CN110320813B - Power management method and device for Internet of things equipment - Google Patents
Power management method and device for Internet of things equipment Download PDFInfo
- Publication number
- CN110320813B CN110320813B CN201910691529.5A CN201910691529A CN110320813B CN 110320813 B CN110320813 B CN 110320813B CN 201910691529 A CN201910691529 A CN 201910691529A CN 110320813 B CN110320813 B CN 110320813B
- Authority
- CN
- China
- Prior art keywords
- power management
- target
- task
- time
- management strategy
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
Abstract
The invention provides a power management method and a power management device for equipment of the Internet of things, wherein the method comprises the following steps: acquiring task information parameters uploaded by the intelligent household appliance; determining a target power management strategy according to the task information parameters; and sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for indicating the intelligent household appliance to carry out power management on the system according to the target power management strategy, so that the problem that the dynamic power management cannot simultaneously ensure low power consumption and low delay in the related technology can be solved, and the power consumption of the intelligent household appliance is cooperatively managed by combining various power management strategies, namely, the optimal power management strategy is determined from the various power management strategies based on the task information parameters of the intelligent household appliance, so that the effect of simultaneously ensuring the low power consumption and the low delay is realized.
Description
Technical Field
The invention relates to the field of intelligent home furnishing, in particular to an intelligent household appliance control method and device based on an internet of things operating system.
Background
In the prior art, the timeout strategy is characterized in that the response performance of an algorithm can be adjusted by controlling a time limit value, but the algorithm can be in a sleep mode after waiting for a period of time no matter how long the idle time is, so that certain power consumption is wasted; the prediction strategy is not stable in efficiency when the task request in the non-steady state is carried out, the response delay cannot be effectively controlled, and the compromise control between the power consumption and the response performance is carried out; the disadvantage of the random strategy is that the service requests are assumed to follow a known distribution, thus limiting their applicability.
With the development of the application of the internet of things technology, more and more objects are embedded into microcomputer equipment, and the objects are controlled by the microcomputer and are networked with other objects. These things include intelligent homes such as intelligent electric light, intelligent refrigerator, intelligent robot, intelligent audio amplifier of sweeping the floor. They all have common characteristics, namely limited hardware resources and computing power, strict heating control and low power consumption requirements.
In order to reduce the power consumption of smart homes, a concept of Dynamic Power Management (DPM) is proposed in the design of an operating system.
The technical problem of the existing DPM management strategy is that the reduction of power consumption can be ensured and the response delay can be controlled at the same time.
No solution has been proposed to the problem in the related art that dynamic power management cannot ensure low power consumption and low latency at the same time.
Disclosure of Invention
The embodiment of the invention provides an intelligent household appliance control method and device based on an Internet of things operating system, and aims to at least solve the problem that dynamic power management cannot simultaneously ensure low power consumption and low delay in the related art.
According to one embodiment of the invention, an intelligent household appliance control method based on an operating system of the Internet of things is provided, and comprises the following steps:
acquiring task information parameters uploaded by the intelligent household appliance;
determining a target power management strategy according to the task information parameters;
and sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for indicating the intelligent household appliance to carry out power management on a system according to the target power management strategy.
Optionally, the determining a target power management policy according to the task information parameter includes:
the task information parameters comprise: under the condition that n tasks, a first moment when the n tasks enter a system state and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1;
performing the following for each of the n tasks, wherein the each task performing the following is referred to as a target task:
determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set;
and determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy.
Optionally, determining a second time at which the target task is expected to be in the system state according to other power management policies except the initial power management policy in the pre-saved power management policy set includes:
under the condition that the other power management strategies comprise a plurality of power management strategies, calculating a plurality of second times that the target task is expected to be in the system state according to the plurality of power management strategies;
determining the target power management policy for the target task based on the first time and the second time comprises:
comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task;
and determining a target power management strategy of the target task according to the comparison results.
Optionally, comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task includes:
respectively subtracting the plurality of second times from the first time of the target task to obtain a plurality of difference values of the target task;
determining a target power management policy of the target task according to the plurality of comparison results comprises:
determining a second time corresponding to a difference value smaller than or equal to a preset threshold value in the plurality of difference values as a target state time;
and determining the power management strategy corresponding to the target state time as the target power management strategy of the target task.
Optionally, determining, as the target state time, a second time corresponding to a difference value smaller than or equal to a predetermined threshold value among the plurality of difference values includes:
and under the condition that more than one difference value which is smaller than or equal to a preset threshold value is selected from the difference values, selecting a second time corresponding to the minimum difference value which is smaller than or equal to the preset threshold value from the difference values to determine the second time as the target state time.
Optionally, the operation instruction carries a target index number of the target power management policy, and the operation instruction is used to instruct the intelligent appliance to perform power management on a system according to the target power management policy corresponding to the target index number.
Optionally, before sending the operation instruction to the smart appliance, the method further includes:
and acquiring a target index number of the target power management strategy from a pre-stored corresponding relation between the power management strategy and the index number.
According to another embodiment of the present invention, there is also provided an intelligent home appliance control method based on an internet of things operating system, including:
acquiring task information parameters and sending the task information parameters to a cloud server;
receiving an operation instruction returned by the cloud server according to the task information parameter, wherein the operation instruction carries a target power management strategy;
and performing power management on the system according to the target power management strategy.
Optionally, performing power management on the system according to the target power management policy includes:
under the condition that the operation instruction carries a target index number of a target power management strategy, acquiring the target power management strategy corresponding to the target index number;
and performing power management on the system according to the target power management strategy.
According to another embodiment of the present invention, there is also provided an intelligent appliance control device based on an internet of things operating system, including:
the first acquisition module is used for acquiring task information parameters uploaded by the intelligent household appliance;
the determining module is used for determining a target power management strategy according to the task information parameters;
the first sending module is used for sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for indicating the intelligent household appliance to carry out power management on a system according to the target power management strategy.
Optionally, the determining module includes:
a determining submodule, configured to, at the task information parameter, include: under the condition that n tasks, a first moment when the n tasks enter a system state and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1;
an execution submodule, configured to perform the following operations on each of the n tasks, where each task that performs the following operations is referred to as a target task:
determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set;
and determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy.
Optionally, the execution submodule includes:
the computing unit is used for computing a plurality of second times of the target task in the system state according to the plurality of power management strategies respectively under the condition that the other power management strategies comprise a plurality of power management strategies;
the comparison unit is used for comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task;
and the determining unit is used for determining a target power management strategy of the target task according to the comparison results.
Optionally, the comparison unit is also used for
Respectively subtracting the plurality of second times from the first time of the target task to obtain a plurality of difference values of the target task;
the determination unit is also used for
Determining a second time corresponding to a difference value smaller than or equal to a preset threshold value in the plurality of difference values as a target state time;
and determining the power management strategy corresponding to the target state time as the target power management strategy of the target task.
Optionally, the determination unit is further configured to
And under the condition that more than one difference value which is smaller than or equal to a preset threshold value is selected from the difference values, selecting a second time corresponding to the minimum difference value which is smaller than or equal to the preset threshold value from the difference values to determine the second time as the target state time.
Optionally, the operation instruction carries a target index number of the target power management policy, and the operation instruction is used to instruct the intelligent appliance to perform power management on a system according to the target power management policy corresponding to the target index number.
Optionally, before sending the operation instruction to the smart appliance, the apparatus further includes:
and the second acquisition module is used for acquiring the target index number of the target power management strategy from the corresponding relation between the pre-stored power management strategy and the index number.
According to another embodiment of the present invention, there is also provided an intelligent appliance control device based on an internet of things operating system, including:
the second sending module is used for acquiring the task information parameters and sending the task information parameters to the cloud server;
the receiving module is used for receiving an operation instruction returned by the cloud server according to the task information parameter, wherein the operation instruction carries a target power management strategy;
and the management module is used for carrying out power management on the system according to the target power management strategy.
Optionally, the management module includes:
the obtaining submodule is used for obtaining a target power management strategy corresponding to a target index number under the condition that the target index number of the target power management strategy is carried in the operation instruction;
and the management submodule is used for carrying out power management on the system according to the target power management strategy.
According to a further embodiment of the present invention, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present invention, there is also provided an electronic device, including a memory in which a computer program is stored and a processor configured to execute the computer program to perform the steps in any of the above method embodiments.
According to the invention, the task information parameters uploaded by the intelligent household appliance are obtained; determining a target power management strategy according to the task information parameters; and sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for indicating the intelligent household appliance to carry out power management on the system according to the target power management strategy, so that the problem that the dynamic power management cannot simultaneously ensure low power consumption and low delay in the related technology can be solved, and the power consumption of the intelligent household appliance is cooperatively managed by combining various power management strategies, namely, the optimal power management strategy is determined from the various power management strategies based on the task information parameters of the intelligent household appliance, so that the effect of simultaneously ensuring the low power consumption and the low delay is realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a block diagram of a hardware structure of a mobile terminal of an intelligent household appliance control method based on an internet of things operating system according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an Internet of things operating system power state transition, according to an embodiment of the invention;
fig. 3 is a first flowchart of a method for controlling an intelligent appliance based on an operating system of the internet of things according to an embodiment of the present invention;
fig. 4 is a block diagram of a structure of an internet of things cloud server system according to an embodiment of the present invention;
fig. 5 is an architecture diagram of an operating system of an intelligent appliance according to an embodiment of the present invention;
fig. 6 is a second flowchart of an intelligent household appliance control method based on an internet of things operating system according to an embodiment of the present invention;
fig. 7 is a flowchart of a power management method of an intelligent appliance according to an embodiment of the present invention;
fig. 8 is a first block diagram of an intelligent household appliance control device based on an operating system of the internet of things according to an embodiment of the present invention;
fig. 9 is a second block diagram of the intelligent household appliance control device based on the internet of things operating system according to the embodiment of the invention.
Detailed Description
The invention will be described in detail hereinafter with reference to the drawings and embodiments. It should be noted that, in the present application, the embodiments and features of the embodiments may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example 1
The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Taking a mobile terminal as an example, fig. 1 is a block diagram of a hardware structure of the mobile terminal of an intelligent appliance control method based on an internet of things operating system according to an embodiment of the present invention, and as shown in fig. 1, a mobile terminal 10 may include one or more processors 102 (only one is shown in fig. 1) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), and a memory 104 for storing data, and optionally, the mobile terminal may further include a transmission device 106 for a communication function and an input/output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration, and does not limit the structure of the mobile terminal. For example, the mobile terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to the message receiving method in the embodiment of the present invention, and the processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, so as to implement the method described above. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the mobile terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio FrequeNcy (RF) module, which is used to communicate with the internet in a wireless manner.
An operating system may have several different power states, energy requirements of each power state are different, which may imply a potential energy saving space, fig. 2 is a schematic diagram of power state transition of an operating system of an internet of things according to an embodiment of the present invention, as shown in fig. 2, including:
1. task 0, task 1, and task 2 are three tasks with different priorities, and when the system runs in a certain task state, the system can respond to an interrupt handler to process an interrupt, can be requested to sleep to enter a sleep state, and can also return to an idle state. Different tasks require different power states, for example, a lamp switch does not need a high processor speed, the frequency of a processor can be adjusted to be low, remote communication control of the intelligent refrigerator requires complex processing operation, the processor needs to be adjusted to the highest frequency under general conditions, and a proper power state needs to be selected according to different task needs.
2. The system enters idle, and can be awakened by interrupt at the moment to process interrupt; DPM provides a managed idle mode that can save power more intelligently.
3. After the interrupt processing program is received to process the interrupt, the system can return to the task state from the interrupt, and can also continue to return to the idle state.
4. The system may enter a sleep mode when in the task state via a request to sleep, e.g., the system may suspend to memory.
From the above analysis, DPM can be classified into three types: system suspend and resume, peripheral power management, and fine-grained power management. System suspend and resume is a coarse-grained dynamic power management used to put the system into sleep when it needs to sleep for a long time or to wake up the system when it needs to; the peripheral power supply management user closes or restores the peripheral in the system; fine power management is used in situations where the power state needs to be changed frequently and at a fine granularity.
The control strategies adopted can be broadly divided into timeout strategies, prediction strategies and random strategies. The basic idea of the time-out strategy is to determine a time limit value based on observed idle time data and to switch to the corresponding sleep mode as soon as the sustained idle time exceeds this time limit.
The prediction strategy predicts the idle time length at the beginning according to a certain rule, once the predicted value exceeds a specific switching threshold value, the power manageable components such as a memory, a CPU, a communication device and the like are switched to a corresponding sleep mode at the beginning, and otherwise, the ready state is kept. The prediction strategies comprise an exponential averaging method, an adaptive learning tree model method, a nonlinear recursive model strategy according to historical tasks and idle time lengths, a time series prediction method of weighted moving average and the like. Taking the exponential averaging method as an example, the exponential averaging method is given by formula (1).
Wherein, InIndicating the predicted current idle time length, In-1Indicates the length of idle time of the last prediction, in-1Represents the last actual idle time length, alpha represents the correlation factor, and alpha is more than or equal to 0 and less than or equal to 1. As shown in equation (1), the exponential-mean prediction method is a recursive algorithm that uses a recursive loop to make nullsThe history of idle times is used in the current prediction. When α is 0, the equation In＝In-1It means that the current prediction is only relevant to the last prediction and not to the history. When α is 1, the equation In＝in-1It means that the current prediction is not related to the last prediction, but to the history. By taking different values for the correlation factor alpha, the degree of correlation of the prediction algorithm with the last predicted value and the actual idle time can be adjusted. Now, equation (1) is developed into equation (2).
The prediction algorithm predicts the length of the current idle time by using the historical record of the idle time as a prediction parameter in a way that the time gradually attenuates from near to far. The prediction algorithm can be adaptively adjusted according to the idle state change of the system. And when the current idle time is predicted to be less than or equal to the idle time threshold, the power consumption mode of the equipment is not switched. Otherwise, if the value is larger than the threshold value, the device is converted into the sleep mode after entering the idle state.
The random strategy is regarded as a random optimization problem, and a random decision model is utilized to solve a control algorithm. The basic idea of the stochastic strategy is that when an event occurs in the system, such as the end of a task, the operating state of the relevant device can be changed, i.e. the device enters the energy-saving state, or the device is switched from the energy-saving state to the operating state. This is an event driven (Events Drive) mechanism, i.e. the occurrence of an event causes a change in the operating state. Because the occurrence of the event is random, the transition of the working state is also random. The stochastic management strategy can eliminate the waiting time in the delayed closing management strategy or the prediction time in the prediction management strategy, so the energy saving effect is better in the aspect. However, when the operation state of the device is switched too frequently, the switching process consumes more energy, and the energy saving effect is not necessarily good, which requires an optimization process. Therefore, the random management process of the power supply is a random optimization process, and the state transition process of the devices in the system needs to be regarded as a random process, such as a Markov process.
Based on the mobile terminal, in this embodiment, an intelligent household appliance control method based on an internet of things operating system is provided, and fig. 3 is a first flowchart of the intelligent household appliance control method based on the internet of things operating system according to the embodiment of the present invention, and as shown in fig. 3, the flowchart includes the following steps:
step S302, task information parameters uploaded by the intelligent household appliance are obtained;
step S304, determining a target power management strategy according to the task information parameters;
step S306, sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for instructing the intelligent household appliance to perform power management on a system according to the target power management strategy.
Optionally, before the step S306, a target index number of the target power management policy is obtained from a pre-stored corresponding relationship between the power management policy and the index number. And sending the target video-following number to the intelligent household appliance, wherein correspondingly, the operation instruction carries the target index number of the target power management strategy, and the intelligent household appliance is instructed to carry out power management on the system according to the target power management strategy corresponding to the target index number.
Acquiring task information parameters uploaded by the intelligent household appliance through the steps S302 to S306; determining a target power management strategy according to the task information parameters; and sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for indicating the intelligent household appliance to carry out power management on the system according to the target power management strategy, so that the problem that the dynamic power management cannot simultaneously ensure low power consumption and low delay in the related technology can be solved, and the power consumption of the intelligent household appliance is cooperatively managed by combining various power management strategies, namely, the optimal power management strategy is determined from the various power management strategies based on the task information parameters of the intelligent household appliance, so that the effect of simultaneously ensuring the low power consumption and the low delay is realized.
The embodiment provides an internet of things cloud server system, which comprises a cloud server and intelligent household appliances, and fig. 4 is a structural block diagram of the internet of things cloud server system according to the embodiment of the invention, as shown in fig. 4, the cloud server is in data communication with the intelligent household appliances, receives task information parameters from the intelligent household appliances, and sends operation instructions to the intelligent household appliances; the cloud server stores a plurality of power management strategies and task information parameters from the intelligent household appliances, analyzes data information from the intelligent household appliances, determines a target power management strategy according to the task information parameters, sends an operation instruction to the intelligent household appliances, and the intelligent household appliances perform power management on the system according to the target power management strategy carried in the operation instruction.
Fig. 5 is an architecture diagram of an operating system of an intelligent appliance according to an embodiment of the present invention, and as shown in fig. 5, the intelligent appliance includes an operating system, and further includes hardware, a device driver layer, a kernel layer, a middle component layer, and an application layer.
The kernel layer comprises a Dynamic Power Management (DPM) module, the DPM module is used for collecting system state information and task information, the system state comprises a working state, an idle state and a dormant state, and the task information comprises idle state entering time, sleep entering time, awakening starting time, closing starting time, working starting time and the like; counting task information executed by system equipment and interpreting the task information into accurate task information parameters; and integrating a plurality of power management strategies and containing index codes of the power management strategies, wherein the index codes are consistent with the codes of the power management strategies stored in the cloud server. The power management strategy comprises a delay closing method in a timeout strategy, an exponential averaging method, a self-adaptive learning tree model method, a nonlinear recursive model strategy according to historical tasks and idle time lengths, a time sequence prediction method of weighted moving average, a Markov decision process model method in a random strategy and the like in a prediction strategy; and the power supply control module selects an optimal power supply management strategy to carry out power supply management on the system equipment according to the operation instruction issued by the cloud server.
And the intermediate component layer is used for uploading information of the intelligent household appliance to the cloud server, the uploaded information comprises task information parameters generated by the DPM module, and the Internet of things protocol stack comprises an NB-IOT protocol, a LoRa protocol, a Sigfox protocol, a 5G communication protocol and the like. In addition, the middle component layer also contains a DPM API interface through which a user program can call DPM functions.
And obtaining system information and task information from the operating system and the hardware equipment, and sending the task information parameters to the cloud server. And the cloud server issues the operation instruction to the DPM module, and the DPM module selects an optimal power management strategy according to the instruction to perform power management on the intelligent household appliance.
In an embodiment of the present invention, the step S304 may specifically include:
the task information parameters comprise: under the condition that n tasks, a first moment when the n tasks enter a system state and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1;
performing the following for each of the n tasks, wherein the each task performing the following is referred to as a target task:
determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set;
and determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy.
Optionally, determining the second time at which the target task is expected to be in the system state according to other power management policies in the pre-saved set of power management policies except the initial power management policy may include:
under the condition that the other power management strategies comprise a plurality of power management strategies, calculating a plurality of second times that the target task is expected to be in the system state according to the plurality of power management strategies;
determining the target power management policy for the target task based on the first time and the second time comprises:
comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task;
and determining a target power management strategy of the target task according to the comparison results.
Further, comparing the first time with the plurality of second times, respectively, to obtain a plurality of comparison results of the target task may include:
respectively subtracting the plurality of second times from the first time of the target task to obtain a plurality of difference values of the target task;
determining a target power management policy of the target task according to the comparison results comprises:
determining a second time corresponding to a difference value smaller than or equal to a preset threshold value in the plurality of difference values as a target state time;
and determining the power management strategy corresponding to the target state time as the target power management strategy of the target task.
Optionally, determining, as the target state time, a second time corresponding to a difference that is less than or equal to a predetermined threshold in the plurality of differences may specifically include:
and under the condition that more than one difference value which is smaller than or equal to a preset threshold value is selected from the difference values, selecting a second time corresponding to the minimum difference value which is smaller than or equal to the preset threshold value from the difference values to determine as the target state time.
Counting the stored n tasks, and setting the time for the kth task to enter an idle state after the execution of the kth task is finished as T1(corresponding to the first time mentioned above), the start execution/working time of the (k + 1) th task thereafter is T2(corresponding to the second time above), then the idle state time T for task kK＝T2-T1. According to the method, the idle state time of 1 to n tasks stored in a data storage module is sequentially calculated and stored, wherein the task n is the current task, K, N is an integer larger than 1, and K is smaller than or equal to n;
respectively calculating the predicted idle time t of the task (n-1) by traversing the prediction strategies stored in the data storage module except for the delayed closing methodn-1(the predicted idle time is calculated based on a prediction strategy), and the actual idle time of the task (n-1) is set as Tn-1If T isn-1And tn-1The relationship between them satisfies the formula: i Tn-1-tn-1And if the | is less than or equal to the δ, taking the prediction strategy corresponding to the predicted idle time as the optimal power management strategy, wherein the δ is a preset threshold value.
If there are multiple prediction strategies that satisfy the above formula, | T is selectedn-1-tn-1And the strategy with the minimum value of | is taken as the optimal power management strategy, namely the prediction strategy with the idle time obtained by prediction being closest to the actual idle time.
Example 2
According to another embodiment of the present invention, there is also provided an intelligent household appliance control method based on an internet of things operating system, fig. 6 is a flowchart of a second intelligent household appliance control method based on an internet of things operating system according to an embodiment of the present invention, as shown in fig. 6, applied to an intelligent household appliance, the process includes the following steps:
step S602, acquiring task information parameters and sending the task information parameters to a cloud server;
step S604, receiving an operation instruction returned by the cloud server according to the task information parameter, wherein the operation instruction carries a target index number of a target power management policy;
and step S606, performing power management on the system according to the target power management strategy corresponding to the target index number.
Through the steps S602 to S606, the intelligent household appliance obtains the task information parameters, sends the task information parameters to the cloud server, and performs power management according to the operation instruction in the cloud server, so that the problem that dynamic power management cannot simultaneously ensure low power consumption and low delay in the related art can be solved, and multiple power management strategies are combined to cooperatively manage the power consumption of the intelligent household appliance, that is, an optimal power management strategy is determined from the multiple power management strategies based on the task information parameters of the intelligent household appliance, so that the effect of simultaneously ensuring low power consumption and low delay is achieved.
Optionally, the step S606 may specifically include:
under the condition that the operation instruction carries a target index number of a target power management strategy, acquiring the target power management strategy corresponding to the target index number;
and performing power management on the system according to the target power management strategy.
Based on the internet of things cloud server system, detailed description is given below of power management between the cloud server and the intelligent household appliance.
Fig. 7 is a flowchart of a power management method for an intelligent home appliance according to an embodiment of the present invention, and as shown in fig. 7, the method specifically includes the following steps:
step S701, in an initial state of the intelligent household appliance operating system, the operating system performs power management on the intelligent household appliance by adopting a delayed closing method, and the intelligent household appliance acquires system state information and task information. The system state comprises a working state, an idle state and a dormant state, and the task information comprises idle state entering time, sleep entering time, wake-up starting time, closing starting time, working starting time and the like;
step S702, the intelligent household appliance interprets the collected system state information and task information into accurate task information parameters and uploads the task information parameters to a cloud server;
step S703, the cloud server stores the task information parameters from the intelligent household electrical appliance;
step S704, the cloud server analyzes the task information parameters, selects an optimal power management policy, and sends an index number of the optimal power management policy to the intelligent appliance in the form of an operation instruction.
Step S705, the intelligent household appliance receives an operation instruction issued by the cloud server, selects an optimal power management strategy according to the strategy index number in the operation instruction, and performs power management on the system equipment.
Through the description of the foregoing embodiments, it is clear to those skilled in the art that the method according to the foregoing embodiments may be implemented by software plus a necessary general hardware platform, and certainly may also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 3
In this embodiment, an intelligent household appliance control device based on an operating system of the internet of things is further provided, and the device is used for implementing the above embodiments and preferred embodiments, and is not described again after being described. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 8 is a first block diagram of an intelligent appliance control device based on an internet of things operating system according to an embodiment of the present invention, as shown in fig. 8, including:
the first obtaining module 82 is configured to obtain task information parameters uploaded by the intelligent appliance;
a determining module 84, configured to determine a target power management policy according to the task information parameter;
the first sending module 86 is configured to send an operation instruction to the intelligent household appliance, where the operation instruction carries the target power management policy, and the operation instruction is used to instruct the intelligent household appliance to perform power management on a system according to the target power management policy.
Optionally, the determining module 84 includes:
a determining submodule, configured to, at the task information parameter, include: under the condition that n tasks, a first moment when the n tasks enter a system state and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1;
an execution submodule, configured to perform the following operations on each of the n tasks, where each task that performs the following operations is referred to as a target task:
determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set;
and determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy.
Optionally, the execution submodule includes:
the computing unit is used for computing a plurality of second times of the target task in the system state according to the plurality of power management strategies respectively under the condition that the other power management strategies comprise a plurality of power management strategies;
the comparison unit is used for comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task;
and the determining unit is used for determining a target power management strategy of the target task according to the comparison results.
Optionally, the comparison unit is also used for
Respectively subtracting the plurality of second times from the first time of the target task to obtain a plurality of difference values of the target task;
the determination unit is also used for
Determining a second time corresponding to a difference value smaller than or equal to a preset threshold value in the plurality of difference values as a target state time;
and determining the power management strategy corresponding to the target state time as the target power management strategy of the target task.
Optionally, the determination unit is further configured to
And under the condition that more than one difference value which is smaller than or equal to a preset threshold value is selected from the difference values, selecting a second time corresponding to the minimum difference value which is smaller than or equal to the preset threshold value from the difference values to determine the second time as the target state time.
Optionally, the operation instruction carries a target index number of the target power management policy, and the operation instruction is used to instruct the intelligent appliance to perform power management on a system according to the target power management policy corresponding to the target index number.
Optionally, before sending the operation instruction to the smart appliance, the apparatus further includes:
and the second acquisition module is used for acquiring the target index number of the target power management strategy from the corresponding relation between the pre-stored power management strategy and the index number.
Example 4
According to another embodiment of the present invention, there is also provided an intelligent household appliance control apparatus based on an operating system of the internet of things, where fig. 9 is a block diagram ii of the intelligent household appliance control apparatus based on an operating system of the internet of things according to the embodiment of the present invention, as shown in fig. 9, the apparatus includes:
the second sending module 92 is configured to obtain task information parameters and send the task information parameters to the cloud server;
a receiving module 94, configured to receive an operation instruction returned by the cloud server according to the task information parameter, where the operation instruction carries a target power management policy;
and the management module 96 is configured to perform power management on the system according to the target power management policy.
Optionally, the management module 96 includes:
the obtaining sub-module is used for obtaining a target power management strategy corresponding to a target index number under the condition that the target index number of the target power management strategy is carried in the operation instruction;
and the management submodule is used for carrying out power management on the system according to the target power management strategy.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
Example 5
An embodiment of the present invention further provides a storage medium having a computer program stored therein, wherein the computer program is configured to perform the steps in any of the method embodiments described above when executed.
Alternatively, in the present embodiment, the storage medium may be configured to store a computer program for executing the steps of:
s11, acquiring task information parameters uploaded by the intelligent household appliance;
s12, determining a target power management strategy according to the task information parameters;
and S13, sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for instructing the intelligent household appliance to perform power management on a system according to the target power management strategy.
Optionally, in this embodiment, the storage medium may be further configured to store a computer program for executing the following steps:
s21, acquiring task information parameters and sending the task information parameters to a cloud server;
s22, receiving an operation instruction returned by the cloud server according to the task information parameter, wherein the operation instruction carries a target power management strategy;
and S23, performing power management on the system according to the target power management strategy.
Optionally, in this embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-ONly Memory (ROM), a RaNdom Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store computer programs.
Example 6
Embodiments of the present invention also provide an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s11, acquiring task information parameters uploaded by the intelligent household appliance;
s12, determining a target power management strategy according to the task information parameters;
and S13, sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for instructing the intelligent household appliance to perform power management on a system according to the target power management strategy.
Optionally, in this embodiment, the processor may be further configured to execute, by the computer program, the following steps:
s21, acquiring task information parameters and sending the task information parameters to a cloud server;
s22, receiving an operation instruction returned by the cloud server according to the task information parameter, wherein the operation instruction carries a target power management strategy;
and S23, performing power management on the system according to the target power management strategy.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized in a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a memory device and executed by a computing device, and in some cases, the steps shown or described may be executed out of order, or separately as individual integrated circuit modules, or multiple modules or steps thereof may be implemented as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the principle of the present invention should be included in the protection scope of the present invention.
Claims (12)
1. An intelligent household appliance control method based on an internet of things operating system is characterized by comprising the following steps:
acquiring task information parameters uploaded by the intelligent household appliance;
determining a target power management policy according to the task information parameters, comprising: the task information parameters comprise: under the conditions of n tasks, a first moment when the n tasks enter a system state, and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1; performing the following for each of the n tasks, wherein the each task performing the following is referred to as a target task: determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set; determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy;
and sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for indicating the intelligent household appliance to carry out power management on a system according to the target power management strategy.
2. The method of claim 1,
determining a second time at which the target task is expected to be in the system state according to other power management policies except the initial power management policy in the pre-saved set of power management policies comprises:
under the condition that the other power management strategies comprise a plurality of power management strategies, calculating a plurality of second times that the target task is expected to be in the system state according to the plurality of power management strategies;
determining the target power management policy for the target task based on the first time and the second time comprises:
comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task;
and determining a target power management strategy of the target task according to the comparison results.
3. The method of claim 2,
comparing the first time with the plurality of second times respectively to obtain a plurality of comparison results of the target task, wherein the comparison results comprise:
respectively subtracting the plurality of second times from the first time of the target task to obtain a plurality of difference values of the target task;
determining a target power management policy of the target task according to the comparison results comprises:
determining a second time corresponding to a difference value smaller than or equal to a preset threshold value in the plurality of difference values as a target state time;
and determining the power management strategy corresponding to the target state time as the target power management strategy of the target task.
4. The method of claim 3, wherein determining a second time corresponding to a difference value of the plurality of difference values that is less than or equal to a predetermined threshold value as the target state time comprises:
and under the condition that more than one difference value which is smaller than or equal to a preset threshold value is selected from the difference values, selecting a second time corresponding to the minimum difference value which is smaller than or equal to the preset threshold value from the difference values to determine the second time as the target state time.
5. The method according to any one of claims 1 to 4, wherein the operation instruction carries a target index number of the target power management policy, and the operation instruction is used to instruct the intelligent appliance to perform power management on a system according to the target power management policy corresponding to the target index number.
6. The method of claim 5, wherein prior to sending the operating instructions to the smart appliance, the method further comprises:
and acquiring a target index number of the target power management strategy from a pre-stored corresponding relation between the power management strategy and the index number.
7. An intelligent household appliance control method based on an Internet of things operating system is characterized by comprising the following steps:
acquiring task information parameters and sending the task information parameters to a cloud server;
receiving an operation instruction returned by the cloud server according to the task information parameters, wherein the operation instruction carries a target power management strategy, the target power management strategy is determined according to the task information parameters, and the task information parameters comprise: under the conditions of n tasks, a first moment when the n tasks enter a system state, and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1; performing the following for each of the n tasks, wherein the each task performing the following is referred to as a target task: determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set; determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy;
and performing power management on the system according to the target power management strategy.
8. The method of claim 7, wherein power managing a system according to the target power management policy comprises:
under the condition that the operation instruction carries a target index number of a target power management strategy, acquiring the target power management strategy corresponding to the target index number;
and performing power management on the system according to the target power management strategy.
9. The utility model provides an intelligent household electrical appliance controlling means based on thing networking operating system which characterized in that includes:
the first acquisition module is used for acquiring task information parameters uploaded by the intelligent household appliance;
the determining module is used for determining a target power management strategy according to the task information parameters;
the first sending module is used for sending an operation instruction to the intelligent household appliance, wherein the operation instruction carries the target power management strategy, and the operation instruction is used for instructing the intelligent household appliance to carry out power management on a system according to the target power management strategy;
the determining module comprises:
a determining submodule, configured to, at the task information parameter, include: under the condition that n tasks, a first moment when the n tasks enter a system state and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1;
an execution submodule, configured to perform the following operations on each of the n tasks, where each task that performs the following operations is referred to as a target task: determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set; and determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy.
10. The utility model provides an intelligent household electrical appliance controlling means based on thing networking operating system which characterized in that includes:
the second sending module is used for acquiring the task information parameters and sending the task information parameters to the cloud server;
a receiving module, configured to receive an operation instruction returned by the cloud server according to the task information parameter, where the operation instruction carries a target power management policy, the target power management policy is determined according to the task information parameter, and the task information parameter includes: under the condition that n tasks, a first moment when the n tasks enter a system state and a second moment when the n tasks start to execute the tasks after the system state, respectively determining first time of the n-1 tasks in the system state according to the first moment and the second moment, wherein the nth task is a current task, the system state comprises an idle state and a sleep state, and n is an integer greater than 1; performing the following for each of the n tasks, wherein the each task performing the following is referred to as a target task: determining a second time when the target task is expected to be in the system state according to other power management strategies except the initial power management strategy in a pre-stored power management strategy set; determining the target power management strategy of the target task according to the first time and the second time, wherein the target power management strategy is an optimal power management strategy;
and the management module is used for carrying out power management on the system according to the target power management strategy.
11. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 8 when executed.
12. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910691529.5A CN110320813B (en) | 2019-07-29 | 2019-07-29 | Power management method and device for Internet of things equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910691529.5A CN110320813B (en) | 2019-07-29 | 2019-07-29 | Power management method and device for Internet of things equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110320813A CN110320813A (en) | 2019-10-11 |
| CN110320813B true CN110320813B (en) | 2022-07-19 |
Family
ID=68124934
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910691529.5A Active CN110320813B (en) | 2019-07-29 | 2019-07-29 | Power management method and device for Internet of things equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110320813B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111757018A (en) * | 2020-06-22 | 2020-10-09 | 季华实验室 | Camera group synchronous exposure control method and system, computer readable storage medium and camera group control system |
| CN114286431B (en) * | 2021-12-02 | 2024-03-22 | 海尔优家智能科技（北京）有限公司 | Method and device for starting low-power mode, storage medium and electronic device |
| CN115296776A (en) * | 2022-08-03 | 2022-11-04 | 宁波方太厨具有限公司 | Communication data checking processing method, system, equipment and medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1752896A (en) * | 2005-11-08 | 2006-03-29 | 北京大学 | Power source management method of embedded equipment under operation system cooperation and its system |
| CN1828481A (en) * | 2006-03-10 | 2006-09-06 | 浙江大学 | Energy-saving switching method for waiting overtime judge of flush type system outer apparatus |
| CN1932721A (en) * | 2006-09-08 | 2007-03-21 | 华南理工大学 | Dynamic power supply management construction based on embedded system |
| CN101067758A (en) * | 2007-06-14 | 2007-11-07 | 华南理工大学 | Energy consumption management method for inserting system |
| CN104865833A (en) * | 2015-04-23 | 2015-08-26 | 青岛海尔智能家电科技有限公司 | Household electrical appliance power supply management method and apparatus |
| CN105912406A (en) * | 2016-05-05 | 2016-08-31 | 中国人民解放军国防科学技术大学 | Low-energy independent task scheduling and resource configuration method |
| CN107546748A (en) * | 2016-08-30 | 2018-01-05 | 江门市联创发展有限公司 | A kind of intelligent electric power management device |
| CN107707603A (en) * | 2017-06-29 | 2018-02-16 | 青岛恒金源电子科技有限公司 | A kind of intelligent home network electric energy management method |
| CN208522531U (en) * | 2018-04-17 | 2019-02-19 | 江苏企想科技有限公司 | A kind of intelligent power management system |
| CN109507905A (en) * | 2018-12-25 | 2019-03-22 | 泛联（北京）科技有限公司 | A kind of association control method and system of domestic environment intelligent management product |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7539882B2 (en) * | 2005-05-30 | 2009-05-26 | Rambus Inc. | Self-powered devices and methods |
| US20100010688A1 (en) * | 2008-07-08 | 2010-01-14 | Hunter Robert R | Energy monitoring and management |
| US10048669B2 (en) * | 2016-02-03 | 2018-08-14 | Sap Se | Optimizing manufacturing schedule with time-dependent energy cost |
-
2019
- 2019-07-29 CN CN201910691529.5A patent/CN110320813B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1752896A (en) * | 2005-11-08 | 2006-03-29 | 北京大学 | Power source management method of embedded equipment under operation system cooperation and its system |
| CN1828481A (en) * | 2006-03-10 | 2006-09-06 | 浙江大学 | Energy-saving switching method for waiting overtime judge of flush type system outer apparatus |
| CN1932721A (en) * | 2006-09-08 | 2007-03-21 | 华南理工大学 | Dynamic power supply management construction based on embedded system |
| CN101067758A (en) * | 2007-06-14 | 2007-11-07 | 华南理工大学 | Energy consumption management method for inserting system |
| CN104865833A (en) * | 2015-04-23 | 2015-08-26 | 青岛海尔智能家电科技有限公司 | Household electrical appliance power supply management method and apparatus |
| CN105912406A (en) * | 2016-05-05 | 2016-08-31 | 中国人民解放军国防科学技术大学 | Low-energy independent task scheduling and resource configuration method |
| CN107546748A (en) * | 2016-08-30 | 2018-01-05 | 江门市联创发展有限公司 | A kind of intelligent electric power management device |
| CN107707603A (en) * | 2017-06-29 | 2018-02-16 | 青岛恒金源电子科技有限公司 | A kind of intelligent home network electric energy management method |
| CN208522531U (en) * | 2018-04-17 | 2019-02-19 | 江苏企想科技有限公司 | A kind of intelligent power management system |
| CN109507905A (en) * | 2018-12-25 | 2019-03-22 | 泛联（北京）科技有限公司 | A kind of association control method and system of domestic environment intelligent management product |
Non-Patent Citations (1)
| Title |
|---|
| 普及计算终端设备的电源管理技术研究;李允等;《电子科技大学学报》;20011028(第05期);57-62 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110320813A (en) | 2019-10-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110320813B (en) | Power management method and device for Internet of things equipment | |
| WO2021012584A1 (en) | Method for formulating single-task migration strategy in mobile edge computing scenario | |
| JP5792836B2 (en) | Method and apparatus for smart power management for mobile communication terminals using power thresholds | |
| KR101529539B1 (en) | Method and apparatus of smart power management for mobile communication terminals | |
| EP1361708B1 (en) | Method and system for managing power consumption of a network interface module in a wireless computing device | |
| KR101624765B1 (en) | Energy-aware server management | |
| Tong et al. | Application-aware traffic scheduling for workload offloading in mobile clouds | |
| CN111625080B (en) | Server energy saving method and device, electronic equipment and storage medium | |
| US20160315998A1 (en) | Method and system for optimizing execution of user commands in relation to power management | |
| CN110601935A (en) | Processing method and device for tasks in intelligent home operating system and cloud platform system | |
| CN113179531B (en) | Terminal working time length prediction and optimization method and system based on 5G slice | |
| CN113986000A (en) | Server power-on method, system, device and medium | |
| Triki et al. | Reinforcement learning-based dynamic power management of a battery-powered system supplying multiple active modes | |
| CN109660995B (en) | AP intelligent energy-saving control method and device in WiFi system based on machine learning algorithm | |
| CN107612705B (en) | Method for saving power for terminal equipment for managing Internet of things in batches and relay equipment | |
| CN114860058A (en) | Low-power-consumption processing method and system for power chip | |
| CN107580017B (en) | Batch power-saving management method and access equipment | |
| Dondi et al. | A scheduling algorithm for consistent monitoring results with solar powered high-performance wireless embedded systems | |
| CN112015260A (en) | Method and device for adjusting performance state of processor, UEFI (unified extensible firmware interface) and storage medium | |
| CN113472649B (en) | Wireless sensing node management method, electronic device and computer readable storage medium | |
| CN117648199B (en) | Wireless edge computing system based on camera | |
| Lim et al. | Queueing analysis of dynamic power management schemes for mobile devices | |
| Kaddah et al. | Advanced demand response solutions based on fine-grained load control | |
| CN117348717A (en) | CPU energy consumption management method and device, storage medium and electronic device | |
| CN116302493A (en) | Operation control method of equipment, server, electronic device and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |