High-efficiency power management method and system for Internet of Things

Abstract

The invention relates to the technical field of power management, in particular to a high-efficiency power management method and system for the Internet of Things. The task scheduling and execution sequence is optimized by classifying a plurality of execution tasks into different task tables, the power consumption mode is dynamically judged according to the real-time operation parameters, it is ensured that the device can quickly respond to the changing working environment and power consumption requirements, and by knowing the current power consumption mode, the operation efficiency of the device can be improved under the condition that key tasks are not affected. Therefore, the working mode is dynamically adjusted according to the task characteristics, unnecessary energy consumption is effectively reduced, the Internet of Things equipment can dynamically adjust according to the real-time working mode and the task power consumption and adapt to different operating environments and demand changes, the adaptability of the system is improved, and the reliability of the system is improved. The power consumption modes are flexibly switched according to the predicted power consumption demand, enough resources can be provided in high power consumption, energy consumption is saved in low power consumption, energy efficiency is improved, and therefore the power management efficiency of the Internet of Things equipment can be remarkably improved.

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 refers to the technology and concept of connecting various physical devices and objects through the Internet so that they can 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, improving efficiency and convenience. The Internet of Things involves multiple fields and generally performs more tasks, so there are certain requirements for power consumption. The existing method is generally to execute tasks in sequence according to the order of input, but this will cause the Internet of Things devices to switch back and forth between several power consumption modes without rules, 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] Acquire historical operating parameters and real-time operating parameters of the IoT device, and determine the current power consumption mode of the IoT device according to 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] Acquire characteristic information of multiple execution tasks of the IoT device, and acquire a priority allocation value of the corresponding execution task according to each of 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 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 comprises:

[0011] Acquire a state indicator according to the real-time operation parameter, and detect state information of the IoT device according to the state indicator, wherein the state 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 judging whether the voltage signal of the IoT device is stable within a first preset time period according to 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 a 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 operation parameters, and determining whether the network activity of the IoT device is constant within a second preset time period according to the network traffic data;

[0015] If the network traffic of the IoT device is constant during 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, the CPU utilization in the real-time operation parameter is obtained, and it is determined whether the CPU utilization 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 comprises:

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

[0020] According to the first environmental characteristic information, a plurality of normalized first temperature data and a plurality of first humidity data are obtained when the Internet of Things device is running in a third preset time period, 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 represents the first heat and humidity index of the ith element, 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] According to the first operation performance characteristic information, a plurality of normalized CPU usage data, a plurality of network traffic data, and a plurality of battery power consumption data when the IoT device is running in a third preset time period are obtained, and a corresponding first resource utilization index is calculated according to each 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, the network traffic data, the battery power consumption data and the first resource utilization index;

[0026] Constructing a preset power consumption demand prediction model according to a plurality of first thermal humidity indexes and a first resource utilization index;

[0027] Input each of the first thermal humidity index and the first resource utilization index as a training set into a preset power consumption demand prediction model for training, and use other multiple first thermal humidity indexes and first resource utilization indexes as verification sets for verification, 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] Acquire a second operation characteristic of the real-time operation parameter, wherein the first operation characteristic includes second environment characteristic information and second operation performance characteristic information;

[0030] Acquire a second heat and humidity index according to the second environment characteristic information, acquire a second resource utilization index according to the second operation 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;

[0031] Among them, 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 of the feature information comprises:

[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 according to the current time, the task deadline, the task impact range and the abnormality degree, wherein 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, the frequency of task impact, the degree of user demand, and the degree of resource demand according to the priority characteristics;

[0039] The priority score is calculated according to the dependency degree, task impact frequency, user demand degree and resource demand degree, 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 comprises:

[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] Acquire 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 of the execution power consumptions is within the power consumption threshold interval;

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

[0050] If the execution power consumption is not within the power consumption threshold interval, 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 interval;

[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 value of the power consumption threshold interval are divided into a second task table in 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 a third task table in the 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 mode of a power consumption mode of an IoT device according to the predicted power consumption requirement, the first task table, the second task table, and the third task table comprises:

[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 execution power consumption that is closest to the predicted power consumption value from the first task table as the immediately executed task;

[0056] Obtaining an operating power consumption value of the IoT device after the immediate execution task is completed, 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 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 interval, 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 interval, the current power consumption mode is switched to the low power consumption mode.

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

[0061] A first acquisition module is used to acquire historical operating parameters and real-time operating parameters of the IoT device, and determine the current power consumption mode of the IoT device according to 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 acquire the predicted power consumption requirement of the Internet of Things device according to the historical operating parameters and the real-time operating parameters;

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

[0064] A division module, used for classifying and dividing a 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 comprises:

[0067] The division module comprises:

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

[0069] An acquisition 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 consumption is within the power consumption threshold interval;

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

[0072] If the execution power consumption is not within the power consumption threshold interval, 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 interval;

[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 value of the power consumption threshold interval are divided into a second task table in 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 a third task table in the 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 on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned high-efficiency power management method for the Internet of Things are implemented.

[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 first under limited resources, thereby optimizing the resource utilization efficiency, and classify multiple execution tasks into different task tables, optimize the task scheduling and execution order, dynamically judge the power consumption mode according to the real-time operation parameters, and ensure that the device can quickly respond to the changing working environment and power consumption requirements. By understanding the current power consumption mode, the execution of low-priority tasks can be reduced without affecting key tasks. In this way, the working mode is dynamically adjusted according to the task characteristics, and unnecessary energy consumption is effectively reduced, 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 The figure is a schematic diagram of a method flow according to an embodiment of the present invention.

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

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

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

[0082] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used 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 the current power consumption mode of the IoT device according to 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, obtaining characteristic information of multiple execution tasks of the IoT device, and obtaining a priority allocation value of the corresponding execution task according to each of the characteristic information;

[0087] S4, classifying and dividing 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 of 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 the above steps S1-S5, the Internet of Things refers to the technology and concept of connecting various physical devices and objects through the Internet so that they can 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 more tasks, so there are certain requirements for power consumption. The existing method is generally to execute the tasks in sequence according to the input order, but this will cause the Internet of Things devices to switch back and forth irregularly in several power consumption modes, 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 robot arm receiving instructions, it may switch modes frequently 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, among which Task 1, Task 2, and Task 3 are input in the order of first, second, and third, Task 1 has a higher power consumption and needs to be switched to a high-power mode. Task 2 has a lower power consumption, and at this time it needs to switch from a high-power mode to a low-power mode. Task 3 has a higher power consumption and needs to switch from a low-power mode to a high-power mode. In this way, the robot arm will switch between high-power and low-power modes in accordance with the input order, and tasks with the same power consumption cannot be processed together.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. Through the priority allocation value, the Internet of Things device can reasonably arrange the task execution order, ensure that important tasks are completed first under limited resources, thereby optimizing the 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, and then classify and divide the multiple execution tasks according to the current power consumption mode and each priority allocation value to obtain the first task table, the second task table and the third task table, classify the multiple execution tasks into different task tables, optimize the task scheduling and execution order, dynamically judge the power consumption mode according to the real-time operation parameters, ensure that the device can quickly respond to the changing working environment and power consumption requirements, and improve 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. The tasks are classified and managed to ensure that the device resources are fully utilized when the power consumption is high, and the power consumption is appropriately reduced when the power consumption is low, thereby improving the resource utilization efficiency. In this way, the working mode is dynamically adjusted according to the task characteristics, and unnecessary energy consumption is effectively reduced. For example, when the task demand is low, it switches to the standby or sleep mode to extend the battery life of the Internet of Things device. High-priority tasks are executed first to ensure that key tasks can respond in time, improve the flexibility and real-time performance of the overall system, and 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 according to 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, ensure that management decisions are more scientific and accurate, and flexibly switch power consumption modes according to predicted power consumption requirements, so as to provide sufficient resources when power consumption is high, save energy when power consumption is low, and improve energy efficiency. Through the priority management of the task table, it is ensured that high-priority tasks are executed in time in appropriate power consumption modes, reduce delays, and switch to low power consumption modes 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, acquiring a state indicator according to the real-time operation parameter, and detecting state information of the IoT device according to the state indicator, wherein the state 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 according to 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 a 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 operation parameters, and determining whether the network activity of the IoT device is constant within a second preset time period according to the network traffic data;

[0095] If the network traffic of the IoT device is constant during 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, obtain the CPU utilization in the real-time operation parameter, and determine whether the CPU utilization exceeds a preset threshold;

[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 consumption mode at this time. When the Internet of Things device is in low power consumption mode, its current output and activity are usually limited. The battery voltage may remain at a high level in this mode. As time goes by, the reduction in battery voltage can indicate that the device has entered a low power consumption state, and the battery voltage provides direct power status feedback. The low power consumption mode usually requires less current to keep the voltage relatively stable. Therefore, the state can be effectively monitored through voltage changes. In low power consumption 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 mode, 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 an active state. In standby mode, activity is determined by network traffic to avoid invalid resource consumption and optimize network usage 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 a 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, accurate identification of the IoT device state is ensured, power management strategies are optimized, and power consumption modes (low power consumption, standby or high power consumption) are automatically adjusted in different states to improve energy efficiency and reduce unnecessary energy consumption.

[0099] In one embodiment, the step S2 of acquiring the predicted power consumption requirement of the IoT device according to 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 a plurality of normalized first temperature data and a plurality of first humidity data when the IoT device is running within a third preset time period according to the first environmental characteristic information, and calculate a corresponding first thermal humidity index according to each of the first temperature data and the first humidity data, wherein the calculation formula is:

[0102]

[0103] Among them, R(Z) i represents the first heat and humidity index of the ith element, 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 sequence number of the first humidity data, the first temperature data and the first heat and humidity index;

[0104] S23. Acquire multiple normalized CPU usage data, multiple network traffic data, and multiple battery power consumption data when the IoT device is running in a third preset time period according to the first operation performance characteristic information, and calculate the corresponding first resource utilization index according to each CPU usage data, network traffic data, and battery power consumption data, wherein the calculation formula is:

[0105]

[0106] 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, 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 the plurality of first thermal humidity indexes and the first resource utilization index;

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

[0109] S26, 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;

[0110] S27, acquiring a second operation characteristic of the real-time operation parameter, wherein the first operation characteristic includes second environment characteristic information and second operation performance characteristic information;

[0111] S28, obtaining a second heat and humidity index according to the second environment characteristic information, obtaining a second resource utilization index according to the second operation 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;

[0112] Among them, 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 in 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 according to 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 determined 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 a plurality of CPU usage data, network traffic data, and battery power consumption data when the IoT device is running within a third preset time period, and calculates a corresponding first resource utilization index according to each CPU usage data, network traffic data, and battery power consumption data, so that the power consumption mode is dynamically adjusted according to the resource utilization index, energy efficiency is improved, unnecessary energy consumption is reduced, and high-priority tasks are ensured to be executed under the optimal resource state, thereby improving the system response speed and efficiency. By real-time monitoring of battery consumption, over-discharge is avoided and the service life of the device is extended. A preset power consumption demand prediction model is constructed according to the plurality of first thermal humidity indices and the first resource utilization indices and trained to obtain a plurality of prediction training results, wherein, for example, when the first thermal humidity index There are three first resource utilization indexes, the first heat humidity indexes are 10, 20, 30, and 40, and the first resource utilization indexes are 2, 4, 6, and 8, respectively. The first heat humidity indexes of 10 and 20 and the first resource utilization indexes 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 indexes of 30 and 40 and the first resource utilization indexes of 6 and 8 are used as validation sets for validation to obtain the training results, and 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, and then the model parameters of the preset power consumption demand prediction model are adjusted according to the multiple prediction training results to obtain the power consumption demand prediction model, and the historical and real-time data are used to obtain the power consumption demand prediction model. According to the model training, it can accurately predict future power consumption requirements, thereby effectively avoiding excessive resource configuration 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 the 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 and prevent equipment from being damaged due to overload, thereby extending its service life.

[0114] In one embodiment, the step S3 of acquiring the priority allocation value of the corresponding execution task according to each of the 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 degree according to the emergency characteristics;

[0117] S33, obtaining the current time, and calculating the urgency value according to the current time, the task deadline, the task impact range and the abnormality degree, wherein 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 the priority score according to the dependency degree, task impact frequency, user demand degree and resource demand degree, 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 the above steps S31-S36, the present invention obtains the task deadline, task impact range and abnormality through the emergency characteristics of the feature information, and calculates the urgency value according to the current time, task deadline, task impact range and abnormality, wherein the emergency feature refers to the time sensitivity feature or urgency feature of executing the task in the energy storage system or environment, and the task impact range refers to the breadth and depth of the impact of the execution of the task or activity on the energy storage system. The urgency value obtained by calculation can effectively identify and prioritize key tasks, thereby ensuring that important tasks are executed in a timely manner. When a high-urgency task occurs, the device can quickly switch to a high-power consumption mode to ensure the timely completion of the task, otherwise it can reduce power consumption. By identifying the task impact range, resources can be reasonably allocated to ensure efficient use of resources. The degree of dependency, task impact frequency, user demand and resource demand are obtained through the priority characteristics of the feature information, and based on the dependency The priority score is calculated based on the degree of relationship, task impact frequency, user demand and resource demand. The calculated priority score can effectively identify high-priority tasks and ensure that critical tasks are given priority. The priority score helps to reasonably allocate resources among different tasks, achieve efficient use of resources and avoid 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 the corresponding execution task is calculated based on each urgency value and priority score. By combining the urgency and priority scores 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, the power consumption of low-priority tasks can be reduced, and the overall energy efficiency can be improved. 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 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:

[0129] S41, obtaining the standard power consumption of each execution task 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;

[0130] S42, acquiring the execution power consumption of the corresponding execution according to each of the priority allocation values ​​and the power consumption-allocation relationship curve;

[0131] S43, 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;

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

[0133] If the execution power consumption is not within the power consumption threshold interval, 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 interval;

[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 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;

[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, and obtains the execution power consumption of the corresponding execution according to each priority allocation value and the power consumption-allocation relationship curve. According to the power consumption-allocation relationship curve, the actual power consumption under each priority allocation value can be accurately predicted, 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 interval, if it is within the power consumption threshold interval, the execution The execution tasks corresponding to the power consumption are divided into the first task table. Otherwise, it is determined whether the power consumption value of the Internet of Things 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 demand, the first task table, the second task table, and the third task table includes:

[0138] S51, obtaining a predicted power consumption value of an 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, selecting from the first task table a to-be-executed task corresponding to the predicted power consumption value closest to the execution power consumption as an immediately executed task;

[0140] S53, obtaining the running power consumption value of the IoT device after running the immediate execution task, and determining whether the running power consumption value is within the 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 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 interval, 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 interval, 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 running 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, automatically switch the power consumption mode according to the running power consumption value, ensure that the device runs in the optimal state under different power consumption conditions, avoid unnecessary energy consumption, flexibly select the execution task according to the power consumption mode, ensure efficient use of resources, reduce waste, and reduce unnecessary energy consumption through effective power consumption management, thereby ensuring that the IoT device can achieve optimal energy allocation and resource utilization in a dynamic environment.

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

[0146] A first acquisition module is used to acquire historical operating parameters and real-time operating parameters of the IoT device, and determine the current power consumption mode of the IoT device according to 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 acquire the predicted power consumption requirement of the Internet of Things device according to the historical operating parameters and the real-time operating parameters;

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

[0149] A division module, used for classifying and dividing a 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 comprises:

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

[0153] An acquisition 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 consumption is within the power consumption threshold interval;

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

[0156] If the execution power consumption is not within the power consumption threshold interval, 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 interval;

[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 value of the power consumption threshold interval are divided into a second task table in 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 a third task table in the 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 on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned high-efficiency power management method for the Internet of Things are implemented.

[0161] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related 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 a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0162] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including 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 specification 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: Acquire historical operating parameters and real-time operating parameters of the IoT device, and determine the current power consumption mode of the IoT device according to 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; Acquire characteristic information of multiple execution tasks of the IoT device, and acquire a priority allocation value of the corresponding execution task according to each of the characteristic information; 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; 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 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 comprises: Acquire a state indicator according to the real-time operation parameter, and detect state information of the IoT device according to the state indicator, wherein the state 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 judging whether the voltage signal of the IoT device is stable within a first preset time period according to 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 a 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 operation parameters, and determining whether the network activity of the IoT device is constant within a second preset time period according to the network traffic data; If the network traffic of the IoT device is constant during 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, the CPU utilization in the real-time operation parameter is obtained, and it is determined whether the CPU utilization 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 obtaining the predicted power consumption requirement of the IoT device according to the historical operating parameters and the real-time operating parameters includes: Acquire first environmental characteristic information and first operating performance characteristic information of the historical operating parameters; According to the first environmental characteristic information, a plurality of normalized first temperature data and a plurality of first humidity data are obtained when the Internet of Things device is running in a third preset time period, 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: Among them, R(Z) i represents the first heat and humidity index of the ith element, 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 sequence number of the first humidity data, the first temperature data and the first heat and humidity index; According to the first operation performance characteristic information, a plurality of normalized CPU usage data, a plurality of network traffic data, and a plurality of battery power consumption data when the IoT device is running in a third preset time period are obtained, and a corresponding first resource utilization index is calculated according to each CPU usage data, network traffic data, and battery power consumption data, wherein the calculation formula is: Among them, Z(L) n Indicates the nth first resource utilization index, C(S) n Indicates the nth CPU usage data, W(L) n Represents 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, the network traffic data, the battery power consumption data and the first resource utilization index; Constructing a preset power consumption demand prediction model according to a plurality of first thermal humidity indexes and a first resource utilization index; sequentially 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 outputting a plurality of prediction training results; 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; Acquire a second operation characteristic of the real-time operation parameter, wherein the first operation characteristic includes second environment characteristic information and second operation performance characteristic information; Acquire a second heat and humidity index according to the second environment characteristic information, acquire a second resource utilization index according to the second operation 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; Among them, 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.

4. The high-efficiency power management method for the Internet of Things according to claim 1, characterized in that: The step of obtaining the priority allocation value of the corresponding execution task according to each of the feature information includes: Acquire an emergency feature and a priority feature according to 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 according to the current time, the task deadline, the task impact range and the abnormality degree, wherein the calculation formula is: 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, the frequency of task impact, the degree of user demand, and the degree of resource demand according to the priority characteristics; The priority score is calculated according to the dependency degree, task impact frequency, user demand degree and resource demand degree, wherein the calculation formula is: 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.

5. 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; Acquire 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 of the execution power consumptions is within the power consumption threshold interval; If the execution power consumption is within the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in the order of the execution power consumption; If the execution power consumption is not within the power consumption threshold interval, 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 interval; 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 value of the power consumption threshold interval are divided into a second task table in 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 a third task table in the 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.

6. 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 demand, 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 execution power consumption that is closest to the predicted power consumption value from the first task table as the immediately executed task; Obtaining an operating power consumption value of the IoT device after the immediate execution task is completed, 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 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 interval, 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 interval, the current power consumption mode is switched to the low power consumption mode.

7. A high-efficiency power management system for the Internet of Things, characterized in that: include: A first acquisition module is used to acquire historical operating parameters and real-time operating parameters of the IoT device, and determine the current power consumption mode of the IoT device according to 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 acquire the predicted power consumption requirement of the Internet of Things device according to the historical operating parameters and the real-time operating parameters; A third acquisition module is used to acquire characteristic information of multiple execution tasks of the IoT device, and acquire a priority allocation value of the corresponding execution task according to each of the characteristic information; A division module, used for classifying and dividing a 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.

8. The high-efficiency power management system for the Internet of Things according to claim 7, characterized in that: The division module comprises: An establishing unit, used to obtain the standard power consumption of each task to be executed under a plurality of preset priority allocation values, and establish a power consumption-allocation relationship curve according to the plurality of standard power consumptions and the corresponding preset priority allocation values; An acquisition 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 consumption is within the power consumption threshold interval; If the execution power consumption is within the power consumption threshold interval, the tasks to be executed corresponding to the execution power consumption are divided into a first task table in the order of the execution power consumption; If the execution power consumption is not within the power consumption threshold interval, 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 interval; 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 value of the power consumption threshold interval are divided into a second task table in 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 a third task table in the 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.

9. 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 6 are implemented.

10. 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 6 are implemented.

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