Internet smart home energy management system

Abstract

The invention relates to the technical field of smart home, in particular to an Internet smart home energy management system, which is characterized in that an energy management prediction module detects energy consumption of a plurality of devices; calculating the overall energy consumption according to the energy consumption of the plurality of devices; a prediction time window is set by a user; the overall energy consumption of the prediction time window is obtained through prediction based on the ARIMA model, and then the overall energy consumption is displayed; after the automatic scene adjusting module judges that the overall energy consumption does not exceed a preset energy consumption threshold value, physiological data of the user is monitored, and then a preset score-emotion-control table is inquired according to the emotional state of the user to obtain a control instruction to control equipment; the remote control module is used for a user to select a remote control instruction, calculating newly increased energy consumption and overall energy consumption of equipment of the remote control instruction to obtain estimated energy consumption, and then displaying a prompt instruction after judging that the newly increased energy consumption and the overall energy consumption exceed a preset energy consumption threshold value; and after judging that the newly increased energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold value, controlling the equipment according to the remote control instruction.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart home, and in particular relates to an Internet smart home energy management system. Background Art

[0002] With the rapid development of social economy and the improvement of people's living standards, energy demand continues to grow, and energy management becomes more complex. Especially with the popularization of smart homes and smart buildings, users' personalized needs for energy management are becoming increasingly obvious. However, many traditional energy management systems often fail to meet this personalized demand, resulting in unsatisfactory overall energy management results and poor user experience. First, the problem of lack of personalized prediction needs is becoming increasingly prominent. When using smart home devices, users often hope that the system can accurately predict and manage energy consumption based on their living habits, usage patterns, and real-time environmental changes. For example, family members' schedules, daily activities, and seasonal changes will affect the energy consumption requirements of the device. If the system cannot be personalized based on these factors, users will face unnecessary energy waste and economic burden. Secondly, traditional energy management systems usually rely on static data analysis and prediction models. These models fail to fully consider users' dynamic needs and environmental changes, resulting in insufficient prediction accuracy. Therefore, users may encounter problems such as frequent switching of devices and energy consumption exceeding expectations in actual use, which not only causes economic losses, but also has a negative impact on users' comfort experience. For example, during the high temperatures of summer, if the control of air-conditioning equipment cannot respond to the needs of users in a timely manner, it may lead to uncomfortable indoor temperature and affect the living experience. Furthermore, the user interface design of energy management systems is often not friendly, and it is difficult for users to quickly and conveniently obtain the required information and make settings. Traditional control methods usually require users to have certain professional knowledge, which increases the complexity of operation. In this context, users often feel at a loss and then resist the use of smart devices, which is also one of the important factors leading to poor user experience. In addition, the lack of an effective feedback mechanism is also an important factor affecting energy management and user experience. Users hope to get real-time feedback when managing energy consumption and understand the energy consumption and efficiency of the equipment. If the system cannot provide clear feedback, users will find it difficult to evaluate their energy usage habits and lack the motivation to improve. Therefore, an Internet smart home energy management system is proposed. Summary of the invention

[0003] The present invention aims to solve the technical problem that the lack of personalized energy prediction demand causes poor energy management and user experience, and provides an Internet smart home energy management system.

[0004] The technical solution adopted by the present invention to solve the technical problem is: an Internet smart home energy management system, including an energy management prediction module, an automatic scene adjustment module, and a remote control module.

[0005] The energy management prediction module is used to detect the energy consumption of several devices and then store it. It is also used to calculate the overall energy consumption based on the energy consumption of several devices and then send it to the automatic scene adjustment module. It is also used for users to set the prediction time window. It is also used to predict the overall energy consumption of the prediction time window based on the ARIMA model and then display it.

[0006] The automatic scene adjustment module is electrically connected to the lighting equipment, music equipment, air conditioning equipment, and electric curtains, and is network-connected to the energy management prediction module, and is used to determine whether the overall energy consumption exceeds the preset energy consumption threshold after receiving the overall energy consumption. And after determining that the overall energy consumption does not exceed the preset energy consumption threshold, it is used to monitor the user's physiological data, and then calculate the user's emotional state score by calculating the physiological data, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control the lighting equipment, music equipment, air conditioning equipment, and electric curtains.

[0007] The remote control module is connected to the energy management prediction module through a network, and is used for allowing the user to select a remote control instruction, parsing the device controlled by the remote control instruction, and then calculating the additional energy consumption and the overall energy consumption of the device of the remote control instruction to obtain the estimated energy consumption, and then judging whether the additional energy consumption and the overall energy consumption exceed the preset energy consumption threshold. It is also used to generate a prompt instruction after judging that the additional energy consumption and the overall energy consumption exceed the preset energy consumption threshold, and then display the prompt instruction. It is also used to control the device according to the remote control instruction after judging that the additional energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold.

[0008] Furthermore, the energy management prediction module includes several smart meters, edge computing layer, analysis module, and display module.

[0009] A number of smart meters are electrically connected to a number of devices and connected to a remote control module network to detect the energy consumption of a number of devices, and then send the energy consumption of a number of devices to the edge computing layer and the remote control module.

[0010] The edge computing layer is connected to several smart meters, automated scene adjustment modules, and remote control modules for users to set time intervals. After receiving the energy consumption of several devices, it calculates the overall energy consumption based on the energy consumption of several devices, and then sends the overall energy consumption to the automated scene adjustment module, analysis module, and remote control module at the set time interval.

[0011] The analysis module is connected to the edge computing layer network, and is used to store the overall energy consumption after continuously receiving it. It is also used for the user to set the prediction time window. It is also used to predict the overall energy consumption of the prediction time window based on the ARIMA model according to the overall energy consumption of the stored K+1 time intervals, and then send it to the display module.

[0012] The display module is connected to the analysis module network and is used for displaying the overall energy consumption after receiving it.

[0013] Furthermore, the edge computing layer calculates the overall energy consumption based on the energy consumption of several devices as follows:

[0014] Where P total is the overall energy consumption in kw / h, P i is the energy consumption of the i-th device, i∈[1,n], in kw / h.

[0015] Furthermore, the analysis module predicts the overall energy consumption of the predicted time window based on the ARIMA model according to the overall energy consumption of the stored K+1 time intervals. The formula is:

[0016] P total (t+Δt)=f(Ptotal(t),P total (t-1), ..., P total (tk)),

[0017] Where P total (t+Δt) is the overall energy consumption of the prediction time window, in kw / h, Δt is the prediction time window, in h, t, t-1...tk are K+1 time intervals, in h, P total (t), P total (t-1)...P total (tk) is the overall energy consumption of the stored K+1 time intervals, in kw / h.

[0018] Furthermore, the automatic scene adjustment module includes a controller, a wearable device,

[0019] The controller is connected to the edge computing layer network, and is used to determine whether the overall energy consumption exceeds a preset energy consumption threshold after receiving the overall energy consumption. And after determining that the overall energy consumption does not exceed the preset energy consumption threshold, it is used to generate a start signal and then send it to the wearable device.

[0020] The wearable device is electrically connected to the controller, lighting equipment, music equipment, air-conditioning equipment, and electric curtains, and is used to monitor the user's physiological data after receiving a start signal, and then obtain the user's emotional state score by calculating the physiological data, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control the lighting equipment, music equipment, air-conditioning equipment, and electric curtains.

[0021] Furthermore, wearable devices include heart rate monitors, blood oxygen monitors, respiratory sensors, MCUs,

[0022] The heart rate monitor is used to monitor the user's heart rate and then send it to the MCU.

[0023] The blood oxygen detector is used to monitor the user's blood oxygen saturation and then send it to the MCU.

[0024] The breathing sensor is used to monitor the user's breathing rate and then send it to the MCU.

[0025] MCU is electrically connected to the heart rate monitor, blood oxygen monitor, breathing sensor, lighting equipment, music equipment, air conditioning equipment, and electric curtains.

[0026] It is used to normalize the heart rate, blood oxygen saturation and respiratory rate to obtain the normalized heart rate, normalized blood oxygen saturation and normalized respiratory rate after receiving the heart rate, blood oxygen saturation and respiratory rate, and then calculate the user's emotional state score according to the normalized heart rate, normalized blood oxygen saturation, normalized respiratory rate and a preset weight coefficient, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control lighting equipment, music equipment, air-conditioning equipment and electric curtains.

[0027] Furthermore, the MCU normalizes the heart rate, the preset maximum heart rate, and the preset minimum heart rate to obtain the normalized heart rate formula:

[0028] Among them, HR norm is the normalized heart rate, HR is the heart rate, the unit is beats / minute, HR min The preset minimum heart rate, in beats per minute, HR max The preset maximum heart rate, in beats per minute.

[0029] And the formula for normalized blood oxygen saturation is calculated by normalizing the blood oxygen saturation, the preset maximum blood oxygen saturation value, and the preset minimum blood oxygen saturation value:

[0030] in, is the normalized heart rate, SpO 2 is the blood oxygen saturation, in percentage, HR min The preset minimum blood oxygen saturation value is expressed in percentage, HR max The preset maximum value of blood oxygen saturation is expressed in percentage.

[0031] And the formula for normalizing the respiratory frequency, calculating the preset maximum respiratory frequency, and the preset minimum respiratory frequency to obtain the normalized respiratory frequency is:

[0032] Among them, RR norm is the normalized respiratory rate, RR is the respiratory rate, in times / minute, RR min The preset minimum respiratory rate, in times / minute, RR max It is the preset maximum respiratory rate, in times / minute.

[0033] Furthermore, the MCU calculates the user's emotional state score according to the normalized heart rate, normalized blood oxygen saturation, normalized respiratory rate, and a preset weight coefficient:

[0034] Where S is the user's emotional state score, is the preset weight coefficient.

[0035] Furthermore, the remote control module includes a web front end and an edge server.

[0036] The web front end is used for users to select remote control instructions, and then send the remote control instructions to the edge server. It is also used for displaying the prompt instructions after receiving the prompt instructions.

[0037] The edge server is connected to the web front end, several smart meters, the edge computing layer, and several device networks. After receiving the remote control command, the overall energy consumption, and the energy consumption of several devices, it is used to parse the device controlled by the remote control command, and then calculate the new energy consumption and the overall energy consumption of the device of the remote control command to obtain the estimated energy consumption, and then determine whether the new energy consumption and the overall energy consumption exceed the preset energy consumption threshold. And after determining that the new energy consumption and the overall energy consumption exceed the preset energy consumption threshold, it is used to generate a prompt instruction and then send it to the web front end. And after determining that the new energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold, it is used to control the device according to the remote control command.

[0038] Furthermore, the edge server calculates the additional energy consumption of the device activated by the remote control command and the overall energy consumption to obtain the estimated energy consumption formula:

[0039] P totalnew =Ptotal +ΔP,

[0040] Among them, P totalnew is the estimated energy consumption in kw / h, P total is the overall energy consumption in kw / h, and ΔP is the additional energy consumption of the device under remote control instructions in kw / h.

[0041] Beneficial effects of the present invention:

[0042] 1. Through the energy management prediction module, the energy consumption data of multiple devices is monitored and stored in real time, the overall energy consumption is calculated, and the user is allowed to customize the prediction time window. The energy consumption is predicted through the ARIMA model to improve the accuracy of the prediction, provide data support for users, and help them optimize energy use.

[0043] 2. The automatic scene adjustment module can determine whether the overall energy consumption exceeds the preset threshold and automatically adjust the device operation. At the same time, by monitoring the user's physiological data, assessing their emotional state, and intelligently adjusting the environment settings, the user's comfort and experience are improved.

[0044] 3. The remote control module allows users to select and execute control commands through the web front end and manage the equipment flexibly. It can calculate the new energy consumption in real time, determine whether it exceeds the preset energy consumption threshold, and generate prompt instructions when necessary to enhance users' energy-saving awareness and ensure safe and reasonable energy consumption management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the system module of the present invention;

[0046] Figure 2 Schematic diagram of a wearable device of the present invention. DETAILED DESCRIPTION

[0047] The following will clearly and completely describe the concept and technical effects of the present invention in combination with the embodiments, so as to fully understand the purpose, features and effects of the present invention. Figure 1 , Figure 2 .

[0048] An Internet smart home energy management system includes an energy management prediction module, an automatic scene adjustment module, and a remote control module.

[0049] The energy management prediction module is used to detect the energy consumption of several devices and then store it. It is also used to calculate the overall energy consumption based on the energy consumption of several devices and then send it to the automatic scene adjustment module. It is also used for users to set the prediction time window. It is also used to predict the overall energy consumption of the prediction time window based on the ARIMA model and then display it.

[0050] The automatic scene adjustment module is electrically connected to the lighting equipment, music equipment, air conditioning equipment, and electric curtains, and is network-connected to the energy management prediction module, and is used to determine whether the overall energy consumption exceeds the preset energy consumption threshold after receiving the overall energy consumption. And after determining that the overall energy consumption does not exceed the preset energy consumption threshold, it is used to monitor the user's physiological data, and then calculate the user's emotional state score by calculating the physiological data, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control the lighting equipment, music equipment, air conditioning equipment, and electric curtains.

[0051] The remote control module is connected to the energy management prediction module through a network, and is used for allowing the user to select a remote control instruction, parsing the device controlled by the remote control instruction, and then calculating the additional energy consumption and the overall energy consumption of the device of the remote control instruction to obtain the estimated energy consumption, and then judging whether the additional energy consumption and the overall energy consumption exceed the preset energy consumption threshold. It is also used to generate a prompt instruction after judging that the additional energy consumption and the overall energy consumption exceed the preset energy consumption threshold, and then display the prompt instruction. It is also used to control the device according to the remote control instruction after judging that the additional energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold.

[0052] In this embodiment, the energy management prediction module can detect the energy consumption of multiple devices in real time and calculate the overall energy consumption. Based on the ARIMA model, energy consumption prediction can be performed to know the energy consumption of a certain time window in the future in advance. This energy consumption prediction function not only improves the accuracy of energy management, but also provides data support for users to help them optimize energy use. The automatic scene adjustment module can further evaluate the user's emotional state based on the user's physiological data when it detects that the overall energy consumption does not exceed the preset threshold. According to the emotional state, the preset control scheme is matched, and a more comfortable environment can be automatically created for the user by adjusting the lighting, music, air conditioning, electric curtains and other equipment. This makes the system not only focus on energy saving, but also provide users with a better life experience. The remote control module enables users to operate home appliances through remote commands. After receiving the user's remote control command, it can not only control the operation of the device, but also calculate the additional energy consumption generated by the command, and compare it with the current overall energy consumption to determine whether it exceeds the preset threshold. If it exceeds, a warning is issued to prevent unnecessary high energy consumption; if it does not exceed, the device is operated normally according to the command. This function not only improves the convenience of remote operation, but also further strengthens the control and management of overall energy consumption. Through energy consumption prediction, automatic adjustment, user physiological status monitoring, and remote control command dual judgment, it can save energy while intelligently providing users with a comfortable living environment. This dynamic balance greatly reduces unnecessary energy consumption and improves energy efficiency while maximizing user comfort.

[0053] In this embodiment, the energy management prediction module includes several smart meters, an edge computing layer, an analysis module, and a display module.

[0054] A number of smart meters are electrically connected to a number of devices and connected to a remote control module network to detect the energy consumption of a number of devices, and then send the energy consumption of a number of devices to the edge computing layer and the remote control module.

[0055] The edge computing layer is connected to several smart meters, automated scene adjustment modules, and remote control modules for users to set time intervals. After receiving the energy consumption of several devices, it calculates the overall energy consumption based on the energy consumption of several devices, and then sends the overall energy consumption to the automated scene adjustment module, analysis module, and remote control module at the set time interval.

[0056] The analysis module is connected to the edge computing layer network, and is used to store the overall energy consumption after continuously receiving it. It is also used for the user to set the prediction time window. It is also used to predict the overall energy consumption of the prediction time window based on the ARIMA model according to the overall energy consumption of the stored K+1 time intervals, and then send it to the display module.

[0057] The display module is connected to the analysis module network and is used for displaying the overall energy consumption after receiving it.

[0058] In this embodiment, through the electrical connection between several smart meters and multiple devices, the energy consumption of each device can be monitored in real time, and the data can be transmitted to the edge computing layer. This ensures the comprehensiveness and timeliness of energy consumption data, so that the energy usage of equipment in a home or building can be accurately grasped. Through the edge computing layer, the received energy consumption data of multiple devices can be processed and calculated to quickly obtain the overall energy consumption. Edge computing also allows users to flexibly set time intervals, and transmit overall energy consumption data to the automated scene adjustment module, analysis module, and remote control module in different time periods to achieve efficient and timely data transmission. The analysis module is based on the ARIMA model and predicts energy consumption based on the overall energy consumption data of K+1 time intervals. This makes it possible to predict energy consumption in the future in advance, provide data support for users, and help them make reasonable energy use plans. This prediction function greatly improves the intelligence level of energy consumption management and avoids energy waste. Through the display module, users can view the overall energy consumption data and energy consumption prediction results in real time. This visual method provides users with intuitive energy usage information, which is convenient for users to monitor and adjust energy consumption strategies and help achieve energy saving effects. Through the collaborative work of smart meters, edge computing, analysis modules, and display modules, not only can the energy consumption of equipment be monitored, but also future energy consumption can be intelligently predicted and displayed intuitively through the display module. This multi-module collaborative work method improves the overall efficiency and control effect. It allows users to set time intervals and prediction time windows, providing a high degree of flexibility. Users can adjust the energy consumption monitoring frequency and prediction cycle according to actual needs, further improving the practicality and operability of the system.

[0059] In this embodiment, the edge computing layer calculates the overall energy consumption based on the energy consumption of several devices as follows:

[0060] Where P total is the overall energy consumption in kw / h, P i is the energy consumption of the i-th device, i∈[1,n], in kw / h,

[0061] For example, n = 3, P 1 =1kw / h,P 2 =2kw / h,P 3 =3kw / h,P total =6kw / h.

[0062] In this embodiment, the analysis module predicts the overall energy consumption of the predicted time window based on the ARIMA model according to the overall energy consumption of the stored K+1 time intervals. The formula is:

[0063] P total (t+Δt)=f(P total (t),Ptotal (t-1),...,P total (tk)),

[0064] Where P total (t+Δt) is the overall energy consumption of the prediction time window, in kw / h, Δt is the prediction time window, in h, t, t-1...tk are K+1 time intervals, in h, P total (t), P total (t-1)...P total (tk) is the overall energy consumption of the K+1 time intervals stored, in kw / h. f() is the ARIMA model. The overall energy consumption of the K+1 time intervals is put into the ARIMA model, and the overall energy consumption of the predicted time window is obtained through the autoregressive part, moving average part, and difference part of the ARIMA model.

[0065] For example, K = 4, P total (t) is 5kw / h, P total (t-1) is 4.8kw / h, P total (t-2) is 4.7kw / h, P total (t-3) is 4.6kw / h, P total (t-4) is 4.5kw / h, and the prediction time window is 1. After the autoregressive part, moving average part, and difference part of the ARIMA model, the overall energy consumption of the prediction time window is 4.9kw / h.

[0066] In this embodiment, the automatic scene adjustment module includes a controller and a wearable device.

[0067] The controller is connected to the edge computing layer network, and is used to determine whether the overall energy consumption exceeds a preset energy consumption threshold after receiving the overall energy consumption. And after determining that the overall energy consumption does not exceed the preset energy consumption threshold, it is used to generate a start signal and then send it to the wearable device.

[0068] The wearable device is electrically connected to the controller, lighting equipment, music equipment, air-conditioning equipment, and electric curtains, and is used to monitor the user's physiological data after receiving a start signal, and then obtain the user's emotional state score by calculating the physiological data, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control the lighting equipment, music equipment, air-conditioning equipment, and electric curtains.

[0069] In this embodiment, the overall energy consumption can be determined by the controller to ensure that the overall energy consumption does not exceed the preset energy consumption threshold before the device is turned on. This can effectively avoid excessive energy consumption, help achieve energy saving goals, and improve energy management efficiency. Under the premise that the energy consumption does not exceed the threshold, the user's emotional state can be monitored and calculated through the wearable device according to the user's physiological data. This intelligent adjustment based on the user's individual physiological feedback enables personalized services to be provided and improve the user's comfort. The wearable device monitors the user's physiological data and automatically adjusts the environmental equipment according to the preset score-emotion-control table to achieve intelligent human-computer interaction. This automated adjustment reduces the need for manual intervention by the user and makes the living environment more adaptable and intelligent. Not only energy consumption issues are taken into account, but also multiple devices in the environment are adjusted according to the user's emotional state, so as to find a balance between energy saving and comfort. For example, when the user is in a low mood, the lighting will be adjusted or soothing music will be played to improve the user's emotional state. : The connection between the controller and lighting equipment, music equipment, air conditioning, and electric curtains ensures that the operation of multiple devices can be flexibly controlled through accurate recognition of user emotions, enhancing the intelligent automation control capabilities of home or work environments.

[0070] By combining the user's physiological data and emotional state, a highly personalized device control experience is provided to the user. For example, when the user is in a relaxed state, the lighting and music may be adjusted to maintain this state and provide a more suitable environment.

[0071] In this embodiment, the wearable device includes a heart rate detector, a blood oxygen detector, a breathing sensor, and an MCU.

[0072] The heart rate monitor is used to monitor the user's heart rate and then send it to the MCU.

[0073] The blood oxygen detector is used to monitor the user's blood oxygen saturation and then send it to the MCU.

[0074] The breathing sensor is used to monitor the user's breathing rate and then send it to the MCU.

[0075] MCU is electrically connected to the heart rate monitor, blood oxygen monitor, breathing sensor, lighting equipment, music equipment, air conditioning equipment, and electric curtains.

[0076] It is used to normalize the heart rate, blood oxygen saturation and respiratory rate to obtain the normalized heart rate, normalized blood oxygen saturation and normalized respiratory rate after receiving the heart rate, blood oxygen saturation and respiratory rate, and then calculate the user's emotional state score according to the normalized heart rate, normalized blood oxygen saturation, normalized respiratory rate and a preset weight coefficient, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control lighting equipment, music equipment, air-conditioning equipment and electric curtains.

[0077] Among them, the score-emotion-control table contains the user's emotional state and the corresponding control instructions corresponding to the user's emotional state score. For example, the score-emotion-control table stores the following content: when the emotional state score is between 0-0.3, the user's emotional state is in a state of tension or anxiety. The corresponding control instruction is that the lighting device will dim the light to 50% to reduce light stimulation and create a quiet environment. The music device plays soft background music with the volume turned down to relieve the user's nervousness. The temperature of the air-conditioning device will be adjusted to 20℃ to provide a warmer and more comfortable environment to help relax the body and mind. The electric curtains will be partially closed to reduce external interference and increase privacy and security. When the emotional state score is between 0.31-0.6, the user is in a calm or neutral state, the lighting device maintains normal brightness, the light is about 75%, to maintain the lighting conditions required for normal activities, and the music device plays soothing background music with a moderate volume to maintain the user's stable emotional state. The air-conditioning device maintains the current temperature setting without additional adjustment. The electric curtains remain the same without any changes. When the emotional state score is between 0.61-0.8, the user is in a state of pleasure or relaxation: the lighting equipment will increase the light brightness to 100%, providing a brighter environment and enhancing the user's positive emotions. The music device plays cheerful background music with the volume slightly turned up to enhance the sense of pleasure. The temperature of the air conditioning equipment is adjusted to 24℃ to create a more refreshing atmosphere and further improve comfort. The electric curtains will open completely to increase the entry of natural light and enhance the sense of openness and positive environment. When the emotional state score is between 0.81-1.0, the user is in a very pleasant or excited state: the lighting equipment will keep the light brightness at 100%, and may switch to color mode to create a more exciting and energetic atmosphere. The music device plays cheerful music with the volume turned up to a high level to match the user's excited mood. The temperature of the air conditioning equipment is adjusted to 26℃, and the wind speed is increased to provide a cooler environment to keep the user energetic. The electric curtains are fully opened and kept in ventilation mode to allow fresh air to circulate, increasing the user's excitement and comfort experience.

[0078] In this embodiment, the MCU normalizes the heart rate, the preset maximum heart rate, and the preset minimum heart rate to obtain a normalized heart rate formula:

[0079] Among them, HR norm is the normalized heart rate, HR is the heart rate, the unit is beats / minute, HR min The preset minimum heart rate, in beats per minute, HR max The preset maximum heart rate, in beats per minute.

[0080] The preset minimum heart rate is 60 beats / minute, the preset maximum heart rate is 100 beats / minute, the heart rate is 75 beats / minute, and the normalized heart rate is 0.375.

[0081] And the formula for normalized blood oxygen saturation is calculated by normalizing the blood oxygen saturation, the preset maximum blood oxygen saturation value, and the preset minimum blood oxygen saturation value:

[0082] in, is the normalized heart rate, SpO 2 is the blood oxygen saturation, in percentage, HR min The preset minimum blood oxygen saturation value is expressed in percentage, HR max The preset maximum value of blood oxygen saturation is expressed in percentage.

[0083] The preset minimum blood oxygen saturation value is 90%, the preset maximum blood oxygen saturation value is 100%, the blood oxygen saturation value is 95%, and the normalized blood oxygen saturation value is 0.6.

[0084] And the formula for normalizing the respiratory frequency, calculating the preset maximum respiratory frequency, and the preset minimum respiratory frequency to obtain the normalized respiratory frequency is:

[0085] Among them, RR norm is the normalized respiratory rate, RR is the respiratory rate, in times / minute, RR min The preset minimum respiratory rate, in times / minute, RR max It is the preset maximum respiratory rate, in times / minute.

[0086] For example, the preset minimum respiratory rate is 12 times / minute, the preset maximum respiratory rate is 20 times / minute, the respiratory rate is 16 times / minute, and the normalized respiratory rate is 0.5.

[0087] In this embodiment, the MCU calculates the user's emotional state score according to the normalized heart rate, normalized blood oxygen saturation, normalized respiratory rate, and a preset weight coefficient as follows:

[0088] Where S is the user's emotional state score, is the preset weight coefficient.

[0089] For example, the normalized respiratory rate is 0.5, the normalized blood oxygen saturation is 0.6, the normalized heart rate is 0.375, ω HR is 0.4, is 0.3, ω RR is 0.3 and S is 0.48.

[0090] In this embodiment, the remote control module includes a web front end and an edge server.

[0091] The web front end is used for users to select remote control instructions, and then send the remote control instructions to the edge server. It is also used for displaying the prompt instructions after receiving the prompt instructions.

[0092] The edge server is connected to the web front end, several smart meters, the edge computing layer, and several device networks. After receiving the remote control instructions, the overall energy consumption, and the energy consumption of several devices, it is used to parse the devices controlled by the remote control instructions, and then calculate the new energy consumption and the overall energy consumption of the devices of the remote control instructions to obtain the estimated energy consumption, and then determine whether the new energy consumption and the overall energy consumption exceed the preset energy consumption threshold. And after determining that the new energy consumption and the overall energy consumption exceed the preset energy consumption threshold, it is used to generate a prompt instruction, and then send it to the web front end. And after determining that the new energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold, it is used to control the device according to the remote control instruction. The edge server stores a large number of remote control instructions and each remote control instruction is mapped to a specific control operation.

[0093] In this embodiment, through the web front end, the user can remotely select and send control instructions, making the device control operation more convenient. Users can remotely control smart devices at home or in the office through the Internet without being near the device, which improves the flexibility and convenience of the system. The edge server can receive the overall energy consumption and the energy consumption information of each device in real time, and estimate the energy consumption of the newly added device. When the energy consumption of the newly added device exceeds the preset threshold with the overall energy consumption, a warning prompt will be generated to avoid device overload or excessive energy consumption. This function improves the transparency and control of energy use and helps users optimize energy consumption management. After analyzing the newly added energy consumption of the remote control instruction device, the edge server can intelligently determine whether the energy consumption of the newly added device will cause the overall energy consumption to exceed the preset threshold. When the energy consumption exceeds the threshold, a prompt message is generated and displayed on the web front end to help users adjust the use of the device in time to avoid unnecessary high energy consumption and potential power load risks. Through distributed computing on the edge server, energy consumption analysis and control instruction parsing are completed on the edge device, reducing dependence on the central server and improving the response speed of the system. Such a design ensures a smooth experience for users when controlling the device, especially in scenarios that require real-time operation, providing a more efficient control mechanism. It can not only control the opening and closing of devices, but also ensure that the energy consumption of the entire environment remains within a reasonable range by judging the energy consumption threshold. This intelligent control reduces the energy waste caused by excessive use of devices and ensures that the system operates within an efficient energy consumption range. The web front end not only supports remote operation by users, but also provides timely feedback and prompts when energy consumption exceeds the standard, so that users can understand the operating status and energy consumption of the equipment. This humanized design enhances the user's sense of control over equipment and energy consumption management, and avoids the cost burden or risk of equipment failure caused by excessive energy consumption. When the energy consumption of the newly added equipment does not exceed the threshold, the operation can be performed immediately according to the remote control instruction, which reduces the waiting time and makes the device respond faster. This mechanism ensures that users can quickly and effectively control the equipment remotely, improving the convenience of smart home or smart office environment.

[0094] In this embodiment, the edge server calculates the additional energy consumption of the remote control command to start the device and the overall energy consumption to obtain the estimated energy consumption formula:

[0095] P totalnew =P total +ΔP,

[0096] Among them, P totalnew is the estimated energy consumption in kw / h, P total is the overall energy consumption in kw / h, and ΔP is the additional energy consumption of the device under remote control instructions in kw / h.

[0097] For example, if the overall energy consumption is 5kw / h, the additional energy consumption of the equipment under remote control instructions is 1.2kw / h, and the estimated energy consumption is 6.2kw / h.

[0098] The above embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work shall all fall within the scope of protection of the present invention.

Claims

1. An Internet smart home energy management system, characterized by: Including energy management prediction module, automatic scene adjustment module, remote control module, The energy management prediction module is used to detect the energy consumption of several devices and then store it; and to calculate the overall energy consumption based on the energy consumption of several devices and then send it to the automatic scene adjustment module; and to allow the user to set the prediction time window; and to predict the overall energy consumption of the prediction time window based on the ARIMA model and then display it; The automated scene adjustment module is electrically connected to the lighting equipment, the music equipment, the air conditioning equipment, and the electric curtains, and is network-connected to the energy management prediction module, and is used to determine whether the overall energy consumption exceeds a preset energy consumption threshold after receiving the overall energy consumption; and is used to monitor the user's physiological data after determining that the overall energy consumption does not exceed the preset energy consumption threshold, and then obtain the user's emotional state score by calculating the physiological data, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control the lighting equipment, the music equipment, the air conditioning equipment, and the electric curtains; The remote control module is connected to the energy management prediction module through a network, and is used for allowing a user to select a remote control instruction, parsing the device controlled by the remote control instruction, and then calculating the additional energy consumption and the overall energy consumption of the device of the remote control instruction to obtain an estimated energy consumption, and then judging whether the additional energy consumption and the overall energy consumption exceed a preset energy consumption threshold; and is used for generating a prompt instruction after judging that the additional energy consumption and the overall energy consumption exceed the preset energy consumption threshold, and then displaying the prompt instruction; and is used for controlling the device according to the remote control instruction after judging that the additional energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold.

2. The Internet smart home energy management system according to claim 1, characterized in that: The energy management prediction module includes several smart meters, edge computing layer, analysis module and display module. The plurality of smart meters are electrically connected to a plurality of devices and are network-connected to the remote control module, so as to detect the energy consumption of the plurality of devices and then send the energy consumption of the plurality of devices to the edge computing layer and the remote control module; The edge computing layer is connected to the plurality of smart meters, the automatic scene adjustment module, and the remote control module through a network, and is used for allowing a user to set a time interval; After receiving the energy consumption of a number of devices, calculate the overall energy consumption according to the energy consumption of the several devices, and then send the overall energy consumption to the automatic scene adjustment module, the analysis module, and the remote control module according to the set time interval; The analysis module is connected to the edge computing layer network, and is used to store the overall energy consumption after continuously receiving it; and is used for the user to set the prediction time window; and is used to predict the overall energy consumption of the prediction time window based on the ARIMA model according to the overall energy consumption of the stored K+1 time intervals, and then send it to the display module; The display module is connected to the analysis module through a network and is used to display the overall energy consumption after receiving it.

3. The Internet smart home energy management system according to claim 2 is characterized by: The edge computing layer calculates the overall energy consumption based on the energy consumption of several devices as follows: Where P total is the overall energy consumption in kw / h, P i is the energy consumption of the i-th device, i∈[1,n], in kw / h.

4. The Internet smart home energy management system according to claim 3 is characterized by: The analysis module predicts the overall energy consumption of the predicted time window based on the ARIMA model according to the overall energy consumption of the stored K+1 time intervals. The formula is: total (t+Δt)=f(P total (t), P total (t-1), ..., P total (tk)), Where P total (t+Δt) is the overall energy consumption of the prediction time window, in kw / h, Δt is the prediction time window, in h, t, t-1...tk are K+1 time intervals, in h, P total (t), P total (t-1)...P total (tk) is the overall energy consumption of the stored K+1 time intervals, in kw / h.

5. The Internet smart home energy management system according to claim 3 is characterized by: The automatic scene adjustment module includes a controller and a wearable device. The controller is connected to the edge computing layer network, and is used to determine whether the overall energy consumption exceeds a preset energy consumption threshold after receiving the overall energy consumption; and is used to generate a start signal after determining that the overall energy consumption does not exceed the preset energy consumption threshold, and then send it to the wearable device; The wearable device is electrically connected to the controller, lighting equipment, music equipment, air conditioning equipment, and electric curtains, and is used to monitor the user's physiological data after receiving a start signal, and then obtain the user's emotional state score by calculating the physiological data, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control the lighting equipment, music equipment, air conditioning equipment, and electric curtains.

6. The Internet smart home energy management system according to claim 5, characterized in that: The wearable device includes a heart rate detector, a blood oxygen detector, a breathing sensor, and an MCU. The heart rate detector is used to monitor the user's heart rate and then send it to the MCU; The blood oxygen detector is used to monitor the user's blood oxygen saturation and then send it to the MCU; The breathing sensor is used to monitor the user's breathing frequency and then send it to the MCU; The MCU is electrically connected to the heart rate detector, the blood oxygen detector, the breathing sensor, the lighting equipment, the music equipment, the air conditioning equipment, and the electric curtain. It is used to normalize the heart rate, blood oxygen saturation and respiratory rate to obtain the normalized heart rate, normalized blood oxygen saturation and normalized respiratory rate after receiving the heart rate, blood oxygen saturation and respiratory rate, and then calculate the user's emotional state score according to the normalized heart rate, normalized blood oxygen saturation, normalized respiratory rate and a preset weight coefficient, and then query the preset score-emotion-control table according to the user's emotional state score to obtain the user's emotional state, and then query the preset score-emotion-control table according to the user's emotional state to obtain control instructions to control lighting equipment, music equipment, air-conditioning equipment and electric curtains.

7. The Internet smart home energy management system according to claim 6, characterized in that: The MCU normalizes the heart rate, the preset maximum heart rate, and the preset minimum heart rate to obtain a normalized heart rate formula: Among them, HR norm is the normalized heart rate, HR is the heart rate, the unit is beats / minute, HR min The preset minimum heart rate, in beats per minute, HR max The preset maximum heart rate, in beats per minute. And the formula for normalized blood oxygen saturation is calculated by normalizing the blood oxygen saturation, the preset maximum blood oxygen saturation value, and the preset minimum blood oxygen saturation value: in, is the normalized heart rate, SpO2 is the blood oxygen saturation, the unit is percentage, HR min The preset minimum blood oxygen saturation value is expressed in percentage, HR max The preset maximum value of blood oxygen saturation is expressed in percentage. And the formula for normalizing the respiratory frequency, calculating the preset maximum respiratory frequency, and the preset minimum respiratory frequency to obtain the normalized respiratory frequency is: Among them, RR norm is the normalized respiratory rate, RR is the respiratory rate, in times / minute, RR min The preset minimum respiratory rate, in times / minute, RR max It is the preset maximum respiratory rate, in times / minute.

8. The Internet smart home energy management system according to claim 6, characterized in that: The MCU calculates the user's emotional state score according to the normalized heart rate, normalized blood oxygen saturation, normalized respiratory rate, and a preset weight coefficient: Where S is the user's emotional state score, is the preset weight coefficient.

9. The Internet smart home energy management system according to claim 6, characterized in that: The remote control module includes a web front end and an edge server. The web front end is used for allowing the user to select a remote control instruction, and then sending the remote control instruction to the edge server; and is used for displaying the prompt instruction after receiving the prompt instruction; The edge server is connected to the web front end, the several smart meters, the edge computing layer, and several device networks, and is used to parse the device controlled by the remote control instruction after receiving the remote control instruction, the overall energy consumption, and the energy consumption of several devices, and then calculate the new energy consumption and the overall energy consumption of the device of the remote control instruction to obtain the estimated energy consumption, and then determine whether the new energy consumption and the overall energy consumption exceed the preset energy consumption threshold; and after determining that the new energy consumption and the overall energy consumption exceed the preset energy consumption threshold, generate a prompt instruction, and then send it to the web front end; and after determining that the new energy consumption and the overall energy consumption do not exceed the preset energy consumption threshold, control the device according to the remote control instruction.

10. The Internet smart home energy management system according to claim 9, characterized in that: The edge server calculates the additional energy consumption of the device activated by the remote control command and the overall energy consumption to obtain the estimated energy consumption formula: P totalnew =P total +ΔP, Among them, P totalnew is the estimated energy consumption in kw / h, P total is the overall energy consumption in kw / h, and ΔP is the additional energy consumption of the device under remote control instructions in kw / h.

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