Power consumption management method and system for IoT devices based on big data

Abstract

The present invention discloses a method and system for power consumption management of IoT devices based on big data, and relates to the technical field of device power consumption management. The method and system for power consumption management of IoT devices based on big data specifically include the following steps: S1, device classification; S2, power consumption evaluation and modeling; S3, implementation of sleep and wake-up strategies; S4, strategy design and dynamic adjustment; by carefully classifying data center servers and IoT devices, the power consumption characteristics of various types of devices can be understood and managed more accurately. 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 ineffective sleep of IoT devices and improves energy utilization efficiency. The dynamic adjustment strategy not only achieves load balancing, but also dynamically adjusts power consumption according to task priority and load conditions, further reducing overall power consumption.

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 management in IoT devices is crucial for extending device lifespan, improving system reliability, and reducing operating costs. Effective power management ensures devices function properly when needed while reducing unnecessary energy waste. IoT devices typically enter a low-power sleep mode when inactive, significantly reducing power consumption. However, the diversity of IoT devices makes it difficult to standardize power management strategies.

[0003] For example, a method, device, and system for managing the operation of electromechanical equipment disclosed in publication number CN115049083A include the following: obtaining the historical data of the total power consumption of the current building; determining the target power consumption based on the obtained 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 control factors of each area determined. 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 operating 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, power consumption management for a large number of devices currently usually uses power regulation to reduce power consumption and avoid waste. When applied to some existing IoT devices, when not in use, they usually use sleep mode to adjust power and reduce power consumption. However, the usage status of different IoT devices varies, and the sleep frequency is also uncontrollable. When the device wakes up from sleep, the power consumption will increase due to the instantaneous high power. Although the time is short, if the device wakes up from sleep frequently within a certain period of time, the effect of reducing power consumption and saving energy will be achieved. How to control the power consumption of different IoT devices during sleep and wakeup is an urgent problem that needs to be solved. Summary of the Invention

[0005] In response to the deficiencies of the existing technology, 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 technology.

[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:

[0007] S1. Device classification: Data center servers and IoT devices are divided into different categories or groups according to their feature types.

[0008] S2. Power Consumption Assessment and Modeling: Using power consumption testing tools, we conduct power consumption tests on various devices under conditions that simulate actual workloads. We record static and dynamic power consumption data and build power consumption models to reflect device power consumption. We also set power consumption thresholds for each device type. When a device's power consumption exceeds the threshold, the corresponding power management policy is triggered.

[0009] S3. Implementation of sleep and wake-up strategies: Statistics and analysis of the sleep time and frequency of IoT devices are collected to calculate the power consumption saved by sleep and the power consumption increased by wake-up. Based on the characteristics and application scenarios of the devices, thresholds for power savings during sleep and power increase during wake-up are set. For devices with short sleep times and frequent sleep times per unit time, the devices are prevented from sleeping during the corresponding time periods.

[0010] S4. Strategy design and dynamic adjustment: For servers, design a dynamic load balancing algorithm 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 management strategies.

[0011] Preferably, the device classification method in S1 specifically includes:

[0012] Data center servers: servers are divided into different categories or groups based on CPU model, memory size, and storage type characteristics;

[0013] IoT devices: IoT devices are classified into different categories or groups based on device type, functional requirements, and power consumption characteristics.

[0014] Preferably, the power consumption evaluation and modeling steps in S2 specifically include:

[0015] Power consumption test: Use power consumption test tools to test the power consumption of each type of equipment under the condition of simulating actual workload; record the static power consumption P static and dynamic power consumption P dynamic (t), where t represents time;

[0016] Power consumption model: Establish power consumption model P(t, type, load) = P static +P dynamic (t, load), where P() represents the total power consumption, t represents the time, type represents the device type, and load represents the device load;

[0017] 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 policy is triggered.

[0018] Preferably, the steps for implementing the sleep-wake-up strategy in S3 specifically include:

[0019] S3.1 Equipment Statistics:

[0020] 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;

[0021] Collect the sleep state and corresponding power consumption data of each device at each time point through the IoT platform or sensors;

[0022] S3.2 Sleep time statistics:

[0023] For each device D i , calculate its average sleep time and sleep frequency f i :

[0024] ;

[0025] Among them S it 1 means sleep, 0 means wake up;

[0026] ;

[0027] S3.3 Power consumption analysis:

[0028] Set device D i The power consumption in sleep state is , the power consumption in the wake-up state is , the power consumption during sleep and wake-up is ;

[0029] Calculate the power consumption saved by sleeping per unit time and wake-up increases power consumption :

[0030] ;

[0031] ;

[0032] S3.4 Threshold setting:

[0033] According to the characteristics and application scenarios of the device, set the threshold value of sleep mode to save power and wake mode to increase power consumption.Save and θ add ;

[0034] if and , then it is considered that the device has a short sleep time and sleeps frequently during this time period;

[0035] S3.5 Intelligent management and control strategy: For devices that have short sleep time and frequent sleep time per unit time, control them to not sleep during the corresponding time period.

[0036] Preferably, in S4, the dynamic load balancing algorithm of the server is designed based on weighted load balancing, and the dynamic load A(t) of the server is expressed as follows:

[0037] ;

[0038] 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.

[0039] 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 system's power consumption while ensuring that tasks are completed on time and data is transmitted accurately; specifically, it includes:

[0040] S4.1 parameter setting:

[0041] Set task priority P(t): represents the priority of the task at time t; divide the tasks into different priority levels according to their importance and urgency;

[0042] Set IoT device load L(t): This represents the load status of the IoT device at time t, which is a comprehensive reflection of the device's current data processing volume, number of connected devices, and transmission data rate. Establish a load status monitoring module to monitor the system's load status in real time or periodically.

[0043] α(P), β(P), and δ(L) are frequency adjustment coefficients, where α(P) is the coefficient for adjusting the operating frequency according to the task priority P; β(P) is the coefficient for adjusting the transmission frequency according to the task priority P; and δ(L) is the coefficient for adjusting the frequency according to the IoT device load L.

[0044] S4.2 Calculate the adjustment factor:

[0045] S4.2.1 Monitor task priority P(t) and IoT device load L(t) in real time or periodically;

[0046] 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:

[0047] α(P(t))= 1+k f × (PP avg );

[0048] β(P(t))= 1+k r × (PP avg );

[0049] Among them, k f and k r is the adjustment slope, P avg is an average or baseline priority;

[0050] S4.2.3 Calculate δ(L(t)) based on the current IoT device load L(t). The function formula is expressed as:

[0051] ;

[0052] Among them, k L（t） is the adjustment slope, L avg (t) is the average or baseline load;

[0053] S4.3 Frequency adjustment:

[0054] S4.3.1 Calculate the new operating frequency f(t) and transmission frequency r(t);

[0055] The operating frequency adjustment formula is expressed as:

[0056] f(t) = f base ×α(P(t))×δ(L(t));

[0057] The transmission frequency adjustment formula is expressed as:

[0058] r(t) = r base ×β(P(t))×δ(L(t));

[0059] 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;

[0060] S4.3.2 The calculated new frequency value f base With r base Applied to processors or work units and transmission modules;

[0061] S4.3.3 Loop monitoring and adjustment: Repeat steps S4.2-S4.3.2 continuously to dynamically adjust the frequency to adapt to changes in task priorities and IoT device loads.

[0062] Preferably, in S4, regularly evaluating and adjusting the power consumption management strategy specifically includes:

[0063] Determine the evaluation cycle:

[0064] Based on application scenarios: Develop an evaluation cycle based on the application scenarios and requirements of IoT devices;

[0065] Consider device characteristics: shorten the evaluation cycle when device hardware or software is updated;

[0066] Effectiveness evaluation: Collect device power consumption according to the evaluation period, compare it with historical power consumption, and evaluate the effectiveness of the power management strategy.

[0067] Coefficient adjustment: After conducting an effectiveness evaluation, if it is determined that the implementation of the power management strategy does not achieve the expected effect, the calculation parameters and formulas of the frequency adjustment coefficients α(P(t)), β(P(t)), and δ(L(t)) are adjusted.

[0068] 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.

[0069] The present invention also discloses an IoT device power consumption management system based on big data, including:

[0070] IoT devices, including all devices connected to an IoT system;

[0071] Data center servers, which store, process, and analyze data from IoT devices;

[0072] The device classification module classifies IoT devices and data center servers by distinguishing feature types;

[0073] The power consumption assessment and modeling unit uses power consumption testing tools to test the power consumption of various devices under conditions that simulate actual workloads and record static and dynamic power consumption data;

[0074] 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;

[0075] The power consumption management module designs dynamic load balancing algorithms and power consumption management strategies for servers and IoT devices in data centers to manage power consumption.

[0076] The monitoring and reporting module is used to monitor the power consumption of the entire IoT system in real time and generate report output.

[0077] 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 policy implementation unit and the policy design and power consumption management module. The generated power consumption management report includes power consumption trends, device status, policy 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.

[0078] The present invention provides a method and system for managing power consumption of IoT devices based on big data. Compared with the existing technology, it has the following advantages:

[0079] 1. This big data-based method for IoT device power consumption management, through detailed classification of data center servers and IoT devices, enables a more precise understanding and management of the power consumption characteristics of various devices. Secondly, through power consumption assessment and modeling, an accurate power consumption model is established and power consumption thresholds are set, enabling real-time monitoring and effective power consumption management. Furthermore, the implementation of a sleep / wake-up strategy avoids ineffective sleep in IoT devices and improves energy efficiency. The dynamic adjustment strategy designed for servers and IoT devices not only achieves load balancing but also dynamically adjusts power consumption based on task priority and load, further reducing overall power consumption. These improvements collectively enhance the energy efficiency management of IoT devices and provide strong support for energy conservation and emission reduction in big data environments.

[0080] 2. This big data-based method for managing IoT device power consumption 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 wakeup per unit time, this method can identify devices with short sleep times and frequent sleep cycles, which may consume unnecessary power during frequent sleep and wakeup. For these devices, the method intelligently controls them from sleeping during the corresponding time period, thereby avoiding ineffective power consumption losses. This refined sleep and wakeup 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-efficient and efficient IoT environment.

[0081] 3. This big data-based method for managing IoT device power consumption utilizes a weighted load balancing algorithm, enabling servers to more flexibly and dynamically adjust weights based on performance and load, thereby achieving more efficient resource allocation and load balancing. Furthermore, IoT devices dynamically adjust their operating and transmission frequencies based on task priority and load status, ensuring timely task completion and accurate data transmission while effectively reducing system power consumption. This dynamic adjustment strategy responds to task changes and load fluctuations in real time, improving system energy efficiency and flexibility. Overall, these improvements enable IoT systems to more intelligently manage resources and power consumption, providing strong support for building an efficient and energy-efficient IoT environment.

[0082] 4. This big data-based approach to IoT device power management uses a data-driven evaluation cycle based on application scenarios and device characteristics, and regularly collects power consumption data for effectiveness assessment. This method can promptly identify deficiencies in power management strategies and optimize them by adjusting the parameters and formulas used to calculate the frequency adjustment coefficient. This regular evaluation and adjustment ensures that power management strategies can continuously and effectively adapt to changes and demands of IoT devices, thereby improving overall energy efficiency and system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 It is a schematic diagram of the overall steps of the present invention;

[0084] Figure 2 This is a system principle block diagram of the present invention. DETAILED DESCRIPTION

[0085] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0086] See Figure 1 The present invention discloses a method for power consumption management of IoT devices based on big data, and provides the following four technical solutions:

[0087] The first implementation method specifically includes the following steps:

[0088] S1. Device Classification: Data center servers and IoT devices are divided into different categories or groups based on their characteristics. Specific device classification methods include:

[0089] Data center servers: servers are divided into different categories or groups based on CPU model, memory size, and storage type characteristics;

[0090] 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.

[0091] S2. Power Consumption Assessment and Modeling: Utilize power consumption testing tools to simulate actual workloads, conduct power consumption tests on various devices, record static and dynamic power consumption data, and establish a power consumption model to reflect device power consumption. Furthermore, power consumption thresholds are set for each device type. When a device exceeds the threshold, the corresponding power management policy is triggered. The steps for power consumption assessment and modeling include:

[0092] Power consumption test: Use power consumption test tools to test the power consumption of each type of equipment under the condition of simulating actual workload; record the static power consumption P static and dynamic power consumption P dynamic (t), where t represents time;

[0093] Power consumption model: Establish power consumption model P(t, type, load) = P static +P dynamic (t, load), where P() represents the total power consumption, t represents the time, type represents the device type (server or IoT device), and load represents the device load (such as CPU usage, data transmission volume, etc.);

[0094] 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 policy is triggered.

[0095] S3. Implementation of sleep and wake-up strategies: Statistics and analysis of the sleep time and frequency of IoT devices are collected to calculate the power consumption saved by sleep and the power consumption increased by wake-up. Based on the characteristics and application scenarios of the devices, thresholds for power savings during sleep and power increase during wake-up are set. For devices with short sleep times and frequent sleep times per unit time, the devices are prevented from sleeping during the corresponding time periods.

[0096] S4. Strategy design and dynamic adjustment: For servers, design a dynamic load balancing algorithm 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 management strategies.

[0097] The following is an example of a device classification list, and the types can be expanded:

[0098] Table 1. Data center server classification

[0099] illustrate:

[0100] Servers in Group A are suitable for tasks requiring high-performance computing, such as big data analysis and real-time data processing;

[0101] Servers in Group B are suitable for medium-load tasks, such as Web servers and database servers;

[0102] Servers in Group C are suitable for scenarios with strict power consumption requirements or low load, such as backup servers and test environments.

[0103] Table 2. Classification of IoT devices

[0104] illustrate:

[0105] The sensor devices in group 1 are mainly used for data collection, have low power consumption, and are suitable for long-term operation;

[0106] Actuator devices in group 2 are used to control other devices. They have medium power consumption and work according to control requirements.

[0107] Smart home appliances in group 3 are used in smart home environments and have high power consumption, but low power consumption when in standby mode;

[0108] Group 4 industrial IoT devices are used for industrial automation control and have high power consumption, which may vary depending on the load conditions.

[0109] By carefully classifying data center servers and IoT devices, this method enables a more precise understanding and management of the power consumption characteristics of each device. Secondly, through power consumption assessment and modeling, an accurate power consumption model is established and power consumption thresholds are set, enabling real-time monitoring and effective power consumption management. Furthermore, the implementation of a sleep / wake-up strategy prevents ineffective sleep in IoT devices, improving energy efficiency. The dynamic adjustment strategy designed for servers and IoT devices not only achieves load balancing but also dynamically adjusts power consumption based on task priority and load, further reducing overall power consumption. These improvements collectively enhance the energy efficiency management of IoT devices and provide strong support for energy conservation and emission reduction in big data environments.

[0110] The second implementation mode differs from the first implementation mode mainly in that the steps for implementing the sleep / wake-up strategy in S3 specifically include:

[0111] S3.1 Equipment Statistics:

[0112] 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;

[0113] Collect the sleep state and corresponding power consumption data of each device at each time point through the IoT platform or sensors;

[0114] S3.2 Sleep time statistics:

[0115] For each device D i , calculate its average sleep time and sleep frequency f i :

[0116] ;

[0117] Among them S it 1 means sleep, 0 means wake up;

[0118] ;

[0119] S3.3 Power consumption analysis:

[0120] Set device D i The power consumption in sleep state is , the power consumption in the wake-up state is , the power consumption during sleep and wake-up is ;

[0121] Calculate the power consumption saved by sleeping per unit time and wake-up increases power consumption :

[0122] ;

[0123] ;

[0124] S3.4 Threshold setting:

[0125] According to the characteristics and application scenarios of the device, set the threshold value of sleep mode to save power and wake mode to increase power consumption. Save and θ add ;

[0126] if and , then it is considered that the device has a short sleep time and sleeps frequently during this time period;

[0127] S3.5 Intelligent management and control strategy: For devices that have short sleep time and frequent sleep time per unit time, control them to not sleep during the corresponding time period.

[0128] 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 wakeup per unit time, this method can identify devices with short sleep times and frequent sleep cycles, which may consume unnecessary power during these frequent sleep and wakeup cycles. For these devices, the method intelligently controls them from sleeping during the corresponding time periods, thereby avoiding ineffective power consumption losses. This refined sleep and wakeup management strategy not only improves the energy utilization efficiency of devices, but also optimizes the overall power consumption performance of the IoT system, providing strong support for building a more energy-efficient and efficient IoT environment.

[0129] The third implementation mode is 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 dynamic load A(t) of the server is expressed as follows:

[0130] ;

[0131] 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.

[0132] In S4, the power consumption management strategy for IoT devices is as follows: 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) based on the current task priority and load status. The goal is to minimize the system's power consumption while ensuring that tasks are completed on time and data is transmitted accurately. Specifically, it includes:

[0133] S4.1 parameter setting:

[0134] Set task priority P(t): This represents the priority of the task at time t. It can be a discrete value (such as high, medium, or low) or a continuous value (such as a floating point number between 0 and 1). Tasks are divided into different priority levels based on their importance and urgency.

[0135] Set IoT device load L(t): This represents the load status of the IoT device at time t. It is a comprehensive reflection of the device's current data processing volume, the number of connected devices, and the transmission data rate. It can be a normalized value (between 0 and 1). Establish a load status monitoring module to monitor the system's load status in real time or periodically.

[0136] α(P), β(P), and δ(L) are frequency adjustment coefficients, where α(P) is the coefficient for adjusting the operating frequency according to the task priority P; β(P) is the coefficient for adjusting the transmission frequency according to the task priority P; and δ(L) is the coefficient for adjusting the frequency according to the IoT device load L.

[0137] S4.2 Calculate the adjustment factor:

[0138] S4.2.1 Monitor task priority P(t) and IoT device load L(t) in real time or periodically;

[0139] 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:

[0140] α(P(t))= 1+k f × (PP avg );

[0141] β(P(t))= 1+k r × (PP avg );

[0142] Among them, k f and k r is the adjustment slope, P avg is an average or baseline priority;

[0143] S4.2.3 Calculate δ(L(t)) based on the current IoT device load L(t). The function formula is expressed as:

[0144] ;

[0145] Among them, k L（t） is the adjustment slope, L avg (t) is the average or baseline load; when the load L(t) is high, the frequency is reduced to reduce the burden on the equipment and save power;

[0146] S4.3 Frequency adjustment:

[0147] S4.3.1 Calculate the new operating frequency f(t) and transmission frequency r(t);

[0148] The operating frequency adjustment formula is expressed as:

[0149] f(t) = f base ×α(P(t))×δ(L(t));

[0150] The transmission frequency adjustment formula is expressed as:

[0151] r(t) = r base×β(P(t))×δ(L(t));

[0152] 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;

[0153] S4.3.2 The calculated new frequency value f base With r base Applied to processors or work units and transmission modules;

[0154] S4.3.3 Loop monitoring and adjustment: Repeat steps S4.2-S4.3.2 continuously to dynamically adjust the frequency to adapt to changes in task priorities and IoT device loads.

[0155] Through a weight-based load balancing algorithm, servers can more flexibly and dynamically adjust weights based on performance and load, achieving more efficient resource allocation and load balancing. Simultaneously, IoT devices dynamically adjust their operating and transmission frequencies based on task priority and load status, ensuring timely task completion and accurate data transmission while effectively reducing system power consumption. This dynamic adjustment strategy responds to task changes and load fluctuations in real time, improving system energy efficiency and flexibility. Overall, these improvements enable IoT systems to more intelligently manage resources and power consumption, providing strong support for building efficient and energy-efficient IoT environments.

[0156] The fourth implementation mode differs from the third implementation mode mainly in that, in S4, periodically evaluating and adjusting the power consumption management strategy specifically includes:

[0157] Determine the evaluation cycle:

[0158] Based on application scenarios: Develop an evaluation cycle based on the application scenarios and requirements of IoT devices. For example, for devices that require long-term stable operation and are power-sensitive, the evaluation cycle may be shorter, such as daily or weekly. For devices with less stringent power requirements and less frequent application scenarios, the evaluation cycle can be longer, such as monthly or quarterly.

[0159] Consider device characteristics: shorten the evaluation cycle when device hardware or software is updated;

[0160] Effectiveness evaluation: Collect device power consumption according to the evaluation period, compare it with historical power consumption, and evaluate the effectiveness of the power management strategy.

[0161] Coefficient adjustment: After conducting an effectiveness evaluation, if it is determined that the implementation of the power management strategy does not achieve the expected effect, the calculation parameters and formulas of the frequency adjustment coefficients α(P(t)), β(P(t)), and δ(L(t)) are adjusted.

[0162] 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.

[0163] By determining the evaluation cycle based on the application scenario and device characteristics, and regularly collecting power consumption data for effectiveness assessment, this method can promptly identify deficiencies in the power management strategy and optimize the strategy by adjusting the parameters and formula for calculating the frequency adjustment coefficient. This regular evaluation and adjustment mechanism ensures that the power management strategy can continuously and effectively adapt to the changes and needs of IoT devices, thereby improving overall energy efficiency and system stability.

[0164] See Figure 2 The present invention discloses an Internet of Things device power consumption management system based on big data, comprising:

[0165] IoT devices, including all devices connected to the IoT system, such as sensors, actuators, and smart appliances, communicate with data center server modules to transmit data and receive control instructions. These devices have different characteristics, such as power consumption, processing power, and communication methods.

[0166] Data center servers are used to store, process, and analyze data from IoT devices. Data exchange between the two is the foundation of the system, providing essential information for subsequent power consumption assessment and strategy design. With powerful computing and storage capabilities, servers are the core of the IoT system. They communicate with IoT devices and the power management module, receiving data, sending control commands, and storing relevant data. They also work in conjunction with the power management module to optimize server resource usage (such as CPU, memory, and storage) to minimize power consumption.

[0167] The device classification module classifies IoT devices and data center servers by distinguishing between characteristic types (such as power consumption, processing power, and communication methods). The classification results are output to the power consumption assessment and modeling unit and the power consumption management module.

[0168] The power consumption assessment and modeling unit uses power consumption testing tools to test the power consumption of various devices under conditions that simulate actual workloads, and records static and dynamic power consumption data. It also sets power consumption thresholds for each type of device and triggers the corresponding power management policy when the device power consumption exceeds the threshold.

[0169] 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;

[0170] The power management module designs dynamic load balancing algorithms and power management strategies for servers and IoT devices to manage power consumption in data centers. For servers, algorithms are designed for load balancing and optimizing resource usage. For IoT devices, power consumption optimization dynamically adjusts operating and transmission frequencies based on task priority and load conditions to minimize power consumption.

[0171] The monitoring and reporting module is used to monitor the power consumption of the entire IoT system in real time and generate report outputs. The monitoring and reporting module collects data from the device classification module, the power consumption assessment and modeling unit, the sleep and wake-up policy implementation unit, and the policy design and power consumption management module. The generated power consumption management report includes power consumption trends, device status, and policy effects. It also 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 adjustments can be made in a timely manner.

[0172] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0173] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0174] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for power consumption management of IoT devices based on big data, characterized in that: The specific steps include: S1. Device classification: Data center servers and IoT devices are divided into different categories or groups according to their feature types. S2. Power Consumption Assessment and Modeling: Using power consumption testing tools, we conduct power consumption tests on various devices under conditions that simulate actual workloads. We record static and dynamic power consumption data and build power consumption models to reflect device power consumption. We also set power consumption thresholds for each device type. When a device's power consumption exceeds the threshold, the corresponding power management policy is triggered. S3. Implementation of sleep and wake-up strategies: Statistics and analysis of the sleep time and frequency of IoT devices are collected to calculate the power consumption saved by sleep and the power consumption increased by wake-up. Based on the characteristics and application scenarios of the devices, thresholds for power consumption saved by sleep and increased by wake-up are set. For devices with short sleep times and frequent sleep times per unit time, the devices are controlled to not sleep during the corresponding time period. Specifically, the following are included: 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 and sleep frequency f i : ； Among them S it 1 means sleep, 0 means wake up; ； S3.3 Power consumption analysis: Set device D i The power consumption in sleep state is , the power consumption in the 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 increases power consumption : ； ； S3.4 Threshold setting: According to the characteristics and application scenarios of the device, set the threshold value of sleep mode to save power and wake mode to increase power consumption. Save and θ add ; if and , then 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 that have short sleep times and frequent sleep times per unit time, control them to not sleep during the corresponding time period; S4. Strategy design and dynamic adjustment: Design dynamic load balancing algorithms for servers to perform load balancing; For IoT devices, the operating frequency and transmission frequency are dynamically adjusted to minimize power consumption based on task priority and load conditions; and the power consumption management strategy is regularly evaluated and adjusted.

2. The method for managing power consumption of IoT devices based on big data according to claim 1, wherein: 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 power consumption management 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 power consumption test tools to test the power consumption of each type of equipment under the condition of simulating actual workload; record the static power consumption P static and dynamic power consumption P dynamic (t), where t represents time; Power consumption model: Establish power consumption model P(t, type, load) = P static +P dynamic (t, load), where P() represents the total power consumption, t represents the time, 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 policy is triggered.

4. The method for power consumption management of IoT devices based on big data according to claim 1, wherein: In S4, the dynamic load balancing algorithm of the server is designed based on weighted load balancing. The dynamic load A(t) of the server is expressed as follows: ； 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.

5. The method for power consumption management of IoT devices based on big data according to claim 4, characterized in that: In S4, the power consumption management strategy of the IoT device is as follows: the system dynamically adjusts the operating frequency f(t) and the transmission frequency r(t) according to the current task priority and load status. The goal is to minimize the system power consumption while ensuring that tasks are completed on time and data is transmitted accurately. Specifically, it includes: S4.1 parameter setting: Set task priority P(t): represents the priority of the task at time t; divide the tasks into different priority levels according to their importance and urgency; Set IoT device load L(t): This represents the load status of the IoT device at time t, which is a comprehensive reflection of the device's current data processing volume, number of connected devices, and transmission data rate. Establish a load status monitoring module to monitor the system's 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 operating frequency according to the task priority P; β(P) is the coefficient for adjusting the transmission frequency according to the task priority P; and 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 Monitor task priority P(t) and IoT device load L(t) in real time or periodically; 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 an 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（t） is the adjustment slope, L avg (t) 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 transmission modules; S4.3.3 Loop monitoring and adjustment: Repeat steps S4.2-S4.3.2 continuously to dynamically adjust the frequency to adapt to changes in task priorities and IoT device loads.

6. The method for power consumption management of IoT devices based on big data according to claim 5, characterized in that: In S4, regularly evaluating and adjusting the power consumption management strategy specifically includes: Determine the evaluation cycle: Based on application scenarios: Develop an evaluation cycle based on the application scenarios and requirements of IoT devices; Consider device characteristics: shorten the evaluation cycle when device hardware or software is updated; Effectiveness evaluation: Collect device power consumption according to the evaluation period, compare it with historical power consumption, and evaluate the effectiveness of the power management strategy. Coefficient adjustment: After conducting an effectiveness evaluation, if it is determined that the implementation of the power management strategy does not achieve the expected effect, the calculation parameters and formulas of the frequency adjustment coefficients α(P(t)), β(P(t)), and δ(L(t)) are adjusted.

7. The method for power consumption management of IoT devices based on big data according to claim 6, 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.

8. An IoT device power consumption management system based on big data, configured to implement the IoT device power consumption management method based on big data according to any one of claims 1 to 7, 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 assessment and modeling unit uses power consumption testing tools to test the power consumption of various devices under conditions that simulate actual workloads and record 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 in data centers to manage power consumption. The monitoring and reporting module is used to monitor the power consumption of the entire IoT system in real time and generate report output.

9. The big data-based IoT device power consumption management system according to claim 8, characterized in that: The monitoring and reporting module collects data from the device classification module, power consumption assessment and modeling unit, sleep and wake-up policy implementation unit and policy design and power consumption management module. The generated power consumption management report includes power consumption trends, device status, policy 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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