Internet of Things equipment power consumption management method and system based on big data

Abstract

The invention discloses an Internet of Things equipment power consumption management method and system based on big data, and relates to the technical field of equipment power consumption management. The Internet of Things equipment power consumption management method and system based on big data specifically comprises the following steps: S1, equipment classification; s2, power consumption evaluation and modeling; s3, implementing a dormancy awakening strategy; s4, strategy design and dynamic adjustment; through careful classification of the data center server and the Internet of Things equipment, power consumption characteristics of various types of equipment can be more accurately understood and managed. And secondly, through power consumption evaluation and modeling, an accurate power consumption model is established, and a power consumption threshold value is set, so that real-time monitoring and effective management of power consumption are realized. In addition, the implementation of the sleep wake-up strategy avoids invalid sleep of the Internet of Things equipment, and improves the energy utilization efficiency. The dynamic adjustment strategy not only realizes load balancing, but also dynamically adjusts the power consumption according to the task priority and the load condition, so that the overall power consumption is further reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of device power consumption management, and in particular to a method and system for power consumption management of IoT devices based on big data. Background Art

[0002] Power consumption management of IoT devices is critical to extending device life, improving system reliability, and reducing operating costs. Effective power consumption management ensures that devices can function properly when needed while reducing unnecessary energy waste. IoT devices typically enter low-power sleep mode when inactive, which can significantly reduce power consumption. Due to the diversity of IoT devices, it is difficult to unify power consumption management strategies.

[0003] For example, a method, device and system for managing the operation of electromechanical equipment disclosed in publication number CN115049083A includes: obtaining the historical data of the total power consumption of the current building; determining the target power consumption based on the historical data of the total power consumption of the current building; establishing a control factor model based on time and flow of people; dividing the area according to the current time and flow of people; determining the control factor of each area according to the control factor model; and regulating the power consumption of the electromechanical equipment in each area according to the determined control factors of each area. The method provided by the invention divides the area into zones by determining the target power consumption, thereby obtaining the control factor of each area according to the target power consumption, and regulating the operation status of the electromechanical equipment in each area according to the obtained control factor, thereby achieving power control. Through precise regional power control, energy waste can be reduced.

[0004] As shown in the above technology, for the power consumption management of a large number of devices, power regulation is usually used to reduce power consumption and avoid waste. When applied to some existing IoT devices, when not in use, sleep mode is usually used to adjust power and reduce power consumption. However, the usage status of different IoT devices is different, and the sleep frequency is uncontrollable. When the device is awakened from sleep, the power consumption will increase due to the high instantaneous power. Although the time is short, if the device is awakened from sleep frequently within a certain time end, it will not achieve the effect of reducing power consumption and saving energy. How to control the power consumption of sleep and wakeup for different IoT devices is an urgent problem to be solved. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a method and system for power consumption management of IoT devices based on big data, which solves the problems of the above-mentioned technologies.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for power consumption management of IoT devices based on big data, specifically comprising the following steps: S1. Device classification: Data center servers and IoT devices are classified into different categories or groups according to different feature types; S2. Power consumption assessment and modeling: Use power consumption test tools to test the power consumption of various devices under the condition of simulating actual workloads, record static and dynamic power consumption data, and establish a power consumption model to reflect the power consumption of devices. At the same time, set power consumption thresholds for each type of device. When the power consumption of a device exceeds the power consumption threshold, trigger the corresponding power consumption management strategy. S3, implementation of sleep and wake-up strategy: statistics and analysis of the sleep time and frequency of IoT devices, calculation of the power consumption saved by sleep and the power consumption increased by wake-up, and setting the thresholds of sleep power saving and wake-up power increase according to the characteristics and application scenarios of the devices. For devices with short sleep time and frequent sleep in a unit time, control the devices not to sleep in the corresponding time period; S4. Strategy design and dynamic adjustment: For servers, design dynamic load balancing algorithms for load balancing; for IoT devices, dynamically adjust the operating frequency and transmission frequency to minimize power consumption based on task priority and load conditions; and regularly evaluate and adjust power consumption management strategies.

[0007] Preferably, the device classification method in S1 specifically includes: Data center servers: servers are divided into different categories or groups based on CPU model, memory size, and storage type characteristics; IoT devices: IoT devices are classified into different categories or groups based on device type, functional requirements, and power consumption characteristics.

[0008] Preferably, the power consumption evaluation and modeling steps in S2 specifically include: Power consumption test: Use the power consumption test tool to test the power consumption of each type of equipment under the condition of simulating the actual workload; record the static power consumption P static and dynamic power consumption P dynamic (t), where t represents time; Power consumption model: Establish the power consumption model P(t, type, load) = P static +P dynamic (t, load), where P(t) represents the total power consumption at time t, type represents the device type, and load represents the device load; Parameter design: Set a power consumption threshold P0 for each type of device. When the device power consumption exceeds the threshold, the power consumption management strategy is triggered.

[0009] Preferably, the steps of implementing the sleep-wake-up strategy in S3 specifically include: S3.1 Equipment Statistics: For all devices, use the set D = {D1, D2, ..., DN}, where N represents the number of IoT devices; each device D i There is a sleep time record sequence S i ={S i1 , S i2 , …, S iT}, where T is the total number of statistical time points; Collect the sleep state (sleep or wake-up) and corresponding power consumption data of each device at each time point through the IoT platform or sensors; S3.2 Sleep time statistics: For each device D i , calculate its average sleep time i and sleep frequency f i : ; Where S it 1 means sleep, 0 means wake up; ; S3.3 Power consumption analysis: Set device D i The power consumption in sleep mode is , the power consumption in wake-up state is , the power consumption during sleep and wake-up is ; Calculate the power consumption saved by sleeping per unit time and wake-up increase power consumption : ; ; S3.4 Threshold setting: According to the characteristics and application scenarios of the device, set the thresholds for saving power when sleeping and increasing power when waking up. Save and θ add ; if and , it is considered that the device has a short sleep time and sleeps frequently during this time period; S3.5 Intelligent management and control strategy: For devices with short sleep time and frequent sleep time per unit time, control them to not sleep during the corresponding time period.

[0010] Preferably, in S4, the dynamic load balancing algorithm of the server is designed based on weighted load balancing, and the formula is: ; Among them, w j(t) represents the weight of server j at time t, l j (t) represents the load of server j at time t, and n represents the total number of servers.

[0011] Preferably, in S4, the power consumption management strategy of the IoT device is: the system dynamically adjusts the operating frequency f(t) and the transmission frequency r(t) according to the current task priority and load status, with the goal of minimizing the power consumption of the system while ensuring that the task is completed on time and the data is transmitted accurately; specifically including: S4.1 parameter setting: Set task priority P(t): represents the task priority at time t; divide tasks into different priority levels according to their importance and urgency; Set the IoT device load L(t): represents the load status of the IoT device at time t, which is a comprehensive reflection of the amount of data currently processed by the device, the number of connected devices, and the transmission data rate indicators; establish a load status monitoring module to monitor the system load status in real time or periodically; α(P), β(P) and δ(L) are frequency adjustment coefficients, where α(P) is the coefficient for adjusting the working frequency according to the task priority P; β(P) is the coefficient for adjusting the transmission frequency according to the task priority P; δ(L) is the coefficient for adjusting the frequency according to the IoT device load L; S4.2 Calculate the adjustment factor: S4.2.1 Real-time or periodic monitoring of task priority P(t) and IoT device load L(t); S4.2.2 Calculate α(P(t)) and β(P(t)) based on the current task priority P(t). The function formula is expressed as: α(P(t))= 1+k f × (PP avg ); β(P(t))= 1+k r × (PP avg ); Among them, k f and k r is the adjustment slope, P avg is the average or baseline priority; S4.2.3 Calculate δ(L(t)) based on the current IoT device load L(t). The function formula is expressed as: ; Among them, k L is the adjustment slope, L avg is the average or baseline load; S4.3 Frequency adjustment: S4.3.1 Calculate the new operating frequency f(t) and transmission frequency r(t); The operating frequency adjustment formula is expressed as: f(t) = f base ×α(P(t))×δ(L(t)); The transmission frequency adjustment formula is expressed as: r(t) = r base ×β(P(t))×δ(L(t)); Among them, f base With r base is the base frequency of the processor or work unit, f base Indicates the basic operating frequency; r base Indicates the basic transmission frequency; S4.3.2 The calculated new frequency value f base With r base Applied to processors or work units and transport modules; S4.3.3 Cyclic monitoring and adjustment: Repeat steps S4.2-S4.3.2 continuously to dynamically adjust the frequency to adapt to changes in task priority and IoT device load.

[0012] Preferably, in S4, regularly evaluating and adjusting the power consumption management strategy specifically includes: Determine the evaluation period: Based on application scenarios: According to the application scenarios and requirements of IoT devices, the evaluation cycle is formulated accordingly; Consider device features: shorten the evaluation cycle when device hardware or software is updated; Effectiveness evaluation: Collect the power consumption of the equipment according to the evaluation period, compare it with the historical power consumption, and evaluate the effectiveness of the power consumption management strategy; Coefficient adjustment: After the effectiveness evaluation is performed, if it is determined that the effect of implementing the power consumption management strategy does not reach the expected value, the calculation parameters and formulas of the frequency adjustment coefficients α(P), β(P) and δ(L) are adjusted.

[0013] Preferably, the specific method for determining whether the effect of implementing the power consumption management strategy does not reach the expected value is: setting a power consumption reduction threshold, calculating the ratio of the power consumption detected after implementing the power consumption management strategy to the power consumption detected before implementation, and when the ratio is greater than the power consumption reduction threshold, determining that the effect of implementing the power consumption management strategy does not reach the expected value.

[0014] The present invention also discloses an IoT device power consumption management system based on big data, including: IoT devices, including all devices connected to an IoT system; Data center servers, which store, process, and analyze data from IoT devices; The device classification module classifies IoT devices and data center servers by distinguishing feature types; The power consumption evaluation and modeling unit uses power consumption testing tools to test the power consumption of various devices under the conditions of simulating actual workloads and records static and dynamic power consumption data; The sleep and wake-up strategy implementation unit is used to count and analyze the sleep time and frequency of IoT devices, calculate the power consumption saved by sleep and increased by wake-up, and control the sleep of IoT devices; The power consumption management module designs dynamic load balancing algorithms and power consumption management strategies for servers and IoT devices to manage the power consumption of data center servers and IoT devices. The monitoring and reporting module is used to monitor the power consumption of the entire IoT system in real time and generate report output.

[0015] Preferably, the monitoring and reporting module collects data from the device classification module, the power consumption assessment and modeling unit, the sleep and wake-up strategy implementation unit, and the strategy design and power consumption management module. The generated power consumption management report includes power consumption trends, device status, strategy effects, and is provided with an alarm function. When the device power consumption exceeds the set threshold, an alarm is triggered and relevant personnel are notified to handle it.

[0016] The present invention provides a method and system for managing power consumption of IoT devices based on big data. Compared with the prior art, it has the following beneficial effects: 1. This method of power consumption management of IoT devices based on big data can more accurately understand and manage the power consumption characteristics of various types of devices by carefully classifying data center servers and IoT devices. Secondly, through power consumption evaluation and modeling, an accurate power consumption model is established, and power consumption thresholds are set, thereby realizing real-time monitoring and effective management of power consumption. In addition, the implementation of the sleep and wake-up strategy avoids the ineffective sleep of IoT devices and improves energy utilization efficiency. The dynamic adjustment strategy designed for servers and IoT devices not only achieves load balancing, but also dynamically adjusts power consumption according to task priority and load conditions, further reducing overall power consumption. These improvements jointly improve the energy efficiency management level of IoT devices and provide strong support for energy conservation and emission reduction in the big data environment.

[0017] 2. This IoT device power consumption management method based on big data can accurately grasp the sleep behavior and power consumption characteristics of each IoT device through detailed device statistics and sleep time analysis. By calculating the average sleep time, sleep frequency, and the power consumption saved by sleep and increased by wake-up per unit time, this method can identify those devices with short sleep time and frequent sleep, which may consume unnecessary power during frequent sleep and wake-up. For these devices, the method intelligently controls them not to sleep during the corresponding time period, thereby avoiding invalid power consumption loss. This refined sleep and wake-up management strategy not only improves the energy utilization efficiency of the device, but also optimizes the overall power consumption performance of the IoT system, providing strong support for building a more energy-saving and efficient IoT environment.

[0018] 3. This method of power consumption management of IoT devices based on big data, through a weight-based load balancing algorithm, enables the server to more flexibly adjust the weight dynamically according to performance and load, thereby achieving more efficient resource allocation and load balancing. At the same time, IoT devices dynamically adjust the working frequency and transmission frequency according to task priority and load status, which not only ensures the timely completion of tasks and accurate data transmission, but also effectively reduces system power consumption. This dynamic adjustment strategy can respond to task changes and load fluctuations in real time, improving the energy efficiency and flexibility of the system. In general, these improvements enable the IoT system to manage resources and power consumption more intelligently, providing strong support for building an efficient and energy-saving IoT environment.

[0019] 4. This IoT device power consumption management method based on big data determines the evaluation cycle according to the application scenario and device characteristics, and regularly collects power consumption data for effectiveness evaluation. This method can timely discover deficiencies in the power consumption management strategy and optimize the strategy by adjusting the calculation parameters and formulas of the frequency adjustment coefficient. This regular evaluation and adjustment mechanism ensures that the power consumption management strategy can continue to effectively adapt to the changes and needs of IoT devices, thereby improving the overall energy efficiency and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the overall steps of the present invention; Figure 2 It is a system principle block diagram of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] See also Figure 1 The present invention discloses a power consumption management method for IoT devices based on big data, and provides the following four technical solutions: The first implementation method includes the following steps: S1. Equipment classification: Data center servers and IoT devices are classified into different categories or groups according to different feature types; the specific equipment classification methods include: Data center servers: servers are divided into different categories or groups based on CPU model, memory size, and storage type characteristics; IoT devices: IoT devices are classified into different categories or groups based on device type (such as sensors, smart wearables, smart homes, etc.), functional requirements (such as data transmission frequency, processing task complexity), power consumption characteristics (such as peak power consumption, average power consumption), etc. S2. Power consumption assessment and modeling: Use power consumption test tools to test the power consumption of various devices under the condition of simulating actual workloads, record static and dynamic power consumption data, and establish a power consumption model to reflect the power consumption of the devices. At the same time, set power consumption thresholds for each type of device. When the power consumption of the device exceeds the power consumption threshold, trigger the corresponding power consumption management strategy. The steps of power consumption assessment and modeling include: Power consumption test: Use the power consumption test tool to test the power consumption of each type of equipment under the condition of simulating the actual workload; record the static power consumption P static and dynamic power consumption P dynamic (t), where t represents time; Power consumption model: Establish the power consumption model P(t, type, load) = P static +P dynamic (t, load), where P(t) represents the total power consumption at time t, type represents the device type (server or IoT device), and load represents the device load (such as CPU usage, data transmission volume, etc.); Parameter design: Set a power consumption threshold P0 for each type of device. When the power consumption of the device exceeds the threshold, the power consumption management strategy is triggered. S3, implementation of sleep and wake-up strategy: statistics and analysis of the sleep time and frequency of IoT devices, calculation of the power consumption saved by sleep and the power consumption increased by wake-up, and setting the thresholds of sleep power saving and wake-up power increase according to the characteristics and application scenarios of the devices. For devices with short sleep time and frequent sleep in a unit time, control the devices not to sleep in the corresponding time period; S4. Strategy design and dynamic adjustment: For servers, design dynamic load balancing algorithms for load balancing; for IoT devices, dynamically adjust the operating frequency and transmission frequency to minimize power consumption based on task priority and load conditions; and regularly evaluate and adjust power consumption management strategies.

[0023] The following is an example of a device classification list, and the types can be expanded: Table 1. Data center server classification table

[0024] illustrate: Servers in Group A are suitable for tasks that require high-performance computing, such as big data analysis and real-time data processing; Servers in Group B are suitable for medium-load tasks, such as Web servers and database servers; Servers in group C are suitable for scenarios with strict requirements on power consumption or low load, such as backup servers and test environments.

[0025] Table 2. Classification of IoT devices

[0026] illustrate: The sensor devices in group 1 are mainly used for data collection, have low power consumption, and are suitable for long-term operation; The actuator devices in group 2 are used to control other devices, have medium power consumption, and work according to the control requirements; The smart home appliances in group 3 are used in a smart home environment and have high power consumption, but low power consumption when in standby mode; Group 4 IIoT devices are used for industrial automation control and have high power consumption, which may change depending on the load conditions.

[0027] By carefully classifying data center servers and IoT devices, this method can more accurately understand and manage the power consumption characteristics of various types of devices. Secondly, through power consumption evaluation and modeling, an accurate power consumption model is established, and power consumption thresholds are set, thereby achieving real-time monitoring and effective management of power consumption. In addition, the implementation of the sleep and wake-up strategy avoids the ineffective sleep of IoT devices and improves energy utilization efficiency. The dynamic adjustment strategy designed for servers and IoT devices not only achieves load balancing, but also dynamically adjusts power consumption according to task priority and load conditions, further reducing overall power consumption. These improvements jointly improve the energy efficiency management level of IoT devices and provide strong support for energy conservation and emission reduction in the big data environment.

[0028] The second implementation mode is mainly different from the first implementation mode in that the steps of implementing the sleep wake-up strategy in S3 specifically include: S3.1 Equipment Statistics: For all devices, use the set D = {D1, D2, ..., D N}, where N represents the number of IoT devices; each device D i There is a sleep time record sequence S i ={S i1 , S i2 , …, S iT}, where T is the total number of time points counted (e.g., time points divided into hours, minutes, etc.); Collect the sleep state (sleep or wake-up) and corresponding power consumption data of each device at each time point through the IoT platform or sensors; S3.2 Sleep time statistics: For each device D i , calculate its average sleep time i and sleep frequency f i : ; Where S it 1 means sleep, 0 means wake up; ; S3.3 Power consumption analysis: Set device D i The power consumption in sleep mode is , the power consumption in wake-up state is , the power consumption during sleep and wake-up is ; Calculate the power consumption saved by sleeping per unit time and wake-up increase power consumption : ; ; S3.4 Threshold setting: According to the characteristics and application scenarios of the device, set the thresholds for saving power when sleeping and increasing power when waking up. Save and θ add ; if and , it is considered that the device has a short sleep time and sleeps frequently during this time period; S3.5 Intelligent management and control strategy: For devices with short sleep time and frequent sleep time per unit time, control them to not sleep during the corresponding time period.

[0029] Through detailed device statistics and sleep time analysis, this method can accurately grasp the sleep behavior and power consumption characteristics of each IoT device. By calculating the average sleep time, sleep frequency, and the power consumption saved by sleep and increased by wake-up per unit time, this method can identify those devices with short sleep time and frequent sleep, which may consume unnecessary power during frequent sleep and wake-up. For these devices, this method intelligently controls them not to sleep during the corresponding time period, thereby avoiding invalid power consumption loss. This refined sleep and wake-up management strategy not only improves the energy utilization efficiency of the device, but also optimizes the overall power consumption performance of the IoT system, providing strong support for building a more energy-saving and efficient IoT environment.

[0030] The third implementation mode is mainly different from the first implementation mode in that: in S4, the dynamic load balancing algorithm of the server is designed based on weighted load balancing, and the formula is: ; Among them, w j (t) represents the weight of server j at time t (dynamically adjusted according to performance, load, etc.), l j (t) represents the load of server j at time t, and n represents the total number of servers.

[0031] In S4, the power consumption management strategy of IoT devices is: the system dynamically adjusts the operating frequency f(t) (i.e., the operating frequency of the processor or other working unit) and the transmission frequency r(t) (i.e., the rate or frequency of data transmission) according to the current task priority and load status. The goal is to minimize the power consumption of the system while ensuring that the task is completed on time and the data is transmitted accurately. Specifically, it includes: S4.1 parameter setting: Set task priority P(t): represents the task priority at time t, which can be a discrete value (such as high, medium, low) or a continuous value (such as a floating point number between 0 and 1); divide tasks into different priority levels according to their importance and urgency; Set the IoT device load L(t): represents the load status of the IoT device at time t, which is a comprehensive reflection of the amount of data currently processed by the device, the number of connected devices, and the transmission data rate indicators. It can be a normalized value (between 0 and 1); establish a load status monitoring module to monitor the system load status in real time or periodically; α(P), β(P) and δ(L) are frequency adjustment coefficients, where α(P) is the coefficient for adjusting the working frequency according to the task priority P; β(P) is the coefficient for adjusting the transmission frequency according to the task priority P; δ(L) is the coefficient for adjusting the frequency according to the IoT device load L; S4.2 Calculate the adjustment factor: S4.2.1 Real-time or periodic monitoring of task priority P(t) and IoT device load L(t); S4.2.2 Calculate α(P(t)) and β(P(t)) based on the current task priority P(t). The function formula is expressed as: α(P(t))= 1+k f × (PP avg ); β(P(t))= 1+k r × (PP avg ); Among them, k f and k r is the adjustment slope, P avg is the average or baseline priority; S4.2.3 Calculate δ(L(t)) based on the current IoT device load L(t). The function formula is expressed as: ; Among them, k L is the adjustment slope, L avg is the average or baseline load; when the load L is high, the frequency is reduced to reduce the burden on the equipment and save power; S4.3 Frequency adjustment: S4.3.1 Calculate the new operating frequency f(t) and transmission frequency r(t); The operating frequency adjustment formula is expressed as: f(t) = f base ×α(P(t))×δ(L(t)); The transmission frequency adjustment formula is expressed as: r(t) = r base ×β(P(t))×δ(L(t)); Among them, f base With r base is the base frequency of the processor or work unit, f base Indicates the basic operating frequency; r base Indicates the basic transmission frequency; S4.3.2 The calculated new frequency value f base With r base Applied to processors or work units and transport modules; S4.3.3 Cyclic monitoring and adjustment: Repeat steps S4.2-S4.3.2 continuously to dynamically adjust the frequency to adapt to changes in task priority and IoT device load.

[0032] Through the weight-based load balancing algorithm, the server can more flexibly adjust the weight dynamically according to performance and load, thereby achieving more efficient resource allocation and load balancing. At the same time, IoT devices dynamically adjust the working frequency and transmission frequency according to task priority and load status, which not only ensures the timely completion of tasks and accurate data transmission, but also effectively reduces system power consumption. This dynamic adjustment strategy can respond to task changes and load fluctuations in real time, improving the energy efficiency and flexibility of the system. In general, these improvements enable IoT systems to manage resources and power consumption more intelligently, providing strong support for building an efficient and energy-saving IoT environment.

[0033] The fourth implementation mode is mainly different from the third implementation mode in that: in S4, periodically evaluating and adjusting the power consumption management strategy specifically includes: Determine the evaluation period: Based on application scenarios: According to the application scenarios and requirements of IoT devices, the evaluation cycle is formulated accordingly. For example, for devices that need to run stably for a long time and are sensitive to power consumption, the evaluation cycle may be shorter, such as daily or weekly. For devices with less stringent power consumption requirements and little change in application scenarios, the evaluation cycle can be relatively long, such as monthly or quarterly. Consider device features: shorten the evaluation cycle when device hardware or software is updated; Effectiveness evaluation: Collect the power consumption of the equipment according to the evaluation period, compare it with the historical power consumption, and evaluate the effectiveness of the power consumption management strategy; Coefficient adjustment: After the effectiveness evaluation is performed, if it is determined that the effect of implementing the power management strategy does not reach the expected value, the calculation parameters and formulas of the frequency adjustment coefficients α(P), β(P) and δ(L) are adjusted.

[0034] The specific method for judging whether the effect of implementing the power consumption management strategy does not reach the expected value is: setting a power consumption reduction threshold, calculating the ratio of the power consumption detected after implementing the power consumption management strategy to the power consumption detected before implementation, and when the ratio is greater than the power consumption reduction threshold, judging that the effect of implementing the power consumption management strategy does not reach the expected value.

[0035] By determining the evaluation cycle according to the application scenario and device characteristics, and regularly collecting power consumption data for effectiveness evaluation, this method can promptly discover deficiencies in the power management strategy, and optimize the strategy by adjusting the calculation parameters and formula of the frequency adjustment coefficient. This regular evaluation and adjustment mechanism ensures that the power management strategy can continue to effectively adapt to the changes and needs of IoT devices, thereby improving overall energy efficiency and system stability.

[0036] See also Figure 2 The present invention discloses an IoT device power consumption management system based on big data, comprising: IoT devices, including all devices connected to the IoT system, such as sensors, actuators, smart appliances, etc., communicate with the data center server modules, transmit data and receive control instructions. These devices have different characteristics, such as power consumption, processing power, communication methods, etc. Data center servers are used to store, process and analyze data from IoT devices. The data interaction between the two is the basis of the system and provides necessary information for subsequent power consumption evaluation and strategy design. The server has powerful computing and storage capabilities and is the core of the IoT system. It communicates with IoT devices and power management modules, receives data, sends control instructions, and stores related data. It also works with the power management module to optimize the server's resource usage (such as CPU, memory, storage, etc.) to minimize power consumption. The device classification module classifies IoT devices and data center servers by distinguishing feature types (such as power consumption, processing power, communication mode, etc.); and outputs the classification results to the power consumption assessment and modeling unit and the power consumption management module; The power consumption assessment and modeling unit uses power consumption testing tools to test the power consumption of various types of equipment under conditions that simulate actual workloads, and records static and dynamic power consumption data; sets power consumption thresholds for each type of equipment, and triggers corresponding power consumption management strategies when the power consumption of the equipment exceeds the threshold; The sleep and wake-up strategy implementation unit is used to count and analyze the sleep time and frequency of IoT devices, calculate the power consumption saved by sleep and increased by wake-up, and control the sleep of IoT devices; Power consumption management module, for servers and IoT devices, designs dynamic load balancing algorithms and power consumption management strategies to manage power consumption of data center servers and IoT devices; for servers, designs algorithms for load balancing and optimizes resource usage; power consumption optimization: for IoT devices, dynamically adjusts the operating frequency and transmission frequency according to task priority and load conditions to minimize power consumption; The monitoring and reporting module is used to monitor the power consumption of the entire Internet of Things system in real time and generate report output; the monitoring and reporting module collects data from the device classification module, the power consumption assessment and modeling unit, the sleep and wake-up strategy implementation unit and the strategy design and power consumption management module, and the generated power consumption management report includes power consumption trend, device status, strategy effect, and has an alarm function. When the device power consumption exceeds the set threshold, an alarm is triggered and relevant personnel are notified to handle it; through reports and alarms, the monitoring and reporting module provides system administrators with an intuitive power consumption management view so that problems can be discovered and adjusted in a timely manner.

[0037] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0039] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power consumption management method for IoT devices based on big data, characterized in that: The specific steps include: S1. Device classification: Data center servers and IoT devices are classified into different categories or groups according to different feature types; S2. Power consumption assessment and modeling: Use power consumption test tools to test the power consumption of various devices under the condition of simulating actual workloads, record static and dynamic power consumption data, and establish a power consumption model to reflect the power consumption of devices. At the same time, set power consumption thresholds for each type of device. When the power consumption of a device exceeds the power consumption threshold, trigger the corresponding power consumption management strategy. S3, implementation of sleep and wake-up strategy: statistics and analysis of the sleep time and frequency of IoT devices, calculation of the power consumption saved by sleep and the power consumption increased by wake-up, and setting the thresholds of sleep power saving and wake-up power increase according to the characteristics and application scenarios of the devices. For devices with short sleep time and frequent sleep in a unit time, control the devices not to sleep in the corresponding time period; S4, Strategy design and dynamic adjustment: Design dynamic load balancing algorithm for servers to perform load balancing; For IoT devices, dynamically adjust the operating frequency and transmission frequency to minimize power consumption based on task priority and load conditions; and regularly evaluate and adjust the power consumption management strategy.

2. The method for managing power consumption of IoT devices based on big data according to claim 1, characterized in that: The device classification method in S1 specifically includes: Data center servers: servers are divided into different categories or groups based on CPU model, memory size, and storage type characteristics; IoT devices: IoT devices are classified into different categories or groups based on device type, functional requirements, and power consumption characteristics.

3. The method for managing power consumption of IoT devices based on big data according to claim 1, characterized in that: The steps of power consumption evaluation and modeling in S2 specifically include: Power consumption test: Use the power consumption test tool to test the power consumption of each type of equipment under the condition of simulating the actual workload; record the static power consumption P static and dynamic power consumption P dynamic (t), where t represents time; Power consumption model: Establish the power consumption model P(t, type, load) = P static +P dynamic (t, load), where P(t) represents the total power consumption at time t, type represents the device type, and load represents the device load; Parameter design: Set a power consumption threshold P0 for each type of device. When the device power consumption exceeds the threshold, the power consumption management strategy is triggered.

4. The method for managing power consumption of IoT devices based on big data according to claim 1, characterized in that: The steps for implementing the sleep wake-up strategy in S3 specifically include: S3.1 Equipment Statistics: For all devices, use the set D = {D1, D2, ..., D N }, where N represents the number of IoT devices; each device D i There is a sleep time record sequence S i ={S i1 , S i2 , …, S iT }, where T is the total number of statistical time points; Collect the sleep state and corresponding power consumption data of each device at each time point through the IoT platform or sensors; S3.2 Sleep time statistics: For each device D i , calculate its average sleep time i and sleep frequency f i : ； Where S it 1 means sleep, 0 means wake up; ； S3.3 Power consumption analysis: Set device D i The power consumption in sleep mode is , the power consumption in wake-up state is , the power consumption during sleep and wake-up is ; Calculate the power consumption saved by sleeping per unit time and wake-up increase power consumption : ； ； S3.4 Threshold setting: According to the characteristics and application scenarios of the device, set the thresholds for saving power when sleeping and increasing power when waking up. Save and θ add ; if and , it is considered that the device has a short sleep time and sleeps frequently during this time period; S3.5 Intelligent management and control strategy: For devices with short sleep time and frequent sleep time per unit time, control them to not sleep during the corresponding time period.

5. The method for managing power consumption of IoT devices based on big data according to claim 1, characterized in that: In S4, the dynamic load balancing algorithm of the server is designed based on weighted load balancing, and the formula is: ； Among them, w j (t) represents the weight of server j at time t, l j (t) represents the load of server j at time t, and n represents the total number of servers.

6. The method for managing power consumption of IoT devices based on big data according to claim 5, characterized in that: In S4, the power consumption management strategy of the IoT device is: the system dynamically adjusts the operating frequency f(t) and the transmission frequency r(t) according to the current task priority and load status, with the goal of minimizing the power consumption of the system while ensuring that the task is completed on time and the data is transmitted accurately; specifically, it includes: S4.1 parameter setting: Set task priority P(t): represents the task priority at time t; divide tasks into different priority levels according to their importance and urgency; Set the IoT device load L(t): represents the load status of the IoT device at time t, which is a comprehensive reflection of the amount of data currently processed by the device, the number of connected devices, and the transmission data rate indicators; establish a load status monitoring module to monitor the system load status in real time or periodically; α(P), β(P) and δ(L) are frequency adjustment coefficients, where α(P) is the coefficient for adjusting the working frequency according to the task priority P; β(P) is the coefficient for adjusting the transmission frequency according to the task priority P; δ(L) is the coefficient for adjusting the frequency according to the IoT device load L; S4.2 Calculate the adjustment factor: S4.2.1 Real-time or periodic monitoring of task priority P(t) and IoT device load L(t); S4.2.2 Calculate α(P(t)) and β(P(t)) based on the current task priority P(t). The function formula is expressed as: α(P(t))= 1+k f ×(P-P avg ); β(P(t))= 1+k r ×(P-P avg ); Among them, k f and k r is the adjustment slope, P avg is the average or baseline priority; S4.2.3 Calculate δ(L(t)) based on the current IoT device load L(t). The function formula is expressed as: ； Among them, k L is the adjustment slope, L avg is the average or baseline load; S4.3 Frequency adjustment: S4.3.1 Calculate the new operating frequency f(t) and transmission frequency r(t); The operating frequency adjustment formula is expressed as: f(t) = f base ×α(P(t))×δ(L(t)); The transmission frequency adjustment formula is expressed as: r(t) = r base ×β(P(t))×δ(L(t)); Among them, f base With r base is the base frequency of the processor or work unit, f base Indicates the basic operating frequency; r base Indicates the basic transmission frequency; S4.3.2 The calculated new frequency value f base With r base Applied to processors or work units and transport modules; S4.3.3 Cyclic monitoring and adjustment: Repeat steps S4.2-S4.3.2 continuously to dynamically adjust the frequency to adapt to changes in task priority and IoT device load.

7. The method for managing power consumption of IoT devices based on big data according to claim 6, characterized in that: In S4, regularly evaluating and adjusting the power consumption management strategy specifically includes: Determine the evaluation period: Based on application scenarios: According to the application scenarios and requirements of IoT devices, the evaluation cycle is formulated accordingly; Consider device features: shorten the evaluation cycle when device hardware or software is updated; Effectiveness evaluation: Collect the power consumption of the equipment according to the evaluation period, compare it with the historical power consumption, and evaluate the effectiveness of the power consumption management strategy; Coefficient adjustment: After the effectiveness evaluation is performed, if it is determined that the effect of implementing the power consumption management strategy does not reach the expected value, the calculation parameters and formulas of the frequency adjustment coefficients α(P), β(P) and δ(L) are adjusted.

8. The method for managing power consumption of IoT devices based on big data according to claim 7, characterized in that: The specific method for judging whether the effect of implementing the power consumption management strategy does not reach the expected value is: setting a power consumption reduction threshold, calculating the ratio of the power consumption detected after implementing the power consumption management strategy to the power consumption detected before implementation, and when the ratio is greater than the power consumption reduction threshold, judging that the effect of implementing the power consumption management strategy does not reach the expected value.

9. An IoT device power consumption management system based on big data, used to implement the IoT device power consumption management method based on big data according to any one of claims 1 to 8, characterized in that: include: IoT devices, including all devices connected to an IoT system; Data center servers, which store, process, and analyze data from IoT devices; The device classification module classifies IoT devices and data center servers by distinguishing feature types; The power consumption evaluation and modeling unit uses power consumption testing tools to test the power consumption of various devices under the conditions of simulating actual workloads and records static and dynamic power consumption data; The sleep and wake-up strategy implementation unit is used to count and analyze the sleep time and frequency of IoT devices, calculate the power consumption saved by sleep and increased by wake-up, and control the sleep of IoT devices; The power consumption management module designs dynamic load balancing algorithms and power consumption management strategies for servers and IoT devices to manage the power consumption of data center servers and IoT devices. The monitoring and reporting module is used to monitor the power consumption of the entire IoT system in real time and generate report output.

10. The IoT device power consumption management system based on big data according to claim 9, characterized in that: The monitoring and reporting module collects data from the device classification module, the power consumption assessment and modeling unit, the sleep and wake-up strategy implementation unit, and the strategy design and power consumption management module. The generated power consumption management report includes power consumption trends, device status, and strategy effects, and is equipped with an alarm function. When the device power consumption exceeds the set threshold, an alarm is triggered and relevant personnel are notified to handle it.

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