Temperature control method, device, apparatus and storage medium

Abstract

This application provides a temperature control method, apparatus, device, and storage medium. The temperature control method includes: acquiring real-time environmental data of multiple areas within a building, and acquiring target temperatures for temperature control devices in different areas. The real-time environmental data includes temperature data and occupant activity data. Using a pre-set artificial intelligence model, based on the real-time environmental data, the method predicts the temperature change trend of each area and identifies temperature differences between different areas. Using a pre-set strategy prediction model, based on the temperature change trend, temperature difference information, and target temperature, the method dynamically adjusts the operating parameters of the temperature control devices. This application achieves real-time monitoring, intelligent analysis, and dynamic control of building interior temperature, improving the efficiency of building interior temperature management and occupant comfort.

Description

[Technical Field]

[0001] This application relates to the field of intelligent control technology, specifically to a temperature control method, device, equipment and storage medium. [Previous Technology]

[0002] With the continuous development of urbanization and building technology, modern buildings face increasingly severe challenges in temperature management. Specifically, due to their large size, complex structure, and diverse usage requirements, temperature control in modern buildings has become an extremely challenging task. This is especially true in large commercial complexes, office buildings, and high-rise residential buildings, where indoor temperature distribution is extremely uneven due to factors such as sunlight intensity, wind direction and speed, and population density in different areas.

[0003] In related technologies, temperature control methods often rely on fixed preset rules and simple logic control, lacking comprehensive consideration of complex environmental factors and dynamic response capabilities. They are difficult to cope with the above-mentioned problem of uneven indoor temperature distribution, resulting in low temperature management efficiency and serious energy waste. This not only affects the comfort of the building's internal environment, but may also have adverse effects on the building structure and equipment lifespan. [Summary of the Invention]

[0004] In view of the above, this application provides a temperature control method, apparatus, device and storage medium to solve the technical problem that conventional temperature control methods lack comprehensive consideration and dynamic response capabilities to complex environmental factors, making it difficult to cope with uneven indoor temperature distribution, resulting in low temperature management efficiency and serious energy waste.

[0005] In a first aspect, embodiments of this application provide a temperature control method, the temperature control method comprising: acquiring real-time environmental data of multiple areas within a building, and acquiring target temperatures of temperature control devices in different areas, the real-time environmental data including temperature data and personnel activity data; using a preset artificial intelligence model, based on the real-time environmental data, predicting the temperature change trend of each area, and identifying temperature difference information between different areas; using a preset strategy prediction model, based on the temperature change trend, the temperature difference information, and the target temperature, dynamically adjusting the operating parameters of the temperature control devices.

[0006] The temperature control method of the above embodiments acquires real-time environmental data of multiple areas within a building and the target temperature of temperature control devices in different areas. The real-time environmental data includes temperature data and personnel activity data. Using a preset artificial intelligence model, based on the real-time environmental data, the method predicts the temperature change trend of each area and identifies temperature differences between different areas. Using a preset strategy prediction model, based on the temperature change trend, temperature difference information, and target temperature, the method dynamically adjusts the operating parameters of the temperature control devices. Based on this, this application achieves real-time monitoring, intelligent analysis, and dynamic control of the temperature inside a building, improving the temperature balance between different areas within the building, enhancing the efficiency of temperature management and the living comfort of users, while also reducing energy consumption and improving energy utilization.

[0007] In some embodiments of this application, the step of dynamically adjusting the operating parameters of the temperature control device based on the temperature change trend, the temperature difference information and the target temperature using a preset strategy prediction model includes: generating a control strategy based on the temperature change trend, the temperature difference information and the target temperature using a preset strategy prediction model; and dynamically adjusting the operating parameters of the temperature control device according to the control strategy.

[0008] In some embodiments of this application, the temperature control method further includes: acquiring energy consumption data of the temperature control device; and adjusting the control strategy based on the energy consumption data, the temperature change trend, and the temperature difference information.

[0009] In some embodiments of this application, the temperature control method further includes: obtaining a query instruction for a control problem of the temperature control device; and providing a control suggestion to the user for the control problem based on the query instruction and the control strategy.

[0010] In some embodiments of this application, providing the user with control suggestions for the control problem based on the query instruction and the control strategy includes: identifying key information from the query instruction; retrieving historical information related to the key information from a preset control database based on the key information; providing the user with control suggestions for the control problem based on the historical information and the control strategy, and displaying the control suggestions via a display device.

[0011] In some embodiments of this application, the temperature control method further includes: obtaining feedback information on the control strategy; and adjusting the control strategy based on the feedback information.

[0012] In some embodiments of this application, the step of using a preset artificial intelligence model to predict the temperature change trend of each region based on the real-time environmental data and to identify the temperature difference information between different regions includes: preprocessing the real-time environmental data of each region to obtain preprocessed real-time environmental data; using the artificial intelligence model to predict the temperature change trend of each region based on the preprocessed real-time environmental data and to identify the temperature difference information between different regions.

[0013] In a second aspect, embodiments of this application also provide a temperature control device, the temperature control device comprising: an acquisition module, configured to acquire real-time environmental data of multiple areas within a building, and acquire target temperatures of temperature control devices in different areas, the real-time environmental data including temperature data and personnel activity data; an analysis module, configured to use a preset artificial intelligence model to predict the temperature change trend of each area based on the real-time environmental data, and identify temperature difference information between different areas; and a control module, configured to use a preset strategy prediction model to dynamically adjust the operating parameters of the temperature control devices based on the temperature change trend, the temperature difference information, and the target temperature.

[0014] In a third aspect, embodiments of this application also provide an electronic device, the electronic device including a memory, a processor, and a temperature control program stored in the memory and executable on the processor, wherein when the temperature control program is executed by the processor, it implements the steps of the temperature control method as described in the above embodiments.

[0015] In a fourth aspect, embodiments of this application also provide a computer-readable storage medium storing a temperature control program, which, when executed by a processor, implements the steps of the temperature control method as described in the above embodiments.

[0016] Understandably, the temperature control device of the second aspect, the electronic device of the third aspect, and the readable storage medium of the fourth aspect provided above all correspond to the temperature control method of the first aspect. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding temperature control method provided above, and will not be repeated here.

Implementation Method

[0021] Embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0022] In the embodiments of this application, it should be noted that, unless otherwise expressly specified and limited, the word "for example" is used to indicate an example, illustration, or description. Any embodiment or design scheme described as "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the word "for example" is intended to present the relevant concepts in a specific manner.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection, an electrical connection, or a connection that allows for mutual communication; they may refer to a direct connection or an indirect connection via an intermediate medium; they may refer to the internal communication between two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in this application can be understood according to the specific circumstances.

[0024] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. Furthermore, in the description of this application, "a plurality of" means two or more, unless otherwise expressly and specifically limited.

[0025] To better understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0026] Conventional temperature control methods often rely on fixed preset rules and simple logic control, lacking comprehensive consideration of complex environmental factors and dynamic response capabilities. They are unable to cope with the problem of extremely uneven indoor temperature distribution, resulting in low temperature management efficiency and serious energy waste. This not only affects the comfort of the building's interior environment, but may also have adverse effects on the building structure and equipment lifespan.

[0027] In view of the above, this application provides a temperature control method, apparatus, device and storage medium to solve the above-mentioned technical problems.

[0028] Please refer to Figure 1, which is a schematic diagram of the steps of a temperature control method provided in an embodiment of this application.

[0029] The temperature control method provided in this application embodiment is applied to one or more electronic devices 10 (as shown in FIG2). The electronic device 10 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0030] In other embodiments, the electronic device 10 can be communicatively connected to devices such as desktop computers, laptops, handheld computers, and cloud servers. Users can interact with the electronic device 10 through a keyboard, mouse, remote control, touchpad, or voice control device.

[0031] Specifically, the temperature control method includes the following steps. Depending on different needs, the order of some steps in the flowchart may be changed, and some steps may be omitted.

[0032] Step S10: Obtain real-time environmental data of multiple areas within the building, and obtain the target temperature of the temperature control equipment in different areas.

[0033] In some embodiments of this application, real-time environmental data includes temperature data and personnel activity data.

[0034] Specifically, the method for acquiring temperature data may include: installing temperature sensors 1 in different areas (e.g., area 1 and area 2) within the building, and having electronic devices 10 communicate with the temperature sensors 1. The temperature sensors 1 monitor the temperature data of each area within the building in real time, thereby allowing electronic devices 10 to collect temperature data from each temperature sensor 1.

[0035] The method for obtaining personnel activity data may include: setting up image sensors 2 (such as cameras) in different areas (e.g., area 1, area 2) within the building, and the electronic device 10 communicating with the image sensors 2. The image sensors 2 collect image data of each area within the building in real time, thereby the electronic device 10 collects image data of each area within the building from each image sensor 2, and performs preprocessing operations such as image denoising, background removal, and image enhancement on the image data, and extracts personnel activity features based on deep learning models (such as convolutional neural networks, recursive neural networks, etc.) to obtain personnel activity data.

[0036] The method for obtaining the target temperature may include: the electronic device 10 may integrate a management system for temperature control equipment, such as a centralized air conditioning control system, so as to obtain the target temperature set by the user for the temperature control equipment in different areas from the management system for integrated temperature control equipment.

[0037] In the above embodiments, temperature sensors 1 and image sensors 2 are installed in different areas of the building, which can monitor temperature data and personnel activity data in different areas in real time. This provides a more comprehensive picture of the actual environmental conditions for subsequent adjustment of the operating parameters of the temperature control equipment, thereby enabling precise adjustment of the temperature control equipment according to the actual environmental conditions. For example, ventilation can be increased in densely populated areas; and the target temperature can be lowered in areas with high temperatures to maintain a comfortable indoor environment.

[0038] In other embodiments, real-time environmental data may include, but is not limited to, humidity data, energy consumption data, sound data, etc.

[0039] In some embodiments of this application, the building may include, but is not limited to, residential buildings, office buildings, shopping malls, etc., and this application does not impose any restrictions on this. Multiple areas may include, but are not limited to, areas located in different locations.

[0040] In some embodiments of this application, temperature control devices are installed in different areas of the building. These devices may be centralized temperature control systems, and may include, but are not limited to, compressors and fans.

[0041] In some embodiments of this application, after acquiring real-time environmental data of multiple areas within a building, the electronic device 10 may also store the real-time environmental data in a preset local database or a preset cloud server.

[0042] In the above embodiments, storing real-time environmental data in a local database can improve the access speed of real-time environmental data, enabling real-time responses when analyzing real-time environmental data in subsequent steps. It also reduces the risk of real-time environmental data being intercepted or leaked during transmission, helping to protect the security and privacy of real-time environmental data within the building. Even in the event of network instability or interruption, the local database can still function normally, ensuring the continuity and integrity of the real-time environmental data. Cloud servers typically have automatic backup and recovery functions, effectively preventing the loss and damage of real-time environmental data, ensuring its integrity and availability. Cloud servers allow building managers, maintenance personnel, and others from different locations to access real-time environmental data via the Internet, enabling remote monitoring and management, supporting multi-person collaboration, and improving work efficiency and the convenience of sharing real-time environmental data.

[0043] Step S11: Using a preset artificial intelligence model, based on real-time environmental data, predict the temperature change trend of each region and identify the temperature difference information between different regions.

[0044] In some embodiments of this application, before performing step S11, the electronic device 10 needs to associate the location marker of each region with the real-time environmental data of each region, so as to predict the temperature change trend of each region and the temperature difference information between different regions based on the real-time environmental data of each region.

[0045] Specifically, step S11, using a preset artificial intelligence model, predicts the temperature change trend of each region based on real-time environmental data, and identifies the temperature difference information between different regions. Specifically, this includes: preprocessing the real-time environmental data of each region to obtain preprocessed real-time environmental data; using an artificial intelligence model, based on the preprocessed real-time environmental data, predicting the temperature change trend of each region, and identifying the temperature difference information between different regions.

[0046] In some embodiments of this application, the operation of preprocessing the real-time environmental data of each region may include, but is not limited to, data cleaning, which can remove noise, missing values ​​or outliers, and ensure the accuracy of the real-time environmental data.

[0047] In some embodiments of this application, the training method for the artificial intelligence model may include: determining a suitable artificial intelligence algorithm based on the characteristics of real-time environmental data, such as a long short-term memory network, a support vector machine, or a deep learning model, for constructing the artificial intelligence model. The artificial intelligence model is trained using historical environmental data and related temperature change trends and temperature difference information as a training set, enabling the artificial intelligence model to accurately predict temperature change trends over a future period and identify temperature difference information between different regions.

[0048] In some embodiments of this application, the temperature control method may further include the following steps: collecting feedback on the actual temperature change trend, actual temperature difference information and the prediction results of the artificial intelligence model, and evaluating the accuracy of the prediction of the artificial intelligence model; continuously optimizing and adjusting the artificial intelligence model based on the feedback information to improve the prediction accuracy and robustness; and regularly updating the artificial intelligence model to adapt to new application scenarios and data characteristics.

[0049] In the above embodiments, artificial intelligence models, through complex mathematical models and machine learning techniques, can identify patterns and trends in data to predict temperature change trends and potential temperature differences over a future period, such as localized overheating or undercooling areas, abnormal temperature fluctuations, etc., which helps to formulate and adjust strategies more scientifically. Furthermore, artificial intelligence models can automatically and quickly process large amounts of real-time environmental data, greatly improving the efficiency and accuracy of data processing. At the same time, artificial intelligence models can continuously learn and optimize, constantly improving their analytical capabilities and prediction accuracy.

[0050] Step S12: Using a preset strategy prediction model, the operating parameters of the temperature control equipment are dynamically adjusted based on the temperature change trend, temperature difference information and target temperature.

[0051] In some embodiments of this application, step S12, which uses a preset strategy prediction model to dynamically adjust the operating parameters of the temperature control device based on temperature change trends, temperature difference information and target temperature, specifically includes: using a preset strategy prediction model to generate a control strategy based on temperature change trends, temperature difference information and target temperature; and dynamically adjusting the operating parameters of the temperature control device according to the control strategy.

[0052] In some embodiments of this application, the operating parameters may include, but are not limited to, cooling power, heating power, wind speed, wind direction, and air volume.

[0053] In some embodiments of this application, the control strategy may include, but is not limited to, adjusting the target temperature of the temperature control device, changing the working mode (e.g., heating, cooling, standby), adjusting the wind speed, adjusting the wind direction, adjusting the air volume, etc.

[0054] In some embodiments of this application, the training method for the policy prediction model may include: determining a suitable machine learning model, such as a decision tree, random forest, or neural network, based on temperature change trends, temperature difference information, and target temperature, to construct the policy prediction model. The pre-stored control strategies and corresponding historical temperature change trends, historical temperature difference information, and historical target temperatures are used as a training set to train the policy prediction model, enabling it to accurately generate control strategies.

[0055] In the above embodiments, by using a strategy prediction model, based on temperature change trends, temperature difference information, and target temperature, accurate prediction of the control strategy is made. This enables dynamic adjustment of the operating parameters of the temperature control equipment to ensure that the indoor temperature is always maintained within the set target range, avoiding excessive temperature fluctuations, improving the accuracy and stability of temperature control, reducing unnecessary energy consumption, and achieving the goal of energy saving and consumption reduction. Because the accuracy and stability of temperature control are improved, users can enjoy a more comfortable environment. Whether in homes, offices, or public places, a balanced and comfortable temperature can be maintained to meet people's comfort needs, providing users with a more convenient and efficient user experience.

[0056] In some embodiments of this application, the temperature control method may further include the following steps:

[0057] Step S13: Obtain a query command for the control problem of the temperature control device.

[0058] In some embodiments of this application, a chatbot 3 may be provided to users (e.g., floor managers, maintenance personnel, residents, etc.) to obtain user query commands regarding temperature control equipment. These query commands may include, but are not limited to, voice information. The chatbot 3 is communicatively connected to the electronic device 10.

[0059] In other embodiments, users can also raise questions about the adjustment of the temperature control device through various electronic devices 10 such as websites, mobile phones, and computers. This application does not limit the user's dialogue mode.

[0060] Step S14: Based on the query command and control strategy, provide the user with control suggestions for the control problem.

[0061] Specifically, step S14, based on the query command and control strategy, provides the user with control suggestions for the control problem, which specifically includes: identifying key information from the query command; retrieving historical information related to the key information from the preset control database based on the key information; providing the user with control suggestions for the control problem based on the historical information and control strategy, and displaying the control suggestions through the display device 4.

[0062] It should be noted that the steps shown in Figure 3 below describe in detail how to provide users with control suggestions for control issues based on query commands and control strategies. To avoid repetition, they will not be repeated here.

[0063] The temperature control method of this application realizes real-time monitoring, intelligent analysis and dynamic control of the temperature inside the building, improves the temperature balance between different areas inside the building, enhances the temperature management efficiency and user comfort inside the building, and also reduces energy consumption and improves energy utilization.

[0064] Please refer to Figure 3, which is a schematic diagram of the steps of a temperature control method provided in an embodiment of this application.

[0065] This embodiment is a detailed explanation of step S14 in the aforementioned embodiment, further illustrating how to provide users with control suggestions for control issues based on query commands and control strategies. Specifically, it includes the following steps:

[0066] Step S141: Identify key information from the query command.

[0067] In some embodiments of this application, key information is accurately extracted from query instructions using natural language processing technology or a preset keyword matching algorithm.

[0068] In some embodiments of this application, key information includes, but is not limited to, operating mode, runtime, and control requirements.

[0069] Step S142: Based on the key information, retrieve historical information related to the key information from the preset control database.

[0070] In some embodiments of this application, the control database stores user control information and habits for different temperature control devices under different environmental conditions. Using preset retrieval algorithms (such as index lookup, full-text search, etc.), historical information related to the current query command is quickly located.

[0071] Step S143: Based on historical information and control strategies, provide users with control suggestions for control issues and display the control suggestions on the display device.

[0072] In some embodiments of this application, the retrieved historical information and the predicted control strategy are combined to perform reasoning and calculation to generate personalized control suggestions for users.

[0073] In some embodiments of this application, the display device 4 may include, but is not limited to, a screen, a projection device, a mobile device (mobile phone, laptop computer, handheld computer), etc.

[0074] In the above embodiments, through automation and intelligence, user queries can be responded to quickly, and personalized control suggestions can be generated. This is more intuitive and convenient, helping to improve user satisfaction and experience. It also enables more precise control of the temperature control equipment, avoiding unnecessary energy waste, thereby achieving the goal of energy conservation and emission reduction. The generated control suggestions are displayed to the user through the display device 4, which is more intuitive and convenient, helping to improve user satisfaction and experience.

[0075] In some embodiments of this application, the temperature control method may further include the following steps: acquiring energy consumption data of the temperature control device; adjusting the control strategy based on the energy consumption data, temperature change trend and temperature difference information.

[0076] Specifically, the temperature control equipment may have an energy consumption monitoring function, which can record and display energy consumption data in real time.

[0077] In the above embodiments, by adjusting the control strategy, the energy consumption of the temperature control equipment can be effectively reduced, and the energy efficiency level can be improved. The optimized control strategy can better adapt to temperature changes and maintain the stability and comfort of the indoor temperature. It can also reduce unnecessary energy consumption and equipment wear and tear, and reduce maintenance costs.

[0078] In some embodiments of this application, the temperature control method may further include the following steps: obtaining feedback information on the control strategy; and adjusting the control strategy based on the feedback information.

[0079] In the above embodiments, by obtaining feedback information, shortcomings of the control strategy in practical applications can be identified in a timely manner, thereby enabling targeted adjustments. This dynamic adjustment process makes the control strategy more adaptable to changes in the actual environment and conditions, enhancing its adaptability and flexibility. By analyzing feedback information, deficiencies in resource allocation and use can be identified, thereby enabling optimization adjustments, which helps reduce resource waste, improve resource utilization efficiency, and lower maintenance costs. By obtaining user feedback information, the actual feelings and satisfaction of users with the control strategy can be understood, thereby enabling improvements and optimizations, and enhancing the overall user experience and satisfaction.

[0080] Figure 4 is a composition diagram of a temperature control device 100 provided in an embodiment of this application.

[0081] In this embodiment, based on the same concept as the temperature control method in the above embodiments, this application also provides a temperature control device 100, which can be used to perform the above temperature control method. For ease of explanation, the schematic diagram of the temperature control device 100 embodiment only shows the parts related to the embodiments of this application. Those skilled in the art will understand that the schematic structure does not constitute a limitation on the temperature control device 100, and may include more or fewer components than shown in the diagram, or combine certain components, or have different component arrangements.

[0082] Specifically, the temperature control device 100 provided in this application embodiment includes an acquisition module 110, an analysis module 120, and a control module 130.

[0083] The acquisition module 110 is used to acquire real-time environmental data of multiple areas within the building and to acquire the target temperature of temperature control equipment in different areas. The real-time environmental data includes temperature data and personnel activity data.

[0084] In some embodiments of this application, real-time environmental data includes temperature data and personnel activity data.

[0085] Specifically, the acquisition module 110 includes a collection unit 111, a transmission unit 112, and a storage unit 113.

[0086] Temperature sensors 1 are installed in different areas (e.g., area 1, area 2) within the building, and electronic devices 10 are communicatively connected to the temperature sensors 1. The temperature sensors 1 are used to monitor the temperature data of each area within the building in real time. The collection unit 111 is used to collect temperature data from each temperature sensor 1.

[0087] Image sensors 2 (such as cameras) are installed in different areas (e.g., area 1, area 2) within the building, and electronic devices 10 are communicatively connected to the image sensors 2. The image sensors 2 collect image data of each area within the building in real time. The collection unit 111 collects image data of each area within the building from each image sensor 2, performs preprocessing operations such as image denoising, background removal, and image enhancement on the image data, and extracts personnel activity features based on deep learning models (such as convolutional neural networks, recursive neural networks, etc.) to obtain personnel activity data.

[0088] Electronic device 10 can integrate a management system for temperature control equipment, such as a centralized air conditioning control system, so that the collection unit 111 can obtain the target temperature set by the user for the temperature control equipment in different areas from the integrated temperature control equipment management system.

[0089] In the above embodiments, temperature sensors 1 and image sensors 2 are installed in different areas of the building, which can monitor temperature data and personnel activity data in different areas in real time. This provides a more comprehensive picture of the actual environmental conditions for subsequent adjustment of the operating parameters of the temperature control equipment, thereby enabling precise adjustment of the temperature control equipment according to the actual environmental conditions. For example, ventilation can be increased in densely populated areas; and the target temperature can be lowered in areas with high temperatures to maintain a comfortable indoor environment.

[0090] In other embodiments, real-time environmental data may include, but is not limited to, humidity data, energy consumption data, sound data, etc.

[0091] In some embodiments of this application, the building may include, but is not limited to, residential buildings, office buildings, shopping malls, etc., and this application does not impose any restrictions on this. Multiple areas may include, but are not limited to, areas located in different locations.

[0092] In some embodiments of this application, temperature control devices are installed in different areas of the building. These devices may be centralized temperature control systems, and may include, but are not limited to, compressors and fans.

[0093] The transmission unit 112 is used to transmit real-time environmental data to the analysis module 120 or the central database via wireless communication (e.g., Bluetooth, Wi-Fi) or wired communication (e.g., fiber optic, Ethernet).

[0094] The storage unit 113 is used to store real-time environmental data to a preset local database or a preset cloud server.

[0095] In the above embodiments, storing real-time environmental data in a local database can improve the access speed of real-time environmental data, enabling real-time responses when analyzing real-time environmental data in subsequent steps. It also reduces the risk of real-time environmental data being intercepted or leaked during transmission, helping to protect the security and privacy of real-time environmental data within the building. Even in the event of network instability or interruption, the local database can still function normally, ensuring the continuity and integrity of the real-time environmental data. Cloud servers typically have automatic backup and recovery functions, effectively preventing the loss and damage of real-time environmental data, ensuring its integrity and availability. Cloud servers allow building managers, maintenance personnel, and others from different locations to access real-time environmental data via the Internet, enabling remote monitoring and management, supporting multi-person collaboration, and improving work efficiency and the convenience of sharing real-time environmental data.

[0096] The analysis module 120 is used to predict the temperature change trend of each area and identify the temperature difference information between different areas by using a preset artificial intelligence model based on real-time environmental data.

[0097] Specifically, the analysis module 120 includes a preprocessing unit 121, a model analysis unit 122, and a system feedback unit 123.

[0098] In some embodiments of this application, it is necessary to associate the location marker of each region with the real-time environmental data of each region in advance, so that the temperature change trend of each region and the temperature difference information between different regions can be predicted based on the real-time environmental data of each region.

[0099] Specifically, the preprocessing unit 121 is used to preprocess the real-time environmental data of each region to obtain preprocessed real-time environmental data; the model analysis unit 122 is used to use an artificial intelligence model to predict the temperature change trend of each region based on the preprocessed real-time environmental data, and to identify the temperature difference information between different regions.

[0100] In some embodiments of this application, the operation of preprocessing the real-time environmental data of each region may include, but is not limited to, data cleaning, which can remove noise, missing values ​​or outliers, and ensure the accuracy of the real-time environmental data.

[0101] In some embodiments of this application, the training method for the artificial intelligence model may include: determining a suitable artificial intelligence algorithm based on the characteristics of real-time environmental data, such as a long short-term memory network, a support vector machine, or a deep learning model, for constructing the artificial intelligence model. The artificial intelligence model is trained using historical environmental data and related temperature change trends and temperature difference information as a training set, enabling the artificial intelligence model to accurately predict temperature change trends over a future period and identify temperature difference information between different regions.

[0102] The system feedback unit 123 is used to collect feedback on the actual temperature change trend, actual temperature difference information and the prediction results of the artificial intelligence model, evaluate the accuracy of the artificial intelligence model prediction; continuously optimize and adjust the artificial intelligence model based on the feedback information to improve the prediction accuracy and robustness; and regularly update the artificial intelligence model to adapt to new application scenarios and data characteristics.

[0103] In the above embodiments, artificial intelligence models, through complex mathematical models and machine learning techniques, can identify patterns and trends in data to predict temperature change trends and potential temperature differences over a future period, such as localized overheating or undercooling areas, abnormal temperature fluctuations, etc., which helps to formulate and adjust strategies more scientifically. Furthermore, artificial intelligence models can automatically and quickly process large amounts of real-time environmental data, greatly improving the efficiency and accuracy of data processing. At the same time, artificial intelligence models can continuously learn and optimize, constantly improving their analytical capabilities and prediction accuracy.

[0104] The control module 130 is used to dynamically adjust the operating parameters of the temperature control device based on the temperature change trend, temperature difference information and target temperature by using a preset strategy prediction model.

[0105] Specifically, the control module 130 includes a strategy generation unit 131 and a control unit 132.

[0106] In some embodiments of this application, the strategy generation unit 131 is used to generate a control strategy based on the temperature change trend, temperature difference information and target temperature using a preset strategy prediction model; the control unit 132 is used to dynamically adjust the operating parameters of the temperature control device according to the control strategy.

[0107] In some embodiments of this application, the operating parameters may include, but are not limited to, cooling power, heating power, wind speed, wind direction, and air volume.

[0108] In some embodiments of this application, the control strategy may include, but is not limited to, adjusting the target temperature of the temperature control device, changing the working mode (e.g., heating, cooling, standby), adjusting the wind speed, adjusting the wind direction, adjusting the air volume, etc.

[0109] In some embodiments of this application, the training method for the policy prediction model may include: determining a suitable machine learning model, such as a decision tree, random forest, neural network, etc., based on temperature change trends, temperature difference information, and target temperature, to construct the policy prediction model. The pre-stored control strategies and corresponding historical temperature change trends, historical temperature difference information, and historical target temperatures are used as a training set to train the policy prediction model, enabling it to accurately generate control strategies.

[0110] In the above embodiments, by using a strategy prediction model, based on temperature change trends, temperature difference information, and target temperature, the control strategy is accurately predicted. This enables dynamic adjustment of the operating parameters of the temperature control equipment to ensure that the indoor temperature remains within the set target range, avoiding excessive temperature fluctuations. This improves the accuracy and stability of temperature control, reduces unnecessary energy consumption, and achieves the goal of energy saving and consumption reduction. Because the accuracy and stability of temperature control are improved, users can enjoy a more comfortable environment. Whether in homes, offices, or public places, a balanced and comfortable temperature can be maintained to meet people's comfort needs, providing users with a more convenient and efficient user experience.

[0111] In some embodiments of this application, the control module 130 may further include an energy-saving management unit 133. The energy-saving management unit 133 is used to acquire energy consumption data of the temperature control device; and adjust the control strategy based on the energy consumption data, temperature change trend and temperature difference information.

[0112] Specifically, the temperature control equipment may have an energy consumption monitoring function, which can record and display energy consumption data in real time.

[0113] In the above embodiments, by adjusting the control strategy, the energy consumption of the temperature control equipment can be effectively reduced, and the energy efficiency level can be improved. The optimized control strategy can better adapt to temperature changes and maintain the stability and comfort of the indoor temperature. It can also reduce unnecessary energy consumption and equipment wear and tear, and reduce maintenance costs.

[0114] In some embodiments of this application, the temperature control device 100 may further include a user interaction module 140 for obtaining query instructions for control issues of the temperature control device; and providing control suggestions to the user for control issues based on the query instructions and control strategies.

[0115] In some embodiments of this application, a chatbot 3 may be provided to users (e.g., floor managers, maintenance personnel, residents, etc.) to obtain query commands from users regarding the adjustment of temperature control equipment. The query commands may include, but are not limited to, voice information. The chatbot 3 is communicatively connected to the electronic device 10.

[0116] In other embodiments, users can also raise questions about the adjustment of temperature control equipment through various electronic devices 10 such as websites, mobile phones, and computers. This application does not limit the user's dialogue mode.

[0117] Specifically, the user interaction module 140 includes a natural language processing unit 141, a retrieval unit 142, and a display unit 143.

[0118] The natural language processing unit 141 is used to identify key information from the query command; the retrieval unit 142 is used to retrieve historical information related to the key information from the preset control database based on the key information; the display unit 143 is used to provide the user with control suggestions for control issues based on historical information and control strategies, and to display the control suggestions through the display device 4.

[0119] In some embodiments of this application, key information is accurately extracted from query instructions using natural language processing technology or a preset keyword matching algorithm.

[0120] In some embodiments of this application, key information includes, but is not limited to, operating mode, runtime, and control requirements.

[0121] In some embodiments of this application, the control database stores user control information and habits regarding different temperature control devices under different environmental conditions. Using preset retrieval algorithms (such as index lookup, full-text search, etc.), historical information related to the current query command is quickly located. In some embodiments of this application, the retrieved historical information and predicted control strategies are combined to perform reasoning and calculation, generating personalized control suggestions for the user.

[0122] In some embodiments of this application, the display device 4 may include, but is not limited to, a screen, a projector, a mobile device (mobile phone, laptop, handheld computer), etc.

[0123] In the above embodiments, by means of automation and intelligence, user query commands can be responded to quickly, and personalized control suggestions can be generated. This is more intuitive and convenient, which helps to improve user satisfaction and experience. It also enables more precise control of the operation of temperature control equipment, avoiding unnecessary energy waste, thereby achieving the goal of energy conservation and emission reduction. The generated control suggestions are displayed to the user through the display device 4, which is more intuitive and convenient, and helps to improve user satisfaction and experience.

[0124] In some embodiments of this application, the user interaction module 140 may further include a user feedback unit 144. The user feedback unit 144 is used to obtain feedback information on the control strategy; and adjust the control strategy based on the feedback information.

[0125] In the above embodiments, by obtaining feedback information, shortcomings of the control strategy in practical applications can be identified in a timely manner, thereby enabling targeted adjustments. This dynamic adjustment process makes the control strategy more adaptable to changes in the actual environment and conditions, enhancing its adaptability and flexibility. By analyzing feedback information, deficiencies in resource allocation and use can be identified, thereby enabling optimization adjustments, which helps reduce resource waste, improve resource utilization efficiency, and lower maintenance costs. By obtaining user feedback information, the actual feelings and satisfaction of users with the control strategy can be understood, thereby enabling improvements and optimizations, and enhancing the overall user experience and satisfaction.

[0126] The temperature control device 100 of this application embodiment realizes real-time monitoring, intelligent analysis and dynamic control of the temperature inside the building, improves the temperature balance between different areas inside the building, enhances the temperature management efficiency and user comfort inside the building, and also reduces energy consumption and improves energy utilization.

[0127] Figure 2 is a schematic diagram of the hardware structure of an electronic device 10 provided in an embodiment of this application.

[0128] In some embodiments of this application, the electronic device 10 includes, but is not limited to, a memory 11, a processor 12, and a computer program 13 stored in the memory 11 and executable on the processor 12, such as a temperature control program.

[0129] Those skilled in the art will understand that the schematic diagram is merely an example of the electronic device 10 and does not constitute a limitation on the electronic device 10. It may include more or fewer components than those shown in the diagram, or combine certain components, or different components. For example, the electronic device 10 may also include input / output devices, network access devices, buses, etc.

[0130] The processor 12 acquires the operating system of the electronic device 10 and various installed applications. The processor 12 acquires the applications to implement the steps in the above-described temperature control method embodiments, such as the steps shown in Figures 1 and 3.

[0131] The exemplary computer program 13 may be divided into one or more modules / units, one or more of which are stored in memory 11 and retrieved by processor 12 to complete this application. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the retrieval process of computer program 13 in electronic device 10.

[0132] In some embodiments of this application, the electronic device 10 includes a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. The hardware of the electronic device 10 includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0133] In some embodiments of this application, the network where the electronic device 10 is located includes, but is not limited to: the Internet, wide area network, urban area network, local area network, virtual private network (VPN), etc.

[0134] In some embodiments of this application, memory 11 is used to store program code and various data, such as temperature control device 100 installed in electronic device 10, and to achieve high-speed and automatic access to programs or data during the operation of electronic device 10. Memory 11 may include read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc memory, magnetic disk memory, magnetic tape memory, or any other computer-readable medium capable of carrying or storing data.

[0135] In some embodiments of this application, memory 11 may also be external memory and / or internal memory of electronic device 10. Furthermore, memory 11 may be memory in physical form, such as memory stick, TF card (Trans-flash Card), etc.

[0136] In some embodiments of this application, the processor 12 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The processor 12 is the computing core and control center of the electronic device 10, connecting various parts of the entire electronic device 10 through various interfaces and lines, and calling data stored in the memory 11 to perform various functions of the electronic device 10 and process data, such as performing temperature control functions.

[0137] In some embodiments of this application, the processor 12 is used to obtain the operating system of the electronic device 10 and various installed applications. For example, the processor 12 obtains a temperature control program to implement the temperature control method of the above embodiments, such as the steps shown in FIG1 and FIG3.

[0138] In one embodiment of this application, the electronic device 10 may further include a power supply (not shown) for supplying power to various components. Preferably, the power supply may be logically connected to the processor 12 via a power management device, thereby enabling functions such as charging, discharging, and power consumption management through the power management device. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 10 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0139] In one embodiment of this application, if the module / sub-module integrated by the electronic device 10 is implemented as a software functional sub-module and sold or used as an independent workpiece, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can also be implemented by the computer program 13 instructing the relevant hardware. The computer program 13 can be stored in a computer-readable storage medium. When the computer program 13 is acquired by the processor 12, it can implement the steps of the various method embodiments shown in FIG1 above.

[0140] In one embodiment of this application, the computer program 13 may include computer program code, which may be source code, objective code, etc. The computer-readable medium may include any entity or device capable of carrying computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, or read-only memory (ROM). The memory 11 in the electronic device 10 stores multiple instructions to implement a temperature control method, and the processor 12 can acquire multiple instructions to implement the temperature control method of the above embodiment.

[0141] Specifically, the specific implementation method of the processor 12 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiments of Figures 1 and 3, which will not be repeated here.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed methods and apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of modules is merely a logical functional division, and there may be other division methods in actual implementation.

[0143] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] The functional modules in the various embodiments of this application can be integrated into a processing sub-module, or each sub-module can exist physically separately, or two or more sub-modules can be integrated into a sub-module. The integrated sub-module can be implemented in hardware form or in the form of hardware plus software functional modules.

[0145] Therefore, the embodiments should be considered exemplary and non-limiting in all respects. The scope of this application is defined by the appended claims rather than the foregoing description, and thus all variations falling within the meaning and scope of equivalent requirements of the claims are intended to be included within this application. Any reference numerals in the claims should not be construed as limiting the scope of the claims.

[0146] Furthermore, it is clear that the word "comprising" does not exclude other sub-modules or steps, and the singular does not exclude the plural. The multiple sub-modules or devices described in this application may also be implemented by a single sub-module or device through software or hardware.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit it. Although this application has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of this application without departing from the spirit and scope of the technical solutions of this application. [Simplified Explanation of the Diagram]

[0017] Figure 1 is a schematic diagram of the steps of a temperature control method provided in an embodiment of this application.

[0018] Figure 2 is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application.

[0019] Figure 3 is a schematic diagram of the steps of a temperature control method provided in another embodiment of this application.

[0020] Figure 4 is a schematic diagram of the functional module composition of a temperature control device provided in an embodiment of this application.

Claims

1. A temperature control method, wherein, The temperature control method includes: acquiring real-time environmental data for multiple areas within a building, and acquiring target temperatures set by users for temperature control devices in different areas, wherein the real-time environmental data includes temperature data and personnel activity data; using a preset artificial intelligence model, based on the real-time environmental data, predicting the temperature change trend of each area and identifying temperature difference information between different areas; using a preset strategy prediction model, based on the temperature change trend, the temperature difference information, and the target temperature, dynamically adjusting the operating parameters of the temperature control device.

2. The temperature control method as described in claim 1, wherein, The step of dynamically adjusting the operating parameters of the temperature control device based on the temperature change trend, the temperature difference information, and the target temperature using a preset strategy prediction model includes: generating a control strategy based on the temperature change trend, the temperature difference information, and the target temperature using a preset strategy prediction model; and dynamically adjusting the operating parameters of the temperature control device according to the control strategy.

3. The temperature control method as described in claim 2, wherein, The temperature control method further includes: acquiring energy consumption data of the temperature control device; and adjusting the control strategy based on the energy consumption data, the temperature change trend, and the temperature difference information.

4. The temperature control method as described in claim 2, wherein, The temperature control method further includes: obtaining a query command for a control problem of the temperature control device; and providing the user with control suggestions for the control problem based on the query command and the control strategy.

5. The temperature control method as described in claim 4, wherein, The step of providing the user with control suggestions for the control problem based on the query command and the control strategy includes: identifying key information from the query command; retrieving historical information related to the key information from a preset control database based on the key information; providing the user with control suggestions for the control problem based on the historical information and the control strategy, and displaying the control suggestions via a display device.

6. The temperature control method as described in claim 2, wherein, The temperature control method further includes: obtaining feedback information regarding the control strategy; and adjusting the control strategy based on the feedback information.

7. The temperature control method as described in claim 1, wherein, The method of using a preset artificial intelligence model to predict the temperature change trend of each region based on the real-time environmental data and to identify the temperature difference information between different regions includes: preprocessing the real-time environmental data of each region to obtain preprocessed real-time environmental data; and using the artificial intelligence model to predict the temperature change trend of each region based on the preprocessed real-time environmental data and to identify the temperature difference information between different regions.

8. A temperature control device, wherein, The temperature control device includes: an acquisition module for acquiring real-time environmental data of multiple areas within a building, and acquiring target temperatures set by the user for temperature control devices in different areas, wherein the real-time environmental data includes temperature data and personnel activity data; an analysis module for using a preset artificial intelligence model to predict the temperature change trend of each area and temperature difference information between different areas based on the real-time environmental data; and a control module for using a preset strategy prediction model to dynamically adjust the operating parameters of the temperature control device based on the temperature change trend, the temperature difference information, and the target temperature.

9. An electronic device, wherein, The electronic device includes a memory, a processor, and instruction code stored in the memory and executable on the processor, wherein the instruction code, when executed by the processor, implements the temperature control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, wherein, The computer-readable storage medium stores instruction code that, when executed by a processor, implements the temperature control method as described in any one of claims 1 to 7.

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