Internet of Things equipment energy management optimization method, equipment and storage medium

Abstract

The invention discloses an Internet of Things equipment energy management optimization method. The method comprises the following steps: S1, collecting and quantifying environment data and equipment state information; s2, analyzing the quantized data, and evaluating and quantifying the current energy consumption state of the equipment; s3, dynamically adjusting the task execution sequence according to the energy consumption mode of the equipment, the task priority and the external environment condition by using a priority-based self-adaptive hierarchical scheduling algorithm; s4, executing the tasks by utilizing the Internet of Things equipment according to the adjusted execution sequence, and preferentially executing the low-energy-consumption or high-efficiency tasks; and S5, further optimizing an intelligent scheduling algorithm according to task execution result feedback, and realizing continuous self-adaptive energy management optimization. By means of combination of hierarchical division and real-time scheduling, complexity of task scheduling is simplified, accuracy and effectiveness of scheduling are guaranteed at the same time, and therefore the problems that an existing method is poor in real-time performance, low in throughput and the like are solved under the condition that algorithm complexity is not remarkably increased.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management optimization, and in particular to an energy management optimization method, device and storage medium for Internet of Things devices. Background Art

[0002] Based on the development of Internet of Things (IoT) technology, it is particularly important to develop effective energy management strategies in order to ensure the efficient and stable operation of smart devices while taking into account the needs of saving resources and protecting the environment.

[0003] The current mainstream energy management methods mainly include energy harvesting, low-power design, dynamic power management, use of energy-saving communication protocols, implementation of data compression and aggregation technology, setting deep sleep mode, and optimizing task scheduling.

[0004] Among them, energy harvesting uses natural renewable energy such as solar or wind energy to power devices; low-power design focuses on reducing unnecessary energy consumption by improving hardware architecture and software algorithms; and dynamic power management flexibly adjusts the system's working state based on actual usage to save power. Among the above-mentioned measures, task scheduling and optimization is one of the key links to improve the efficiency of the entire network. Traditionally, the process of task scheduling and optimization is usually based on pre-set task priorities: high-priority tasks will be given higher execution weights and processed in order from high to low. Although this method is simple and intuitive, it has certain limitations when facing complex and changing application scenarios.

[0005] Although the task scheduling mechanism based on fixed priority sorting can meet basic needs to a certain extent, it also has the following shortcomings:

[0006] (1) When a high-priority task takes up too much computing resources and takes too long to execute, other pending tasks will have to wait for a long time, which not only reduces the overall response speed of the system but also may affect the user experience;

[0007] (2) In certain situations, due to the lack of effective management of dependencies between tasks, it may happen that even lower priority but more urgent tasks cannot be processed in a timely manner, thus affecting the real-time performance of the system. In addition, this static allocation method is difficult to adapt to the ever-changing workload environment, limiting the maximization of system throughput.

[0008] To this end, the present application specifically proposes an IoT device energy management optimization method, device and storage medium to solve the above technical problems. Summary of the invention

[0009] The main purpose of the present invention is to provide an energy management optimization method, device and storage medium for Internet of Things devices to solve the technical problems raised in the background technology.

[0010] The present invention adopts the following technical solutions to solve the above technical problems:

[0011] A method for optimizing energy management of IoT devices includes the following specific steps:

[0012] S1. Data collection: IoT devices collect and quantify environmental data and device status information through built-in sensors;

[0013] S2. Energy management module: An energy management module is set up on the device to analyze the collected quantified data, evaluate the current energy consumption status of the device and quantify it;

[0014] S3. Intelligent Scheduling Algorithm: Use a priority-based adaptive hierarchical scheduling algorithm to dynamically adjust the order of task execution according to the device's energy consumption pattern, task priority, and external environmental conditions;

[0015] S4. Task execution: Based on the results of the intelligent scheduling algorithm, IoT devices are used to execute tasks in the adjusted execution order, giving priority to low-energy or high-efficiency tasks;

[0016] S5. Energy optimization feedback: The internal management system of the IoT device further optimizes the intelligent scheduling algorithm based on the feedback of task execution results to achieve continuous adaptive energy management optimization.

[0017] Preferably, the environmental data in step S1 include temperature, humidity, and air pressure, and the device status includes the current power consumption P(t) and remaining power B of the device. remaining 、Equipment load condition L current .

[0018] Preferably, the specific operation steps of the energy management module analysis and evaluation operation in step S2 include:

[0019] S21. Evaluate and calculate the current power consumption P(t): The real-time power value read by the sensor is calculated using the formula:

[0020] P(t)=V(t)×I(t)

[0021] Where V(t) is voltage and I(t) is current;

[0022] S22. Calculate the remaining power B remaining : Expressed as a percentage of remaining power, or estimated by the total energy consumption rate;

[0023] S23. Evaluate and calculate the energy consumption rate RE (t): Evaluates the energy consumption rate within a specified time based on the frequency and power consumption of task execution:

[0024]

[0025] Among them, T current is the time taken for the current task;

[0026] S24. Evaluate and calculate the energy load ratio L b (t): It is used to measure the ratio of equipment load to energy consumption and is defined as:

[0027]

[0028] Load ratio L b (t) is used as an indicator to evaluate whether the device is in an overload state to help optimize subsequent task scheduling.

[0029] Preferably, the adaptive hierarchical intelligent scheduling algorithm in step S3 includes:

[0030] S31. Classify all tasks and calculate T for each task i The initial priority P(T i ), dynamically adjust task priorities based on task urgency, energy consumption requirements, and execution time windows;

[0031] S32. Dynamically adjust the initial default value according to the specific application scenario, and then adaptively and dynamically optimize the priority of task execution based on feedback, thereby optimizing the overall scheduling order;

[0032] S33. According to the priority P(T i ), divide the tasks into different levels, execute tasks in parallel within the level, and execute tasks between levels in order of priority. Then, according to the current device status and load conditions, the task allocation of each level is adaptively adjusted. There are:

[0033] L i ={T j |P(T i ) th (L i )}

[0034] Among them, L i is the set of tasks at level i, P th (L i ) is the priority threshold of level i.

[0035] Preferably, in the step S31, the initial priority P(T i ) include:

[0036] S311. Preset U(T i ) is the urgency of the task to measure the importance of the task. There are:

[0037]

[0038] Where T deadline is the deadline of the task, T current is the current time, and the more urgent the task deadline is, the greater the U(T i ) is higher;

[0039] S312. Preset E(T i ) is the energy consumption requirement of the task, and there is:

[0040] E(T i )=P avg (T i )×T exec (T i )

[0041] Where P avg (T i ) is the average energy consumption of the task estimated through historical data or real-time analysis, T exec (T i ) is the expected time for task execution;

[0042] S313. Preset D(T i ) is the time window for task execution, which is defined as the time period within which the task must be completed. There are:

[0043] D(T i )=T end (T i )-T start (T i )

[0044] Where T end (T i ) is the end time of task execution, T start (T i ) is the time opening for task execution, and D(T i ) can be used as an indicator to measure the flexibility of task scheduling: the larger the time window, the more flexible the task scheduling;

[0045] S314. For the initial priority P(T i ), with preset weight factors α, β, γ, there are:

[0046] P(T i )=α·U(T i )+β·E(T i )+γ·D(Ti )

[0047] in:

[0048] α is represented as the urgency weight, which is used to reflect the impact of the urgency of the task on the priority;

[0049] β is represented as the energy consumption weight, which is used to reflect the impact of energy consumption on task priority. For energy-constrained scenarios, the weight of β is lower;

[0050] γ is represented as the time window weight, which is used to reflect the impact of task scheduling flexibility;

[0051] P(T i ) is larger, the higher the priority, indicating that the more urgent the task is or the lower the energy consumption is, the more likely it is to be executed first;

[0052] P(T i ) is smaller, which means the priority is lower, the task is not urgent, the energy consumption is high, and the task with a longer time window may be postponed.

[0053] Preferably, in the specific operation process of adaptively and dynamically optimizing the priority of task execution in step S32, the priority of task execution is dynamically optimized under different operating states by adaptively and dynamically adjusting weight factors α, β, and γ according to feedback, wherein the specific adjustment rules for dynamically adjusting weight factors include:

[0054] In energy-intensive situations: reduce the value of β, lower the priority of high-energy-consuming tasks, and reduce the energy consumption of equipment;

[0055] When the task is urgent: increase the value of α, give priority to urgent tasks, and ensure the timeliness of the tasks;

[0056] When the time window is ample: reduce γ and postpone tasks that can be flexibly scheduled, thereby freeing up resources to perform other urgent tasks.

[0057] Preferably, the specific operation steps of adaptively adjusting the task allocation at each level in step S33 include:

[0058] S331. Execute task scheduling: Starting from the highest priority level, tasks are executed in order by level, and tasks within each level are executed in parallel;

[0059] S332. Adaptive adjustment: Real-time monitoring of device load and task execution, dynamic adjustment of priority threshold P th (L i ) and task allocation strategies;

[0060] S333. Real-time load balancing: During the execution of tasks at each level, the device load is monitored in real time, and the task allocation and scheduling strategies are dynamically adjusted according to the load conditions to avoid excessive concentration and resource overload, so as to ensure the continuous and efficient execution of tasks.

[0061]

[0062] Among them, L b is the current level L i The load factor, E max The maximum available energy of the device.

[0063] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0064] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0065] It can be seen from the above technical solution that the present invention provides an IoT device energy management optimization method, device and storage medium. Compared with the prior art, the present invention has the following advantages:

[0066] 1. The present invention simplifies the complexity of task scheduling by setting an intelligent scheduling algorithm and utilizing the combination of hierarchical division and real-time scheduling, while ensuring the accuracy and effectiveness of scheduling. Thus, without significantly increasing the complexity of the algorithm, the present invention solves the problems of poor real-time performance and low throughput in the existing methods, reduces unnecessary energy waste, and improves overall energy efficiency, so as to further enhance the energy management efficiency and task execution effect of IoT devices.

[0067] 2. The present invention performs energy management through load balancing and dynamic priority adjustment, maximizes energy utilization efficiency, extends equipment operation time, thereby optimizing energy use, reducing equipment losses caused by excessive use, and at the same time reducing energy consumption and reducing impact on the environment.

[0068] 3. The present invention reduces resource contention and conflict between tasks by executing a hierarchical scheduling strategy, improves the real-time response and throughput of the system, and continuously optimizes the energy management strategy by utilizing execution result feedback, thereby ensuring that the scheduling strategy can always adapt to the current environment, thereby ensuring environmental adaptability and real-time stability when executing energy management optimization operations.

[0069] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become easy to understand through the following description. Of course, it is not necessary to achieve all of the advantages described above simultaneously for any product implementing the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0071] Figure 1 It is a schematic diagram of the algorithm logic flow of the present invention;

[0072] Figure 2 It is a schematic flow chart of the operation method of the present invention. DETAILED DESCRIPTION

[0073] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. In the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0074] In the embodiment, see Figure 1 to Figure 2 .

[0075] like Figure 1 and Figure 2 As shown, an IoT device energy management optimization method proposed in an embodiment of the present invention includes the following specific operation steps:

[0076] S1. Data collection: IoT devices collect and quantify environmental data and device status information through built-in sensors;

[0077] The environmental data includes temperature, humidity, and air pressure, and the device status includes the current power consumption P(t) and remaining power B remaining 、Equipment load condition L current wait;

[0078] S2. Energy management module: An energy management module is set up on the device to analyze the collected quantified data, evaluate the current energy consumption status of the device and quantify it;

[0079] The specific steps of energy management module analysis and evaluation operations include:

[0080] S21. Evaluate and calculate the current power consumption P(t): The real-time power value read by the sensor is calculated using the formula:

[0081] P(t)=V(t)×I(t)

[0082] Where V(t) is voltage and I(t) is current;

[0083] S22. Evaluate or calculate the remaining power B remaining : Expressed as a percentage of remaining power (e.g., 70% battery remaining), or estimated by the total energy consumption rate;

[0084] S23. Evaluate and calculate the energy consumption rate R E (t): Evaluates the energy consumption rate within a specified time based on the frequency and power consumption of task execution:

[0085]

[0086] Among them, T current is the time taken for the current task;

[0087] S24. Evaluate and calculate the energy load ratio L b (t): It is used to measure the ratio of equipment load to energy consumption and is defined as:

[0088]

[0089] Load ratio L b (t) used as an indicator for evaluating whether a device is in an overload state, so as to help optimize subsequent task scheduling;

[0090] S3. Intelligent Scheduling Algorithm: Use a priority-based adaptive hierarchical scheduling algorithm to dynamically adjust the order of task execution according to the device's energy consumption pattern, task priority, and external environmental conditions;

[0091] The adaptive hierarchical intelligent scheduling algorithm includes:

[0092] S31. Classify all tasks and calculate T for each task i The initial priority P(T i ), dynamically adjust task priorities based on factors such as task urgency, energy consumption requirements, and execution time windows;

[0093] At this time, calculate the initial priority P(T i ) include:

[0094] S311. Preset U(T i ) is the urgency of the task, which measures the importance of the task and usually depends on external factors. There are:

[0095]

[0096] Where T deadline is the deadline of the task, T current is the current time, and the more urgent the task deadline is, the greater the U(T i ) is higher;

[0097] S312. Preset E(T i ) is the energy consumption requirement of the task, and there is:

[0098] E(T i )=P avg (T i )×T exec (T i )

[0099] Where P avg (T i ) is the average energy consumption of the task estimated through historical data or real-time analysis, T exec (T i ) is the expected time for task execution;

[0100] S313. Preset D(T i ) is the time window for task execution, which is defined as the time period within which the task must be completed. There are:

[0101] D(T i )=T end (T i )-T start (T i )

[0102] Where T end (T i ) is the end time of task execution, T start (T i ) is the time opening for task execution, and D(T i ) can be used as an indicator to measure the flexibility of task scheduling: the larger the time window, the greater the scheduling flexibility of the task;

[0103] S314. For the initial priority P(T i ), with preset weight factors α, β, γ, there are:

[0104] P(T i )=α·U(T i )+β·E(T i )+γ·D(T i )

[0105] in:

[0106] P(T i ) is task T i Priority;

[0107] α is represented as the urgency weight, which is used to reflect the impact of the urgency of the task on the priority;

[0108] β is represented as the energy consumption weight, which is used to reflect the impact of energy consumption on task priority. For energy-constrained scenarios, the weight of β is lower;

[0109] γ is represented as the time window weight, which is used to reflect the impact of task scheduling flexibility;

[0110] P(T i ) is larger, the higher the priority, indicating that the more urgent the task is or the lower the energy consumption is, the more likely it is to be executed first;

[0111] P(T i ) is smaller, which means the priority is lower, the task is not urgent, the energy consumption is high, and the task with a longer time window may be postponed;

[0112] At the same time, the initial default value is dynamically adjusted according to the specific application scenario, and the weight factors α, β, and γ are adaptively adjusted dynamically based on feedback. The system can dynamically optimize the priority of task execution under different operating conditions;

[0113] S32. Dynamically adjust the initial default value according to the specific application scenario, and then adaptively and dynamically optimize the priority of task execution based on feedback, thereby optimizing the overall scheduling order;

[0114] At this time, in the specific operation process of adaptively and dynamically optimizing the priority of task execution, the priority of task execution is dynamically optimized under different operating conditions by adaptively and dynamically adjusting the weight factors α, β, and γ according to the feedback. The specific adjustment rules for dynamically adjusting the weight factors include:

[0115] (1) Under energy-intensive conditions: reduce the value of β, lower the priority of high-energy-consuming tasks, and reduce the energy consumption of equipment;

[0116] (2) When the task is urgent: increase the value of α, give priority to urgent tasks, and ensure the timeliness of the tasks;

[0117] (3) When the time window is ample: reduce γ and postpone tasks that can be flexibly scheduled, thereby freeing up resources to perform other urgent tasks;

[0118] At this time, the dynamic adjustment of priorities enables the system to flexibly respond to the urgency and resource requirements of different tasks, thereby optimizing the overall scheduling order;

[0119] S33. According to the priority P(T i), divide the tasks into different levels, execute tasks in parallel within the level, and execute tasks between levels in order of priority. Then, according to the current device status and load conditions, the task allocation of each level is adaptively adjusted. There are:

[0120] L i ={T j |P(T i ) th (L i )}

[0121] Among them, L i is the set of tasks at level i, P th (L i ) is the priority threshold of level i;

[0122] This hierarchical division method combined with the adaptive adjustment strategy can effectively reduce task conflicts and resource contention, and improve the flexibility and adaptability of system scheduling;

[0123] Therefore, by implementing a hierarchical scheduling strategy, resource contention and conflict between tasks are reduced, the real-time response and throughput of the system are improved, and by using the feedback of execution results to continuously optimize the energy management strategy, it can be ensured that the scheduling strategy can always adapt to the current environment, thereby ensuring the environmental adaptability and real-time stability when executing energy management optimization operations;

[0124] At this time, the task allocation at each level is adaptively adjusted according to the results of the intelligent scheduling algorithm. The IoT devices execute tasks and give priority to low-energy or high-efficiency tasks. The specific operation steps include:

[0125] S41. Execute task scheduling: Starting from the highest priority level, tasks are executed in order by level, and tasks within each level are executed in parallel;

[0126] S42. Adaptive adjustment: Real-time monitoring of device load and task execution, dynamic adjustment of priority threshold P th (L i ) and task allocation strategy, at this time, the task execution order is dynamically adjusted through real-time data to optimize energy consumption;

[0127] S43. Real-time load balancing: During the execution of tasks at each level, the device load is monitored in real time, and the task allocation and scheduling strategies are dynamically adjusted according to the load conditions to avoid over-concentration and resource overload.

[0128]

[0129] Among them, L b is the current level L i The load factor, E max is the maximum available energy of the device;

[0130] At this time, real-time load balancing is used to prevent excessive resource consumption and ensure continuous and efficient execution of tasks;

[0131] Therefore, energy management through load balancing and dynamic priority adjustment can maximize energy efficiency, extend equipment operation time, optimize energy use, reduce equipment losses caused by excessive use, and reduce energy consumption and environmental impact.

[0132] S4. Task execution: Based on the results of the intelligent scheduling algorithm, IoT devices are used to execute tasks in the adjusted execution order, giving priority to low-energy or high-efficiency tasks;

[0133] S5. Energy optimization feedback: The internal management system of the IoT device further optimizes the intelligent scheduling algorithm based on the feedback of task execution results, and realizes continuous adaptive energy management optimization. Specifically, according to the task execution results and device status, the parameters such as α, β, and γ are dynamically adjusted to optimize the scheduling effect. The algorithm for adaptively adjusting the weight factor combines the reinforcement learning Q-learning and feedback control ideas to dynamically and adaptively adjust the weight factor α, β, and γ parameters.

[0134] In summary, the present invention simplifies the complexity of task scheduling by setting an intelligent scheduling algorithm and utilizing the combination of hierarchical division and real-time scheduling, while ensuring the accuracy and effectiveness of scheduling. Therefore, the energy consumption of the equipment can be monitored in real time without significantly increasing the complexity of the algorithm, and the problems of poor real-time performance and low throughput in the existing methods can be solved, thereby reducing unnecessary energy waste and improving overall energy efficiency, so as to further enhance the energy management efficiency and task execution effect of the Internet of Things devices, and provide optimization suggestions.

[0135] On the other hand, the present invention further discloses a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor executes the steps of the above method.

[0136] On the other hand, the present invention further discloses a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above method.

[0137] In another embodiment provided in the present application, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute any of the IoT device energy management optimization methods in the above embodiments.

[0138] It is understandable that the system provided by the embodiment of the present invention corresponds to the method provided by the embodiment of the present invention, and the explanation, examples and beneficial effects of the relevant contents can refer to the corresponding parts in the above method.

[0139] The embodiment of the present application also provides an electronic device, including a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus.

[0140] Memory, used to store computer programs;

[0141] The processor is used to implement the above-mentioned IoT device energy management optimization method when executing the program stored in the memory.

[0142] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc.

[0143] The communication interface is used for communication between the above electronic device and other devices.

[0144] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0145] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, and discrete hardware components.

[0146] It should also be noted that electronic devices also include terminal devices, which can also be called terminals, user equipment (UE), mobile stations (MS), mobile terminals (MT), etc. Terminal devices can be mobile phones, smart TVs, wearable devices, tablet computers (Pad), computers with wireless transceiver functions, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc. The embodiments of the present application do not limit the specific technology and specific device form adopted by the terminal devices.

[0147] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive Solid State Disk (SSD)), etc.

[0148] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0149] In addition, it should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0150] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or suggesting their relative importance or implicitly indicating the number of technical features indicated. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme in which A and B are satisfied at the same time. In addition, in the embodiments of the present invention, "multiple" refers to more than two. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

Claims

1. A method for optimizing energy management of IoT devices, characterized in that: The specific steps are as follows: S1. Data collection: IoT devices collect and quantify environmental data and device status information through built-in sensors; S2. Energy management module: An energy management module is set up on the device to analyze the collected quantified data, evaluate the current energy consumption status of the device and quantify it; S3. Intelligent Scheduling Algorithm: Use a priority-based adaptive hierarchical scheduling algorithm to dynamically adjust the order of task execution according to the device's energy consumption pattern, task priority, and external environmental conditions; S4. Task execution: Based on the results of the intelligent scheduling algorithm, IoT devices are used to execute tasks in the adjusted execution order, giving priority to low-energy or high-efficiency tasks; S5. Energy optimization feedback: The internal management system of the IoT device further optimizes the intelligent scheduling algorithm based on the feedback of task execution results to achieve continuous adaptive energy management optimization.

2. The method for optimizing energy management of IoT devices according to claim 1, characterized in that: The environmental data in step S1 include temperature, humidity, and air pressure, and the device status includes the current power consumption P(t) and remaining power B of the device. remaining 、Equipment load condition L current .

3. The method for optimizing energy management of IoT devices according to claim 1, characterized in that: The specific operation steps of the energy management module analysis and evaluation operation in step S2 include: S21. Evaluate and calculate the current power consumption P(t): The real-time power value read by the sensor is calculated using the formula: P(t)=V(t)×I(t) Where V(t) is voltage and I(t) is current; S22. Calculate the remaining power B remaining : Expressed as a percentage of remaining power; S23. Evaluate and calculate the energy consumption rate R E (t): Evaluates the energy consumption rate within a specified time based on the frequency and power consumption of task execution: Among them, T current is the time taken for the current task; S24. Evaluate and calculate the energy load ratio L b (t): It is used to measure the ratio of equipment load to energy consumption and is defined as: Load ratio L b (t) is used as an indicator to evaluate whether the device is in an overload state to help optimize subsequent task scheduling.

4. The method for optimizing energy management of IoT devices according to claim 1, characterized in that: The adaptive hierarchical intelligent scheduling algorithm in step S3 includes: S31. Classify all tasks and calculate T for each task i The initial priority P(T i ), dynamically adjust task priorities based on task urgency, energy consumption requirements, and execution time windows; S32. Dynamically adjust the initial default value according to the specific application scenario, and then adaptively and dynamically optimize the priority of task execution based on feedback, thereby optimizing the overall scheduling order; S33. According to the priority P(T i ), divide the tasks into different levels, execute tasks in parallel within the level, and execute tasks between levels in order of priority. Then, according to the current device status and load conditions, the task allocation of each level is adaptively adjusted. There are: L i ={T j |P(T i )P th (L i )} Among them, L i is the set of tasks at level i, P th (L i ) is the priority threshold of level i.

5. The method for optimizing energy management of IoT devices according to claim 4, characterized in that: In the step S31, the initial priority P(T i The specific steps of the operation include: S311. Preset U(T i ) is the urgency of the task to measure the importance of the task. There are: Where T deadline is the deadline of the task, T current is the current time, and the more urgent the task deadline is, the greater the U(T i ) is higher; S312. Preset E(T i ) is the energy consumption requirement of the task, and there is: E(T i )=P avg (T i )×T exec (T i ) Where P avg (T i ) is the average energy consumption of the task estimated through historical data or real-time analysis, T exec (T i ) is the expected time for task execution; S313. Preset D(T i ) is the time window for task execution, which is defined as the time period within which the task must be completed. There are: D(T i )=T end (T i )-T start (T i ) Where T end (T i ) is the end time of task execution, T start (T i ) is the time opening for task execution, and D(T i ) can be used as an indicator to measure the flexibility of task scheduling: the larger the time window, the more flexible the task scheduling; S314. For the initial priority P(T i ), with preset weight factors α, β, γ, there are: P(T i )=α·U(T i )+β·E(T i )+γ·D(T i ) in: α is represented as the urgency weight, which is used to reflect the impact of the urgency of the task on the priority; β is represented as the energy consumption weight, which is used to reflect the impact of energy consumption on task priority. For energy-constrained scenarios, the weight of β is lower; γ is represented as the time window weight, which is used to reflect the impact of task scheduling flexibility; P(T i ) is larger, the higher the priority, indicating that the more urgent the task is or the lower the energy consumption is, the more likely it is to be executed first; P(T i ) is smaller, which means the priority is lower, the task is not urgent, the energy consumption is high, and the task with a longer time window may be postponed.

6. The method for optimizing energy management of IoT devices according to claim 5, characterized in that: In the specific operation process of adaptively and dynamically optimizing the priority of task execution in step S32, the priority of task execution is dynamically optimized under different operating states by adaptively and dynamically adjusting the weight factors α, β, and γ according to feedback, wherein the specific adjustment rules for dynamically adjusting the weight factors include: In energy-intensive situations: reduce the value of β, lower the priority of high-energy-consuming tasks, and reduce the energy consumption of equipment; When the task is urgent: increase the value of α, give priority to urgent tasks, and ensure the timeliness of the tasks; When the time window is ample: reduce γ and postpone tasks that can be flexibly scheduled, thereby freeing up resources to perform other urgent tasks.

7. The method for optimizing energy management of IoT devices according to claim 4, characterized in that: The specific operation steps of adaptively adjusting the task allocation at each level in step S33 include: S331. Execute task scheduling: Starting from the highest priority level, tasks are executed in order by level, and tasks within each level are executed in parallel; S332. Adaptive adjustment: Real-time monitoring of device load and task execution, dynamic adjustment of priority threshold P th (L i ) and task allocation strategies; S333. Real-time load balancing: During the execution of tasks at each level, the device load is monitored in real time, and the task allocation and scheduling strategies are dynamically adjusted according to the load conditions to avoid excessive concentration and resource overload, so as to ensure the continuous and efficient execution of tasks. Among them, L b is the current level L i The load factor, E max The maximum available energy of the device.

8. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

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