Method and apparatus for voice-controlled air conditioner, air conditioner, storage medium

Abstract

This application relates to the field of smart home appliance technology, and discloses a method for voice-controlled air conditioners. The method includes, when a voice module receives an externally input voice command, sending the voice command to a voice classification module to determine whether the voice command is an offline voice command; if the voice command is an offline voice command, feeding the voice command back to the voice module to control the air conditioner's operation; if the voice command is not an offline voice command, transmitting the voice command to the cloud for parsing and recognition before feeding it back to the voice module to control the air conditioner's operation. By first determining whether the user's voice command is an offline voice command, and then performing the corresponding offline or online control, this avoids using online cloud AI to parse and recognize the voice command at the initial stage, reducing the frequency of using online AI for voice-controlled air conditioners and saving system resources. This application also discloses a device, an air conditioner, and a storage medium for voice-controlled air conditioners.

Description

Technical Field

[0001] This application relates to the field of smart home appliance technology, such as a method and apparatus for voice-controlled air conditioners, an air conditioner, and a storage medium. Background Technology

[0002] Currently, with the advent of the AI ​​(Artificial Intelligence) era, speech recognition has entered various fields of society, and smart air conditioning technology, which incorporates speech recognition into air conditioners, has emerged accordingly. Existing voice-controlled air conditioner solutions are divided into offline and online voice module solutions, controlling the air conditioner through fixed commands or cloud-based recognition, each with its own advantages and disadvantages. Offline voice solutions have the advantages of not requiring a network, not requiring application installation, and having a fast response speed, but also have disadvantages such as short command word length, limited number of commands, and lack of support for semantic understanding and recognition. Online voice solutions have the advantages of no limitations on the length and number of command words, and support for semantic understanding and recognition, but also have disadvantages such as requiring a network connection to work and having a slower response speed.

[0003] The related technology discloses a method for controlling intelligent devices that combines online and offline voice communication, including the following steps: The processing end recognizes the received voice information through a voice recognition module and converts the recognized information into a signal, which is then sent to the processing unit; after receiving the information, the processing unit extracts request keywords and compares them with a command recognition database; based on the comparison result, the request keywords are sent to the online processing unit or the offline processing unit, which then provides feedback according to the instruction, sending a control command. This method can automatically allocate offline or online processing modules based on the instruction content for corresponding control.

[0004] In the process of implementing the embodiments of this disclosure, at least the following problems were found in the related art:

[0005] The relevant technology requires comparing and recognizing the keywords of the voice commands with offline and online databases respectively, and then sending the keywords to offline and / or online processing units for feedback control. The voice processing process is highly dependent on online cloud AI, which greatly wastes system resources and results in a poor user experience. Summary of the Invention

[0006] To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended as a general commentary, nor is it intended to identify key / important components or describe the scope of protection of these embodiments, but rather as a prelude to the detailed description that follows.

[0007] This disclosure provides a method and apparatus for voice-controlled air conditioners, an air conditioner, and a storage medium, to reduce the reliance of voice recognition on online AI, save system resources, and improve user experience.

[0008] In some embodiments, the air conditioner includes a voice module and a voice classification module; the method includes: when the voice module receives an externally input voice command, sending the voice command to the voice classification module to determine whether the voice command is an offline voice command; if the voice command is an offline voice command, feeding the voice command back to the voice module to control the operation of the air conditioner; if the voice command is a non-offline voice command, transmitting the voice command to the cloud for parsing and recognition, and then feeding it back to the voice module to control the operation of the air conditioner.

[0009] In some embodiments, the apparatus includes a processor and a memory storing program instructions, wherein the processor is configured to execute the method for voice-controlled air conditioner when executing the program instructions.

[0010] In some embodiments, the air conditioner includes: a voice module and a voice classification module, the voice classification module including a local terminology database; and the aforementioned apparatus for voice control of the air conditioner.

[0011] In some embodiments, the storage medium stores program instructions that, when executed, perform the method described above for voice-controlled air conditioners.

[0012] The method, apparatus, air conditioner, and storage medium for voice-controlled air conditioners provided in this disclosure can achieve the following technical effects:

[0013] When the voice module receives an externally input voice command, it sends the command to the voice classification module to determine if it is an offline command. If the command is offline, it is then fed back to the voice module to control the air conditioner. If the command is not offline, it is transmitted to the cloud for parsing and recognition before being fed back to the voice module to control the air conditioner. By first determining whether the user's voice command is offline before performing the corresponding offline or online control, the system avoids relying on online cloud AI for voice command parsing and recognition at the initial stage. This reduces the frequency of using online AI for voice control of the air conditioner, saves system resources, and improves the user experience.

[0014] The above general description and the description below are exemplary and illustrative only and are not intended to limit this application. Attached Figure Description

[0015] One or more embodiments are illustrated by way of example with reference to the accompanying drawings. These illustrations and drawings do not constitute a limitation on the embodiments. Elements having the same reference numerals in the drawings are shown as similar elements. The drawings are not to be scaled. And wherein:

[0016] Figure 1 This is a schematic diagram of a method for voice-controlled air conditioner provided in an embodiment of this disclosure;

[0017] Figure 2 This is a schematic diagram of another method for voice-controlled air conditioners provided in an embodiment of this disclosure;

[0018] Figure 3 This is a schematic diagram of another method for voice-controlled air conditioners provided in an embodiment of this disclosure;

[0019] Figure 4 This is a schematic diagram of another method for voice-controlled air conditioners provided in an embodiment of this disclosure;

[0020] Figure 5 This is a schematic diagram of another method for voice-controlled air conditioners provided in an embodiment of this disclosure;

[0021] Figure 6 This is a schematic diagram of another method for voice-controlled air conditioners provided in an embodiment of this disclosure;

[0022] Figure 7 This is a schematic diagram of a device for voice-controlled air conditioner provided in an embodiment of this disclosure. Detailed Implementation

[0023] To provide a more detailed understanding of the features and technical content of the embodiments of this disclosure, the implementation of the embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for illustrative purposes only and are not intended to limit the embodiments of this disclosure. In the following technical description, for ease of explanation, several details are used to provide a full understanding of the disclosed embodiments. However, one or more embodiments may still be implemented without these details. In other cases, well-known structures and devices may be simplified in their depiction to simplify the drawings.

[0024] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this disclosure described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion.

[0025] Unless otherwise stated, the term "multiple" means two or more.

[0026] In this embodiment of the disclosure, the character " / " indicates that the objects before and after it are in an "or" relationship. For example, A / B means: A or B.

[0027] The term "and / or" describes an association between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or A and B.

[0028] The term "correspondence" can refer to an association or binding relationship. The correspondence between A and B means that there is an association or binding relationship between A and B.

[0029] In this embodiment of the disclosure, smart home appliances refer to home appliances formed by introducing microprocessors, sensor technology and network communication technology into home appliances. They have the characteristics of intelligent control, intelligent sensing and intelligent application. The operation of smart home appliances often relies on the application and processing of modern technologies such as the Internet of Things, the Internet and electronic chips. For example, smart home appliances can be connected to electronic devices to enable users to remotely control and manage smart home appliances.

[0030] In the disclosed embodiments, the terminal device refers to an electronic device with wireless connectivity. The terminal device can communicate with the aforementioned smart home appliances via the internet, or directly via Bluetooth, Wi-Fi, or other methods. In some embodiments, the terminal device may be, for example, a mobile device, a computer, or an in-vehicle device built into a hovercraft, or any combination thereof. Mobile devices may include, for example, mobile phones, smart home devices, wearable devices, smart mobile devices, virtual reality devices, or any combination thereof. Wearable devices may include, for example, smartwatches, smart bracelets, pedometers, etc.

[0031] This disclosure provides an air conditioner, including a voice module and a voice classification module. The voice module receives external voice commands and feeds back control commands to the air conditioner's main control board to control the air conditioner's operation. The voice classification module classifies the voice commands.

[0032] Combination Figure 1 As shown, this disclosure provides a method for voice-controlled air conditioners, including:

[0033] S01, when the air conditioner receives an externally input voice command in the voice module, it sends the voice command to the voice classification module to determine whether the voice command is an offline voice command.

[0034] S02, when the voice command is an offline voice command, the air conditioner feeds back the voice command to the voice module to control the operation of the air conditioner.

[0035] S03: When the voice command is not an offline voice command, the air conditioner transmits the voice command to the cloud for parsing and recognition, and then feeds it back to the voice module to control the operation of the air conditioner.

[0036] In this process, after the cloud performs parsing and recognition to generate parsing instructions, the cloud feeds back the parsing instructions to the voice module, which then controls the air conditioner to operate via the main control board according to the parsing instructions.

[0037] The method for voice-controlled air conditioners provided in this disclosure, when the voice module receives an externally input voice command, sends the voice command to a voice classification module to determine whether the voice command is an offline voice command. By first determining whether the user's voice command is an offline voice command, and then performing the corresponding offline or online control, there is no need to use online cloud AI to parse and recognize the voice command at the beginning to distinguish between offline and online voice commands. This reduces the dependence of voice-controlled air conditioners on online AI and saves system resources. Next, if the voice command is an offline voice command, it is fed back to the voice module to control the air conditioner. If the voice command is not an offline voice command, it is transmitted to the cloud for parsing and recognition before being fed back to the voice module to control the air conditioner. This method not only has the advantages of high recognition accuracy and efficiency of offline voice control solutions, but also the intelligence of online voice control solutions in accessing cloud content services and converting speech to text, thus improving the user experience.

[0038] Optionally, combined Figure 2 As shown, the air conditioner determines whether a voice command is an offline voice command by including:

[0039] S21, the air conditioner matches the voice command with entries in the local dictionary database.

[0040] S22, if the air conditioner is successfully paired, the voice command is determined to be an offline voice command.

[0041] S23, if the air conditioner fails to match, it determines that the voice command is a non-offline voice command.

[0042] In this way, the air conditioner matches the voice command with entries in its local dictionary database. If a match is successful, the voice command is determined to be an offline voice command. If no match is found, the voice command is determined to be a non-offline voice command. As can be seen, in the initial stage of the voice-controlled air conditioner solution, the system first determines whether the voice command is an offline command by matching it against the local dictionary database. If it is an offline voice command, it can be directly sent to the voice module for control without the need for cloud AI, thus reducing the frequency of cloud AI usage and saving system resources.

[0043] Optionally, combined Figure 3 As shown, before the air conditioner matches the voice command with entries in the local dictionary database, it also includes:

[0044] S31, the length of the words used to obtain voice commands by the air conditioner.

[0045] S32, if the length of the word is greater than the length threshold, the air conditioner determines that the voice command is a non-offline voice command.

[0046] The length threshold is the length of the longest term in the local term database.

[0047] In this way, the air conditioner obtains the length of the voice command and determines that the voice command is a non-offline voice command if the length exceeds a length threshold. When the voice command length exceeds the threshold, there is no need for cloud AI to parse and recognize the voice command, nor is it necessary to match the voice command with entries in the local word database. Simply comparing the length of the voice command with the length threshold is sufficient to determine that the voice command is a non-offline voice command, further improving the efficiency of voice control of the air conditioner.

[0048] Optionally, combined Figure 4 As shown, the air conditioner feeds back voice commands to the voice module to control its operation, including:

[0049] S41, the air conditioner control voice module generates the first control command based on the voice command.

[0050] S42, the air conditioner controls its operation according to the first control command.

[0051] In this way, the air conditioner control voice module generates a first control command based on the voice instruction, and controls the operation of the air conditioner according to the first control command. When the voice command is an offline voice command, the first control command is directly fed back to the air conditioner main control board through the voice module to control the operation of the air conditioner.

[0052] Optionally, combined Figure 5 As shown, the air conditioner transmits voice commands to the cloud for parsing and recognition, and then feeds them back to the voice module to control the operation of the air conditioner, including:

[0053] S51, the air conditioner control voice module transmits voice commands to the cloud.

[0054] S52, the air conditioner uses cloud-based artificial intelligence (AI) to analyze and recognize voice commands and generate a second control command.

[0055] S53, the air conditioner controls the operation of the air conditioner according to the second control command.

[0056] In this way, the air conditioner's voice control module transmits voice commands to the cloud. Cloud AI then parses and recognizes the voice commands to generate a second control command, which is used to control the air conditioner's operation. Only when the voice command is not offline is the cloud AI activated to parse and recognize the voice command and generate a second control command, which is then directly fed back to the air conditioner's main control board via the voice module to control the air conditioner's operation. Whether the voice command is offline or not, the final control command is fed back to the main control board via the voice module to control the air conditioner's operation, eliminating the need for separate offline and online processing units. This simplifies the control process, reduces control costs, and improves the air conditioner's operating efficiency.

[0057] In practical use, the voice module connects to the air conditioner's main control board, which can control the air conditioner itself. After the user issues a voice command, it is transmitted to the voice module. The voice module then forwards the command to the voice classification module, which receives the command. Upon receiving an externally input voice command, the module generates a corresponding voice sequence number. If the command is determined to be an offline command, the result is directly fed back to the voice module, which then controls the air conditioner via the main control board to execute the voice command. If the received voice command, after processing by the voice classification module, is determined not to be an offline command, it is directly transmitted to the cloud server. After cloud AI analysis and recognition, the command is fed back to the voice module, which then controls the air conditioner via the main control board or provides feedback to the user through the speaker.

[0058] Combination Figure 6 As shown, this disclosure provides a method for voice-controlled air conditioners, including:

[0059] S01, when the air conditioner receives an externally input voice command in the voice module, it sends the voice command to the voice classification module to determine whether the voice command is an offline voice command.

[0060] S02, when the voice command is an offline voice command, the air conditioner feeds back the voice command to the voice module to control the operation of the air conditioner.

[0061] S03: When the voice command is not an offline voice command, the air conditioner transmits the voice command to the cloud for parsing and recognition, and then feeds it back to the voice module to control the operation of the air conditioner.

[0062] When the voice command for the S61 air conditioner is to turn it on, it will push recommended parameters to the user based on the user's usage habits.

[0063] S62, the air conditioner controls its operation based on user feedback on recommended parameters.

[0064] The method for voice-controlled air conditioners provided in this disclosure allows the air conditioner to push recommended parameters to the user based on their usage habits when the voice command is a power-on command. The method then controls the air conditioner's operation based on the user's feedback on these parameters. By recommending parameters to the user based on their habits when the air conditioner is turned on, the user can control the air conditioner according to these recommended parameters, eliminating the need for cumbersome startup settings and improving the user experience.

[0065] Optionally, the air conditioner pushes recommended parameters to the user based on the user's usage habits, including: the air conditioner determines the score of each settable parameter in each working scenario based on the usage of settable parameters within a set time before the air conditioner is turned on; the air conditioner determines recommended parameters in each working scenario based on the score of each settable parameter; and the air conditioner pushes recommended parameters corresponding to the current working scenario to the user based on the current indoor working scenario when the air conditioner is turned on.

[0066] The configurable parameters include air conditioner mode and values. For example, recommended parameters include cooling mode, humidity of 45%, temperature of 26°C, fan speed of medium speed, and fan direction of 45°.

[0067] The duration can be set to any time, depending on your needs. For example, it can be a week, 5 days, or 9 days, etc.

[0068] The baseline is an indoor temperature of 5°C and a humidity of 20%. A change of more than 5°C in temperature or more than 20% in humidity constitutes a new working scenario. A change of less than 5°C in temperature and less than 20% in humidity constitutes the same working scenario.

[0069] In this way, the system determines the score of each settable parameter in each working scenario based on the usage of settable parameters within a set time period. Then, based on these scores, recommended parameters for each working scenario are generated, reflecting the most frequently used parameters by users in each scenario. Finally, when the air conditioner is turned on, the system pushes the most frequently used recommended parameters to the user based on the current indoor working scenario and controls the air conditioner's operation based on user feedback. By pushing the most frequently used recommended parameters to the user when the air conditioner is turned on, based on the usage of each settable parameter, users can directly use the recommended parameters to make the air conditioner operate according to the recommended parameters, thus avoiding cumbersome settings when the air conditioner is turned on. This simplifies the operation process when turning on the air conditioner and improves the user experience. Furthermore, since the recommended parameters are determined based on the usage of each settable parameter and reflect the most frequently used parameters in different scenarios, the recommended parameters are more in line with user habits, further enhancing the user experience.

[0070] Optionally, the air conditioner determines the score of each settable parameter in each working scenario based on the usage of settable parameters within a set time period before the air conditioner is turned on. This includes: scoring each settable parameter based on its initial usage upon startup, the number of times it is used daily, and the duration of its use in each working time period of the air conditioner within the set time period before the air conditioner is turned on; and determining the final score of each settable parameter in each working scenario after the scoring is completed. The working time period is the time period from when the air conditioner is turned on to when it is turned off.

[0071] In this way, within a set time period, each settable parameter is scored based on its initial startup usage, daily usage frequency, and usage duration during each operating period of the air conditioner. Upon reaching the set time, the final score for each settable parameter in each operating scenario is determined. By considering initial startup usage, daily usage frequency, and usage duration during each operating period, the settable parameters are scored from three dimensions. This allows for understanding user behavior across different operating scenarios and generating recommended parameters that best match user habits, thus improving the user experience.

[0072] Optionally, the air conditioner scores each settable parameter based on its initial start-up usage, the number of times each settable parameter is used daily, and the usage duration of each settable parameter during each working period of the air conditioner. This includes: the air conditioner adding a first score to each settable parameter used within the first hour after the first start-up each day; the air conditioner adding a second score to each settable parameter used each day outside of the first hour; and the air conditioner adding a corresponding score to each settable parameter based on the usage duration of each settable parameter during the working period.

[0073] The first fraction can be equal to the second fraction.

[0074] The air conditioner awards points to each settable parameter based on the usage duration within the operating period. Specifically, the ratio of usage duration to the operating period is calculated; this ratio is multiplied by the total score for using a specific mode or value completely within the operating period to obtain the corresponding score; and the corresponding score is added to each settable parameter. For example, a complete usage period is defined as the entire time from when the air conditioner is turned on to when it is turned off, say 100 minutes. Using a specific mode or value continuously within the complete usage period awards 2.5 points. If a specific mode or value is used for only 20 minutes, and the ratio of usage duration to the operating period is 20%, then 2.5 * 20% = 0.5 points are awarded.

[0075] In this way, the air conditioner adds a first score to each settable parameter used within the first hour after the first power-on each day, and a second score for each settable parameter used at other times of the day. The score is further increased based on the duration of use of each settable parameter within the working hours. For example, settable parameters used within the first 2 minutes of power-on earn 1 point. Settable parameters used within a 24-hour period (excluding the first 2 minutes of power-on) also earn 1 point. By scoring settable parameters from three dimensions, the system understands user usage patterns across various work scenarios, enabling the generation of recommended parameters that best match user habits for each scenario, thus improving the user experience.

[0076] Optionally, the air conditioner determines the recommended parameters for each working scenario based on the scores of each settable parameter, including: the air conditioner obtaining the parameter type of each settable parameter in each working scenario; and the air conditioner determining the settable parameter with the highest score among each parameter type as the recommended parameter for the corresponding working scenario.

[0077] Here, "parameter type" refers to different kinds of parameters, such as temperature and humidity. The settable parameter with the highest score within each parameter type refers to the specific parameter with the highest score under that type. For example, in the temperature parameter type, there are only two temperatures: 25℃ and 26℃. Since temperature 26℃ has a higher score than temperature 25℃, then temperature 26℃ is the settable parameter with the highest score in the temperature parameter type.

[0078] In this way, the air conditioner obtains the parameter types of each settable parameter under various working scenarios and determines the settable parameter with the highest score among each parameter type as the recommended parameter for the corresponding working scenario. Thus, the recommended parameters are all settable parameters with the highest scores in each working scenario, which can maximize the satisfaction of users' needs for air conditioner control in various working scenarios.

[0079] Optionally, the air conditioner pushes recommended parameters corresponding to the current working scenario to the user based on the current indoor working scenario, including: the air conditioner determines the current working scenario based on indoor environmental parameters; the air conditioner determines the target recommended parameters corresponding to the working scenario based on the current working scenario; and the air conditioner pushes the target recommended parameters to the user.

[0080] In this way, when the air conditioner is turned on, the system determines the current operating scenario based on indoor environmental parameters, and then determines the corresponding target recommended parameters, finally pushing these parameters to the user. When the user turns on the air conditioner, the system pushes recommended parameters specifically to the user based on the current operating scenario, allowing the user to directly use these recommended parameters to control the air conditioner's operation, simplifying the cumbersome startup process and improving the user experience. The terminal device also controls the air conditioner's operation based on user feedback; users can also provide feedback if they do not wish to use the recommended parameters, further enhancing the user experience.

[0081] Optionally, the air conditioner determines the current working scenario based on indoor environmental parameters, including: the air conditioner determines the working scenario corresponding to indoor temperature, indoor humidity, and carbon dioxide concentration based on a preset correspondence; the air conditioner sets the working scenario as the current working scenario; wherein the environmental parameters include indoor temperature, indoor humidity, and carbon dioxide concentration.

[0082] In this way, the air conditioner determines the operating scenario corresponding to indoor temperature, humidity, and carbon dioxide concentration based on a preset correspondence, and sets this scenario as the current operating scenario. By distinguishing different operating scenarios using indoor temperature, humidity, and carbon dioxide concentration, the air conditioner can determine the current indoor operating scenario and identify recommended parameters corresponding to that scenario. This ensures that the recommended parameters are adapted to the current indoor environment and user needs, thereby improving the user experience.

[0083] Optionally, the air conditioner controls its operation based on user feedback, including: if the user confirms that the air conditioner is operating according to the recommended parameters, the air conditioner is controlled to operate according to the recommended parameters; if the user modifies the recommended parameters, the air conditioner is controlled to operate according to the modified recommended parameters.

[0084] In this way, if the user confirms that the air conditioner is running according to the recommended parameters, the air conditioner will operate according to those parameters; if the user modifies the recommended parameters, the air conditioner will operate according to the modified parameters. After pushing the recommended parameters to the user, the system also controls the air conditioner's operation based on user feedback. If the user does not want to use the recommended parameters, they can modify them, making the parameters more suitable for their current needs and enhancing the user experience.

[0085] In practical use, various sensors connect to the air conditioner's main control board, providing various monitoring data. The air conditioner connects to a network module, reporting data to terminal devices via the network module, while the terminal devices can also send commands to the air conditioner through the network module. A mobile app can control the smart air conditioner by sending commands from the terminal device. Simultaneously, user usage data is recorded in the cloud and transmitted to an intelligent recommendation algorithm. After one to two weeks of data processing, the algorithm determines the user's most frequently used air conditioning mode and various parameter values ​​within a certain temperature and humidity range. This data is then transmitted to the mobile app via the terminal device. Upon receiving the data, the mobile app's intelligent recommendation mode is enabled, allowing the user to use this mode via their phone. If user habits change, the corresponding data will also change, and the parameters within the recommended mode will adjust accordingly, ensuring the mode's intelligence and practicality.

[0086] Combination Figure 7 As shown, this disclosure provides an apparatus for voice-controlled air conditioners, including a processor 100 and a memory 101. Optionally, the apparatus may further include a communication interface 102 and a bus 103. The processor 100, communication interface 102, and memory 101 can communicate with each other via the bus 103. The communication interface 102 can be used for information transmission. The processor 100 can call logical instructions in the memory 101 to execute the method for voice-controlled air conditioners described in the above embodiment.

[0087] Furthermore, the logic instructions in the aforementioned memory 101 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium.

[0088] The memory 101, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as program instructions / modules corresponding to the methods in the embodiments of this disclosure. The processor 100 executes functional applications and data processing by running the program instructions / modules stored in the memory 101, that is, it implements the method for voice-controlled air conditioners in the above embodiments.

[0089] The memory 101 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the terminal device. Furthermore, the memory 101 may include high-speed random access memory and may also include non-volatile memory.

[0090] This disclosure provides an air conditioner, including a voice module and a voice classification module, the voice classification module including a local terminology database; and the aforementioned apparatus for voice control of the air conditioner.

[0091] This disclosure provides a storage medium storing computer-executable instructions configured to perform the above-described method for voice-controlled air conditioners.

[0092] The aforementioned storage medium can be either transient or non-transient.

[0093] The technical solutions of this disclosure can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes one or more instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in this disclosure. The aforementioned storage medium can be a non-transitory storage medium, including: 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, and other media capable of storing program code; it can also be a transient storage medium.

[0094] The foregoing description and accompanying drawings fully illustrate embodiments of this disclosure to enable those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, procedural, and other changes. The embodiments represent only possible variations. Individual components and functions are optional unless explicitly required, and the order of operation may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terminology used in this application is for describing embodiments only and is not intended to limit the claims. As used in the description of embodiments and claims, the singular forms “a,” “an,” and “the” are intended to equally include the plural forms unless the context clearly indicates otherwise. Similarly, the term “and / or” as used in this application means including one or more of the associated listed items and all possible combinations thereof. Additionally, when used in this application, the term "comprise" and its variations "comprises" and / or "comprising" refer to the presence of stated features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof. Without further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. In this document, each embodiment may focus on the differences from other embodiments, and similar or identical parts between embodiments can be referred to mutually. For methods, products, etc., disclosed in the embodiments, if they correspond to the method section disclosed in the embodiments, the relevant parts can be referred to the description of the method section.

[0095] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of this disclosure. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] The methods and products (including but not limited to devices and equipment) disclosed in the embodiments herein can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units may be merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to implement this embodiment according to actual needs. Furthermore, the functional units in the embodiments of this disclosure may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0097] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than that shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different blocks may also occur in a different order than disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. Each block in a block diagram and / or flowchart, and combinations of blocks in a block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

Claims

1. A method for voice controlling an air conditioner, characterized by, The air conditioner comprises a voice module and a voice classification module; wherein the voice module is used for receiving external voice instructions and feeding back control instructions to an air conditioner main control board to control the air conditioner to operate; the voice classification module is used for classifying voice instructions; the method comprises: In the case where the voice module receives external input voice instructions, the voice instructions are sent to the voice classification module to determine whether the voice instructions are offline voice instructions; In the case where the voice instructions are offline voice instructions, the voice instructions are fed back to the voice module to control the air conditioner to operate; In the case where the voice instructions are non-offline voice instructions, the voice instructions are transmitted to the cloud for analysis and identification and then fed back to the voice module to control the air conditioner to operate; Wherein, the determination of whether the voice instructions are offline voice instructions comprises: obtaining the word length of the voice instructions; in the case where the word length is greater than the length threshold, it is determined that the voice instructions are non-offline voice instructions; wherein the length threshold is the length value of the longest word in the local word database; In the case where the voice instructions are power-on instructions, within a set time length before the air conditioner is powered on, each settable parameter is scored according to the first power-on use condition of each settable parameter, the use frequency of each settable parameter per day and the use time length of each settable parameter in each working time period of the air conditioner; in the case where the scoring is completed, the final score value of each settable parameter in each working scenario is determined; According to the score value of each settable parameter, the recommended parameters in each working scenario are determined; In the case where the air conditioner is powered on, the recommended parameters corresponding to the current working scenario are pushed to the user according to the current working scenario.

2. The method of claim 1, wherein, The voice classification module comprises a local word database; the determination of whether the voice instructions are offline voice instructions further comprises: In the case where the word length is less than or equal to the length threshold, the voice instructions are matched with the words in the local word database; In the case where the matching is successful, it is determined that the voice instructions are offline voice instructions; In the case where the matching is unsuccessful, it is determined that the voice instructions are non-offline voice instructions.

3. The method of claim 1, wherein, The feeding back of the voice instructions to the voice module to control the air conditioner to operate comprises: Controlling the voice module to generate a first control instruction according to the voice instructions; Controlling the air conditioner to operate according to the first control instruction.

4. The method of claim 1, wherein, The transmission of the voice instructions to the cloud for analysis and identification and then the feeding back to the voice module to control the air conditioner to operate comprises: Controlling the voice module to transmit the voice instructions to the cloud; Analyzing and identifying the voice instructions through cloud artificial intelligence (AI) to generate a second control instruction; Controlling the air conditioner to operate according to the second control instruction.

5. The method according to any one of claims 1 to 4, characterized in that, After the recommended parameters corresponding to the current working scenario are pushed to the user, it further comprises: Controlling the air conditioner to operate according to the feedback information of the user on the recommended parameters.

6. The method according to any one of claims 1 to 4, characterized in that, The first use of each settable parameter according to each settable parameter, the number of times each settable parameter is used per day, and the use time of each settable parameter in each working time period of the air conditioner are scored, including: A first score is added to each settable parameter used within a first time period after the first start-up of each day; A second score is added to each settable parameter used at a time other than the first time period each day, each time; A corresponding score is added to each settable parameter according to the use time of each settable parameter in the working time period.

7. The method of claim 6, wherein, The corresponding score added to each settable parameter according to the use time of each settable parameter in the working time period, including: The ratio of the use time of each settable parameter to the working time period is calculated respectively; The ratio is multiplied by the total score of using the corresponding settable parameter in the working time period to obtain the corresponding score; The corresponding score is added to each settable parameter.

8. An apparatus for voice control of an air conditioner, comprising a processor and a memory having stored program instructions, characterized in that, The processor is configured to execute the method for voice control of the air conditioner as claimed in any one of claims 1 to 7 when executing the program instructions.

9. An air conditioner characterized by comprising: The device for voice control of the air conditioner as claimed in claim 8, including a voice module and a voice classification module, the voice classification module including a local term database.

10. A storage medium storing program instructions, characterized in that, The program instructions execute the method for voice control of the air conditioner as claimed in any one of claims 1 to 7 when running.

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