A smart home control method and system based on the Internet of Things

Abstract

The present invention discloses a smart home control method and system based on the Internet of Things, the method comprising: sending a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of the monitoring data include: temperature, humidity, brightness, image, and video data; real-time calculation of the individual performance index of each smart home and the overall performance index of all smart homes; according to the monitoring data, the individual performance index, and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to indoor scenarios while controlling energy consumption balance. By using the embodiments of the present invention, smart home devices can be dynamically controlled to achieve a balance between individual and overall performance, while being able to automatically adapt to environmental scenarios and meet user needs.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart home, and in particular to a smart home control method and system based on the Internet of Things. Background Art

[0002] With the continuous advancement and popularization of Internet of Things (IoT) technology, smart home systems have become an important part of modern life. Smart home control systems mainly use various sensors, actuators, controllers and communication technologies to monitor and manage the home environment to provide a more comfortable, convenient and energy-saving living experience. Smart home systems can achieve home safety, convenience, comfort and energy saving by connecting various smart devices in the home. However, when controlling and managing smart devices, traditional smart home systems can often only follow preset modes and rules, and do not take into account factors such as user personalized needs and environmental changes, resulting in low execution efficiency. Summary of the invention

[0003] The purpose of the present invention is to provide a smart home control method and system based on the Internet of Things to address the deficiencies in the prior art, to dynamically control smart home devices, to achieve a balance between individual and overall performance, and to automatically adapt to environmental changes and meet user needs.

[0004] An embodiment of the present application provides a smart home control method based on the Internet of Things, the method comprising:

[0005] Sending a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data;

[0006] Real-time calculation of the individual performance indicators of each smart home and the overall performance indicators of all smart homes;

[0007] According to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario while controlling the energy consumption balance.

[0008] Optionally, the calculation formula of the individual performance index is:

[0009] S_i=(1-E_{real,i} / E_{expected,i})·W_i·P_i+C_i

[0010] Among them, S_i is the individual performance index of the i-th smart home, E_{real,i} is the actual energy consumption of the i-th smart home, E_{expected,i} is the expected energy consumption of the i-th smart home, W_i is the dynamic weight based on the frequency of home use, P_i is the adjustment coefficient based on environmental parameters, and C_i is the correction constant based on the home energy efficiency category.

[0011] Optionally, the calculation formula of the overall effectiveness index is:

[0012]

[0013] Wherein, S_{total} is the overall performance index, R is the adjustment factor of the overall home use efficiency, and D is the dynamic adjustment coefficient based on home diversity and interconnection functions.

[0014] Optionally, executing a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index includes:

[0015] For a smart home whose individual performance index exceeds a first preset performance threshold, if other smart homes have sensors of the same type as the smart home, the sensors are turned off to determine whether the individual performance index after being turned off exceeds the first preset performance threshold, wherein different smart homes correspond to different first preset performance thresholds;

[0016] If the first preset performance threshold is exceeded, continue to adjust the device parameters of the smart home so that the individual performance index does not exceed the first preset performance threshold and the overall performance index does not exceed the second preset performance threshold;

[0017] If other smart homes do not have sensors of the same type as the smart home, or for a smart home whose individual performance index does not exceed a first preset performance threshold, the indoor scenario environment is determined based on the monitoring data, and the device parameters of the smart home are dynamically adjusted, wherein during the adjustment process, it is ensured that the individual performance index does not exceed the first preset performance threshold, and the overall performance index does not exceed the second preset performance threshold.

[0018] Another embodiment of the present application provides a smart home control system based on the Internet of Things, the system comprising:

[0019] A collection module is used to send a collection request for monitoring data to each smart home, so that each smart home can call its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data;

[0020] A calculation module is used to calculate the individual performance index of each smart home and the overall performance index of all smart homes in real time;

[0021] The control module is used to execute a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index to adapt to the indoor scenario and control the energy consumption balance at the same time.

[0022] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0023] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0024] Compared with the prior art, the present invention provides a smart home control method based on the Internet of Things, which sends a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of the monitoring data include: temperature, humidity, brightness, image, and video data; real-time calculation of the individual performance index of each smart home and the overall performance index of all smart homes; according to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario while controlling the energy consumption balance, so that the smart home devices can be dynamically controlled to achieve a balance between individual and overall performance, while automatically adapting to environmental changes and meeting user needs. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A hardware structure block diagram of a computer terminal for a smart home control method based on the Internet of Things provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a flow chart of a smart home control method based on the Internet of Things provided by an embodiment of the present invention;

[0027] Figure 3 A schematic structural diagram of a smart home control system based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0029] The embodiment of the present invention first provides a smart home control method based on the Internet of Things. The method can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0030] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a smart home control method based on the Internet of Things provided by an embodiment of the present invention. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0031] The memory 104 can be used to store software programs and modules of application software, such as program instructions / modules corresponding to the smart home control method based on the Internet of Things in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] See also Figure 2The embodiment of the present invention provides a smart home control method based on the Internet of Things, which may include the following steps:

[0034] S201, sending a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data;

[0035] Specifically, the Internet of Things technology can be used to achieve collaborative work between smart home devices, so that they can communicate with each other and call each other's sensors. In this way, each smart home device calls its built-in sensor to collect corresponding monitoring data according to the received collection request. The types of these monitoring data include but are not limited to: temperature, humidity, brightness, image, video data, etc. These data reflect the real-time status of the home environment and provide a basis for subsequent intelligent processing and control.

[0036] First, by collecting monitoring data in real time, the system can keep abreast of changes in the home environment, laying the foundation for providing personalized and intelligent services. Second, monitoring data can not only be used for automatic adjustment of the environment, but also improve the efficiency of energy use and achieve the goal of energy conservation and emission reduction. Finally, this interconnection between devices also greatly enhances the scalability and flexibility of the system, facilitating subsequent equipment upgrades and function expansion. One implementation method may include:

[0037] 1. Communication protocol setting: First, a unified IoT communication protocol, such as MQTT, CoAP, etc., needs to be set for communication between smart home devices to ensure that information transmission between devices is efficient and secure.

[0038] 2. Sensor calling mechanism: Each smart home device calls the corresponding sensor through the built-in software platform or operating system according to the collection request received. This process can be achieved through a preset API (application programming interface). Each sensor (such as temperature, humidity sensor, etc.) has a corresponding API to start data collection.

[0039] 3. Data collection and transmission: After the sensor collects the monitoring data, it sends the data to the central processing unit or cloud platform through the communication module of the device according to the set protocol. During the data transmission process, encryption technology must be used to ensure the security of the data.

[0040] S202, calculating the individual performance index of each smart home and the overall performance index of all smart homes in real time;

[0041] Specifically, in the process of real-time calculation of individual and overall performance indicators, the core purpose is to comprehensively evaluate the energy consumption performance of each smart home device and the energy efficiency performance of the entire smart home system, so as to achieve refined energy management and scheduling. This process not only focuses on the energy consumption of a single device, but also takes into account the interaction and complementary relationship between home devices. Through the dual evaluation of overall and individual performance, the energy efficiency and user experience of the entire family are optimized. One implementation method may include:

[0042] Based on the Internet of Things communication technology, the energy consumption data (E_real,i) and usage frequency of each smart home device are collected. The collected data is preprocessed, such as outlier processing and data normalization, to ensure the accuracy and stability of subsequent calculations.

[0043] For each smart home device, its individual performance index is calculated according to the formula. This step can be performed on the central processing unit or cloud platform by writing a corresponding data processing algorithm. A calculation formula for an individual performance index can be:

[0044] S_i=(1-E_{real,i} / E_{expected,i})·W_i·P_i+C_i

[0045] Among them, S_i is the individual performance index of the i-th smart home. The goal of this index is to measure the gap between the actual energy consumption of the device and the expected energy consumption, and evaluate the operating efficiency of the device based on this difference. By designing such a formula, the performance of each device in the smart home system can be clarified, providing guidance for optimizing device energy consumption and improving the energy efficiency of the entire system.

[0046] E_{real,i} is the actual energy consumption of the i-th smart home, which can be determined by collecting energy consumption-related data in real time through energy consumption monitoring devices or built-in sensors.

[0047] E_{expected,i} is the expected energy consumption of the ith smart home. The standard energy consumption of the equipment can be estimated based on historical data and typical usage patterns, or it can be estimated based on the standard energy consumption data provided by the equipment manufacturer and user usage habits analysis.

[0048] W_i is a dynamic weight based on the frequency of home use, which reflects the impact of equipment use frequency on energy efficiency. It can be dynamically adjusted based on the user's actual usage frequency statistics or the usage data monitored by the equipment sensor. For example, the higher the frequency of equipment use, the larger the Wi value. Assume that the frequency of use of home equipment is divided into three levels from low to high: low frequency use: W_i = 0.5, medium frequency use: W_i = 1.0, high frequency use: W_i = 1.5.

[0049] P_i is an adjustment coefficient based on environmental parameters (for example, temperature, humidity, etc.). It is appropriately adjusted according to the possible changes in equipment energy consumption under different environmental conditions. The adjustment coefficient can be set by analyzing the energy consumption performance of the equipment under different environmental conditions, such as the influence of temperature and humidity. For example, when the ambient temperature difference is large (such as outdoor equipment), P_i=1.2; when the ambient temperature difference is small (such as indoor equipment), P_i=1.0.

[0050] C_i is a correction constant based on the household energy efficiency category, which is used to correct the evaluation standard deviation caused by different equipment efficiency. It can be determined based on the energy efficiency level of the equipment and the manufacturer's energy consumption standards. Equipment with different energy efficiency levels will have different C_i values, for example, high-efficiency energy consumption equipment: C_i = -0.2, medium-efficiency energy consumption equipment: C_i = 0, and low-efficiency energy consumption equipment: C_i = 0.2.

[0051] The formula takes into account the deviation between actual and expected energy consumption, equipment usage frequency, environmental impact and energy efficiency level to ensure that the calculation results of the energy consumption score are both comprehensive and targeted.

[0052] Based on the individual performance indicators and actual energy consumption of all devices, the overall performance index is calculated according to the formula. This calculation step is also implemented by the data processing algorithm on the central processing unit or cloud platform. A calculation formula for the overall performance index can be:

[0053]

[0054] Among them, S_{total} is the overall performance index, which provides an index reflecting the overall energy efficiency of the home system. The formula combines the individual performance and actual energy consumption data of all devices, taking into account the energy efficiency performance at the system level and its relationship with the performance of individual devices. This formula helps to comprehensively evaluate and manage the energy efficiency of the entire smart home system and guide system-level energy efficiency optimization measures.

[0055] R is the adjustment factor for the overall home usage efficiency. It reflects the impact of user usage patterns and preferences on the energy efficiency of the entire home system. It can be obtained through long-term observation and analysis of user usage habits and life patterns, and can also be evaluated through surveys or user feedback. For example, if low energy consumption is achieved during high-demand periods, the R value is higher. The default value of R can be 0.8.

[0056] D is a dynamic adjustment coefficient based on home diversity and interconnection functions, reflecting the complementarity and collaborative contribution of different devices to energy efficiency. It can be set by evaluating the actual situation of interactive collaboration and functional complementarity between devices. It may need to be adjusted according to different system configurations and device combinations, such as low interconnection functions: D = 0.9, high interconnection functions: D = 1.1.

[0057] This formula calculates the overall energy consumption score by considering the relationship between the energy consumption score of each device and the actual energy consumption, combined with the overall system efficiency and intelligence level. This ensures that the scoring mechanism not only focuses on the energy efficiency of a single device, but also reflects the energy consumption performance of the entire smart home system as a whole.

[0058] The calculation of individual performance indicators helps identify which devices are consuming more energy than expected and which are operating efficiently, thus providing a basis for subsequent energy consumption adjustments; the calculation of overall performance indicators reflects the overall energy consumption performance of the home. By optimizing the overall performance indicators, the optimal allocation of household energy use can be achieved, ensuring that energy is energy-saving and emission-reducing while meeting user needs. This calculation process can also timely discover potential equipment failures or abnormal energy consumption phenomena, providing data support for the maintenance and upgrade of smart home systems.

[0059] The calculation formulas for the individual energy consumption score and the overall energy consumption score described above achieve a comprehensive evaluation of the energy efficiency of smart home devices by introducing a variety of dynamic weights and adjustment factors. The complexity of this method lies in the consideration of factors of different dimensions (such as device usage frequency, environmental conditions, energy efficiency level, etc.), which not only considers the performance of the device itself, but also does not ignore the impact of the external environment and usage patterns on energy consumption, thus being able to fairly and accurately reflect the actual energy consumption performance of smart home devices and systems.

[0060] S203: Execute a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index to adapt to the indoor scenario while controlling the energy consumption balance.

[0061] The steps to implement the dynamic energy consumption control strategy are essentially an intelligent optimization process, which aims to maximize energy conservation and improve the overall energy efficiency of the smart home system while meeting user needs and indoor scenario needs. This process not only involves the control of individual devices, but also includes the coordinated optimization of the entire smart home system. By analyzing monitoring data, individual and overall performance indicators in real time, the system can respond sensitively to environmental changes and user needs, dynamically adjust device parameters, and achieve the goal of energy balance.

[0062] Its meaning includes but is not limited to:

[0063] 1. Energy saving and emission reduction: Through refined control strategies, unnecessary energy consumption can be reduced, which is conducive to achieving a green home environment and reducing carbon footprint.

[0064] 2. Improve user experience: Intelligently adjust device operation to adapt to indoor scenarios, ensuring that users can live and work in a comfortable environment and improve their quality of life.

[0065] 3. Maintain equipment health: Reasonably control equipment operation, avoid excessive use, extend equipment life, and reduce maintenance costs.

[0066] Specifically, for a smart home whose individual performance index exceeds a first preset performance threshold, if other smart homes have sensors of the same type as the smart home, the sensor is turned off to determine whether the individual performance index after being turned off exceeds the first preset performance threshold, wherein different smart homes correspond to different first preset performance thresholds; for example, for low-energy consumption smart home devices: the first preset performance threshold = 0.7, for conventional devices: the first preset performance threshold = 0.5, for high-energy consumption devices: the first preset performance threshold = 0.3;

[0067] For smart homes whose individual performance indicators exceed the first preset performance threshold, first try to turn off device sensors with redundant functions in the same environment. In a smart home system, the individual performance indicators of devices may exceed the first preset threshold due to various reasons, such as device aging, changes in environmental factors, etc. The practice of turning off sensors of the same type in this step is based on a concept: reduce duplication of work and excessive energy consumption, and meet needs by optimizing the use of the most effective devices.

[0068] For example, a decision tree algorithm can be used to implement this step. First, all devices are sorted according to the individual performance indicators of each device. Then, functionally similar or duplicate devices are checked, and the decision tree algorithm will choose which device's sensor to keep turned on and turn off the corresponding sensors of other devices based on preset rules (such as performance indicators, device usage frequency, etc.). In this process, the performance indicators of the turned-off devices must be continuously monitored to ensure that they still meet the requirements.

[0069] If the first preset performance threshold is exceeded, continue to adjust the device parameters of the smart home so that the individual performance index does not exceed the first preset performance threshold, and the overall performance index does not exceed the second preset performance threshold; the second preset performance threshold is intended to protect the performance of the entire system and prevent the performance of the entire system from being reduced due to the adjustment of a single device, for example, 0.6;

[0070] If the individual performance index of the device still exceeds the first preset performance threshold after the sensor is turned off, a deeper level of intervention is needed to reduce energy consumption by directly adjusting the device's operating parameters, which also demonstrates the smart home system's ability to flexibly respond to abnormal situations. The device's operating parameters can be further adjusted, such as adjusting the operating mode, reducing the operating frequency, etc., to achieve energy saving. This step must ensure that the individual performance is improved while not causing the overall performance index to exceed the second preset performance threshold.

[0071] If other smart homes do not have sensors of the same type as the smart home, or for a smart home whose individual performance index does not exceed a first preset performance threshold, the indoor scenario environment is determined based on the monitoring data, and the device parameters of the smart home are dynamically adjusted, wherein during the adjustment process, it is ensured that the individual performance index does not exceed the first preset performance threshold, and the overall performance index does not exceed the second preset performance threshold.

[0072] This step is for devices that are already in a relatively ideal state, and for devices that do not have the same type of sensors as other smart homes. Device parameters can be adjusted to reduce individual performance indicators (for devices that do not have the same type of sensors as other smart homes and whose individual performance indicators exceed the threshold, the sensors can be left on according to actual needs). At the same time, device parameters can be adjusted through real-time monitoring data to adapt to the changing indoor scenarios. This dynamic adjustment strategy is the key to achieving efficient energy consumption control, ensuring that the smart home system can flexibly respond to environmental changes while ensuring the optimization of performance indicators.

[0073] Based on real-time monitoring data analysis, the indoor scenario environment is determined, such as the distribution of people in the room, the intensity of light in the room, etc., and the equipment parameters are dynamically adjusted to adapt to this environment, while ensuring that both individual and overall performance indicators do not exceed the preset performance thresholds.

[0074] For example, deep learning algorithms, especially convolutional neural networks (CNNs), can be used to analyze real-time monitoring data and identify changes in indoor scenarios. CNNs can accurately identify indoor scenarios (e.g., whether there are people in the room, light intensity, etc.) based on images, videos, temperature, humidity and other data. Then, according to the identified changes in scenarios, the device parameters are dynamically adjusted (such as adjusting light brightness, adjusting temperature, etc.), and the device operating status is optimized while ensuring that individual and overall performance indicators do not exceed the preset thresholds.

[0075] The core of the entire dynamic energy consumption control strategy is to achieve an intelligent balance: maintaining and improving the quality of the user's living environment while minimizing energy consumption. Through real-time monitoring and intelligent decision support, the smart home system can achieve efficient use of energy while ensuring user comfort.

[0076] From these steps, we can see that the dynamic energy consumption control strategy of the smart home system is designed to be flexible and comprehensive. It starts with identifying the devices that need to be optimized, and gradually refines the control strategy through a variety of technical algorithms, and finally achieves personalized energy consumption optimization. This coarse-to-fine control strategy can not only ensure effective management of energy consumption, but also improve the user's living experience.

[0077] It can be seen that by sending a collection request for monitoring data to each smart home, each smart home can call its own sensor to collect the corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data; real-time calculation of the individual performance index of each smart home and the overall performance index of all smart homes; according to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario while controlling the energy consumption balance, so that the smart home equipment can be dynamically controlled to achieve a balance between individual and overall performance, while being able to automatically adapt to environmental changes and meet user needs.

[0078] Another embodiment of the present invention provides a smart home control system based on the Internet of Things. Figure 3 , the system may include:

[0079] The collection module 301 is used to send a collection request for monitoring data to each smart home, so that each smart home can call its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data;

[0080] The calculation module 302 is used to calculate the individual performance index of each smart home and the overall performance index of all smart homes in real time;

[0081] The control module 303 is used to execute a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index to adapt to the indoor scenario and control the energy consumption balance at the same time.

[0082] It can be seen that by sending a collection request for monitoring data to each smart home, each smart home can call its own sensor to collect the corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data; real-time calculation of the individual performance index of each smart home and the overall performance index of all smart homes; according to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario while controlling the energy consumption balance, so that the smart home equipment can be dynamically controlled to achieve a balance between individual and overall performance, while being able to automatically adapt to environmental changes and meet user needs.

[0083] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0084] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps:

[0085] S201, sending a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data;

[0086] S202, calculating the individual performance index of each smart home and the overall performance index of all smart homes in real time;

[0087] S203: Execute a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index to adapt to the indoor scenario while controlling the energy consumption balance.

[0088] It can be seen that by sending a collection request for monitoring data to each smart home, each smart home can call its own sensor to collect the corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data; real-time calculation of the individual performance index of each smart home and the overall performance index of all smart homes; according to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario while controlling the energy consumption balance, so that the smart home equipment can be dynamically controlled to achieve a balance between individual and overall performance, while being able to automatically adapt to environmental changes and meet user needs.

[0089] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0090] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0091] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0092] S201, sending a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data;

[0093] S202, calculating the individual performance index of each smart home and the overall performance index of all smart homes in real time;

[0094] S203: Execute a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index to adapt to the indoor scenario while controlling the energy consumption balance.

[0095] It can be seen that by sending a collection request for monitoring data to each smart home, each smart home can call its own sensor to collect the corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data; real-time calculation of the individual performance index of each smart home and the overall performance index of all smart homes; according to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario while controlling the energy consumption balance, so that the smart home equipment can be dynamically controlled to achieve a balance between individual and overall performance, while being able to automatically adapt to environmental changes and meet user needs.

[0096] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A smart home control method based on the Internet of Things, characterized in that: The method comprises: Sending a collection request for monitoring data to each smart home, so that each smart home calls its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data; The individual performance index of each smart home and the overall performance index of all smart homes are calculated in real time; the calculation formula of the individual performance index is: Wherein, S_i is the individual performance index of the ith smart home, E_{real,i} is the actual energy consumption of the ith smart home, E_{expected,i} is the expected energy consumption of the ith smart home, W_i is the dynamic weight based on the frequency of home use, P_i is the adjustment coefficient based on environmental parameters, and C_i is the correction constant based on the home energy efficiency category; According to the monitoring data, the individual performance index and the overall performance index, a dynamic energy consumption control strategy is executed to adapt to the indoor scenario and control the energy consumption balance at the same time; the dynamic energy consumption control strategy is executed according to the monitoring data, the individual performance index and the overall performance index, including: For a smart home whose individual performance index exceeds a first preset performance threshold, if other smart homes have sensors of the same type as the smart home, the sensors are turned off to determine whether the individual performance index after being turned off exceeds the first preset performance threshold, wherein different smart homes correspond to different first preset performance thresholds; If the first preset performance threshold is exceeded, continue to adjust the device parameters of the smart home so that the individual performance index does not exceed the first preset performance threshold and the overall performance index does not exceed the second preset performance threshold; If other smart homes do not have sensors of the same type as the smart home, or for a smart home whose individual performance index does not exceed a first preset performance threshold, the indoor scenario environment is determined based on the monitoring data, and the device parameters of the smart home are dynamically adjusted, wherein during the adjustment process, it is ensured that the individual performance index does not exceed the first preset performance threshold, and the overall performance index does not exceed the second preset performance threshold.

2. The method according to claim 1, characterized in that The calculation formula of the overall performance index is: Among them, S_{total} is the overall performance index, R is the adjustment factor of the overall home use efficiency, and D is the dynamic adjustment coefficient based on home diversity and interconnection functions.

3. A smart home control system based on the Internet of Things, characterized in that: The system comprises: A collection module is used to send a collection request for monitoring data to each smart home, so that each smart home can call its own sensor to collect corresponding monitoring data according to the collection request, wherein each smart home communicates with each other through the Internet of Things and can call each other's own sensors, and the types of monitoring data include: temperature, humidity, brightness, image, and video data; The calculation module is used to calculate the individual performance index of each smart home and the overall performance index of all smart homes in real time; the calculation formula of the individual performance index is: Wherein, S_i is the individual performance index of the ith smart home, E_{real,i} is the actual energy consumption of the ith smart home, E_{expected,i} is the expected energy consumption of the ith smart home, W_i is the dynamic weight based on the frequency of home use, P_i is the adjustment coefficient based on environmental parameters, and C_i is the correction constant based on the home energy efficiency category; A control module is used to execute a dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index to adapt to the indoor scenario and control the energy consumption balance at the same time; the execution of the dynamic energy consumption control strategy according to the monitoring data, the individual performance index and the overall performance index includes: For a smart home whose individual performance index exceeds a first preset performance threshold, if other smart homes have sensors of the same type as the smart home, the sensors are turned off to determine whether the individual performance index after being turned off exceeds the first preset performance threshold, wherein different smart homes correspond to different first preset performance thresholds; If the first preset performance threshold is exceeded, continue to adjust the device parameters of the smart home so that the individual performance index does not exceed the first preset performance threshold and the overall performance index does not exceed the second preset performance threshold; If other smart homes do not have sensors of the same type as the smart home, or for a smart home whose individual performance index does not exceed a first preset performance threshold, the indoor scenario environment is determined based on the monitoring data, and the device parameters of the smart home are dynamically adjusted, wherein during the adjustment process, it is ensured that the individual performance index does not exceed the first preset performance threshold, and the overall performance index does not exceed the second preset performance threshold.

4. The system according to claim 3, characterized in that The calculation formula of the overall performance index is: Among them, S_{total} is the overall performance index, R is the adjustment factor of the overall home use efficiency, and D is the dynamic adjustment coefficient based on home diversity and interconnection functions.

5. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 2 when executed.

6. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 2.

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