Internet of Things traffic periodic distribution control method and system, electronic equipment and medium

Abstract

The invention discloses an Internet of Things traffic periodic distribution control method and system, electronic equipment and a medium. The method comprises the following steps: acquiring multi-dimensional operation data of each intelligent equipment in an Internet of Things system; based on the multi-dimensional operation data, adopting a preset model to generate a periodic flow distribution scheme of each intelligent device; wherein the periodic traffic distribution scheme comprises a total traffic quota and a time-phased traffic quota of each intelligent device in a target period; according to the periodic flow distribution scheme, making a time period strategy comprising a plurality of operation stages for each intelligent device, and realizing jump of each operation stage based on a trigger condition; and monitoring the actual flow consumption condition and the stage jump state of each intelligent device, and optimizing the parameters and the time period strategy of the preset model according to the monitoring result. According to the invention, the operation reliability of the Internet of Things system can be guaranteed and the flow resource utilization efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to IoT traffic periodic allocation control methods, systems, electronic devices, and media. Background Technology

[0002] With the rapid popularization of IoT technology, the deployment scale of smart devices in IoT systems continues to expand. Different types of smart devices need to achieve data interaction and functional collaboration through traffic transmission during operation. Therefore, periodic allocation of traffic to smart devices has become a fundamental support for ensuring the stable and efficient operation of the system. Currently, the industry has formed a common practice of periodically allocating traffic resources. This involves developing an allocation plan based on relevant device operation information and adjusting the strategy according to the device's operating status to adapt to the device's traffic usage needs in different periods.

[0003] However, existing periodic traffic allocation schemes suffer from insufficient accuracy. Because the traffic demands of devices in an IoT environment dynamically change with factors such as operating scenarios and usage frequency, existing allocation schemes often fail to fully capture these dynamic characteristics. This leads to discrepancies between the generated periodic traffic allocation results and the actual demands of devices at different times within the target period. This can result in either wasted traffic during certain periods or insufficient traffic supply during critical periods, ultimately affecting the overall reliability and resource utilization efficiency of the IoT system. Summary of the Invention

[0004] This application aims to address at least one of the technical problems existing in the prior art. To this end, this application proposes a method, system, electronic device, and medium for periodic allocation control of Internet of Things (IoT) traffic, which can ensure the reliability of IoT system operation and improve the efficiency of traffic resource utilization.

[0005] Firstly, this application provides a method for periodic allocation control of Internet of Things (IoT) traffic, including: Collect multi-dimensional operational data from various smart devices in the Internet of Things (IoT) system; Based on the multi-dimensional operational data, a pre-set model is used to generate a periodic traffic allocation scheme for each smart device; wherein, the periodic traffic allocation scheme includes the total traffic quota and time-period traffic quota for each smart device within the target period. According to the aforementioned periodic traffic allocation scheme, a time period strategy containing multiple operating phases is formulated for each smart device, and the transition between each operating phase is realized based on trigger conditions. Monitor the actual traffic consumption and stage transition status of each smart device, and optimize the parameters and time period strategy of the preset model based on the monitoring results.

[0006] The IoT traffic periodic allocation control method according to the first aspect of this application has at least the following beneficial effects: First, multi-dimensional operational data of each smart device in the IoT system is collected. Then, based on the multi-dimensional operational data, a periodic traffic allocation scheme containing the total traffic quota and time-segmented traffic quota of each smart device within the target period is generated through a preset model. Subsequently, a time-cycle strategy containing multiple operational stages is formulated for each device according to the allocation scheme, and the transition between each operational stage is realized based on trigger conditions. Finally, the parameters of the preset model and the time-cycle strategy are optimized by monitoring the actual traffic consumption and stage transition status of each device. This process comprehensively captures the traffic demand characteristics of devices by collecting multi-dimensional operational data, accurately matches the demand of devices in each time period by combining the allocation scheme containing time-segmented quotas, and adapts the traffic consumption characteristics of different operational stages with multi-stage periodic strategies and dynamic trigger transitions. Subsequent monitoring and optimization can continuously correct deviations, thereby effectively solving the problem of insufficient traffic allocation accuracy in existing schemes, ultimately avoiding traffic idle waste and insufficient supply during critical periods, ensuring the reliability of IoT system operation and improving traffic resource utilization efficiency.

[0007] According to some embodiments of the first aspect of this application, the collection of multi-dimensional operational data of various smart devices in the Internet of Things system includes: Collect basic traffic data of each smart device over several historical periods; wherein, the basic traffic data includes the total consumption of the period and the peak traffic period; Collect scene attribute data bound to each smart device; wherein, the scene attribute data includes the scene type to which the smart device belongs and environmental association factors; Collect user priority data from various smart devices; wherein, the user priority data includes user-preset static priorities and dynamic priorities generated based on user behavior learning; The basic traffic data, the operation scenario type, and the user priority data are integrated and processed to obtain multi-dimensional operation data.

[0008] According to some embodiments of the first aspect of this application, the step of generating a periodic traffic allocation scheme for each smart device based on the multi-dimensional operational data and using a preset model includes: The multi-dimensional operational data is standardized, and the total consumption of the cycle and the peak traffic period are converted into historical demand benchmark values, the scenario type is converted into scenario weight coefficient, the environmental correlation factor is converted into environmental correction coefficient, the static priority is converted into static priority correction coefficient, and the dynamic priority is converted into dynamic priority correction coefficient. The basic traffic quota scheme for each smart device is generated by the basic quota calculation module of the preset model, combined with the historical demand baseline value, the scenario weight coefficient and the static priority correction coefficient. Obtain the current environmental status and traffic fluctuation trends over the past few periods; Based on the current environmental state, the environmental correction coefficient, the traffic fluctuation trend, and the dynamic priority correction coefficient, a dynamic traffic quota scheme is obtained. Based on the dynamic traffic quota scheme, the basic traffic quota scheme is adjusted to output the total traffic quota and time-based traffic quota for each smart device within the target period, thus forming a periodic traffic allocation scheme.

[0009] According to some embodiments of the first aspect of this application, the step of formulating a time-cycle strategy comprising multiple operating phases for each smart device based on the periodic traffic allocation scheme includes: Based on the total traffic quota and the time-sharing traffic quota, the time periods for the smart device to operate in multiple continuous phases within the target period are determined; wherein, the operating phases include a warm-up period, a formal operation period, a recovery period, and a dormant period; Based on the total traffic quota and the preset allocation rules for each operating phase, the traffic allocation of the smart device in each operating phase is obtained; The time periods and traffic allocation for each operational phase are divided to form a time cycle strategy for each smart device.

[0010] According to some embodiments of the first aspect of this application, determining the time period division of the smart device in multiple continuous operation phases within a target period based on the total traffic quota and the time-based traffic quota includes: Based on the time-sharing traffic quota, identify the high-demand periods and low-demand periods within the target period, and determine the high-demand periods as the time range of the formal period; Based on the start time of the formal period, the warm-up period is set according to a preset advance amount. The start time of the warm-up period is the start time of the formal period minus the preset advance amount, and the end time is the same as the start time of the formal period. Based on the end time of the formal period, the recovery period is set according to a preset delay amount. The start time of the recovery period is the same as the end time of the formal period, and the end time is the end time of the formal period plus the preset delay amount. The remaining time period within the target cycle, excluding the warm-up period, the formal period, and the recovery period, is designated as the silent period, and the silent period covers all low-demand periods.

[0011] According to some embodiments of the first aspect of this application, obtaining the traffic allocation of the smart device in each of the operating phases based on the total traffic quota and the preset allocation rules for each operating phase includes: Obtain the preset allocation rules for each of the operation stages to obtain the first preset percentage of the warm-up period, the second preset percentage of the formal period, the third preset percentage of the recovery period, and the fourth preset percentage of the dormant period; Based on the first preset ratio, the second preset ratio, the third preset ratio, the fourth preset ratio, and the total flow quota, the first flow allocation for the warm-up period, the second flow allocation for the formal period, the third flow allocation for the recovery period, and the fourth flow allocation for the quiescent period are obtained respectively. The first traffic allocation, the second traffic allocation, the third traffic allocation, and the fourth traffic allocation are integrated to determine the traffic allocation situation.

[0012] According to some embodiments of the first aspect of this application, the dynamic priority is generated according to the following steps: Collect user interaction behavior data with each of the aforementioned smart devices; wherein, the interaction behavior data includes the frequency and duration of user operations on the devices; Set a user behavior learning cycle, and within each user behavior learning cycle, statistically analyze the operation frequency feature value and operation time period feature value of each smart device based on the operation frequency and the operation time period; If the operation frequency characteristic value of the smart device is greater than a preset first threshold, or the operation time period characteristic value is greater than a preset second threshold, the dynamic priority adjustment is triggered. If the operation frequency characteristic value of the smart device is less than a preset third threshold and the operation time period characteristic value is less than a preset fourth threshold, the dynamic priority downgrade is triggered; wherein, the third threshold is less than the first threshold and the fourth threshold is less than the second threshold; The adjusted dynamic priority will be synchronized to the user priority data.

[0013] Secondly, this application also provides an IoT traffic periodic allocation control system, comprising: The data acquisition unit is used to collect multi-dimensional operational data from various smart devices in the Internet of Things (IoT) system. The analysis unit is used to generate a periodic traffic allocation scheme for each smart device based on the multi-dimensional operational data and using a preset model; wherein, the periodic traffic allocation scheme includes the total traffic quota and time-period traffic quota for each smart device within the target period; The allocation unit is used to formulate a time period strategy containing multiple operating stages for each smart device according to the periodic flow allocation scheme, and to realize the jump of each operating stage based on the triggering conditions. The monitoring unit is used to monitor the actual traffic consumption and stage transition status of each smart device, and optimize the parameters and time period strategy of the preset model based on the monitoring results.

[0014] Thirdly, this application also provides an electronic device, including: At least one memory; At least one processor; At least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement the IoT traffic periodic allocation control method as described in any embodiment of the first aspect.

[0015] Fourthly, this application also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable signals for performing the Internet of Things traffic periodic allocation control method as described in any embodiment of the first aspect.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] Additional aspects and advantages of this application will become apparent and readily understood in conjunction with the following description of the embodiments, in which: Figure 1 Flowcharts of IoT traffic periodic allocation control methods provided in some embodiments of this application; Figure 2 A schematic diagram of an IoT traffic periodic allocation control system provided in some embodiments of this application. Detailed Implementation

[0018] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0019] In the description of this application, it should be understood that the orientation descriptions, such as up, down, front, back, left, right, etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0020] In the description of this application, the use of "first" and "second" is for the purpose of distinguishing technical features only, and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or the order of the technical features indicated.

[0021] In the description of this application, unless otherwise expressly defined, terms such as "setup," "installation," and "connection" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this application in conjunction with the specific content of the technical solution.

[0022] With the rapid popularization of IoT technology, the deployment scale of smart devices in IoT systems continues to expand. Different types of smart devices need to achieve data interaction and functional collaboration through traffic transmission during operation. Therefore, periodic allocation of traffic to smart devices has become a fundamental support for ensuring the stable and efficient operation of the system. Currently, the industry has formed a common practice of periodically allocating traffic resources. This involves developing an allocation plan based on relevant device operation information and adjusting the strategy according to the device's operating status to adapt to the device's traffic usage needs in different periods.

[0023] However, existing periodic traffic allocation schemes suffer from insufficient accuracy. Because the traffic demands of devices in an IoT environment dynamically change with factors such as operating scenarios and usage frequency, existing allocation schemes often fail to fully capture these dynamic characteristics. This leads to discrepancies between the generated periodic traffic allocation results and the actual demands of devices at different times within the target period. This can result in either wasted traffic during certain periods or insufficient traffic supply during critical periods, ultimately affecting the overall reliability and resource utilization efficiency of the IoT system.

[0024] Based on this, this application provides a method, system, electronic device and medium for periodic allocation control of Internet of Things traffic to solve the above-mentioned technical problems. The technical solutions provided by this application will be described in detail below.

[0025] In a first aspect, this application provides a method for periodic allocation control of Internet of Things (IoT) traffic, which may include, but is not limited to, the following steps: Step S110: Collect multi-dimensional operational data of each smart device in the IoT system.

[0026] Step S120: Based on multi-dimensional operational data, a periodic traffic allocation scheme for each smart device is generated using a preset model; wherein, the periodic traffic allocation scheme includes the total traffic quota and time-period traffic quota for each smart device within the target period.

[0027] Step S130: Based on the periodic traffic allocation scheme, formulate a time period strategy for each smart device that includes multiple operating phases, and realize the jump of each operating phase based on the trigger conditions.

[0028] Step S140: Monitor the actual traffic consumption and stage transition status of each smart device, and optimize the parameters and time period strategy of the preset model based on the monitoring results.

[0029] In steps S110 to S140, multi-dimensional operational data of each smart device in the IoT system is first collected. Then, based on the multi-dimensional operational data, a periodic traffic allocation scheme containing the total traffic quota and time-segmented traffic quota of each smart device within the target period is generated through a preset model. Subsequently, a time-cycle strategy containing multiple operational stages is formulated for each device according to the allocation scheme, and the transition between each operational stage is realized based on trigger conditions. Finally, the parameters of the preset model and the time-cycle strategy are optimized by monitoring the actual traffic consumption and stage transition status of each device. This process comprehensively captures the traffic demand characteristics of devices by collecting multi-dimensional operational data, accurately matches the demand of devices in different time periods by combining the allocation scheme with time-segmented quotas, and adapts the traffic consumption characteristics of different operational stages with multi-stage periodic strategies and dynamic trigger transitions. Subsequent monitoring and optimization can continuously correct deviations, thereby effectively solving the problem of insufficient traffic allocation accuracy in existing schemes, ultimately avoiding traffic idle waste and insufficient supply during critical periods, ensuring the reliability of IoT system operation and improving traffic resource utilization efficiency.

[0030] It is understood that step S110 may include, but is not limited to, the following steps: Step S210: Collect basic traffic data of each smart device over several historical periods; wherein, the basic traffic data includes the total consumption of the period and the peak traffic period.

[0031] Step S220: Collect scene attribute data bound to each smart device; wherein, scene attribute data includes the scene type to which the smart device belongs and environmental association factors.

[0032] Step S230: Collect user priority data for each smart device; wherein, the user priority data includes user-preset static priorities and dynamic priorities generated based on user behavior learning.

[0033] Step S240: Integrate and process the basic traffic data, operation scenario type and user priority data to obtain multi-dimensional operation data.

[0034] In step S210, basic traffic data for several historical periods is collected, including the total consumption and peak traffic periods of each period. The basic traffic data is used to capture the regular characteristics of the smart device's past traffic usage, avoiding allocation deviations caused by relying solely on single-period data.

[0035] In step S220, the bound scene attribute data is collected, including scene type and environmental correlation factors. This scene attribute data can be used to adapt to the differentiated traffic requirements of devices in different scenarios. For example, the traffic requirements of emergency monitoring scenarios are different from those of ordinary home scenarios. This data can make the allocation scheme more in line with the actual scenario. The bound environmental correlation factors include real-time weather data. For example, on sunny days, the device transmits monitoring images at a normal frequency of 10 minutes / time, and the corresponding time-segment traffic quota remains stable. When the weather turns into heavy rain (severe weather), the heavy rain may cause water accumulation and blurring of the lens, and sudden changes in road conditions require more timely monitoring. At this time, this factor will trigger the device to increase the data transmission frequency to 3 minutes / time, and at the same time, the traffic allocation scheme will temporarily increase the traffic quota for the corresponding time period to ensure that the backend can obtain clear images in real time and promptly grasp the road flooding or traffic abnormalities.

[0036] In step S230, user priority data containing static and dynamic priorities is collected. This can not only meet users' preset traffic guarantee requirements for important devices, but also adapt to the needs brought about by changes in user behavior through dynamic priorities.

[0037] In step S240, the integration of the three types of data forms multi-dimensional operational data that can comprehensively cover the historical patterns, scenario characteristics, and user needs related to device traffic. This effectively avoids the limitations of a single data dimension, lays the foundation for generating accurate periodic traffic allocation schemes, and further improves the matching degree between traffic allocation and the actual operational needs of the device.

[0038] It is understood that step S120 may include, but is not limited to, the following steps: Step S310: Standardize the multi-dimensional operation data, convert the total consumption of the cycle and the peak traffic period into historical demand benchmark values, convert the scenario type into scenario weight coefficients, convert the environmental correlation factors into environmental correction coefficients, convert the static priority into static priority correction coefficients, and convert the dynamic priority into dynamic priority correction coefficients.

[0039] Step S320: Using the basic quota calculation module of the preset model, combined with historical demand baseline values, scenario weight coefficients and static priority correction coefficients, generate a basic traffic quota scheme for each smart device.

[0040] Step S330: Obtain the current environmental status and the traffic fluctuation trend over the past few periods.

[0041] Step S340: Based on the current environmental status, environmental correction coefficient, traffic fluctuation trend, and dynamic priority correction coefficient, obtain the dynamic traffic quota scheme.

[0042] Step S350: Based on the dynamic traffic quota scheme, adjust the basic traffic quota scheme, output the total traffic quota and time-based traffic quota of each smart device in the target period, and form a periodic traffic allocation scheme.

[0043] In steps S310 to S350, the calculation dimensions of multi-dimensional operational data are unified through standardization processing. Then, a two-step process—basic quota generation and dynamic quota adjustment—is used to generate a periodic traffic allocation scheme. This ensures the stability of the allocation while adapting to real-time changes, effectively improving the accuracy and adaptability of traffic allocation. Standardization processing transforms different types of multi-dimensional data into directly calculable benchmark values ​​and coefficients, avoiding calculation deviations caused by differences in data types and allowing data from various dimensions to collaboratively support quota generation. Basic quota calculation combines historical demand, scenario weights, and static priorities to ensure that the allocation scheme aligns with the long-term usage patterns of equipment and core user needs, laying a stable quota foundation. The dynamic quota scheme combines the current environmental state, environmental correction coefficients, traffic fluctuation trends, and dynamic priorities to respond in real-time to environmental changes (such as severe weather) and fluctuations in traffic demand, compensating for the limitations of basic quotas in handling real-time changes. Ultimately, by dynamically adjusting the base quota, the total output traffic and time-based traffic quota can both follow historical patterns and user presets, and adapt to the current situation, making the periodic traffic allocation scheme more in line with the actual needs of smart devices within the target period, reducing traffic waste or insufficient supply.

[0044] It is understood that step S130 may include, but is not limited to, the following steps: Step S410: Based on the total traffic quota and time-sharing traffic quota, determine the time period division of multiple continuous operation phases of the smart device within the target period; wherein, the operation phase includes the warm-up period, the formal operation period, the recovery period, and the dormant period.

[0045] Step S420: Based on the total traffic quota and the preset allocation rules for each operating phase, obtain the traffic allocation of the smart device in each operating phase.

[0046] Step S430: Divide the time periods and traffic allocation of each operation phase to form a time cycle strategy for each smart device.

[0047] In steps S410 to S430, by combining the total traffic quota and time-sharing traffic quota in the periodic traffic allocation scheme, a time-cycle strategy with multiple operating stages is formulated step by step. This allows for precise adaptation of traffic allocation in both time and usage dimensions, effectively improving the utilization efficiency of traffic resources and the stability of equipment operation. From the perspective of time period division, determining the time ranges for the warm-up period, formalization period, recovery period, and quiet period based on the total traffic quota and time-sharing traffic quota allows each stage to accurately correspond to the traffic demand characteristics of the equipment at different times within the target cycle. For example, the formalization period corresponds to the high-demand period in the time-sharing traffic quota, and the quiet period corresponds to the low-demand period, avoiding a disconnect between stage division and actual demand. From the perspective of traffic allocation, allocating traffic to each stage according to the total traffic quota and preset rules ensures that the traffic supply for each stage meets functional requirements without causing idleness. For example, the warm-up period and recovery period can avoid traffic surges caused by sudden high-frequency transmission of the equipment through reasonable traffic transitions, while the quiet period allocates traffic according to low demand to reduce waste. The final integrated time-cycle strategy ensures that the timing and amount of traffic usage are highly aligned with the actual operational needs of the devices, further mitigating the potential time-dimensional compatibility issues that may arise from relying solely on quota allocation. This guarantees that devices receive appropriate traffic support at each stage of operation.

[0048] It is understood that step S410 may include, but is not limited to, the following steps: Step S510: Based on the time-sharing traffic quota, identify the high-demand periods and low-demand periods within the target period, and determine the high-demand periods as the time range of the official period.

[0049] Step S520: Based on the start time of the official period, set the warm-up period according to the preset advance amount. The start time of the warm-up period is the start time of the official period minus the preset advance amount, and the end time is the same as the start time of the official period.

[0050] Step S530: Based on the end time of the formal period, set the time period of the recovery period according to the preset delay amount. The start time of the recovery period is the same as the end time of the formal period, and the end time is the end time of the formal period plus the preset delay amount.

[0051] Step S540: Determine the remaining time period within the target cycle, excluding the warm-up period, the formal period, and the recovery period, as the silent period, and the silent period covers all low-demand periods.

[0052] In steps S510 to S540, by using the demand differences in the time-sharing traffic quota as the core dividing criterion, the time periods of each operating stage are precisely matched with the actual traffic demand of the equipment. This ensures efficient traffic supply during high-demand periods and avoids resource waste during low-demand periods. By directly designating the high-demand periods identified by the time-sharing traffic quota as the official period, it ensures that the equipment has complete time support during critical traffic consumption periods, avoiding traffic supply and demand mismatch caused by the misalignment between the official period and high-demand periods. The warm-up period is set in advance according to the start time of the official period, and the recovery period is set in advance according to the end time of the official period. This serves as a transition phase for the official period, allowing the equipment to smoothly enter the high-demand operating state and completely finish the remaining data transmission during the high-demand period, avoiding traffic loss or functional interruption caused by abrupt transitions between stages. The silent period covers the remaining time periods within the target cycle and includes all low-demand periods, binding low traffic consumption demand with specific time periods, reducing traffic occupation during unnecessary periods. Ultimately, through the precise division of time periods in each stage, a reasonable allocation of traffic resources in the time dimension is achieved, further improving overall traffic utilization efficiency and equipment operation stability.

[0053] It is understood that step S420 may include, but is not limited to, the following steps: Step S610: Obtain the preset allocation rules for each running stage, and get the first preset percentage for the warm-up period, the second preset percentage for the formal period, the third preset percentage for the recovery period, and the fourth preset percentage for the dormant period.

[0054] Step S620: Based on the first preset percentage, the second preset percentage, the third preset percentage, the fourth preset percentage, and the total flow quota, obtain the first flow allocation for the warm-up period, the second flow allocation for the formal period, the third flow allocation for the recovery period, and the fourth flow allocation for the silent period.

[0055] Step S630: Integrate the first traffic allocation, the second traffic allocation, the third traffic allocation, and the fourth traffic allocation to determine the traffic allocation situation.

[0056] In steps S610 to S630, by pre-setting traffic proportion rules for each operational stage and combining the total traffic quota to break down the specific allocation for each stage, precise control of the total traffic can be achieved, while ensuring that the traffic supply for each stage is highly adapted to its own functional positioning, effectively avoiding traffic allocation imbalance. First, the pre-set proportions for the warm-up period, formalization period, recovery period, and dormancy period are clearly defined, providing a clear rule basis for traffic allocation in each stage and avoiding arbitrariness in the allocation process. Then, based on the total traffic quota, the specific traffic for each stage is calculated according to the pre-set proportions, ensuring that the total traffic for each stage does not exceed the total quota, achieving overall traffic controllability. Simultaneously, the formalization period receives a traffic proportion matching high demand, and the dormancy period receives a traffic proportion suitable for low demand, avoiding insufficient traffic in high-demand stages or idle traffic in low-demand stages. Finally, the allocation amounts for each stage are integrated to determine the final traffic allocation, making the entire allocation process logically coherent and the results clear. This further ensures that the traffic supply for each stage in the time-cycle strategy both conforms to the total quota constraints and meets its own operational needs, improving the accuracy of traffic resource utilization.

[0057] It is understandable that the dynamic priority in step S230 is generated according to the following steps: Step S710: Collect user interaction behavior data with each smart device; wherein, the interaction behavior data includes the frequency and duration of user operations on the devices.

[0058] Step S720: Set a user behavior learning cycle, and within each user behavior learning cycle, statistically analyze the operation frequency characteristic value and operation time period characteristic value of each smart device based on the operation frequency and operation time period.

[0059] Step S730: If the operation frequency characteristic value of the smart device is greater than the preset first threshold, or the operation time period characteristic value is greater than the preset second threshold, dynamic priority adjustment is triggered.

[0060] Step S740: If the operation frequency characteristic value of the smart device is less than the preset third threshold and the operation time period characteristic value is less than the preset fourth threshold, a dynamic priority downgrade is triggered; wherein, the third threshold is less than the first threshold and the fourth threshold is less than the second threshold.

[0061] Step S750: Synchronize the adjusted dynamic priority to the user priority data.

[0062] In steps S710 to S750, by generating and adjusting dynamic priorities based on real user interaction data, user priority data can be more closely aligned with users' real-time usage habits, providing precise demand guidance for subsequent traffic allocation. By setting a learning cycle to continuously capture changes in user behavior, the problem of dynamic priorities becoming detached from actual usage scenarios is avoided. Simultaneously, threshold rules that trigger adjustments via "OR logic" and adjustments via "AND logic" reduce the risk of misadjustment. Finally, synchronizing the adjusted dynamic priorities to user priority data ensures that subsequent traffic allocation can promptly tilt towards devices that users are more concerned about, matching traffic resources to users' real needs, improving user experience while avoiding resource misallocation.

[0063] Secondly, this application also provides an IoT traffic periodic distribution control system 800, comprising: The acquisition unit 810 is used to collect multi-dimensional operational data from various smart devices in the Internet of Things system. Analysis unit 820 is used to generate a periodic traffic allocation scheme for each smart device based on multi-dimensional operational data and a preset model; wherein, the periodic traffic allocation scheme includes the total traffic quota and time-period traffic quota for each smart device in the target period; The allocation unit 830 is used to formulate a time period strategy containing multiple operating phases for each smart device according to the periodic flow allocation scheme, and to realize the jump of each operating phase based on the triggering conditions. The monitoring unit 840 is used to monitor the actual traffic consumption and stage transition status of each smart device, and optimize the parameters and time period strategy of the preset model based on the monitoring results.

[0064] The specific implementation of the IoT traffic periodic allocation control system 800 is basically the same as the specific implementation of the IoT traffic periodic allocation control method described above, and will not be repeated here.

[0065] Thirdly, this application also provides an electronic device, including: at least one memory; at least one processor; at least one program; the program is stored in the memory, and the processor executes the at least one program to implement the Internet of Things traffic periodic allocation control method as described in any embodiment of the first aspect.

[0066] This electronic device first collects multi-dimensional operational data from various smart devices in the IoT system. Then, based on this multi-dimensional operational data, it generates a periodic traffic allocation scheme through a preset model, which includes the total traffic quota and time-segmented traffic quota for each smart device within the target period. Subsequently, based on this allocation scheme, it formulates a time-cycle strategy for each device, including multiple operational stages, and implements the transition between each operational stage based on trigger conditions. Finally, by monitoring the actual traffic consumption and stage transition status of each device, it optimizes the parameters of the preset model and the time-cycle strategy. This process comprehensively captures the traffic demand characteristics of devices by collecting multi-dimensional operational data, accurately matches the needs of devices in different time periods with an allocation scheme including time-segmented quotas, and adapts the traffic consumption characteristics of different operational stages with a multi-stage periodic strategy and dynamic trigger transitions. Subsequent monitoring and optimization can continuously correct deviations, thereby effectively solving the problem of insufficient traffic allocation accuracy in existing solutions. Ultimately, it avoids traffic idle waste and insufficient supply during critical periods, ensuring the reliability of IoT system operation and improving traffic resource utilization efficiency.

[0067] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and signals, such as the program instructions / signals corresponding to the processing module in the embodiments of this application. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and signals stored in the memory, thereby implementing the IoT traffic periodic allocation control method of the above method embodiments.

[0068] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store relevant data for the aforementioned IoT traffic periodic allocation control method. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processing module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0069] One or more signals are stored in memory, and when executed by one or more processors, the IoT traffic periodic allocation control method in any of the above method embodiments is executed.

[0070] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that is executed by one or more processors, enabling the one or more processors to perform the IoT traffic periodic allocation control method in the above method embodiments.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] Based on the above description of the embodiments, those skilled in the art will understand that all or some of the steps and systems in the methods disclosed above can be implemented as software, firmware, hardware, and suitable combinations thereof. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable signals, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible by a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable signals, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0073] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0075] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0076] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0077] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] The embodiments of this application have been described in detail above with reference to the accompanying drawings. However, this application is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of this application.

Claims

1. A method for controlling periodic allocation of Internet of Things traffic, characterized in that, The method comprises the following steps: Collecting multi-dimensional running data of each intelligent device in the Internet of Things system; Based on the multi-dimensional running data, a preset model is used to generate a periodic traffic allocation scheme for each intelligent device; wherein the periodic traffic allocation scheme includes the total traffic quota and the time period traffic quota of each intelligent device in the target period; According to the periodic traffic allocation scheme, a time period strategy containing multiple running stages is formulated for each intelligent device, and the jump of each running stage is realized based on the trigger condition; Monitoring the actual traffic consumption and stage jump state of each intelligent device, and optimizing the parameters of the preset model and the time period strategy according to the monitoring result.

2. The IoT traffic periodic allocation control method of claim 1, wherein, The method of collecting multi-dimensional running data of each intelligent device in the Internet of Things system comprises the following steps: Collecting basic traffic data of each intelligent device in a history period; wherein the basic traffic data includes the total consumption and the peak traffic period; Collecting scene attribute data bound to each intelligent device; wherein the scene attribute data includes the scene type and the environment correlation factor to which the intelligent device belongs; Collecting user priority data of each intelligent device; wherein the user priority data includes the static priority preset by the user and the dynamic priority generated based on user behavior learning; The basic traffic data, the running scene type and the user priority data are integrated and processed to obtain multi-dimensional running data.

3. The IoT traffic periodic allocation control method of claim 2, wherein, The method of generating a periodic traffic allocation scheme for each intelligent device based on the multi-dimensional running data comprises the following steps: Standardizing the multi-dimensional running data, converting the total consumption and the peak traffic period into historical demand reference values, converting the scene type into a scene weight coefficient, converting the environment correlation factor into an environment correction coefficient, converting the static priority into a static priority correction coefficient, and converting the dynamic priority into a dynamic priority correction coefficient; Through the basic quota calculation module of the preset model, the historical demand reference values, the scene weight coefficient and the static priority correction coefficient are combined to generate a basic traffic quota scheme for each intelligent device; Obtaining the current environment state and the traffic fluctuation trend in the last several periods; According to the current environment state, the environment correction coefficient, the traffic fluctuation trend and the dynamic priority correction coefficient, a dynamic traffic quota scheme is obtained; Based on the dynamic traffic quota scheme, the basic traffic quota scheme is adjusted, and the total traffic quota and the time period traffic quota of each intelligent device in the target period are output to form a periodic traffic allocation scheme.

4. The IoT traffic periodic allocation control method of claim 3, wherein, The method of formulating a time period strategy containing multiple running stages for each intelligent device according to the periodic traffic allocation scheme comprises the following steps: According to the total traffic quota and the time period traffic quota, the time period division of multiple continuous running stages of the intelligent device in the target period is determined; wherein the running stage includes a preheating period, a formal period, a recovery period and a silent period; According to the total traffic quota and the allocation rule preset for each running stage, the traffic allocation of the intelligent device in each running stage is obtained; The time period division and the flow distribution of each operation stage are divided to form a time period strategy for each intelligent device.

5. The IoT traffic periodic allocation control method of claim 4, wherein, The time period division of the intelligent device in a plurality of continuous operation stages in the target period is determined according to the total flow quota and the time-sharing flow quota. According to the time-sharing flow quota, a high demand period and a low demand period in the target period are identified, and the high demand period is determined as the time period range of the formal period; According to the starting time of the formal period, the time period of the warm-up period is set by a preset advance amount, the starting time of the warm-up period is the starting time of the formal period minus the preset advance amount, and the ending time is consistent with the starting time of the formal period; According to the ending time of the formal period, the time period of the recovery period is set by a preset delay amount, the starting time of the recovery period is consistent with the ending time of the formal period, and the ending time is the ending time of the formal period plus the preset delay amount; The remaining time period in the target period, except for the warm-up period, the formal period and the recovery period, is determined as the time period of the silent period, and the silent period covers all low demand periods.

6. The IoT traffic periodic allocation control method of claim 4, wherein, The flow distribution of each operation stage of the intelligent device is obtained according to the total flow quota and the preset distribution rule of each operation stage, including: Obtaining the first preset proportion of the warm-up period, the second preset proportion of the formal period, the third preset proportion of the recovery period and the fourth preset proportion of the silent period according to the preset distribution rule of each operation stage; According to the first preset proportion, the second preset proportion, the third preset proportion, the fourth preset proportion and the total flow quota, the first flow distribution amount of the warm-up period, the second flow distribution amount of the formal period, the third flow distribution amount of the recovery period and the fourth flow distribution amount of the silent period are obtained respectively; The first flow distribution amount, the second flow distribution amount, the third flow distribution amount and the fourth flow distribution amount are integrated to determine the flow distribution.

7. The IoT traffic periodic allocation control method of claim 2, wherein, The dynamic priority is generated according to the following steps: Collecting interaction behavior data of users and each intelligent device; wherein the interaction behavior data includes operation frequency and operation time period of users to the device; Setting a user behavior learning period, and in each user behavior learning period, the operation frequency characteristic value and the operation time period characteristic value of each intelligent device are counted according to the operation frequency and the operation time period; If the operation frequency characteristic value of the intelligent device is greater than a preset first threshold value, or the operation time period characteristic value is greater than a preset second threshold value, the dynamic priority is triggered to be adjusted up; If the operation frequency characteristic value of the intelligent device is less than a preset third threshold value, and the operation time period characteristic value is less than a preset fourth threshold value, the dynamic priority is triggered to be adjusted down; wherein the third threshold value is less than the first threshold value, and the fourth threshold value is less than the second threshold value; The adjusted dynamic priority is synchronized to the user priority data.

8. An Internet of Things traffic periodic allocation control system characterized by, It includes: The acquisition unit is used for collecting multi-dimensional operation data of each intelligent device in the Internet of Things system; An analysis unit is configured to generate a periodic flow allocation scheme for each smart device based on the multidimensional operation data and a preset model, wherein the periodic flow allocation scheme comprises a total flow quota and a time-period flow quota of each smart device in a target period; An allocation unit is configured to formulate a time period strategy comprising multiple operation stages for each smart device according to the periodic flow allocation scheme, and to realize the jump of each operation stage based on a trigger condition; A monitoring unit is configured to monitor the actual flow consumption and stage jump state of each smart device, and to optimize the parameters of the preset model and the time period strategy according to the monitoring result.

9. An electronic device, comprising: Comprise: At least one memory; At least one processor; At least one program; The program is stored in the memory, and the processor executes at least one program to realize the Internet of Things flow periodic allocation control method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable signals, and the computer executable signals are used to execute the Internet of Things flow periodic allocation control method of any one of claims 1 to 7.

Images