Home control method and system based on intelligent ring and charging equipment

Abstract

The invention discloses a home control method and system based on an intelligent ring and charging equipment. The method comprises the following steps: acquiring user sensing data through the intelligent ring and acquiring environment sensing data through the charging equipment of the intelligent ring; according to the user sensing data and the environment sensing data, analyzing a user comfort index and an environment discomfort reason; determining a corresponding equipment execution instruction based on an equipment collaborative optimization algorithm according to the user comfort index and the environmental discomfort reason; and sending the equipment execution instruction to corresponding home equipment for execution so as to improve the user comfort index and the environmental discomfort reason. Therefore, accurate user comfort regulation and control based on multi-source sensing and intelligent optimization of the ring and the charging equipment can be realized, the self-adaptability and user experience of an intelligent home environment are improved, and the user health and comfort risks caused by the fact that the environment is not improved in time due to discomfort are reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a home control method and system based on a smart ring and charging device. Background Technology

[0002] With the rapid popularization of smart home systems in daily life, users are increasingly valuing the improvement of living comfort and health through adaptive environmental control. A key technical challenge is how to achieve precise comfort analysis and collaborative optimization of home devices to reduce environmental discomfort. Existing technologies typically collect user or environmental sensor data through a single device, using fixed thresholds or simple rules to adjust the operating status of home devices to maintain a comfortable indoor environment. However, existing solutions lack the fusion analysis of user physiological data from smart rings and environmental data from charging devices, as well as the dynamic execution of device collaborative optimization algorithms. This makes it difficult to accurately identify user comfort levels and the causes of environmental discomfort and generate targeted control commands. Commonly used isolated or static control strategies cannot adapt to individual differences and real-time changes, resulting in insufficient environmental adaptability. This can easily lead to health or comfort risks for users due to unaddressed environmental discomfort, limiting the user experience and actual effectiveness of smart home systems. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a home control method and system based on a smart ring and a charging device, which can realize precise user comfort control based on multi-source sensing and intelligent optimization of the ring and charging device, improve the adaptability of the smart home environment and user experience, and reduce the risk to user health and comfort caused by the failure to improve the environment in a timely manner.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a home control method based on a smart ring and a charging device, the method comprising: User sensor data is acquired through the smart ring, and environmental sensor data is acquired through the charging device of the smart ring. Based on the user sensor data and the environmental sensor data, analyze the user comfort index and the reasons for environmental discomfort; Based on the user comfort index and the reasons for environmental discomfort, the corresponding device execution instructions are determined using a device collaborative optimization algorithm. The device executes instructions to the corresponding home appliances to improve the user's comfort level and the causes of environmental discomfort.

[0005] As an optional implementation, in a first aspect of the invention, the user sensing data includes at least one of the user's heart rate data, HRV data, skin temperature data, and blood oxygen data.

[0006] As an optional implementation, in a first aspect of the invention, the environmental sensing data includes at least one of temperature data, humidity data, PM2.5 data, TVOC data, CO2 data, light intensity data, and noise data in the user's environment.

[0007] As an optional implementation, in the first aspect of the present invention, the step of analyzing the user comfort index and the causes of environmental discomfort based on the user sensing data and the environmental sensing data includes: The user sensor data and the environmental sensor data are combined into multi-source sensor data; The multi-source sensor data is input into the trained comfort prediction model to obtain the output comfort prediction result; the comfort prediction result includes the user comfort index and the corresponding environmental discomfort cause; the comfort prediction model is trained using a training dataset that includes multiple training multi-source sensor data and corresponding user comfort index labels and environmental cause labels that cause discomfort.

[0008] As an optional implementation, in the first aspect of the present invention, determining the corresponding device execution instruction based on the user comfort index and the cause of environmental discomfort using a device collaborative optimization algorithm includes: Determine whether the user comfort index meets the preset index decrease rule, and obtain the determination result; When the judgment result is yes, based on the device collaborative optimization algorithm, according to the user comfort index and the reasons for environmental discomfort, as well as the algorithm function related to energy consumption and comfort, the device execution instructions are calculated; the device execution instructions include control instructions corresponding to multiple home devices.

[0009] As an optional implementation, in the first aspect of the invention, the algorithm function is used to limit: The calculated energy consumption of the device corresponding to the executed instructions is minimized. The calculated environmental comfort effect corresponding to the device's execution instructions reaches its maximum.

[0010] As an optional implementation, in the first aspect of the invention, the algorithm function is used to limit: The calculated device execution instructions do not contain any of the multiple control instructions that conform to the preset device conflict rules.

[0011] As an optional implementation, in the first aspect of the present invention, the method further includes: After executing the device execution instructions, it continues to acquire new user sensor data and environmental sensor data; Based on the new user sensor data and environmental sensor data, a new user comfort index is analyzed. Determine whether the new user comfort index meets the preset improvement value rules to obtain a second determination result; If the second determination result is yes, the current device execution strategy is maintained; If the second judgment result is negative, a new device execution instruction is determined based on the device collaborative optimization algorithm and sent for execution.

[0012] A second aspect of this invention discloses a home control system based on a smart ring and a charging device, the system comprising: The acquisition module is used to acquire user sensor data through the smart ring and environmental sensor data through the charging device of the smart ring. The analysis module is used to analyze the user comfort index and the causes of environmental discomfort based on the user sensor data and the environmental sensor data. The determination module is used to determine the corresponding device execution instructions based on the user comfort index and the reasons for environmental discomfort, using a device collaborative optimization algorithm. The control module is used to send the device execution instructions to the corresponding home appliances for execution in order to improve the user's comfort index and the causes of environmental discomfort.

[0013] As an optional implementation, in a second aspect of the invention, the user sensing data includes at least one of the user's heart rate data, HRV data, skin temperature data, and blood oxygen data.

[0014] As an optional implementation, in a second aspect of the invention, the environmental sensing data includes at least one of temperature data, humidity data, PM2.5 data, TVOC data, CO2 data, light intensity data, and noise data in the user's environment.

[0015] As an optional implementation, in a second aspect of the invention, the specific method by which the analysis module analyzes the user comfort index and the causes of environmental discomfort based on the user sensor data and the environmental sensor data includes: The user sensor data and the environmental sensor data are combined into multi-source sensor data; The multi-source sensor data is input into the trained comfort prediction model to obtain the output comfort prediction result; the comfort prediction result includes the user comfort index and the corresponding environmental discomfort cause; the comfort prediction model is trained using a training dataset that includes multiple training multi-source sensor data and corresponding user comfort index labels and environmental cause labels that cause discomfort.

[0016] As an optional implementation, in a second aspect of the invention, the determining module determines the specific method of executing the corresponding device instruction based on the user comfort index and the cause of environmental discomfort, using a device collaborative optimization algorithm, including: Determine whether the user comfort index meets the preset index decrease rule, and obtain the determination result; When the judgment result is yes, based on the device collaborative optimization algorithm, according to the user comfort index and the reasons for environmental discomfort, as well as the algorithm function related to energy consumption and comfort, the device execution instructions are calculated; the device execution instructions include control instructions corresponding to multiple home devices.

[0017] As an optional implementation, in a second aspect of the invention, the algorithm function is used to limit: The calculated energy consumption of the device corresponding to the executed instructions is minimized. The calculated environmental comfort effect corresponding to the device's execution instructions reaches its maximum.

[0018] As an optional implementation, in a second aspect of the invention, the algorithm function is used to limit: The calculated device execution instructions do not contain any of the multiple control instructions that conform to the preset device conflict rules.

[0019] As an optional implementation, in a second aspect of the invention, the system is further configured to perform the following steps: After executing the device execution instructions, it continues to acquire new user sensor data and environmental sensor data; Based on the new user sensor data and environmental sensor data, a new user comfort index is analyzed. Determine whether the new user comfort index meets the preset improvement value rules to obtain a second determination result; If the second determination result is yes, the current device execution strategy is maintained; If the second judgment result is negative, a new device execution instruction is determined based on the device collaborative optimization algorithm and sent for execution.

[0020] A third aspect of the present invention discloses another home control system based on a smart ring and a charging device, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the home control method based on a smart ring and charging device disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the home control method based on a smart ring and a charging device disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires user sensor data through a smart ring and environmental sensor data through a charging device. By combining and analyzing user comfort index and the causes of environmental discomfort, it generates and executes home device commands based on a device collaborative optimization algorithm. This enables precise user comfort control based on multi-source sensing and intelligent optimization of the ring and charging device, improving the adaptability of the smart home environment and user experience, and reducing the risks to user health and comfort caused by the failure to address environmental discomfort in a timely manner. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart of a home control method based on a smart ring and a charging device disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a home control system based on a smart ring and charging device disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another home control system based on a smart ring and charging device disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a home control method and system based on a smart ring and a charging device. It acquires user sensor data through the smart ring and environmental sensor data through the charging device. By combining and analyzing user comfort index and the causes of environmental discomfort, it generates and executes home device commands based on a device collaborative optimization algorithm. This enables precise user comfort control through multi-source sensing and intelligent optimization based on the ring and charging device, improving the adaptability and user experience of the smart home environment, and reducing the health and comfort risks to users caused by untimely improvement of environmental discomfort. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a home control method based on a smart ring and a charging device, as disclosed in an embodiment of the present invention. Figure 1 The described home control method based on a smart ring and charging device can be applied to data processing systems / data processing devices / data processing servers (wherein the server includes a local processing server or a cloud processing server). For example... Figure 1 As shown, this home control method based on a smart ring and charging device may include the following operations: 101. Acquire user sensor data through the smart ring and acquire environmental sensor data through the charging device of the smart ring.

[0032] Optionally, the user sensor data includes at least one of the user's heart rate data, HRV data, skin temperature data, and blood oxygen data.

[0033] Optionally, the environmental sensing data may include at least one of the following: temperature data, humidity data, PM2.5 data, TVOC data, CO2 data, light intensity data, and noise data in the user's environment.

[0034] 102. Based on user sensor data and environmental sensor data, analyze the user comfort index and the reasons for environmental discomfort.

[0035] 103. Based on the user comfort index and the reasons for environmental discomfort, determine the corresponding device execution instructions based on the device collaborative optimization algorithm.

[0036] 104. Send the device execution command to the corresponding home device for execution to improve the user's comfort level and the cause of environmental discomfort.

[0037] As can be seen, the above-mentioned embodiments of the invention acquire user sensor data through a smart ring and environmental sensor data through a charging device. By combining and analyzing the user comfort index and the causes of environmental discomfort, and generating and executing home device commands based on a device collaborative optimization algorithm, it is possible to achieve precise user comfort control based on multi-source sensing and intelligent optimization of the ring and charging device, improve the adaptability of the smart home environment and user experience, and reduce the risks to user health and comfort caused by the failure to improve environmental discomfort in a timely manner.

[0038] As an optional embodiment, the step above, analyzing the user comfort index and the causes of environmental discomfort based on user sensor data and environmental sensor data, includes: Combine user sensor data and environmental sensor data into multi-source sensor data; Multi-source sensor data is input into a trained comfort prediction model to obtain the output comfort prediction results.

[0039] Optionally, the comfort prediction results include the user comfort index and the corresponding environmental discomfort reasons.

[0040] Optionally, the comfort prediction model is trained using a training dataset that includes multiple training multi-source sensor data and corresponding user comfort index labels and environmental cause labels that cause discomfort.

[0041] As can be seen, through the above optional embodiments, by combining user sensor data and environmental sensor data into multi-source sensor data input to a trained comfort prediction model, the user comfort index and the cause of environmental discomfort are directly output. Thus, based on accurate comfort analysis, the multi-modal fusion prediction model improves the accuracy of analysis and the ability to trace causes, providing a high-quality decision basis for equipment collaborative optimization and reducing the risk of misjudgment of comfort caused by a single data source.

[0042] As an optional embodiment, the step described above, determining the corresponding device execution command based on the device collaborative optimization algorithm according to the user comfort index and the cause of environmental discomfort, includes: Determine whether the user comfort index meets the preset index decrease rule, and obtain the judgment result; When the judgment result is yes, based on the device collaborative optimization algorithm, according to the user comfort index and the reasons for environmental discomfort, as well as the algorithm functions related to energy consumption and comfort, the device execution instructions are calculated; the device execution instructions include control instructions corresponding to multiple home devices.

[0043] Optionally, the algorithm function is used to limit: The calculated device execution instructions correspond to the minimum device energy consumption. The calculated environmental comfort effect corresponding to the device execution instructions is maximized.

[0044] Optionally, the algorithm function is used to limit: The calculated device execution instructions do not contain any of the multiple control instructions that conform to the preset device conflict rules.

[0045] As can be seen, through the above optional embodiments, by determining whether the user comfort index triggers the decline rule and calculating the device execution command based on the device collaborative optimization algorithm when it is triggered, the security of the command is improved through multi-objective constraint optimization, realizing green and efficient collaboration of smart homes, and reducing the risk of energy waste and adjustment failure caused by device conflicts or over-adjustment.

[0046] As an optional embodiment, the method further includes: After executing the device's execution instructions, it continues to acquire new user sensor data and environmental sensor data; A new user comfort index is analyzed based on new user sensor data and environmental sensor data; Determine whether the new user comfort index meets the preset improvement value rules to obtain the second judgment result; If the second judgment result is yes, the current device execution strategy is maintained; If the second judgment result is negative, a new device execution instruction is determined based on the device collaborative optimization algorithm and sent for execution.

[0047] As can be seen, through the above optional embodiments, by continuously acquiring new sensor data and evaluating new user comfort indices after executing device commands, and deciding whether to maintain or re-optimize strategies based on whether improvement rules are met, the continuity and adaptability of the adjustment process are improved through closed-loop feedback and dynamic re-optimization on the basis of precise comfort control, ensuring long-term comfort maintenance and reducing the risk of repeated comfort changes caused by environmental changes after a one-time adjustment.

[0048] In one specific implementation scheme, a smart home linkage control system based on the multi-source data fusion of a smart ring and a charging box, as disclosed in the embodiments of this invention, is realized. It utilizes the smart ring and charging box as fixed environmental monitoring nodes and the smart ring as a mobile physiological monitoring node. Through a cloud-based AI platform, it fuses and analyzes environmental and human data, thereby achieving automated, personalized, and precise control of smart home devices. Specifically, current mainstream smart home control systems mainly have the following limitations: 1. Limited triggering conditions: Control relies heavily on preset timers, geofencing, or single sensor thresholds (e.g., turning on the air conditioner when the temperature exceeds 26°C). This "if-then" rule is static and fails to consider the overall environmental quality, let alone the user's physical condition and actual needs.

[0049] 2. Lack of human-centered perception: The system only knows "what's wrong with the environment," not "what's wrong with the people." For example, the system detects that the temperature is moderate, but the user may feel cold due to a cold; the room's CO2 concentration is slowly rising but not exceeding the standard, yet the user has already begun to feel stuffy and tired. Existing systems cannot respond to such subtle, proactive changes in needs.

[0050] 3. Poor inter-device coordination: Equipment such as air conditioners, air purifiers, fresh air systems, and lights usually work independently, lacking a coordinated control strategy based on the overall optimization of the "human-environment" system.

[0051] In this solution, the smart ring and its charging case offer a completely new data dimension and technological approach to addressing the aforementioned issues. The charging case serves as a fixed, powerful environmental monitoring station, while the ring acts as a mobile, personal physiological monitor. By fusing the data from both, a smart home control system can be constructed that simultaneously senses the state of both the "person" and the "environment" and makes optimal decisions. The specific system includes: 1. Data perception layer: Environmental monitoring charging box: As a fixed node, it continuously collects environmental data such as temperature, humidity, PM2.5, TVOC, CO2, light intensity, and noise, and uploads them via Wi-Fi.

[0052] Smart ring: As a mobile node, it continuously collects physiological data such as user heart rate, HRV, skin temperature, and blood oxygen, and uploads them via Bluetooth relay.

[0053] 2. Intelligent Decision-Making Layer (Cloud AI Platform): Multi-source data fusion module: Receives and synchronizes environmental and physiological data in time.

[0054] Comfort and Health Quantification Model: Calculates an individual's health and comfort index and analyzes the main causes of discomfort (such as "heat discomfort" and "stuffy air").

[0055] Home appliance control strategy engine: It includes a device collaborative optimization algorithm. Its decision logic is not a simple on / off switch, but rather an optimization problem that aims to "maximize human health and comfort with minimal energy consumption." For example, in winter, it prioritizes humidification over increasing the air conditioner temperature to improve comfort; when CO2 levels are high but PM2.5 levels are good, it prioritizes turning on the fresh air system over the air purifier.

[0056] 3. Execution layer, including: Smart home devices include air conditioners, fresh air systems, air purifiers, humidifiers / dehumidifiers, smart lights, curtains, etc.

[0057] Smart home gateway / cloud: Receives control command sets from the cloud AI platform and distributes them to specific execution devices.

[0058] Specifically, one example of the home appliance linkage control method implemented in this solution includes the following steps: S1: Continuous data collection and uploading: The charging case and ring continuously collect environmental and physiological data and upload them to the cloud.

[0059] S2: Status Assessment and Root Cause Analysis: The cloud-based AI model calculates the current overall comfort index and determines the dominant factor causing the index to decline (is it heat, air, or light?).

[0060] S3: Generate the optimal set of device control strategies: Based on the root cause analysis results, the control strategy engine generates a set of coordinated control instructions. For example, if the determination is that "high CO2 concentration causes a decrease in user HRV and stuffiness," then the strategy is: {"Device 1": "Fresh air system", "Action": "On to medium speed", "Duration": "30 minutes"}, {"Device 2": "Air conditioner", "Action": "Lower by 1℃"}.

[0061] S4: Command Execution and Effect Verification: Control commands are sent to the corresponding devices for execution via the smart home gateway. The system continuously monitors changes in environmental and physiological data to verify the control effect. If the effect does not meet expectations, a new round of decision optimization (closed-loop feedback) is triggered.

[0062] Specifically, taking a winter nighttime sleep scenario as an example, we can illustrate a practical application process of this invention: 1. Data Sensing: The charging case detected that the bedroom humidity dropped to 30% (dry), while the CO2 concentration slowly rose to 1300ppm. The ring detected a slight decrease in the user's skin temperature and a decrease in HRV.

[0063] 2. Status Assessment: Cloud-based AI calculated that the health and comfort index had declined, and the root cause analysis result was: "Mainly dry air, accompanied by mild hypoxia".

[0064] 3. Strategy Generation: The control engine determines the optimal solution: prioritizing the drying problem while simultaneously assisting with ventilation in a low-energy-consumption manner. The generated instruction set is: {"Humidifier": "On", "Target Humidity": "45%"}, {"Fresh Air Unit": "On Low", "Duration": "20 minutes"}. This avoids directly raising the air conditioning temperature, which is more energy-intensive.

[0065] 4. Execution and Verification: After the equipment was executed, the system monitored a steady increase in humidity and a slow decrease in CO2 concentration, while the user's HRV data gradually returned to normal. The system determined that the control strategy was effective and maintained the current state.

[0066] 5. The next morning, the user woke up without feeling dry or stuffy, having had a high-quality sleep, without any manual intervention required.

[0067] In summary, the embodiments of the present invention have the following advantages: 1. Foresight and proactivity: It can detect environmental problems in advance through subtle changes in physiological data before users feel obvious discomfort, and proactively adjust accordingly to achieve "unnoticeable" comfort.

[0068] 2. True humanistic control: The core of control decisions is the user's physiological feedback, rather than cold environmental parameter thresholds, truly "serving people".

[0069] 3. Multi-device collaborative optimization: Starting from the overall optimal approach, a device linkage strategy is formulated to avoid conflicts between devices (such as dehumidifying and humidifying being turned on at the same time), thereby improving efficiency and saving energy.

[0070] 4. Ultimate Personalized Experience: The system learns the preferences of different users in different states to provide a customized environmental experience for every member of the family.

[0071] 5. Value closed loop: A complete closed loop of "perception-analysis-decision-execution-verification" is formed, making the system more and more intelligent the more it is used.

[0072] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a home control system based on a smart ring and charging device, as disclosed in an embodiment of the present invention. Figure 2The described home control system based on a smart ring and charging device can be applied to data processing systems / data processing devices / data processing servers (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 2 As shown, the home control system based on the smart ring and charging device may include: The acquisition module 201 is used to acquire user sensor data through the smart ring and environmental sensor data through the charging device of the smart ring.

[0073] Analysis module 202 is used to analyze user comfort index and causes of environmental discomfort based on user sensor data and environmental sensor data.

[0074] The determination module 203 is used to determine the corresponding device execution instructions based on the user comfort index and the reasons for environmental discomfort, using a device collaborative optimization algorithm.

[0075] The control module 204 is used to send device execution instructions to the corresponding home appliances for execution in order to improve user comfort and address environmental discomfort.

[0076] As can be seen, the above-mentioned embodiments of the invention acquire user sensor data through a smart ring and environmental sensor data through a charging device. By combining and analyzing the user comfort index and the causes of environmental discomfort, and generating and executing home device commands based on a device collaborative optimization algorithm, it is possible to achieve precise user comfort control based on multi-source sensing and intelligent optimization of the ring and charging device, improve the adaptability of the smart home environment and user experience, and reduce the risks to user health and comfort caused by the failure to improve environmental discomfort in a timely manner.

[0077] As an optional embodiment, the user sensing data includes at least one of the user's heart rate data, HRV data, skin temperature data, and blood oxygen data.

[0078] As can be seen, the above optional embodiments limit the content of user sensor data, enabling the smart ring to accurately and comprehensively acquire the physiological characteristics related to the user's experience, assisting in the intelligent optimization of precise user comfort control, improving the adaptability of the smart home environment and user experience, and reducing the risk to user health and comfort caused by the failure to promptly improve unsuitable environments.

[0079] As an optional embodiment, the environmental sensing data includes at least one of the following: temperature data, humidity data, PM2.5 data, TVOC data, CO2 data, light intensity data, and noise data in the user's environment.

[0080] As can be seen, the above optional embodiments limit the content of environmental sensing data, enabling the charging device of the smart ring to accurately and comprehensively acquire user experience-related features in the environment, assisting in achieving precise user comfort control through intelligent optimization, improving the adaptability and user experience of the smart home environment, and reducing the risk to user health and comfort caused by the failure to promptly address unsuitable environments.

[0081] As an optional embodiment, the analysis module analyzes the user comfort index and the specific reasons for environmental discomfort based on user sensor data and environmental sensor data, including: Combine user sensor data and environmental sensor data into multi-source sensor data; Multi-source sensor data is input into a trained comfort prediction model to obtain the output comfort prediction result; optionally, the comfort prediction result includes the user comfort index and the corresponding environmental discomfort cause; the comfort prediction model is trained using a training dataset that includes multiple training multi-source sensor data and corresponding user comfort index labels and environmental cause labels that cause discomfort.

[0082] As can be seen, through the above optional embodiments, by combining user sensor data and environmental sensor data into multi-source sensor data input to a trained comfort prediction model, the user comfort index and the cause of environmental discomfort are directly output. Thus, based on accurate comfort analysis, the multi-modal fusion prediction model improves the accuracy of analysis and the ability to trace causes, providing a high-quality decision basis for equipment collaborative optimization and reducing the risk of misjudgment of comfort caused by a single data source.

[0083] As an optional embodiment, the determining module determines the specific method of executing the corresponding device instruction based on the user comfort index and the reasons for environmental discomfort, using a device collaborative optimization algorithm, including: Determine whether the user comfort index meets the preset index decrease rule, and obtain the judgment result; When the judgment result is yes, based on the device collaborative optimization algorithm, according to the user comfort index and the reasons for environmental discomfort, as well as the algorithm functions related to energy consumption and comfort, the device execution instructions are calculated; the device execution instructions include control instructions corresponding to multiple home devices.

[0084] As can be seen, through the above optional embodiments, by determining whether the user comfort index triggers a decline rule and calculating the device execution command based on the device collaborative optimization algorithm when triggered, the green and efficient collaboration of smart homes can be achieved on the basis of precise home device control.

[0085] As an optional implementation, the algorithm function is used to define: The calculated device execution instructions correspond to the minimum device energy consumption. The calculated environmental comfort effect corresponding to the device execution instructions is maximized.

[0086] As can be seen, through the above optional embodiments, by limiting the algorithm function so that the calculation of the device collaborative optimization algorithm takes into account the rule of "lowest energy consumption and highest comfort", the energy-saving and effectiveness of the instructions are improved through target constraint optimization, realizing green and efficient collaboration of smart homes and reducing energy waste and adjustment failure risk caused by over-adjustment.

[0087] As an optional implementation, the algorithm function is used to define: The calculated device execution instructions do not contain any of the multiple control instructions that conform to the preset device conflict rules.

[0088] As can be seen, through the above optional embodiments, by limiting the algorithm function so that the calculation of the device collaborative optimization algorithm takes into account the rule of no device conflict, the security of the instruction is improved through target constraint optimization, realizing green and efficient collaboration of smart homes, and reducing the risk of energy waste and adjustment failure caused by device conflict or over-adjustment.

[0089] As an optional embodiment, the system is also used to perform the following steps: After executing the device's execution instructions, it continues to acquire new user sensor data and environmental sensor data; A new user comfort index is analyzed based on new user sensor data and environmental sensor data; Determine whether the new user comfort index meets the preset improvement value rules to obtain the second judgment result; If the second judgment result is yes, the current device execution strategy is maintained; If the second judgment result is negative, a new device execution instruction is determined based on the device collaborative optimization algorithm and sent for execution.

[0090] As can be seen, through the above optional embodiments, by continuously acquiring new sensor data and evaluating new user comfort indices after executing device commands, and deciding whether to maintain or re-optimize strategies based on whether improvement rules are met, the continuity and adaptability of the adjustment process are improved through closed-loop feedback and dynamic re-optimization on the basis of precise comfort control, ensuring long-term comfort maintenance and reducing the risk of repeated comfort changes caused by environmental changes after a one-time adjustment.

[0091] Example 3 Please see Figure 3 , Figure 3 This is another home control system based on a smart ring and charging device disclosed in the embodiments of the present invention. Figure 3The described home control system based on a smart ring and charging device is applied in a data processing system / data processing device / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, the home control system based on the smart ring and charging device may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the home control method based on a smart ring and a charging device described in Embodiment 1.

[0092] Example 4 This invention discloses a computer read storage medium storing a computer program for electronic data exchange, wherein the computer program causes a computer to execute the steps of the home control method based on a smart ring and charging device described in Embodiment 1.

[0093] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the home control method based on a smart ring and charging device described in Embodiment 1.

[0094] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0096] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0097] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0098] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0099] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0100] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0101] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0102] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0103] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0104] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0105] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0106] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0107] Finally, it should be noted that the home control method and system based on a smart ring and charging device disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A home control method based on a smart ring and a charging device, characterized in that, The method includes: User sensor data is acquired through the smart ring, and environmental sensor data is acquired through the charging device of the smart ring. Based on the user sensor data and the environmental sensor data, analyze the user comfort index and the reasons for environmental discomfort; Based on the user comfort index and the reasons for environmental discomfort, the corresponding device execution instructions are determined using a device collaborative optimization algorithm. The device executes instructions to the corresponding home appliances to improve the user's comfort level and the causes of environmental discomfort.

2. The home control method based on a smart ring and charging device according to claim 1, characterized in that, The user sensor data includes at least one of the user's heart rate data, HRV data, skin temperature data, and blood oxygen data.

3. The home control method based on a smart ring and charging device according to claim 1, characterized in that, The environmental sensing data includes at least one of the following: temperature data, humidity data, PM2.5 data, TVOC data, CO2 data, light intensity data, and noise data in the user's environment.

4. The home control method based on a smart ring and charging device according to claim 1, characterized in that, The step of analyzing the user comfort index and the causes of environmental discomfort based on the user sensor data and the environmental sensor data includes: The user sensor data and the environmental sensor data are combined into multi-source sensor data; The multi-source sensor data is input into the trained comfort prediction model to obtain the output comfort prediction result; the comfort prediction result includes the user comfort index and the corresponding environmental discomfort cause; the comfort prediction model is trained using a training dataset that includes multiple training multi-source sensor data and corresponding user comfort index labels and environmental cause labels that cause discomfort.

5. The home control method based on a smart ring and charging device according to claim 1, characterized in that, The step of determining the corresponding device execution instructions based on the user comfort index and the reasons for environmental discomfort, using a device collaborative optimization algorithm, includes: Determine whether the user comfort index meets the preset index decrease rule, and obtain the determination result; When the judgment result is yes, based on the device collaborative optimization algorithm, according to the user comfort index and the reasons for environmental discomfort, as well as the algorithm function related to energy consumption and comfort, the device execution instructions are calculated; the device execution instructions include control instructions corresponding to multiple home devices.

6. The home control method based on a smart ring and charging device according to claim 5, characterized in that, The algorithm function is used to limit: The calculated energy consumption of the device corresponding to the executed instructions is minimized. The calculated environmental comfort effect corresponding to the device's execution instructions reaches its maximum.

7. The home control method based on a smart ring and charging device according to claim 5, characterized in that, The algorithm function is used to limit: The calculated device execution instructions do not contain any of the multiple control instructions that conform to the preset device conflict rules.

8. The home control method based on a smart ring and charging device according to claim 1, characterized in that, The method further includes: After executing the device execution instructions, it continues to acquire new user sensor data and environmental sensor data; Based on the new user sensor data and environmental sensor data, a new user comfort index is analyzed. Determine whether the new user comfort index meets the preset improvement value rules to obtain a second determination result; If the second determination result is yes, the current device execution strategy is maintained; If the second judgment result is negative, a new device execution instruction is determined based on the device collaborative optimization algorithm and sent for execution.

9. A home control system based on a smart ring and charging device, characterized in that, The system includes: The acquisition module is used to acquire user sensor data through the smart ring and environmental sensor data through the charging device of the smart ring. The analysis module is used to analyze the user comfort index and the causes of environmental discomfort based on the user sensor data and the environmental sensor data. The determination module is used to determine the corresponding device execution instructions based on the user comfort index and the reasons for environmental discomfort, using a device collaborative optimization algorithm. The control module is used to send the device execution instructions to the corresponding home appliances for execution in order to improve the user's comfort index and the causes of environmental discomfort.

10. A home control system based on a smart ring and charging device, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the home control method based on a smart ring and charging device as described in any one of claims 1-8.

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