A cloud data scheduling method and system based on the Internet of Things

Abstract

The present invention is applicable to the field of cloud data mobilization, and provides a cloud data scheduling method and system based on the Internet of Things. The system includes: a data acquisition and classification module, a device status evaluation and priority allocation module, a priority score allocation module, a data upload queue management module, an upload result recording and monitoring module, a cloud storage and data life cycle management module, and an upload feedback analysis and report generation module. By evaluating the current status and network environment of the Internet of Things device, the system can dynamically set the priority interval. This mechanism enables the data upload process to have flexibility and intelligent adaptability. Real-time monitoring of device status and network conditions ensures that the system can reasonably allocate resources according to actual conditions, thereby ensuring that high-priority data can be uploaded in time at the right time. This method not only improves the upload success rate, but also effectively reduces the upload delay caused by network congestion or equipment failure.

Description

Technical Field

[0001] The present invention belongs to the field of cloud data mobilization, and in particular relates to a cloud data scheduling method and system based on the Internet of Things. Background Art

[0002] The field of cloud data orchestration involves technologies and strategies for effectively managing and optimizing data storage, processing, and transmission in cloud computing environments. Its main goal is to ensure that data can flow efficiently between different cloud services and resources to meet the performance, availability, and cost requirements of applications.

[0003] The field of cloud data scheduling is a crucial part of the cloud computing environment, involving various technologies and strategies for effectively managing and optimizing the storage, processing and transmission of data. With the surge in data volume and the diversification of application requirements, how to ensure the efficient flow of data between different cloud services and resources has become a core issue that enterprises and organizations need to solve. The main goal of this field is to balance performance, availability and cost to ensure the best use of cloud resources.

[0004] In the traditional IoT data upload process, the lack of an effective priority mechanism is a significant flaw. This results in the system being unable to identify and process key data in a timely manner, and important information may be uploaded late, affecting real-time decision-making and response. In addition, existing technologies often adopt static resource allocation strategies that cannot adapt to rapidly changing environments. This static nature makes the system inefficient and unable to reasonably allocate resources when faced with emergencies or network load fluctuations.

[0005] Network congestion and equipment failure are also major problems with traditional upload methods, often resulting in high latency, affecting the timeliness and reliability of data. In the case of network congestion, uploaded data may be processed late, while equipment failure may result in data loss, further exacerbating system instability. Summary of the invention

[0006] The purpose of the present invention is to provide a cloud data scheduling method based on the Internet of Things, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0007] The present invention is implemented as follows: a cloud data scheduling method based on the Internet of Things, the method comprising:

[0008] Get the stored data to be uploaded from IoT devices, classify the data by importance, timestamp and type, and mark the data as to be uploaded;

[0009] Evaluate the current status and network environment of IoT devices, set several priority intervals, and dynamically allocate the data to be uploaded to several priority intervals according to the device status;

[0010] Use a random number generator to assign a priority score to each piece of data to be uploaded in each priority interval, set a priority threshold, and mark the data above the priority threshold as uploadable data based on the priority score;

[0011] In each time window, a predetermined amount of data is randomly selected from the data marked as uploadable for upload to form a data upload queue, and the transmission frequency and upload strategy are adjusted according to the network status;

[0012] Record the success and failure results of each upload, monitor latency and bandwidth usage, and generate system logs;

[0013] Store the uploaded data in the cloud database and perform data lifecycle management;

[0014] Analyze upload feedback, identify the relationship between upload success rate, latency, and network status, and generate upload reports accordingly.

[0015] As a further solution of the present invention, the method of obtaining the stored data to be uploaded from the IoT device and classifying the data according to importance, timestamp and type specifically includes:

[0016] Establish a connection with the IoT device, read the storage area of ​​the device, and extract the data to be uploaded;

[0017] Mark the importance of data according to its content, source and application scenario; extract timestamps from data and classify them according to the extraction time; and then perform secondary classification of data types based on data content;

[0018] Add status identification to the classified data and mark it as to be uploaded.

[0019] As a further solution of the present invention, the current state and network environment of the IoT device are evaluated, a number of priority intervals are set, and the data to be uploaded is dynamically allocated to the number of priority intervals according to the device state, specifically including:

[0020] Obtain status information of IoT devices in real time and evaluate the current network environment, including network bandwidth, latency, packet loss rate, and connection stability;

[0021] According to the evaluation results of the device status and network environment, several priority intervals are set, and thresholds of the priority intervals are set, including high priority, medium priority and low priority, and the number of priority intervals and the type of data contained in each interval are determined;

[0022] According to the device status and network status, the data to be uploaded is dynamically allocated to several priority intervals.

[0023] As a further solution of the present invention, the random number generator is used to assign a priority score to each piece of data to be uploaded in each priority interval, and a priority threshold is set. According to the priority score, data above the priority threshold is marked as uploadable data, specifically including:

[0024] Initialize a priority score storage structure for each data to be uploaded;

[0025] Set a random number generator to traverse the data to be uploaded in each priority interval and generate a random priority score for each data;

[0026] A priority threshold is set according to system requirements and network status, and data with a priority score higher than the priority threshold is filtered and uploaded from the data to be uploaded according to the set priority threshold.

[0027] As a further solution of the present invention, within each time window, a predetermined amount of data is randomly selected from the data marked as uploadable for uploading to form a data upload queue, and the transmission frequency and upload strategy are adjusted according to the network status, specifically including:

[0028] Set time windows and record the start and end time of each time window. Determine the number of uploads scheduled in each time window based on system capabilities and network status.

[0029] Filter out data with a priority higher than a priority threshold from the data to be uploaded, and within each time window, use a random number generator to randomly select a predetermined number of data to be uploaded to form a data upload queue;

[0030] Monitor the network status in real time, and dynamically adjust the frequency and strategy of data upload based on the real-time monitored network status.

[0031] As a further solution of the present invention, the recording of success and failure results of each upload, monitoring of delay and bandwidth usage, and generation of system logs specifically include:

[0032] Record the results of each upload, including success and failure status, monitor the time required for each upload, record latency, and monitor the bandwidth consumed during the data upload process;

[0033] Integrate the collected content and generate a system log to record the information uploaded this time.

[0034] As a further solution of the present invention, the uploaded data is stored in a cloud database and data lifecycle management is performed, specifically including:

[0035] Build a data upload interface to upload the data to be stored to the selected cloud database and verify the data after it is stored;

[0036] Develop a data lifecycle management strategy, determine the lifecycle stages of different types of data, and set time limits and archiving strategies for data storage based on the importance and frequency of use of the data;

[0037] Set the backup frequency to back up the data in the cloud database;

[0038] Monitor data storage and lifecycle management effectiveness, and generate corresponding reports.

[0039] As a further solution of the present invention, the analysis of upload feedback, identification of the relationship between upload success rate, delay and network status, and generation of an upload report accordingly, specifically includes:

[0040] Organize the success and failure results of each upload, calculate the upload success rate of each type of data, analyze the upload performance of data of different priorities, calculate the average delay of each type of data, and analyze the bandwidth usage under different network conditions;

[0041] Use statistical methods to analyze the correlation between upload success rate and latency and bandwidth usage to identify potential patterns and trends that affect upload performance;

[0042] Generate an upload report based on the analysis results.

[0043] Another object of the present invention is to provide a cloud data scheduling system based on the Internet of Things, the system comprising:

[0044] The data acquisition and classification module is used to obtain the stored data to be uploaded from the IoT devices, classify the data by importance, timestamp and type, and mark the data as to be uploaded;

[0045] The device status assessment and priority allocation module is used to assess the current status and network environment of IoT devices, set several priority intervals, and dynamically allocate the data to be uploaded to several priority intervals according to the device status;

[0046] A priority score assignment module is used to assign a priority score to each piece of data to be uploaded in each priority interval using a random number generator, and to set a priority threshold. According to the priority score, data above the priority threshold is marked as uploadable data.

[0047] The data upload queue management module is used to randomly select a predetermined amount of data from the data marked as uploadable in each time window for uploading to form a data upload queue, and adjust the transmission frequency and upload strategy according to the network status;

[0048] The upload result recording and monitoring module is used to record the success and failure results of each upload, monitor latency and bandwidth usage, and generate system logs;

[0049] The cloud storage and data lifecycle management module is used to store the uploaded data in the cloud database and perform data lifecycle management;

[0050] The upload feedback analysis and report generation module is used to analyze the upload feedback, identify the relationship between the upload success rate, delay and network status, and generate an upload report accordingly.

[0051] As a further solution of the present invention, the device status evaluation and priority allocation module includes:

[0052] The status information acquisition unit is used to obtain the status information of IoT devices in real time and evaluate the current network environment, including network bandwidth, latency, packet loss rate, and connection stability;

[0053] A priority interval setting unit, used to set a number of priority intervals according to the evaluation results of the device status and the network environment, and set the thresholds of the priority intervals, including high priority, medium priority and low priority, and determine the number of priority intervals and the type of data contained in each interval;

[0054] The priority interval data allocation unit is used to dynamically allocate the data to be uploaded to several priority intervals according to the device status and the network status.

[0055] The beneficial effects of the present invention are:

[0056] By evaluating the current status of IoT devices and the network environment, the system can dynamically set the priority interval. This mechanism makes the data upload process flexible and intelligently adaptable. Real-time monitoring of device status and network conditions ensures that the system can reasonably allocate resources according to actual conditions, thereby ensuring that high-priority data can be uploaded in a timely manner at the right time. This method not only improves the upload success rate, but also effectively reduces upload delays caused by network congestion or device failure.

[0057] A random number generator is introduced to assign a priority score to each piece of data to be uploaded, and a priority threshold is set for screening. This step ensures that high-priority data is processed first during the upload process by quantifying the importance of the data. This method reduces the possibility of human intervention, improves the automation level of the system, and also reduces the waste of resources that may be caused by selection bias.

[0058] By randomly selecting uploadable data to form an upload queue and dynamically adjusting the transmission frequency and upload strategy according to the network status, the system's adaptability under different network conditions is further improved. This flexible upload mechanism enables the system to respond to changes in the network environment in real time, ensuring more data transmission when the network load is low, and reasonably controlling the upload frequency when the network is congested, thereby avoiding upload failures or excessive delays. This intelligent scheduling not only improves the success rate of data uploads, but also optimizes bandwidth utilization and reduces resource waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a cloud data scheduling method based on the Internet of Things provided by an embodiment of the present invention;

[0060] Figure 2 A flowchart of obtaining stored data to be uploaded from an Internet of Things device provided by an embodiment of the present invention;

[0061] Figure 3 A flowchart of dynamically allocating data to be uploaded into several priority intervals provided by an embodiment of the present invention;

[0062] Figure 4 A flowchart of randomly selecting some data with priority scores higher than a priority threshold for uploading provided by an embodiment of the present invention;

[0063] Figure 5 A flow chart of adjusting transmission frequency and upload strategy provided by an embodiment of the present invention;

[0064] Figure 6 A flowchart for recording the success and failure results of each upload and monitoring delay and bandwidth usage provided by an embodiment of the present invention;

[0065] Figure 7 A flowchart of storing uploaded data in a cloud database and performing data lifecycle management provided by an embodiment of the present invention;

[0066] Figure 8 A flowchart of analyzing upload feedback and identifying the relationship between upload success rate, delay and network status provided by an embodiment of the present invention;

[0067] Fig. 9 A structural block diagram of a cloud data scheduling system based on the Internet of Things provided by an embodiment of the present invention;

[0068] Fig.10 This is a structural block diagram of the device status evaluation and priority allocation module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] 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.

[0070] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0071] Figure 1 A flowchart of a cloud data scheduling method based on the Internet of Things provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes:

[0072] S100, obtaining stored data to be uploaded from the IoT device, classifying the data according to importance, timestamp and type, and marking the data as to be uploaded;

[0073] like Figure 2 As shown, the method obtains the stored data to be uploaded from the IoT device and classifies the data according to importance, timestamp and type, specifically including:

[0074] S110, establishing a connection with an IoT device, and reading a storage area of ​​the device to extract data to be uploaded;

[0075] S120, marking the importance of the data according to the content, source and application scenario of the data; extracting timestamps from the data and classifying the data according to the extraction time; and then performing secondary classification of the data according to the data content;

[0076] S130, adding a status mark to the classified data, marking it as to be uploaded.

[0077] This step establishes a real-time connection with the IoT device through a secure communication protocol (such as MQTT, HTTPs, CoAP, etc.), ensures the stability and security of the connection, and adopts a verification mechanism to prevent unauthorized access. Next, access the internal storage of the device to extract the data to be uploaded, and use an efficient data reading method to ensure that a large amount of data is quickly obtained to avoid device performance limitations. Through data parsing tools, filter out valid data to be uploaded from the read data, and standardize the data format for subsequent processing and classification.

[0078] On this basis, the importance of the data is evaluated through preset rules according to the content, source and application scenario of the data. For example, critical business data (such as sensor monitoring data) is marked as high importance, while non-critical data (such as historical data) is marked as low importance. At the same time, timestamp information is extracted from the data, and the data is classified according to the extraction time to determine the newness of the data and give priority to the latest data. Combined with the data content, the data is secondary classified by type, such as by sensor type, data format, etc., for subsequent processing. This process can set different classification standards according to the needs of the application scenario.

[0079] Finally, add a status mark to the classified data and mark it as "pending upload" to ensure the consistency of the status mark during the data transmission process to facilitate subsequent monitoring and management.

[0080] Through this step, data quality is improved, ensuring that the uploaded data is of high value, which helps with subsequent analysis and decision-making. In addition, uploading only the required data can reduce the network burden, improve upload speed and efficiency, and avoid the transmission of redundant data. Establishing a secure connection and verification mechanism also increases the reliability of the data upload process and reduces the risk of data loss or leakage. Dynamic classification and tagging strategies enable the system to adapt to different application scenarios and needs, facilitating future expansion. By setting status identification, it is easy to monitor and manage data in real time, ensure the transparency and traceability of the upload process, thereby improving the user experience, ensuring that the uploaded data is the information that users care about most, and enhancing users' trust and dependence on the system.

[0081] S200, evaluating the current state and network environment of the IoT device, setting a number of priority intervals, and dynamically allocating the data to be uploaded to the number of priority intervals according to the device state;

[0082] like Figure 3 As shown, the current state and network environment of the IoT device are evaluated, several priority intervals are set, and the data to be uploaded is dynamically allocated to several priority intervals according to the device state, specifically including:

[0083] S210, obtains the status information of IoT devices in real time and evaluates the current network environment, including network bandwidth, latency, packet loss rate, and connection stability;

[0084] S220, according to the evaluation results of the device status and the network environment, set a number of priority intervals, set thresholds of the priority intervals, including high priority, medium priority and low priority, determine the number of priority intervals and the type of data contained in each interval;

[0085] S230, dynamically allocating the data to be uploaded into a plurality of priority intervals according to the device status and the network status.

[0086] In this step, we first obtain the status information of IoT devices in real time to ensure that we can accurately grasp the operating status of the devices, including key indicators such as battery power, processor load, and sensor working status. At the same time, we evaluate the current network environment, focusing on parameters such as network bandwidth, latency, packet loss rate, and connection stability. This information will provide basic data support for subsequent data upload decisions.

[0087] Next, according to the evaluation results of the device status and network environment, several priority intervals are set, usually including high priority, medium priority and low priority. The number of priority intervals and specific threshold settings need to be adjusted according to the special needs of the application scenario. For example, for emergency monitoring data, lower delay and packet loss rate thresholds may be set to ensure that high-priority data can be uploaded in a timely manner. At the same time, the type of data contained in each priority interval is clarified so that the system can be dynamically adjusted according to actual conditions.

[0088] Finally, the data to be uploaded is dynamically allocated to several priority intervals according to the device status and network status. The system will combine real-time monitoring data to achieve accurate classification and allocation of data to be uploaded, ensuring that high-priority data can be uploaded first when the network is in good condition, and can adjust the upload strategy according to the situation when the network is congested or the device is in poor condition.

[0089] This step can significantly improve the efficiency and reliability of data upload. First, real-time acquisition of device and network status information enables the system to make more intelligent decisions and ensure priority upload of important data. Second, dynamic setting of priority intervals and thresholds makes the system flexible and adaptable to different environments and needs, avoiding data loss or delays caused by network instability. In addition, this step can also optimize the use of network resources, ensuring that the system can effectively manage upload tasks when network conditions are poor, thereby improving the overall user experience and enhancing user trust in the system.

[0090] S300, using a random number generator to assign a priority score to each piece of data to be uploaded in each priority interval, and setting a priority threshold, and marking data above the priority threshold as uploadable data according to the priority score;

[0091] like Figure 4 As shown, the random number generator is used to assign a priority score to each piece of data to be uploaded in each priority interval, and a priority threshold is set. According to the priority score, data above the priority threshold is marked as uploadable data, specifically including:

[0092] S310, initializing a storage structure of a priority score for each data to be uploaded;

[0093] S320, setting a random number generator, traversing the data to be uploaded in each priority interval, and generating a random priority score for each data;

[0094] S330, setting a priority threshold according to system requirements and network status, and filtering and uploading data having a priority score higher than the priority threshold from the data to be uploaded according to the set priority threshold.

[0095] In this step, a storage structure of priority scores is first initialized for each piece of data to be uploaded. This structure can effectively organize the priority information of each piece of data to be uploaded, ensuring that the system can quickly access and update these scores for use in subsequent decision-making processes.

[0096] Then, a random number generator is set up to traverse the data to be uploaded in each priority interval and generate a random priority score for each piece of data. In this process, the advantage of using a random number generator is that it can introduce a certain degree of randomness, help break the potential data upload bias, and ensure that different data can get a fair chance to be uploaded to a certain extent.

[0097] Next, according to the system requirements and network status, set the priority threshold. This threshold is set based on the device's workload, the current network environment (such as latency and bandwidth), and the importance of the data. According to the set priority threshold, the data to be uploaded with a priority score higher than the threshold is filtered and marked as uploadable data. This process ensures that only the most valuable and most urgent data is uploaded first, thereby optimizing the upload process.

[0098] This step significantly improves the flexibility and efficiency of data upload. First, the generation of random priority scores introduces fairness, avoids bias in the system when processing large amounts of data, and ensures that all types of data have the opportunity to be uploaded, which is particularly critical for balancing the importance of different data. Secondly, the priority thresholds that are dynamically set based on system requirements and network status enable the system to respond to changes in real time, improving the intelligence level of data upload. In addition, this step can effectively reduce the waste of network resources and only upload the most needed data, thereby optimizing bandwidth usage, improving user experience and enhancing system reliability. Overall, this step provides efficient and dynamic decision support for data selection in the cloud data scheduling process, making the entire process more intelligent and adaptive.

[0099] S400, within each time window, randomly select a predetermined amount of data from the data marked as uploadable to upload, form a data upload queue, and adjust the transmission frequency and upload strategy according to the network status;

[0100] like Figure 5 As shown, in each time window, a predetermined amount of data is randomly selected from the data marked as uploadable to be uploaded to form a data upload queue, and the transmission frequency and upload strategy are adjusted according to the network status, specifically including:

[0101] S410, setting a time window, and recording the start and end time of each time window, and determining the scheduled upload quantity in each time window according to the system capability and network status;

[0102] S420, filtering out data with a priority higher than a priority threshold from the data to be uploaded, and in each time window, randomly selecting a predetermined number of data to be uploaded using a random number generator to form a data upload queue;

[0103] S430, monitor the network status in real time, and dynamically adjust the frequency and strategy of data uploading according to the real-time monitored network status.

[0104] If the network status is good, increase the upload frequency and select more data to upload; if the network status is poor, reduce the upload frequency.

[0105] Adjust the amount of data uploaded each time and the selection strategy (such as uploading more important data first) based on the network quality.

[0106] Record the strategy and reasons for each adjustment for subsequent optimization.

[0107] In this step, you first need to set a time window, which will define the start and end time of each data upload operation, such as every 5 minutes, 10 minutes or longer to suit the specific application scenario. The length of the time window should be dynamically adjusted according to the device's upload frequency, data generation rate and network conditions. Based on the system's capabilities (such as processing power, storage capacity) and real-time evaluation of network status, determine the number of uploads scheduled in each time window, and refer to the system's processing power, storage limit and historical upload success rate to set the number of uploads in each time window. For example, if the number of successful uploads in the previous time window is 50, then the number can be appropriately increased or decreased in the next window. When the network condition is good, increase the number of uploads; when the network is unstable, reduce the number of uploads to ensure that it does not affect other key operations. The scheduled number can be dynamically adjusted based on previous upload history and system performance to ensure that it does not exceed the system's processing capacity while maintaining efficient uploads.

[0108] Next, data with a priority higher than the set priority threshold is filtered out from the data marked for upload. This step ensures that only the most important data enters the upload queue, preventing invalid or low-value data from taking up precious bandwidth and upload time.

[0109] Using a random number generator, the system will randomly select a predetermined number of data to be uploaded within each time window to form a data upload queue. This random selection mechanism helps avoid selection bias and ensures that all types of data have the opportunity to be uploaded, thereby achieving more balanced data processing.

[0110] Real-time monitoring of network status is a key step in this step. By monitoring network bandwidth, latency, packet loss rate and other indicators, the system can dynamically adjust the frequency and strategy of data upload. For example, if the network status is good, the system can increase the upload frequency and select more data for upload; when the network status is poor, the system will reduce the upload frequency and select more important data for upload first to ensure the timely transmission of key data.

[0111] In addition, it is very important to record the strategy and reasons for each adjustment. This process not only helps the system understand the performance under different network conditions, but also provides data support for future optimization. By analyzing historical records, the system can gradually improve the upload strategy and improve the efficiency and success rate of data upload.

[0112] This step can dynamically adjust based on the real-time network status to ensure that the upload process can adapt to different network conditions, thereby improving the upload success rate and efficiency. In addition, efficient data utilization can be achieved, and high-priority data upload is given priority to ensure timely processing of key data when resources are limited, and avoid low-priority data from occupying bandwidth. The random selection mechanism ensures the fairness and diversity of uploaded data and avoids excessive concentration on certain data types. By recording upload strategies and results, it supports subsequent historical data analysis, provides a basis for system optimization, and helps decision makers understand the relationship between various indicators, thereby continuously improving system performance. Ultimately, ensuring the timely upload of key data, improving users' trust in the system and the practicality of IoT devices, and reasonable upload frequency and quantity settings can also significantly reduce system resource consumption and improve overall operational efficiency.

[0113] S500, records the success and failure results of each upload, monitors latency and bandwidth usage, and generates system logs;

[0114] like Figure 6 As shown, the success and failure results of each upload are recorded, latency and bandwidth usage are monitored, and system logs are generated, including:

[0115] S510, recording the result of each upload, including the status of success and failure, and monitoring the time required for each upload, recording the delay, and monitoring the bandwidth consumed during the data upload process;

[0116] S520, integrating the collected content and generating a system log to record the information uploaded this time.

[0117] This step will record the results of each upload and clearly mark the upload status of each piece of data as "successful" or "failed". This information can not only help users understand the upload status, but also provide a basis for subsequent troubleshooting.

[0118] Secondly, the system will monitor and record the time required for each upload. This time record is used to evaluate the efficiency of the upload and help analyze the upload situation under different conditions. By comparing the time of each upload, potential performance bottlenecks can be discovered, thus providing data support for subsequent optimization.

[0119] In addition, latency monitoring is an important part of this step. The system tracks network latency and records the highest, lowest, and average latency observed during data upload. These latency data can reveal changes in network status and their impact on upload performance, helping the system make better decisions during future uploads.

[0120] Finally, the system will also monitor the bandwidth consumed during the data upload process. By recording bandwidth usage, the system can evaluate whether the current network environment is suitable for data upload and dynamically adjust the upload strategy based on bandwidth changes.

[0121] All collected information will be integrated and summarized to generate system logs. These logs will not only record detailed information of the upload, including uploaded data type, quantity, upload results, latency, and bandwidth usage, but also serve as a historical record of system operation status, providing an important basis for subsequent performance analysis and fault diagnosis.

[0122] This step can provide the system with real-time feedback information to ensure the transparency and traceability of the upload process. By recording the success and failure status of each upload, the system can identify problems in a timely manner and conduct targeted troubleshooting, thereby improving system reliability and user experience. In addition, monitoring the time and delay required for upload can help the system analyze and optimize upload performance and ensure data upload efficiency under various network conditions.

[0123] Monitoring bandwidth usage is also important. The system can dynamically adjust the upload strategy according to changes in the network environment to avoid uploading large amounts of data when the network is congested and reduce the upload failure rate. The generated system log becomes a historical record of system operation, which can provide data support for subsequent upload strategy adjustments and system optimization, helping decision makers understand the relationship between system performance and network status.

[0124] S600, storing the uploaded data in a cloud database and performing data lifecycle management;

[0125] like Figure 7 As shown, the uploaded data is stored in the cloud database and data life cycle management is performed, specifically including:

[0126] S610, constructing a data upload interface, uploading the data to be stored to the selected cloud database, and verifying the data after it is stored;

[0127] S620, formulate a data lifecycle management strategy, determine the lifecycle stages of different types of data, and set time limits and archiving strategies for data storage based on the importance and frequency of use of the data;

[0128] S630, setting a backup frequency to back up data in the cloud database;

[0129] S640, monitor data storage status and lifecycle management effects, and generate corresponding reports.

[0130] Building a data upload interface is the basis for effective communication between data and cloud databases. The interface should be efficient and reliable, support uploading of multiple data formats, and ensure that the data will not be damaged during transmission. After the data is stored, the system will perform a verification step to ensure the integrity and consistency of the uploaded data and prevent data loss or errors caused by upload errors.

[0131] Secondly, developing a data lifecycle management strategy is an important part of this step. According to the characteristics of different types of data, the lifecycle stages are divided, such as creation, storage, use, archiving, and destruction. According to the importance and frequency of use of the data, the system will set a time limit for data storage and an archiving strategy. For important data that is frequently accessed, it may be chosen to be stored for a long time; for data that is less frequently used, it can be archived or cleaned up after a certain storage period, ensuring the efficient operation of the cloud database.

[0132] In addition, setting the backup frequency is also an important measure to ensure data security. The system will formulate a regular backup plan based on the importance of the data and the storage strategy to ensure that the data can be quickly restored in the event of data loss or damage. Backup can be performed in the form of incremental backup or full backup to reduce storage space usage and backup time.

[0133] Finally, monitoring data storage and lifecycle management is the key to continuous improvement. The system will regularly evaluate the use of stored data, the effectiveness of lifecycle policy execution, and generate corresponding reports. These reports will provide information such as data access frequency, storage duration, backup status, and lifecycle stage transitions to support subsequent decision-making.

[0134] By building an efficient data upload interface, accurate data upload and storage are guaranteed, reducing data risks caused by transmission errors. After formulating a data lifecycle management strategy, the system can flexibly adjust the storage plan according to the actual use of data, optimize resource utilization efficiency, and avoid unnecessary storage costs.

[0135] The setting and monitoring of backup frequency further enhances data security, ensuring that data can be quickly restored even in the event of an accident, thereby reducing losses caused by data loss. In addition, the ability to monitor and analyze data storage and lifecycle management effects provides a continuous feedback mechanism to help managers dynamically optimize data management strategies and improve overall operational efficiency.

[0136] S700, analyze the upload feedback, identify the relationship between the upload success rate, delay and network status, and generate an upload report accordingly.

[0137] like Figure 8 As shown, the upload feedback is analyzed to identify the relationship between the upload success rate, delay and network status, and an upload report is generated accordingly, specifically including:

[0138] S710, sorting out the success and failure results of each upload, calculating the upload success rate of each type of data, analyzing the upload performance of data of different priorities, calculating the average delay of each type of data, and analyzing the bandwidth usage under different network conditions;

[0139] S720, using statistical methods to analyze the correlation between the upload success rate and the latency and bandwidth usage, and identify potential patterns and trends that affect the upload performance;

[0140] S730, generating an upload report according to the analysis result.

[0141] This step will collect and organize the success and failure results of each upload, which will cover information such as the type of data uploaded, quantity, upload time, status (successful or failed), etc. Classify the successful and failed upload results, calculate the upload success rate of each type of data, identify which type of data is more likely to be uploaded successfully, and which type of data faces more challenges. Next, the system will analyze the upload performance of each type of data according to the priority range. For example, whether the upload success rate of high-priority data is significantly higher than that of low-priority data, or whether the failure rate of data in certain priority categories increases under certain circumstances. At the same time, the system records the average delay of data of different priorities, analyzes the relationship between priority and upload delay, and identifies whether there is a phenomenon in which priority affects upload efficiency.

[0142] In addition, the system calculates the average latency of all uploaded data and compares the latency of different categories of data to find the cause and propose optimization solutions. In terms of bandwidth usage, the system will count the bandwidth usage under different network conditions, analyze the relationship between bandwidth usage and upload success rate and latency, and identify the difference in upload performance under congested or good network conditions. By applying statistical methods (such as correlation analysis, regression analysis, etc.), the system will analyze the correlation between upload success rate, latency and bandwidth usage, identify which factors have a significant impact on upload performance, and discover potential patterns and trends. Finally, the system will generate a detailed upload report based on the above analysis results. The report will include success rate, average latency, bandwidth usage, and potential influencing factors related to these indicators. These reports provide decision makers with clear visual data and insights to help them formulate more accurate data upload strategies.

[0143] By collating and analyzing the upload results, the upload success rate and latency can be quantified, providing a deeper understanding of the system's performance. This data analysis provides managers with powerful information to identify potential bottlenecks and sources of problems in the upload process. Analyzing the upload performance of data of different priorities helps the system optimize the upload strategy to ensure that high-priority data can be uploaded faster and more efficiently, thereby improving overall business efficiency. Statistics on the relationship between bandwidth usage and success rate and latency can provide data support for the dynamic allocation of network resources and improve the system's adaptability in different environments. Ultimately, the generated upload report not only provides feedback for system operation, but also provides a basis for future policy adjustments and optimizations. Through continuous monitoring and analysis, the system can continue to improve and adapt to the rapidly changing IoT environment.

[0144] Fig. 9 A structural block diagram of a cloud data scheduling system based on the Internet of Things provided by an embodiment of the present invention, such as Fig. 9 As shown, the system comprises:

[0145] The data acquisition and classification module 100 is used to acquire the stored data to be uploaded from the IoT device, classify the data by importance, timestamp and type, and mark the data as to be uploaded;

[0146] The device status evaluation and priority allocation module 200 is used to evaluate the current status and network environment of the IoT device, set a number of priority intervals, and dynamically allocate the data to be uploaded to the number of priority intervals according to the device status;

[0147] The priority score assignment module 300 is used to assign a priority score to each piece of data to be uploaded in each priority interval using a random number generator, and to set a priority threshold, and to mark data above the priority threshold as uploadable data according to the priority score;

[0148] The data upload queue management module 400 is used to randomly select a predetermined amount of data from the data marked as uploadable in each time window for uploading to form a data upload queue, and adjust the transmission frequency and upload strategy according to the network status;

[0149] The upload result recording and monitoring module 500 is used to record the success and failure results of each upload, monitor the delay and bandwidth usage, and generate system logs;

[0150] The cloud storage and data lifecycle management module 600 is used to store the uploaded data in the cloud database and perform data lifecycle management;

[0151] The upload feedback analysis and report generation module 700 is used to analyze the upload feedback, identify the relationship between the upload success rate, delay and network status, and generate an upload report accordingly.

[0152] like Fig.10 As shown, the device status evaluation and priority allocation module includes:

[0153] The status information acquisition unit 210 is used to acquire the status information of the IoT device in real time and evaluate the current network environment, including network bandwidth, delay, packet loss rate and connection stability;

[0154] The priority interval setting unit 220 is used to set a number of priority intervals according to the evaluation results of the device status and the network environment, and set the thresholds of the priority intervals, including high priority, medium priority and low priority, and determine the number of priority intervals and the type of data contained in each interval;

[0155] The priority interval data allocation unit 230 is used to dynamically allocate the data to be uploaded to a number of priority intervals according to the device status and the network status.

[0156] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0157] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0158] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0159] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

[0160] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud data scheduling method based on the Internet of Things, characterized in that: The method comprises: Get the stored data to be uploaded from IoT devices, classify the data by importance, timestamp and type, and mark the data as to be uploaded; Evaluate the current status and network environment of IoT devices, set several priority intervals, and dynamically allocate the data to be uploaded to several priority intervals according to the device status; Use a random number generator to assign a priority score to each piece of data to be uploaded in each priority interval, set a priority threshold, and mark the data above the priority threshold as uploadable data based on the priority score; In each time window, a predetermined amount of data is randomly selected from the data marked as uploadable for upload to form a data upload queue, and the transmission frequency and upload strategy are adjusted according to the network status; Record the success and failure results of each upload, monitor latency and bandwidth usage, and generate system logs; Store the uploaded data in the cloud database and perform data lifecycle management; Analyze upload feedback, identify the relationship between upload success rate, latency, and network status, and generate upload reports accordingly; The process of evaluating the current status and network environment of the IoT device, setting a number of priority intervals, and dynamically allocating the data to be uploaded to the number of priority intervals according to the device status specifically includes: Obtain status information of IoT devices in real time and evaluate the current network environment, including network bandwidth, latency, packet loss rate, and connection stability; According to the evaluation results of the device status and network environment, several priority intervals are set, and thresholds of the priority intervals are set, including high priority, medium priority and low priority, and the number of priority intervals and the type of data contained in each interval are determined; Dynamically allocate the data to be uploaded to several priority intervals according to the device status and network status; The random number generator is used to assign a priority score to each piece of data to be uploaded in each priority interval, and a priority threshold is set. According to the priority score, data above the priority threshold is marked as uploadable data, specifically including: Initialize a priority score storage structure for each data to be uploaded; Set a random number generator to traverse the data to be uploaded in each priority interval and generate a random priority score for each data; According to the system requirements and network status, a priority threshold is set, and according to the set priority threshold, data with a priority score higher than the priority threshold is filtered out from the data to be uploaded and uploaded; In each time window, a predetermined amount of data is randomly selected from the data marked as uploadable for uploading to form a data upload queue, and the transmission frequency and upload strategy are adjusted according to the network status, specifically including: Set time windows and record the start and end time of each time window. Determine the number of uploads scheduled in each time window based on system capabilities and network status. Filter out data with a priority higher than a priority threshold from the data to be uploaded, and within each time window, use a random number generator to randomly select a predetermined number of data to be uploaded to form a data upload queue; Monitor the network status in real time, and dynamically adjust the frequency and strategy of data upload based on the real-time monitored network status.

2. The method according to claim 1, characterized in that The method of obtaining the stored data to be uploaded from the IoT device and classifying the data by importance, timestamp and type includes: Establish a connection with the IoT device, read the storage area of ​​the device, and extract the data to be uploaded; Mark the importance of data according to its content, source and application scenario; extract timestamps from data and classify them according to the extraction time; and then perform secondary classification of data types based on data content; Add status identification to the classified data and mark it as to be uploaded.

3. The method according to claim 2, characterized in that The recording of success and failure results of each upload, monitoring of latency and bandwidth usage, and generation of system logs specifically include: Record the results of each upload, including success and failure status, monitor the time required for each upload, record latency, and monitor the bandwidth consumed during the data upload process; Integrate the collected content and generate a system log to record the information uploaded this time.

4. The method according to claim 3, characterized in that: The uploaded data is stored in the cloud database and data life cycle management is performed, specifically including: Build a data upload interface to upload the data to be stored to the selected cloud database and verify the data after it is stored; Develop a data lifecycle management strategy, determine the lifecycle stages of different types of data, and set time limits and archiving strategies for data storage based on the importance and frequency of use of the data; Set the backup frequency to back up the data in the cloud database; Monitor data storage and lifecycle management effectiveness, and generate corresponding reports.

5. The method according to claim 4, characterized in that The analysis of upload feedback, identifying the relationship between upload success rate, delay and network status, and generating an upload report accordingly, specifically includes: Organize the success and failure results of each upload, calculate the upload success rate of each type of data, analyze the upload performance of data of different priorities, calculate the average delay of each type of data, and analyze the bandwidth usage under different network conditions; Use statistical methods to analyze the correlation between upload success rate and latency and bandwidth usage to identify potential patterns and trends that affect upload performance; Generate an upload report based on the analysis results.

6. A cloud data scheduling system based on the Internet of Things, characterized in that: The system comprises: The data acquisition and classification module is used to obtain the stored data to be uploaded from the IoT devices, classify the data by importance, timestamp and type, and mark the data as to be uploaded; The device status assessment and priority allocation module is used to assess the current status and network environment of IoT devices, set several priority intervals, and dynamically allocate the data to be uploaded to several priority intervals according to the device status; A priority score assignment module is used to assign a priority score to each piece of data to be uploaded in each priority interval using a random number generator, and to set a priority threshold. According to the priority score, data above the priority threshold is marked as uploadable data. The data upload queue management module is used to randomly select a predetermined amount of data from the data marked as uploadable in each time window for uploading to form a data upload queue, and adjust the transmission frequency and upload strategy according to the network status; The upload result recording and monitoring module is used to record the success and failure results of each upload, monitor latency and bandwidth usage, and generate system logs; The cloud storage and data lifecycle management module is used to store the uploaded data in the cloud database and perform data lifecycle management; The upload feedback analysis and report generation module is used to analyze the upload feedback, identify the relationship between the upload success rate, latency and network status, and generate an upload report accordingly; Wherein, the device status assessment and priority allocation module includes: The status information acquisition unit is used to obtain the status information of IoT devices in real time and evaluate the current network environment, including network bandwidth, latency, packet loss rate, and connection stability; A priority interval setting unit, used to set a number of priority intervals according to the evaluation results of the device status and the network environment, and set the thresholds of the priority intervals, including high priority, medium priority and low priority, and determine the number of priority intervals and the type of data contained in each interval; A priority interval data allocation unit, used to dynamically allocate the data to be uploaded to a number of priority intervals according to the device status and network status; The working steps of the priority score allocation module are: Initialize a priority score storage structure for each data to be uploaded; Set a random number generator to traverse the data to be uploaded in each priority interval and generate a random priority score for each data; According to the system requirements and network status, a priority threshold is set, and according to the set priority threshold, data with a priority score higher than the priority threshold is filtered out from the data to be uploaded and uploaded; The working steps of the data upload queue management module are: Set time windows and record the start and end time of each time window. Determine the number of uploads scheduled in each time window based on system capabilities and network status. Filter out data with a priority higher than a priority threshold from the data to be uploaded, and within each time window, use a random number generator to randomly select a predetermined number of data to be uploaded to form a data upload queue; Monitor the network status in real time, and dynamically adjust the frequency and strategy of data upload based on the real-time monitored network status.

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