Distributed edge computing scheduling system and method for sensing energy consumption of same-screen device

Abstract

The invention discloses a distributed edge computing scheduling system and method for sensing energy consumption of a same-screen device, and relates to the technical field of same-screen device scheduling systems.The distributed edge computing scheduling system and method for sensing energy consumption of the same-screen device comprise the same-screen device which is used for collecting energy consumption data during operation of the same-screen device and sending the energy consumption data to a scheduling center; the plurality of edge computing nodes are distributed at different geographic positions, respectively have computing resources and can communicate with the dispatching center; the dispatching center is used for receiving the energy consumption data sent by the same-screen device and dispatching and distributing the distributed edge computing tasks; by adopting the same-screen device, energy consumption data of the same-screen device in different working modes can be accurately collected in real time, and the energy efficiency performance of the device is improved; the dispatching center can accurately judge the energy consumption state of the screen sharing device through the energy consumption analysis module, and the operation efficiency and the energy utilization efficiency of the whole distributed edge computing system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of the same-screen device scheduling system, and in particular to a distributed edge computing scheduling system and method for energy consumption awareness of the same-screen device. Background Art

[0002] In the current information technology field, with the wide application of devices such as the same-screen device, the energy consumption problem has attracted increasing attention. Traditional same-screen devices often lack effective awareness and management of their own energy consumption, resulting in high energy consumption during operation, especially when performing some complex computing tasks, and the increase in energy consumption is more obvious.

[0003] In terms of distributed edge computing, although there are already some related technologies and systems, most systems have deficiencies in energy consumption awareness and scheduling. For example, some systems fail to achieve real-time monitoring of the energy consumption of the same-screen device and cannot perform accurate task scheduling according to the energy consumption status; or when performing task scheduling, they do not fully consider the energy consumption situation and geographical location factors of the edge computing nodes, resulting in high overall energy consumption and low efficiency of the system.

[0004] Therefore, in view of this current situation, there is an urgent need for a distributed edge computing scheduling system and method for energy consumption awareness of the same-screen device to solve the problems of high energy consumption and low efficiency existing in the prior art. Summary of the Invention

[0005] In view of this, in view of the deficiencies of the prior art, the main purpose of the present invention is to provide a distributed edge computing scheduling system for energy consumption awareness of the same-screen device, which can accurately collect the energy consumption data of the same-screen device itself in different working modes in real time through the same-screen device, improving the energy efficiency performance of the device; the scheduling center can accurately judge the energy consumption status of the same-screen device through the energy consumption analysis module, improving the operation efficiency and energy utilization efficiency of the entire distributed edge computing system; the computing resource management unit and energy consumption adjustment module equipped with multiple edge computing nodes enhance the reliability and flexibility of the system.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A distributed edge computing scheduling system for energy consumption awareness of the same-screen device, including a same-screen device: used to collect the energy consumption data during its own operation and send it to the scheduling center; Multiple edge computing nodes: distributed in different geographical locations, each having computing resources and capable of communicating with the scheduling center; Dispatch Center: Receives the energy consumption data sent by the screen synchronizer device, and based on this data and the characteristics of the computing tasks, conducts the scheduling and allocation of distributed edge computing tasks, assigns appropriate computing tasks to the corresponding edge computing nodes, and monitors the computing status and energy consumption of each edge computing node in real time to achieve optimized management of energy consumption and efficient execution of computing tasks.

[0007] As a preferred solution: The screen synchronizer device is built-in with an energy consumption sensor, which can monitor the energy consumption changes of the screen synchronizer in different working modes in real time, monitor the energy consumption values of standby energy consumption and normal working energy consumption, and send these real-time energy consumption data to the dispatch center via a communication protocol.

[0008] As a preferred solution: The dispatch center includes an energy consumption analysis module, which deeply analyzes the energy consumption data sent by the screen synchronizer device. By establishing an energy consumption model, it determines whether the current energy consumption status of the screen synchronizer is in a low energy consumption mode, a medium energy consumption mode, or a high energy consumption mode.

[0009] As a preferred solution: The dispatch center also includes a task priority evaluation module, which assigns corresponding priorities to each computing task according to factors such as the urgency of the computing task and the amount of data. It preferentially assigns high-priority computing tasks to edge computing nodes with lower energy consumption to ensure the timely processing of critical tasks and achieve reasonable control of energy consumption.

[0010] As a preferred solution: Each of the multiple edge computing nodes is equipped with a local computing resource management unit, which can monitor the usage of its own computing resources in real time, including CPU usage rate and memory occupancy rate, and feedback the resource usage information to the dispatch center. The dispatch center conducts reasonable task scheduling and resource allocation according to the overall computing task requirements and the resource conditions of each node.

[0011] As a preferred solution: Each of the multiple edge computing nodes is also provided with an energy consumption adjustment module, which can adjust its own energy consumption according to the instructions of the dispatch center. By reducing the computing frequency and turning off some computing components, it realizes the reduction of energy consumption while ensuring the continuity and stability of computing tasks.

[0012] A method for a distributed edge computing scheduling system with screen synchronizer energy consumption perception includes the following steps: First, the screen synchronizer device continuously collects its own energy consumption data and sends it to the dispatch center; Second, the dispatch center receives the energy consumption data and conducts analysis to judge the energy consumption status of the screen synchronizer; Third, according to the priority of the computing tasks and the resource and energy consumption situations of each edge computing node, allocate the computing tasks to the appropriate edge computing nodes; Fourth, the edge computing nodes execute the allocated computing tasks and feedback the computing progress and energy consumption situation to the scheduling center; Fifth, the scheduling center adjusts the allocation of computing tasks and the energy consumption control strategies of each node in real time according to the feedback information.

[0013] As an optimal solution: the time interval for the screen synchronizer device to collect energy consumption data is set according to actual needs to ensure that the scheduling center can obtain the latest energy consumption information in a timely manner and make accurate scheduling decisions.

[0014] As an optimal solution: when the scheduling center allocates computing tasks, it considers the energy consumption situation of the edge computing nodes and the geographical location factors of the nodes, and allocates the computing tasks to the edge computing nodes closer to the screen synchronizer device to reduce the delay and energy consumption of data transmission.

[0015] As an optimal solution: when the screen synchronizer device is in a low energy consumption state for a long time, the scheduling center can automatically adjust the computing task allocation strategy to increase its computing task volume.

[0016] Compared with the prior art, the present invention has obvious advantages and beneficial effects. Specifically, as can be seen from the above technical solutions: First, by adopting the screen synchronizer device, it can accurately collect its own energy consumption data in different working modes in real time, such as standby energy consumption and normal working energy consumption, providing detailed and accurate basic information for subsequent scheduling decisions, which helps equipment manufacturers optimize the energy consumption design of the screen synchronizer and improve the energy efficiency performance of the equipment.

[0017] Second, for distributed edge computing: the scheduling center can accurately judge the energy consumption state of the screen synchronizer through the energy consumption analysis module, and allocate tasks according to this state combined with the characteristics of the computing tasks. High-priority computing tasks can be preferentially allocated to edge computing nodes with lower energy consumption, ensuring the timely processing of key tasks while achieving reasonable control of energy consumption, and improving the operation efficiency and energy utilization efficiency of the entire distributed edge computing system.

[0018] Third, the computing resource management units and energy consumption regulation modules equipped in multiple edge computing nodes can monitor their own resource usage in real time and adjust the energy consumption according to the instructions of the scheduling center, reducing the energy consumption without affecting the execution of computing tasks, ensuring the continuity and stability of computing tasks, and enhancing the reliability and flexibility of the system.

[0019] Fourthly, the overall system realizes effective perception of the energy consumption of the screen mirroring device and optimized scheduling of distributed edge computing tasks, avoiding waste of resources and unnecessary increase in energy consumption. Under the premise of meeting user needs, the entire system can minimize energy consumption, improve the overall performance and energy efficiency ratio of the system, and provide users with a more efficient and energy-saving service experience, having important practical application value and market competitiveness.

[0020] To more clearly elaborate on the structural features and effects of the present invention, the following will describe it in detail in conjunction with the accompanying drawings and specific embodiments. Description of the Drawings

[0021] Figure 1 It is a flowchart of the distributed edge computing scheduling system for energy consumption perception of the screen mirroring device of the present invention; Figure 2 It is a data transmission flowchart of the distributed edge computing scheduling system for energy consumption perception of the screen mirroring device of the present invention. Detailed Embodiments

[0022] As shown in the present invention Figure 1 to FIG. 2, a distributed edge computing scheduling system for energy consumption perception of a screen mirroring device includes a screen mirroring device: which is used to collect the energy consumption data during its own operation and send it to the scheduling center; Multiple edge computing nodes: distributed in different geographical locations, each having computing resources and capable of communicating with the scheduling center; The scheduling center: receives the energy consumption data sent by the screen mirroring device, based on this data and the characteristics of the computing tasks, conducts scheduling and allocation of distributed edge computing tasks, allocates appropriate computing tasks to the corresponding edge computing nodes, and monitors the computing status and energy consumption of each edge computing node in real time to achieve optimized management of energy consumption and efficient execution of computing tasks.

[0023] The screen mirroring device is built-in with an energy consumption sensor, which can monitor the energy consumption changes of the screen mirroring device in different working modes in real time, monitor the energy consumption values of standby energy consumption and normal working energy consumption, and send these real-time energy consumption data to the scheduling center through a communication protocol.

[0024] The scheduling center includes an energy consumption analysis module, which deeply analyzes the energy consumption data sent by the screen mirroring device, and judges whether the current energy consumption status of the screen mirroring device is in a low energy consumption mode, a medium energy consumption mode or a high energy consumption mode by establishing an energy consumption model.

[0025] The dispatching center also includes a task priority evaluation module. The task priority evaluation module assigns corresponding priorities to each computing task according to factors such as the urgency of the computing task and the size of the data volume, and preferentially allocates high-priority computing tasks to edge computing nodes with lower energy consumption, ensuring the timely processing of critical tasks while achieving reasonable control of energy consumption.

[0026] Each of the multiple edge computing nodes is equipped with a local computing resource management unit. The computing resource management unit can monitor the usage of its own computing resources in real time, including CPU usage rate and memory occupancy rate, and feedback the resource usage information to the dispatching center. The dispatching center performs reasonable task scheduling and resource allocation according to the overall computing task requirements and the resource conditions of each node.

[0027] Each of the multiple edge computing nodes is also provided with an energy consumption adjustment module. The energy consumption adjustment module can adjust its own energy consumption according to the instructions of the dispatching center, and reduce energy consumption by reducing the computing frequency and turning off some computing components, while ensuring the continuity and stability of the computing tasks.

[0028] A method for a distributed edge computing scheduling system with energy consumption awareness of a screen synchronizer includes the following steps: First, the screen synchronizer device continuously collects its own energy consumption data and sends it to the dispatching center; Second, the dispatching center receives the energy consumption data and analyzes it to judge the energy consumption status of the screen synchronizer; Third, according to the priority of the computing task and the resources and energy consumption conditions of each edge computing node, allocate the computing task to a suitable edge computing node; Fourth, the edge computing node executes the allocated computing task and feeds back the computing progress and energy consumption situation to the dispatching center; Fifth, the dispatching center adjusts the allocation of the computing task and the energy consumption control strategy of each node in real time according to the feedback information.

[0029] The time interval for the screen synchronizer device to collect energy consumption data is set according to actual needs to ensure that the dispatching center can obtain the latest energy consumption information in time and make accurate scheduling decisions.

[0030] When performing computing task allocation, the dispatching center considers the energy consumption situation of the edge computing node and the geographical location factor of the node, and allocates the computing task to the edge computing node closer to the screen synchronizer device to reduce data transmission delay and energy consumption.

[0031] When the screen synchronizer device is in a low energy consumption state for a long time, the dispatching center can automatically adjust the computing task allocation strategy and increase its computing task volume.

[0032] The present invention provides a distributed edge computing scheduling system for energy consumption awareness of a screen synchronizer, aiming to achieve effective awareness of the energy consumption of the screen synchronizer and optimize the scheduling of distributed edge computing tasks. The system consists of a screen synchronizer device, multiple edge computing nodes, and a scheduling center.

[0033] The screen synchronizer device is responsible for collecting the energy consumption data during its own operation and sending this data to the scheduling center in real time. Multiple edge computing nodes are distributed in different geographical locations, each having computing resources and being able to communicate with the scheduling center. The scheduling center receives the energy consumption data sent by the screen synchronizer device, and based on this data and the characteristics of the computing tasks, conducts the scheduling and allocation of distributed edge computing tasks, allocates appropriate computing tasks to the corresponding edge computing nodes, and monitors the computing status and energy consumption of each edge computing node in real time to achieve optimized management of energy consumption and efficient execution of computing tasks.

[0034] Screen synchronizer device: The screen synchronizer device is built-in with a high-precision energy consumption sensor. This energy consumption sensor can monitor the energy consumption changes of the screen synchronizer in different working modes in real time and accurately, including energy consumption values in various states such as standby energy consumption and normal working energy consumption, and send these real-time energy consumption data to the scheduling center through a specific communication protocol.

[0035] Scheduling center: The scheduling center includes key components such as an energy consumption analysis module and a task priority evaluation module.

[0036] Energy consumption analysis module: Deeply analyzes the energy consumption data sent by the screen synchronizer device. By establishing an energy consumption model, it can accurately judge whether the current energy consumption state of the screen synchronizer is in a low energy consumption mode, a medium energy consumption mode, or a high energy consumption mode.

[0037] Task priority evaluation module: Assigns corresponding priorities to each computing task according to factors such as the urgency of the computing task and the size of the data volume. For example, for a real-time video transmission task, due to its high urgency and large data volume, a higher priority is assigned; while for some non-real-time document editing tasks, the priority is relatively low. In this way, when performing scheduling and allocation, high-priority computing tasks can be preferentially allocated to edge computing nodes with lower energy consumption to ensure the timely processing of key tasks and achieve reasonable control of energy consumption.

[0038] Edge computing node: Each edge computing node is equipped with a local computing resource management unit and an energy consumption adjustment module.

[0039] Computing resource management unit: Can monitor the usage of its own computing resources in real time, such as CPU usage rate, memory occupancy rate, etc., and feedback this resource usage information to the scheduling center. By feeding this information back to the scheduling center, the scheduling center can better understand the resource usage of each node and conduct reasonable task scheduling and resource allocation.

[0040] Energy consumption regulation module: It can regulate its own energy consumption according to the instructions of the dispatching center without affecting the execution of computing tasks.

[0041] Implementation steps of the method: 1. The screen synchronizer device continuously collects its own energy consumption data and sends it to the dispatching center. The collection time interval can be set according to actual needs. For example, it can be set to collect energy consumption data once per second to ensure that the dispatching center can obtain the latest energy consumption information in a timely manner, so as to make accurate dispatching decisions.

[0042] 2. The dispatching center receives the energy consumption data and analyzes it to judge the energy consumption status of the screen synchronizer, such as the judgment methods of low energy consumption, medium energy consumption, and high energy consumption modes described above.

[0043] 3. According to the priority of computing tasks and the resources and energy consumption of each edge computing node, the computing tasks are allocated to appropriate edge computing nodes. For example, for a high-priority real-time video encoding task, if the dispatching center finds that an edge computing node is in the low energy consumption mode and has sufficient computing resources at this time, it will allocate this task to this node; for a low-priority data analysis task, the dispatching center may allocate it to another edge computing node with relatively higher energy consumption but also more sufficient resources.

[0044] 4. The edge computing node executes the allocated computing task and feeds back the computing progress and energy consumption status to the dispatching center. For example, when the edge computing node is processing a video encoding task, every time a certain proportion of the encoding work is completed, it feeds back the current progress and the energy consumption status at this time to the dispatching center.

[0045] 5. The dispatching center adjusts the allocation of computing tasks and the energy consumption control strategies of each node in real time according to the feedback information. If the energy consumption of an edge computing node suddenly increases, it may be due to reasons such as an increase in the complexity of the computing task or a node failure. The dispatching center will timely adjust the task allocation, transfer some tasks to other nodes with lower energy consumption; or adjust the energy consumption regulation strategy of the node according to the energy consumption situation of the node, such as further reducing the computing frequency of a certain node, etc., to achieve the energy consumption optimization of the overall system and the efficient execution of computing tasks.

[0046] For example, in an actual office environment, the distributed edge computing scheduling system with energy consumption awareness of the screen mirroring device is used. When the screen mirroring device is in the low energy consumption mode, the scheduling center will automatically allocate some simple document editing tasks to the edge computing nodes with lower energy consumption for processing. When the screen mirroring device switches to the high energy consumption mode, such as when starting to play high-definition videos, the scheduling center will quickly allocate the relevant video decoding and processing tasks to the edge computing nodes with stronger performance but relatively higher energy consumption, and at the same time, adjust the allocation of computing resources in real time according to the energy consumption of the nodes to ensure the stable operation of the entire system and the reasonable control of energy consumption.

[0047] Through the implementation of the above system and method, it is possible to effectively realize the awareness of the energy consumption of the screen mirroring device and the optimized scheduling of distributed edge computing tasks, improve the performance and energy efficiency of the system, and provide users with more efficient and energy-saving services.

[0048] The design focus of the present invention lies in: First, by using the screen mirroring device, it can accurately collect its own energy consumption data in different working modes in real time, such as standby energy consumption and normal working energy consumption, providing detailed and accurate basic information for subsequent scheduling decisions, which helps equipment manufacturers optimize the energy consumption design of the screen mirroring device and improve the energy efficiency performance of the device.

[0049] Second, for distributed edge computing: the scheduling center can accurately judge the energy consumption status of the screen mirroring device through the energy consumption analysis module, and allocate tasks according to this status combined with the characteristics of the computing tasks. High-priority computing tasks can be preferentially allocated to the edge computing nodes with lower energy consumption, ensuring the timely processing of key tasks while realizing the reasonable control of energy consumption, and improving the operation efficiency and energy utilization efficiency of the entire distributed edge computing system.

[0050] Third, the computing resource management unit and energy consumption regulation module equipped with multiple edge computing nodes can monitor their own resource usage in real time and adjust the energy consumption according to the instructions of the scheduling center, reducing the energy consumption without affecting the execution of computing tasks, ensuring the continuity and stability of the computing tasks, and enhancing the reliability and flexibility of the system.

[0051] Fourth, the overall system realizes the effective awareness of the energy consumption of the screen mirroring device and the optimized scheduling of distributed edge computing tasks, avoiding waste of resources and unnecessary increase in energy consumption. Under the premise of meeting the user's needs, the entire system can minimize energy consumption, improve the overall performance and energy efficiency ratio of the system, and provide users with a more efficient and energy-saving service experience, having important practical application value and market competitiveness.

[0052] The above are only the preferred embodiments of the present invention, and do not impose any limitation on the technical scope of the present invention. Therefore, any minor modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A distributed edge computing scheduling system with energy consumption awareness for devices on the same screen, characterized by: Including the same screen device: used to collect its own energy consumption data during operation and send it to the dispatch center; Multiple edge computing nodes: distributed in different geographical locations, each with computing resources and able to communicate with the dispatch center; Dispatching center: Receives energy consumption data sent by the same-screen devices, and based on this data and the characteristics of the computing tasks, dispatches and allocates distributed edge computing tasks, assigns appropriate computing tasks to the corresponding edge computing nodes, and monitors the computing status and energy consumption of each edge computing node in real time to achieve optimal management of energy consumption and execution of computing tasks.

2. According to claim 1, the distributed edge computing scheduling system with energy consumption awareness of the same-screen device is characterized by: The on-screen device is equipped with an energy consumption sensor, which can monitor the energy consumption changes of the on-screen device in different working modes in real time, monitor the energy consumption values ​​of standby energy consumption and normal working energy consumption, and send these real-time energy consumption data to the dispatch center via a communication protocol.

3. According to claim 1, the distributed edge computing scheduling system with energy consumption awareness of the same-screen device is characterized by: The dispatch center includes an energy consumption analysis module, which performs in-depth analysis on the energy consumption data sent by the same-screen device, and determines whether the current energy consumption state of the same-screen device is in low energy consumption mode, medium energy consumption mode or high energy consumption mode by establishing an energy consumption model.

4. According to claim 1, the distributed edge computing scheduling system with energy consumption awareness of the same-screen device is characterized by: The dispatch center also includes a task priority evaluation module, which assigns a corresponding priority to each computing task according to the urgency of the computing task and the size of the data, and preferentially allocates high-priority computing tasks to edge computing nodes with lower energy consumption, ensuring timely processing of critical tasks while achieving reasonable control of energy consumption.

5. The distributed edge computing scheduling system with energy consumption awareness of the same-screen device according to claim 1 is characterized in that: The multiple edge computing nodes are all equipped with a local computing resource management unit, which can monitor its own computing resource usage in real time, including CPU usage and memory occupancy, and feed back the resource usage information to the scheduling center. The scheduling center performs reasonable task scheduling and resource allocation based on the overall computing task requirements and the resource conditions of each node.

6. The distributed edge computing scheduling system with energy consumption awareness of the same-screen device according to claim 1 is characterized in that: The multiple edge computing nodes are also provided with an energy consumption regulation module, which can regulate its own energy consumption according to the instructions of the dispatching center, and reduce energy consumption by reducing the computing frequency and shutting down some computing components, while ensuring the continuity and stability of computing tasks.

7. A method for a distributed edge computing scheduling system based on the energy consumption awareness of the same-screen device according to any one of claims 1 to 6, characterized in that: The following steps are involved: First, the same-screen device continuously collects its own energy consumption data and sends it to the dispatch center; Second, the dispatch center receives and analyzes the energy consumption data to determine the energy consumption status of the same-screen device; Third, the computing tasks are assigned to appropriate edge computing nodes according to the priority of the computing tasks and the resources and energy consumption of each edge computing node; Fourth, the edge computing nodes perform the assigned computing tasks and feed back the computing progress and energy consumption to the dispatch center; Fifth, the dispatch center adjusts the allocation of computing tasks and the energy consumption control strategy of each node in real time based on feedback information.

8. The method of the distributed edge computing scheduling system with energy consumption awareness of the same-screen device according to claim 7 is characterized in that: The time interval for collecting energy consumption data by the on-screen device is set according to actual needs to ensure that the dispatch center can obtain the latest energy consumption information in a timely manner and make accurate dispatch decisions.

9. The method of the distributed edge computing scheduling system with energy consumption awareness of the same-screen device according to claim 7 is characterized in that: When allocating computing tasks, the dispatch center considers the energy consumption of edge computing nodes and the geographical location of the nodes, and allocates computing tasks to edge computing nodes that are closer to the same-screen devices to reduce data transmission delays and energy consumption.

10. The method of the distributed edge computing scheduling system with energy consumption awareness of the same-screen device according to claim 7, characterized in that: When the on-screen device is in a low-energy consumption state for a long time, the dispatch center can automatically adjust the allocation strategy of computing tasks to increase its computing task volume.

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