Method and device for controlling intelligent network device and intelligent air conditioner
By combining weather, region and user factors, and combining the historical usage data of the smart network device for self-learning, the problem of inaccurate learning results of the smart network device is solved, and the accuracy of learning results and user experience is improved.
Patent Information
- Application Number
- CN202510352657.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-24
- Publication Date
- 2025-06-17
AI Technical Summary
When learning user habits, existing smart network devices are difficult to accurately consider seasonal changes, weather changes and regional differences, resulting in inaccurate learning results.
By determining the target factors that affect the parameter setting of the smart network device, including weather factors, regional factors and user factors, and combining the historical usage data of the smart network device for self-learning, we can obtain target setting parameters that are more in line with user expectations.
It improves the accuracy of learning results of the smart network device and can more accurately meet the user's comfort needs.
Smart Images
Figure CN120160256A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of smart home appliances, for example, to a method and device for controlling a smart network device, and a smart air conditioner. Background Art
[0002] Currently, in the era of the Internet of Things, big data technology has been widely applied to the field of smart home. With the improvement of the intelligence level of smart network devices, learning about user behavior habits has become a popular technology. By collecting and analyzing a large amount of user behavior habit data, the usage experience of smart network devices is optimized. Moreover, with the development of the Internet of Things and artificial intelligence technologies, the functions of smart network devices are becoming more and more complex, and users' demand for personalized experiences is also getting higher and higher.
[0003] In related technologies, a smart network device, such as a smart air conditioner, learns about users' usage habits by analyzing users' historical usage data, such as set temperature, usage duration, usage frequency, etc. For example, the system will record the temperature set by the user in the past period of time and learn based on this data to recommend a commonly used temperature.
[0004] In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in related technologies:
[0005] In a real environment, seasonal changes, weather changes, and regional differences will all affect user comfort. If only considering users' historical usage data, the learning results of smart network devices will be inaccurate.
[0006] It should be noted that the information disclosed in the above background art section is only used to enhance the understanding of the background of this application, and thus may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention
[0007] To have a basic understanding of some aspects of the disclosed embodiments, a simple summary is given below. This summary is not a general review, nor is it intended to identify key / important constituent elements or delineate the protection scope of these embodiments, but rather serves as a preamble to the subsequent detailed description.
[0008] The embodiments of the present disclosure provide a method and device for controlling a smart network device, and a smart air conditioner to improve the accuracy of the learning results of the smart network device.
[0009] In some embodiments, the method for controlling a smart network device includes: determining an expected set parameter according to a target factor affecting the parameter setting of the smart network device; determining a commonly used set parameter according to the historical usage data of the smart network device; and performing self-learning based on the expected set parameter and the commonly used set parameter to obtain the target set parameter of the smart network device.
[0010] Optionally, the target factors include one or more of weather factors, regional factors, and user factors; according to the target factors affecting the parameter settings of the intelligent network device, determining the expected setting parameters includes one or more of the following methods: determining the first expected setting parameter according to the current weather information of the area where the intelligent network device is located; determining the second expected setting parameter according to the usage data of other users in the current season within the area where the intelligent network device is located; determining the third expected setting parameter according to the current user of the intelligent network device.
[0011] Optionally, the current user of the intelligent network device is determined in the following manner: obtaining the voiceprint information of the current user; based on a pre-trained voiceprint recognition model, determining the identity of the current user according to the voiceprint information.
[0012] Optionally, according to the historical usage data of the intelligent network device, determining the common setting parameters includes: in the case where the historical usage duration of the intelligent network device reaches the preset duration, determining the common setting parameters according to the historical setting parameters of the intelligent network device within the preset duration; or, in the case where the historical usage duration of the intelligent network device does not reach the preset duration, determining that there are no common setting parameters.
[0013] Optionally, according to the historical setting parameters of the intelligent network device within the preset duration, determining the common setting parameters includes: obtaining a plurality of historical setting parameters of the intelligent network device within the preset duration, as well as the usage frequency and usage duration of each historical setting parameter; scoring each historical setting parameter according to the usage frequency and usage duration of each historical setting parameter to obtain a learning score; determining the historical setting parameter with the highest learning score as the common setting parameter.
[0014] Optionally, self-learning is performed according to the expected setting parameters and the common setting parameters to obtain the target setting parameters of the intelligent network device, including: assigning corresponding weight coefficients to the expected setting parameters and the common setting parameters; calculating the target setting parameters according to the expected setting parameters, the common setting parameters, and the weight coefficients.
[0015] Optionally, the target setting parameters are calculated according to the following formula:
[0016]
[0017] where T is the target setting parameter, j is the number of expected setting parameters, j≥1, t i is the expected setting parameter, w i is the weight coefficient of the expected setting parameter, t0 is the common setting parameter, w0 is the weight coefficient of the common setting parameter, and t is the compensation parameter.
[0018] Optionally, the method for controlling the intelligent network device further includes: during the use of the intelligent network device, monitoring the user's usage behavior; dynamically adjusting the target setting parameters according to the user's usage behavior.
[0019] In some embodiments, the apparatus for controlling an intelligent network device includes a processor and a memory storing program instructions, and the processor is configured to execute the method for controlling an intelligent network device as described above when running the program instructions.
[0020] In some embodiments, an intelligent air conditioner includes: an intelligent air conditioner body; and the apparatus for controlling an intelligent network device as described above, which is installed on the intelligent air conditioner body.
[0021] The method and apparatus for controlling an intelligent network device and the intelligent air conditioner provided by the embodiments of the present disclosure can achieve the following technical effects:
[0022] In the embodiments of the present disclosure, determining the expected setting parameters according to the target factors affecting the parameter settings of the intelligent network device and determining the common setting parameters according to the historical usage data of the intelligent network device can comprehensively consider the target factors and the historical usage data during the self-learning process of the intelligent network device, so as to obtain target setting parameters that better meet the user's expectations and improve the accuracy of the learning results of the intelligent network device.
[0023] The above general description and the following description are only exemplary and explanatory, and are not used to limit this application. BRIEF DESCRIPTION OF THE DRAWINGS
[0024] One or more embodiments are exemplarily illustrated by the corresponding drawings. These exemplary illustrations and the drawings do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements. The drawings do not constitute a scale limitation, and among them:
[0025] Figure 1 is a schematic diagram of a method for controlling an intelligent network device provided by an embodiment of the present disclosure;
[0026] Figure 2 is a schematic diagram of another method for controlling an intelligent network device provided by an embodiment of the present disclosure;
[0027] Figure 3 is a schematic diagram of another method for controlling an intelligent network device provided by an embodiment of the present disclosure;
[0028] Figure 4 is a schematic diagram of another method for controlling an intelligent network device provided by an embodiment of the present disclosure;
[0029] Figure 5 is a schematic diagram of an apparatus for controlling an intelligent network device provided by an embodiment of the present disclosure;
[0030] Figure 6 is a schematic diagram of an intelligent air conditioner provided by an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0031] In order to understand the features and technical content of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration purposes only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the sake of explanation, numerous details are provided to give a thorough 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 shown in a simplified manner to simplify the drawings.
[0032] In the technical solutions described in this application, terms such as "first" and "second" are used to distinguish similar objects and do not necessarily have to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so as to implement the embodiments of the present disclosure described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion.
[0033] Unless otherwise specified, the term "plurality" means two or more.
[0034] In the embodiments of the present disclosure, the character " / " indicates that the objects before and after are in an "or" relationship. For example, A / B means: A or B.
[0035] The term "and / or" is a description of the association relationship of an object, indicating that three relationships can exist. For example, A and / or B means: A or B, or, the three relationships of A and B.
[0036] The term "corresponding" may refer to an association relationship or a binding relationship. A corresponding to B means that there is an association relationship or a binding relationship between A and B.
[0037] Combined with Figure 1 As shown, the embodiments of the present disclosure provide a method for controlling an intelligent network device. The execution subject of this method can be a processor, and the method includes:
[0038] S101, the processor determines the expected setting parameters according to the target factors affecting the parameter settings of the intelligent network device.
[0039] S102, the processor determines the commonly used setting parameters according to the historical usage data of the intelligent network device.
[0040] S103, the processor performs self-learning according to the expected setting parameters and the commonly used setting parameters to obtain the target setting parameters of the intelligent network device.
[0041] In the embodiments of the present disclosure, the expected setting parameters are determined according to the target factors affecting the parameter settings of the intelligent network device, and the common setting parameters are determined according to the historical usage data of the intelligent network device. It is possible to comprehensively consider the target factors and the historical usage data during the self-learning process of the intelligent network device, so as to obtain target setting parameters that better meet the user's expectations and improve the accuracy of the learning results of the intelligent network device.
[0042] Optionally, the intelligent network device includes devices that are connected to the Internet through Internet of Things technology and can achieve intelligent operation and remote control. For example: intelligent air conditioners, intelligent refrigerators, intelligent speakers, intelligent lighting, intelligent ovens, intelligent water heaters, intelligent washing machines, intelligent air purifiers, intelligent humidifiers, etc.
[0043] In this embodiment, the intelligent network device has functions such as self-learning, automation, and remote control, and can automatically adjust the parameter settings of the intelligent network device according to the user's needs and environmental changes. For example: intelligent air conditioner temperature setting, intelligent refrigerator temperature setting, intelligent speaker volume setting, intelligent lighting brightness setting, etc.
[0044] Optionally, the target factors include one or more of weather factors, geographical factors, and user factors.
[0045] In this embodiment, the target factors are the key factors affecting the parameter settings of the intelligent network device, and these factors can be used to determine the expected setting parameters of the intelligent network device. Among them, the weather factor refers to the weather conditions in the area where the current device is located, including temperature, humidity, wind speed, rainfall, etc., and the current weather information can be obtained through the weather forecast API or the sensors built into the intelligent network device. The weather factor directly affects the user's setting requirements for the intelligent network device. For example: in high-temperature weather, the user may need a lower air conditioner set temperature; in high-humidity weather, the user may need to turn on the dehumidification function. The geographical factor refers to the geographical location of the device and its corresponding climate, cultural habits, etc., and the area where the device is located can be obtained through the geographical location information of the device, and analyzed in combination with the historical usage data of this area. There may be significant differences in the setting requirements of users in different regions for the intelligent network device. For example: in the northern region, it is cold in winter, and users may need a higher air conditioner heating temperature; in the southern region, it is hot in summer, and users may need a lower air conditioner cooling temperature. The user factor refers to the personal characteristics and usage habits of the user, and the age and gender of the user can be judged through voiceprint recognition technology, and then the personal characteristics and usage habits of the user can be determined. There may be differences in the setting requirements of different user groups for the intelligent network device. For example: children and the elderly are more sensitive to temperature and may require more moderate setting parameters; users of different genders may have different preferences for parameters such as wind speed and humidity. In practical applications, the target factors are usually used comprehensively. By comprehensively considering these factors, the intelligent network device can provide more accurate and personalized setting parameters.
[0046] Optionally, according to the target factors affecting the parameter settings of the intelligent network device, determine the expected setting parameters, including one or more of the following methods: determine the first expected setting parameter according to the current weather information in the area where the intelligent network device is located; determine the second expected setting parameter according to the usage data of other users in the current season in the area where the intelligent network device is located; determine the third expected setting parameter according to the current user of the intelligent network device.
[0047] In this embodiment, the first expected setting parameter is the setting parameter of the intelligent network device preliminarily determined according to the current weather information, and its influencing factors include temperature, humidity, wind speed and rainfall. According to the preset mapping relationship between weather parameters and setting parameters, the first expected setting parameter can be determined. The second expected setting parameter is the setting parameter of the intelligent network device preliminarily determined according to the usage data of other users in the current season in the area, and its influencing factors include the commonly used setting parameters of other users in the current season, as well as the frequency and duration of each commonly used setting parameter. According to the usage data of other users in the area, count the setting parameter with the highest usage frequency and the longest usage duration as the second expected setting parameter. The third expected setting parameter is the setting parameter of the intelligent network device preliminarily determined according to the personal characteristics and usage habits of the current user, and its influencing factors include the user's age, gender and historical usage data. According to the mapping relationship between specific users and setting parameters, the third expected parameter can be determined.
[0048] Optionally, determine the current user of the intelligent network device in the following manner: obtain the voiceprint information of the current user; based on a pre-trained voiceprint recognition model, determine the identity of the current user according to the voiceprint information.
[0049] In this embodiment, determining the current user of the intelligent network device through voiceprint recognition technology is an efficient and non-intrusive user identification method. Voiceprint recognition technology can accurately identify information such as the user's identity, age and gender by analyzing the user's voice characteristics (such as frequency, pitch, timbre, etc.). In the specific implementation process, the voice data of the user can be collected first through the microphone built in the intelligent network device or an external voice device. Then, perform noise reduction, voice activity detection and frame division processing on the collected voice data, and extract features such as MFCC (Mel-Frequency Cepstral Coefficients), pitch and formants of the voice as voiceprint information. Finally, input the voiceprint information into a pre-trained voiceprint recognition model to match the user's identity, age and gender, so as to determine the identity of the current user.
[0050] Optionally, based on the historical usage data of the intelligent network device, determine the common setting parameters, including: when the historical usage duration of the intelligent network device reaches a preset duration, determine the common setting parameters according to the historical setting parameters of the intelligent network device within the preset duration; or, when the historical usage duration of the intelligent network device does not reach the preset duration, determine that there are no common setting parameters.
[0051] As shown in combination with Figure 2 Another method for controlling an intelligent network device provided by an embodiment of the present disclosure includes:
[0052] S201, the processor determines the expected setting parameters according to the target factors affecting the parameter setting of the intelligent network device.
[0053] S202, the processor determines whether the historical usage duration of the intelligent network device reaches the preset duration. If so, execute S203; if not, execute S205.
[0054] S203, the processor determines the common setting parameters according to the historical setting parameters of the intelligent network device within the preset duration.
[0055] S204, the processor performs self-learning according to the expected setting parameters and the common setting parameters to obtain the target setting parameters of the intelligent network device.
[0056] S205, the processor performs self-learning according to the expected setting parameters to obtain the target setting parameters of the intelligent network device.
[0057] In this embodiment, in the self-learning function of the intelligent network device, historical usage data is an important basis for determining common setting parameters. By analyzing the historical setting parameters of the user within the preset duration, the system can determine the common setting parameters of the user, thereby providing a more personalized and comfortable experience for the user. In addition, when the historical usage duration does not reach the preset duration, the historical usage data may not be of reference significance, and the intelligent network device only performs self-learning according to the expected setting parameters.
[0058] Optionally, the value range of the preset duration includes 5 days to 10 days.
[0059] Optionally, determining the common setting parameters according to the historical setting parameters of the intelligent network device within the preset duration includes: obtaining multiple historical setting parameters of the intelligent network device within the preset duration, as well as the usage frequency and usage duration of each historical setting parameter; scoring each historical setting parameter according to the usage frequency and usage duration of each historical setting parameter to obtain a learning score; determining the historical setting parameter with the highest learning score as the common setting parameter.
[0060] In this embodiment, by analyzing the historical setting parameters within a preset duration, the user's frequently used setting parameters can be determined. Based on the usage records of the intelligent network device, the historical setting parameters of the user within the preset duration are obtained, and then scored according to the usage frequency and usage duration of the historical setting parameters. Finally, the historical setting parameter with the highest learning score is determined as the frequently used setting parameter. By analyzing the user's historical usage data, more accurate frequently used setting parameters can be provided, improving the user's comfort experience. In addition, according to the calculated learning scores, the learning scores can be divided into different intervals to represent the user's habit degree of a certain historical setting parameter. For example, the learning score interval is (0, 0.6], indicating that the user has a low habit degree for this historical setting parameter; the learning score interval is (0.6, 0.8]: indicating that the user has a medium habit degree for this historical setting parameter; the learning score interval is (0.8, 1]: indicating that the user has a high habit degree for this historical setting parameter.
[0061] Optionally, the learning score of the target historical setting parameter is calculated according to the following formula:
[0062]
[0063] Where S is the learning score, a0 is the number of times the target historical setting parameter is used, a is the number of times all historical setting parameters are used, α is the usage frequency weight coefficient, b0 is the usage duration of the target historical setting parameter, a is the usage duration of all historical setting parameters, and β is the usage duration weight coefficient.
[0064] Optionally, when calculating the learning score, the time decay factor is considered, and different weights are assigned to the historical setting parameters of each day. For example: the weight of the first day is 1; the weight of the second day is 0.9; the weight of the third day is 0.8.
[0065] Optionally, self-learning is performed based on the expected setting parameters and the frequently used setting parameters to obtain the target setting parameters of the intelligent network device, including: assigning corresponding weight coefficients to the expected setting parameters and the frequently used setting parameters; calculating the target setting parameters according to the expected setting parameters, the frequently used setting parameters, and the weight coefficients.
[0066] Combined with Figure 3 As shown, another method for controlling an intelligent network device provided by an embodiment of the present disclosure includes:
[0067] S301, the processor determines the expected setting parameters according to the target factors affecting the parameter setting of the intelligent network device.
[0068] S302, the processor determines the frequently used setting parameters according to the historical usage data of the intelligent network device.
[0069] S303, the processor assigns corresponding weight coefficients to the expected setting parameters and the common setting parameters.
[0070] S304, the processor calculates the target setting parameters according to the expected setting parameters, the common setting parameters, and the weight coefficients.
[0071] In the self - learning function of the intelligent network device, by comprehensively considering the expected setting parameters and the common setting parameters and assigning corresponding weight coefficients, the final target setting parameters can be calculated. The weight coefficient is used to represent the importance of the expected setting parameters and the common setting parameters when calculating the target setting parameters. The weight coefficient can be dynamically adjusted according to factors such as season, weather, and user preferences. For example: in high - temperature weather, the weight coefficient of the expected setting parameters can be appropriately increased; when a user frequently uses a certain setting parameter, the weight coefficient of the common setting parameter can be appropriately increased.
[0072] Taking the temperature setting of an intelligent air conditioner as an example, the corresponding weight coefficients assigned to the expected setting parameters and the common setting parameters are shown in Table 1.
[0073] Table 1
[0074]
[0075] When using Table 1 to determine the weight coefficients, it is necessary to first determine the expected setting parameters and the common setting parameters. For example, in the case where the current weather factor is outdoor temperature > 30°C, the first expected setting parameter is determined to be 25°C; according to the current regional factor, the set temperature with the highest usage frequency of users in the current area is determined as the second expected setting parameter; in the case where the current user is an adult, the third expected setting parameter is determined to be 26°C. Then, according to the historical usage data of the intelligent network device, it is determined whether there are common setting parameters. If there are common setting parameters and the learning score of the common setting parameter is 0.7, the second common temperature is determined as the common setting parameter, and the weight coefficients of the first expected setting parameter, the second expected setting parameter, the third expected setting parameter, and the common setting parameter are respectively: 0.15, 0.05, 0.05, and 0.65. If there are no common setting parameters, the weight coefficients of the first expected setting parameter, the second expected setting parameter, and the third expected setting parameter are respectively: 0.3, 0.2, 0.5.
[0076] Optionally, calculate the target setting parameter according to the following formula:
[0077]
[0078] where T is the target setting parameter, j is the number of expected setting parameters, j ≥ 1, t i is the expected setting parameter, w iis the weight coefficient for the desired setting parameter, t0 is the common setting parameter, w0 is the weight coefficient for the common setting parameter, and t is the compensation parameter.
[0079] In this embodiment, according to the actual situation, appropriate desired setting parameters can be selected. For example, when not considering user factors, only the first and second desired setting parameters can be selected for calculating the target setting parameter. In addition, if there is no common setting parameter, the weight coefficient w0 of the common setting parameter only needs to be set to 0.
[0080] In a specific embodiment, after the intelligent air conditioner in a certain area is turned on, it is recognized that the weather today is rainy and the outdoor humidity > 70%; the most frequently used set temperature by other users in this area is 26°C; the current user is an elderly person; according to the historical usage data of the intelligent air conditioner, the common setting parameter is determined to be 26°C and the learning score is 0.9. After obtaining the corresponding weight coefficient according to Table 1, if the compensation parameter is 26°C, the calculation formula for the target set temperature is:
[0081] T = 0.1×26 + 0.05×26 + 0.05×27 + 0.75×26 + (1 - 0.1 - 0.05 - 0.05 - 0.75)×26 = 26.05
[0082] After rounding the calculation result, the value of the target set temperature is 26°C.
[0083] Optionally, the method for controlling the intelligent network device further includes: during the use of the intelligent network device, monitoring the user's usage behavior; and dynamically adjusting the target setting parameter according to the user's usage behavior.
[0084] Combining Figure 4 As shown, the embodiments of the present disclosure provide another method for controlling an intelligent network device, including:
[0085] S401, the processor determines the desired setting parameter according to the target factors affecting the parameter setting of the intelligent network device.
[0086] S402, the processor determines the common setting parameter according to the historical usage data of the intelligent network device.
[0087] S403, the processor performs self-learning according to the desired setting parameter and the common setting parameter to obtain the target setting parameter of the intelligent network device.
[0088] S404, the processor monitors the user's usage behavior during the use of the intelligent network device.
[0089] S405, the processor dynamically adjusts the target setting parameter according to the user's usage behavior.
[0090] In this embodiment, by monitoring the user's usage behavior in real time and dynamically adjusting the target setting parameters according to these behaviors, the intelligence level and user experience of the intelligent network device can be further improved. According to the user's latest usage behavior, it can be determined whether the user's preference has changed. For example, if the user frequently lowers the temperature, it may indicate that the user prefers a lower temperature. According to the real-time monitored environmental data, the target setting parameters are dynamically adjusted. For example, when the outdoor temperature rises, the indoor set temperature is automatically lowered. According to the user's usage habits at different time periods, the target setting parameters are adjusted. For example, the user prefers a lower temperature at night and a higher temperature during the day. The adjusted target setting parameters are sent to the intelligent network device, and the user's usage behavior is continuously monitored to further optimize the target setting parameters.
[0091] Combined with Figure 5 As shown, an embodiment of the present disclosure provides a device 500 for controlling an intelligent network device, including a processor 501 and a memory 502. Optionally, the device may further include a communication interface 503 and a bus 504. Among them, the processor 501, the communication interface 503, and the memory 502 can communicate with each other through the bus 504. The communication interface 503 can be used for information transmission. The processor 501 can call the logical instructions in the memory 502 to execute the method for controlling the intelligent network device in the above embodiment.
[0092] In addition, when the logical instructions in the above-mentioned memory 502 are implemented in the form of a software functional unit and sold or used as an independent product, they can be stored in a computer-readable storage medium.
[0093] The memory 502, as a computer-readable storage medium, can be used to store software programs and computer-executable programs, such as the program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 501 executes functional applications and data processing by running the program instructions / modules stored in the memory 502, that is, implements the method for controlling the intelligent network device in the above embodiment.
[0094] The memory 502 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system and application programs required for at least one function; the storage data area can store data created according to the use of the terminal device, etc. In addition, the memory 502 may include a high-speed random access memory and may also include a non-volatile memory.
[0095] Combined with Figure 6As shown, an embodiment of the present disclosure provides an intelligent air conditioner 600, including: an intelligent air conditioner body, and the above-mentioned device 500 for controlling the intelligent network device. The device 500 for controlling the intelligent network device is installed on the intelligent air conditioner body. The installation relationship described here is not limited to being placed inside the intelligent air conditioner, but also includes installation connections with other components of the intelligent air conditioner, including but not limited to physical connections, electrical connections, or signal transmission connections, etc. Those skilled in the art can understand that the device 500 for controlling the intelligent network device can be adapted to a feasible intelligent air conditioner main body, thereby implementing other feasible embodiments.
[0096] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, and the computer-executable instructions are configured to execute the above-mentioned method for controlling the intelligent network device.
[0097] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The foregoing storage medium may be a non-transitory storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk, or an optical disc, etc., which are various media that can store program codes.
[0098] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure, enabling those skilled in the art to practice them. Other embodiments may include structural, logical, electrical, process, and other changes. Embodiments merely represent possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the terms used in this application are only for describing embodiments and do not limit the technical solutions described in this application. As used in the technical solutions described in this application, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to also include the plural forms. Similarly, as used in this application, the term "and / or" refers to any and all possible combinations including one or more of the associated listed items. Additionally, when used in this application, the term "comprise" and its variants "comprises" and / or "comprising" etc. mean the presence of the stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or groupings of these. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, or device comprising the element. In this article, what each embodiment focuses on can be the differences from other embodiments, and the same or similar parts among various embodiments can be referred to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method parts disclosed in the embodiments, the relevant parts can refer to the description of the method parts.
[0099] Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner can depend on the specific application and design constraints of the technical solution. The skilled person can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the embodiments of the present disclosure. The skilled person can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.
[0100] In the embodiments disclosed in this document, the disclosed methods, products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units can be merely a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms. The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to implement this embodiment. Additionally, in the embodiments of the present disclosure, each functional unit can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit.
[0101] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to the embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the block can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, which can depend 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 can also occur in a different order than that disclosed in the description. Sometimes, there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, which can depend on the functions involved. Each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.
Claims
1. A method for controlling an intelligent network device, characterized in that: include: Determine the expected setting parameters according to the target factors that affect the setting of intelligent network device parameters; Determine common setting parameters based on historical usage data of smart network devices; Self-learning is performed based on the expected setting parameters and commonly used setting parameters to obtain the target setting parameters of the intelligent network device.
2. The method according to claim 1, characterized in that The target factors include one or more of weather factors, geographical factors and user factors. According to the target factors affecting the parameter setting of the intelligent network device, the expected setting parameters are determined, including one or more of the following methods: Determining a first desired setting parameter according to current weather information in the area where the intelligent network device is located; Determine the second desired setting parameter according to the usage data of other users in the area where the smart network device is located in the current season; A third desired setting parameter is determined according to a current user of the intelligent network device.
3. The method according to claim 2, characterized in that The current user of the smart device is determined as follows: Get the voiceprint information of the current user; Based on the pre-trained voiceprint recognition model, the identity of the current user is determined according to the voiceprint information.
4. The method according to claim 1, characterized in that Based on the historical usage data of the smart network device, determine the commonly used setting parameters, including: When the historical usage time of the smart network device reaches a preset time, determining the commonly used setting parameters according to the historical setting parameters of the smart network device within the preset time; or, When the historical usage time of the intelligent network device does not reach the preset time, it is determined that there are no commonly used setting parameters.
5. The method according to claim 4, characterized in that According to the historical setting parameters of the intelligent network device within the preset time, the commonly used setting parameters are determined, including: Obtain multiple historical setting parameters of the intelligent network device within a preset time period, as well as the usage frequency and usage duration of each historical setting parameter; According to the usage frequency and usage duration of each historical setting parameter, score each historical setting parameter to obtain a learning score; The historical setting parameters with the highest learning score are determined as the commonly used setting parameters.
6. The method according to claim 1, characterized in that Self-learning is performed based on the expected setting parameters and common setting parameters to obtain the target setting parameters of the intelligent network device, including: Assign corresponding weight coefficients to the expected setting parameters and the commonly used setting parameters; Calculate target setting parameters based on expected setting parameters, common setting parameters and weight coefficients.
7. The method according to claim 6, characterized in that Calculate the target setting parameters according to the following formula: Where T is the target setting parameter, j is the number of expected setting parameters, j ≥ 1, t i Set the parameters for expectation, w i is the weight coefficient of the expected setting parameter, t0 is the common setting parameter, w0 is the weight coefficient of the common setting parameter, and t is the compensation parameter.
8. The method according to any one of claims 1 to 7, characterized in that: Also includes: During the use of the smart network device, monitor the user's usage behavior; Dynamically adjust target setting parameters based on user usage behavior.
9. A device for controlling an intelligent network device, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for controlling an intelligent network device according to any one of claims 1 to 8 when running the program instructions.
10. A smart air conditioner, characterized in that: include: Smart air conditioner body; The device for controlling a smart network device as claimed in claim 9 is installed on the smart air conditioner body.