Cloud-based smart home control system

Abstract

The invention relates to the technical field of smart home, in particular to a cloud-based smart home control system, which comprises an environment data processing module, a user interaction module, a drive update analysis module and an equipment update control module. According to the invention, by collecting and analyzing the home environment data in real time, the uploading frequency of the data is optimized, unnecessary network flow and server load are reduced, the smart home equipment is monitored and adjusted, the equipment operation is ensured to be accurately matched with the user demand, and by evaluating and comparing the equipment operation data under different drive versions, the user experience is improved. According to the method, the optimal updating opportunity can be intelligently determined on the basis of considering factors such as festivals, so that the optimal performance of equipment is guaranteed, updating operation in peak periods is avoided, interference to daily activities of families is reduced, the intelligent response capability of the system is improved, energy utilization and safety are optimized, and the energy utilization rate is increased. And a more intelligent and efficient living environment is created for the user.

Description

Technical Field

[0001] The present invention relates to the field of smart home technology, and in particular to a cloud-based smart home control system. Background Art

[0002] The field of smart home technology involves the automation and intelligent management of home facilities. It includes functions such as automatic adjustment of the environment, remote control of home appliances, security monitoring, and energy management. Smart home systems can be connected through the Internet, allowing users to monitor and control in real time through smart devices such as mobile phones, tablets or other interfaces. In addition, the system can learn the user's habits and preferences, and automatically adjust settings to improve efficiency and comfort.

[0003] Among them, smart home control systems involve the design and implementation of technical solutions for managing and monitoring various smart devices in the home. The core of the control system is that it can connect various smart devices in the home, such as lighting systems, security equipment, HVAC systems, etc., and allow users to remotely control and monitor through smartphones, tablets or other network-connected devices, thereby improving the convenience and comfort of family life and enhancing energy efficiency and safety. Through the smart home control system, users can more accurately adjust the home environment, optimize home energy use, and remotely monitor home security.

[0004] Traditional control systems rely on fixed-frequency data uploading and processing, which increases the burden of data transmission and causes the system to consume high energy and processing resources at non-essential times. Traditional systems lack flexibility in device driver update strategies and fail to fully consider changes in device usage frequency and future time periods, resulting in update operations that may be performed when users use the device at a high frequency, affecting normal home use and device performance, and reducing the system's adaptability and user experience. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a cloud-based smart home control system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a cloud-based smart home control system, the system comprising: The environmental data processing module collects the environmental data of each room in real time based on the smart home operating environment, transmits the real-time environmental data to the cloud server through the network, evaluates the volatility of the environmental data, adjusts the frequency of uploading the environmental data, and obtains the environmental data upload results; The user interaction module transmits the real-time environmental data to the user's mobile device based on the environmental data upload result, receives the user's target adjustment effect on the smart home device through the cloud server, adjusts the working parameters of the smart home device, and obtains the device monitoring adjustment result; Based on the monitoring adjustment results, the driver update analysis module collects the operation data of the smart home device in real time when there is an updated driver for the smart home device, extracts the operation data of the smart home device with the latest driver under the same working environment, evaluates the deviation of the operation effect of the smart home device under the latest driver and the current driver, obtains the operation effect deviation information, and determines whether the driver needs to be updated in combination with the stability of the driver, and obtains the driver update demand analysis result; The device update control module analyzes the operating frequency of smart home devices in different time periods based on the driver update demand analysis results, and selects the target time period to implement device driver updates in combination with the impact of festivals in future time periods to obtain smart home device update control results.

[0007] The present invention is improved in that the step of evaluating the volatility of environmental data is: Based on the smart home operating environment, the environmental data of each room, including temperature, humidity and light intensity, is collected in real time through smart home devices to obtain a real-time environmental data set; Based on the real-time environment data set, the formula is: ; Calculate the standard deviation of each environmental parameter; in, is the standard deviation, is the average value of the environmental parameter, is the total number of data points, For the Environmental parameters; Based on the standard deviation of each environmental parameter, by the formula: ; Calculate the room environment data volatility index to obtain the room environment volatility assessment result; in, , , is the weighting coefficient, is the environmental data volatility index, represents the standard deviation of the temperature data, represents the standard deviation of humidity data, Represents the standard deviation of the light intensity data.

[0008] The present invention is improved in that the steps of obtaining the environmental data upload result are: Based on the room environment volatility assessment result, extract the basic upload frequency of the environment data to obtain data upload related information; Based on the data upload related information, through the formula: ; Calculate the adjusted upload frequency; in, is the adjusted upload frequency, is the basic upload frequency, is the maximum upload frequency, is the environmental data volatility index, is the maximum volatility threshold set.

[0009] Based on the adjusted upload frequency, the real-time environmental data is transmitted to the cloud server through the network to obtain the environmental data upload result.

[0010] The present invention is improved in that the step of obtaining the equipment monitoring adjustment result is: Based on the environmental data upload result, the real-time environmental data is transmitted to the user's mobile device, and the user's target adjustment effect on the smart home device is received through the cloud server to obtain the target setting parameters; Based on the target setting parameters, through the formula: ; Calculate the adjusted equipment operating parameters, and adjust the equipment operating parameters multiple times according to the preset adjustment times to match the user's adjustment goals, and obtain the equipment monitoring adjustment results; in, Represents the user goal setting parameters, Represents the actual working parameters of the current device. is the adjusted equipment operating parameter, is a dynamic adjustment factor.

[0011] The present invention is improved in that the step of obtaining the operation effect deviation information is as follows: Based on the monitoring and adjustment results, when there is an updated driver for the smart home device, the operation stability and power consumption data of the smart home device under the latest driver and the current driver are collected to obtain the driving effect related data; Based on the driving effect correlation data, through the formula: ; Calculate the operation effect deviation index to obtain the operation effect deviation information; in, is the operation effect deviation index, and They are the running stability index under the latest and current drivers, and They are the latest and current driving power consumption respectively. and is the weight factor, is the attenuation coefficient.

[0012] The present invention is improved in that the step of obtaining the driver update demand analysis result is: Based on the operation effect deviation information, the frequency of BUGs occurring during the operation of the latest driver is collected to obtain driver instability data; Based on the driving instability data, the formula: ; Calculate the driver update demand index; in, To drive the update demand index, is the attenuation coefficient, is the weight coefficient, is the operation effect deviation index, Indicates the frequency of driver bugs; Based on the driver update requirement index, a comparison is performed with a preset update threshold. If the driver update requirement index is greater than the preset update threshold, it is determined that the device needs to perform a driver update, and a driver update requirement analysis result is obtained.

[0013] The present invention is improved in that the step of analyzing the operating frequency of the smart home device in different time periods is: Based on the driver update demand analysis results, collect the operation data of the smart home device, record the startup and shutdown time of the device in a day, and obtain the operation record of the device; Based on the operation records of the equipment, the time periods are divided according to the target time intervals, and the data is classified according to the timestamps, each start and stop record is assigned to a corresponding time period, and the number of starts and stops in each time period is counted to obtain segmented statistical data; Based on the segmented statistical data, the formula is: ; Calculate the operating frequency of the device in each time period and obtain the device operating frequency analysis results: in, is the total number of times the device starts and stops within the time period. is the total duration of the time period, The device operation frequency for the time period.

[0014] The present invention is improved in that the steps of obtaining the update control result of the smart home device are: Based on the equipment operation frequency analysis results and combined with the impact of festivals in the future period, the formula is: ; Calculate the probability of the device being idle in the future period; in, is the probability of the device being idle in the future period, The device operation frequency for the time period. is the festival impact factor, The maximum theoretical operating frequency of the device.

[0015] Based on the idle probability of the device in the future time period, by analyzing the size of the idle probability of the differentiated time period, the target time period is selected to implement the device driver update, and the smart home device update control result is obtained.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by real-time collection and analysis of home environment data, the frequency of data upload is optimized, unnecessary network traffic and server load are reduced, and environmental control is made more precise and energy-saving, thereby improving the overall efficiency of smart home devices and the user experience. The monitoring and adjustment of smart home devices realizes detailed user setting feedback, ensures that device operation is accurately matched with user needs, and improves living comfort and device operation efficiency. By evaluating and comparing device operation data under different driver versions, the best update time can be intelligently determined based on factors such as festivals, thereby ensuring the optimal performance of the device while avoiding update operations during peak hours, reducing interference with daily family activities, improving the system's intelligent response capability, optimizing energy utilization and safety, and creating a smarter and more efficient living environment for users. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 A flow chart for evaluating the volatility of environmental data for the present invention; Figure 3 A flowchart of the present invention for obtaining the result of uploading environmental data; Figure 4 A flow chart for obtaining equipment monitoring adjustment results according to the present invention; Figure 5 A flow chart for obtaining operation effect deviation information of the present invention; Figure 6 A flowchart of the present invention for obtaining the driver update demand analysis result; Figure 7 A flow chart of analyzing the operating frequency of smart home devices in different time periods according to the present invention; Figure 8 This is a flow chart of obtaining the update control results of smart home devices in the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1 The present invention provides a technical solution: a cloud-based smart home control system, the system comprising: The environmental data processing module is based on the smart home operating environment. Through smart home devices, it collects the environmental data of each room in real time, including temperature, humidity and light intensity, transmits the real-time environmental data to the cloud server through the network, analyzes the environmental data over a period of time, evaluates the volatility of the environmental data, adjusts the frequency of uploading the environmental data according to the volatility of the environmental data, and obtains the environmental data upload result; The user interaction module transmits the real-time environmental data to the user's mobile device based on the environmental data upload result, and receives the user's target adjustment effect on the smart home device through the cloud server, adjusts the working parameters of the smart home device to match the user's adjustment target, and obtains the device monitoring adjustment result; The driver update analysis module is based on the monitoring adjustment results. When there is an updated driver for the smart home device, the operation data of the smart home device is collected in real time, and the operation data of the smart home device with the latest driver under the same working environment is extracted. The operation data includes operation stability and operation power consumption. The deviation of the operation effect of the smart home device under the latest driver and the current driver is evaluated to obtain the operation effect deviation information. Combined with the stability of the driver, the driver update demand index is evaluated to determine whether the driver needs to be updated, and the driver update demand analysis result is obtained; The device update control module analyzes the operating frequency of smart home devices in different time periods based on the driver update demand analysis results, and evaluates the idle probability of smart home devices in future time periods in combination with the impact of festivals in future time periods. It selects the target time period to implement device driver updates and obtains the smart home device update control results.

[0021] The environmental data upload results include the adjusted data upload frequency, independent temperature, humidity and light intensity data for each room; the equipment monitoring adjustment results include the equipment's operating status information, the user-set target effect adjustment results and the equipment's working parameter adjustment records; the driver update demand analysis results include the comparison of the operating stability of the new and old drivers, operating power consumption data and update demand index; the smart home device update control results include the selected update time period, the expected equipment idle probability and holiday impact considerations.

[0022] See also Figure 2 , the steps to assess the volatility of environmental data are: Based on the smart home operating environment, the environmental data of each room, including temperature, humidity and light intensity, is collected in real time through smart home devices to obtain a real-time environmental data set; Based on the real-time environment data set, the formula is: ; Calculate the standard deviation of each environmental parameter; in, is the standard deviation, is the average value of the environmental parameter, is the total number of data points, For the Environmental parameters; Based on the standard deviation of each environmental parameter, by the formula: ; Calculate the room environment data volatility index to obtain the room environment volatility assessment result; in, , , is the weighting coefficient, is the environmental data volatility index, represents the standard deviation of the temperature data, represents the standard deviation of humidity data, Represents the standard deviation of the light intensity data.

[0023] formula: ; and ; Parameter details and how to obtain them: : No. An environmental parameter refers to the environmental data collected by smart home devices, which can be temperature, humidity or light intensity. These data are read directly from the sensors of smart home devices, and the devices record the environmental status of each room at regular intervals.

[0024] : The average value of environmental parameters, calculated by The arithmetic mean of the data. Calculation It helps to determine the center point of environmental data, so as to further analyze the dispersion of data.

[0025] : represents the standard deviation of temperature data, which is used to measure the volatility of temperature data. It calculates the standard deviation of temperature data over a period of time. Relative to its average The degree of discreteness is obtained.

[0026] : represents the standard deviation of humidity data, which is used to measure the volatility of humidity data. It calculates the standard deviation of humidity data over a period of time. Relative to its average The degree of discreteness is obtained.

[0027] : It represents the standard deviation of the light intensity data, which is used to measure the volatility of the light intensity data. Relative to its average The degree of discreteness is obtained.

[0028] , , : Weighting coefficient, used to control the impact of different environmental parameters on volatility, preset according to demand.

[0029] Calculation example: Set the three standard deviations to , and , , , .

[0030] Calculating the Volatility Index :

[0031] Results Volatility Index , is a quantitative index that comprehensively considers the volatility of three environmental factors: temperature, humidity and light. Shows the comprehensive volatility of environmental changes, with light intensity being the largest factor The volatility index is most affected by high standard deviation and low standard deviation. This index can help the smart home system determine the frequency of uploading environmental data. For example, when the environment fluctuates greatly, the frequency of data upload can be increased to more accurately monitor and adjust the home environment.

[0032] See also Figure 3 , the steps to obtain the environmental data upload results are: Based on the room environment volatility assessment results, the basic upload frequency of environmental data is extracted to obtain data upload related information; Based on the data upload related information, through the formula: ; Calculate the adjusted upload frequency; in, is the adjusted upload frequency, is the basic upload frequency, is the maximum upload frequency, is the environmental data volatility index, is the maximum volatility threshold set.

[0033] Based on the adjusted upload frequency, the real-time environmental data is transmitted to the cloud server through the network to obtain the environmental data upload result.

[0034] formula: ; Parameter meaning and acquisition method: :The basic upload frequency is a constant set at the beginning of system design based on network bandwidth and server processing capacity. Usually, this value is obtained through network performance testing and preliminary environmental monitoring. For example, it is set to 5 times / hour, which means the data upload frequency under the lowest fluctuation.

[0035] : Maximum upload frequency, which is also an upper limit set based on network and server capabilities. For example, it is set to 30 times / hour to ensure that environmental changes can be captured and uploaded in a timely manner when data fluctuates greatly.

[0036] : Environmental data volatility index, calculated by the previous steps.

[0037] : The maximum volatility threshold set, used as The reference standard can be set as , representing the maximum volatility expected under extreme conditions.

[0038] Calculation example: Set the parameters as follows: , , , .

[0039] Calculate adjusted upload frequency .

[0040] Calculation results times / hour, which means the volatility index based on the current environmental data , the data upload frequency should be adjusted to about 25.295 times / hour. This frequency can effectively capture environmental changes without excessively occupying network resources.

[0041] See also Figure 4 , the steps to obtain the equipment monitoring adjustment results are: Based on the environmental data upload results, the real-time environmental data is transmitted to the user's mobile device, and the user's target adjustment effect on the smart home device is received through the cloud server to obtain the target setting parameters; Based on the target setting parameters, through the formula: ; Calculate the adjusted equipment operating parameters, and adjust the equipment operating parameters multiple times according to the preset adjustment times to match the user's adjustment goals, and obtain the equipment monitoring adjustment results; in, Represents the user goal setting parameters, Represents the actual working parameters of the current device. is the adjusted equipment operating parameter, is a dynamic adjustment factor.

[0042] formula: ; Parameter details and how to obtain them: : User goal setting parameters, obtained through user input on the mobile device. For example, the user may want to set the temperature of the living space to a specific comfortable temperature.

[0043] : The current operating parameters of the equipment, usually read directly from the equipment's sensors or internal records. This reflects the actual working status of the equipment before adjustment.

[0044] : Dynamic adjustment factor is a pre-set coefficient determined by the device's response speed and the user's need for adjustment sensitivity. This factor helps control the speed and amplitude of parameter adjustment to achieve a smooth transition.

[0045] Calculation example: Set the parameters as follows: (the temperature the user wishes to set), (the current temperature of the device), (The set adjustment factor reflects the medium adjustment speed).

[0046] Calculate using formula :

[0047] Calculation results Indicates that according to the dynamic adjustment factor According to the user's goal setting, the smart home system should adjust the room temperature to 21 degrees Celsius. The adjustment reflects a compromise strategy, not jumping to the target temperature immediately, but adjusting gradually to avoid excessive energy consumption or sudden changes to the living environment. This adjustment process helps maintain the comfort of the environment while taking into account energy efficiency and equipment durability.

[0048] See also Figure 5 ,The steps for obtaining the operation effect deviation information are: Based on the monitoring and adjustment results, when there is an updated driver for the smart home device, the operation stability and power consumption data of the smart home device under the latest driver and the current driver are collected to obtain the driving effect related data; Based on the driving effect correlation data, through the formula: ; Calculate the operation effect deviation index to obtain the operation effect deviation information; in, is the operation effect deviation index, and They are the running stability index under the latest and current drivers, and They are the latest and current driving power consumption respectively. and is the weight factor, is the attenuation coefficient.

[0049] formula: ; Parameter details and how to obtain them: and :The operation stability index under the latest and current drive is obtained by statistically analyzing the failure rate of the equipment (the ratio of the number of failures to the operating time) or the mean time between failures (MTBF, Mean Time Between Failures).

[0050] and : The latest and current operating power consumption under the current drive. The power consumption data can be obtained by directly measuring the power consumption of the device under different working conditions.

[0051] and : Weight factor, used to adjust the degree of influence of stability and power consumption on the total deviation during the calculation process. The weight is set based on business needs or device sensitivity, and is usually determined by the device management team based on experience or device performance requirements.

[0052] : Attenuation factor, used to adjust the effect of stability deviation in the total deviation calculation. This factor can be set based on the sensitivity of the device adjustment and the results of historical data analysis to ensure that the impact of stability changes is neither overestimated nor underestimated.

[0053] Calculation example: Set the following specific values: , (Stability index, the closer to 1, the more stable), , (power consumption), , (weight of stability and power consumption), (Attenuation coefficient).

[0054] Calculate the operation effect deviation index :

[0055] Calculation results , indicating that under the given weight and decay coefficient settings, the latest driver has improved the overall performance of stability and power consumption by about 2.2 units compared to the current driver. The results can help decision makers understand the potential value of driver updates, especially when stability improvements have a greater impact on overall performance.

[0056] See also Figure 6 , the steps to obtain the driver update requirement analysis results are: Based on the running effect deviation information, the frequency of bugs in the latest driver during operation is collected to obtain the driver instability data; Based on the drive instability data, the formula: ; Calculate the driver update demand index; in, To drive the update demand index, is the attenuation coefficient, is the weight coefficient, is the operation effect deviation index, Indicates the frequency of driver bugs; Based on the driver update requirement index, it is compared with the preset update threshold. If the driver update requirement index is greater than the preset update threshold, it is determined that the device needs to perform a driver update, and a driver update requirement analysis result is obtained.

[0057] formula: ; Parameter details and how to obtain them: : Operation effect deviation index, calculated by the previous steps.

[0058] : The frequency of driver bugs, usually obtained through the device's fault log system statistics, expressed as the number of times the bug occurs within a time period.

[0059] : Attenuation coefficient, used to adjust the influence of the bug frequency in the exponential function. It is a pre-set coefficient that is adjusted based on historical data and an assessment of the device's sensitivity.

[0060] : Weight coefficient, used to adjust the overall impact of the bug frequency on the update demand index. This coefficient is set based on the operational importance of the device and the need for stability.

[0061] Calculation example: Set the following parameters: (Operational Performance Deviation Index), (bug frequency, indicating that bugs occur 0.04 times per unit time), (attenuation coefficient), (Weight coefficient).

[0062] Calculate using formula :

[0063] Calculation results , indicating that under the influence of the adjusted attenuation coefficient and weight coefficient, the driver update demand index is 1.031. If the preset update threshold is 1, the result , indicating that there is a need to update the driver under the current conditions, because exceeds the threshold, making it more likely that a driver update will be recommended.

[0064] See also Figure 7 ,The steps to analyze the operating frequency of smart home devices in different time periods are: Based on the analysis results of driver update requirements, the operation data of smart home devices is collected, the startup and shutdown time of the devices in a day is recorded, and the operation records of the devices are obtained; Based on the operation records of the equipment, the time periods are divided according to the target time intervals, and the data is classified according to the timestamps. Each start and stop record is assigned to the corresponding time period, and the number of starts and stops in each time period is counted to obtain segmented statistical data; Based on segmented statistical data, the formula is: ; Calculate the operating frequency of the device in each time period and obtain the device operating frequency analysis results: in, is the total number of times the device starts and stops within the time period. is the total duration of the time period, The device operation frequency for the time period.

[0065] formula: ; Parameter details and how to obtain them: : The total number of times the device starts and stops within a time period. The timestamp is extracted from the device operation log, and the startup records of each time period are counted to obtain the number of startups within the time period. : The total duration of a time period, in hours, usually calculated from a preset time period (such as 3 hours from 5:00 to 8:00 in the morning) or the actual time span recorded in the log.

[0066] Calculation example: Set the following parameters: Research time period: afternoon (1:00PM4:00PM), corresponding to hours; the number of device startups in this time period is counted from the logs: .

[0067] Calculate the equipment operating frequency according to the formula: ; Calculation results , indicating that the device is started an average of 5 times per hour in the afternoon. This result provides a quantitative indicator of the device usage intensity during this period, providing a reference for subsequent energy-saving analysis, load forecasting, or driver update time selection.

[0068] See also Figure 8 , the steps to obtain the update control results of smart home devices are: Based on the equipment operation frequency analysis results and combined with the impact of festivals in the future period, the formula is: ; Calculate the probability of the device being idle in the future period; in, is the probability of the device being idle in the future period, The device operation frequency for the time period. is the festival impact factor, The maximum theoretical operating frequency of the device.

[0069] Based on the idle probability of the device in the future time period, by analyzing the size of the idle probability of the differentiated time period, the target time period is selected to implement the device driver update, and the update control result of the smart home device is obtained.

[0070] formula: ; Parameter details and how to obtain them The device operating frequency in the time period is calculated in the previous steps.

[0071] : Holiday impact factor, used to adjust the device's operating frequency during a specific period of time, determined based on the impact of holidays or special events on device usage habits. By analyzing historical data, it is found that the holiday operating frequency drops by 20%, so set .

[0072] : The maximum theoretical operating frequency of the equipment, used for normalization, usually obtained from the design specifications of the equipment. For example, if the maximum number of times the equipment runs per hour is 10 times, then .

[0073] Calculation example: Set the following parameters: times / hour (calculated from the device operation log, the average number of times the device is started in a specific time period), (Holiday impact factor, indicating a 20% drop in activation frequency), times / hour (the maximum theoretical operating frequency of the equipment, derived from the equipment design specifications).

[0074] Calculation process:

[0075] Calculation results , indicating that the probability of the device being idle in the future period is This probability value provides decision makers with a time window reference for device driver updates. A higher idle probability indicates that this period is suitable for device updates.

[0076] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cloud-based smart home control system, characterized in that: The system comprises: The environmental data processing module collects the environmental data of each room in real time based on the smart home operating environment, transmits the real-time environmental data to the cloud server through the network, evaluates the volatility of the environmental data, adjusts the frequency of uploading the environmental data, and obtains the environmental data upload results; The user interaction module transmits the real-time environmental data to the user's mobile device based on the environmental data upload result, receives the user's target adjustment effect on the smart home device through the cloud server, adjusts the working parameters of the smart home device, and obtains the device monitoring adjustment result; Based on the monitoring adjustment results, the driver update analysis module collects the operation data of the smart home device in real time when there is an updated driver for the smart home device, extracts the operation data of the smart home device with the latest driver under the same working environment, evaluates the deviation of the operation effect of the smart home device under the latest driver and the current driver, obtains the operation effect deviation information, and determines whether the driver needs to be updated in combination with the stability of the driver, and obtains the driver update demand analysis result; The device update control module analyzes the operating frequency of smart home devices in different time periods based on the driver update demand analysis results, and selects the target time period to implement device driver updates in combination with the impact of festivals in future time periods to obtain smart home device update control results.

2. The cloud-based smart home control system according to claim 1, characterized in that: The steps of evaluating the volatility of environmental data are: Based on the smart home operating environment, the environmental data of each room, including temperature, humidity and light intensity, is collected in real time through smart home devices to obtain a real-time environmental data set; Based on the real-time environment data set, the formula is: ； Calculate the standard deviation of each environmental parameter; in, is the standard deviation, is the average value of the environmental parameter, is the total number of data points, For the Environmental parameters; Based on the standard deviation of each environmental parameter, by the formula: ； Calculate the room environment data volatility index to obtain the room environment volatility assessment result; in, , , is the weighting coefficient, is the environmental data volatility index, represents the standard deviation of the temperature data, represents the standard deviation of humidity data, Represents the standard deviation of the light intensity data.

3. The cloud-based smart home control system according to claim 2, characterized in that: The steps for obtaining the environmental data upload result are: Based on the room environment volatility assessment result, extract the basic upload frequency of the environment data to obtain data upload related information; Based on the data upload related information, through the formula: ； Calculate the adjusted upload frequency; in, is the adjusted upload frequency, is the basic upload frequency, is the maximum upload frequency, is the environmental data volatility index, is the maximum volatility threshold set; Based on the adjusted upload frequency, the real-time environmental data is transmitted to the cloud server through the network to obtain the environmental data upload result.

4. The cloud-based smart home control system according to claim 1, characterized in that: The steps for obtaining the equipment monitoring adjustment result are: Based on the environmental data upload result, the real-time environmental data is transmitted to the user's mobile device, and the user's target adjustment effect on the smart home device is received through the cloud server to obtain the target setting parameters; Based on the target setting parameters, through the formula: ； Calculate the adjusted equipment working parameters, and adjust the equipment working parameters multiple times according to the preset adjustment times to match the user's adjustment goals and obtain the equipment monitoring adjustment results; in, Represents the user goal setting parameters, Represents the actual working parameters of the current device. is the adjusted equipment operating parameter, is a dynamic adjustment factor.

5. The cloud-based smart home control system according to claim 1, characterized in that: The step of obtaining the operation effect deviation information is as follows: Based on the monitoring and adjustment results, when there is an updated driver for the smart home device, the operation stability and power consumption data of the smart home device under the latest driver and the current driver are collected to obtain the driving effect related data; Based on the driving effect correlation data, through the formula: ； Calculate the operation effect deviation index to obtain the operation effect deviation information; in, is the operation effect deviation index, and They are the running stability index under the latest and current drivers, and They are the latest and current driving power consumption respectively. and is the weight factor, is the attenuation coefficient.

6. The cloud-based smart home control system according to claim 5, characterized in that: The steps for obtaining the driver update demand analysis result are as follows: Based on the operation effect deviation information, the frequency of BUGs occurring during the operation of the latest driver is collected to obtain driver instability data; Based on the driving instability data, the formula: ； Calculate the driver update demand index; in, To drive the update demand index, is the attenuation coefficient, is the weight coefficient, is the operation effect deviation index, Indicates the frequency of driver bugs; Based on the driver update requirement index, a comparison is performed with a preset update threshold. If the driver update requirement index is greater than the preset update threshold, it is determined that the device needs to perform a driver update, and a driver update requirement analysis result is obtained.

7. The cloud-based smart home control system according to claim 1, characterized in that: The steps of analyzing the operating frequency of smart home devices in different time periods are as follows: Based on the driver update demand analysis results, collect the operation data of the smart home device, record the startup and shutdown time of the device in a day, and obtain the operation record of the device; Based on the operation records of the equipment, the time periods are divided according to the target time intervals, and the data is classified according to the timestamps, each start and stop record is assigned to a corresponding time period, and the number of starts and stops in each time period is counted to obtain segmented statistical data; Based on the segmented statistical data, the formula is: ； Calculate the operating frequency of the device in each time period and obtain the device operating frequency analysis results: in, is the total number of times the device starts and stops within the time period. is the total duration of the time period, The device operation frequency for the time period.

8. The cloud-based smart home control system according to claim 7, characterized in that: The steps for obtaining the update control result of the smart home device are as follows: Based on the equipment operation frequency analysis results and combined with the impact of festivals in the future period, the formula is: ； Calculate the probability of the device being idle in the future period; in, is the probability of the device being idle in the future period, The device operation frequency for the time period. is the festival impact factor, is the maximum theoretical operating frequency of the device; Based on the idle probability of the device in the future time period, by analyzing the size of the idle probability of the differentiated time period, the target time period is selected to implement the device driver update, and the smart home device update control result is obtained.

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