Data synchronization method for cloud storage and cloud storage system

Abstract

The invention discloses a cloud storage data synchronization method and a cloud storage system, and relates to the technical field of data synchronization, and the method comprises the steps: calculating the priority Pi of each device based on a household appliance standard data set of household appliances through a synchronization priority calculation formula; and according to a comparison result with a preset priority threshold Pth, the system can flexibly generate a synchronization strategy. And when the priority of the equipment is higher, the system can improve the data synchronization frequency to ensure that the equipment is in a real-time response state. And for the equipment with lower priority, the system reduces the synchronization frequency and reduces unnecessary energy consumption through delay synchronization and an energy-saving strategy. Through the time sequence analysis of the user behavior mode, the system can predict the behavior of the user in advance and generate a linkage control instruction Ci, for example, when the user is about to go home, an air conditioner is automatically turned on, and the light brightness is adjusted. The intelligent linkage control not only improves the response speed of the equipment, but also enables different equipment to work cooperatively.

Description

Technical Field

[0001] The present invention relates to the technical field of data synchronization, and in particular to a cloud storage data synchronization method and a cloud storage system. Background Art

[0002] With the development of smart homes, smart homes are becoming a key component of users' lives. Using sensors and network technology, remote monitoring and linkage control of various home appliances can be achieved. In this context, status monitoring, data uploading and synchronization of home appliances have become key links, which involve the collection and analysis of multi-dimensional data such as air conditioners, refrigerators, lights, air purifiers and other devices. This method is committed to processing and optimizing the collected data through cloud storage in multi-device scenarios, so that devices can be efficiently linked and synchronized at the right time.

[0003] Although the current smart home system can realize data collection and linkage control of household appliances, the main problem is that the data synchronization frequency and linkage control between devices lack intelligence and accuracy. Many existing systems can only switch and control devices based on simple timing rules or feedback from a single sensor, and cannot fully integrate the status information of multiple devices and respond in real time according to user behavior patterns. In addition, existing data synchronization is often not optimized in resource allocation. For devices with lower priority, excessive synchronization operations may be performed, resulting in unnecessary network traffic and device energy consumption; for devices with higher priority, it is difficult to respond quickly to user needs when data updates lag. These deficiencies make the response efficiency of the device low, making it difficult to meet users' needs for comfort and efficiency in the smart home environment. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a cloud storage data synchronization method and a cloud storage system, which solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented by the following technical scheme: comprising the following steps: S1. Install sensor groups inside home appliances to collect home appliance data in real time, and transmit the home appliance data to a cloud server through a wireless network. In the cloud server, pre-process the collected home appliance data to obtain a home appliance standard data set, and build a cloud database to store the home appliance standard data set; S2. Use the synchronization priority calculation formula to calculate and output the priority P of the home appliance equipment i , and preset the priority threshold P th Priority P for home appliances i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; S3, based on the corresponding synchronization strategy generated by the preliminary comparative evaluation, calculate and output the synchronization frequency F of each device uploading data i , analyze the frequency of communication between home appliances and the cloud, and execute the corresponding home appliance operation mode; S4, through the cloud intelligent linkage control and home appliances linkage, generate linkage control instructions C i , control the coordinated operation of multiple home appliances; S5. After the initial home appliance linkage and control strategy execution, the cloud service calculates the device status and outputs the optimized device synchronization priority O i , and preset the optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation and analyze the synchronization frequency F i and the effectiveness of linkage control.

[0006] Preferably, S1 includes S11, S12 and S13; S11, collect home appliance data in real time through sensor groups integrated inside home appliances and installed indoors; The sensor group includes an acceleration sensor, a gas detection module, an electromagnetic radiation sensor and a timer; Home appliance data includes equipment vibration frequency VF, indoor air composition AC, electromagnetic radiation intensity EMR and user behavior pattern BPD; The indoor air composition AC is detected by the gas detection module, and the composition of each indoor air is detected and obtained through comprehensive analysis; The user behavior pattern BPD tracks the user's geographic location through the GPS and WIFI data of the smartphone, and then records when the user turns on or off the device through the historical usage data of smart home devices such as smart lights, air conditioners and door locks. Through machine learning algorithms, the user's life patterns are inferred from these data, and then a time series analysis is performed on the user's geographic location and the user's life patterns to identify patterns and obtain behavior predictions. For example, the system can predict the user's possible operating behavior in a specific time period through daily historical data.

[0007] Preferably, S12, connecting the sensor group to the cloud server through the Internet of Things (IOT), transmitting the collected home appliance data to the cloud server, and processing the collected home appliance data in the cloud server; Preprocessing methods include outlier detection and data smoothing and normalization; Outlier detection is used to filter the collected equipment vibration frequency VF by 3 times the standard deviation to remove noise interference and obtain the standard equipment vibration frequency VF clean , the specific filtering algorithm formula is , where VFi represents the vibration frequency of the i-th household appliance, Represents the mean value of the vibration frequency of the equipment, Indicates the standard deviation of the vibration frequency of the equipment; Data smoothing and normalization are used to normalize the electromagnetic radiation intensity EMR to prevent different data magnitudes from affecting the subsequent algorithm accuracy and obtain the standard electromagnetic radiation intensity EMR. b , the specific normalization algorithm formula is: ; Where, EMR i Represents the electromagnetic radiation intensity of the i-th household appliance, EMR min Indicates the upper limit of electromagnetic radiation intensity, EMR max Indicates the lower limit of electromagnetic radiation intensity; Then the standard equipment vibration frequency VF obtained after preprocessing clean and standard electromagnetic radiation intensity EMR b Integrate with the indoor air composition AC and user behavior pattern BPD in the home appliance data to obtain the home appliance standard data set; S13, constructing a cloud database in the cloud server, and setting a write port and a write port, and writing the acquired home appliance data set into the cloud database through the write port for storage.

[0008] Preferably, S2 includes S21 and S23; S21. Calculate the home appliance priority P of each smart home appliance through the synchronization priority calculation formula. i , determines the priority of the current home appliance data when synchronizing in the cloud; Home appliances priority P i The output is calculated by the following algorithm formula; ; Where P i Specifically, VF represents the priority of the i-th home appliance. max Indicates the upper limit of the equipment vibration frequency, BPD i represents the user behavior pattern of the i-th home appliance, represents the normalized value of vibration frequency, w1, w2, w3 and w4 represent the normalized value of vibration frequency, indoor air composition AC, standard electromagnetic radiation intensity EMR respectively b and the preset weight value of the user behavior pattern BPD, whose specific parameters are set by the user, and w1+w2+w3+w4=1.

[0009] Preferably, S23, based on the user's preset priority threshold P th Then compare it with the obtained home appliance equipment priority P iConduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; When the home appliance priority P i ≥Priority threshold P th When , it means that the current home appliance is in real-time response state, and the synchronization frequency strategy is executed at this time; When the home appliance priority P i <Priority threshold P th , it indicates that the current home appliance is in standby mode, and the data delay and energy-saving synchronization strategies are implemented at this time.

[0010] Preferably, S3 includes S31; S31, executing the corresponding synchronization strategy according to the preliminary comparison and evaluation results, the synchronization strategy includes the synchronization frequency strategy and the data delay and energy-saving synchronization strategy. When executing the synchronization frequency strategy, the data delay and energy-saving synchronization strategy are cancelled to restore the normal synchronization frequency of the home appliance. When executing the data delay and energy-saving synchronization strategy, the home appliance equipment is automatically switched to the low power consumption mode. At this time, the output synchronization frequency F is calculated by the synchronization frequency formula i ; Synchronous frequency F i The output is calculated by the following synchronization frequency formula; ; Where 1 represents the output synchronization frequency F when executing the synchronization frequency strategy i , 0 means the output synchronization frequency F when executing data delay and energy saving synchronization strategy i , F 0 Indicates the lower limit of the synchronization frequency, indicating that even low-priority devices have a basic synchronization frequency. Indicates the synchronization frequency gain coefficient, according to the priority P of the home appliance i To increase the synchronization frequency of high priority devices, represents the synchronization frequency reduction factor, The closer it is to 0, the greater the reduction in synchronization frequency. The significance of this strategy is that the current state of the i-th home appliance is of high importance or urgency, and the system needs to prioritize its data synchronization to ensure that the device uploads data in a timely manner and provides real-time response. For example, if the user behavior pattern BPD detects that the user is about to return home, such as through GPS data, the priority of air conditioners, lights and other devices increases, and the system increases the data synchronization frequency of these devices to adjust the working status of the devices in advance. For devices with lower priority, the system automatically switches them to low-power mode to reduce energy and power consumption. For example, smart cameras enter low-power monitoring mode when there are no major security events.

[0011] Preferably, S4 includes S41; S41, extracting the user behavior pattern BPD through the write port of the cloud database, extracting the rules from the user's historical use of home appliances through the cloud time series analysis and clustering algorithm, and predicting the future user behavior pattern to generate linkage control instructions C i ; Generate linkage control instruction C i The output is calculated by the following algorithm formula; ; In the formula, N represents the total number of household electrical appliances. represents the adjustment coefficient of the i-th home device, f represents the behavior pattern function, BPD i represents the user behavior pattern of the i-th device, represents the device state adjustment coefficient of the i-th home device, T i represents the current temperature of the i-th home appliance, T desired，i represents the user's expected temperature of the i-th home appliance, S i represents the state of the i-th household appliance, S desired，i represents the user's expected home appliance state of the i-th home appliance, and t represents time.

[0012] Preferably, S5 includes S51 and S52; S51, after executing the linkage control and synchronization strategy, the cloud service calculates and outputs the optimized device synchronization priority O for the device status. i , analyze the synchronization frequency F i And linkage control instruction C i effectiveness; Device Sync Priority O i The output is calculated by the following algorithm formula; ; In the formula, Indicates the synchronization frequency F i The control coefficient of Indicates linkage control instruction C i The control coefficient of .

[0013] Preferably, S52, based on the user preset optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation to analyze the current situation of home appliance data synchronization and execution linkage control. The specific evaluation contents are as follows; When the device synchronization priority is 0 i ≥Optimization threshold O th When it is, it means that there is an abnormality in the data synchronization strategy and linkage control of the current home appliance. At this time, an early warning message is issued to remind the user that there is an abnormality in the operation of the home appliance and to perform detection and maintenance; When the device synchronization priority is 0 i <Optimization threshold O th , it indicates that the data synchronization strategy and linkage control of the current home appliance are normal and no adjustment is required. At this time, the linkage control and synchronization strategy of the current home appliance are stored in the cloud database through the write port.

[0014] A cloud storage data synchronization cloud storage system, comprising a data acquisition and preprocessing module, a data synchronization priority analysis module, a synchronization frequency strategy adjustment module, an intelligent linkage control module and a comprehensive evaluation and optimization module; The data collection and preprocessing module collects home appliance data in real time by installing sensor groups inside home appliances, and transmits the home appliance data to the cloud server through a wireless network. In the cloud server, the collected home appliance data is preprocessed to obtain a home appliance standard data set, and a cloud database is built to store the home appliance standard data set; The data synchronization priority analysis module calculates and outputs the priority level of home appliances P by using the synchronization priority calculation formula. i , and preset the priority threshold P th Priority P for home appliances i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; The synchronization frequency strategy adjustment module calculates and outputs the synchronization frequency F of each device uploading data through the corresponding synchronization strategy generated based on the preliminary comparison and evaluation. i , analyze the frequency of communication between home appliances and the cloud, and execute the corresponding home appliance operation mode; The intelligent linkage control module links with the home appliances through the intelligent linkage control of the cloud to generate linkage control instructions C i , control the coordinated operation of multiple home appliances; After the comprehensive evaluation and optimization module performs the initial linkage and control strategy execution of home appliances, the cloud service calculates the device status and outputs the optimized device synchronization priority. i , and preset the optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation and analyze the synchronization frequency F i and the effectiveness of linkage control.

[0015] The present invention provides a cloud storage data synchronization method and a cloud storage system, which have the following beneficial effects: (1) This method collects home appliance data in real time by installing multiple sensors inside home appliances. These data are uploaded to the cloud server through the Internet of Things. After outlier detection, data smoothing and normalization, a standardized home appliance standard data set is generated and stored in the cloud database. This multi-dimensional data collection and intelligent preprocessing mechanism can effectively remove noise and outliers to ensure the accuracy and reliability of data synchronization. At the same time, by constructing a standard data set, unified data management between different devices can be achieved, improving the accuracy of the entire smart home system, enabling the system to flexibly respond to status changes of different home appliances and avoid affecting the synchronization process due to inconsistent or abnormal device data.

[0016] (2) This method calculates the priority P of each device based on the home appliance standard data set through the synchronization priority calculation formula. i . According to the preset priority threshold P th Based on the comparison results, the system can flexibly generate synchronization strategies. When the device priority is high, the system will increase the data synchronization frequency to ensure that the device is in a real-time response state. For devices with lower priority, the system will reduce the synchronization frequency and unnecessary energy consumption through delayed synchronization and energy-saving strategies. Through the time series analysis of user behavior patterns, the system can also predict user behavior in advance and generate linkage control instructions C i , such as automatically turning on the air conditioner and adjusting the light brightness when the user is about to return home. This intelligent linkage control not only improves the response speed of the device, but also enables different devices to work together to provide a more efficient control solution, ensuring that users get the best device service at critical moments.

[0017] (3) This method uses the system to continuously monitor the status of the home appliance during its operation and calculate the optimized synchronization priority O of the device. i , and with the optimized threshold O th Perform comparative evaluation to ensure the effectiveness of the synchronization frequency and linkage control strategy. i Above the optimization threshold O th When the system detects abnormal conditions, it will issue early warning information in time to remind users that the device may have abnormal conditions and needs to be inspected or maintained. This mechanism effectively prevents damage to the device due to abnormal operation for a long time and reduces the failure rate of the device. At the same time, for devices with lower priority or normal status, the system will automatically switch them to low power mode to reduce energy consumption by reducing unnecessary data synchronization and resource consumption. This not only improves the overall energy-saving effect of the system, but also extends the service life of the device, ensuring that the device can maintain efficient operation while avoiding excessive energy consumption and frequent equipment failures. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A schematic diagram of the steps of a cloud storage data synchronization method of the present invention; Figure 2 The present invention is a schematic diagram of a cloud storage data synchronization cloud storage system process. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 The present invention provides a cloud storage data synchronization method. To achieve the above purpose, the present invention is implemented through the following technical scheme: comprising the following steps: S1. Install sensor groups inside home appliances to collect home appliance data in real time, and transmit the home appliance data to a cloud server through a wireless network. In the cloud server, pre-process the collected home appliance data to obtain a home appliance standard data set, and build a cloud database to store the home appliance standard data set; S2. Use the synchronization priority calculation formula to calculate and output the priority P of the home appliance equipment i , and preset the priority threshold P th Priority P for home appliances i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; S3, based on the corresponding synchronization strategy generated by the preliminary comparative evaluation, calculate and output the synchronization frequency F of each device uploading data i , analyze the frequency of communication between home appliances and the cloud, and execute the corresponding home appliance operation mode; S4, through the cloud intelligent linkage control and home appliances linkage, generate linkage control instructions C i , control the coordinated operation of multiple home appliances; S5. After the initial home appliance linkage and control strategy execution, the cloud service calculates the device status and outputs the optimized device synchronization priority O i , and preset the optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation and analyze the synchronization frequency F i and the effectiveness of linkage control.

[0021] In this embodiment, the method collects data of household appliances in real time through a sensor group and uploads it to the cloud for preprocessing and storage to ensure the accuracy and consistency of the data; based on the synchronization priority calculation formula, the priority P of the household appliances is flexibly calculated according to the device status. i , and through the priority threshold P th Assess the importance of the device and generate a suitable synchronization strategy; further calculate the synchronization frequency F i , optimize the communication frequency between the device and the cloud, ensure the real-time synchronization of data of high-priority devices, and reduce the energy consumption of low-priority devices; generate control instructions C through intelligent linkage control of the cloud i , realize the coordinated operation of multiple devices, improve the intelligence and flexibility of device response; through the optimized synchronization priority O i and the optimized threshold O th The secondary evaluation further optimizes the effectiveness of the synchronization frequency and linkage control, ensuring continuous monitoring and optimization of the equipment's operating status. Compared with traditional home appliance control methods, this method effectively improves the equipment's operating efficiency and response speed through dynamic adjustment of data priority and synchronization frequency, and significantly reduces energy consumption through intelligent energy-saving strategies. At the same time, the intelligent linkage control in the cloud enables devices to automatically collaborate based on user behavior patterns, improving the user experience. Overall, this method not only improves the accuracy and efficiency of data synchronization, but also extends the service life of the equipment, reduces the failure rate, and realizes efficient, energy-saving and intelligent linkage management of smart home devices, achieving significant technical improvements and performance improvements.

[0022] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: S1 includes S11, S12 and S13; S11, collect home appliance data in real time through sensor groups integrated inside home appliances and installed indoors; The sensor group includes an acceleration sensor, a gas detection module, an electromagnetic radiation sensor and a timer; Home appliance data includes equipment vibration frequency VF, indoor air composition AC, electromagnetic radiation intensity EMR and user behavior pattern BPD; The indoor air composition AC is detected by the gas detection module, and the composition of each indoor air is detected and obtained through comprehensive analysis; The user behavior pattern BPD tracks the user's geographic location through the GPS and WIFI data of the smartphone, and then records when the user turns on or off the device through the historical usage data of smart home devices such as smart lights, air conditioners and door locks. Through machine learning algorithms, the user's life patterns are inferred from these data, and then a time series analysis is performed on the user's geographic location and the user's life patterns to identify patterns and obtain behavior predictions. For example, the system can predict the user's possible operating behavior in a specific time period through daily historical data.

[0023] S12, connecting the sensor group to the cloud server through the Internet of Things (IOT), transmitting the collected home appliance data to the cloud server, and processing the collected home appliance data in the cloud server; Preprocessing methods include outlier detection and data smoothing and normalization; Outlier detection is used to filter the collected equipment vibration frequency VF by 3 times the standard deviation to remove noise interference and obtain the standard equipment vibration frequency VF clean , the specific filtering algorithm formula is , where VF i represents the vibration frequency of the i-th household appliance, Represents the mean value of the vibration frequency of the equipment, Indicates the standard deviation of the vibration frequency of the equipment; Data smoothing and normalization are used to normalize the electromagnetic radiation intensity EMR to prevent different data magnitudes from affecting the subsequent algorithm accuracy and obtain the standard electromagnetic radiation intensity EMR. b , the specific normalization algorithm formula is: ; Where, EMR i Represents the electromagnetic radiation intensity of the i-th household appliance, EMR min Indicates the upper limit of electromagnetic radiation intensity, EMR max Indicates the lower limit of electromagnetic radiation intensity; Then the standard equipment vibration frequency VF obtained after preprocessing clean and standard electromagnetic radiation intensity EMR b Integrate with the indoor air composition AC and user behavior pattern BPD in the home appliance data to obtain the home appliance standard data set; S13, constructing a cloud database in the cloud server, and setting a write port and a write port, and writing the acquired home appliance data set into the cloud database through the write port for storage.

[0024] In this embodiment, the method collects multi-dimensional data of household appliances in real time through a sensor group to ensure comprehensive monitoring of the device status and environmental information. The data is transmitted to the cloud server through the Internet of Things IoT connection, and the data is pre-processed, including outlier detection and data normalization, thereby improving the accuracy and consistency of the data. Through the construction of a cloud database, the processed standard data set is stored to ensure that the system can continuously access and utilize these high-quality data. Compared with traditional household appliance management technology, this method overcomes the problems of inaccurate data and untimely processing in the prior art through intelligent collection and preprocessing of multi-dimensional data. Real-time data processing and anomaly detection ensure accurate judgment of the device status, while data smoothing and normalization eliminate the influence of different sensor data magnitudes, making subsequent synchronization and linkage control more accurate. At the same time, through the construction and data storage of the cloud database, the system realizes long-term tracking and analysis of the device status.

[0025] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S2 includes S21 and S23; S21. Calculate the home appliance priority P of each smart home appliance through the synchronization priority calculation formula. i , determines the priority of the current home appliance data when synchronizing in the cloud; Home appliances priority P i The output is calculated by the following algorithm formula; ; Where P i Specifically, VF represents the priority of the i-th home appliance. max Indicates the upper limit of the equipment vibration frequency, BPD i represents the user behavior pattern of the i-th home appliance, represents the normalized value of vibration frequency, w1, w2, w3 and w4 represent the normalized value of vibration frequency, indoor air composition AC, standard electromagnetic radiation intensity EMR respectively b and the preset weight value of the user behavior pattern BPD, whose specific parameters are set by the user, and w1+w2+w3+w4=1.

[0026] S23: Preset priority threshold P based on user th Then compare it with the obtained home appliance equipment priority P i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; When the home appliance priority P i ≥Priority threshold P th When , it means that the current home appliance is in real-time response state, and the synchronization frequency strategy is executed at this time; When the home appliance priority P i <Priority threshold P th , it indicates that the current home appliance is in standby mode, and the data delay and energy-saving synchronization strategies are implemented at this time.

[0027] In this embodiment, the method calculates the synchronization priority P of each device by combining the vibration frequency VF, air composition AC, electromagnetic radiation intensity EMR and user behavior pattern BPD of the home appliance through the synchronization priority calculation formula. i , thereby determining the importance of the device in cloud synchronization. This calculation process introduces a weighted combination of multiple parameters to ensure the flexibility and accuracy of device priority. Then, based on the priority threshold P preset by the user th , for device priority P i For high-priority devices, the system will implement a high-frequency data synchronization strategy to ensure real-time response; for low-priority devices, it will implement a delayed synchronization and energy-saving strategy to reduce energy consumption.

[0028] In this embodiment, the method calculates the priority level of each device P by combining the vibration frequency VF, air composition AC, electromagnetic radiation intensity EMR and user behavior pattern BPD of the home appliance through the synchronization priority calculation formula. i , thereby determining the importance of the device in cloud synchronization. This calculation process introduces a weighted combination of multiple parameters to ensure the flexibility and accuracy of device priority. Then, based on the priority threshold P preset by the user th , the priority of home appliances is P i A preliminary comparative evaluation is conducted. For high-priority devices, a high-frequency data synchronization strategy is implemented to ensure real-time response; for low-priority devices, a delayed synchronization and energy-saving strategy is implemented to reduce energy consumption.

[0029] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S3 includes S31; S31, executing the corresponding synchronization strategy according to the preliminary comparison and evaluation results, the synchronization strategy includes the synchronization frequency strategy and the data delay and energy-saving synchronization strategy. When executing the synchronization frequency strategy, the data delay and energy-saving synchronization strategy are cancelled to restore the normal synchronization frequency of the home appliance. When executing the data delay and energy-saving synchronization strategy, the home appliance equipment is automatically switched to the low power consumption mode. At this time, the output synchronization frequency F is calculated by the synchronization frequency formula i ; Synchronous frequency F i The output is calculated by the following synchronization frequency formula; ; Where 1 represents the output synchronization frequency F when executing the synchronization frequency strategyi , 0 means the output synchronization frequency F when executing data delay and energy saving synchronization strategy i , F 0 Indicates the lower limit of the synchronization frequency, indicating that even low-priority devices have a basic synchronization frequency. Indicates the synchronization frequency gain coefficient, according to the priority P of the home appliance i To increase the synchronization frequency of high priority devices, represents the synchronization frequency reduction factor, The closer it is to 0, the greater the reduction in synchronization frequency. The significance of this strategy is that the current state of the i-th home appliance is of high importance or urgency, and the system needs to prioritize its data synchronization to ensure that the device uploads data in a timely manner and provides real-time response. For example, if the user behavior pattern BPD detects that the user is about to return home, such as through GPS data, the priority of air conditioners, lights and other devices increases, and the system increases the data synchronization frequency of these devices to adjust the working status of the devices in advance. For devices with lower priority, the system automatically switches them to low-power mode to reduce energy and power consumption. For example, smart cameras enter low-power monitoring mode when there are no major security events.

[0030] In this embodiment, the method executes corresponding synchronization strategies for different device states based on the previous priority evaluation results, including synchronization frequency strategy and data delay and energy-saving synchronization strategy. For devices with higher priority, the system restores the normal synchronization frequency of the device by canceling the delay and energy-saving mode to ensure that the device can upload data in time and achieve real-time response. For devices with lower priority, the system automatically switches to low-power mode and calculates the corresponding synchronization frequency F through the synchronization frequency formula. i , reducing the power consumption of the device and unnecessary resource consumption. This method significantly improves the efficiency and accuracy of data synchronization compared to traditional technologies. Traditional technologies often use a fixed synchronization frequency and cannot flexibly adjust the synchronization strategy according to the actual needs of the device, resulting in waste of device resources or delayed response.

[0031] This embodiment is explained in Example 3, please refer to Figure 1 , specifically: S4 includes S41; S41, extracting the user behavior pattern BPD through the write port of the cloud database, extracting the rules from the user's historical use of home appliances through the cloud time series analysis and clustering algorithm, and predicting the future user behavior pattern to generate linkage control instructions C i ; Generate linkage control instruction C i The output is calculated by the following algorithm formula; ; In the formula, N represents the total number of household electrical appliances. represents the adjustment coefficient of the i-th home device, f represents the behavior pattern function, BPD i represents the user behavior pattern of the i-th device, represents the device state adjustment coefficient of the i-th home device, T i represents the current temperature of the i-th home appliance, T desired，i represents the user's expected temperature of the i-th home appliance, S i represents the state of the i-th household appliance, S desired，i represents the user's expected home appliance state of the i-th home appliance, and t represents time.

[0032] In this embodiment, the method extracts the user behavior pattern BPD through the cloud database, uses time series analysis and clustering algorithms to extract rules from the user's historical usage data, and predicts future user behavior. This intelligent analysis can adjust the device status in advance according to the user's living habits, generate linkage control instructions Ci, and calculate the linkage strategy of each device through a formula to ensure the coordinated operation of the device. For example, the system can pre-adjust the temperature of the air conditioner, turn on the lighting equipment, etc. according to the user's home time, thereby improving the response speed of the device and the user experience. Compared with traditional device control methods, this method accurately predicts the user's behavior through intelligent algorithms, breaking through the limitations of traditional reliance on simple timing or manual control. Users no longer need to manually operate the device, and the system can automatically adjust the device according to the user's historical behavior to achieve the expected comfortable environment. This automated linkage based on behavioral patterns not only improves the operating efficiency of home appliances, but also brings users a more convenient and personalized smart home experience.

[0033] This embodiment is explained in Example 2. Please refer to Figure 1 , specifically: S5 includes S51 and S52; S51, after executing the linkage control and synchronization strategy, the cloud service calculates and outputs the optimized device synchronization priority O for the device status. i , analyze the synchronization frequency F i And linkage control instruction C i effectiveness; Device Sync Priority O i The output is calculated by the following algorithm formula; ; In the formula, Indicates the synchronization frequency F i The control coefficient of Indicates linkage control instruction C i The control coefficient of .

[0034] S52: Optimizing threshold value based on user preset th Sync with device priority O i Conduct a secondary comparative evaluation to analyze the current situation of home appliance data synchronization and execution linkage control. The specific evaluation contents are as follows; When the device synchronization priority is 0 i ≥Optimization threshold O th When it is, it means that there is an abnormality in the data synchronization strategy and linkage control of the current home appliance. At this time, an early warning message is issued to remind the user that there is an abnormality in the operation of the home appliance and to perform detection and maintenance; When the device synchronization priority is 0 i <Optimization threshold O th , it indicates that the data synchronization strategy and linkage control of the current home appliance are normal and no adjustment is required. At this time, the linkage control and synchronization strategy of the current home appliance are stored in the cloud database through the write port.

[0035] In this embodiment, the method extracts the user behavior pattern BPD from the cloud database, uses time series analysis and clustering algorithms to extract rules from the user's historical usage data, and predicts future user behavior. This intelligent analysis can adjust the device status in advance according to the user's living habits and generate linkage control instructions C i , and calculate the linkage strategy of each device through formulas to ensure the coordinated operation of the devices. For example, the system can pre-adjust the temperature of the air conditioner, turn on the lighting equipment, etc. according to the user's home time, improving the response speed of the device and user experience. Compared with traditional device control methods, this method accurately predicts user behavior through intelligent algorithms, breaking through the limitations of traditional reliance on simple timing or manual control. Users no longer need to manually operate the device, and the system can automatically adjust the device according to the user's historical behavior to achieve the expected comfortable environment. This automated linkage based on behavioral patterns not only improves the operating efficiency of home appliances, but also brings users a more convenient and personalized smart home experience. At the same time, by predicting user needs in advance, the system can also reduce equipment energy consumption while reducing waiting time, achieve more efficient energy management and smarter equipment linkage control, and further improve the overall operation effect of the smart home system.

[0036] See also Figure 1 and Figure 2 , a cloud storage data synchronization cloud storage system, including a data acquisition and preprocessing module, a data synchronization priority analysis module, a synchronization frequency strategy adjustment module, an intelligent linkage control module and a comprehensive evaluation and optimization module; The data collection and preprocessing module collects home appliance data in real time by installing sensor groups inside home appliances, and transmits the home appliance data to the cloud server through a wireless network. In the cloud server, the collected home appliance data is preprocessed to obtain a home appliance standard data set, and a cloud database is built to store the home appliance standard data set; The data synchronization priority analysis module calculates and outputs the priority level of home appliances P by using the synchronization priority calculation formula. i , and preset the priority threshold P th Priority P for home appliances i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; The synchronization frequency strategy adjustment module calculates and outputs the synchronization frequency F of each device uploading data through the corresponding synchronization strategy generated based on the preliminary comparison and evaluation. i , analyze the frequency of communication between home appliances and the cloud, and execute the corresponding home appliance operation mode; The intelligent linkage control module links with the home appliances through the intelligent linkage control of the cloud to generate linkage control instructions C i , control the coordinated operation of multiple home appliances; After the comprehensive evaluation and optimization module performs the initial linkage and control strategy execution of home appliances, the cloud service calculates the device status and outputs the optimized device synchronization priority. i , and preset the optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation and analyze the synchronization frequency F i and the effectiveness of linkage control.

[0037] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A cloud storage data synchronization method, characterized in that: The following steps are involved: S1. Install sensor groups inside home appliances to collect home appliance data in real time, and transmit the home appliance data to a cloud server through a wireless network. In the cloud server, pre-process the collected home appliance data to obtain a home appliance standard data set, and build a cloud database to store the home appliance standard data set; S2. Use the synchronization priority calculation formula to calculate and output the priority P of the home appliance equipment i , and preset the priority threshold P th Priority P for home appliances i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; S3, based on the corresponding synchronization strategy generated by the preliminary comparative evaluation, calculate and output the synchronization frequency F of each device uploading data i , analyze the frequency of communication between home appliances and the cloud, and execute the corresponding home appliance operation mode; S4, through the cloud intelligent linkage control and home appliances linkage, generate linkage control instructions C i , control the coordinated operation of multiple home appliances; S5. After the initial home appliance linkage and control strategy execution, the cloud service calculates the device status and outputs the optimized device synchronization priority O i , and preset the optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation and analyze the synchronization frequency F i and the effectiveness of linkage control.

2. A cloud storage data synchronization method according to claim 1, characterized in that: Said S1 includes S11, S12 and S13; S11, collect home appliance data in real time through sensor groups integrated inside home appliances and installed indoors; The sensor group includes an acceleration sensor, a gas detection module, an electromagnetic radiation sensor and a timer; The home appliance data includes device vibration frequency VF, indoor air composition AC, electromagnetic radiation intensity EMR and user behavior pattern BPD; The indoor air composition AC is detected by a gas detection module, and the composition of each indoor air is detected and obtained through comprehensive analysis; The user behavior pattern BPD tracks the user's geographic location through the GPS and WIFI data of the smartphone, and then infers the user's life pattern from these data through smart home devices and machine learning algorithms, and then performs time series analysis on the user's geographic location and the user's life pattern to identify the pattern and obtain predictions.

3. A cloud storage data synchronization method according to claim 2, characterized in that: S12, connecting the sensor group to the cloud server through the Internet of Things (IOT), transmitting the collected home appliance data to the cloud server, and then processing the collected home appliance data in the cloud server; The preprocessing method includes outlier detection and data smoothing and normalization; The outlier detection is used to filter the collected equipment vibration frequency VF by 3 times the standard deviation to remove noise interference and obtain the standard equipment vibration frequency VF clean , the specific filtering algorithm formula is , where VF i represents the vibration frequency of the i-th household appliance, Represents the mean value of the vibration frequency of the equipment, Indicates the standard deviation of the vibration frequency of the equipment; The data smoothing and normalization are used to normalize the electromagnetic radiation intensity EMR to obtain the standard electromagnetic radiation intensity EMR. b , the specific normalization algorithm formula is: ; Where, EMR i Represents the electromagnetic radiation intensity of the i-th household appliance, EMR min Indicates the upper limit of electromagnetic radiation intensity, EMR max Indicates the lower limit of electromagnetic radiation intensity; Then the standard equipment vibration frequency VF obtained after preprocessing clean and standard electromagnetic radiation intensity EMR b Integrate with the indoor air composition AC and user behavior pattern BPD in the home appliance data to obtain the home appliance standard data set; S13, constructing a cloud database in the cloud server, and setting a write port and a write port, and writing the acquired home appliance data set into the cloud database through the write port for storage.

4. A cloud storage data synchronization method according to claim 3, characterized in that: Said S2 includes S21 and S23; S21. Calculate the home appliance priority P of each smart home appliance through the synchronization priority calculation formula. i , determines the priority of the current home appliance data when synchronizing in the cloud; The home appliance priority P i The output is calculated by the following algorithm formula; ； Where P i Specifically, VF represents the priority of the i-th home appliance. max Indicates the upper limit of the equipment vibration frequency, BPD i represents the user behavior pattern of the i-th home appliance, represents the normalized value of vibration frequency, w1, w2, w3 and w4 represent the normalized value of vibration frequency, indoor air composition AC, standard electromagnetic radiation intensity EMR respectively b and the preset weight value of the user behavior pattern BPD, whose specific parameters are set by the user, and w1+w2+w3+w4=1.

5. A cloud storage data synchronization method according to claim 4, characterized in that: S23: Preset priority threshold P based on user th Then compare it with the obtained home appliance equipment priority P i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; When the home appliance priority P i ≥Priority threshold P th When , it means that the current home appliance is in real-time response state, and the synchronization frequency strategy is executed at this time; When the home appliance priority P i <Priority threshold P th , it indicates that the current home appliance is in standby mode, and the data delay and energy-saving synchronization strategies are implemented at this time.

6. A cloud storage data synchronization method according to claim 5, characterized in that: The S3 includes S31; S31, executing the corresponding synchronization strategy according to the preliminary comparison and evaluation results, wherein the synchronization strategy includes the synchronization frequency strategy and the data delay and energy-saving synchronization strategy. When executing the synchronization frequency strategy, the data delay and energy-saving synchronization strategy are cancelled to restore the normal synchronization frequency of the home appliance. When executing the data delay and energy-saving synchronization strategy, the home appliance is automatically switched to a low power consumption mode. At this time, the output synchronization frequency F is calculated by the synchronization frequency formula i ; The synchronization frequency F i The output is calculated by the following synchronization frequency formula; ； Where 1 represents the output synchronization frequency F when executing the synchronization frequency strategy i , 0 means the output synchronization frequency F when executing data delay and energy saving synchronization strategy i , F0 represents the lower limit of synchronization frequency, Indicates the synchronization frequency gain coefficient, according to the priority P of the home appliance i To increase the synchronization frequency of high priority devices, represents the synchronization frequency reduction factor, The closer it is to 0, the greater the reduction in synchronization frequency.

7. A cloud storage data synchronization method according to claim 6, characterized in that: The S4 includes S41; S41, extracting the user behavior pattern BPD through the write port of the cloud database, extracting the rules from the user's historical use of home appliances through the cloud time series analysis and clustering algorithm, and predicting the future user behavior pattern to generate linkage control instructions C i ; The generating linkage control instruction C i The output is calculated by the following algorithm formula; ； In the formula, N represents the total number of household electrical appliances. represents the adjustment coefficient of the i-th home device, f represents the behavior pattern function, BPD i represents the user behavior pattern of the i-th device, represents the device state adjustment coefficient of the i-th home device, T i represents the current temperature of the i-th home appliance, T desired，i represents the user's expected temperature of the i-th home appliance, S i represents the state of the i-th household appliance, S desired，i represents the user's expected home appliance state of the i-th home appliance, and t represents time.

8. A cloud storage data synchronization method according to claim 6, characterized in that: The S5 includes S51 and S52; S51, after executing the linkage control and synchronization strategy, the cloud service calculates and outputs the optimized device synchronization priority O for the device status. i , analyze the synchronization frequency F i And linkage control instruction C i effectiveness; The device synchronization priority is 0 i The output is calculated by the following algorithm formula; ； In the formula, Indicates the synchronization frequency F i The control coefficient of Indicates linkage control instruction C i The control coefficient of .

9. A cloud storage data synchronization method according to claim 8, characterized in that: S52: Optimizing threshold value based on user preset th Sync with device priority O i Conduct a secondary comparative evaluation to analyze the current situation of home appliance data synchronization and execution linkage control. The specific evaluation contents are as follows; When the device synchronization priority is 0 i ≥Optimization threshold O th When it is, it means that there is an abnormality in the data synchronization strategy and linkage control of the current home appliance. At this time, an early warning message is issued to remind the user that there is an abnormality in the operation of the home appliance and to perform detection and maintenance; When the device synchronization priority is 0 i <Optimization threshold O th , it indicates that the data synchronization strategy and linkage control of the current home appliance are normal and no adjustment is required. At this time, the linkage control and synchronization strategy of the current home appliance are stored in the cloud database through the write port.

10. A cloud storage data synchronization cloud storage system, applied to a cloud storage data synchronization method according to any one of claims 1 to 9, characterized in that: It includes data acquisition and preprocessing module, data synchronization priority analysis module, synchronization frequency strategy adjustment module, intelligent linkage control module and comprehensive evaluation and optimization module; The data collection and preprocessing module collects home appliance data in real time by installing sensor groups inside home appliances, and transmits the home appliance data to a cloud server through a wireless network. In the cloud server, the collected home appliance data is preprocessed to obtain a home appliance standard data set, and a cloud database is constructed to store the home appliance standard data set. The data synchronization priority analysis module calculates and outputs the home appliance device priority P by using the synchronization priority calculation formula. i , and preset the priority threshold P th Priority P for home appliances i Conduct preliminary comparative evaluation and generate corresponding synchronization strategies based on the preliminary comparative evaluation results; The synchronization frequency strategy adjustment module calculates and outputs the synchronization frequency F of each device uploading data based on the corresponding synchronization strategy generated based on the preliminary comparison and evaluation. i , analyze the frequency of communication between home appliances and the cloud, and execute the corresponding home appliance operation mode; The intelligent linkage control module is linked with the home appliance through the intelligent linkage control on the cloud to generate a linkage control instruction C i , control the coordinated operation of multiple home appliances; The comprehensive evaluation and optimization module calculates the device status and outputs the optimized device synchronization priority after the initial home appliance linkage and control strategy execution. i , and preset the optimization threshold O th Sync with device priority O i Conduct a secondary comparative evaluation and analyze the synchronization frequency F i and the effectiveness of linkage control.

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