Equipment group power supply management method and system

Abstract

The invention provides an equipment group power supply management method and system, and the method comprises the steps: a management system continuously monitors the power supply operation state and energy consumption condition of each piece of equipment, and if the deviation between the power supply operation state and energy consumption condition of each piece of equipment and the energy prediction information of each piece of equipment exceeds a monitoring threshold value, the equipment group is started; if yes, summarizing and analyzing the acquired shared information by adopting a distributed edge calculation method, updating the total energy demand and supply condition of the equipment group, and calculating to obtain an updated global optimal power management scheme; dynamically adjusting the damping factor of the equipment according to the real-time energy change rate; and adjusting a power management state adaptive switching strategy, a switching sensitivity threshold value and switching delay time of the equipment according to the power management state adaptive switching frequency, the damping factor size and the energy fluctuation tolerance dynamic state of the equipment so as to realize adaptive optimization of power management of the equipment group. And the system stability and the energy utilization efficiency are effectively balanced.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a method and system for managing power supply of a device group. Background Art

[0002] In a complex environment where multiple devices work together, how to achieve global optimal power management for a group of devices is a major challenge. Traditional single-device power management methods are difficult to adapt to the needs of group collaboration and cannot fully utilize overall energy resources. The mechanism for energy information sharing and global coordination between devices is still imperfect, resulting in uneven energy distribution and low overall utilization efficiency. At the same time, the dynamic changes and mutual influences between devices make power management more complicated. How to achieve flexible switching of group power states while ensuring system stability is also a difficult problem.

[0003] In addition, different devices have different importance and energy consumption characteristics, so how to formulate differentiated management strategies also needs to be considered. In scenarios where there are a large number of devices and they are widely distributed, the limitations of centralized management methods are becoming increasingly prominent, and distributed collaborative management has become an inevitable trend.

[0004] However, how to achieve efficient sharing of global information and decision optimization in a distributed architecture, as well as how to balance the relationship between local autonomous management and global coordination, are urgent issues to be solved. These challenges combined form a complex multi-objective optimization problem that requires a balance between energy efficiency, system stability, and management complexity. Summary of the invention

[0005] The present invention provides a device group power management method and system, aiming to solve the problems of unbalanced energy distribution and low overall utilization efficiency caused by the existing single device power management strategy.

[0006] The present invention provides a device group power management method, comprising: The management system continuously monitors the power supply operation status and energy consumption of each device; If it is detected that the power supply operation status and energy consumption of the device deviate from the energy prediction information of the device by more than a monitoring threshold, the management system uses a distributed edge computing method to summarize and analyze the acquired shared information, update the overall energy demand and supply of the device group, and calculate an updated global optimal power management solution; The shared information includes energy prediction information, energy change rate, power management state adaptive switching strategy and remaining available power of each device; The management system dynamically adjusts the damping factor of the device according to the real-time energy change rate; The management system adaptively switches the frequency, the magnitude of the damping factor, and the energy fluctuation tolerance according to the power management state of the device, and dynamically adjusts the power management state adaptive switching strategy, the switching sensitivity threshold, and the switching delay time of the device.

[0007] Further, before the management system continuously monitors the power operation status and energy consumption of each device, it further includes: The management system uses a genetic algorithm to calculate the current global optimal power management solution and the initial value of the damping factor according to the overall energy demand and supply situation of the current device group; Each device adjusts the power management state adaptive switching strategy of the local device according to the current global optimal power management solution to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold.

[0008] Further, before the management system uses a genetic algorithm to calculate the current global optimal power management solution and the initial value of the damping factor according to the overall energy demand and supply situation of the current device group, it further includes: The management system uses a distributed edge computing method to summarize and analyze the received shared information and calculate the overall energy demand and supply situation of the device group.

[0009] Further, before the management system uses a distributed edge computing method to summarize and analyze the received shared information and calculate the overall energy demand and supply situation of the device group, it further includes: The management system shares the energy prediction information, the energy change rate, the power management state adaptive switching strategy, and the remaining available power of each device through a low-power communication network between devices.

[0010] Further, the management system dynamically adjusts the magnitude of the damping factor of the device according to the real-time energy change rate, including: The management system dynamically adjusts the magnitude of the damping factor according to the real-time monitored energy change rate; If the energy change rate exceeds the preset change rate upper limit, the damping factor is increased; if it is lower than the preset change rate lower limit, the damping factor is decreased.

[0011] Further, the management system adaptively switches the frequency, the magnitude of the damping factor, and the energy fluctuation tolerance according to the power management state of the device, and dynamically adjusts the power management state adaptive switching strategy, the switching sensitivity threshold, and the switching delay time of the device, including: The management system uses a system oscillation detection module to monitor the power management state adaptive switching frequency in real time. If the switching frequency exceeds the preset switching frequency threshold, the damping factor and the switching delay time are increased; Dynamically adjust the adaptive switching sensitivity threshold of the power management state according to the damping factor magnitude and the energy fluctuation tolerance. If the damping factor is higher than the preset damping factor threshold, increase the switching sensitivity threshold and reduce the adaptive switching frequency of the power management state.

[0012] Furthermore, after the management system dynamically adjusts the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device's power management state according to the adaptive switching frequency of the device's power management state, the damping factor magnitude, and the energy fluctuation tolerance, it further includes: The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead.

[0013] Furthermore, after the management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead, it further includes: The management system sets different sensitivity levels for different devices according to the device importance, energy status, and standby power consumption level.

[0014] Furthermore, the management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead, including: The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state. If the energy consumption overhead exceeds the preset overhead threshold, increase the switching delay time and sensitivity threshold.

[0015] Furthermore, it further includes: The management system dynamically adjusts the damping adjustment step size and response time according to the damping effect evaluation feedback.

[0016] Furthermore, the management system dynamically adjusts the damping adjustment step size and response time according to the damping effect evaluation feedback, including: The management system dynamically adjusts the damping adjustment step size and response time according to the evaluation result of the damping effect evaluation feedback module. If the adjustment step size exceeds the preset adjustment threshold, reduce the adjustment step size to achieve adaptive optimization of damping control.

[0017] Furthermore, the management system sets different sensitivity levels for different devices according to the device importance, energy status, and standby power consumption level, including: The management system sets different sensitivity levels for different devices according to the importance of the devices, the energy status, and the standby power consumption level through the sensitivity classification setting module.

[0018] Furthermore, the management system uses the genetic algorithm to calculate the initial values of the current global optimal power management scheme and the damping factor according to the overall energy demand and supply of the current device group, including: The management system uses the genetic algorithm to calculate the global optimal power management scheme and the initial value of the damping factor that maximizes the overall benefit of the group according to the overall energy demand and supply of the device group.

[0019] Furthermore, the management system shares the energy prediction information, energy change rate, local power status adaptive switching management strategy, and remaining available power of each device through the low-power communication network between devices, including: The management system broadcasts and shares the energy prediction information, energy change rate, current power management strategy, and remaining available power of each device to all devices in the group through the low-power communication network between devices.

[0020] Furthermore, each device adjusts the local device's power management state adaptive switching strategy according to the current global optimal power management scheme to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold, including: Each device adjusts the local power management strategy accordingly according to the global optimal power management scheme, the battery health status of the device, and the energy storage efficiency to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold.

[0021] Furthermore, the present application also provides a device group power management system, including: A monitoring module for continuously monitoring the power operation status and energy consumption of each device; A processing module, if it detects that the deviation between the power operation status and energy consumption of the device and the energy prediction information of the device exceeds the monitoring threshold, then uses the distributed edge computing method to summarize and analyze the obtained shared information, update the overall energy demand and supply of the device group, and calculate the updated global optimal power management scheme; the shared information includes the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device; An adjustment module for dynamically adjusting the size of the damping factor of the device according to the real-time energy change rate; The adjustment module is further configured to adaptively switch the frequency, the damping factor magnitude, and the energy fluctuation tolerance dynamically according to the power management state of the device, and adjust the adaptive switching strategy, the switching sensitivity threshold, and the switching delay time of the power management state of the device.

[0022] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: The present invention discloses an intelligent device group power management method and system. Aiming at the energy management problem in a multi-device collaborative working environment, the present invention adopts distributed edge computing and genetic algorithms to achieve global optimal power management of the device group. By sharing the energy information of each device through a low-power communication network, calculating the overall energy demand and supply of the group, and obtaining a global optimal power management scheme. Each device adjusts its local power management strategy according to this scheme. The present invention also introduces a damping factor, dynamically adjusts its magnitude according to the real-time energy change rate, and adjusts the switching strategy, the sensitivity threshold, and the delay time of the device power management state accordingly. At the same time, a mode switching cost evaluation algorithm is adopted to calculate the energy consumption overhead of each state switch, and further optimize the switching parameters. In addition, the present invention also sets different sensitivity levels according to factors such as the importance of the device. Through these innovative designs, the present invention realizes the adaptive optimization of the device group power management, effectively balances the system stability and the energy utilization efficiency, and significantly improves the overall energy utilization rate of the device group. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flowchart of a device group power management method provided by an embodiment of the present invention.

[0024] Figure 2 It is a flowchart of another device group power management method provided by an embodiment of the present invention.

[0025] Figure 3 It is a schematic diagram of a device group power management system provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To further understand the content of the present invention, the present invention will be described in detail in combination with the drawings and embodiments. The following further describes the present application in detail with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related invention, rather than limiting the invention. In addition, it should be noted that only the parts related to the invention are shown in the drawings for the convenience of description.

[0027] Please refer to Figure 1 , Figure 1 It is a flowchart of a device group power management method provided by an embodiment of the present invention, as Figure 1As shown in the figure, a device group power management method provided in this embodiment may specifically include the following steps: S110. The management system continuously monitors the power operation status and energy consumption of each device; By continuously monitoring the power operation status and energy consumption of each device, the management system can grasp the energy usage of the devices in real time. For example, the management system can collect parameters such as voltage and current of each device at regular time intervals (such as 1 minute), calculate the real-time power and energy consumption of the device. At the same time, the management system can also predict the energy demand of the device in the next period according to historical data and device characteristics.

[0028] S120. If it is detected that the deviation between the power operation status and energy consumption of the device and the energy prediction information of the device exceeds the monitoring threshold, the management system uses a distributed edge computing method to summarize and analyze the obtained shared information, updates the overall energy demand and supply of the device group, and calculates an updated global optimal power management plan; The shared information includes the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device.

[0029] By comparing the actual energy consumption with the predicted energy consumption, the management system can find whether the energy usage of the device is abnormal. When the management system detects that the deviation between the actual energy consumption and the predicted energy consumption of the device exceeds a certain threshold (such as 20%), it indicates that the energy usage of the device has an abnormal situation, which may affect the overall energy balance of the device group. At this time, the management system needs to take measures to optimize the energy distribution of the device group.

[0030] The management system uses a distributed edge computing method to collect energy-related shared information from each device, including the energy prediction information, energy change rate, power management state switching strategy, remaining available power, etc. of the device. Using this information distributed on different devices, the management system can more comprehensively analyze the energy demand and supply of the device group.

[0031] For example, the management system finds that the energy change rate of a certain device suddenly increases, indicating that the device may be about to enter a high energy consumption state. At the same time, another device has more remaining power and its energy change rate is stable. Therefore, the management system can migrate some tasks from the high energy consumption device to the low energy consumption device to balance the energy consumption. By summarizing and analyzing the collected shared information, the management system can update the overall energy demand and supply of the device group and calculate a global optimal power management plan.

[0032] The solution may include adjusting the working mode of the device (such as reducing the frequency, going into sleep mode), optimizing task allocation (such as task migration, load balancing), etc., with the aim of minimizing the overall energy consumption while ensuring the device performance. The management system distributes the updated global energy optimization solution to each device to guide them in adjusting the power management state. At the same time, the management system will also dynamically adjust the damping factor of the device according to the real-time energy change rate of the device, making the switching of its power management state smoother and reducing the impact on the system performance. In addition, the management system will comprehensively consider factors such as the switching frequency of the device power state, the damping factor, and the energy fluctuation tolerance, and dynamically adjust the power management strategy of the device.

[0033] For example, for a device with a high switching frequency and large energy fluctuations, the management system can appropriately increase its switching sensitivity threshold and delay time to avoid a decline in system performance caused by frequent power state switching. By continuously monitoring the device energy consumption, summarizing and analyzing the shared information, and dynamically optimizing the power management strategy, this method of power management for a group of devices can achieve dynamic balance and optimization of the overall energy consumption while meeting the performance requirements, and improve the energy utilization efficiency of the group of devices. This is of great significance for large-scale device clusters (such as data centers, Internet of Things, etc.), helping to reduce energy costs and achieve green and energy-saving operation.

[0034] Step S130, the management system dynamically adjusts the magnitude of the damping factor of the device according to the real-time energy change rate; The management system dynamically adjusting the magnitude of the damping factor of the device according to the real-time energy change rate can effectively control the energy consumption of the device. For example, when the energy change rate of the device is high, the management system can increase the damping factor, making the change in the energy consumption of the device smoother and avoiding violent fluctuations in energy. On the contrary, when the energy change rate of the device is low, the management system can appropriately reduce the damping factor, enabling the device to respond more sensitively to energy changes and improving the energy utilization efficiency. By dynamically adjusting the damping factor, the management system can achieve refined control of the device energy consumption according to the actual energy change situation of the device, thereby optimizing the energy management of the entire group of devices.

[0035] S140. The management system dynamically adjusts the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the power management state of the device according to the adaptive switching frequency of the power management state of the device, the magnitude of the damping factor, and the energy fluctuation tolerance.

[0036] The management system can adaptively switch factors such as frequency, damping factor magnitude, and energy fluctuation tolerance according to the power management state of the device, and dynamically adjust the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device. The purpose of doing this is to enable the power management state of the device to better adapt to the actual energy change situation and improve the efficiency and reliability of power management.

[0037] For example, when the power management state adaptive switching frequency of the device is relatively high, the management system can appropriately increase the switching sensitivity threshold and extend the switching delay time to avoid energy waste caused by frequent state switching. At the same time, the management system can also dynamically adjust the switching strategy according to the magnitude of the damping factor and the energy fluctuation tolerance, so that the device can respond quickly when the energy fluctuation is large, and can maintain the current state when the energy fluctuation is small, reducing unnecessary switching.

[0038] Through this dynamic adjustment mechanism, the management system can better balance the energy consumption and performance requirements of the device, and achieve more intelligent and efficient power management. By dynamically adjusting the damping factor and adaptively adjusting the power management state switching strategy, the management system can, according to the actual energy change situation of the device, achieve refined control and optimization of the energy consumption of the device, and improve the energy management efficiency and reliability of the entire device group. This dynamic adjustment mechanism fully considers the energy change characteristics and power management requirements of the device, and through adaptive strategy adjustment, makes the power management of the device more intelligent and efficient, providing strong support for the energy optimization of the device group.

[0039] Please refer to Figure 2 , Figure 2 which is a flowchart of another device group power management method provided by an embodiment of the present invention. As Figure 2 shown, based on Figure 1 the device group power management method shown, before step S110, that is, before the management system continuously monitors the power operation status and energy consumption of each device, it further includes: S10. The management system uses a genetic algorithm to calculate the current global optimal power management solution and the initial value of the damping factor according to the overall energy demand and supply situation of the current device group; Before the management system continuously monitors the power operation status and energy consumption of each device, first, the management system needs to use a genetic algorithm to calculate the current global optimal power management solution and the initial value of the damping factor according to the overall energy demand and supply situation of the current device group. The genetic algorithm is a random search algorithm that simulates the natural selection and genetic mechanism of biological evolution theory. By simulating the natural evolution process to search for the optimal solution, it does not require taking derivatives, can jump out of the local optimal solution, and has strong search ability.

[0040] The management system can take the overall energy demand and supply situation of the device group as the input of the genetic algorithm, take the power management scheme and damping factor as the parameters to be optimized, and generate new solutions through operations such as selection, crossover, and mutation of the genetic algorithm. Finally, the global optimal power management scheme and the initial value of the damping factor that maximize the overall benefit in the current situation are obtained.

[0041] For example, the device group includes 10 devices. The current overall energy demand is 1000W, and the energy supply is 800W. The management system can search through the genetic algorithm to obtain an optimal power management scheme: 6 devices work, 4 devices sleep, and the damping factor of the sleeping devices is set to 0.2. Thus, while ensuring that critical devices work, the total energy consumption is minimized so that it does not exceed the energy supply.

[0042] S11. Each device adjusts the power management state adaptive switching strategy of the local device according to the current global optimal power management scheme to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold.

[0043] After obtaining the current global optimal power management scheme, each device needs to adjust the power management state adaptive switching strategy of the local device according to this scheme to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold. Since the environments and tasks of each device are different, their energy demand and supply situations are also different. Therefore, it cannot be blindly adjusted according to the global scheme and needs to be adaptively switched according to the local situation.

[0044] For example, if a certain device currently has sufficient energy supply and is responsible for important tasks, it can appropriately increase the switching sensitivity threshold and extend the working time; while another device has a tight energy supply and is responsible for secondary tasks, it can lower the switching sensitivity threshold and extend the sleeping time. The device power management strategy can also be dynamically adjusted according to the energy demand change trend, its own power situation, etc., to improve the flexibility of power management. For example, if a certain device finds that the energy demand has decreased by more than 20% for 3 consecutive cycles and the power is sufficient, it can consider reducing the working power and extending the sleeping time to store energy to cope with possible demand peaks.

[0045] Through the combination of centralized optimization by the management system and local adaptive adjustment of the devices, a power management scheme with high overall energy utilization efficiency and strong local pertinence can be obtained, thereby effectively improving the energy utilization efficiency of the device group.

[0046] The management system is responsible for macro-control and optimization of the device group from a global perspective, providing decision-making basis and constraint conditions for each device; while each device makes adaptive adjustments to the global scheme according to the local specific situation to improve the flexibility and robustness of power management. The two cooperate with each other to better cope with the complex and changeable energy demand and supply situations.

[0047] like Figure 2 As shown, before step S10, that is, before the management system uses the genetic algorithm to calculate the current global optimal power management solution and the initial value of the damping factor according to the overall energy demand and supply of the current device group, it also includes: S08. The management system uses a distributed edge computing method to summarize and analyze the received shared information and calculate the overall energy demand and supply of the device group.

[0048] Before the management system uses genetic algorithms to calculate the global optimal power management solution and the initial value of the damping factor, it is necessary to first use distributed edge computing methods to summarize and analyze the received shared information and calculate the overall energy demand and supply of the device group.

[0049] The purpose of this step is to provide the necessary input data and constraints for the subsequent genetic algorithm optimization. Specifically, the management system first needs to collect energy-related shared information from each device node, such as the current power status of the device, energy consumption level, power supply capacity, etc.

[0050] Due to the large number of devices and their dispersed geographical locations, the traditional centralized information collection method has high communication overhead and high latency, making it difficult to meet the needs of real-time optimization. Therefore, the use of distributed edge computing methods to locally process and aggregate device shared information can significantly reduce communication load and improve computing efficiency.

[0051] For example, the management system divides the device nodes into several areas and deploys an edge computing server in each area. The device sends shared information to the edge server in the area, which summarizes and analyzes the received information, calculates the overall energy demand and supply of the devices in the area, and reports the results to the management system. The management system then integrates the calculation results of each area to obtain a global view of the energy demand and supply of the entire device group.

[0052] When aggregating and analyzing shared information, edge computing servers can adopt a variety of strategies to improve computing efficiency and accuracy.

[0053] One approach is to set the information sampling frequency and dynamically adjust the sampling frequency according to the dynamic characteristics of the device energy change, so as to reduce unnecessary communication while ensuring the timeliness of information. For example, the sampling frequency can be lowered for devices with slow energy changes, and the sampling frequency can be increased for devices with large energy fluctuations.

[0054] Another approach is to introduce an information compression and fusion mechanism. The edge server extracts features from the massive device-shared information collected, compresses redundant information, and reduces the data dimension. Meanwhile, a multi-source information fusion algorithm is adopted to comprehensively integrate information from different sources and of different types, and comprehensively evaluate the energy state of the devices. For example, not only the device power but also environmental factors affecting energy consumption, such as the temperature and humidity where the device is located, are considered to improve the accuracy of energy demand prediction. After the management system receives the aggregated energy demand and supply information of the device group, it can be used as the input for the genetic algorithm to solve the global optimal power management solution. At the same time, based on the overall energy status of the device group, the initial population and constraint conditions of the genetic algorithm are reasonably set, such as the selection of the initial value of the damping factor, to improve the algorithm convergence speed and optimization effect.

[0055] Distributed edge computing is a key technical means for the management system to efficiently aggregate the energy demand and supply information of the device group. Through in-situ processing and hierarchical aggregation, communication overhead can be reduced, computing efficiency can be improved, and high-quality decision-making basis can be provided for subsequent global optimization. At the same time, strategies such as sampling, compression, and fusion can further improve the intelligent level of information processing, enabling the management system to more accurately and dynamically grasp the energy status of the device group and make optimal power management decisions.

[0056] Before step S08, that is, before the management system uses the distributed edge computing method to aggregate and analyze the received shared information and calculate the overall energy demand and supply of the device group, it further includes: S06. The management system shares the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device through the low-power communication network between devices.

[0057] The management system shares the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device through the low-power communication network between devices. This process can be achieved through the following implementation methods: On the one hand, to achieve the sharing of energy prediction information, the management system can use machine learning algorithms to predict the energy demand of each device for a period of time in the future based on the device historical energy consumption data, usage patterns, environmental factors, etc. The predicted information can be shared between devices through a low-power communication network, such as Bluetooth, Zigbee, etc. In this way, the management system can master the energy demand situation of the entire device group and provide a basis for subsequent energy scheduling and optimization.

[0058] On the other hand, sharing of the energy change rate is achieved. The energy change rate of a device reflects the dynamic change trend of its energy consumption. The management system can calculate the energy change rate of each device based on the energy consumption data reported by the device in real time. For example, for an electric vehicle, its energy change rate can be calculated according to the change speed of the battery power. Sharing the energy change rates of various devices with the management system through a low-power communication network can help the management system grasp the energy consumption trend of the device group in real time and adjust the energy distribution strategy in a timely manner.

[0059] On the other hand, sharing of the adaptive switching strategy of the power management state is achieved. To improve the energy efficiency of devices, the management system can dynamically adjust its power management strategy according to the usage situation and energy state of the devices. For example, when a device is in an idle state for a long time, it can be switched to the low-power mode; when the device needs to execute computationally intensive tasks, it can be switched to the high-performance mode. Sharing the adaptive switching strategy of the power management state of each device with the management system can enable the management system to globally optimize the power management of the device group and avoid the situation where the overall performance is affected due to improper power management strategies of individual devices. Sharing of the remaining available power of the device. The remaining available power of a device is a key factor affecting its ability to execute tasks. The management system needs to grasp the remaining power situation of each device in real time so as to reasonably schedule tasks and energy.

[0060] Through a low-power communication network, a device can share its remaining power information with the management system. For example, an electric vehicle can report the current power of its battery and the estimated remaining mileage to the management system. After the management system aggregates the remaining power information of all devices, it can foreseeably plan the work of the device group and avoid the situation where tasks are interrupted due to the exhaustion of the power of some devices.

[0061] By sharing energy prediction information, energy change rate, adaptive switching strategy of power management state, and remaining available power of the device among devices through a low-power communication network, the management system can comprehensively grasp the energy demand and supply situation of the device group. On this basis, the management system can use the method of distributed edge computing to allocate computing tasks to each device, make full use of the computing resources and energy of the device group, and improve the overall energy efficiency and performance. At the same time, the management system can also dynamically optimize the energy distribution and task scheduling strategies of the device group according to the shared information to ensure that the device group can operate efficiently for a long time.

[0062] Preferably, based on the above step S130, the management system dynamically adjusts the size of the damping factor of the device according to the real-time energy change rate, including: The management system dynamically adjusts the size of the damping factor according to the real-time monitored energy change rate. If the energy change rate exceeds the preset change rate upper limit, the damping factor is increased; if it is lower than the preset change rate lower limit, the damping factor is decreased.

[0063] The management system dynamically adjusts the damping factor according to the real-time monitored energy change rate, which can be specifically achieved through the following methods: First, the management system needs to obtain the energy change situation of the device in real time. This can be done by installing sensors on the device to regularly collect the energy data of the device and report it to the management system. After receiving the data, the management system can calculate the energy change rate within a certain time range, such as calculating the change amplitude of energy in the last 5 minutes.

[0064] Then, the management system needs to set the upper and lower threshold values of the energy change rate. Exceeding the upper limit means that the energy fluctuates greatly, and it is necessary to increase the damping factor to suppress the fluctuation; being lower than the lower limit means that the energy is relatively stable, and the damping factor can be reduced. The setting of the threshold needs to be based on the actual situation of the device and management requirements, and can be obtained through empirical values or previous data analysis. After obtaining the real-time energy change rate, the management system compares it with the threshold. If it exceeds the upper threshold, for example, the energy change rate reaches 20%, then multiply the damping factor by a coefficient greater than 1, such as 1.2, to increase the damping effect. If it is lower than the lower threshold, for example, the energy change rate is less than 5%, then multiply the damping factor by a coefficient less than 1, such as 0.8, to reduce the damping.

[0065] The adjustment amplitude of the damping factor also needs to be dynamically optimized according to the actual effect. The size of the damping factor directly affects the response sensitivity of the device during energy fluctuations. The larger the damping factor, the slower the response of the device, which is beneficial to maintaining the stability of energy and reducing frequent state switches. The smaller the damping factor, the more sensitive the response of the device, and it can quickly adapt to energy changes, but it may cause system oscillations.

[0066] Therefore, the management system needs to balance stability and sensitivity according to the working characteristics of the device to dynamically adjust the damping factor to achieve the best energy management effect. At the same time, the adjustment of the damping factor also needs to consider the constraints of the device itself, such as the maximum power, voltage range, etc., to avoid excessive damping causing the device to malfunction. The management system can use these constraints as the boundaries of damping adjustment and optimize the damping strategy as much as possible on the premise of ensuring the safe operation of the device.

[0067] In addition, the dynamic adjustment process of the damping factor itself will also consume a certain amount of computing resources and energy. Therefore, when the management system implements this function, it needs to take into account the time granularity and adjustment amplitude of the adjustment, avoid introducing additional overhead due to overly frequent adjustments, and reduce the overall energy efficiency level.

[0068] For example, an adjustment period can be set, such as evaluating whether adjustment is needed every 1 minute, or triggering adjustment when the energy change rate exceeds a certain range, to reduce unnecessary calculations. The management system dynamically adjusts the damping factor according to the real-time energy change rate, which can effectively suppress energy fluctuations, improve the energy stability of the device group, and at the same time take into account the response sensitivity of the devices. This method makes full use of the energy monitoring data, adaptively optimizes the damping parameters, has strong practicability and robustness, and provides an efficient solution for the energy management of the device group.

[0069] Preferably, based on the above step S140, the management system adaptively switches the frequency, the magnitude of the damping factor, and the energy fluctuation tolerance according to the power management state of the device, and dynamically adjusts the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device, including: The management system monitors the power management state adaptive switching frequency in real time through the system oscillation detection module. If the switching frequency exceeds the preset switching frequency threshold, the damping factor and the switching delay time are increased; Dynamically adjust the power management state adaptive switching sensitivity threshold according to the magnitude of the damping factor and the energy fluctuation tolerance. If the damping factor is higher than the preset damping factor threshold, the switching sensitivity threshold is increased, and the power management state adaptive switching frequency is reduced.

[0070] The management system adaptively switches the frequency, the magnitude of the damping factor, and the energy fluctuation tolerance according to the power management state of the device, and dynamically adjusts the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device. Specifically, the management system monitors the power management state adaptive switching frequency in real time through the system oscillation detection module. When it is detected that the switching frequency exceeds the preset switching frequency threshold, the system will correspondingly increase the damping factor and the switching delay time. The increase in the damping factor can play a role in suppressing the frequent switching of the power management state, avoiding problems such as increased system energy consumption and decreased performance caused by overly frequent switching of the power management state. At the same time, the increase in the switching delay time can also delay the switching of the power management state, further reducing the switching frequency.

[0071] In addition, the management system also dynamically adjusts the sensitivity threshold of the power management state adaptive switching according to the magnitude of the damping factor and the energy fluctuation tolerance. When the damping factor is higher than the preset damping factor threshold, the system will correspondingly increase the switching sensitivity threshold. The increase in the switching sensitivity threshold means that the energy change amplitude required to trigger the switching of the power management state increases, thereby reducing the sensitivity of the power management state adaptive switching. This can avoid frequently triggering the switching of the power management state under small energy fluctuations, reduce unnecessary switching operations, and improve the stability and energy efficiency of the system.

[0072] For example, assume that the preset switching frequency threshold is 10 times per minute. When the management system detects that the adaptive switching frequency of a device's power management state reaches 15 times per minute, the system will increase the damping factor of the device from 0.5 to 0.8, and at the same time increase the switching delay time from 1 second to 2 seconds. This can effectively reduce the switching frequency of the device's power management state and avoid the negative impact of overly frequent switching on system performance and energy consumption.

[0073] On the other hand, assume that the preset damping factor threshold is 0.7. When the damping factor of a device reaches 0.75, the management system will increase the adaptive switching sensitivity threshold of the device's power management state from 5% to 8%. This means that only when the energy change amplitude of the device exceeds 8% will the switching of the power management state be triggered. By increasing the switching sensitivity threshold, frequent switching in the case of small energy fluctuations can be reduced, and the stability and energy efficiency of the system can be improved.

[0074] By dynamically adjusting the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device's power management state, the management system can effectively respond to the dynamic changes in the device's energy change and usage conditions, realizing the intelligentization and optimization of the device's power management. This can not only reduce the energy consumption of the device and extend the battery usage time, but also improve the system performance and user experience.

[0075] Preferably, after the above step S140, that is, after the management system dynamically adjusts the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device's power management state according to the adaptive switching frequency of the device's power management state, the magnitude of the damping factor, and the energy fluctuation tolerance, it further includes: The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead of each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead.

[0076] The management system dynamically adjusts the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device's power management state according to the adaptive switching frequency of the device's power management state, the magnitude of the damping factor, and the energy fluctuation tolerance. This is to dynamically optimize the power management strategy according to the actual operating conditions of the device and improve energy efficiency.

[0077] For example, when it is detected that the switching frequency of a device's power state is relatively high, its damping factor can be appropriately increased, the switching sensitivity threshold can be reduced, and the switching delay time can be extended to avoid energy consumption waste caused by frequent switching. Another example is that for a device with large energy fluctuations, its energy fluctuation tolerance can be increased, allowing energy fluctuations within a larger range, and reducing unnecessary power state switching.

[0078] On this basis, the management system also adopts a mode switching cost evaluation algorithm to calculate the energy consumption overhead of each adaptive switching of the device's power management state, and further adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead situation. This is to balance the energy-saving benefits brought by the power management state switching and the energy consumption overhead of the switching itself, and seek the optimal balance point.

[0079] Specifically, when the calculated energy consumption overhead of a certain power state switching is large, the switching delay time can be appropriately extended, the switching sensitivity threshold can be increased, and the number of power state switchings of the device can be reduced; conversely, if the switching energy consumption overhead is small, the switching delay time can be shortened, the switching sensitivity threshold can be decreased, so that the power management state of the device can more sensitively adapt to the energy change.

[0080] Through the above steps, the management system can dynamically adjust its power management strategy and related parameters according to the actual operating conditions of the device, while meeting the performance requirements of the device, minimizing the energy consumption of the entire device group. This adaptive power management method can not only effectively improve the energy efficiency level of the device group, but also extend the service life of the device and reduce the operation and maintenance costs.

[0081] Preferably, after the management system uses the mode switching cost evaluation algorithm to calculate the energy consumption overhead of each adaptive switching of the device's power management state and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead, it further includes: The management system sets different sensitivity levels for different devices according to the device importance, energy status, and standby power consumption level.

[0082] Furthermore, the management system sets different sensitivity levels for different devices according to the device importance, energy status, and standby power consumption level, including: The management system sets different sensitivity levels for different devices through a sensitivity grading setting module according to the device importance, energy status, and standby power consumption level.

[0083] After the management system uses the mode switching cost evaluation algorithm to calculate the energy consumption overhead of each adaptive switching of the device's power management state and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead, it will also set different sensitivity levels for different devices according to the device importance, energy status, and standby power consumption level. This is because in actual application scenarios, different devices may have different importance and energy status, and their power management requirements are also different. For example, for some key devices such as servers and gateways, due to their importance in the system, it is necessary to ensure that they can operate continuously and stably. Therefore, a higher sensitivity level can be set for them so that they can quickly respond to the adjustment of the power management strategy.

[0084] For some relatively less important devices, such as sensor nodes, etc., their importance level is relatively low, and they are usually powered by batteries with limited energy. Therefore, a lower sensitivity level can be set for them to reduce the frequency of power management state switching, so as to save energy and extend their service life. In addition, the energy status of the device is also an important factor affecting the setting of the sensitivity level.

[0085] For devices with sufficient energy, the sensitivity level can be appropriately increased so that they can more flexibly adapt to changes in the power management strategy. For devices with low energy, the sensitivity level needs to be reduced to reduce unnecessary power management state switching to extend their operating time as much as possible. At the same time, the standby power consumption level of the device also needs to be taken into account.

[0086] For devices with high standby power consumption, the sensitivity level can be appropriately reduced to reduce the frequency of their entering the standby state, so as to reduce the energy consumption of the entire system. For devices with low standby power consumption, the sensitivity level can be increased so that they can respond more quickly to adjustments in the power management strategy.

[0087] The management system, through the sensitivity classification setting module, comprehensively considers factors such as the importance level, energy status, and standby power consumption level of the device, and sets different sensitivity levels for different devices. For example, devices can be classified into several levels according to their importance, such as critical devices, important devices, and less important devices, and different sensitivity levels can be further divided within each level according to their energy status and standby power consumption level.

[0088] For example, for a critical device, if it has sufficient energy and low standby power consumption, its sensitivity level can be set to the highest, such as level 1. In this way, the device can respond most quickly to changes in the power management strategy and ensure its continuous and stable operation. For a less important device, if it has low energy and high standby power consumption, its sensitivity level can be set to a lower level, such as level 4 or level 5. Therefore, the frequency of power management state switching of this device will be reduced to save its limited energy and extend its service life. Through this differential sensitivity level setting, the management system can adjust its power management strategy according to the actual situation of different devices, while ensuring the stable operation of critical devices, and also maximizing the energy consumption savings of the entire system and improving energy utilization efficiency. This dynamic adjustment method can make the power management of the device group more intelligent and refined, thus realizing the energy consumption optimization and performance improvement of the entire system.

[0089] Preferably, the management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the power management state of the device, and adjusts the switching delay time and the switching sensitivity threshold according to the energy consumption overhead, including: The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the power management state of the device. If the energy consumption overhead exceeds a preset overhead threshold, the switching delay time and the sensitivity threshold are increased.

[0090] Furthermore, it also includes: The management system dynamically adjusts the damping adjustment step size and the response time according to the damping effect evaluation feedback.

[0091] The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the power management state of the device, and dynamically adjusts the switching delay time and the switching sensitivity threshold according to the energy consumption overhead. Specifically, the management system first obtains the historical energy consumption data and the power management state switching records of the device, and establishes an energy consumption overhead evaluation model by analyzing the power consumption characteristics and switching frequencies under different power management modes. This model comprehensively considers the impacts of factors such as the device's workload, power management state, and number of switches on the overall energy consumption.

[0092] During the actual operation process, the management system monitors the switching situation of the device's power management state in real time. Whenever a switching occurs, the established evaluation model is used to calculate the energy consumption overhead brought by this switching. If the calculated energy consumption overhead exceeds the preset overhead threshold, it is determined that the current switching strategy may lead to too frequent switching of the power management state, thus causing additional energy consumption losses. Therefore, the management system will correspondingly increase the switching delay time, that is, extend the time interval between two power management state switches to reduce the switching frequency. At the same time, the switching sensitivity threshold will also be increased, so that the power management state switch is only triggered when there are significant changes in the device's workload or external environment, avoiding unnecessary switches under small load fluctuations.

[0093] For example, assume that the power management states of a device include a working mode and a sleep mode. The initial value of the switching delay time is set to 10 minutes, and the initial value of the switching sensitivity threshold is set to 20%. Through energy consumption overhead evaluation, the management system finds that the device has switched between the working mode and the sleep mode 10 times within 1 hour, and each switch has increased the energy consumption overhead by 0.5 watt-hours on average. The cumulative increased energy consumption overhead reaches 5 watt-hours, exceeding the preset overhead threshold of 3 watt-hours. The management system adjusts the switching delay time to 15 minutes and the switching sensitivity threshold to 30%. After the adjustment, the device only undergoes 3 power management state switches within the next 1 hour. The average energy consumption overhead per switch drops to 0.3 watt-hours, and the cumulative energy consumption overhead is reduced to 0.9 watt-hours, not exceeding the preset threshold, achieving a more energy-efficient and efficient power management.

[0094] In addition, the management system will also dynamically adjust the damping adjustment step size and response time according to the evaluation feedback of the damping effect to further optimize the smoothness and stability of the power management state switching. The damping effect evaluation mainly analyzes the power management state switching of the device over a period of time, calculates indicators such as the switching frequency and energy consumption fluctuation, and judges the rationality of the current damping parameter settings.

[0095] If it is found that the switching is too frequent or the energy consumption fluctuation is large, the damping adjustment step size is appropriately increased and the response time is extended, making the power management state switching smoother and reducing the impact on the device performance and energy consumption. On the contrary, if the switching is relatively slow or the damping effect is not obvious, the damping adjustment step size can be appropriately reduced and the response time can be shortened to improve the sensitivity and timeliness of the power management.

[0096] For example, for the damping parameters of a device, the initial adjustment step size is 10% and the response time is 5 minutes. Through continuous monitoring, it is found that the number of power management state switches of the device within 20 minutes is 8 times, and the energy consumption fluctuation caused by each switch is 1 watt-hour on average, exceeding the expected fluctuation threshold of 0.5 watt-hour. This indicates that the current damping effect is not ideal and the switching is too sensitive. Therefore, the management system increases the damping adjustment step size to 15% and extends the response time to 8 minutes. After the adjustment, the device only undergoes 3 power management state switches within the next 20 minutes, and the average energy consumption fluctuation drops to 0.4 watt-hour, meeting the expected threshold requirements and achieving a more stable power management process.

[0097] By adopting a mode switching cost evaluation algorithm and a damping effect evaluation feedback mechanism, the management system can dynamically adjust the strategies and parameters of power management state switching according to the actual operating conditions of the device. While ensuring the device performance, it minimizes the energy consumption overhead caused by switching and improves the energy efficiency level of the entire system. This adaptive power management method, without manual intervention, can automatically make optimal decisions according to the changes in the device workload, has strong practicability and robustness, and provides an effective technical means for realizing intelligent power management of a group of devices.

[0098] Preferably, the management system dynamically adjusts the damping adjustment step and response time according to the damping effect evaluation feedback, including: The management system dynamically adjusts the damping adjustment step and response time according to the evaluation results of the damping effect evaluation feedback module. If the adjustment step exceeds the preset adjustment threshold, the adjustment step is reduced to achieve adaptive optimization of damping control.

[0099] Preferably, the management system uses a genetic algorithm to calculate the initial values of the current global optimal power management scheme and the damping factor according to the overall energy demand and supply situation of the current device group, including: The management system uses a genetic algorithm to calculate the global optimal power management scheme and the initial value of the damping factor that maximizes the overall interests of the group according to the overall energy demand and supply situation of the device group.

[0100] Preferably, the management system shares the energy prediction information, energy change rate, local power state adaptive switching management strategy, and remaining available power of each device through a low-power communication network between devices, including: The management system broadcasts and shares the energy prediction information, energy change rate, current power management strategy, and remaining available power of each device to all devices in the group through a low-power communication network between devices.

[0101] Preferably, each device adjusts the local device's power management state adaptive switching strategy according to the current global optimal power management scheme to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold, including: Each device adjusts the local power management strategy accordingly according to the global optimal power management scheme, as well as according to the device battery health status and energy storage efficiency, to obtain the updated device power management strategy and the initial value of the switching sensitivity threshold.

[0102] Please refer to Figure 3 , Figure 3 which is a schematic diagram of a device group power management system provided by an embodiment of the present invention, as Figure 3As shown in the figure, an embodiment of the present invention further provides a device group power management system for implementing the steps achieved by any of the above device group power management system methods. The system includes: A monitoring module 310 for continuously monitoring the power operation status and energy consumption of each device; A processing module 320. If it is detected that the deviation between the power operation status and energy consumption of the device and the energy prediction information of the device exceeds the monitoring threshold, a distributed edge computing method is used to summarize and analyze the obtained shared information, update the overall energy demand and supply of the device group, and calculate an updated global optimal power management scheme. The shared information includes the energy prediction information, energy change rate, power management status adaptive switching strategy, and remaining available power of each device; An adjustment module 330 for dynamically adjusting the damping factor of the device according to the real-time energy change rate; The adjustment module 330 is further configured to dynamically adjust the power management status adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device according to the power management status adaptive switching frequency, the damping factor, and the energy fluctuation tolerance of the device.

[0103] The monitoring module 310 continuously monitors the power operation status and energy consumption of each device, which can be achieved by installing sensors on the devices. For example, current sensors and voltage sensors can be installed at the power input end of the device to collect the current and voltage data of the device in real time, and calculate the power and energy consumption of the device according to the current and voltage. At the same time, the temperature change of the device can also be monitored through a temperature sensor to determine whether the device is in a normal working state. The monitoring module 310 can also record the historical energy consumption data of the device. By analyzing the historical data, the energy consumption of the device in the next period of time can be predicted, providing a basis for subsequent power management.

[0104] The function of the processing module 320 is to, when it is detected that the deviation between the power operation status and energy consumption of the device and the predicted value is large, use a distributed edge computing method to summarize and analyze the information shared by each device, update the overall energy demand and supply of the device group, and calculate an updated global optimal power management scheme.

[0105] Among them, the shared information includes the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device. By analyzing this shared information, the processing module 320 can keep track of the energy demand and supply of the device group in real time and optimize the power management plan based on this information. For example, when the energy consumption of a certain device suddenly increases, resulting in a large deviation between its energy prediction information and actual consumption, the processing module 320 can timely adjust the power management strategy of this device, such as reducing its working frequency or entering the sleep state, to reduce energy consumption. At the same time, the processing module 320 can also dynamically adjust the energy distribution among devices according to the overall energy demand and supply of the device group to ensure the overall energy balance and efficient utilization of the device group.

[0106] The function of the adjustment module 330 is to dynamically adjust the damping factor of the device according to the real-time energy change rate, and to dynamically adjust the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device according to parameters such as the power management state adaptive switching frequency, damping factor size, and energy fluctuation tolerance of the device. Among them, the damping factor is an important parameter that determines the response speed and amplitude of the device during energy fluctuations.

[0107] When the energy change rate is large, the adjustment module 330 will increase the damping factor to make the response of the device smoother and avoid frequent power management state switching. On the contrary, when the energy change rate is small, the adjustment module 330 will decrease the damping factor to enable the device to respond to energy changes faster. In addition, the adjustment module 330 will also dynamically adjust the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device according to parameters such as the power management state adaptive switching frequency, damping factor size, and energy fluctuation tolerance of the device. For example, when the power management state switching frequency of the device is high, the adjustment module 330 will appropriately increase the switching sensitivity threshold and delay time to reduce unnecessary power management state switching. At the same time, the adjustment module 330 will also dynamically adjust the size of the damping factor according to the energy fluctuation tolerance of the device to adapt to different energy fluctuation situations.

[0108] In summary, for the energy management problem in a multi-device collaborative working environment, the intelligent device group power management method and system provided by the embodiments of the present invention adopt distributed edge computing and genetic algorithms to achieve global optimal power management for the device group. By sharing the energy information of each device through a low-power communication network, the overall energy demand and supply of the group are calculated to obtain a global optimal power management solution. Each device adjusts its local power management strategy according to this solution. The embodiments of the present invention also introduce a damping factor, dynamically adjust its magnitude according to the real-time energy change rate, and accordingly adjust the switching strategy, sensitivity threshold, and delay time of the device power management state. At the same time, a mode switching cost evaluation algorithm is adopted to calculate the energy consumption overhead of each state switch, further optimizing the switching parameters. In addition, the embodiments of the present invention also set different sensitivity levels according to factors such as the importance of the device. Through these innovative designs, the present invention realizes the adaptive optimization of the device group power management, effectively balances the system stability and energy utilization efficiency, and significantly improves the overall energy utilization rate of the device group.

[0109] The foregoing is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for power management of a group of devices, characterized in that Including: The management system continuously monitors the power operation status and energy consumption of each device; If it is detected that the deviation between the power operation status and energy consumption of the device and the energy prediction information of the device exceeds the monitoring threshold, the management system uses a distributed edge computing method to summarize and analyze the obtained shared information, updates the overall energy demand and supply of the device group, and calculates an updated global optimal power management scheme; the shared information includes the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device; The management system dynamically adjusts the damping factor of the device according to the real-time energy change rate; The management system dynamically adjusts the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device according to the power management state adaptive switching frequency, the damping factor, and the energy fluctuation tolerance of the device.

2. The method for power management of a device group as claimed in claim 1, wherein Before the management system continuously monitors the power operation status and energy consumption of each device, it further includes: The management system uses a genetic algorithm to calculate the current global optimal power management scheme and the initial value of the damping factor according to the overall energy demand and supply of the current device group; Each device adjusts the power management state adaptive switching strategy of the local device according to the current global optimal power management scheme to obtain an updated device power management strategy and the initial value of the switching sensitivity threshold.

3. The method for power management of a device group as described in claim 2, wherein, Before the management system uses a genetic algorithm to calculate the current global optimal power management scheme and the initial value of the damping factor according to the overall energy demand and supply of the current device group, it further includes: The management system uses a distributed edge computing method to summarize and analyze the received shared information and calculates the overall energy demand and supply of the device group.

4. The method for power management of a device group as claimed in claim 3, wherein, Before the management system uses a distributed edge computing method to summarize and analyze the received shared information and calculates the overall energy demand and supply of the device group, it further includes: The management system shares the energy prediction information, energy change rate, power management state adaptive switching strategy, and remaining available power of each device through a low-power communication network between devices.

5. The method for power management of a group of devices according to claim 1, wherein The management system dynamically adjusts the damping factor of the device according to the real-time energy change rate, including: The management system dynamically adjusts the damping factor according to the real-time monitored energy change rate. If the energy change rate exceeds the preset change rate upper limit, the damping factor is increased; if it is lower than the preset change rate lower limit, the damping factor is decreased.

6. The method for power management of a device group according to claim 1, characterized in that, The management system dynamically adjusts the power management state adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device according to the power management state adaptive switching frequency, the damping factor, and the energy fluctuation tolerance of the device, including: The management system uses the system oscillation detection module to monitor the power management state adaptive switching frequency in real time. If the switching frequency exceeds the preset switching frequency threshold, the damping factor and the switching delay time are increased; Dynamically adjust the adaptive switching sensitivity threshold of the power management state according to the damping factor magnitude and the energy fluctuation tolerance. If the damping factor is higher than the preset damping factor threshold, increase the switching sensitivity threshold and reduce the adaptive switching frequency of the power management state.

7. The method for power management of a group of devices according to claim 1, characterized in that, After the management system dynamically adjusts the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device's power management state according to the adaptive switching frequency of the device's power management state, the damping factor magnitude, and the energy fluctuation tolerance, it further includes: The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead.

8. The method for power management of a group of devices according to claim 7, characterized in that, After the management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead, it further includes: The management system sets different sensitivity levels for different devices according to the device importance, energy status, and standby power consumption level.

9. The method for power management of a device group according to claim 8, wherein The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state, and adjusts the switching delay time and switching sensitivity threshold according to the energy consumption overhead, including: The management system uses a mode switching cost evaluation algorithm to calculate the energy consumption overhead for each adaptive switching of the device's power management state. If the energy consumption overhead exceeds the preset overhead threshold, increase the switching delay time and sensitivity threshold.

10. A device group power management system, characterized in that, It includes: A monitoring module for continuously monitoring the power operation status and energy consumption of each device; A processing module. If it detects that the deviation between the power operation status and energy consumption of the device and the energy prediction information of the device exceeds the monitoring threshold, it uses a distributed edge computing method to summarize and analyze the obtained shared information, update the overall energy demand and supply of the device group, and calculate an updated global optimal power management solution; the shared information includes the energy prediction information, energy change rate, adaptive switching strategy of the power management state, and remaining available power of each device; An adjustment module for dynamically adjusting the damping factor magnitude of the device according to the real-time energy change rate; The adjustment module is further configured to dynamically adjust the adaptive switching strategy, switching sensitivity threshold, and switching delay time of the device's power management state according to the adaptive switching frequency of the device's power management state, the damping factor magnitude, and the energy fluctuation tolerance.

Images