α≤π / 4, represents taking the partial derivative with respect to the variable θ;

[0126] In the second aspect of the embodiments of the present invention, an intelligent temperature control device for an outdoor building is disclosed, which is used to implement the intelligent temperature control method for the outdoor building, and includes: a meteorological parameter acquisition module, a temperature acquisition module, a control module, and a heating module;

[0127] The meteorological parameter acquisition module includes a visibility acquisition sub-module, a wind speed acquisition sub-module, an ambient temperature acquisition sub-module, and a solar irradiance acquisition sub-module; the visibility acquisition sub-module is used to acquire a visibility sequence around the outdoor building;

[0128] The wind speed acquisition sub-module is used to acquire a wind speed sequence around the outdoor building;

[0129] The ambient temperature acquisition sub-module is used to acquire an ambient temperature sequence around the outdoor building;

[0130] The solar irradiance acquisition sub-module is used to acquire a solar irradiance parameter sequence around the outdoor building;

[0131] The temperature acquisition module includes a building temperature acquisition sub-module and a ground temperature acquisition sub-module; the building temperature acquisition sub-module is arranged on the surface of the outdoor building and is used to acquire a building surface temperature sequence;

[0132] The ground temperature acquisition sub-module is arranged on the ground around the outdoor building and is used to acquire a ground temperature sequence;

[0133] The control module is respectively connected to the meteorological parameter acquisition module and the temperature acquisition module, and is used to process the meteorological parameter value set and the ambient temperature information set by using a preset temperature difference control model to obtain temperature control voltage information, and send the temperature control voltage information to the heating module;

[0134] The heating module is connected to the control module and is used to heat the outdoor building and adjust the output heat value according to the input temperature control voltage information;

[0135] The heating module includes a mounting frame, a heat insulation layer, a heating layer and a heat conduction layer;

[0136] The heating layer is used to adjust the voltage of the heating resistance wire according to the input temperature control voltage information.

[0137] Inside the mounting frame, the heat insulation layer, the heating layer and the heat conduction layer are placed from bottom to top; the heat conduction layer is close to the building surface;

[0138] The heating layer is a heating film made of a silicone-encapsulated heating resistance wire;

[0139] Meteorological station parameters, solar irradiance parameters, surface temperature values, building surface temperature values;

[0140] In the third aspect of the present invention, an intelligent temperature control device for an outdoor building is disclosed, and the device includes:

[0141] A memory storing executable program code;

[0142] A processor coupled to the memory;

[0143] The processor calls the executable program code stored in the memory to execute the intelligent temperature control method for the outdoor building.

[0144] In the fourth aspect of the present invention, a computer-readable storage medium is disclosed, and the computer-readable storage medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the intelligent temperature control method for the outdoor building.

[0145] In the fifth aspect of the present invention, an information data processing terminal is disclosed, and the information data processing terminal is used to implement the intelligent temperature control method for the outdoor building.

[0146] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the scope of the claims of the present invention.

Claims

1. An intelligent temperature control method for an outdoor building, characterized in that: include: S1, collecting and obtaining a meteorological parameter value set and an ambient temperature information set; the meteorological parameter value set includes a visibility sequence, a wind speed sequence, an ambient temperature sequence, and a solar radiation parameter sequence; the ambient temperature information set includes a building surface temperature sequence and a ground temperature sequence; S2, using a preset temperature difference control model to process the meteorological parameter value set and the ambient temperature information set to obtain temperature control voltage information; S3, inputting the temperature control voltage information into the heating module, and using the heating model to heat the outdoor building; the heating module is arranged on the surface or inside the outdoor building, and is used to adjust the output heat value according to the input temperature control voltage information.

2. The intelligent temperature control method for outdoor buildings according to claim 1, characterized in that: The temperature difference control model comprises: Obtaining a standard meteorological value information set and a target temperature difference value; the standard meteorological value information set includes a visibility standard value, a wind speed standard value, an ambient temperature standard value, and a solar radiation parameter standard value; Using the first feedback sub-model, the standard meteorological value information set and the meteorological parameter value set are calculated and processed to obtain a meteorological control factor sequence; Using the second feedback submodule, the target temperature difference value, the building surface temperature sequence and the ground temperature sequence are calculated and processed to obtain a temperature difference control factor sequence; Using the meteorological control factor sequence and the temperature difference control factor sequence, a temperature difference feedback control sub-model is constructed; The temperature control voltage information is calculated using the temperature difference feedback control sub-model.

3. The intelligent temperature control method for outdoor buildings according to claim 2, characterized in that: The expression of the first feedback sub-model is: Where k1(0) is the preset feedback factor, α1 is the preset proportional factor, are the values ​​of the visibility sequence, wind speed sequence, ambient temperature sequence and solar radiation parameter sequence at time t, and the differences between them and the corresponding standard values, L2() is a quadratic Laguerre polynomial, and k1(t) is the value of the meteorological control factor sequence at time t; The expression of the second feedback sub-model is: Among them, α2 and α3 are preset proportional factors, T1(t) and T2(t) represent the values ​​of the building surface temperature sequence and the ground temperature sequence at time t, Δt represents the target temperature difference, and k2(t) is the value of the temperature difference control factor sequence at time t; The expression of the temperature difference feedback control sub-model is: Wherein, T is the length of the wind speed sequence, and u(t) is the temperature control voltage information at time t.

4. The intelligent temperature control method for outdoor buildings according to claim 2, characterized in that: The temperature difference control model includes an environment processing sub-model and a temperature difference processing sub-model; The input end of the environmental processing sub-model is used to receive a set of meteorological parameter values; The first input end of the temperature difference processing sub-model is used to receive the obtained environment temperature information set and the target temperature difference value; the second input end of the temperature difference processing sub-model is connected to the output end of the environment processing sub-model; The output terminal of the temperature difference processing sub-model is used to output the predicted value of the temperature control voltage information; The environment processing sub-model includes: a first residual multi-head self-attention module, a second residual multi-head self-attention module, a third residual multi-head self-attention module, a fourth residual multi-head self-attention module and a multi-head mutual attention module; The first residual multi-head self-attention module, the second residual multi-head self-attention module, the third residual multi-head self-attention module, and the fourth residual multi-head self-attention module are respectively connected to the multi-head mutual attention module, and are used to respectively receive the visibility sequence, the wind speed sequence, the ambient temperature sequence, and the solar irradiation parameter sequence, and perform self-attention feature extraction on the received sequences to obtain corresponding feature information; The multi-head mutual attention module is used to extract multi-head mutual attention features from the feature information corresponding to each sequence to obtain mutual attention features; The output end of the multi-head mutual attention module is connected to the input module of the temperature difference processing sub-model.

5. The intelligent temperature control method for outdoor buildings according to claim 4, characterized in that: The temperature difference processing sub-model includes: an input module, a first convolution module, a depth-separable convolution module, a first dimension-raising convolution module, a second dimension-raising convolution module, a third dimension-raising convolution module, a fourth dimension-raising convolution module, a second convolution module, a first pooling module, a third convolution module and a first fully connected module; The input end of the input module of the temperature difference processing sub-model is used to receive the ambient temperature information set, the target temperature difference value and the output value of the ambient processing sub-model; the output end of the input module of the temperature difference processing sub-model is connected to the input end of the first convolution module of the temperature difference processing sub-model; the output end of the first convolution module of the temperature difference processing sub-model is connected to the input end of the depthwise separable convolution module of the temperature difference processing sub-model; the output end of the depthwise separable convolution module of the temperature difference processing sub-model is connected to the input end of the first dimensionally increased convolution module of the temperature difference processing sub-model; the output end of the first dimensionally increased convolution module of the temperature difference processing sub-model is connected to the input end of the second dimensionally increased convolution module of the temperature difference processing sub-model; the output end of the second dimensionally increased convolution module of the temperature difference processing sub-model is connected to the third dimensionally increased convolution module of the temperature difference processing sub-model; The input end of the product module is connected; the output end of the third dimensionality-raising convolution module of the temperature difference processing sub-model is connected to the input end of the fourth dimensionality-raising convolution module of the temperature difference processing sub-model; the output end of the fourth dimensionality-raising convolution module of the temperature difference processing sub-model is connected to the input end of the second convolution module of the temperature difference processing sub-model; the output end of the second convolution module of the temperature difference processing sub-model is connected to the input end of the first pooling module of the temperature difference processing sub-model; the output end of the first pooling module of the temperature difference processing sub-model is connected to the input end of the third convolution module of the temperature difference processing sub-model; the output end of the third convolution module of the temperature difference processing sub-model is connected to the input end of the first fully-connected module of the temperature difference processing sub-model; the output end of the first fully-connected module of the temperature difference processing sub-model is used to output the predicted value of the temperature control voltage information.

6. The intelligent temperature control method for outdoor buildings according to claim 4, characterized in that: The temperature difference control model is first trained before use; The training process of the temperature difference control model includes: Initialize the number of training iterations; obtain a training data set; the training data set includes training data and corresponding label information; the training data includes visibility sequence, wind speed sequence, ambient temperature sequence, solar radiation parameter sequence, building surface temperature sequence, ground temperature sequence and target temperature difference value; the label information includes temperature control voltage information corresponding to the training data; Input the training data in the training data set as input data into the temperature difference control model; Using the temperature difference control model, the input data is processed to obtain a predicted value; Performing difference calculation processing on the obtained predicted value and the label information corresponding to the input data to obtain a difference value; Determine whether the difference value satisfies a convergence condition, and obtain a first determination result; When the first judgment result is no, judging whether the training iteration number value is equal to the training number threshold, and obtaining a second judgment result; When the second judgment result is no, determining that the model training state does not meet the training termination condition; When the second judgment result is yes, determining that the model training state satisfies the training termination condition; When the first judgment result is yes, determining that the model training state satisfies a training termination condition; When the model training state does not meet the termination training condition, the temperature difference control model is updated with the parameter update model, the training iteration number value is increased by 1, and the execution is triggered to input the training data in the training data set as input data into the temperature difference control model; When the model training state satisfies the training termination condition, the training process of the temperature difference control model is completed to obtain a trained temperature difference control model.

7. An intelligent temperature control device for an outdoor building, used to implement the intelligent temperature control method for an outdoor building according to any one of claims 1 to 6, comprising: Meteorological parameter acquisition module, temperature acquisition module, control module, heating module; The meteorological parameter collection module includes a visibility collection submodule, a wind speed collection submodule, an ambient temperature collection submodule, and a solar radiation collection submodule; The visibility collection submodule is used to collect visibility sequences around outdoor buildings; The wind speed collection submodule is used to collect the wind speed sequence around the outdoor building; The ambient temperature acquisition submodule is used to acquire the ambient temperature sequence around the outdoor building; The solar radiation collection submodule is used to collect and obtain a solar radiation parameter sequence around an outdoor building; The temperature acquisition module includes a building temperature acquisition submodule and a ground temperature acquisition submodule; The building temperature collection submodule is arranged on the outdoor building surface and is used to collect and obtain the building surface temperature sequence; The ground temperature collection submodule is arranged on the ground around the outdoor building and is used to collect and obtain the ground temperature sequence; The control module is connected to the meteorological parameter acquisition module and the temperature acquisition module respectively, and is used to process the meteorological parameter value set and the ambient temperature information set using a preset temperature difference control model to obtain temperature control voltage information, and send the temperature control voltage information to the heating module; The heating module is connected to the control module and is used to heat the outdoor building and adjust the output heat value according to the input temperature control voltage information.

8. An intelligent temperature control device for an outdoor building, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent temperature control method for an outdoor building according to any one of claims 1 to 6.

9. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the intelligent temperature control method for an outdoor building according to any one of claims 1 to 6.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the intelligent temperature control method for an outdoor building as described in any one of claims 1 to 6.

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