An efficient power management method and system for the internet of things

Abstract

The present application relates to the technical field of power management, in particular to a high-efficiency power management method and system for Internet of Things. The present application optimizes task scheduling and execution sequence by classifying multiple execution tasks into different task tables, dynamically judges power consumption mode according to real-time running parameters, ensures that the device can quickly respond to the changing working environment and power consumption demand, understands the current power consumption mode, reduces the execution of low-priority tasks without affecting key tasks, dynamically adjusts the working mode according to the task characteristics, effectively reduces unnecessary energy consumption, makes the Internet of Things device dynamically adjust according to the real-time working mode and task power consumption, adapts to different running environments and demand changes, improves the adaptability of the system, flexibly switches the power consumption mode according to the predicted power consumption demand, can provide sufficient resources in high power consumption and save energy in low power consumption, improves energy efficiency, thereby significantly improves the power management efficiency of the Internet of Things device.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a high-efficiency power management method and system for the Internet of Things. Background Art

[0002] The Internet of Things (IoT) refers to the technology and concept of connecting various physical devices and objects via the internet, enabling them to exchange data and communicate. These devices can include household appliances, industrial machinery, vehicles, environmental sensors, and more. The core idea of ​​the IoT is to interconnect these devices through a network to form an intelligent network, enabling automated management and data analysis, improving efficiency and convenience. The IoT spans multiple fields and generally performs a large number of tasks, thus imposing certain power consumption requirements. Existing approaches generally execute tasks sequentially based on the order in which they are input. However, this causes IoT devices to erratically switch between different power consumption modes, resulting in excessive power consumption. Summary of the Invention

[0003] The main purpose of the present invention is to provide a high-efficiency power management method for the Internet of Things, aiming to solve the technical problems in the prior art.

[0004] The present invention proposes a high-efficiency power management method for the Internet of Things, comprising:

[0005] Obtain historical operating parameters and real-time operating parameters of the IoT device, and determine a current power consumption mode of the IoT device based on the real-time operating parameters, wherein the current power consumption mode includes a high power consumption mode, a medium power consumption mode, and a low power consumption mode;

[0006] Obtaining predicted power consumption requirements of the IoT device based on the historical operating parameters and the real-time operating parameters;

[0007] Obtaining characteristic information of multiple execution tasks of the IoT device, and obtaining a priority allocation value corresponding to each execution task based on the characteristic information;

[0008] Classify and divide the multiple execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table;

[0009] The switching mode of the power consumption mode of the Internet of Things device is determined according to the predicted power consumption demand, the first task table, the second task table and the third task table, so as to manage the power supply of the Internet of Things device.

[0010] Preferably, the step of determining the current power consumption mode of the IoT device according to the real-time operating parameters includes:

[0011] Acquire a status indicator according to the real-time operating parameter, and detect status information of the IoT device according to the status indicator, wherein the status information includes an active state, a standby state, and a high power consumption state;

[0012] When it is detected that the IoT device is in an active state, obtaining battery voltage data in the real-time operating parameters, and determining whether the voltage signal of the IoT device is stable within a first preset time period based on the battery voltage data;

[0013] If the voltage signal of the IoT device is stable within the first preset time period, it is determined that the IoT device is in low power consumption mode at this time;

[0014] When it is detected that the IoT device is in a standby state, obtaining network traffic data in the real-time operating parameters, and determining whether the network activity of the IoT device is constant within a second preset time period based on the network traffic data;

[0015] If the network traffic of the IoT device is constant within the second preset time period, it is determined that the IoT device is in standby mode at this time;

[0016] When it is detected that the IoT device is in a high power consumption state, obtaining the CPU utilization rate in the real-time operating parameters, and determining whether the CPU utilization rate exceeds a preset threshold;

[0017] If the CPU utilization exceeds a preset threshold, it is determined that the IoT device is in a high power consumption mode.

[0018] Preferably, the step of obtaining the predicted power consumption requirement of the IoT device according to the historical operating parameters and the real-time operating parameters includes:

[0019] Acquire first environmental characteristic information and first operating performance characteristic information of the historical operating parameters;

[0020] A plurality of normalized first temperature data and a plurality of first humidity data are obtained when the IoT device is running within a third preset time period according to the first environmental characteristic information, and a corresponding first thermal humidity index is calculated according to each of the first temperature data and the first humidity data, wherein the calculation formula is:

[0021]

[0022] Among them, R(Z) i Indicates the first heat and humidity index of the i-th order, S(D) i represents the i-th first humidity data, a represents the weight of the first humidity data, W(D) irepresents the i-th first temperature data, b represents the weight of the first temperature data, and i represents the sequence number of the first humidity data, the first temperature data, and the first heat and humidity index;

[0023] A plurality of normalized CPU usage data, a plurality of network traffic data, and a plurality of battery power consumption data are obtained when the IoT device is operating within a third preset time period according to the first operating performance characteristic information, and a corresponding first resource utilization index is calculated according to each piece of CPU usage data, network traffic data, and battery power consumption data, wherein the calculation formula is:

[0024]

[0025] Among them, Z(L) n Indicates the nth first resource utilization index, C(S) n Indicates the nth CPU usage data, W(L) n Indicates the nth network traffic data, D(L) n represents the nth battery power consumption data, where n represents the sequence number of the CPU usage data, network traffic data, battery power consumption data, and the first resource utilization index;

[0026] Building a preset power consumption demand prediction model according to the plurality of first heat and humidity indices and the first resource utilization index;

[0027] Inputting each of the first heat and humidity index and the first resource utilization index as a training set into a preset power consumption demand prediction model for training, and using multiple other first heat and humidity indices and first resource utilization indices as validation sets for validation, to obtain multiple prediction training results;

[0028] Adjusting the model parameters of the preset power consumption demand prediction model according to the plurality of prediction training results to obtain the power consumption demand prediction model;

[0029] Acquiring a second operating characteristic of the real-time operating parameter, wherein the first operating characteristic includes second environmental characteristic information and second operating performance characteristic information;

[0030] Obtaining a second heat and humidity index based on the second environmental characteristic information, obtaining a second resource utilization index based on the second operating performance characteristic information, and inputting the second heat and humidity index and the second resource utilization index into a power consumption demand prediction model to obtain a power consumption demand prediction value, wherein the power consumption demand prediction model is: Y(F)=α*R(Z)²+β*Z(L)²;

[0031] Wherein, Y(F) represents the power consumption demand prediction value, α represents the first model parameter, R(Z)2 represents the second thermal humidity index, β represents the second model parameter, and Z(L)2 represents the second resource utilization index.

[0032] Preferably, the step of obtaining the priority allocation value of the corresponding execution task according to each piece of feature information includes:

[0033] Acquire an emergency feature and a priority feature according to the feature information;

[0034] Obtaining the task deadline, task impact scope, and abnormality degree according to the emergency characteristics;

[0035] The current time is obtained, and the urgency value is calculated based on the current time, task deadline, task impact range, and abnormality level. The calculation formula is:

[0036]

[0037] Among them, J(C) represents the urgency value, J(S) represents the task deadline, D(S) represents the current time, R(F) represents the task impact range, and Y(C) represents the abnormality degree;

[0038] Obtaining the degree of dependency, task impact frequency, user demand, and resource demand based on the priority characteristics;

[0039] The priority score is calculated based on the dependency level, task impact frequency, user demand level, and resource demand level, wherein the calculation formula is:

[0040]

[0041] Among them, Y(P) represents the priority score, Y(C) represents the degree of dependency, R(P) represents the frequency of task impact, Y(X) represents the degree of user demand, and Z(X) represents the degree of resource demand;

[0042] The priority allocation value of the corresponding execution task is calculated according to each of the urgency values ​​and the priority score, wherein the calculation formula is:

[0043]

[0044] Among them, Y(F) represents the priority allocation value, J(C) represents the urgency value, and Y(P) represents the priority score.

[0045] Preferably, the step of classifying and dividing the plurality of execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table includes:

[0046] Obtaining the standard power consumption of each task to be executed under multiple preset priority allocation values, and establishing a power consumption-allocation relationship curve according to the multiple standard power consumptions and the corresponding preset priority allocation values;

[0047] Obtaining the execution power consumption of the corresponding execution according to each of the priority allocation values ​​and the power consumption-allocation relationship curve;

[0048] Obtaining a power consumption threshold interval in the current power consumption mode, and determining whether each execution power consumption is within the power consumption threshold interval;

[0049] If the execution power consumption is within the power consumption threshold range, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in order of the execution power consumption;

[0050] If the execution power consumption is not within the power consumption threshold range, obtaining the power consumption value of the IoT device according to the current power consumption mode, and determining whether the power consumption value is greater than the middle value of the power consumption threshold range;

[0051] If the power consumption value is greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit of the power consumption threshold interval are divided into a second task table in order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit of the power consumption threshold interval are divided into a third task table in order of the execution power consumption;

[0052] If the power consumption value is not greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table according to the order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table according to the order of the execution power consumption.

[0053] Preferably, the step of determining a switching method of the power consumption mode of the IoT device according to the predicted power consumption requirement, the first task table, the second task table, and the third task table includes:

[0054] Obtaining a predicted power consumption value of the IoT device according to the predicted power consumption requirement;

[0055] When the current power consumption mode is the medium power consumption mode, selecting a to-be-executed task corresponding to the predicted power consumption value closest to the execution power consumption from the first task table as the immediately executed task;

[0056] Obtaining an operating power consumption value of the IoT device after completing the immediate execution task, and determining whether the operating power consumption value is within a power consumption threshold range;

[0057] If the running power consumption value is within the power consumption threshold range, selecting the task to be executed that is ranked one step behind the task to be executed immediately as the task to be prepared for execution;

[0058] If the operating power consumption value is greater than the upper limit of the power consumption threshold range, switching the current power consumption mode to a high power consumption mode;

[0059] If the operating power consumption value is less than the lower limit of the power consumption threshold range, the current power consumption mode is switched to the low power consumption mode.

[0060] This application also provides a high-efficiency power management system for the Internet of Things, including:

[0061] a first acquisition module, configured to acquire historical operating parameters and real-time operating parameters of the IoT device, and determine a current power consumption mode of the IoT device based on the real-time operating parameters, wherein the current power consumption mode includes a high power consumption mode, a medium power consumption mode, and a low power consumption mode;

[0062] A second acquisition module is used to obtain the predicted power consumption requirement of the Internet of Things device based on the historical operating parameters and the real-time operating parameters;

[0063] A third acquisition module is used to obtain characteristic information of multiple execution tasks of the IoT device, and obtain a priority allocation value corresponding to each execution task according to the characteristic information;

[0064] a division module, configured to classify and divide the plurality of execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table;

[0065] The determination module is used to determine the switching mode of the power consumption mode of the Internet of Things device according to the predicted power consumption demand, the first task table, the second task table and the third task table, so as to manage the power supply of the Internet of Things device.

[0066] Preferably, the division module includes:

[0067] The division module includes:

[0068] An establishing unit, configured to obtain a standard power consumption of each to-be-executed task under a plurality of preset priority allocation values, and to establish a power consumption-allocation relationship curve according to the plurality of standard power consumptions and the corresponding preset priority allocation values;

[0069] an acquiring unit, configured to acquire the execution power consumption of the corresponding execution according to each of the priority allocation values ​​and the power consumption-allocation relationship curve;

[0070] a judging unit, configured to obtain a power consumption threshold interval in a current power consumption mode, and judge whether each of the execution power consumptions is within the power consumption threshold interval;

[0071] If the execution power consumption is within the power consumption threshold range, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in order of the execution power consumption;

[0072] If the execution power consumption is not within the power consumption threshold range, obtaining the power consumption value of the IoT device according to the current power consumption mode, and determining whether the power consumption value is greater than the middle value of the power consumption threshold range;

[0073] If the power consumption value is greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit of the power consumption threshold interval are divided into a second task table in order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit of the power consumption threshold interval are divided into a third task table in order of the execution power consumption;

[0074] If the power consumption value is not greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table according to the order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table according to the order of the execution power consumption.

[0075] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned high-efficiency power management method for the Internet of Things when executing the computer program.

[0076] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned high-efficiency power management method for the Internet of Things.

[0077] The beneficial effects of the present invention are as follows: the present invention obtains the priority allocation value of the corresponding execution task through the characteristic information of multiple execution tasks of the Internet of Things device, so that the Internet of Things device can reasonably arrange the task execution order, ensure that important tasks are completed in priority under limited resources, thereby optimizing the resource utilization efficiency, and classify multiple execution tasks into different task tables, thereby optimizing task scheduling and execution order, and dynamically judging the power consumption mode according to real-time operating parameters to ensure that the device can quickly respond to changing working environments and power consumption requirements. By understanding the current power consumption mode, the execution of low-priority tasks can be reduced without affecting critical tasks. In this way, the working mode is dynamically adjusted according to the task characteristics, effectively reducing unnecessary energy consumption, so that the Internet of Things device can be dynamically adjusted according to the real-time working mode and task power consumption, adapt to different operating environments and demand changes, improve the adaptability of the system, and flexibly switch the power consumption mode according to the predicted power consumption requirements. It can provide sufficient resources when the power consumption is high, save energy consumption when the power consumption is low, and improve energy efficiency, thereby significantly improving the power management efficiency of the Internet of Things device. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 Schematic diagram of a method flow according to an embodiment of the present invention.

[0079] Figure 2 FIG. 1 is a schematic diagram of the device structure according to an embodiment of the present invention.

[0080] Figure 3 This is a schematic diagram of the internal structure of a computer device according to an embodiment of the present application.

[0081] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0082] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0083] like Figure 1-Figure 3 As shown, the present application provides a high-efficiency power management method for the Internet of Things, including:

[0084] S1. Obtain historical operating parameters and real-time operating parameters of the IoT device, and determine a current power consumption mode of the IoT device based on the real-time operating parameters, wherein the current power consumption mode includes a high power consumption mode, a medium power consumption mode, and a low power consumption mode;

[0085] S2. Obtaining predicted power consumption requirements of the IoT device based on the historical operating parameters and the real-time operating parameters;

[0086] S3. Obtain characteristic information of multiple execution tasks of the IoT device, and obtain a priority allocation value corresponding to each execution task based on the characteristic information;

[0087] S4. Classify and divide the multiple execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table;

[0088] S5. Determine a switching method for the power consumption mode of the IoT device according to the predicted power consumption demand, the first task table, the second task table, and the third task table to manage the power supply of the IoT device.

[0089] As described in steps S1-S5 above, the Internet of Things refers to the technology and concept of connecting various physical devices and objects through the Internet to enable them to exchange data and communicate. These devices can be household appliances, industrial machinery, vehicles, environmental sensors, etc. The core idea of ​​the Internet of Things is to interconnect these devices through the network to form an intelligent network, thereby realizing automated management and data analysis, and improving efficiency and convenience. The Internet of Things involves multiple fields and generally performs many tasks, so there are certain requirements for power consumption. The existing method generally executes tasks in sequence according to the input order. However, this will cause IoT devices to switch back and forth between several power consumption modes erratically, resulting in excessive power consumption. For example, in modern intelligent manufacturing, industrial robot arms are widely used in automated production lines, responsible for tasks such as assembly, handling, and welding. In actual operation, due to the randomness of the instructions received by the robot arm, it may frequently switch modes in a short period of time. For example, after completing a high-power handling, it receives a low-priority instruction and switches to standby mode. Each switch requires additional energy to stabilize the state and adjust the motor speed. Or when the robot arm receives three tasks, where Task 1, Task 2, and Task 3 are input in the order of first, second, and third, Task 1 has higher power consumption and needs to switch to high-power mode. Task 2 has lower power consumption and needs to switch from high-power mode to low-power mode at this time. Task 3 has higher power consumption and needs to switch from low-power mode to high-power mode. In this way, the robot arm will switch between high-power and low-power modes according to the input order, and tasks with the same power consumption cannot be processed together.The present invention obtains priority allocation values ​​of corresponding execution tasks through characteristic information of multiple execution tasks of an Internet of Things device. Through the priority allocation values, the Internet of Things device can reasonably arrange the task execution order, ensure that important tasks are completed first under limited resources, thereby optimizing resource utilization efficiency, and judge the current power consumption mode of the Internet of Things device through the real-time operation parameters of the Internet of Things device. Then, according to the current power consumption mode and each priority allocation value, multiple execution tasks are classified and divided to obtain a first task table, a second task table and a third task table, and multiple execution tasks are classified into different task tables, thereby optimizing task scheduling and execution order, dynamically judging the power consumption mode according to the real-time operation parameters, ensuring that the device can quickly respond to changing working environments and power consumption requirements, and improving the adaptability of the system. By understanding the current power consumption mode, the execution of low-priority tasks can be reduced without affecting key tasks, thereby saving energy consumption and extending the life of the device. Tasks are classified and managed to ensure that device resources are fully utilized when power consumption is high, and appropriately reduce power consumption when power consumption is low, thereby improving resource utilization efficiency. In this way, the working mode is dynamically adjusted according to the task characteristics, effectively reducing unnecessary energy consumption. For example, when the task demand is low, it switches to standby or sleep mode, thereby extending the battery life of the Internet of Things device. High-priority tasks are executed first to ensure that critical tasks can respond in a timely manner, improve the flexibility and real-time performance of the overall system, enable the Internet of Things devices to dynamically adjust according to the real-time working mode and task power consumption, adapt to different operating environments and demand changes, and improve the adaptability of the system. The present invention obtains the predicted power consumption requirements of the Internet of Things devices based on historical operating parameters and real-time operating parameters, wherein the predicted power consumption requirements refer to predicting the resource requirements at a specific time point or time period in the future by analyzing historical data and trends, and then determining the switching method of the power consumption mode of the Internet of Things device based on the predicted power consumption requirements, the first task table, the second task table and the third task table, so that the power management of the Internet of Things devices uses historical operating parameters and real-time operating parameters to predict power consumption requirements, ensuring that management decisions are more scientific and accurate, and flexibly switching power consumption modes according to predicted power consumption requirements. It can provide sufficient resources when power consumption is high, save energy when power consumption is low, and improve energy efficiency. Through priority management of the task table, it is ensured that high-priority tasks are executed in an appropriate power consumption mode in a timely manner, reducing delays, and switching to a low power consumption mode when full power consumption is not required, which helps to extend battery life and the overall service life of the device, thereby significantly improving the power management efficiency of the Internet of Things devices.

[0090] In one embodiment, the step S1 of determining the current power consumption mode of the IoT device according to the real-time operating parameters includes:

[0091] S11. Acquire a status indicator according to the real-time operating parameter, and detect status information of the IoT device according to the status indicator, wherein the status information includes an active state, a standby state, and a high power consumption state;

[0092] S12. When it is detected that the IoT device is in an active state, obtaining battery voltage data in the real-time operating parameters, and determining whether a voltage signal of the IoT device is stable within a first preset time period based on the battery voltage data;

[0093] If the voltage signal of the IoT device is stable within the first preset time period, it is determined that the IoT device is in low power consumption mode at this time;

[0094] S13. When it is detected that the IoT device is in a standby state, obtaining network traffic data in the real-time operating parameters, and determining whether the network activity of the IoT device is constant within a second preset time period based on the network traffic data;

[0095] If the network traffic of the IoT device is constant within the second preset time period, it is determined that the IoT device is in standby mode at this time;

[0096] S14. When it is detected that the IoT device is in a high power consumption state, obtaining a CPU utilization rate in the real-time operating parameters, and determining whether the CPU utilization rate exceeds a preset threshold value;

[0097] If the CPU utilization exceeds a preset threshold, it is determined that the IoT device is in a high power consumption mode.

[0098] As described in the above steps S11-S14, the present invention obtains status indicators through real-time operating parameters, and detects status information of the Internet of Things device based on the status indicators. When it is detected that the Internet of Things device is in an active state, it is determined whether the voltage signal of the Internet of Things device is stable within a first preset time period based on the battery voltage data. If it is stable, it is determined that the Internet of Things device is in low power mode at this time. When the Internet of Things device is in low power mode, its current output and activity are usually limited. The battery voltage may remain at a high level in this mode. Over time, a decrease in battery voltage can indicate that the device has entered a low power state, and the battery voltage provides direct power status feedback. The low power mode usually requires less current, thereby keeping the voltage relatively stable. Therefore, the status can be effectively monitored through voltage changes. In low power mode, a stable voltage signal can effectively reduce battery consumption and extend the device usage time. When it is detected that the Internet of Things device is in standby state, it is determined whether the network activity of the Internet of Things device is constant within a second preset time period based on the network traffic data. If In standby mode, the device usually reduces the frequency of network communication and the amount of data transmission, so the network traffic data will be significantly reduced or close to zero. The CPU utilization may also have certain activities in standby mode. Using network traffic data can help accurately determine whether the device is truly in standby mode, rather than misjudging it as active. In standby mode, activity is determined by network traffic, avoiding invalid resource consumption and optimizing network utilization efficiency. When the IoT device is detected to be in a high power consumption state, it is determined whether the CPU utilization exceeds the preset threshold. If it exceeds the preset threshold, it is determined that the IoT device is in high power consumption mode. In the high power consumption state, the CPU utilization is monitored to ensure that the device can provide the required performance while avoiding failures caused by overload. By real-time monitoring of voltage, network traffic and CPU utilization, the state of the IoT device can be accurately identified, the power management strategy is optimized, and the power consumption mode (low power, standby or high power) is automatically adjusted in different states to improve energy efficiency and reduce unnecessary energy consumption.

[0099] In one embodiment, the step S2 of obtaining the predicted power consumption requirement of the IoT device based on the historical operating parameters and the real-time operating parameters includes:

[0100] S21, obtaining first environmental characteristic information and first operating performance characteristic information of the historical operating parameters;

[0101] S22. Acquire multiple normalized first temperature data and multiple normalized first humidity data when the IoT device is running within a third preset time period based on the first environmental characteristic information, and calculate a corresponding first thermal humidity index based on each of the first temperature data and the first humidity data, wherein the calculation formula is:

[0102]

[0103] wherein, R(Z) i represents the i-th first thermal humidity index, S(D) i represents the i-th first humidity data, a represents the weight of the first humidity data, W(D) i represents the i-th first temperature data, b represents the weight of the first temperature data, and i represents the serial number of the first humidity data, the first temperature data and the first thermal humidity index;

[0104] S23, according to the first running performance characteristic information, obtaining a plurality of normalized CPU usage rate data, a plurality of network traffic data and a plurality of battery power consumption data of the Internet of Things device running in a third preset time period, and calculating a corresponding first resource utilization index according to each CPU usage rate data, network traffic data and battery power consumption data, wherein the calculation formula is:

[0105]

[0106] wherein, Z(L) n represents the n-th first resource utilization index, C(S) n represents the n-th CPU usage rate data, W(L) n represents the n-th network traffic data, D(L) n represents the n-th battery power consumption data, and n represents the serial number of the CPU usage rate data, the network traffic data, the battery power consumption data and the first resource utilization index;

[0107] S24, constructing a preset power consumption demand prediction model according to a plurality of first thermal humidity indexes and first resource utilization indexes;

[0108] S25, sequentially inputting each of the first thermal humidity index and the first resource utilization index as a training set into the preset power consumption demand prediction model for training, and inputting other first thermal humidity indexes and first resource utilization indexes as a verification set for verification, to obtain a plurality of prediction training results;

[0109] S26, adjusting the model parameters of the preset power consumption demand prediction model according to a plurality of prediction training results, to obtain a power consumption demand prediction model;

[0110] S27, obtaining a second running characteristic of real-time running parameters, wherein the first running characteristic includes second environmental characteristic information and second running performance characteristic information;

[0111] S28. Obtain a second heat and humidity index based on the second environmental characteristic information, obtain a second resource utilization index based on the second operating performance characteristic information, and input the second heat and humidity index and the second resource utilization index into a power consumption demand prediction model to obtain a power consumption demand prediction value, wherein the power consumption demand prediction model is: Y(F)=α*R(Z)²+β*Z(L)²;

[0112] Wherein, Y(F) represents the power consumption demand prediction value, α represents the first model parameter, R(Z)2 represents the second thermal humidity index, β represents the second model parameter, and z(L)2 represents the second resource utilization index.

[0113] As described in the above steps S21-S28, the present invention obtains multiple first temperature data and first humidity data of the Internet of Things device when it is running within a third preset time period through the first environmental characteristic information of the first operating characteristic of the historical operating parameters, and calculates the corresponding first thermal humidity index based on each first temperature data and first humidity data. In this way, by obtaining the first temperature and first humidity data in real time, it is helpful for the Internet of Things device to dynamically adjust the working mode according to environmental changes and optimize performance. The best power consumption mode of the Internet of Things device in a specific environment is judged by the thermal humidity index to ensure efficient operation and reduce energy consumption. At the same time, the working mode is adjusted according to the thermal humidity index to avoid equipment damage caused by adverse environmental conditions and extend the service life. The method obtains multiple CPU usage data, network traffic data and battery power consumption data of the Internet of Things device when it is running within a third preset time period, and calculates the corresponding first resource utilization index based on each CPU usage data, network traffic data and battery power consumption data. In this way, the power consumption mode is dynamically adjusted according to the resource utilization index to improve energy efficiency, reduce unnecessary energy consumption, ensure that high-priority tasks are executed in the best resource state, improve system response speed and efficiency, avoid over-discharge and extend the service life of the equipment by monitoring battery consumption in real time, and construct a preset power consumption demand prediction model based on multiple first thermal humidity indices and first resource utilization indices and train it to obtain multiple prediction training results. For example, when the first thermal humidity index There are 3 first resource utilization indexes, the first heat humidity index is 10, 20, 30, and 40 respectively, and the first resource utilization index is 2, 4, 6, and 8 respectively. The first heat humidity index of 10 and 20, and the first resource utilization index of 2 and 4 are input as training sets into the preset power consumption demand prediction model for training, and then the first heat humidity index of 30 and 40, and the first resource utilization index of 6 and 8 are used as validation sets for verification to obtain the training results. The loss is obtained according to the difference between the training results and the actual values, and then the model parameters can be adjusted according to the loss value. Then, the model parameters of the preset power consumption demand prediction model are adjusted according to multiple prediction training results to obtain the power consumption demand prediction model. The historical and real-time data are used to perform the power consumption demand prediction model. Based on the model training, it is possible to accurately predict future power consumption requirements, thereby effectively avoiding excessive resource allocation and reducing unnecessary energy consumption. The second thermal humidity index is obtained through the second environmental characteristic information of the real-time operating parameters, and the second resource utilization index is obtained according to the second operating performance characteristic information. The second thermal humidity index and the second resource utilization index are input into the power consumption demand prediction model to obtain a power consumption demand prediction value, wherein the first model parameter refers to the weight parameter of the thermal humidity index in the power consumption demand prediction model, and the second model parameter refers to the weight parameter of the resource utilization index in the power consumption demand prediction model. By combining real-time data and environmental characteristics, the accuracy of the power consumption demand prediction model is enhanced, and the working mode and resource configuration of the equipment are dynamically adjusted according to the predicted value.Improve energy efficiency and reduce energy consumption. Predicting power consumption needs helps to reasonably adjust the working mode, prevent equipment from being damaged due to overload, and thus extend its service life.

[0114] In one embodiment, the step S3 of obtaining the priority allocation value of the corresponding execution task according to each piece of feature information includes:

[0115] S31, acquiring an emergency feature and a priority feature according to the feature information;

[0116] S32. Obtaining the task deadline, task impact scope, and abnormality level based on the emergency characteristics;

[0117] S33. Obtain the current time, and calculate the urgency value based on the current time, task deadline, task impact range, and abnormality level. The calculation formula is:

[0118]

[0119] Among them, J(C) represents the urgency value, J(S) represents the task deadline, D(S) represents the current time, R(F) represents the task impact range, and Y(C) represents the abnormality degree;

[0120] S34. Obtaining the dependency degree, task impact frequency, user demand degree, and resource demand degree according to the priority characteristics;

[0121] S35. Calculate a priority score based on the dependency level, task impact frequency, user demand level, and resource demand level, wherein the calculation formula is:

[0122]

[0123] Among them, Y(P) represents the priority score, Y(C) represents the degree of dependency, R(P) represents the frequency of task impact, Y(X) represents the degree of user demand, and Z(X) represents the degree of resource demand;

[0124] S36. Calculate the priority allocation value of the corresponding execution task according to each of the urgency values ​​and the priority score, wherein the calculation formula is:

[0125]

[0126] Among them, Y(F) represents the priority allocation value, J(C) represents the urgency value, and Y(P) represents the priority score.

[0127] As described in steps S31-S36, the application obtains the task deadline, task impact range and abnormality degree of the emergency feature information, and calculates the emergency degree value according to the current time, task deadline, task impact range and abnormality degree, wherein the emergency feature refers to the time sensitivity or urgency of the task performed in the energy storage system or environment, and the task impact range refers to the breadth and depth of the impact of the task or activity on the energy storage system. The emergency degree value calculated can effectively identify and prioritize critical tasks, ensuring that important tasks are performed in a timely manner. In the presence of high-urgency tasks, the device can quickly switch to a high-power consumption mode to ensure timely completion of the task, otherwise it can reduce power consumption. By identifying the task impact range, resources can be allocated reasonably to ensure efficient use of resources. The priority score is calculated according to the dependency degree, task impact frequency, user demand degree and resource demand degree of the priority feature information. The priority score calculated can effectively identify high-priority tasks and ensure that critical tasks are prioritized. The priority score helps to allocate resources reasonably among different tasks, achieving efficient use of resources and avoiding waste. The system can quickly adjust the working mode according to the priority score to meet the performance requirements of high-demand tasks and improve response speed. The priority allocation value of each executed task is calculated according to the emergency degree value and the priority score. By combining the emergency degree and the priority score to calculate the priority allocation value, the importance of the task can be fully evaluated to ensure that critical tasks are executed first. Based on the priority allocation value, more energy can be allocated to high-priority tasks and the power consumption of low-priority tasks can be reduced, improving overall energy efficiency. Real-time calculation of the priority allocation value allows the device to quickly adjust the working mode to meet the needs of different tasks, improve response speed and reduce unnecessary energy consumption.

[0128] In one embodiment, the step S4 of classifying and dividing a plurality of executed tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table and a third task table comprises:

[0129] S41, obtaining the standard power consumption of each executed task under a plurality of preset priority allocation values, and establishing a power consumption-allocation relationship curve according to a plurality of standard power consumptions and corresponding preset priority allocation values;

[0130] S42, obtaining the execution power consumption of each executed task according to each priority allocation value and the power consumption-allocation relationship curve;

[0131] S43, obtaining the power consumption threshold interval under the current power consumption mode, and determining whether each execution power consumption is located within the power consumption threshold interval;

[0132] If the execution power consumption is within the power consumption threshold range, dividing the execution tasks corresponding to the execution power consumption into a first task table;

[0133] If the execution power consumption is not within the power consumption threshold range, obtaining the power consumption value of the IoT device according to the current power consumption mode, and determining whether the power consumption value is greater than the middle value of the power consumption threshold range;

[0134] If the power consumption value is greater than the middle value of the power consumption threshold interval, the execution tasks corresponding to the execution power consumption greater than the upper limit of the power consumption threshold interval are divided into the second task table, and the execution tasks corresponding to the execution power consumption less than the lower limit of the power consumption threshold interval are divided into the third task table;

[0135] If the power consumption value is not greater than the middle value of the power consumption threshold interval, the execution tasks corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table, and the execution tasks corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table.

[0136] As described in the above steps S41-S43, the present invention obtains the standard power consumption of each execution task under multiple preset priority allocation values, and establishes a power consumption-allocation relationship curve according to the multiple standard power consumptions and the corresponding preset priority allocation values. By obtaining the execution power consumption of the corresponding execution according to each priority allocation value and the power consumption-allocation relationship curve, the actual power consumption under each priority allocation value can be accurately predicted according to the power consumption-allocation relationship curve, thereby improving the accuracy of the prediction. The optimal power consumption strategy can be dynamically selected according to the real-time task requirements and the curve to improve the execution efficiency. By accurately matching the priority allocation value and power consumption of the task, the optimization of energy use is ensured, unnecessary energy consumption is reduced, and the execution power consumption is quickly obtained, which helps to speed up the response time of task execution, reduce unnecessary energy consumption, and improve the overall efficiency of the system. By judging whether each execution power consumption is within the power consumption threshold range, if it is within the power consumption threshold range, the execution is The execution tasks corresponding to the power consumption are divided into the first task table. Otherwise, by judging whether the power consumption value of the IoT device is greater than the middle value of the power consumption threshold interval, if it is greater than the middle value of the power consumption threshold interval, the execution tasks corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the second task table, and the execution tasks corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the third task table. Otherwise, the execution tasks corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table, and the execution tasks corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table. Tasks are divided into different task tables according to power consumption, which is convenient for clarifying priorities, thereby achieving more efficient task scheduling, being able to adjust task classification in real time, and flexibly switching working modes according to current power consumption conditions. By clearly distinguishing tasks in different power consumption ranges, excessive use and waste of resources can be avoided.

[0137] In one embodiment, the step S5 of determining the switching mode of the power consumption mode of the IoT device according to the predicted power consumption requirement, the first task table, the second task table, and the third task table includes:

[0138] S51. Obtain a predicted power consumption value of the Internet of Things device according to the predicted power consumption requirement;

[0139] S52: When the current power consumption mode is the medium power consumption mode, select from the first task table a to-be-executed task corresponding to a predicted power consumption value closest to the execution power consumption as an immediately-executed task;

[0140] S53, obtaining an operating power consumption value of the IoT device after completing the immediate execution task, and determining whether the operating power consumption value is within a power consumption threshold range;

[0141] If the running power consumption value is within the power consumption threshold range, selecting the task to be executed that is ranked one step behind the task to be executed immediately as the task to be prepared for execution;

[0142] If the operating power consumption value is greater than the upper limit of the power consumption threshold range, switching the current power consumption mode to a high power consumption mode;

[0143] If the operating power consumption value is less than the lower limit of the power consumption threshold range, the current power consumption mode is switched to the low power consumption mode.

[0144] As described in the above steps S51-S53, the present invention obtains the predicted power consumption value of the Internet of Things device by predicting the power consumption demand, and when the current power consumption mode is the medium power consumption mode, selects the to-be-executed task corresponding to the execution power consumption closest to the predicted power consumption value from the first task table as the immediately executed task, and then obtains the running power consumption value of the Internet of Things device after running the immediately executed task, and judges whether the running power consumption value is within the power consumption threshold interval. If the running power consumption value is within the power consumption threshold interval, the to-be-executed task ranked after the immediately executed task is selected as the ready-to-execute task. If the running power consumption value is greater than the upper limit value of the power consumption threshold interval, the current power consumption mode is switched to the high power consumption mode, otherwise the current power consumption mode is switched to the low power consumption mode. Similarly, when the current power consumption mode is the low power consumption mode, if the running power consumption value is greater than the upper limit value of the power consumption threshold interval of the low power consumption mode, the current power consumption mode is switched to the medium power consumption mode. If the Internet of Things device is If the running power consumption value is greater than the upper limit of the power consumption threshold interval, the current power consumption mode is switched to the high power consumption mode. Similarly, when the current power consumption mode is the high power consumption mode, if the running power consumption value is less than the lower limit of the power consumption threshold interval of the high power consumption mode, the current power consumption mode is switched to the medium power consumption mode. If the running power consumption value of the IoT device after completing the task to be executed is less than the lower limit of the power consumption threshold interval, the current power consumption mode is switched to the low power consumption mode. By selecting the task to be executed that is closest to the predicted power consumption value, it is possible to ensure that the executed task strikes a balance between power consumption and performance, improve efficiency, and automatically switch the power consumption mode according to the running power consumption value to ensure that the device operates in the optimal state under different power consumption conditions, avoiding unnecessary energy consumption. Flexible selection of execution tasks according to the power consumption mode ensures efficient use of resources and reduces waste. Through effective power consumption management, unnecessary energy consumption can be reduced, thereby ensuring that IoT devices achieve optimal energy allocation and resource utilization in a dynamic environment.

[0145] This application also provides a high-efficiency power management system for the Internet of Things, including:

[0146] a first acquisition module, configured to acquire historical operating parameters and real-time operating parameters of the IoT device, and determine a current power consumption mode of the IoT device based on the real-time operating parameters, wherein the current power consumption mode includes a high power consumption mode, a medium power consumption mode, and a low power consumption mode;

[0147] A second acquisition module is used to obtain the predicted power consumption requirement of the Internet of Things device based on the historical operating parameters and the real-time operating parameters;

[0148] A third acquisition module is used to obtain characteristic information of multiple execution tasks of the IoT device, and obtain a priority allocation value corresponding to each execution task according to the characteristic information;

[0149] a division module, configured to classify and divide the plurality of execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table;

[0150] The determination module is used to determine the switching mode of the power consumption mode of the Internet of Things device according to the predicted power consumption demand, the first task table, the second task table and the third task table, so as to manage the power supply of the Internet of Things device.

[0151] In one embodiment, the partitioning module includes:

[0152] An establishing unit, configured to obtain a standard power consumption of each to-be-executed task under a plurality of preset priority allocation values, and to establish a power consumption-allocation relationship curve according to the plurality of standard power consumptions and the corresponding preset priority allocation values;

[0153] an acquiring unit, configured to acquire the execution power consumption of the corresponding execution according to each of the priority allocation values ​​and the power consumption-allocation relationship curve;

[0154] a judging unit, configured to obtain a power consumption threshold interval in a current power consumption mode, and judge whether each of the execution power consumptions is within the power consumption threshold interval;

[0155] If the execution power consumption is within the power consumption threshold range, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in order of the execution power consumption;

[0156] If the execution power consumption is not within the power consumption threshold range, obtaining the power consumption value of the IoT device according to the current power consumption mode, and determining whether the power consumption value is greater than the middle value of the power consumption threshold range;

[0157] If the power consumption value is greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit of the power consumption threshold interval are divided into a second task table in order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit of the power consumption threshold interval are divided into a third task table in order of the execution power consumption;

[0158] If the power consumption value is not greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table according to the order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table according to the order of the execution power consumption.

[0159] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned high-efficiency power management method for the Internet of Things when executing the computer program.

[0160] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-mentioned high-efficiency power management method for the Internet of Things.

[0161] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0162] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0163] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A high-efficiency power management method for the Internet of Things, characterized in that: include: Obtain historical operating parameters and real-time operating parameters of the IoT device, and determine a current power consumption mode of the IoT device based on the real-time operating parameters, wherein the current power consumption mode includes a high power consumption mode, a medium power consumption mode, and a low power consumption mode; Obtaining predicted power consumption requirements of the IoT device based on the historical operating parameters and the real-time operating parameters; Obtain characteristic information of multiple execution tasks of the IoT device, and obtain emergency characteristics and priority characteristics based on the characteristic information; Obtaining the task deadline, task impact scope, and abnormality degree according to the emergency characteristics; The current time is obtained, and the urgency value is calculated based on the current time, task deadline, task impact range, and abnormality level. The calculation formula is: \mathrm{J}\left ( {\mathrm{C}} \right )=\frac {1} {\mathrm{J}(\mathrm{S})-\mathrm{D}(\mathrm{S})}+\left [ {\mathrm{R}\left ( {\mathrm{F}} \right )*{\mathrm{e}}^{-\mathrm{Y}(\mathrm{C})}} \right ] ; Among them, J(C) represents the urgency value, J(S) represents the task deadline, D(S) represents the current time, R(F) represents the task impact range, and Y(C) represents the abnormality degree; Obtaining the degree of dependency, task impact frequency, user demand, and resource demand based on the priority characteristics; The priority score is calculated based on the dependency level, task impact frequency, user demand level, and resource demand level, wherein the calculation formula is: \mathrm{Y}(\mathrm{P})=\frac {\mathrm{Y}\left ( {\mathrm{C}} \right )*\left [ {\mathrm{R}\left ( {\mathrm{P}} \right )+\mathrm{Y}\left ( {\mathrm{X}} \right )} \right ]} {\mathrm{Z}(\mathrm{X})+1} ; Among them, Y(P) represents the priority score, Y(C) represents the degree of dependency, R(P) represents the frequency of task impact, Y(X) represents the degree of user demand, and Z(X) represents the degree of resource demand; The priority allocation value of the corresponding execution task is calculated according to each of the urgency values ​​and the priority score, wherein the calculation formula is: ； Among them, Y(F) represents the priority allocation value, J(C) represents the urgency value, and Y(P) represents the priority score; Classify and divide the plurality of execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table; The switching mode of the power consumption mode of the Internet of Things device is determined according to the predicted power consumption demand, the first task table, the second task table and the third task table, so as to manage the power supply of the Internet of Things device.

2. The high-efficiency power management method for the Internet of Things according to claim 1, characterized in that: The step of determining the current power consumption mode of the IoT device according to the real-time operating parameters includes: Acquire a status indicator according to the real-time operating parameter, and detect status information of the IoT device according to the status indicator, wherein the status information includes an active state, a standby state, and a high power consumption state; When it is detected that the IoT device is in an active state, obtaining battery voltage data in the real-time operating parameters, and determining whether the voltage signal of the IoT device is stable within a first preset time period based on the battery voltage data; If the voltage signal of the IoT device is stable within the first preset time period, it is determined that the IoT device is in low power consumption mode at this time; When it is detected that the IoT device is in a standby state, obtaining network traffic data in the real-time operating parameters, and determining whether the network activity of the IoT device is constant within a second preset time period based on the network traffic data; If the network traffic of the IoT device is constant within the second preset time period, it is determined that the IoT device is in standby mode at this time; When it is detected that the IoT device is in a high power consumption state, obtaining the CPU utilization rate in the real-time operating parameters, and determining whether the CPU utilization rate exceeds a preset threshold; If the CPU utilization exceeds a preset threshold, it is determined that the IoT device is in a high power consumption mode.

3. The high-efficiency power management method for the Internet of Things according to claim 1, characterized in that: The step of classifying and dividing the plurality of execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table includes: Obtaining the standard power consumption of each task to be executed under multiple preset priority allocation values, and establishing a power consumption-allocation relationship curve according to the multiple standard power consumptions and the corresponding preset priority allocation values; Obtaining the execution power consumption of the corresponding execution according to each of the priority allocation values ​​and the power consumption-allocation relationship curve; Obtaining a power consumption threshold interval in the current power consumption mode, and determining whether each execution power consumption is within the power consumption threshold interval; If the execution power consumption is within the power consumption threshold range, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in order of the execution power consumption; If the execution power consumption is not within the power consumption threshold range, obtaining the power consumption value of the IoT device according to the current power consumption mode, and determining whether the power consumption value is greater than the middle value of the power consumption threshold range; If the power consumption value is greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit of the power consumption threshold interval are divided into a second task table in order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit of the power consumption threshold interval are divided into a third task table in order of the execution power consumption; If the power consumption value is not greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table according to the order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table according to the order of the execution power consumption.

4. The high-efficiency power management method for the Internet of Things according to claim 1, characterized in that: The step of determining a switching method of the power consumption mode of the IoT device according to the predicted power consumption requirement, the first task table, the second task table, and the third task table includes: Obtaining a predicted power consumption value of the IoT device according to the predicted power consumption requirement; When the current power consumption mode is the medium power consumption mode, selecting a to-be-executed task corresponding to the predicted power consumption value closest to the execution power consumption from the first task table as the immediately executed task; Obtaining an operating power consumption value of the IoT device after completing the immediate execution task, and determining whether the operating power consumption value is within a power consumption threshold range; If the running power consumption value is within the power consumption threshold range, selecting the task to be executed that is ranked one step behind the task to be executed immediately as the task to be prepared for execution; If the operating power consumption value is greater than the upper limit of the power consumption threshold range, switching the current power consumption mode to a high power consumption mode; If the operating power consumption value is less than the lower limit of the power consumption threshold range, the current power consumption mode is switched to the low power consumption mode.

5. A high-efficiency power management system for the Internet of Things, characterized in that: include: a first acquisition module, configured to acquire historical operating parameters and real-time operating parameters of the IoT device, and determine a current power consumption mode of the IoT device based on the real-time operating parameters, wherein the current power consumption mode includes a high power consumption mode, a medium power consumption mode, and a low power consumption mode; A second acquisition module is used to obtain the predicted power consumption requirement of the Internet of Things device based on the historical operating parameters and the real-time operating parameters; A third acquisition module is used to obtain feature information of multiple execution tasks of the IoT device, and obtain an emergency feature and a priority feature based on the feature information; Obtaining the task deadline, task impact scope, and abnormality degree according to the emergency characteristics; The current time is obtained, and the urgency value is calculated based on the current time, task deadline, task impact range, and abnormality level. The calculation formula is: \mathrm{J}\left ( {\mathrm{C}} \right )=\frac {1} {\mathrm{J}(\mathrm{S})-\mathrm{D}(\mathrm{S})}+\left [ {\mathrm{R}\left ( {\mathrm{F}} \right )*{\mathrm{e}}^{-\mathrm{Y}(\mathrm{C})}} \right ] ; Among them, J(C) represents the urgency value, J(S) represents the task deadline, D(S) represents the current time, R(F) represents the task impact range, and Y(C) represents the abnormality degree; Obtaining the degree of dependency, task impact frequency, user demand, and resource demand based on the priority characteristics; The priority score is calculated based on the dependency level, task impact frequency, user demand level, and resource demand level, wherein the calculation formula is: \mathrm{Y}(\mathrm{P})=\frac {\mathrm{Y}\left ( {\mathrm{C}} \right )*\left [ {\mathrm{R}\left ( {\mathrm{P}} \right )+\mathrm{Y}\left ( {\mathrm{X}} \right )} \right ]} {\mathrm{Z}(\mathrm{X})+1} ; Among them, Y(P) represents the priority score, Y(C) represents the degree of dependency, R(P) represents the frequency of task impact, Y(X) represents the degree of user demand, and Z(X) represents the degree of resource demand; The priority allocation value of the corresponding execution task is calculated according to each of the urgency values ​​and the priority score, wherein the calculation formula is: ； Among them, Y(F) represents the priority allocation value, J(C) represents the urgency value, and Y(P) represents the priority score; a division module, configured to classify and divide the plurality of execution tasks according to the current power consumption mode and each priority allocation value to obtain a first task table, a second task table, and a third task table; The determination module is used to determine the switching mode of the power consumption mode of the Internet of Things device according to the predicted power consumption demand, the first task table, the second task table and the third task table, so as to manage the power supply of the Internet of Things device.

6. The high-efficiency power management system for the Internet of Things according to claim 5, characterized in that: The division module includes: An establishing unit, configured to obtain a standard power consumption of each to-be-executed task under a plurality of preset priority allocation values, and to establish a power consumption-allocation relationship curve according to the plurality of standard power consumptions and the corresponding preset priority allocation values; an acquiring unit, configured to acquire the execution power consumption of the corresponding execution according to each of the priority allocation values ​​and the power consumption-allocation relationship curve; a judging unit, configured to obtain a power consumption threshold interval in a current power consumption mode, and judge whether each of the execution power consumptions is within the power consumption threshold interval; If the execution power consumption is within the power consumption threshold range, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in order of the execution power consumption; If the execution power consumption is not within the power consumption threshold range, obtaining the power consumption value of the IoT device according to the current power consumption mode, and determining whether the power consumption value is greater than the middle value of the power consumption threshold range; If the power consumption value is greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit of the power consumption threshold interval are divided into a second task table in order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit of the power consumption threshold interval are divided into a third task table in order of the execution power consumption; If the power consumption value is not greater than the middle value of the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption greater than the upper limit value of the power consumption threshold interval are divided into the third task table according to the order of the execution power consumption, and the tasks to be executed corresponding to the execution power consumption less than the lower limit value of the power consumption threshold interval are divided into the second task table according to the order of the execution power consumption.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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