A power management method based on cloud computing, a terminal device and a storage medium

Abstract

The present application relates to the technical field of cloud computing, in particular to a power management method based on cloud computing, a terminal device and a storage medium, in the present application, a cloud management platform dynamically divides virtual synergies according to power grid dispatching requirements, geographical continuity and electrical coupling, solves the problem of device configuration mismatch; based on the callable regulation capacity dataset, the total power regulation amount and the regulation time window of each synergy are calculated, the regulation amount is reduced in proportion when the boundary is updated, and the target value is avoided to exceed the device capacity; under the condition of meeting the total power regulation amount and the regulation time window constraint, the real-time adjustable power boundary value of itself is broadcast to other edge devices in the virtual synergy, and the power distribution scheme of the device is iteratively calculated and generated based on the received real-time adjustable power boundary value; finally, the power distribution scheme is executed and the actual regulation amount is fed back, and when the deviation rate exceeds the threshold, the synergy update or target recalculation is dynamically triggered, forming a closed-loop optimization mechanism.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud computing, in particular to a power management method based on cloud computing, a terminal device and a storage medium. BACKGROUND

[0002] The scale of distributed edge devices in the existing energy system continues to expand, and there are two key challenges in responding to grid dispatch instructions. The traditional cloud-edge collaborative architecture adopts a static device grouping strategy. When devices frequently access or exit, such as photovoltaic array start-stop or energy storage charge-discharge switching, the original device cluster topology cannot be dynamically updated, resulting in a serious mismatch between adjustment task allocation and electrical actual constraints, typically manifested as electrical island devices still being assigned adjustment tasks.

[0003] At the same time, the adjustment capabilities of devices differ significantly, with millisecond-level response energy storage devices coexisting with minute-level ramping gas turbines. Centralized optimization calculation in the cloud cannot adapt to the dynamic state of devices in real time, causing power oscillation or time window overrun in the adjustment process. For example, after a device cluster loses significant adjustment capability due to device exit, it is still forced to allocate the original power adjustment amount, and during execution, slow devices are forced to respond at high speed, triggering protection shutdown, ultimately causing grid frequency fluctuations. SUMMARY

[0004] The present application aims to solve the problems raised in the background art by providing a power management method based on cloud computing, a terminal device and a storage medium. The specific technical problems include how to build a flexible virtual collaborative body structure and implement a dynamic allocation mechanism with time and space constraints to solve the problem of collaborative adjustment failure caused by frequent device changes and response heterogeneity.

[0005] To achieve the above-mentioned purpose, one of the purposes of the present application is a power management method based on cloud computing, comprising the following method steps:

[0006] S1, the cloud management platform dynamically divides a plurality of virtual collaborative bodies according to the target adjustment direction, quantitative adjustment indicators, time constraint conditions and safety boundary parameters of the grid dispatch demand, the geographical continuity relationship of the devices within the distribution network branch line coverage, and the electrical coupling relationship formed by the distribution transformer, bus or feeder direct connection; when new devices are accessed or existing devices are exited, the electrical coupling relationship network is reconstructed based on the latest device topology, the membership is adjusted according to the real-time electrical connection strength and geographical coordinate coverage range, the electrical island formed by the device exit is integrated into the adjacent virtual collaborative body, or an independent virtual collaborative body is created for the new device group, and the updated member list is synchronized to the relevant local proxy node in real time; this scheme ensures that the collaborative body always maintains the physical realizability of geographical proximity of devices to reduce communication delay, and the electrical controllability of electrical coupling to ensure the effectiveness of power adjustment, eliminating the risk of instruction failure caused by device changes.

[0007] S2, the cloud management platform collects the callable adjustment capability dataset of all distributed edge devices in each virtual synergy body in real time, which includes device type, real-time output state, and adjustable power boundary value reported by the local agent node, calculates the absolute value of the total power adjustment amount that the virtual synergy body needs to bear according to the quantitative adjustment index, and generates an adjustment time window according to the time constraint condition. If a virtual synergy body boundary range update event is detected, the total adjustable power of the synergy body is recalculated based on the callable adjustment capability dataset of the remaining members after the device exits, the quantitative adjustment index of the power grid dispatching demand is proportionally reduced and distributed to the synergy body to generate a new total power adjustment amount absolute value, while the original adjustment time window remains unchanged to generate an updated active power synergy adjustment target; this process avoids the target value exceeding the actual capacity after the device exits, preventing subsequent iterative calculation divergence or execution deviation from exceeding the limit.

[0008] S3, after each edge device receives the active power synergy adjustment target through the local agent node, it broadcasts its real-time adjustable power boundary value to other devices in the virtual synergy body under the constraint conditions of total power adjustment amount and adjustment time window; through a distributed consensus algorithm, the sum of all device power distribution values gradually converges to the absolute value of the total power adjustment amount, while according to the continuous length and start and end nodes of the adjustment time window, based on the response speed characteristics determined by the device type and real-time output state, the device with fast response characteristics is assigned a high rate power task in the front segment of the adjustment time window, and the slow device is assigned a low rate power task in the rear segment, and the iteration continues until the sum of all device power distribution values is equal to the total power adjustment amount and the output change curve combination slope adapts to the rate framework of the adjustment time window; this scheme ensures accurate matching of the total power amount through spatial dimension rigid constraint, and eliminates power shock caused by response speed difference through time dimension flexible adaptation.

[0009] S4, after each edge device executes the power distribution scheme, it feeds back actual adjustment amount data such as actual power change value and cumulative adjustment energy within the time window to the cloud management platform; the cloud management platform aggregates all device feedback data in each virtual synergy body, calculates the deviation rate of the actual achieved value and the target value of the total active power adjustment, and if the deviation rate exceeds the set threshold, when the deviation is caused by the interruption of electrical coupling relationship or the fracture of geographical continuity within the virtual synergy body due to device exit, an instruction to update the boundary range of the virtual synergy body is triggered; when the deviation is caused by the total adjustable power of the remaining members of the virtual synergy body being lower than the original total power adjustment amount after the device exits, an instruction to recalculate the active power synergy adjustment target is triggered; this closed-loop mechanism forms a complete chain from device execution to cloud verification to dynamic optimization, ensuring continuous and effective convergence of the adjustment target in long-term operation.

[0010] The second object of the present application is a terminal device comprising a processor and a memory storing a computer program which, when executed, implements a cloud computing-based power management method.

[0011] The third object of the present application is a storage medium storing a computer program which, when executed, implements a cloud computing-based power management method.

[0012] Compared with the prior art, the present application has the following beneficial effects:

[0013] The cloud management platform dynamically divides the virtual collaborative body and updates the boundary range, improving the adaptability of power regulation tasks under the device access or exit scenario; by real-time collection of callable regulation capacity data set to calculate the total power regulation amount and regulation time window, and proportionally reducing the total power regulation amount when detecting boundary range update events, the executable of the target value is effectively guaranteed.

[0014] Relying on the device broadcast real-time adjustable power boundary value, the sum of the power distribution value is equal to the total power regulation amount and the output change curve slope combination adapts to the regulation time window rate framework, ensuring that the dual constraints of spatial and time dimensions are synchronously met; based on the deviation rate feedback mechanism of the actual achieved value and the target value of the total active power regulation, dynamically triggering the update of the virtual collaborative body boundary range or recalculating the active power collaborative regulation target, realizing closed-loop continuous optimization; ultimately improving the grid dispatching demand response accuracy and collaborative regulation reliability. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The present application is a whole method schematic diagram. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] Next, please refer to Figure 1 The first object of the present application is a cloud computing-based power management method, comprising the following steps:

[0018] S1, the cloud management platform dynamically divides the physically connected distributed edge device cluster into multiple virtual collaborative bodies according to the geographical continuity relationship and the electrical coupling relationship according to the grid dispatching demand;

[0019] S2, the cloud management platform calculates the active power cooperative regulation target of each virtual synergy, and the active power cooperative regulation target includes the total power regulation amount and the regulation time window, and issues the active power cooperative regulation target to the local agent node of each virtual synergy;

[0020] S3, after the edge devices in each virtual synergy receive the active power cooperative regulation target through the local agent node, under the condition of meeting the total power regulation amount and the regulation time window constraint, they broadcast their real-time adjustable power boundary value to other edge devices in the same virtual synergy, and iteratively calculate and generate the power distribution scheme of the device based on the received real-time adjustable power boundary value;

[0021] S4, each edge device executes the power distribution scheme and feeds back the actual regulation amount to the cloud management platform.

[0022] The specific process of the above steps is as follows:

[0023] S1, the cloud management platform obtains the topology information of the physically connected distributed edge device cluster according to the power grid dispatching demand, dynamically divides to form multiple virtual synergies based on the geographical continuity relationship and the electrical coupling relationship of the devices in the cluster; wherein the cloud management platform refers to the core control system deployed in the cloud computing infrastructure, which is the two-way interaction hub of the physically connected distributed edge device cluster and the upper power grid control system; the geographical continuity relationship refers to the characteristic that the physical location of the device is continuously distributed within the coverage range of the distribution network branch line; the electrical coupling relationship refers to the characteristic that the device is directly connected to form an electrical association through the distribution transformer, bus or feeder; the power grid dispatching demand refers to the structured instruction issued by the upper power grid control system to the cloud management platform, which includes four core elements: target regulation direction, quantitative regulation index, time constraint condition and safety boundary parameter, and the cloud management platform selects the device group that meets the geographical continuity relationship and the electrical coupling relationship in the physically connected distributed edge device cluster in S1 step, and dynamically divides to generate virtual synergies;

[0024] In the division process, the cloud management platform continuously detects the device online state and the communication link quality, and updates the boundary range of the virtual synergy when a new device is accessed or an existing device is exited; the virtual synergy established in this step provides a clear synergy unit framework for the subsequent steps, so that the cloud management platform can independently calculate the active power cooperative regulation target for each virtual synergy in step S2, while ensuring that the broadcast interaction of each edge device within the synergy in step S3 is physically achievable (due to the geographical proximity of the devices reducing communication delay) and electrically controllable (due to the electrical coupling ensuring the effectiveness of power regulation); wherein the process of updating the boundary range of the virtual synergy is as follows:

[0025] Based on the latest device topology, the electrical coupling relationship network is reconstructed, and the membership of the virtual collaborative body is adjusted according to the real-time electrical connection strength and the geographical coordinate coverage range, for example, the electrical island formed due to the device exit is integrated into the adjacent virtual collaborative body, or a new device group is created to form an independent virtual collaborative body. The updated member list is synchronized to the related local agent node in real time, ensuring that the accurate range of the broadcast interaction in the subsequent S4 step is always consistent with the latest power grid structure, maintaining the effectiveness of the active power collaborative adjustment target.

[0026] S2, the cloud management platform, based on the target adjustment direction, quantitative adjustment index and time constraint condition in the power grid dispatching demand, for each virtual collaborative body dynamically divided in S1 step, executes active power collaborative adjustment target calculation, specifically including:

[0027] First, the callable adjustment capability dataset of all distributed edge devices in each virtual collaborative body is collected in real time (including device type, real-time output state and adjustable power boundary value reported by the local agent node), which is verified in combination with the safety boundary parameters of the power grid dispatching demand; the absolute value of the total power adjustment amount that the virtual collaborative body needs to bear is calculated according to the quantitative adjustment index, and the corresponding adjustment time window is generated according to the time constraint condition; during the calculation process, the boundary range of the virtual collaborative body is dynamically adapted (such as reducing the total power adjustment amount when the adjustment capability is attenuated due to device exit), and finally the active power collaborative adjustment target containing the total power adjustment amount and the adjustment time window is generated; the complete target parameters in the active power collaborative adjustment target are issued to the local agent node of each virtual collaborative body through the power special communication protocol, which provides a collaborative constraint framework for step S3, ensuring that the edge devices always converge to the global adjustment task of the virtual collaborative body in the broadcast interaction and iterative calculation; wherein during the calculation of the active power collaborative adjustment target, if an update event of the boundary range of the virtual collaborative body in S1 step is detected (such as adjustment capability attenuation due to device exit), the following adaptation operation is executed in real time:

[0028] First, based on the callable adjustment capability dataset of the remaining members after the device exit, the total adjustable power of the virtual collaborative body is recalculated; then the quantitative adjustment index of the power grid dispatching demand is proportionally reduced and allocated to the virtual collaborative body to generate a new absolute value of the total power adjustment amount; at the same time, the original adjustment time window remains unchanged (because the time constraint condition has not changed), and finally the updated active power collaborative adjustment target is generated; this adaptation mechanism ensures that the total power adjustment amount issued always matches the actual adjustment capability of the collaborative body when the boundary range of the virtual collaborative body changes, preventing the iterative calculation from diverging or the execution deviation from exceeding the limit in S3 step due to the target value exceeding the device adjustable boundary.

[0029] S3, the edge devices in each virtual collaborator receive the active power collaborative adjustment target issued in step S2 through the local agent node, take the total power adjustment amount in the active power collaborative adjustment target as the upper limit of the global constraint of the virtual collaborator, and take the adjustment time window as the time constraint boundary, and execute local collaborative operation, which specifically includes:

[0030] First, each device broadcasts its real-time adjustable power boundary value (which inherits the core parameters in the callable adjustment capability data set collected in S2) to other edge devices in the same virtual collaborator through the local agent node in a unidirectional manner;

[0031] Subsequently, based on the received real-time adjustable power boundary values of all member devices of the virtual collaborator, iterative calculation is performed inside the local agent node, which dynamically generates a power allocation scheme that meets the total power adjustment amount requirement and adapts to the adjustment time window distribution rhythm through multiple rounds of boundary constraint exchange and output value negotiation; the iterative process continues until the sum of the allocation values of all devices converges to the absolute value of the total power adjustment amount, and the power change rate of each device meets the rate framework of the adjustment time window, ensuring that the output result fully matches the instructions of the active power collaborative adjustment target; during the iterative calculation stage, each local agent node performs multiple rounds of boundary constraint exchange and output value negotiation based on the received real-time adjustable power boundary values of the member devices of the virtual collaborator, which specifically includes:

[0032] First, a distributed consensus algorithm is used to gradually converge the sum of the power allocation values of all devices to the absolute value of the total power adjustment amount (spatial dimension rigid constraint); at the same time, according to the duration and start and end nodes of the adjustment time window, the adjustment task rhythm is dynamically allocated, the high change rate initial main adjustment amount is allocated to devices with fast response characteristics, which is used for high change rate power adjustment tasks allocated to devices with fast response characteristics, and the low change rate late smooth tracking amount is allocated to slow devices (time dimension flexible adaptation), which is used for low change rate power adjustment tasks allocated to slow response devices; only when the sum of the power allocation values of all devices is equal to the total power adjustment amount and the output change curve slope adapts to the rate framework of the adjustment time window, the iteration is terminated and the power allocation scheme is output; wherein the determination of fast response characteristics and slow devices is based on the device type and real-time output state parameters contained in the callable adjustment capability data set issued by the cloud management platform in step S2.

[0033] S4, each edge device performs the actual power adjustment operation according to the power distribution scheme generated in S3, and feeds back the actual adjustment amount data packet to the cloud management platform through the local agent node within the adjustment time window; wherein the actual adjustment amount includes the completion degree index of the distribution task undertaken by the device in the power distribution scheme (such as the actual power change value, the cumulative adjustment energy within the time window), the cloud management platform aggregates the feedback data of all edge devices in the virtual synergy body, calculates the deviation rate of the actual achievement value of the total active power adjustment of the virtual synergy body from the target value, and if the deviation rate exceeds the set threshold, triggers the update of the virtual synergy body boundary range in S1 or the recalculation of the active power collaborative adjustment target in S2; wherein:

[0034] If the deviation is caused by the device exit, which interrupts the electrical coupling relationship or breaks the geographical continuity within the virtual synergy body (specifically, an electrical island is formed or the device position is dispersed), an instruction to update the virtual synergy body boundary range is sent to step S1; if the deviation is caused by the device exit, which causes the total adjustable power of the remaining members of the virtual synergy body to be lower than the total power adjustment amount in the original active power collaborative adjustment target, an instruction to recalculate the active power collaborative adjustment target is sent to step S2; the feedback mechanism forms a closed-loop adjustment chain of "device execution -> cloud verification -> dynamic optimization", which ensures the effective convergence of the active power collaborative adjustment target, and provides a verifiable adjustment amount certificate for the power grid dispatching system.

[0035] The second purpose of the embodiment is to provide a terminal device, which includes a processor and a memory, and the memory stores a computer program which, when executed, implements a power management method based on cloud computing, specifically including:

[0036] The cloud management platform performs S1, dynamically divides the physically connected distributed edge device cluster into multiple virtual synergy bodies according to the geographical continuity relationship and the electrical coupling relationship according to the power grid dispatching demand; if a new device is detected to be connected or an existing device is detected to be disconnected during the division process, the boundary range of the virtual synergy body is updated, and the updated member list is synchronized to the local agent node;

[0037] The cloud management platform performs S2, calculates the active power collaborative adjustment target (including the total power adjustment amount and the adjustment time window) of each virtual synergy body, and issues the target to the local agent node of each virtual synergy body; if a virtual synergy body boundary range update event is detected, a new total power adjustment amount is generated based on the callable adjustment capability data set (including device type, real-time output state and adjustable power boundary value) in proportion;

[0038] Each edge device performs S3 step through the local agent node, broadcasts its real-time adjustable power boundary value to other devices in the same virtual community under the constraints of total power adjustment amount and adjustment time window, iteratively generates power allocation scheme through distributed consensus algorithm (allocation rule: allocate high change rate power task in the front segment of the adjustment time window to fast response characteristic devices, and allocate low change rate power task in the rear segment to slow devices), until the sum of power allocation values is equal to the total power adjustment amount and the combined slope of the output change curve adapts to the rate framework of the adjustment time window;

[0039] Each edge device performs the power allocation scheme of S4 step, and feeds back the actual adjustment amount to the cloud management platform; the cloud management platform calculates the deviation rate of the actual achieved value and the target value of the total active power adjustment, and if the threshold is exceeded, triggers the update of the virtual community boundary range or the recalculation of the active power cooperative adjustment target.

[0040] The third purpose of the embodiment is to provide a storage medium storing a computer program which, when executed, implements a cloud computing-based power management method, specifically comprising:

[0041] Read the S1 step algorithm from the medium, dynamically divide the virtual community according to the grid dispatching requirements (including target adjustment direction, quantitative adjustment index, time constraint condition and safety boundary parameter), continuously detect device status and update the virtual community boundary range when devices are accessed / withdrawn, and integrate the electrical island into the adjacent virtual community or create an independent virtual community for the newly added device group;

[0042] Call the S2 step program, collect the callable adjustment capability dataset of all devices in the virtual community, calculate the absolute value of the total power adjustment amount and the adjustment time window to generate the active power cooperative adjustment target, and issue it to the local agent node through the power special communication protocol; if a boundary range update event is identified, recalculate the total adjustable power and proportionally reduce the total power adjustment amount;

[0043] Load the S3 step iteration program, perform multiple rounds of boundary constraint exchange and output value negotiation in the local agent node based on the real-time adjustable power boundary value, dynamically allocate adjustment task rhythm, and finally output the power allocation scheme that meets the spatial dimension rigidity constraint and the time dimension flexible adaptation;

[0044] Perform the S4 step feedback logic to collect actual adjustment amount data packets (including cumulative adjustment energy within the time window), calculate the deviation rate, and trigger instructions when the threshold is exceeded: update the virtual community boundary range instruction when the electrical coupling relationship is interrupted or the geographical continuity is broken; recalculate the active power cooperative adjustment target instruction when the total adjustable power is lower than the original total power adjustment amount.

[0045] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only preferred examples of the present application and are not intended to limit the present application. Various changes and improvements can be made to the present application without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A power management method based on cloud computing, characterized in that, The methods and steps include the following: S1. The cloud management platform dynamically divides the physically connected distributed edge device clusters into multiple virtual collaborative entities according to the power grid dispatching requirements based on geographical continuity and electrical coupling relationships. The dynamic partitioning process specifically includes: When a new device is added or an existing device is removed, the boundary range of the virtual collaborative entity is updated. Based on the latest equipment topology, the electrical coupling relationship network is reconstructed. The affiliation of virtual cooperators is adjusted according to the real-time electrical connection strength and geographical coordinate coverage. Electrical islands formed by equipment withdrawal are merged into neighboring virtual cooperators, or independent virtual cooperators are created for new equipment groups. The updated member list is synchronized to the relevant local proxy nodes in real time; S2. The cloud management platform calculates the active power coordinated adjustment target of each virtual cooperating entity. The active power coordinated adjustment target includes the total power adjustment amount and the adjustment time window, and sends the active power coordinated adjustment target to the local agent node of each virtual cooperating entity. S3. After receiving the active power collaborative adjustment target through the local proxy node, each edge device in the virtual collaborative body broadcasts its own real-time adjustable power boundary value to other edge devices in the same virtual collaborative body, and generates the power allocation scheme of the device based on the received real-time adjustable power boundary value. S4. Each edge device executes the power allocation scheme and reports the actual adjustment amount to the cloud management platform.

2. The power management method based on cloud computing according to claim 1, characterized in that, The geographical continuity relationship in step S1 refers to the characteristic that the physical location of the equipment is continuously distributed within the coverage area of ​​the distribution network branch lines; the electrical coupling relationship refers to the characteristic that the equipment is electrically associated through direct connection with distribution transformers, buses or feeders; the power grid dispatching requirements include four core elements: target adjustment direction, quantitative adjustment index, time constraint conditions and safety boundary parameters.

3. The power management method based on cloud computing according to claim 1, characterized in that, In step S2, when calculating the active power coordinated adjustment target, the callable adjustment capability dataset of all distributed edge devices in each virtual cooperative body is collected in real time. The callable adjustment capability dataset includes device type, real-time output status and adjustable power boundary value reported by local agent node; the absolute value of the total power adjustment of the virtual cooperative body is calculated according to the quantitative adjustment index, and an adjustment time window is generated according to the time constraint condition.

4. The power management method based on cloud computing according to claim 1, characterized in that, When a virtual cooperative entity boundary range update event is detected in step S2, the following operations are performed: Based on the dataset of callable adjustment capabilities of the remaining members after the device exits, the total adjustable power of the virtual consortium is recalculated; The quantitative adjustment indicators of power grid dispatch demand are proportionally reduced and allocated to this virtual cooperative entity to generate a new absolute value of total power adjustment. Keeping the original adjustment time window unchanged, an updated active power coordinated adjustment target is generated.

5. The power management method based on cloud computing according to claim 1, characterized in that, The iterative calculation process in step S3 specifically includes: The power allocation values ​​of all devices are converged to the absolute value of the total power adjustment through a distributed consensus algorithm. The task rhythm is dynamically allocated based on the duration and start and end points of the adjustment time window; The iteration terminates when the sum of the power allocation values ​​of all devices is always equal to the total power adjustment and the slope of the combined output change curves fits the rate frame of the adjustment time window.

6. The cloud computing-based power management method according to claim 5, characterized in that, The process of dynamically allocating and adjusting the task rhythm specifically includes: High-rate-of-change power tasks are assigned to devices with fast response times during the early part of the adjustment time window, while low-rate-of-change power tasks are assigned to devices with slow response times during the later part of the adjustment time window. The criteria for determining the fast response characteristics come from the device types and real-time output status parameters contained in the callable adjustment capability dataset issued by the cloud management platform in step S2.

7. The power management method based on cloud computing according to claim 1, characterized in that, After the actual adjustment amount is fed back in S4, the cloud management platform calculates the deviation rate between the actual achieved value and the target value of the total active power adjustment of the virtual collaborative body. If the deviation rate exceeds the set threshold, one of the following operations is triggered: When a deviation causes the electrical coupling within the virtual collaborator to be interrupted or the geographical continuity to be broken due to the device exiting, the boundary range of the virtual collaborator is updated. When the total adjustable power of the remaining members of the virtual collaborator is lower than the original total power adjustment amount after the deviation is removed from the device, the active power collaborative adjustment target is recalculated.

8. A terminal device, characterized in that, It includes a processor and a memory, wherein the memory stores a computer program that, when executed, implements the cloud-based power management method as described in any one of claims 1-7.

9. A storage medium, characterized in that, A computer program is stored that, when executed, implements the cloud-based power management method as described in any one of claims 1-7.

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