University campus warm corridor energy supply regulation and control method based on space-time people flow monitoring and prediction

Abstract

The invention provides a university campus heating corridor energy supply regulation and control method based on space-time people flow monitoring and prediction, and belongs to the technical field of intelligent prediction. Image data and environment data of a heating corridor are collected through various sensors; training an image recognition model by using a semantic segmentation algorithm and a deep learning algorithm; processing the picture data to obtain data of the number of people in the warm corridor sections at different time and in different spaces; processing the data acquired by the monitoring equipment, and analyzing the warm corridor space through a thermal comfort evaluation index; according to the number of monitored people, classifying the heating corridors in different sections, and establishing a time sequence database of the use frequency and the thermal comfort degree of the heating corridors in different sections; constructing and training a prediction model through a deep learning algorithm based on the time sequence database; and based on the successfully trained prediction model, carrying out data analysis to form the space-time difference heating corridor adaptive regulation and control method. The energy utilization efficiency can be remarkably improved, and the system has great significance in promoting green campus construction.

Description

Technical Field

[0001] The invention belongs to the technical field of intelligent prediction, and specifically relates to a method for regulating and controlling the energy supply of a university campus warm corridor based on spatio-temporal human flow monitoring and prediction. Background Art

[0002] With the improvement and upgrading of campus infrastructure construction, the happiness of teachers and students has been continuously enhanced, and the construction of a campus that is suitable for learning, teaching and living has been promoted. Harbin Institute of Technology has completed the construction of the campus sky warm corridor and achieved remarkable results, which is sufficient to predict that the warm corridor space is expected to be further developed and popularized in other universities and industrial parks. However, as a public place with a large number of people, high traffic flow and round-the-clock operation, the problem of energy consumption in the operation of the warm corridor space on campus is particularly prominent.

[0003] As an emerging architectural space, the warm corridor space on campus has spatial particularities compared with other traditional spaces. As a transitional space, the warm corridor not only has a traffic function, but also provides a place for students to stay and relax. This spatial characteristic makes the utilization rate and the distribution of the number of people in the warm corridor show certain rules at different time periods and different spatial positions. Therefore, different from the all-day constant temperature control method, the warm corridor space can adopt an intelligent control system to accurately control the temperature and manage the number of people to reduce the heating energy consumption.

[0004] It is difficult to achieve the adjustment of multiple heating devices in the warm corridor area only by manual operation; secondly, using the existing technology to automatically control the warm corridor relies too much on sensor devices and the system response time is long, and it is impossible to effectively predict the rapid change of the number of people. As a result, the heating effect often lags behind the actual demand and cannot meet the immediate requirements of users for comfort. Summary of the Invention

[0005] In order to solve the above problems, the invention proposes a method for regulating and controlling the energy supply of a university campus warm corridor based on spatio-temporal human flow monitoring and prediction to meet the immediate requirements of users for the temperature comfort of the warm corridor space.

[0006] The invention is realized through the following technical solutions:

[0007] A method for regulating and controlling the energy supply of a university campus warm corridor based on spatio-temporal human flow monitoring and prediction:

[0008] Step 1: Collect image data and environmental data of the warm corridor through a thermal imaging camera, a temperature and humidity measuring instrument, a black globe thermometer and an anemometer monitor;

[0009] Step 2: Use a semantic segmentation algorithm and a deep learning algorithm to train an image recognition model to improve the recognition accuracy of the image data;

[0010] Step 3: Process the picture data to obtain the number of people data in different warm corridor sections at different times and spaces;

[0011] Step 4: Process the data obtained by the monitoring device and analyze the warm corridor space through thermal comfort evaluation indicators;

[0012] Step 5: Classify the warm corridors in different sections according to the number of monitored people, and establish a time-series database of the usage frequency and thermal comfort of the warm corridors in different sections;

[0013] Step 6: Based on the time-series database, construct and train a prediction model through a deep learning algorithm;

[0014] Step 7: Based on the successfully trained prediction model, conduct data analysis to form an adaptive control method for the warm corridor with spatio-temporal differences.

[0015] Furthermore, in Step 1,

[0016] Install a thermal imaging camera in the warm corridor so that its shooting angle can cover the entire warm corridor area, regularly shoot thermal images, and record the thermal distribution of people and the environment in the warm corridor;

[0017] Install temperature and humidity measuring instruments at different positions in the warm corridor, and collect temperature data and humidity data at regular intervals;

[0018] Measure the mean radiant temperature of the warm corridor through a black globe thermometer, and its measurement interval is the same as that of the temperature and humidity measuring instrument;

[0019] Monitor and collect the wind speed and wind direction in the warm corridor through an anemometer, and the collection frequency is the same as the measurement intervals of the temperature measuring instrument and the black globe thermometer.

[0020] Furthermore, in Step 2, it includes:

[0021] Step 2.1: Use the collected thermal images as training data, and mark the personnel areas and background areas in the images;

[0022] Step 2.2: Use the marked thermal image data set to train a semantic segmentation model, adjust the training strategy according to the evaluation results, and improve the recognition accuracy of the model for people;

[0023] Step 2.3: Use the thermal images processed by the semantic segmentation algorithm as input, extract features through a convolutional neural network, and realize the classification of people and other objects in the images.

[0024] Furthermore, in Step 3, it includes:

[0025] Step 3.1: Image preprocessing: Perform noise reduction and enhancement operations on the images with improved accuracy in Step 2, and retain the detailed information in the thermal images;

[0026] Step 3.2, Section Division: According to the characteristics and length of the two buildings connected by the warm corridor, divide the warm corridor into sections at regular intervals to separately count the number of people in each section;

[0027] Step 3.3, Use the image recognition model trained in Step 2 to detect the preprocessed thermal imaging images. For each divided warm corridor section, count the number of detected people, continuously track each detected person target, record their position information at different times, obtain the number data of people in the warm corridor sections at different times and different spatial positions, and establish a database.

[0028] Furthermore, in Step 4,

[0029] The thermal comfort evaluation index UTCI is:

[0030] UTCI = ∫(T a ; T mrt ; v 10 ; RH; Clo) = T a +Offect(T a ; T mrt ; v 10 ; RH; Clo)

[0031] In the formula, T a is the air temperature, T mrt is the maximum temperature, v 10 is the wind speed at a height of 10 m, RH is the relative humidity, and Clo is the clothing insulation value;

[0032] Evaluate when the "comfortable" standard is reached in the warm corridor through the following formula:

[0033]

[0034] In the formula, PC i represents the percentage of the thermally comfortable grid in the total grid of the separate section corridor space in the i-th hour, n i represents the number of comfortable grids in the i-th hour, and N is the total number of grids in the atrium area.

[0035] Furthermore, Step 5 includes:

[0036] Step 5.1, Warm Corridor Section Classification: According to the number data of people in the warm corridor sections at different times and spaces obtained in Step 3, divide the warm corridor into different sections with high, medium, and low usage frequencies;

[0037] Step 5.2, Establish a Time Series Database: Create an independent data table for each warm corridor section. The table includes time, usage frequency, environmental parameters, and thermal comfort, store the data table, and update it in real time.

[0038] Further, in step 6, it includes:

[0039] Step 6.1, extract time features, environmental features, usage frequency, and corresponding thermal comfort data from the time series database;

[0040] Step 6.2, train a prediction model using the extracted feature data based on a time series prediction algorithm.

[0041] Further, in step 7,

[0042] Step 7.1, use the trained prediction model to predict the future usage frequency and thermal comfort of the warm corridor;

[0043] Step 7.2, according to the prediction results, combined with the current energy supply situation and thermal comfort requirements, formulate an energy supply control strategy for the warm corridor;

[0044] For the warm corridor sections with high predicted usage frequency, increase the power of heating or ventilation equipment in advance to meet the thermal comfort needs of more people; according to the predicted thermal comfort index UTCI, when it is predicted that the thermal comfort of a certain warm corridor section is about to exceed the comfortable range, adjust the equipment operation status in a timely manner; achieve adaptive control of the warm corridor space.

[0045] An electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0046] A computer-readable storage medium is used to store computer instructions, and when the computer instructions are executed by a processor, the steps of the above method are implemented.

[0047] Advantages of the present invention

[0048] Compared with the prior art, the method for regulating the energy supply of the warm corridor in a university campus based on spatio-temporal pedestrian flow monitoring and prediction of the present invention has the following advantages:

[0049] 1. The present invention can capture and analyze the dynamic changes of the pedestrian flow density on campus in real time, and accurately adjust the energy supply level of the warm corridor system accordingly. This on-demand energy supply strategy effectively avoids unnecessary waste of energy, can significantly improve the energy utilization efficiency, and is of great significance for promoting the construction of a green campus.

[0050] 2. Traditional regulation methods often rely on fixed preset parameters and are difficult to cope with emergencies. However, the present invention can quickly respond and predict the changes in pedestrian flow in advance through real-time monitoring data, and adjust the energy supply strategy in a timely manner, enhancing the flexibility and response speed of the system.

[0051] 3. The intelligent control method of the present invention greatly improves the usage experience of teachers and students, optimizes the comfort of the indoor environment, and provides a demonstration guidance plan for the construction of universities, enterprises, industrial parks, etc. in cold regions. Description of the Drawings

[0052] Figure 1 It is a flowchart of the method of the present invention. Detailed Embodiment

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] The experimental methods used in the following embodiments are all conventional methods unless otherwise specified. The materials, reagents, methods, and instruments used are all conventional materials, reagents, methods, and instruments in the art unless otherwise specified, and those skilled in the art can obtain them through commercial channels.

[0055] A method for regulating the energy supply of a university campus warm corridor based on spatio-temporal pedestrian flow monitoring and prediction:

[0056] The method specifically includes the following steps:

[0057] Step 1: Collect image data and environmental data of the warm corridor through a thermal imaging camera, a temperature and humidity measuring instrument, a black globe thermometer, and an anemometer monitor;

[0058] Install a thermal imaging camera in the warm corridor so that its shooting angle can cover the entire warm corridor area, regularly shoot thermal images, and record the thermal distribution of people and the environment in the warm corridor;

[0059] Install temperature and humidity measuring instruments at different positions in the warm corridor, and collect temperature data and humidity data at regular intervals;

[0060] Measure the average radiant temperature of the warm corridor through a black globe thermometer, and its measurement interval is the same as that of the temperature and humidity measuring instrument;

[0061] Collect the wind speed and wind direction in the warm corridor through the anemometer monitor, and the collection frequency is the same as the measurement interval of the temperature measuring instrument and the black globe thermometer.

[0062] Step 2: Use semantic segmentation algorithms and deep learning algorithms to train an image recognition model to improve the recognition accuracy of image data;

[0063] Step 2.1, Use the collected thermal images as training data, and mark the personnel area and background area in the images;

[0064] Step 2.2: Using the labeled thermal imaging image dataset to train a semantic segmentation model, adjusting the training strategy according to the evaluation results to improve the recognition accuracy of the model for personnel;

[0065] Step 2.3: Taking the thermal imaging image processed by the semantic segmentation algorithm as input, extracting features through a convolutional neural network to achieve the classification of personnel and other objects in the image.

[0066] Step 3: Process the picture data to obtain the number data of people in the warm corridor sections at different times and spaces;

[0067] Step 3.1: Image preprocessing: Perform noise reduction and enhancement operations on the images with improved accuracy in Step 2, and retain the detailed information in the thermal imaging images;

[0068] Step 3.2: Section division: Divide a section at a certain distance according to the length of the warm corridor to separately count the number of people in it;

[0069] Step 3.3: Using the image recognition model trained in Step 2, detect the preprocessed thermal imaging images. For each divided warm corridor section, count the number of detected personnel, continuously track each detected personnel target, record its position information at different times, and obtain the number data of people in the warm corridor sections at different times and different spatial positions.

[0070] Step 4: Process the data obtained by the monitoring device and analyze the warm corridor space through the thermal comfort evaluation index;

[0071] The thermal comfort evaluation index UTCI is as follows:

[0072] UTCI = ∫(T a ; T mrt ; v 10 ; RH; Clo) = T a +Ofect(T a ; T mrt ; v 10 ; RH; Clo)

[0073] In the formula, T a is the air temperature, T mrt is the highest temperature, v 10 is the wind speed at a height of 10 m, RH is the relative humidity, and Clo is the clothing insulation value (clothing index); in order to obtain accurate evaluation results, the clothing index is set to 0.5 for spring and autumn, 0.3 for summer, and 1 for winter according to different seasons.

[0074] Evaluate when the "comfortable" standard is reached in the warm corridor through the following formula:

[0075]

[0076] where PC i represents the percentage of the thermal comfort (9°C < UTCI < 26°C) grids in the total grids of the separate section corridor space in the i-th hour (i = 1, 2, 3, …, 6570). ni represents the number of comfortable grids in the i-th hour, and N is the total number of grids in the atrium area. The operation time of the corridor is set from 5 am to 11 pm every day from January 1st to December 31st of each year, totaling 6570 hours. The internal space of the corridor is divided into grids with rectangular units of side length 0.5 m.

[0077] Step 5: Classify the warm corridors in different sections according to the monitored number of people, and establish a time series database of the usage frequency and thermal comfort of the warm corridors in different sections;

[0078] Step 5.1, Classification of warm corridor sections: According to the number of people in the warm corridor sections at different times and spaces obtained in Step 3, divide the warm corridors into different sections with high, medium, and low usage frequencies.

[0079] Step 5.2, Establish a time series database: Create an independent data table for each warm corridor section, including time, usage frequency, environmental parameters, and thermal comfort, store the data table, and update it in real time.

[0080] Step 6: Based on the time series database, construct and train a prediction model through a deep learning algorithm;

[0081] Step 6.1, Extract time features, environmental features, usage frequency, and corresponding thermal comfort data from the time series database;

[0082] Step 6.2, Train the prediction model using the extracted feature data based on the time series prediction algorithm (CNN-LSTM algorithm).

[0083] Among them, the CNN model is developed on the PyTorch platform, and the model structure consists of 3 convolutional layers, 3 pooling layers, and 2 fully connected layers. The filter size of the convolutional layer is fixed at 3×3, the kernel size of the pooling layer is 3×3, and the stride is 3.

[0084] The LSTM hyperparameters (input sequence length, number of LSTM layers, number of units, learning rate, batch size) need to be specifically analyzed according to the corridors in different sections.

[0085] Step 7: Based on the successfully trained prediction model, conduct data analysis to form an adaptive control method for the warm corridor with spatio-temporal differences.

[0086] Step 7.1, Use the trained prediction model to predict the future usage frequency and thermal comfort of the warm corridor;

[0087] Step 7.2: According to the prediction results, combined with the current energy supply situation and thermal comfort requirements, formulate an energy supply control strategy for the warm corridor;

[0088] For the warm corridor sections with high predicted usage frequencies, increase the power of heating or ventilation equipment in advance to meet the thermal comfort requirements of more people; according to the predicted thermal comfort index UTCI, when it is predicted that the thermal comfort of a certain warm corridor section is about to exceed the comfortable range, adjust the equipment operation status in a timely manner; realize the adaptive control of the warm corridor space.

[0089] Step 8: Based on the objective evaluation of the thermal comfort of the warm corridor space after the above control method, verify and adjust and optimize the algorithm through the combined method of equipment monitoring and questionnaire survey.

[0090] First, after implementing the control method, continue to continuously and long-term monitor the thermal comfort status of the warm corridor space through devices such as thermal imaging cameras and temperature measuring instruments.

[0091] Then, conduct a satisfaction survey on the people using the warm corridor through a questionnaire to understand the subjective feelings of the thermal comfort of the warm corridor.

[0092] Finally, according to the results of equipment monitoring and questionnaire survey, analyze the effect of the control method, and adjust and optimize the prediction model and control algorithm.

[0093] An electronic device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the above method are implemented.

[0094] A computer-readable storage medium for storing computer instructions, and when the computer instructions are executed by a processor, the steps of the above method are implemented.

[0095] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory of the method described in the present invention is intended to include but not limited to these and any other suitable types of memories.

[0096] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire such as coaxial cable, optical fiber, digital subscriber line (DSL), or wirelessly such as infrared, wireless, microwave, etc. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes one or more integrated available media. The available media can be magnetic media such as floppy disks, hard disks, magnetic tapes, optical media such as high-density digital video discs (DVDs), or semiconductor media such as solid state discs (SSDs), etc.

[0097] In the implementation process, the steps of the above method can be completed by the integrated logic circuit in the hardware of the processor or the instructions in the form of software. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by the hardware processor, or executed by a combination of the hardware and software modules in the processor. The software module can be located in a mature storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method. To avoid repetition, it will not be described in detail here.

[0098] It should be noted that the processor in the embodiments of the present application may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method embodiments can be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above processor may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed and completed by a hardware decoding processor, or executed and completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0099] The above has introduced in detail a method for regulating the energy supply of a university campus warm corridor based on spatio-temporal pedestrian flow monitoring and prediction proposed by the present invention, and has elaborated on the principle and implementation manner of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention; at the same time, for those of ordinary skill in the art, according to the idea of the present invention, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present invention.

Claims

1. A method for regulating the energy supply of a university campus warm corridor based on spatio-temporal pedestrian flow monitoring and prediction, characterized in that: The method specifically includes the following steps: Step 1: Collect image data and environmental data of the warm corridor through a thermal imaging camera, a temperature and humidity measuring instrument, a black globe thermometer, and an anemometer monitor; Step 2: Use a semantic segmentation algorithm and a deep learning algorithm to train an image recognition model to improve the recognition accuracy of the image data; Step 3: Process the picture data to obtain the number of people in different warm corridor sections at different times and spaces; Step 4: Process the data obtained by the monitoring equipment and analyze the warm corridor space through thermal comfort evaluation indicators; Step 5: Classify different sections of the warm corridor according to the monitored number of people, and establish a time series database of the usage frequency and thermal comfort of different sections of the warm corridor; Step 6: Based on the time series database, construct and train a prediction model through a deep learning algorithm; Step 7: Based on the successfully trained prediction model, conduct data analysis to form a spatio-temporal differential adaptive regulation method for the warm corridor.

2. The energy supply regulation method according to claim 1, wherein: In Step 1, Install a thermal imaging camera in the warm corridor so that its shooting angle can cover the entire warm corridor area, regularly take thermal imaging pictures, and record the thermal distribution of people and the environment in the warm corridor; Install temperature and humidity measuring instruments at different positions in the warm corridor, and collect temperature data and humidity data at regular intervals; Measure the average radiant temperature of the warm corridor through a black globe thermometer, and its measurement interval is the same as that of the temperature and humidity measuring instrument; Monitor and collect the wind speed and wind direction in the warm corridor through an anemometer, and the collection frequency is the same as the measurement intervals of the temperature measuring instrument and the black globe thermometer.

3. The energy supply regulation method according to claim 2, wherein: In Step 2, it includes: Step 2.1: Use the collected thermal imaging pictures as training data, and mark the person areas and background areas in the images; Step 2.2: Use the marked thermal imaging image data set to train a semantic segmentation model, adjust the training strategy according to the evaluation results, and improve the recognition accuracy of the model for people; Step 2.3: Use the thermal imaging images processed by the semantic segmentation algorithm as input, extract features through a convolutional neural network, and realize the classification of people and other objects in the images.

4. The energy supply regulation method according to claim 3, characterized in that: In Step 3, it includes: Step 3.1: Image preprocessing: Perform noise reduction and enhancement operations on the images with improved accuracy in Step 2, and retain the detailed information in the thermal imaging images; Step 3.2: Section division: According to the characteristics and length of the two end buildings connected by the warm corridor, divide a section at a certain distance to separately count the number of people in it; Step 3.3: Use the image recognition model trained in Step 2 to detect the preprocessed thermal imaging images. For each divided warm corridor section, count the number of people detected in it, continuously track each detected person target, record its position information at different times, obtain the number of people data in different warm corridor sections at different times and different spatial positions, and establish a database.

5. The energy supply regulation method according to claim 4, characterized in that: In Step 4, The thermal comfort evaluation index UTCI is: where T a is the air temperature, T mrt is the maximum temperature, v 10 is the wind speed at a height of 10 m, RH is the relative humidity, and Clo is the clothing insulation value; Evaluate when the "comfortable" standard is reached in the warm corridor through the following formula: where PC i represents the percentage of the thermally comfortable grids in the total grids of the single-section corridor space in the \(i\)-th hour, \(n\) i represents the number of comfortable grids in the \(i\)-th hour, and \(N\) is the total number of grids in the atrium area.

6. The energy supply regulation method according to claim 5, characterized in that: In Step 5, it includes: Step 5.1, Classification of the sunroom section: According to the number data of people in the sunroom section at different times and spaces obtained in Step 3, the sunroom is divided into different sections with high, medium, and low usage frequencies. Step 5.2, Establish a time-series database: Create an independent data table for each sunroom section, which includes time, usage frequency, environmental parameters, and thermal comfort. Store the data table and update it in real time.

7. The energy supply regulation method according to claim 6, wherein: In Step 6, it includes: Step 6.1, Extract time features, environmental features, usage frequency, and corresponding thermal comfort data from the time-series database. Step 6.2, Train a prediction model using the extracted feature data based on the time-series prediction algorithm.

8. The energy supply regulation method according to claim 7, wherein: In Step 7, Step 7.1, Use the trained prediction model to predict the future usage frequency and thermal comfort of the sunroom. Step 7.2, According to the prediction results, combined with the current energy supply situation and thermal comfort requirements, formulate an energy supply control strategy for the sunroom. For the sunroom section with a high predicted usage frequency, increase the power of heating or ventilation equipment in advance to meet the thermal comfort needs of more people; according to the predicted thermal comfort index UTCI, when it is predicted that the thermal comfort of a certain sunroom section is about to exceed the comfort range, adjust the equipment operation status in a timely manner; achieve adaptive control of the sunroom space.

9. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 8.

10. A computer-readable storage medium for storing computer instructions, characterized in that, When the computer instructions are executed by the processor, it implements the steps of the method described in any one of claims 1 to 8.

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