Communication method based on power gap operating system

Abstract

The invention relates to a communication method based on a power gap operating system, which comprises the following steps of: dividing communication data into a plurality of levels, and compressing each level to obtain compressed communication data; inputting the compressed communication data into each node of an edge layer of the power gap operating system, and constructing a self-adaptive computing resource allocation model to perform computing resource allocation of the edge layer; each node in the edge layer calculates the compressed communication data distributed by the node according to the distributed computing resources to obtain the calculated communication data; and constructing a delay optimization model in each edge node, and selecting an optimal channel to transmit data to a corresponding device connected with the edge node through the delay optimization model.

Description

Technical Field

[0001] The present invention relates to a method for communicating based on an electric power Hongmeng operating system, and belongs to the technical field of electric power communications. Background Art

[0002] As an intelligent, multi-terminal collaborative distributed operating system platform, the power Hongmeng system provides the ability for interconnection and resource sharing between devices. However, it still has the following shortcomings in the data transmission process:

[0003] Existing compression methods are not adaptive and cannot be flexibly adjusted according to data priority and transmission environment, resulting in reduced transmission efficiency under high load or burst data demand.

[0004] Resource allocation is not flexible enough to adapt to scenarios with large fluctuations in data traffic or frequent changes in task priorities, resulting in low utilization of computing resources.

[0005] The lack of dynamic path optimization and delay management mechanisms leads to uncontrollable delays under complex network conditions, affecting the real-time and accuracy of data. Summary of the invention

[0006] In order to solve the above-mentioned problems existing in the prior art, the present invention proposes a method for communication based on the power Hongmeng operating system.

[0007] The technical solution of the present invention is as follows:

[0008] On the one hand, the present invention provides a method for communication based on the power Hongmeng operating system, comprising the following steps:

[0009] Divide the communication data into multiple levels, and compress each level to obtain compressed communication data;

[0010] Input the compressed communication data into each node of the edge layer of the Electric Power Hongmeng operating system, and build an adaptive computing resource allocation model to allocate computing resources at the edge layer;

[0011] Each node in the edge layer calculates the compressed communication data assigned to it according to the assigned computing resources to obtain the calculated communication data;

[0012] A delay optimization model is built in each edge node, and the optimal channel is selected through the delay optimization model to transmit data to the corresponding device connected to the edge node.

[0013] As a preferred embodiment of the present invention, the steps of compressing communication data are as follows:

[0014] Assuming that the communication data is divided into L levels, the compressed communication data is specifically shown as follows:

[0015]

[0016] Where: D c Indicates the compressed communication data; D l Represents the communication data of layer l; R l represents the compression ratio of the lth layer; W l represents the compression weight matrix of the lth layer, which is dynamically updated according to the environment state matrix and the data priority matrix, as shown in the following formula:

[0017] W l =f(E l , P l )

[0018] Where: E l represents the environment state matrix of the lth layer; P l Represents the data priority matrix of layer l.

[0019] As a preferred embodiment of the present invention, an adaptive computing resource allocation model is constructed to allocate computing resources at the edge layer. The adaptive computing resource allocation model is specifically shown in the following formula:

[0020]

[0021] Where: J represents the total number of nodes; I represents the total number of computing tasks; C j represents the overall computing resources of node j; C ij The computing resources allocated to computing task i by node j; M j Represents the task matrix of node j, as shown in the following formula:

[0022]

[0023] Where: j represents the task priority weight matrix of node j; V j represents the resource consumption matrix of node j; △t j represents the delay matrix of node j; △T max Indicates the maximum delay tolerance.

[0024] As a preferred embodiment of the present invention, the specific steps of selecting the optimal channel through the delay optimization model are as follows:

[0025] Assuming that there are n channels connecting the edge node to any device, the delay of each channel is calculated as shown in the following formula:

[0026] △T n =(△t sence，n +△t proc，n +△t trans，n +△tbuffer，n +σ n )

[0027] Where: △T n represents the delay of the nth channel; Δt sence，n Indicates the data acquisition delay of the nth channel; Δt proc，n represents the edge computing processing delay of the nth channel; Δt trans，n represents the transmission delay of the nth channel; Δt buffer，n represents the buffer delay of the nth channel; σ n It represents the noise impact of the nth channel, as shown in the following formula:

[0028] σ n =η·γ n log(0.24+SNR n )

[0029] Where: η represents the channel effect coefficient; γ n Indicates the number of hops of the nth channel; SNR n represents the signal-to-noise ratio of the nth channel;

[0030] When the delay calculation of all channels is completed, the channel with the lowest delay is selected as the data transmission channel.

[0031] On the other hand, the present invention also provides a system for communicating based on the electric power Hongmeng operating system, including a data compression module, a resource allocation module, a data calculation module and a delay optimization module;

[0032] The data compression module is used to divide the communication data into multiple levels and compress each level to obtain compressed communication data;

[0033] The resource allocation module is used to input the compressed communication data into each node of the edge layer of the power Hongmeng operating system, and build an adaptive computing resource allocation model to allocate computing resources at the edge layer;

[0034] The data calculation module is used for each node to calculate the communication data after the compressed communication data allocated to it to obtain the calculated communication data;

[0035] The delay optimization module is used to construct a delay optimization model in each edge node, and select an optimal channel through the delay optimization model to transmit data to a corresponding device connected to the edge node.

[0036] As a preferred embodiment of the present invention, the steps of compressing communication data are as follows:

[0037] Assuming that the communication data is divided into L levels, the compressed communication data is specifically shown as follows:

[0038]

[0039] Where: D c Indicates the compressed communication data; D l Represents the communication data of layer l; R l represents the compression ratio of the lth layer; W l represents the compression weight matrix of the lth layer, which is dynamically updated according to the environment state matrix and the data priority matrix, as shown in the following formula:

[0040] W l =f(E l , P l )

[0041] Where: E l represents the environment state matrix of the lth layer; P l Represents the data priority matrix of layer l.

[0042] As a preferred embodiment of the present invention, an adaptive computing resource allocation model is constructed to allocate computing resources at the edge layer. The adaptive computing resource allocation model is specifically shown in the following formula:

[0043]

[0044] Where: J represents the total number of nodes; I represents the total number of computing tasks; C j represents the overall computing resources of node j; C ij The computing resources allocated to computing task i by node j; M j Represents the task matrix of node j, as shown in the following formula:

[0045]

[0046] Where: j represents the task priority weight matrix of node j; V j represents the resource consumption matrix of node j; Δt j represents the delay matrix of node j; △T max Indicates the maximum delay tolerance.

[0047] As a preferred embodiment of the present invention, the specific steps of selecting the optimal channel through the delay optimization model are as follows:

[0048] Assuming that there are n channels connecting the edge node to any device, the delay of each channel is calculated as shown in the following formula:

[0049] △T n =(△t sence，n +△t proc，n +△t trans，n +△tbuffer，n +σ n )

[0050] Where: △T n represents the delay of the nth channel; Δt sence，n Indicates the data acquisition delay of the nth channel; Δt proc，n represents the edge computing processing delay of the nth channel; Δt trans，n represents the transmission delay of the nth channel; Δt buffer，n represents the buffer delay of the nth channel; σ n It represents the noise impact of the nth channel, as shown in the following formula:

[0051] σ n =η·γ n log(0.24+SNR n )

[0052] Where: η represents the channel effect coefficient; γ n Indicates the number of hops of the nth channel; SNR n represents the signal-to-noise ratio of the nth channel;

[0053] When the delay calculation of all channels is completed, the channel with the lowest delay is selected as the data transmission channel.

[0054] On the other hand, the present invention further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0055] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in any embodiment of the present invention.

[0056] The present invention has the following beneficial effects:

[0057] 1. The present invention designs a multi-level adaptive compression mechanism, which dynamically adjusts the compression ratio according to the data type, priority, network status and bandwidth conditions, thereby improving the flexibility of data compression and transmission efficiency.

[0058] 2. The present invention realizes the dynamic allocation of edge computing resources by calculating the priority weight matrix, so that the system can flexibly respond to changes in task priority and real-time performance, thereby improving resource utilization.

[0059] 3. Through multi-dimensional transmission delay modeling and dynamic path selection, the present invention can evaluate the network status in real time, dynamically adjust the transmission path, and significantly reduce data transmission delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0061] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] It should be understood that the step numbers used in this document are only for convenience of description and are not intended to limit the order in which the steps are executed.

[0063] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0064] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0065] The term "and / or" means and includes any and all possible combinations of one or more of the associated listed items.

[0066] Embodiment 1:

[0067] See also Figure 1 , a method for communication based on the power Hongmeng operating system, comprising the following steps:

[0068] Divide the communication data into multiple levels, and compress each level to obtain compressed communication data;

[0069] Input the compressed communication data into each node of the edge layer of the Electric Power Hongmeng operating system, and build an adaptive computing resource allocation model to allocate computing resources at the edge layer;

[0070] Each node in the edge layer calculates the compressed communication data assigned to it according to the assigned computing resources to obtain the calculated communication data;

[0071] A delay optimization model is built in each edge node, and the optimal channel is selected through the delay optimization model to transmit data to the corresponding device connected to the edge node.

[0072] As a preferred implementation of this embodiment, the steps of compressing communication data are:

[0073] Assuming that the communication data is divided into L levels, the compressed communication data is specifically shown as follows:

[0074]

[0075] Where: D c Indicates the compressed communication data; D l Represents the communication data of layer l; R l represents the compression ratio of the lth layer; W l represents the compression weight matrix of the lth layer, which is dynamically updated according to the environment state matrix and the data priority matrix, as shown in the following formula:

[0076] W l =f(E l , P l )

[0077] Where: E l represents the environment state matrix of the lth layer; P l Represents the data priority matrix of layer l.

[0078] As a preferred implementation of this embodiment, an adaptive computing resource allocation model is constructed to allocate computing resources at the edge layer. The adaptive computing resource allocation model is specifically shown in the following formula:

[0079]

[0080] Where: J represents the total number of nodes; I represents the total number of computing tasks; C j represents the overall computing resources of node j; C ij The computing resources allocated to computing task i by node j; M j Represents the task matrix of node j, as shown in the following formula:

[0081]

[0082] Where: j represents the task priority weight matrix of node j; V j represents the resource consumption matrix of node j; △t j represents the delay matrix of node j; △T max Indicates the maximum delay tolerance.

[0083] As a preferred implementation of this embodiment, the specific steps of selecting the optimal channel through the delay optimization model are as follows:

[0084] Assuming that there are n channels connecting the edge node to any device, the delay of each channel is calculated as shown in the following formula:

[0085] △T n =(△t sence，n +△t proc，n +△t trans，n +△t buffer，n +σ n )

[0086] Where: △T n represents the delay of the nth channel; Δ tsence，n Indicates the data acquisition delay of the nth channel; Δt proc，n represents the edge computing processing delay of the nth channel; Δt trans，n represents the transmission delay of the nth channel; Δt buffer，n represents the buffer delay of the nth channel; σ n It represents the noise impact of the nth channel, as shown in the following formula:

[0087] σ n =η·γ n log(0.24+SNR n )

[0088] Where: η represents the channel effect coefficient; γ n Indicates the number of hops of the nth channel; SNR n represents the signal-to-noise ratio of the nth channel;

[0089] When the delay calculation of all channels is completed, the channel with the lowest delay is selected as the data transmission channel.

[0090] Embodiment 2:

[0091] A system for communicating based on the electric power Hongmeng operating system, including a data compression module, a resource allocation module, a data calculation module and a delay optimization module;

[0092] The data compression module is used to divide the communication data into multiple levels and compress each level to obtain compressed communication data;

[0093] The resource allocation module is used to input the compressed communication data into each node of the edge layer of the power Hongmeng operating system, and build an adaptive computing resource allocation model to allocate computing resources at the edge layer;

[0094] The data calculation module is used for each node to calculate the communication data after the compressed communication data allocated to it to obtain the calculated communication data;

[0095] The delay optimization module is used to construct a delay optimization model in each edge node, and select an optimal channel through the delay optimization model to transmit data to a corresponding device connected to the edge node.

[0096] As a preferred implementation of this embodiment, the steps of compressing communication data are:

[0097] Assuming that the communication data is divided into L levels, the compressed communication data is specifically shown as follows:

[0098]

[0099] Where: D c Indicates the compressed communication data; D l Represents the communication data of layer l; R l represents the compression ratio of the lth layer; W l represents the compression weight matrix of the lth layer, which is dynamically updated according to the environment state matrix and the data priority matrix, as shown in the following formula:

[0100] W l =f(E l , P l )

[0101] Where: E l represents the environment state matrix of the lth layer; P l Represents the data priority matrix of layer l.

[0102] As a preferred implementation of this embodiment, an adaptive computing resource allocation model is constructed to allocate computing resources at the edge layer. The adaptive computing resource allocation model is specifically shown in the following formula:

[0103]

[0104] Where: J represents the total number of nodes; I represents the total number of computing tasks; C j represents the overall computing resources of node j; C ij The computing resources allocated to computing task i by node j; M j Represents the task matrix of node j, as shown in the following formula:

[0105]

[0106] Where: j represents the task priority weight matrix of node j; V j represents the resource consumption matrix of node j; △t j represents the delay matrix of node j; △T max Indicates the maximum delay tolerance.

[0107] As a preferred implementation of this embodiment, the specific steps of selecting the optimal channel through the delay optimization model are as follows:

[0108] Assuming that there are n channels connecting the edge node to any device, the delay of each channel is calculated as shown in the following formula:

[0109] △T n =(△t sence，n +△t proc，n +△t trans，n +△t buffer，n +σ n )

[0110] Where: △T n represents the delay of the nth channel; Δt sence，n Indicates the data acquisition delay of the nth channel; Δt proc，n represents the edge computing processing delay of the nth channel; Δt trans，n represents the transmission delay of the nth channel; Δt buffer，n represents the buffer delay of the nth channel; σ n It represents the noise impact of the nth channel, as shown in the following formula:

[0111] σ n =η·γ n log(0.24+SNR n )

[0112] Where: η represents the channel effect coefficient; γ n Indicates the number of hops of the nth channel; SNR n represents the signal-to-noise ratio of the nth channel;

[0113] When the delay calculation of all channels is completed, the channel with the lowest delay is selected as the data transmission channel.

[0114] Embodiment three:

[0115] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in any embodiment of the present invention when executing the program.

[0116] Embodiment 4:

[0117] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in any embodiment of the present invention is implemented.

[0118] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0119] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0120] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0121] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0122] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for communication based on the power Hongmeng operating system, characterized in that: The following steps are involved: Divide the communication data into multiple levels, and compress each level to obtain compressed communication data; Input the compressed communication data into each node of the edge layer of the Electric Power Hongmeng operating system, and build an adaptive computing resource allocation model to allocate computing resources at the edge layer; Each node in the edge layer calculates the compressed communication data assigned to it according to the assigned computing resources to obtain the calculated communication data; A delay optimization model is built in each edge node, and the optimal channel is selected through the delay optimization model to transmit data to the corresponding device connected to the edge node.

2. According to claim 1, a method for communication based on the power Hongmeng operating system is characterized in that: The steps for compressing communication data are: Assuming that the communication data is divided into L levels, the compressed communication data is specifically shown as follows: Where: D c Indicates the compressed communication data; D l Represents the communication data of layer l; R l represents the compression ratio of the lth layer; W l represents the compression weight matrix of the lth layer, which is dynamically updated according to the environment state matrix and the data priority matrix, as shown in the following formula: W l =f(E l ,P l ) Where: E l represents the environment state matrix of the lth layer; P l Represents the data priority matrix of layer l.

3. According to claim 1, a method for communication based on the power Hongmeng operating system is characterized in that: An adaptive computing resource allocation model is constructed to allocate computing resources at the edge layer. The adaptive computing resource allocation model is specifically shown in the following formula: Where: J represents the total number of nodes; I represents the total number of computing tasks; C j represents the overall computing resources of node j; C ij The computing resources allocated to computing task i by node j; M j Represents the task matrix of node j, as shown in the following formula: Where: j represents the task priority weight matrix of node j; V j represents the resource consumption matrix of node j; Δt j represents the delay matrix of node j; ΔT max Indicates the maximum delay tolerance.

4. According to claim 1, a method for communication based on the power Hongmeng operating system is characterized in that: The specific steps for selecting the optimal channel through the delay optimization model are: Assuming that there are n channels connecting the edge node to any device, the delay of each channel is calculated as shown in the following formula: ΔT n =(Δt sence,n +Δt proc,n +Δt trans,n +Δt buffer,n +s n ) Where: ΔT n represents the delay of the nth channel; Δt sence,n Indicates the data acquisition delay of the nth channel; Δt proc,n represents the edge computing processing delay of the nth channel; Δt trans,n represents the transmission delay of the nth channel; Δt buffer,n represents the buffer delay of the nth channel; σ n It represents the noise impact of the nth channel, as shown in the following formula: s n =η·γ n ·log(0.24+SNR n ) Where: η represents the channel effect coefficient; γ n Indicates the number of hops of the nth channel; SNR n represents the signal-to-noise ratio of the nth channel; When the delay calculation of all channels is completed, the channel with the lowest delay is selected as the data transmission channel.

5. A system for communication based on the power Hongmeng operating system, characterized in that: It includes a data compression module, a resource allocation module, a data calculation module and a delay optimization module; The data compression module is used to divide the communication data into multiple levels and compress each level to obtain compressed communication data; The resource allocation module is used to input the compressed communication data into each node of the edge layer of the power Hongmeng operating system, and build an adaptive computing resource allocation model to allocate computing resources at the edge layer; The data calculation module is used for each node to calculate the communication data after the compressed communication data allocated to it to obtain the calculated communication data; The delay optimization module is used to construct a delay optimization model in each edge node, and select an optimal channel through the delay optimization model to transmit data to a corresponding device connected to the edge node.

6. A system for communication based on the power Hongmeng operating system according to claim 5, characterized in that: The steps for compressing communication data are: Assuming that the communication data is divided into L levels, the compressed communication data is specifically shown as follows: Where: D c Indicates the compressed communication data; D l Represents the communication data of layer l; R l represents the compression ratio of the lth layer; W l represents the compression weight matrix of the lth layer, which is dynamically updated according to the environment state matrix and the data priority matrix, as shown in the following formula: W l =f(E l ,P l ) Where: E l represents the environment state matrix of the lth layer; P l Represents the data priority matrix of layer l.

7. According to claim 5, a system for communication based on the power Hongmeng operating system is characterized in that: An adaptive computing resource allocation model is constructed to allocate computing resources at the edge layer. The adaptive computing resource allocation model is specifically shown in the following formula: Where: J represents the total number of nodes; I represents the total number of computing tasks; C j represents the overall computing resources of node j; C ij The computing resources allocated to computing task i by node j; M j Represents the task matrix of node j, as shown in the following formula: Where: j represents the task priority weight matrix of node j; V j represents the resource consumption matrix of node j; Δt j represents the delay matrix of node j; ΔT max Indicates the maximum delay tolerance.

8. According to claim 5, a system for communication based on the power Hongmeng operating system is characterized in that: The specific steps for selecting the optimal channel through the delay optimization model are: Assuming that there are n channels connecting the edge node to any device, the delay of each channel is calculated as shown in the following formula: ΔT n =(Δt sence,n +Δt proc,n +Δt trans,n +Δt buffer,n +s n ) Where: ΔT n represents the delay of the nth channel; Δt sence,n Indicates the data acquisition delay of the nth channel; Δt proc,n represents the edge computing processing delay of the nth channel; Δt trans,n represents the transmission delay of the nth channel; Δt buffer,n represents the buffer delay of the nth channel; σ n It represents the noise impact of the nth channel, as shown in the following formula: s n =η·γ n ·log(0.24+SNR n ) Where: η represents the channel effect coefficient; γ n Indicates the number of hops of the nth channel; SNR n represents the signal-to-noise ratio of the nth channel; When the delay calculation of all channels is completed, the channel with the lowest delay is selected as the data transmission channel.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 4 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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