Air conditioner control method and device and electronic equipment

Abstract

The application provides an air conditioner control method and device and electronic equipment, and belongs to the technical field of smart home. The application obtains environment parameters in response to a first voice of a user. First control parameters of the air conditioner are generated based on the first voice, the environment parameters, current operation parameters of the air conditioner, air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information. The air conditioner is controlled to operate according to the first control parameters. The method can generate suitable air conditioner mode parameters for the user and execute them through the voice of the user in combination with current indoor and outdoor temperature and humidity and other environment information, realizes intelligent air conditioner adjustment based on user demand, and improves the convenience and comfort of air conditioner use.

Description

Technical Field

[0001] This application relates to the field of smart home technology. More specifically, it relates to an air conditioning control method, apparatus, and electronic device. Background Technology

[0002] In recent years, with the rapid development of artificial intelligence and Internet of Things technologies, smart air conditioners have gradually emerged and become popular products in the market. These smart air conditioners integrate voice recognition technology, allowing users to control the air conditioner's on / off state and adjust the temperature through simple voice commands, greatly improving the convenience of operation.

[0003] However, although existing smart air conditioners have made some progress in voice recognition, their functions are still relatively limited, mainly limited to the recognition and execution of simple voice commands.

[0004] These smart air conditioners often can only recognize and execute direct commands given by users, but cannot accurately understand users' spoken commands, resulting in a poor user experience. Summary of the Invention

[0005] This application provides an air conditioning control method, device, and electronic device that can accurately respond to the user's voice requests, ensure that the obtained control parameters match the user's expectations, and significantly improve the user's overall experience.

[0006] In a first aspect, embodiments of this application provide an air conditioning control method, including:

[0007] Responding to the user's first voice command, obtain environmental parameters;

[0008] Based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information, the first control parameters of the air conditioner are generated.

[0009] The air conditioner is operated according to the first control parameter.

[0010] In the above technical solution, by receiving the user's voice and combining environmental and air conditioning parameters, appropriate air conditioning mode parameters are generated and executed for the user, realizing intelligent adjustment of the air conditioner according to the user's spoken instructions and individual needs, which significantly improves the convenience and comfort of air conditioning use.

[0011] In some embodiments, generating the first control parameters of the air conditioner based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information includes:

[0012] Recognize the user's first voice and obtain the first text content corresponding to the first voice;

[0013] Based on the first text content, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information, the target prompt words are obtained;

[0014] The target prompt words are processed by a large model to generate the first control parameters.

[0015] In the above technical solution, the corresponding text content is obtained by recognizing the user's first voice, and the target prompt word is generated by combining environmental parameters, current air conditioner operating parameters, air conditioner model parameters, air conditioner parameter adjustment rules and user preference information. Then, the target prompt word is processed by a large model to finally generate the first control parameters of the air conditioner. This realizes intelligent and precise control of the air conditioner based on the user's voice command and multi-dimensional parameters, and improves the personalization and intelligence level of air conditioner control.

[0016] In some embodiments, obtaining the target prompt word based on the first text content, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information includes:

[0017] Based on the first text content, the environmental parameters, and the current operating parameters, retrieve search information related to the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information from the plug-in knowledge base;

[0018] Based on the first text content, the environmental parameters, and the search information, target prompt words are obtained.

[0019] In the above technical solution, by obtaining information related to the user's current intent from the external knowledge base and generating target prompt words in combination with environmental parameters, a more targeted and accurate input basis is provided for the subsequent generation of the first control parameters of the air conditioner through a large model, which effectively improves the intelligence and personalization of the generation of air conditioner control parameters.

[0020] In some embodiments, the step of processing the target prompt word using a large model to generate the first control parameter includes:

[0021] The large model is used to process the first prompt word in the target prompt words to obtain the baseline control parameters. The first prompt word is obtained based on the environmental parameters and the current operating parameters.

[0022] Based on the second prompt word in the target prompt word, the baseline control parameters are adjusted to obtain personalized control parameters; the second prompt word is obtained based on the first text content and the user preference information.

[0023] Based on the third prompt word in the target prompt word, the personalized control parameters are mutually exclusive to obtain the first control parameter, so that the first control parameter does not include parameters that make the air conditioning functions mutually exclusive; the third prompt word is obtained based on the air conditioning parameter adjustment rules and the air conditioning model parameters.

[0024] In the above technical solution, the first prompt word based on environmental parameters and air conditioning operating parameters is processed by a large model to obtain the baseline control parameters. Then, the baseline parameters are adjusted in a personalized manner based on the second prompt word that integrates user voice commands and preference information to form personalized control parameters. Finally, the third prompt word generated based on air conditioning parameter adjustment rules and model parameters is used to perform mutual exclusion verification and eliminate parameters that may cause functional conflicts. In the end, accurate, safe and user-friendly first control parameters are generated, which significantly improves the intelligence, personalization and reliability of air conditioning control.

[0025] In some embodiments, the environmental parameters include the temperature, humidity, and current climate of the user's current environment, and the user's first voice includes the user's activity information.

[0026] In the above technical solution, by taking environmental factors into account, the system can accurately adjust the operation of the air conditioner according to the actual external conditions. By incorporating the user's activity information into the air conditioner adjustment considerations, the air conditioner can better match the user's feelings under different behaviors, providing the user with a more personalized and comfortable air environment.

[0027] In some embodiments, the method further includes:

[0028] During the operation of the air conditioner according to the first control parameters, in response to the user's second voice, the first control parameters are adjusted based on the second voice, the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information, to generate the second control parameters of the air conditioner.

[0029] The air conditioner is controlled to operate according to the second control parameter.

[0030] In the above technical solution, by monitoring the user's voice in real time, and upon receiving a second voice command from the user, the air conditioner control parameters are adjusted in real time according to the second voice command, ensuring that the air conditioner control mode can accurately meet the user's personalized needs in real time. This improvement not only significantly enhances the comfort and satisfaction of the user experience, but also realizes a deep understanding and efficient response of the smart home system to user needs.

[0031] In some embodiments, adjusting the first control parameters based on the second voice, the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information to generate the second control parameters of the air conditioner includes:

[0032] Recognize the user's second voice and obtain the second text content corresponding to the second voice;

[0033] Based on the first text content and the second text content, determine the user's intent information;

[0034] Based on the intent information, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information, obtain new prompt words;

[0035] The second control parameter is generated by adjusting the first control parameter based on the new prompt word using a large model.

[0036] In the above technical solution, the user's second voice is analyzed to obtain new intent information. Simultaneously, current environmental and air conditioning parameters are collected again. The first control parameter is adjusted based on the new intent information and these parameters to generate a second control parameter. This second control parameter more accurately meets the user's real-time needs, improving user comfort.

[0037] In some embodiments, the method further includes:

[0038] Obtain user feedback information;

[0039] Based on the feedback information, the air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information stored in the plug-in knowledge base are updated.

[0040] In the above technical solution, by analyzing user feedback, new user needs, new habits, or dissatisfaction with existing services are identified. Then, the relevant parameters and rules in the plug-in knowledge base are updated to ensure that the system can respond to user needs more accurately and provide more considerate and personalized air conditioning control services.

[0041] Secondly, this application provides an air conditioning control device, comprising:

[0042] The acquisition module is used to acquire environmental parameters in response to the user's first voice command;

[0043] The processing module is used to generate the first control parameters of the air conditioner based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information.

[0044] The control module is used to control the operation of the air conditioner according to the first control parameters.

[0045] The air conditioning control method, device, and electronic equipment provided in this application collect ambient environmental data in real time after receiving voice commands from the user. By comprehensively analyzing the user's voice content, ambient temperature and humidity, real-time air conditioning operating status, equipment model, air conditioning parameter adjustment rules, and the user's personalized habits, a personalized air conditioning operation plan is intelligently generated. The system automatically executes air conditioning adjustments according to this plan, achieving an intelligent control experience centered on natural language interaction. This method, by accurately interpreting user needs and integrating multi-dimensional environmental parameters and equipment characteristics, dynamically generates control strategies, effectively improving the intelligence level of air conditioning operation and the accuracy of matching environmental comfort. Attached Figure Description

[0046] To more clearly illustrate the implementation methods in the embodiments of this application or related technologies, the accompanying drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0047] Figure 1 A schematic diagram of a scenario for an air conditioning control method provided in this application;

[0048] Figure 2 A flowchart illustrating an air conditioning control method provided in this application. Figure 1 ;

[0049] Figure 3 A flowchart illustrating an air conditioning control method provided in this application. Figure 2 ;

[0050] Figure 4 A flowchart illustrating an air conditioning control method provided in this application. Figure 3 ;

[0051] Figure 5 A flowchart illustrating an air conditioning control method provided in this application. Figure 4 ;

[0052] Figure 6 A flowchart illustrating an air conditioning control method provided in this application. Figure 5 ;

[0053] Figure 7 A flowchart illustrating an air conditioning control method provided in this application. Figure 6 ;

[0054] Figure 8 A schematic diagram illustrating the process of generating parameters for the large model provided in this application;

[0055] Figure 9 This is a schematic diagram of the structure of an air conditioning control device 60 provided in this application;

[0056] Figure 10 This is a structural schematic diagram of an air conditioning control device 70 provided in this application. Detailed Implementation

[0057] To make the objectives, implementation methods and advantages of this application clearer, the exemplary implementation methods of this application will be clearly and completely described below with reference to the accompanying drawings of the exemplary embodiments of this application. Obviously, the described exemplary embodiments are only some embodiments of this application, and not all embodiments.

[0058] It should be noted that the brief descriptions of terms in this application are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this application. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0059] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclusively include, for example, a product or device that includes a series of components is not necessarily limited to those that are explicitly listed, but may include other components that are not explicitly listed or that are inherent to such product or device.

[0060] With advancements in technology and improvements in living standards, people's demands for comfort in their home environments are constantly increasing. As a key device for regulating indoor temperature and humidity, the intelligentization of air conditioning control systems has become an important direction for enhancing user experience. Traditional air conditioning control systems mainly rely on users manually selecting fixed modes (such as cooling, heating, dehumidifying, and ventilation) and adjusting relevant parameters. While this method meets basic temperature control needs to a certain extent, it is significantly lacking in terms of ease of operation and personalized services.

[0061] In recent years, with the rapid development of artificial intelligence and the Internet of Things (IoT) technologies, smart air conditioners have gradually emerged and become popular products in the market. These smart air conditioners integrate voice recognition technology, allowing users to control the air conditioner's on / off state and adjust the temperature using simple voice commands, greatly improving ease of operation. However, although existing smart air conditioners have made some progress in voice recognition, their functions are still relatively limited, mainly focusing on recognizing and executing simple voice commands.

[0062] While existing smart air conditioners have made breakthroughs in voice recognition, they still have significant shortcomings in deeply understanding user intent and comprehensively considering environmental factors. Specifically, these smart air conditioners often can only recognize and execute direct commands given by the user, but cannot accurately understand more colloquial voice commands. When the user's voice commands are unclear, they may fail to be recognized, or the control of the air conditioner may not meet the user's needs, resulting in a poor user experience.

[0063] To address the aforementioned issues, this application proposes an air conditioning control method. By capturing users' spoken commands and deeply analyzing their perceived temperature preferences expressed in conversation, the method integrates indoor and outdoor environmental parameters such as temperature and humidity, as well as the user's past adjustment habits. It automatically generates and executes the most suitable air conditioning operating parameters, thus enabling precise air conditioning control with just a single sentence. Simultaneously, it ensures that the adjusted air conditioning parameters accurately match the user's personalized needs, thereby enhancing the user experience.

[0064] Figure 1 This is a schematic diagram of a scenario for an air conditioning control method provided in this application, such as... Figure 1 As shown, users can directly adjust the air conditioner using spoken commands. The system can combine the air conditioner's version parameters, user needs, environmental information, and other factors to generate and execute operating parameters that meet the user's personalized needs.

[0065] The technical solutions of this application will be described in detail below with reference to specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0066] Figure 2 A flowchart illustrating an air conditioning control method provided in this application. Figure 1 ,like Figure 2 As shown, the method includes:

[0067] S101: Responds to the user's first voice command and obtains environmental parameters.

[0068] Understandably, upon receiving a user's voice command, the system will activate a series of sensors or connect to external data sources to collect relevant parameters of the current environment. These environmental parameters may include, but are not limited to, indoor temperature, humidity, air quality (such as PM2.5 concentration), light intensity, and potential external weather conditions (such as weather forecast information obtained via the internet). By acquiring these environmental parameters, the system can gain a more comprehensive understanding of the user's actual environmental conditions, providing fundamental data support for subsequently generating accurate control parameters.

[0069] S102: Generate the first control parameters of the air conditioner based on the first voice, environmental parameters, current operating parameters of the air conditioner, air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information.

[0070] Understandably, at this stage, the system intelligently generates air conditioner control parameters by comprehensively utilizing multiple information sources. First, it considers the user's voice commands, which directly reflect the user's immediate needs or expectations. Second, combining previously acquired environmental parameters, the system can assess whether the current environment is comfortable and adjust the control strategy accordingly. Simultaneously, the air conditioner's current operating parameters (such as fan speed, temperature setting, and mode) are also important references, reflecting the air conditioner's current working status. The air conditioner model parameters determine its performance characteristics and adjustable range; the air conditioner parameter adjustment rules are a series of preset logics or algorithms that determine whether the air conditioner parameters are reasonable and feasible. Finally, user preference information (such as preferred temperature range, fan speed level, and energy-saving mode) is also taken into consideration to ensure that the generated control parameters not only meet the current environmental requirements but also satisfy the user's personalized preferences.

[0071] S103: Control the operation of the air conditioner according to the first control parameter.

[0072] Understandably, after generating precise control parameters, the system sends these parameters to the air conditioner's control unit to achieve precise control over the air conditioner's operation. This includes adjusting the air conditioner's temperature setting, fan speed, airflow direction, mode (such as cooling, heating, dehumidification, and ventilation), and any energy-saving settings. By adjusting these parameters in real time, the system can ensure that the air conditioner provides a comfortable environment while also maximizing energy efficiency and minimizing energy waste.

[0073] The air conditioning control method provided in this embodiment acquires environmental parameters in response to a user's first voice command. Based on the first voice command, environmental parameters, current air conditioning operating parameters, air conditioning model parameters, air conditioning parameter adjustment rules, and user preference information, it generates first control parameters for the air conditioning. The air conditioning is then controlled according to these first control parameters. This method can understand a user's perception of hot or cold temperature through a single sentence and, combined with current indoor and outdoor temperature and humidity information, generates and executes suitable air conditioning mode parameters for the user, achieving intelligent air conditioning adjustment based on user needs and significantly improving the convenience and comfort of air conditioning use.

[0074] Figure 3 A flowchart illustrating an air conditioning control method provided in this application. Figure 2 ,like Figure 3 As shown, in this embodiment... Figure 2 Based on the embodiments, the process of generating the first control parameter is described in detail, and the method includes:

[0075] S201: Recognize the user's first speech and obtain the first text content corresponding to the first speech.

[0076] One method for recognizing user speech is through Automatic Speech Recognition (ASR), a technology that converts human speech into text. ASR utilizes computer algorithms and models to process and analyze the input speech signal, transforming it into a corresponding written representation.

[0077] Understandably, the system uses advanced speech recognition technology to accurately capture the user's initial speech signal. The speech recognition system meticulously analyzes various elements in the speech, such as pronunciation, intonation, and speech rate. Then, using a pre-trained large language model, it converts the speech signal into text that the computer can understand and process. This yields the initial text content that precisely corresponds to the initial speech and meets the air conditioning control requirements, providing foundational data for subsequent processing and analysis.

[0078] S202: Based on the first text content, environmental parameters, and current operating parameters, retrieve search information related to air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information from the plug-in knowledge base.

[0079] Understandably, the system first conducts a thorough analysis of the initial text content to understand the user's intent and needs. Simultaneously, it considers environmental parameters such as indoor and outdoor temperature, humidity, and air quality, as well as the air conditioner's current operating parameters, including cooling / heating mode, fan speed, and temperature setting. Based on this information, the system performs targeted searches within the external knowledge base. This knowledge base stores a large amount of air conditioner-related data, from which the system extracts search information closely related to air conditioner model parameters (characteristics and performance of the air conditioner model corresponding to the user's room), air conditioner parameter adjustment rules (how to reasonably adjust air conditioner parameters according to various situations), and user preference information (the user's past air conditioner usage habits and preferred settings), providing a basis for subsequent decision-making.

[0080] S203: Based on the first text content, environmental parameters, and search information, obtain target prompt words.

[0081] Understandably, the system comprehensively considers the user needs reflected in the initial text content, the actual situation presented by the environmental parameters, and relevant information retrieved from the plug-in knowledge base. Through in-depth analysis and logical reasoning of this information, the system extracts key elements and transforms them into clearly directional target prompts. These target prompts accurately summarize the user's needs, environmental conditions, and corresponding processing directions, providing clear and accurate guidance for subsequent generation of control parameters using a large model.

[0082] S204: Process the target prompt words using a large model to generate the first control parameters.

[0083] Understandably, the system utilizes a powerful large-scale model to process the target prompts. This large-scale model possesses strong language understanding and generation capabilities, enabling it to deeply interpret the target prompts and understand their implied semantics and intent. Based on this understanding, the large-scale model employs its internally learned knowledge and patterns to perform complex calculations and reasoning. Ultimately, based on the information conveyed by the target prompts, it generates primary control parameters related to air conditioning control. These parameters can be directly used to adjust the air conditioning operation to meet user needs and adapt to environmental conditions.

[0084] The air conditioning control method provided in this embodiment intelligently responds to user voice commands, not only acquiring environmental parameters in real time but also deeply analyzing the voice content and converting it into text. It then retrieves key information related to the air conditioner model, adjustment rules, and user preferences from an external knowledge base. Based on environmental and air conditioning information, a large-scale model is used to generate baseline control parameters. These parameters are then personalized according to user preferences, and mutual exclusion checks ensure the validity of the control parameters, avoiding functional conflicts and generating accurate primary control parameters. This method integrates multiple technologies, including speech recognition, environmental perception, knowledge retrieval, and large-scale model processing, to accurately capture user needs and intelligently optimize air conditioning control, significantly improving the user experience.

[0085] In some embodiments, Figure 4 A flowchart illustrating an air conditioning control method provided in this application. Figure 3 ,like Figure 4 As shown, the processing steps for the large model to generate the first control parameter based on the target prompt word include:

[0086] S301: Using a large model, the first prompt word in the target prompt words is processed to obtain the baseline control parameters.

[0087] The first prompt is based on environmental parameters and current operating parameters. For example, is it winter or summer, and should the air conditioner be turned on in cooling or heating mode? What is the current fan speed of the air conditioner, and does the fan speed need to be adjusted based on the environmental information?

[0088] Understandably, the large-scale model, with its powerful data processing and pattern recognition capabilities, deeply analyzes the initial prompt. Based on massive amounts of pre-learned data and algorithmic logic, it comprehensively considers the complex relationship between environmental factors and the current operating state of the air conditioner. Through a series of complex calculations and inferences, it generates baseline control parameters. These baseline control parameters are fundamental control parameters determined based on the current environment and the air conditioner's operating state, without considering personalized user needs or mutually exclusive air conditioner functions. They provide a basic framework for further adjustments.

[0089] S302: Adjust the baseline control parameters according to the second prompt word in the target prompt words to obtain personalized control parameters.

[0090] The second prompt is derived from the first text content and user preference information. For example, the first text content indicates the user's current feeling, whether it's too cold or too hot, the user's preferred temperature setting, wind speed preference, and specific air quality needs.

[0091] Understandably, the system will adjust the baseline control parameters based on this information. For example, if the user's initial text indicates they have just finished exercising, it's inferred that the user needs a lower temperature to cool down, and the system will accordingly lower the temperature setting based on the baseline control parameters. If the user prefers a lower temperature, the system will appropriately lower the temperature setpoint based on the baseline control parameters; if the user wants a gentler fan speed, the system will adjust the fan speed parameter accordingly. Through these adjustments, the control parameters are made more aligned with the user's personal preferences and usage habits, ultimately resulting in personalized control parameters that improve user satisfaction and comfort with the air conditioning control.

[0092] S303: Based on the third prompt word in the target prompt word, perform mutual exclusion verification on the personalized control parameters to obtain the first control parameter, so that the first control parameter does not include parameters that make the air conditioning function mutually exclusive.

[0093] The third prompt is derived from the air conditioner parameter adjustment rules and air conditioner model parameters, which includes the constraints and limitations between various air conditioner functions.

[0094] Understandably, after generating personalized control parameters, these parameters need to be verified to ensure they do not cause mutually exclusive functions in the air conditioning system. For example, some air conditioning models may not be able to simultaneously perform certain combinations of functions in specific modes. The system will carefully verify the personalized control parameters according to the rules specified in the third prompt. It will check for conflicts between parameters, and if it finds any parameter combinations that may cause functional conflicts, it will adjust or remove them appropriately. After such mutual exclusion verification, the final first control parameters ensure that all functions of the air conditioning system operate normally, avoiding functional conflicts.

[0095] In some embodiments, environmental parameters include the temperature, humidity, and current climate of the user's current environment, and the user's first voice includes the user's activity information.

[0096] Understandably, environmental parameters encompass the user's current environment's temperature, humidity, and climate conditions—information crucial for controlling the air conditioner. Considering environmental factors allows for precise adjustments to the air conditioner's operation based on actual external conditions. For example, in hot and humid climates, appropriately lowering the temperature and enhancing dehumidification can improve comfort. Furthermore, the user's initial voice input contains activity information, such as being about to sleep, reading, or exercising. Different activities lead to different perceptions of temperature and fan speed. When about to sleep, a quieter, more comfortable environment may be desired; while reading, good air circulation and a stable temperature are preferred; and during exercise, a slightly higher fan speed may be needed for better cooling. Incorporating this activity information into the air conditioner's adjustments allows it to better match the user's feelings under different behaviors, providing a more personalized and comfortable air environment.

[0097] Figure 5 A flowchart illustrating an air conditioning control method provided in this application. Figure 4 ,like Figure 5 As shown, in this embodiment... Figure 2 Based on the embodiments, a method for multi-round voice control of air conditioning is described in detail, which includes:

[0098] S401: Responding to the user's second voice command while the air conditioner is operating according to the first control parameters.

[0099] Understandably, while the air conditioner is running stably according to the previously generated first control parameters, the system constantly monitors the user's voice input. Once a second voice command is detected, the system responds quickly, preparing to receive and process this new voice information. This demonstrates the system's real-time interactive capabilities, allowing it to adjust according to the user's dynamic needs, ensuring the air conditioner's operation always aligns with the user's expectations and providing a more flexible and convenient user experience.

[0100] S402: Recognize the user's second speech and obtain the second text content corresponding to the second speech.

[0101] Understandably, after obtaining the second speech, the system will continue to recognize the second speech to obtain the second text content, which is the same as the processing method of the first speech in step S201, and will not be repeated here.

[0102] S403: Determine the user's intent information based on the first text content and the second text content.

[0103] Understandably, by comprehensively analyzing the first and second text contents and employing techniques such as comparison, correlation, and semantic understanding, the system can gain a deeper understanding of the user's true intent. For example, the first text content might indicate that the user wants the air conditioner to reach a certain basic state under specific conditions, while the second text content might reflect the user's need for further adjustments to the air conditioner's state during the current operation. By comprehensively analyzing these two parts, the system can accurately determine the user's current intent information and provide new adjustment directions for the air conditioner control. For instance, the user's first text content might indicate that they have just finished exercising and need to cool down, so the corresponding first control parameter, the fan speed, is relatively high. However, the second text content might indicate that they have rested for a while and no longer need to cool down, but need to adjust to a suitable temperature; that is, the user's intent information has changed.

[0104] S404: Obtain new prompt words based on intent information, environmental parameters, current operating parameters, air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information.

[0105] Understandably, upon receiving new intent information from the user, the system will perform a series of operations to generate more accurate prompts. Specifically, the system will again collect the following key information: current environmental parameters, air conditioner operating status parameters, air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information.

[0106] Reacquiring new environmental parameters is crucial, as weather conditions can change at any time, directly affecting the appropriate operating mode of the air conditioner. Simultaneously, the system will reconfirm information such as the air conditioner model and current operating parameters, taking into account potential changes in the user's room. If the user's room changes, the air conditioning equipment requiring control and adjustment may also change accordingly. By recollecting this information, the system can more accurately understand user needs and generate appropriate prompts, thus providing more effective air conditioning control suggestions. The steps for obtaining prompts are similar to step S203 and will not be repeated here.

[0107] S405: The first control parameter is adjusted based on the new prompt word using a large model to generate the second control parameter.

[0108] Understandably, adjusting the first control parameter through a large model is similar to step S204, and will not be repeated here.

[0109] The air conditioning control method provided in this embodiment intelligently responds to the user's second voice command. First, it performs high-precision analysis and recognition of the collected second voice signal. Based on the user's further specific needs, it dynamically and flexibly adjusts the air conditioning's operating mode, including parameters such as temperature, fan speed, and humidity, ensuring that the air conditioning control mode can accurately meet the user's personalized needs in real time. This improvement not only significantly enhances the comfort and satisfaction of the user experience but also enables the smart home system to deeply understand and efficiently respond to user needs.

[0110] In some embodiments, Figure 6 A flowchart illustrating an air conditioning control method provided in this application. Figure 5 ,like Figure 6 As shown, the steps for updating the knowledge base include:

[0111] S501: Obtain user feedback information.

[0112] Understandably, user feedback is crucial for system optimization and user experience. This feedback can be obtained through various channels, such as voice commands and the air conditioner's corresponding app. While using the air conditioner, users may use voice commands to express satisfaction or dissatisfaction, adjust temperature, fan speed, or mode based on their comfort level, environmental changes, or specific needs. Furthermore, the air conditioner's app is also an important platform for collecting user feedback. Users can view the air conditioner's operating status and history, and submit user experiences, fault reports, or improvement suggestions through the built-in feedback function. This feedback includes not only direct evaluations of the air conditioner's current operating status but also indirect feedback on its functions, performance, and interface design, providing valuable data support for subsequent system optimization.

[0113] S502: Based on the feedback information, update the air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information stored in the plug-in knowledge base.

[0114] Understandably, updating the air conditioner model parameters, parameter adjustment rules, and user preference information stored in the plug-in knowledge base based on feedback is a crucial step in achieving intelligent and personalized air conditioning services. The plug-in knowledge base, as the cornerstone of system decision-making and response, stores air conditioner model parameters that determine the system's control capabilities for different models. User preference information records users' personalized habits during air conditioner use, such as preferred temperature range, fan speed, and scheduled on / off times. When the system receives user feedback, it intelligently analyzes this information, identifies new user needs, new habits, or dissatisfaction with existing services, and then updates the relevant parameters and rules in the plug-in knowledge base to ensure the system can more accurately respond to user needs and provide more considerate and personalized air conditioning services.

[0115] For example, the following section provides a distance-based explanation of the air conditioning control method. Figure 7 A flowchart illustrating an air conditioning control method provided in this application. Figure 6 ,like Figure 7 As shown, the implementation steps of the method include:

[0116] 1. Speech understanding and intent recognition:

[0117] Users interact with the system via voice commands. Voice input is first converted into text data by the Automatic Speech Recognition (ASR) module. The ASR system is implemented using a mature third-party solution, which will not be described in detail here.

[0118] After text conversion, the system uses a large language model to perform semantic analysis (NLU) on the user's statement to determine whether the user's input involves an intention to adjust the air conditioning. If the statement is unrelated to air conditioning adjustment, it is considered a casual conversation, and the system will use the large model's chat capabilities to engage in dialogue with the user. If the statement is identified as having an intention related to air conditioning adjustment, the system will extract key information from the statement, such as the user's temperature perception, current activity, timer or reservation settings, and the user's age.

[0119] For example: A user says, "I just went out to play ball, I'll adjust the air conditioning." The system can infer from the user's ball-playing behavior that the user might feel hot. A user says, "I'm going to sleep, I'll turn on the air conditioning at 10 PM and turn it off at 2 AM." The system can recognize the user's sleep intentions and the specific times to schedule the air conditioning on and off.

[0120] 2. Air Conditioner Mode Recognition Module:

[0121] Figure 8 A schematic diagram illustrating the process of generating parameters for the large model provided in this application. For example... Figure 8As shown, traditional air conditioning systems rely on preset fixed modes to handle different environments and user needs. This application, through the reasoning capabilities of a large language model, combines information such as user specific needs, indoor and outdoor temperatures, humidity, air quality, user activities, and local climate. It guides the large language model's reasoning through prompt word engineering (setting air conditioning mode adjustment rules to guide the large model's reasoning) and an external knowledge base (storing mode parameters and user preferences for different air conditioning models). These structures guiding the large language model's reasoning, together with the large language model, generate a large-scale intelligent air conditioning control model, which is the large model used in this application specifically for generating air conditioning control modes, thereby generating the optimal air conditioning adjustment program.

[0122] The following information is mainly considered when reasoning with large models:

[0123] User needs (e.g., "rapid cooling", "energy saving mode", "sleep mode");

[0124] Environmental factors (such as indoor and outdoor temperature, humidity, and air quality);

[0125] User activities (e.g., after exercising, before going to bed, while working);

[0126] Historical preference information;

[0127] Equipment limitations (e.g., the functions and parameters of different air conditioner models, current equipment status);

[0128] The large model generation mode is mainly divided into three parts: basic pattern reasoning, special requirement adjustment, and mutual exclusion rule verification.

[0129] Basic mode reasoning: Based on the current ambient temperature and humidity, season, and air conditioner operating status, a baseline control mode is derived. This mode provides the basis for subsequent adjustments.

[0130] Special needs adjustment: Based on the user's current activity (such as reading, eating hot pot, or just taking a shower) and user preferences (such as being afraid of the cold or liking to turn on the fresh air function in winter), personalized adjustments are made on the basis of the baseline control mode to better meet the user's needs.

[0131] Mutual exclusion rule verification: Finally, check and adjust according to the mutual exclusion rules between air conditioning modes to ensure that the final air conditioning mode is reasonable and feasible.

[0132] Through these three steps, the system can comprehensively consider multiple factors and provide users with the most suitable air conditioning control solution.

[0133] For example, a user might say, "I just went out to play ball, so I'm adjusting the air conditioning."

[0134] The system infers from intent recognition that the user is currently feeling hot and may need to cool down quickly. Combining the current season (winter) and indoor and outdoor temperatures (outdoor temperature is approximately 0 degrees Celsius), the system infers from the basic mode: heating mode, with a set temperature of 23 degrees Celsius.

[0135] Next, the system was further optimized based on specific needs: the user had just finished exercising and felt quite hot; the user preferred to turn on the fresh air function in winter. Therefore, the system lowered the temperature by 2°C to 21°C and turned on the fresh air function.

[0136] Finally, the system checked for mutual exclusion rules and confirmed there were no conflicts. The final output air conditioning mode was: heating mode, 21℃, with fresh air function enabled.

[0137] This application system supports historical preference recording. The system will record the user's preference information during daily use. For example, if the user has previously preferred energy-saving mode, the system will prioritize energy-saving settings when generating adjustment programs to ensure both comfort and energy conservation. If the user prefers to turn on the fresh air system in winter because their home has heating, the system will prioritize fresh air settings when adjusting programs in winter.

[0138] This application's system supports multi-turn voice interaction. After the user inputs a command, the system leverages the powerful contextual understanding capabilities of the large model to process multi-turn dialogues and respond in real time, dynamically adjusting the air conditioning mode based on the user's further needs. For example, if the user specifies "rapid cooling" in the first interaction, and then mentions "quieter" in the next round of dialogue, the system will automatically combine the preceding and following contexts, combining rapid cooling with a low-noise mode to generate an optimal air conditioning adjustment program.

[0139] 3. The composition of the knowledge base:

[0140] When generating air conditioning control programs, the system utilizes Retrieval Enhanced Generation (RAG) technology to combine knowledge graphs of air conditioning parameters for various models with a large language model, structuring information on environmental factors, control procedures, and equipment limitations. This allows the system to generate control modes more accurately and provide a personalized experience.

[0141] Components of a knowledge base:

[0142] 1) Mode parameter information for each air conditioner model;

[0143] Basic information: Includes detailed parameters of different brands and models of air conditioners, such as power, energy efficiency rating, cooling / heating range, etc.

[0144] Operating modes: Each air conditioner supports different modes (such as cooling, heating, dehumidification, and ventilation) and their corresponding temperature setting range, fan speed options, timer functions, etc.

[0145] Special features: such as fresh air function, air purification, energy-saving mode, rapid cooling / heating, etc.

[0146] Maintenance recommendations: Regularly clean the filter and replace consumable parts.

[0147] 2) Air conditioning function mutual exclusion table information;

[0148] List the mutual exclusion relationships between the various functions of the air conditioner to ensure that conflicting functions are not used simultaneously. For example, cooling mode and heating mode cannot be turned on at the same time. The fresh air function and energy-saving mode may be incompatible.

[0149] When multiple requirements exist simultaneously, determine which features should be prioritized. For example, the priority between user preferences and security settings.

[0150] 3) User preference information;

[0151] Historical usage records: Record information such as the air conditioning mode, temperature setting, and fan speed selection used by the user each time, in order to analyze the user's habits.

[0152] Personalized settings: User-defined preferences, such as automatically adjusting the temperature at specific times, preferred fan speed modes, and whether to keep the fresh air function on.

[0153] Activity Preferences: Rules for adjusting air conditioning mode based on user activities (such as after exercise, eating hot pot, or taking a shower) to meet comfort needs in different scenarios.

[0154] 4. Air Conditioning Command Issuance Module:

[0155] After the air conditioning adjustment command is generated, the system sends the adjustment parameters to the air conditioner to start the corresponding mode by issuing IoT commands.

[0156] Figure 9 This is a structural schematic diagram of an air conditioning control device 60 provided in this application. Figure 9 As shown, the device includes:

[0157] The acquisition module 601 is used to acquire environmental parameters in response to the user's first voice command;

[0158] Processing module 602 is used to generate first control parameters for the air conditioner based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information;

[0159] The control module 603 is used to control the operation of the air conditioner according to the first control parameters.

[0160] In one possible implementation, the acquisition module 601 is further configured to recognize the user's first voice and acquire the first text content corresponding to the first voice.

[0161] The processing module 602 is further configured to obtain target prompt words based on the first text content, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information;

[0162] The processing module 602 is further configured to process the target prompt word using a large model to generate the first control parameter.

[0163] In one possible implementation, the acquisition module 601 is further configured to acquire retrieval information related to the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information from the plug-in knowledge base based on the first text content, the environmental parameters, and the current operating parameters;

[0164] The processing module 602 is further configured to obtain target prompt words based on the first text content, the environmental parameters, and the search information.

[0165] In one possible implementation, the processing module 602 is further configured to process the first prompt word in the target prompt word through the large model to obtain baseline control parameters, wherein the first prompt word is obtained based on the environmental parameters and the current operating parameters;

[0166] The processing module 602 is further configured to adjust the baseline control parameters according to the second prompt word in the target prompt word to obtain personalized control parameters; the second prompt word is obtained based on the first text content and the user preference information;

[0167] The processing module 602 is further configured to perform mutual exclusion verification on the personalized control parameters based on the third prompt word in the target prompt word to obtain the first control parameter, so that the first control parameter does not include parameters that cause the air conditioning functions to be mutually exclusive; the third prompt word is obtained based on the air conditioning parameter adjustment rules and the air conditioning model parameters.

[0168] In one possible implementation, the environmental parameters include the temperature, humidity, and current climate of the user's current environment, and the user's first voice includes the user's activity information.

[0169] In one possible implementation, the processing module 602 is further configured to, during the operation of the air conditioner according to the first control parameters, respond to the user's second voice and adjust the first control parameters based on the second voice, the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information, to generate the second control parameters of the air conditioner.

[0170] The control module 603 is also used to control the operation of the air conditioner according to the second control parameters.

[0171] In one possible implementation, the device further includes: a determining module 604;

[0172] The acquisition module 601 is also used to recognize the user's second voice and acquire the second text content corresponding to the second voice;

[0173] The determining module 604 is used to determine the user's intent information based on the first text content and the second text content;

[0174] The processing module 602 is further configured to obtain new prompt words based on the intent information, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information;

[0175] The processing module 602 is further configured to adjust the first control parameter based on the new prompt word using a large model, and generate the second control parameter.

[0176] In one possible implementation, the acquisition module 601 is further configured to acquire user feedback information;

[0177] The processing module 602 is also used to update the air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information stored in the plug-in knowledge base based on the feedback information.

[0178] The air conditioning control device 60 provided in this application embodiment can execute the air conditioning control method in the above method embodiment. Its implementation principle and technical effects are similar, and will not be repeated here. It should be noted that the above... Figure 9 The division of modules shown is merely illustrative. This application does not limit the division of modules or the naming of modules.

[0179] Figure 10 This is a structural schematic diagram of an air conditioning control device 70 provided in this application. Figure 10 As shown, the electronic device 70 provided in this embodiment includes at least one processor 701 and a memory 702. Optionally, the device 70 further includes a communication component 703. The processor 701, memory 702, and communication component 703 are connected via a bus 704.

[0180] In a specific implementation, at least one processor 701 executes computer execution instructions stored in memory 702, causing at least one processor 701 to perform the above-described method.

[0181] The specific implementation process of processor 701 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0182] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0183] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0184] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0185] This application also provides a computer-readable storage medium, which may include various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. Specifically, the computer-readable storage medium stores program instructions, which are used in the methods described in the above embodiments.

[0186] This application also provides a program product including execution instructions stored in a readable storage medium. At least one control module of the display device can read the execution instructions from the readable storage medium, and the at least one control module executes the execution instructions to cause the display device to implement the air conditioning control methods provided in the various embodiments described above.

[0187] 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 them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

[0188] For ease of explanation, the above description has been provided in conjunction with specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or to limit the embodiments to the specific forms disclosed above. Various modifications and variations can be obtained based on the above teachings. The selection and description of the above embodiments are for the purpose of better explaining the principles and practical applications, thereby enabling those skilled in the art to better utilize the described embodiments and various different variations of embodiments suitable for specific use considerations.

Claims

1. An air conditioning control method, characterized in that, The method includes: Responding to the user's first voice command, obtain environmental parameters; Based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information, the first control parameters of the air conditioner are generated. The air conditioner is operated according to the first control parameter.

2. The method according to claim 1, characterized in that, The first control parameters for the air conditioner are generated based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information, including: Recognize the user's first voice and obtain the first text content corresponding to the first voice; Based on the first text content, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information, the target prompt words are obtained; The target prompt words are processed by a large model to generate the first control parameters.

3. The method according to claim 2, characterized in that, The step of obtaining target prompt words based on the first text content, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information includes: Based on the first text content, the environmental parameters, and the current operating parameters, retrieve search information related to the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information from the plug-in knowledge base; Based on the first text content, the environmental parameters, and the search information, target prompt words are obtained.

4. The method according to claim 3, characterized in that, The process of processing the target prompt words using a large model to generate the first control parameters includes: The large model is used to process the first prompt word in the target prompt words to obtain the baseline control parameters. The first prompt word is obtained based on the environmental parameters and the current operating parameters. Based on the second prompt word in the target prompt word, the baseline control parameters are adjusted to obtain personalized control parameters; the second prompt word is obtained based on the first text content and the user preference information. Based on the third prompt word in the target prompt word, the personalized control parameters are mutually exclusive to obtain the first control parameter, so that the first control parameter does not include parameters that make the air conditioning functions mutually exclusive; the third prompt word is obtained based on the air conditioning parameter adjustment rules and the air conditioning model parameters.

5. The method according to any one of claims 1-4, characterized in that, The environmental parameters include the temperature, humidity, and current climate of the user's current environment, and the user's first voice includes the user's activity information.

6. The method according to any one of claims 1-4, characterized in that, The method further includes: During the operation of the air conditioner according to the first control parameters, in response to the user's second voice, the first control parameters are adjusted based on the second voice, the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information, to generate the second control parameters of the air conditioner. The air conditioner is controlled to operate according to the second control parameter.

7. The method according to claim 6, characterized in that, The process of adjusting the first control parameters based on the second voice, the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information to generate the second control parameters of the air conditioner includes: Recognize the user's second voice and obtain the second text content corresponding to the second voice; Based on the first text content and the second text content, determine the user's intent information; Based on the intent information, the environmental parameters, the current operating parameters, the air conditioner model parameters, the air conditioner parameter adjustment rules, and the user preference information, obtain new prompt words; The second control parameter is generated by adjusting the first control parameter based on the new prompt word using a large model.

8. The method according to any one of claims 1-4, characterized in that, The method further includes: Obtain user feedback information; Based on the feedback information, the air conditioner model parameters, air conditioner parameter adjustment rules, and user preference information stored in the plug-in knowledge base are updated.

9. An air conditioning control device, characterized in that, The device includes: The acquisition module is used to acquire environmental parameters in response to the user's first voice command; The processing module is used to generate the first control parameters of the air conditioner based on the first voice, the environmental parameters, the current operating parameters of the air conditioner, the air conditioner model parameters, the air conditioner parameter adjustment rules, and user preference information. The control module is used to control the operation of the air conditioner according to the first control parameters.

10. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-8.

Images