Private domain control method, device and equipment of intelligent air conditioning system and medium

Abstract

The invention discloses a private domain control method, device and equipment of an intelligent air conditioning system and a medium. The method is applied to a local intelligent air conditioning system and comprises the steps that intelligent equipment in linkage with air conditioning equipment and a preset linkage rule are determined according to linkage information input by a user; the air conditioning equipment and the intelligent equipment are controlled to operate according to the preset linkage rule, and historical use data of a user are collected; generating an automatic adjustment strategy through a preset local private domain large model according to the historical use data of the user and a preset linkage rule; and the operation states of the air conditioning equipment and the intelligent equipment are controlled according to the automatic adjustment strategy. By means of the method, the problem that in the prior art, the safety and individuality of an intelligent air conditioner system are limited can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart homes, and in particular to a private domain control method, device, equipment and medium for a smart air-conditioning system. Background Art

[0002] With the rapid advancement of science and technology, smart home technology is changing people's lifestyles at an unprecedented speed. Traditional air conditioners have gradually transformed into smart air conditioners. With the power of the Internet and applications, users can remotely control air conditioning equipment anytime and anywhere, enjoying an unprecedented convenient experience. However, most of the current smart air conditioning systems rely on cloud artificial intelligence to operate. While this model brings convenience, it also causes a series of problems. Among them, the risk of privacy leakage and strong network dependence have become the two most prominent challenges. Sensitive information such as users' air conditioning usage habits and preferences are at risk of being leaked during cloud processing, and once the network is interrupted, the function of the smart air conditioning system will be greatly reduced. As a result, it is difficult to achieve truly safe and personalized intelligent control in the smart air conditioning system in the existing technology. Summary of the invention

[0003] The embodiments of the present invention provide a private domain control method, device, equipment and medium for an intelligent air-conditioning system, aiming to solve the problems of limited security and personalization of the intelligent air-conditioning system in the prior art.

[0004] In the first aspect, an embodiment of the present invention provides a private domain control method for an intelligent air-conditioning system, which is applied to a local intelligent air-conditioning system, including: determining the intelligent device to be linked with the air-conditioning device and the preset linkage rules according to the linkage information input by the user; controlling the operation of the air-conditioning device and the intelligent device according to the preset linkage rules, and collecting user historical usage data; generating an automatic adjustment strategy through a preset local private domain big model according to the user historical usage data and the preset linkage rules; and controlling the operating status of the air-conditioning device and the intelligent device according to the automatic adjustment strategy.

[0005] In the second aspect, an embodiment of the present invention also provides a private domain control device for an intelligent air-conditioning system, which is applied to a local intelligent air-conditioning system, and includes: a linkage unit, for determining the intelligent device to be linked with the air-conditioning device and the preset linkage rules according to the linkage information input by the user; a collection unit, for controlling the operation of the air-conditioning device and the intelligent device according to the preset linkage rules, and collecting the user's historical usage data; a generation unit, for generating an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and the preset linkage rules; and a control unit, for controlling the operating status of the air-conditioning device and the intelligent device according to the automatic adjustment strategy.

[0006] In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the computer program.

[0007] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a computer program, wherein the computer program includes program instructions, and the program instructions can implement the above method when executed by a processor.

[0008] The embodiment of the present invention provides a private domain control method, device, equipment and medium of an intelligent air conditioning system, which is applied to a local intelligent air conditioning system, and includes: determining the intelligent device and preset linkage rules that are linked with the air conditioning device according to the linkage information input by the user; controlling the operation of the air conditioning device and the intelligent device according to the preset linkage rules, and collecting the user's historical usage data; generating an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and the preset linkage rules; and controlling the operation status of the air conditioning device and the intelligent device according to the automatic adjustment strategy. The embodiment of the present invention determines the intelligent device and preset linkage rules that are linked with the air conditioning device according to the user's selection, so as to determine which intelligent devices will work in linkage with the air conditioning device according to the specific needs or preferences of the user, and controls the operation of the air conditioning device and the intelligent device according to the preset linkage rules, so as to realize the automation and convenience of the smart home. At the same time, the user's usage habits and historical data will be collected and recorded to provide a basis for subsequent analysis and optimization. According to the collected user's historical usage data and the preset linkage rules, an automatic adjustment strategy is generated through a preset local private domain big model, so as to generate a more refined and personalized automatic adjustment strategy according to the user's preferences and behavior patterns, so as to optimize the operation efficiency of the device and the comfort of the user. By generating automatic adjustment strategies based on the local private domain big model, continuous learning and personalized training of user data can be achieved, so that local smart air conditioners can adapt to user usage habits more intelligently, and user privacy data does not need to be uploaded to the cloud, thereby increasing data confidentiality, reducing dependence on cloud platforms, and reducing network load and resource consumption, thereby making the smart air conditioning system more secure and personalized. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0010] Figure 1 A schematic diagram of a flow chart of a private domain control method for an intelligent air-conditioning system provided by an embodiment of the present invention; Figure 2 A schematic diagram of a first sub-process of a private domain control method for an intelligent air-conditioning system provided by an embodiment of the present invention; Figure 3 A schematic diagram of a second sub-process of the private domain control method of the intelligent air-conditioning system provided by an embodiment of the present invention; Figure 4 A schematic diagram of a third sub-process of the private domain control method for the intelligent air-conditioning system provided by an embodiment of the present invention; Figure 5 A schematic diagram of a fourth sub-process of the private domain control method of the intelligent air-conditioning system provided by an embodiment of the present invention; Figure 6 A schematic diagram of a fifth sub-process of the private domain control method of the intelligent air-conditioning system provided by an embodiment of the present invention; Figure 7 A schematic diagram of a sixth sub-process of the private domain control method for the intelligent air-conditioning system provided by an embodiment of the present invention; Figure 8 A schematic block diagram of a private domain control device for an intelligent air conditioning system provided by an embodiment of the present invention; Fig. 9 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0013] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0014] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] See also Figure 1 , Figure 1 A flowchart of a private domain control method for an intelligent air-conditioning system provided in an embodiment of the present invention. The private domain control method for an intelligent air-conditioning system in this embodiment can be applied to a local intelligent air-conditioning system, in which a local private domain large model trained locally is provided. The model runs within the user's home network, and the processing and storage of data are limited to the home, isolated from the external network, thereby ensuring the privacy of user data. By adopting the method of the present invention, the confidentiality of data can be increased, the dependence on the cloud platform can be reduced, and the network load and resource consumption can be reduced, thereby making the intelligent air-conditioning system more secure and personalized.

[0016] Figure 1 1 is a flow chart of a private domain control method of an intelligent air conditioning system provided by an embodiment of the present invention. As shown in the figure, the method includes the following steps S110-S140.

[0017] S110. Determine a smart device to be linked with the air-conditioning device and preset linkage rules according to linkage information input by the user.

[0018] In this embodiment, the smart device refers to other devices that can be accessed through the network and controlled by the smart home system, such as smart bulbs, smart sockets, smart door locks, smart curtains, etc., in addition to the air conditioner. The linkage information is the initial configuration information of the air conditioner and the linkage device set by the user through the user interaction page when the system is installed and used for the first time. Specifically, the linkage information includes the user-defined smart devices and linkage rules for linkage with the air conditioner. The preset linkage rule refers to the automatic coordination rules between the smart device and the air conditioner set in advance by the user according to his or her living habits and needs. For example, the preset linkage rule can be set as follows: when the air conditioner is turned on and set to the "cooling" mode, and the temperature is lower than 22°C, the living room lights are automatically dimmed to dim the lights during cooling to create a comfortable night atmosphere; when the user activates the "sleep mode" of the air conditioner, the living room lighting and TV are automatically turned off, and the door is locked. Turn off other unnecessary devices when entering the sleep mode to ensure safety and energy saving, and provide users with a good sleeping environment. By determining the smart devices and linkage rules for linkage with the air conditioner according to the linkage information input by the user, the automation and intelligent control of the smart home system can be realized.

[0019] S120: Control the operation of the air-conditioning device and the smart device according to the preset linkage rule, and collect user historical usage data.

[0020] In this embodiment, the user historical usage data is the data set or generated by the user when the air conditioner and the smart device are used. For example, the use time, temperature setting, wind speed adjustment and other information of the air conditioner; the switch time, brightness adjustment and other information of the smart light bulb. The air conditioner and the smart device are controlled to operate according to the preset linkage rules. Specifically, when the sensor (such as temperature sensor, light sensor, etc.) in the smart home system detects the conditions that meet the preset linkage rules, the system will automatically trigger the corresponding action to control the air conditioner and the smart device to operate according to the preset rules. For example, when the temperature sensor detects that the indoor temperature is higher than 28 degrees Celsius, the air conditioner will be automatically turned on and adjusted to the cooling mode; at the same time, if the light sensor detects that the indoor light is insufficient, the smart light bulb will be automatically turned on and the user's usage data will be collected at all times. Specifically, the local smart air conditioner system will collect data, send instructions or update status from a variety of different devices through a unified data interface. The user's historical usage data is collected when the air conditioner and the smart device are controlled to operate according to the preset linkage rules to analyze the user's usage habits, so as to further optimize the preset linkage rules and provide more personalized services.

[0021] In one embodiment, if Figure 2 As shown, the step S120 includes steps S121-S122.

[0022] S121, connecting the smart device to a preset local proxy module through a preset data interface or communication protocol; S122: Controlling the operation of the smart device according to the preset linkage rule through the preset local proxy module.

[0023] In this embodiment, the preset data interface or communication protocol is a data interface (such as API, Bluetooth, Zigbee, Wi-Fi, etc.) or a communication protocol (such as MQTT, Zigbee, Matter, etc.) supported by the smart device, and the preset local proxy module is a bridge between the smart device and the control system. It receives instructions from the control system, then converts these instructions into a format that the smart device can understand, and sends them to the smart device. At the same time, it can also collect the status information of the smart device, and define and store the collected information in a collection manner according to the corresponding device, and feed it back to the control system. Specifically, the local proxy module refers to an intermediate layer or module for unifying the interface so that the system does not need to process the interface and communication logic of each device separately. Among them, there is no limitation on the specific local proxy module, and it can realize the corresponding function. Among them, it can be understood that the linked smart device is a device that can provide real-time status data (such as on / off status, working mode, current temperature or humidity) and support control instructions (such as adjusting temperature, changing mode, etc.), such as lighting, dehumidifiers, air purifiers, curtains and other devices. After the smart device is connected to the preset local proxy module through the preset data interface or communication protocol, the corresponding control parameters can be sent to the smart device through the local proxy module. For example, if the rule stipulates that all lights are automatically turned off at a specific time, then when this time point is reached, the local proxy module will send corresponding instructions to all smart bulbs to turn them off. By controlling the operation of the smart device according to the preset local proxy module, the system does not need to process the interface and communication logic of each device separately, and the smart home system can automatically adjust according to the needs of the user, providing a more convenient and comfortable living or working environment.

[0024] S130: Generate an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and preset linkage rules.

[0025] In this embodiment, the preset local private domain big model refers to a learning model deployed locally and specifically used to process specific fields or tasks. It has a high degree of customization and can be trained and optimized for specific scenarios or business needs. At the same time, since it is deployed locally, the security and privacy of the data can be ensured. According to the user's historical usage data and the preset linkage rules, an automatic adjustment strategy is generated through the preset local private domain big model. Specifically, the user's historical usage data is input into the preset local private domain big model, and the model matches and analyzes the input data according to the preset linkage rules. Based on the analysis result model, an automatic adjustment strategy that meets user needs and preset goals is automatically generated. The generated automatic adjustment strategy may include specific measures such as device setting adjustment and resource allocation optimization. For example, the user manually reduces the air conditioning temperature from 24°C to 22°C many times. Then record and learn the user's operations, and generate automatic adjustment strategies based on their behavioral habits. That is, on summer nights, an automatic adjustment strategy that automatically sets the temperature to 22°C will be generated to meet the user's preferences. If the user has not made manual adjustments for a long time, it will further confirm that this is the user's ideal temperature to reduce the frequency of manual adjustments. Or the user feedback is "a bit cold", and the current temperature is set to 22°C. Then the "cold" command is identified and combined with the current temperature setting, room humidity and other parameters to determine that the user needs a higher temperature. Based on the training data and the user's comfort tendency, an automatic adjustment strategy is generated to raise the temperature to 24°C, while evaluating the humidity and airflow in the room to ensure air circulation. By generating an automatic adjustment strategy based on the preset local private domain large model, the system's intelligence level and user experience are improved while ensuring data privacy.

[0026] In one embodiment, if Figure 3 As shown, the step S130 also includes steps S1301-S1302 before the step S130.

[0027] S1301, constructing a user behavior model through a preset domain algorithm according to the linkage information and the preset linkage rules; S1302: Train the preset local private domain big model according to the user behavior model.

[0028] In this embodiment, the preset domain algorithm is an algorithm for constructing and optimizing user behavior modeling in the smart home control scenario, such as a machine learning algorithm, a deep learning algorithm, a statistical model, etc. The user behavior model is constructed by the preset domain algorithm according to the linkage information and the preset linkage rules. Specifically, the user's characteristics and behavior patterns are extracted according to the initial configuration information of the air conditioner and linkage devices set by the user through the user interaction page when the system is installed and used for the first time and the preset linkage rules set, and a user behavior model is constructed based on these characteristics and behavior patterns through the preset domain algorithm (such as a collaborative filtering algorithm). Among them, there is no limitation on the specific domain algorithm, and an algorithm that can construct and optimize user behavior modeling is sufficient. The preset local private domain large model is trained according to the user behavior model, wherein the user behavior model is used as an important input data or feature set to guide the training and optimization of the model. Specifically, the training process usually includes data preprocessing, model initialization, parameter update, performance evaluation and other steps, wherein the specific training process is not limited. By extracting user characteristics and behavior patterns from linkage information, building a user behavior model, and using this model to train the preset local private domain large model, the system can more accurately understand user behavior, predict user needs, and provide users with more personalized and intelligent services.

[0029] In one embodiment, if Figure 4 As shown, step S1302 includes steps S1303-S1304.

[0030] S1303, determining whether the user's historical usage data reaches a training critical value; S1304: If reached, retrain and update the preset local private domain big model according to the user's historical usage data.

[0031] In this embodiment, the training critical value is a preset threshold or condition used to determine whether the currently collected user historical usage data is rich, diverse or representative enough to be used to retrain or optimize the machine learning model. The setting of the critical value can be based on multiple dimensions such as the quantity, quality, diversity, and time span of the data, and there is no limitation on this. To determine whether the user historical usage data reaches the training critical value, specifically, the user historical usage data of the user and the air conditioner and its linkage devices can be continuously collected through the data acquisition module. These data include not only the user's explicit operations (such as adjusting the temperature, mode switching, etc.), but also implicit data (such as power consumption, device working time, usage habits, etc.). And the user's historical usage data will be asynchronously executed into the preset local private domain big model. If the user's historical usage data reaches the training critical value, the preset local private domain big model will be retrained and updated according to the user's historical usage data. Specifically, all or part of the new data can be used to retrain the model, or the preset local private domain big model can be optimized by fine-tuning, wherein the specific retraining method is not limited. By retraining and updating the preset local private domain large model based on the user's historical usage data, the model can better adapt to current user needs, thereby improving the model's accuracy, generalization ability and user experience.

[0032] In one embodiment, if Figure 5 As shown, step S130 also includes steps S1305-S1306.

[0033] S1305: judging whether the operating parameters set by the user are within a preset reasonable range according to the preset health standard, the user's historical usage data, and the preset local private domain big model; S1306. If exceeded, the operating parameters are automatically adjusted to within the preset reasonable range and / or corresponding health reminder information is generated.

[0034] In this embodiment, the preset health standard is a standard or threshold for evaluating whether a health-related indicator is within a normal or expected range, which can be formulated according to scientific research, medical guidelines or industry consensus, and is not limited to this. The operating parameters set by the user are parameters set by the user on the device according to their own needs or preferences. The preset reasonable range is a parameter range that is considered reasonable or acceptable based on the preset health standard and the user's historical usage data, and analyzed by the updated local private domain big model. According to the preset health standard, the user's historical usage data and the preset local private domain big model, it is judged whether the operating parameters set by the user are within the preset reasonable range. For example, when the user sets the air conditioner operating temperature, the preset local private domain big model can judge whether the operating parameters set by the user are within the preset reasonable range according to its internal health standards, or national health standards (such as indoor air index standards, etc.) combined with the user's historical data. If it is found that the air conditioner temperature is too low and not within the preset reasonable range, the temperature is automatically adjusted to a more reasonable range, or a warm reminder is provided to protect the user's health. By judging whether the operating parameters set by the user are within the preset reasonable range, the corresponding operating parameters are adjusted to ensure the health and safety of the user.

[0035] S140. Control the operating status of the air-conditioning device and the smart device according to the automatic adjustment strategy.

[0036] In this embodiment, the automatic adjustment strategy is a strategy generated by the local private domain big model based on information such as user historical usage data, which is used to automatically adjust the operating status of air-conditioning equipment and smart devices. According to the automatic adjustment strategy, instructions are sent to the corresponding air-conditioning equipment and smart devices, requiring them to operate according to the parameters and modes specified by the strategy. These instructions may include turning on / off the device, adjusting the temperature / humidity / light intensity, etc. For example, if the user feedback is "a bit cold" and the current temperature is set to 22°C, an automatic adjustment strategy for adjusting the temperature to 26 degrees Celsius is generated, and an instruction to adjust the temperature to 26 degrees Celsius is sent to the air-conditioning equipment to control the operating status of the air-conditioning equipment. By controlling the operating status of the air-conditioning equipment and the smart device according to the automatic adjustment strategy, a more convenient, comfortable and efficient living and office experience is provided to the user.

[0037] In one embodiment, if Figure 6 As shown, step S140 also includes steps S1401-S1402.

[0038] S1401. If the regional linkage information set by the user is received, the device data of the new air-conditioning device and the new smart device in the area to be connected are connected to the preset local private domain big model, wherein the regional linkage information includes the new air-conditioning device and the new smart device in the area to be connected; S1402. Linkage control is performed on the new air-conditioning equipment and the new smart device through the preset local private domain big model.

[0039] In this embodiment, the regional linkage information is the information of the smart devices that need to be linked in other areas set by the user according to actual needs. The user can decide whether to access the devices that need to be managed by agents in other areas through the configuration interface. Specifically, the current local private domain large model is limited to the local air conditioner and the associated smart devices. The user can choose to connect the air conditioning equipment and smart devices in other areas (for example, other floors or other buildings) to the local smart air conditioning system to uniformly control and link the multiple connected air conditioners. If the regional linkage information set by the user is received, the device data of the new air conditioning equipment and the new smart device in the area to be connected is connected to the preset local private domain large model. The large model will automatically adjust the working state of the device according to the linkage rules set by the user and the real-time collected device data (such as the current indoor temperature, humidity, device status, etc.) to achieve the linkage effect. Among them, the area to be connected is the newly added area selected by the user, such as the first floor area, then the air conditioning and other smart devices in the first floor area selected by the user are new air conditioning equipment and new smart devices. The new air-conditioning equipment and the new smart device are controlled in linkage through the preset local private domain big model. For example, the preset linkage rule sets that when the indoor temperature exceeds 26°C, the air conditioner automatically turns on and adjusts to the cooling mode, and the electric fan controlled by the smart socket also starts automatically. Then, when the big model detects that the indoor temperature of the newly connected first-floor area exceeds 26°C, the air-conditioning equipment and the electric fan on the first floor will be controlled to perform these operations. Among them, the user can also modify the permissions of the local private domain big model so that it can access the smart device data in the entire local area network to perform linkage control on other air-conditioning equipment and new smart devices. By performing linkage control on the new air-conditioning equipment and the new smart device according to the regional linkage information, a wider range of linkage control can be achieved.

[0040] In one embodiment, if Figure 7 As shown, step S140 also includes steps S1403-S1404.

[0041] S1403, storing the user's historical usage data; S1404: If a cleaning instruction and / or a marking instruction is received from a user, the corresponding historical usage data of the user is cleared according to the cleaning instruction, and / or the corresponding historical usage data of the user is marked as not participating in training according to the marking instruction.

[0042] In this embodiment, the preset local private domain large model will give priority to storing data that has a significant impact on the user's daily habits, such as long-term temperature settings, air conditioning working modes and other user historical usage data. The intelligent air conditioning system allows users to manage training data, and users can choose to regularly clean up historical data, such as data in certain time periods. If a cleaning instruction issued by the user is received, the corresponding user historical usage data is found according to the conditions specified in the instruction (such as time range, data type, etc.), and deleted from the storage. If a marking instruction issued by the user is received, the corresponding user historical usage data is found according to the conditions specified in the instruction and marked. This mark may be a simple flag bit or a more detailed metadata to indicate that these data should not be used for training models. By clearing and marking the data according to the instructions issued by the user, the user data is respected and protected, allowing the user to control the storage and use of the data according to their own needs. By providing two operations, cleaning and marking, the user's needs for privacy protection are met, and the possibility of data analysis and personalized recommendations is retained.

[0043] Figure 8 is a schematic block diagram of a private domain control device 200 of an intelligent air conditioning system provided by an embodiment of the present invention. Figure 8 As shown, corresponding to the private domain control method of the above intelligent air conditioning system, the present invention also provides a private domain control device of the intelligent air conditioning system. The private domain control device of the intelligent air conditioning system includes a unit for executing the private domain control method of the above intelligent air conditioning system, and the device can be configured in a desktop computer, a tablet computer, a laptop computer, etc. For details, please refer to Figure 8 The private domain control device of the intelligent air-conditioning system includes a linkage unit 210, a collection unit 220, a generation unit 230 and a control unit 240.

[0044] The linkage unit 210 is used to determine the smart device to be linked with the air-conditioning device and preset linkage rules according to the linkage information input by the user.

[0045] The collection unit 220 is used to control the operation of the air-conditioning device and the smart device according to the preset linkage rules, and collect user historical usage data.

[0046] In one embodiment, the collection unit 220 includes a connection unit and a rule control unit.

[0047] A connection unit, used to connect the smart device to a preset local proxy module via a preset data interface or communication protocol; A rule control unit is used to control the operation of the smart device according to the preset linkage rule through the preset local proxy module.

[0048] The generating unit 230 is used to generate an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and preset linkage rules.

[0049] In one embodiment, the generating unit 230 includes a constructing unit and a training unit.

[0050] A construction unit, configured to construct a user behavior model through a preset domain algorithm according to the linkage information and the preset linkage rules; A training unit is used to train the preset local private domain large model according to the user behavior model.

[0051] In one embodiment, the generating unit 230 includes a determining unit and an updating unit.

[0052] A judging unit, used to judge whether the user's historical usage data reaches a training critical value; An updating unit is used to retrain and update the preset local private domain large model according to the user's historical usage data if the requirement is reached.

[0053] In one embodiment, the generating unit 230 includes a range determining unit and an adjusting unit.

[0054] A range judgment unit, used to judge whether the operating parameters set by the user are within a preset reasonable range according to the preset health standard, the user's historical usage data and the preset local private domain big model; The adjustment unit is used to automatically adjust the operating parameters to within the preset reasonable range and / or generate corresponding health warning information if the range is exceeded.

[0055] The control unit 240 is used to control the operating status of the air-conditioning device and the smart device according to the automatic adjustment strategy.

[0056] In one embodiment, the control unit 240 includes a configuration unit.

[0057] an access unit, configured to access the device data of the new air-conditioning device and the new smart device in the area to be accessed to the preset local private domain macro model if receiving the area linkage information set by the user, wherein the area linkage information includes the new air-conditioning device and the new smart device in the area to be accessed; A linkage control unit is used to perform linkage control on the new air-conditioning equipment and the new smart device through the preset local private domain big model.

[0058] In one embodiment, the control unit 240 includes a configuration unit.

[0059] A storage unit, used to store the user's historical usage data; A clearing unit is used to, if receiving a clearing instruction and / or marking instruction issued by a user, clear the corresponding user historical usage data according to the clearing instruction, and / or mark the corresponding user historical usage data as not participating in training according to the marking instruction.

[0060] It should be noted that technical personnel in the relevant field can clearly understand that the specific implementation process of the private domain control device 200 and each unit of the above-mentioned intelligent air-conditioning system can refer to the corresponding description in the aforementioned method embodiment, and for the convenience and conciseness of the description, it will not be repeated here.

[0061] The private domain control device of the above-mentioned intelligent air conditioning system can be implemented in the form of a computer program, which can be used in Fig. 9 Runs on the computer device shown.

[0062] See also Fig. 9 , Fig. 9 5 is a schematic block diagram of a computer device provided in an embodiment of the present application. The computer device 500 may be a terminal or a server, wherein the terminal may be an electronic device with communication functions such as a smart phone, a tablet computer, a laptop computer, a desktop computer, a personal digital assistant, and a wearable device. The server may be an independent server or a server cluster composed of multiple servers.

[0063] See also Fig. 9 The computer device 500 includes a processor 502 , a memory, and a network interface 505 connected via a system bus 501 , wherein the memory may include a non-volatile storage medium 503 and an internal memory 504 .

[0064] The non-volatile storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032 includes program instructions, and when the program instructions are executed, the processor 502 can execute a private domain control method for an intelligent air conditioning system.

[0065] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500 .

[0066] The internal memory 504 provides an environment for the operation of the computer program 5032 in the non-volatile storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can execute a private domain control method for an intelligent air-conditioning system.

[0067] The network interface 505 is used to communicate with other devices over the network. Fig. 9The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application, and does not constitute a limitation on the computer device 500 to which the solution of the present application is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0068] The processor 502 is used to run a computer program 5032 stored in the memory to implement the steps of the above method.

[0069] It should be understood that in the embodiment of the present application, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0070] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0071] Therefore, the present invention also provides a storage medium. The storage medium may be a computer-readable storage medium. The storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the steps of the above method.

[0072] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0073] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0074] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0075] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0076] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0077] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A private domain control method for an intelligent air conditioning system, characterized in that: The method is applied to a local intelligent air conditioning system, and the method comprises: Determine the smart device to be linked with the air-conditioning equipment and preset linkage rules according to the linkage information input by the user; Controlling the operation of the air conditioning equipment and the smart device according to the preset linkage rules, and collecting historical user usage data; Generate an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and preset linkage rules; The operating states of the air conditioning device and the smart device are controlled according to the automatic adjustment strategy.

2. The method according to claim 1, characterized in that The step of controlling the operation of the air conditioning device and the smart device according to the preset linkage rule comprises: Connecting the smart device to a preset local proxy module via a preset data interface or communication protocol; The operation of the smart device is controlled by the preset local proxy module according to the preset linkage rule.

3. The method according to claim 1, characterized in that Before the step of generating an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and the preset linkage rules, the method includes: Constructing a user behavior model through a preset domain algorithm according to the linkage information and the preset linkage rules; The preset local private domain large model is trained according to the user behavior model.

4. The method according to claim 1, characterized in that: After the step of training the preset local private domain large model according to the user behavior model, the method further includes: Determining whether the user's historical usage data reaches a training critical value; If reached, the preset local private domain big model is retrained and updated according to the user's historical usage data.

5. The method according to claim 4, characterized in that After the step of generating an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and the preset linkage rules, the method includes: Determine whether the operating parameters set by the user are within a preset reasonable range according to the preset health standards, the user's historical usage data, and the preset local private domain big model; If exceeded, the operating parameters will be automatically adjusted to within the preset reasonable range and / or corresponding health reminder information will be generated.

6. The method according to claim 1, characterized in that After the step of controlling the operating states of the air conditioning device and the smart device according to the automatic adjustment strategy, the method further comprises: If the regional linkage information set by the user is received, the device data of the new air-conditioning device and the new smart device in the area to be connected are connected to the preset local private domain big model, wherein the regional linkage information includes the new air-conditioning device and the new smart device in the area to be connected; The new air-conditioning equipment and the new smart device are controlled in linkage through the preset local private domain big model.

7. The method according to claim 1, characterized in that After the step of controlling the operating states of the air conditioning device and the smart device according to the automatic adjustment strategy, the method further comprises: Storing the user's historical usage data; If a cleaning instruction and / or a marking instruction is received from the user, the corresponding user historical usage data is cleared according to the cleaning instruction, and / or the corresponding user historical usage data is marked as not participating in training according to the marking instruction.

8. A private domain control device for an intelligent air conditioning system, characterized in that: The device is applied to a local intelligent air conditioning system, and the device comprises: A linkage unit, used to determine the smart device to be linked with the air-conditioning device and preset linkage rules according to the linkage information input by the user; A collection unit, used to control the operation of the air-conditioning device and the smart device according to the preset linkage rules, and collect user historical usage data; A generating unit, configured to generate an automatic adjustment strategy through a preset local private domain big model according to the user's historical usage data and preset linkage rules; A control unit is used to control the operating status of the air-conditioning device and the smart device according to the automatic adjustment strategy.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1 to 7 can be implemented.

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