Edge computing-based intelligent multi-node online monitoring method and system

Abstract

The present invention relates to the technical field of node online monitoring, in particular to an edge computing-based intelligent multi-node online monitoring method and system. The monitoring method comprises: using sensors to acquire data of network nodes; on the basis of an edge computing method, performing data processing and analysis on the acquired data; and by means of the principle of magnetic induction, performing online monitoring of intelligent nodes. The present invention uses a highly-efficient sensor data acquisition module to accurately and stably acquire data of network nodes; by means of using the edge computing technology, the present invention reduces the data transmission delay, and improves the real-time performance of monitoring; by means of using the principle of magnetic induction, the present invention achieves accurate monitoring of node status within an intelligent-node online monitoring module; and comprehensively using the sensor data acquisition module, an edge computing and data processing and analysis module and the intelligent-node online monitoring module achieves cooperative intelligent online monitoring of multiple nodes.

Description

An intelligent multi-node online monitoring method and system based on edge computing Technical Field

[0001] The present invention relates to the technical field of node online monitoring, and specifically to an intelligent multi-node online monitoring method and system based on edge computing. Background Art

[0002] Transmission line monitoring technologies, both domestically and internationally, focus on monitoring icing, tower tilt, insulator contamination, and line temperature. However, various monitoring devices suffer from data format and backend processing redundancy, making the monitoring data integration process cumbersome and complex, and online monitoring operations and maintenance difficult. This places higher demands on the data processing and transmission capabilities of data conversion systems.

[0003] Edge computing, as an emerging technology, is in its infancy globally. With the continued advancement of the Internet of Things (IoT) across various industries, pilot research on edge computing has been conducted in numerous fields with large and dispersed data volumes and high real-time response requirements. These achievements have been widely recognized and are attracting increasing attention from the IoT industry. While there is currently no unified definition of edge computing, a widely accepted view is that it is a platform that provides real-time intelligent services at the edge of the network, close to end devices or the source of end data. This platform integrates multiple capabilities, including networking, computing, storage, and application core capabilities, to meet the digital needs of various IoT industries, including security and privacy protection, real-time business processing, and intelligent applications.

[0004] Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the present invention provides a method for an intelligent multi-node online monitoring method based on edge computing, which can accurately and stably obtain data of network nodes through an efficient sensor data acquisition module; by adopting edge computing technology, the present invention reduces data transmission delay and improves the real-time performance of monitoring.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: an intelligent multi-node online monitoring method based on edge computing, comprising the following steps:

[0008] Use sensors to obtain network node data;

[0009] Perform data processing and analysis on the acquired data based on edge computing methods;

[0010] Online monitoring of intelligent nodes is carried out through the principle of magnetic induction.

[0011] As a preferred solution of the intelligent multi-node online monitoring method based on edge computing described in the present invention, wherein: the acquisition of network node data by using sensors is achieved by reading sensor data SD from the end node M of the power line and then establishing communication with node M-1, which is specifically implemented as follows:

[0012] When node M sends a signal to node M-1 at t j+1 When an RTS request packet is sent within a time slice, the RTS request packet carries the node's time slot status table, and the node's time slot status table satisfies the following rules:

[0013] t j+2 The time slice is set to receive state, used to receive SD data, t j+3 The time slice is set to the sending state and is used to send SD data;

[0014] When node M-1 is at t j+2 When an RTS data packet is received within the time slice, the time slot arrangement of node M is understood based on the RTS data packet. When the time slot arrangement of node M-1 does not conflict with the time slot arrangement of node M, the data returns a CTS clear packet. When the time slot arrangement of node M-1 conflicts with the time slot arrangement of node M, the data returns an RTS clear packet.

[0015] When node M is scheduled at time t j+3 When the time slice transmits SD data to node M-1, after node M-1 receives the SD data, the collection of network node data is completed.

[0016] As a preferred solution of the intelligent multi-node online monitoring method based on edge computing described in the present invention, wherein: the data processing and analysis based on the edge computing method for the acquired data is to process and securely transmit various types of monitoring data of the terminal through the edge computing method, thereby realizing the stable connection between the monitoring network status maintenance and the power system, and realizing the stable connection between the detection network status maintenance and the power system by calculating the minimum average task completion delay of the power system business resources under the delay requirement condition, and the calculation of the minimum average task completion delay of the power system business resources under the delay requirement condition includes the allocation of the power system business resources and the minimum average task completion delay of the allocated tasks under the delay requirement condition,

[0017] The allocation of the power system business resources is achieved through the particle swarm algorithm, which divides the business resources into particles for allocation calculation. The specific implementation formula is as follows:

[0018] GS best =(g1,g2,...,g D )

[0019] Among them, Y represents the particle population size, S represents the power system business resource size, l represents the D-dimensional position vector of the lth particle, Indicates the optimal position searched by the lth particle so far, GS best Represents the global optimal value of the optimal position searched by the entire particle swarm so far, represents the two collaborative nodes selected by the i-th terminal node, Indicates that the jth subtask of the i-th terminal node is Execute on.

[0020] As a preferred solution of the intelligent multi-node online monitoring method based on edge computing described in the present invention, the allocation of power system business resources is achieved by evaluating the particle quality of the particle swarm algorithm using the Fitness function. The specific implementation formula is as follows:

[0021] Where G(S) represents the sum of the computing resource constraint violations of all edge nodes and satisfies the formula G(S)>0, γ∈(0,1) represents the constraint violation factor, Represents the constraint target, Cross() and Mutate() represent crossover and mutation operations respectively. Indicates the optimal position searched by the lth particle so far, GS best represents the global optimal value of the optimal position searched by the entire particle swarm so far, and c represents the data propagation delay of the wired channel.

[0022] As a preferred solution of the intelligent multi-node online monitoring method based on edge computing described in the present invention, the minimized average task completion delay of the assigned tasks under the delay requirement condition is calculated by calculating the transmission delay of the detection data between the two collaboration points. The specific calculation process is as follows:

[0023] When there are N terminal nodes UE and M edge nodes EN in the network, and the terminal nodes UE and edge nodes EN satisfy the sets N = {1, 2, 3, ..., N} and M = {1, 2, 3, ..., M} respectively, then:

[0024] Among them, x ijk 、 represents the constraints on data transmission between collaboration points, v i Indicates the data transmission rate, c ij Indicates the resource requirements of the calculation, d ij Indicates the amount of input data, e ij represents the storage resource requirement, C k Represents the data of the virtual computing unit, y i Represents the signal-to-noise ratio of two data access points, P h Indicates the transmission power of the data access point, represents the channel gain of the two data access points, σ 2 represents the additive white Gaussian noise power, In particular, is the physical distance between the two data access points, c represents the data propagation delay of the wired channel, Indicates the transmission delay of detection data between two coordination points.

[0025] As a preferred solution of the intelligent multi-node online monitoring method based on edge computing described in the present invention, the online monitoring of intelligent nodes based on the magnetic induction principle is to analyze the working principle of the energy-taking coil through the self-inductance model, and establish an equivalent circuit model of the energy-taking coil, derive the expression of the output power of the energy-taking coil in the linear state, establish the relationship between the output power, primary side current, secondary side turns and magnetic core, and analyze the change trend of the output power of the energy-taking coil in the saturated state, thereby realizing the online monitoring of the intelligent nodes.

[0026] The magnetic induction principle includes a coil equivalent model and an energy extraction coil equivalent circuit; the coil equivalent model includes a primary winding and a secondary winding, and the primary winding and the secondary winding are both wound on the same magnetic core and maintain a coupling relationship between the two coils. The specific implementation is as follows:

[0027] When the equivalent coils are wound on the same magnetic core, the number of turns of the primary winding of the coil is N1, the number of turns of the secondary winding is N2, the secondary winding is open, and the secondary winding has no effect on the magnetic flux in the primary winding;

[0028] When a sinusoidal current I flows through the primary winding 10 When the sinusoidal current generates a magnetic flux φ in the magnetic core 10 The N1 coil turns are hinged, then: φ 10 =BS

[0029] in, Represents magnetic flux, B represents the magnetic induction intensity in the core, S represents the cross-sectional area of ​​the core, and the magnetic field intensity H in the core magnetic circuit l is equal everywhere. According to the total current law, we have:

[0030] Among them, R m is the magnetic core's magnetic resistance, m is the core's magnetic permeability, I 10 Represents the sinusoidal current in the primary winding, L s10 It represents leakage inductance, which is the ratio of leakage magnetic flux to primary current. L1 represents the self-inductance of the primary winding, L2 represents the self-inductance of the secondary winding, and l represents the magnetic circuit of the core.

[0031] As a preferred solution of the intelligent multi-node online monitoring method based on edge computing described in the present invention, the equivalent circuit model of the energy-taking coil is established by fully hinged the equivalent circuits of the two windings and satisfying the complete coupling relationship. The specific implementation is as follows:

[0032] When the secondary side of the energy-harvesting coil is connected to the load resistor R, an excitation signal is applied across the primary winding. Current I1 flows through the primary winding, and the mutual inductance between the two coils is M. When current I2 flows through the secondary winding, then:

[0033] Among them, L s1 Represents the leakage inductance of the primary winding, L s2 Represents the leakage inductance of the secondary winding, r1 represents the internal resistance of the secondary winding itself, Indicates the primary winding input voltage, Represents the voltage at the load resistance terminal of the secondary winding, It represents the voltage across the primary winding self-inductance, Indicates the voltage across the secondary winding self-inductance;

[0034] When the primary winding leakage impedance satisfies: Z s1 =r1+jwL s1 , and the secondary winding leakage impedance satisfies: Z s2 =r2+jwL s2 When , it means that the magnetic core passes through the primary winding and the secondary winding at the same time, then:

[0035] Where M represents the mutual inductance between the two coils, Represents the equivalent current passing through the primary side, represents the equivalent current passing through the secondary side, It represents the voltage across the primary winding self-inductance, It represents the voltage across the secondary winding self-inductance, P represents the output power of the energy-taking coil, q represents the angle of the equivalent current, S represents the cross-sectional area of ​​the magnetic core, and R represents the resistance of the load resistor.

[0036] Another object of the present invention is to provide an intelligent multi-node online monitoring method system based on edge computing, which can realize accurate monitoring of node status in the intelligent node online monitoring module by adopting the magnetic induction principle; and realize collaborative intelligent online monitoring of multiple nodes by comprehensively using the sensor data acquisition module, edge computing and data processing and analysis module, and intelligent node online monitoring module.

[0037] As a preferred solution of the intelligent multi-node online monitoring method system based on edge computing described in the present invention, it includes: a data acquisition module, an edge computing and data processing and analysis module, and an intelligent node online monitoring module; the data acquisition module is used to collect data from network nodes; the edge computing and data processing module is used to process and analyze the collected data using edge computing technology; the intelligent node online monitoring module is used to perform online monitoring of intelligent nodes.

[0038] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the steps of an intelligent multi-node online monitoring method based on edge computing.

[0039] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of an intelligent multi-node online monitoring method based on edge computing are implemented.

[0040] The beneficial effects of the present invention are as follows: the present invention can accurately and stably obtain data of network nodes through an efficient sensor data acquisition module; by adopting edge computing technology, the present invention reduces data transmission delay and improves the real-time performance of monitoring; by adopting the magnetic induction principle, accurate monitoring of node status is achieved in the intelligent node online monitoring module; by comprehensively using the sensor data acquisition module, edge computing and data processing and analysis module, and the intelligent node online monitoring module, collaborative intelligent online monitoring of multiple nodes is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0042] FIG1 is a schematic diagram of the overall method step structure of an intelligent multi-node online monitoring method based on edge computing of the present invention.

[0043] Figure 2 is an equivalent circuit diagram of the primary winding described in an intelligent multi-node online monitoring method based on edge computing in the present invention.

[0044] FIG3 is an equivalent circuit diagram of an energy extraction coil according to an intelligent multi-node online monitoring method based on edge computing of the present invention.

[0045] FIG4 is a schematic diagram of the overall composition structure of an intelligent multi-node online monitoring system based on edge computing according to the present invention. DETAILED DESCRIPTION

[0046] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0047] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0048] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0049] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0050] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0051] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0052] Example 1

[0053] 1 to 3 , which are the first embodiment of the present invention, provide an intelligent multi-node online monitoring method based on edge computing, including the following steps:

[0054] S1: Use sensors to obtain network node data.

[0055] Specifically, the method of obtaining network node data by using sensors is to read sensor data SD from the end node M of the power line and then establish communication with the node M-1. The specific implementation is as follows:

[0056] When node M sends a signal to node M-1 at t j+1 When an RTS request packet is sent within a time slice, the RTS request packet carries the node's time slot status table, and the node's time slot status table satisfies the following rules:

[0057] t j+2 The time slice is set to receive state, used to receive SD data, t j+3 The time slice is set to the sending state and is used to send SD data;

[0058] When node M-1 is at t j+2 When an RTS data packet is received within the time slice, the time slot arrangement of node M is understood based on the RTS data packet. When the time slot arrangement of node M-1 does not conflict with the time slot arrangement of node M, the data returns a CTS clear packet. When the time slot arrangement of node M-1 conflicts with the time slot arrangement of node M, the data returns an RTS clear packet.

[0059] When node M is scheduled at time t j+3When the time slice transmits SD data to node M-1, after node M-1 receives the SD data, the collection of network node data is completed.

[0060] S2: Perform data processing and analysis on the acquired data based on edge computing methods.

[0061] Specifically, the data processing and analysis based on the edge computing method is to process and securely transmit various types of terminal monitoring data through the edge computing method, thereby realizing the stable connection between the monitoring network status maintenance and the power system. The stable connection between the monitoring network status maintenance and the power system is realized by calculating the minimum average task completion delay of the power system business resources under the delay requirement conditions. The calculation of the minimum average task completion delay of the power system business resources under the delay requirement conditions includes the allocation of the power system business resources and the minimum average task completion delay of the allocated tasks under the delay requirement conditions.

[0062] Furthermore, the allocation of the power system business resources is achieved through a particle swarm algorithm, which divides the business resources into particles for allocation calculation. The specific implementation formula is as follows:

[0063] GS best =(g1,g2,...,g D )

[0064] Among them, Y represents the particle population size, S represents the power system business resource size, l represents the D-dimensional position vector of the lth particle, Indicates the optimal position searched by the lth particle so far, GS best Represents the global optimal value of the optimal position searched by the entire particle swarm so far, represents the two collaborative nodes selected by the i-th terminal node, Indicates that the jth subtask of the i-th terminal node is Execute on.

[0065] Furthermore, the allocation of power system business resources is achieved by evaluating the particle quality of the particle swarm algorithm using the Fitness function. The specific implementation formula is as follows:

[0066] Where G(S) represents the sum of the computing resource constraint violations of all edge nodes and satisfies the formula G(S)>0, γ∈(0,1) represents the constraint violation factor, Represents the constraint target, Cross() and Mutate() represent the crossover operation and mutation operation respectively. Indicates the optimal position searched by the lth particle so far, GS best It represents the global optimal value of the optimal position searched by the entire particle swarm so far, and c represents the data propagation delay of the wired channel.

[0067] Furthermore, the minimum average task completion delay of the assigned tasks under the delay requirement condition is calculated by calculating the transmission delay of the detection data between the two cooperation points. The specific calculation process is as follows:

[0068] When there are N terminal nodes UE and M edge nodes EN in the network, and the terminal nodes UE and edge nodes EN satisfy the sets N = {1, 2, 3, ..., N} and M = {1, 2, 3, ..., M} respectively, then:

[0069] Among them, x ijk 、 represents the constraints on data transmission between collaboration points, v i Indicates the data transmission rate, c ij Indicates the resource requirements of the calculation, d ij Indicates the amount of input data, e ij represents the storage resource requirement, C k Represents the data of the virtual computing unit, y i Represents the signal-to-noise ratio of two data access points, P h Indicates the transmission power of the data access point, represents the channel gain of the two data access points, σ 2 represents the additive white Gaussian noise power, In particular, is the physical distance between the two data access points, c represents the data propagation delay of the wired channel, Indicates the transmission delay of detection data between two coordination points.

[0070] S3: Online monitoring of intelligent nodes through magnetic induction principle.

[0071] Specifically, the online monitoring of intelligent nodes based on the magnetic induction principle is to analyze the working principle of the energy acquisition coil through the self-inductance model, and establish an equivalent circuit model of the energy acquisition coil, derive the expression of the output power of the energy acquisition coil in the linear state, establish the relationship between the output power, primary side current, secondary side turns and magnetic core, and analyze the changing trend of the output power of the energy acquisition coil in the saturated state, thereby realizing online monitoring of the intelligent node.

[0072] Furthermore, the magnetic induction principle includes a coil equivalent model and an energy extraction coil equivalent circuit; the coil equivalent model includes a primary winding and a secondary winding, and the primary winding and the secondary winding are both wound on the same magnetic core and maintain a coupling relationship between the two coils, which is specifically implemented as follows:

[0073] When the equivalent coils are wound on the same magnetic core, the number of turns of the primary winding of the coil is N1, the number of turns of the secondary winding is N2, the secondary winding is open, and the secondary winding has no effect on the magnetic flux in the primary winding;

[0074] When a sinusoidal current I flows through the primary winding 10 When the sinusoidal current generates a magnetic flux φ in the magnetic core 10 The N1 coil turns are hinged, then: φ 10 =BS

[0075] in, Represents magnetic flux, B represents the magnetic induction intensity in the core, S represents the cross-sectional area of ​​the core, and the magnetic field intensity H in the core magnetic circuit l is equal everywhere. According to the total current law, we have:

[0076] Among them, R m is the magnetic core's magnetic resistance, m is the core's magnetic permeability, I 10 Represents the sinusoidal current in the primary winding, L s10 It represents leakage inductance, which is the ratio of leakage magnetic flux to primary current. L1 represents the self-inductance of the primary winding, L2 represents the self-inductance of the secondary winding, and l represents the magnetic circuit of the core.

[0077] Furthermore, the equivalent circuit model of the energy extraction coil is established by fully hinged the equivalent circuits of the two windings and satisfying the complete coupling relationship. The specific implementation is as follows:

[0078] When the secondary side of the energy-harvesting coil is connected to the load resistor R, an excitation signal is applied across the primary winding. Current I1 flows through the primary winding, and the mutual inductance between the two coils is M. When current I2 flows through the secondary winding, then:

[0079] Among them, L s1 Represents the leakage inductance of the primary winding, L s2 Represents the leakage inductance of the secondary winding, r1 represents the internal resistance of the secondary winding itself, Indicates the primary winding input voltage, Represents the voltage at the load resistance terminal of the secondary winding, It represents the voltage across the primary winding self-inductance, Indicates the voltage across the secondary winding self-inductance;

[0080] When the primary winding leakage impedance satisfies: Z s1 =r1+jwL s1 , and the secondary winding leakage impedance satisfies: Z s2 =r2+jwL s2 When , it means that the magnetic core passes through the primary winding and the secondary winding at the same time, then:

[0081] Where M represents the mutual inductance between the two coils, Represents the equivalent current passing through the primary side, represents the equivalent current passing through the secondary side, It represents the voltage across the primary winding self-inductance, It represents the voltage across the secondary winding self-inductance, P represents the output power of the energy-taking coil, q represents the angle of the equivalent current, S represents the cross-sectional area of ​​the magnetic core, and R represents the resistance of the load resistor.

[0082] Example 2

[0083] 4 , which is a second embodiment of the present invention, provides an intelligent multi-node online monitoring system based on edge computing, including a data acquisition module, an edge computing and data processing and analysis module, and an intelligent node online monitoring module;

[0084] Specifically, the data acquisition module is used to collect data from network nodes; the edge computing and data processing module is used to process and analyze the collected data using edge computing technology; and the intelligent node online monitoring module is used to perform online monitoring of intelligent nodes.

[0085] Furthermore, in order to demonstrate the beneficial effects of the present invention, the technical features of the present invention are compared with the prior art. The specific comparison results are shown in the following table:

[0086] It is not difficult to find from the above comparison table that after adopting our invention, compared with the existing technology, the present invention has advantages in data acquisition capabilities, data quality control, real-time analysis and decision-making, health status assessment and safety assessment, and can improve the performance and reliability of the multi-node online monitoring system.

[0087] Furthermore, if the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0088] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0089] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0090] Additionally, in order to provide a concise description of exemplary embodiments, all features of an actual embodiment (ie, those features that are not relevant to the best mode presently contemplated for carrying out the invention or those that are not relevant to implementing the invention) may not be described.

[0091] It will be appreciated that in the development of any actual embodiment, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort may be complex and time-consuming, but will, for those of ordinary skill having the benefit of this disclosure, be a routine undertaking of design, fabrication, and production without undue experimentation.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An intelligent multi-node online monitoring method based on edge computing, characterized in that: The following steps are included: Use sensors to obtain network node data; Perform data processing and analysis on the acquired data based on edge computing methods; Online monitoring of intelligent nodes is carried out through the principle of magnetic induction.

2. The intelligent multi-node online monitoring method based on edge computing according to claim 1, characterized in that: The method of using sensors to obtain network node data is to establish communication with node M-1 after reading sensor data SD from the end node M of the power line. The specific implementation is as follows: When node M sends a signal to node M-1 at t j+1 When an RTS request packet is sent within a time slice, the RTS request packet carries a slot status table of the node, and the slot status table of the node satisfies the following rules: t j+2 The time slice is set to receive state, used to receive SD data, t j+3 The time slice is set to the sending state for sending SD data; When node M-1 is at t j+2 When an RTS data packet is received within the time slice, the time slot arrangement of node M is understood based on the RTS data packet. When the time slot arrangement of node M-1 does not conflict with the time slot arrangement of node M, the data returns a CTS clear packet. When the time slot arrangement of node M-1 conflicts with the time slot arrangement of node M, the data returns an RTS clear packet. When node M is scheduled at time t j+3 When the time slice transmits SD data to node M-1, after node M-1 receives the SD data, the collection of network node data is completed.

3. The intelligent multi-node online monitoring method based on edge computing as claimed in claim 2, characterized in that: The data processing and analysis based on the edge computing method is to process and securely transmit various types of terminal monitoring data through the edge computing method, thereby realizing the stable connection between the monitoring network status maintenance and the power system. The stable connection between the detection network status maintenance and the power system is realized by calculating the minimum average task completion delay of the power system business resources under the delay requirement condition. The calculation of the minimum average task completion delay of the power system business resources under the delay requirement condition includes the allocation of the power system business resources and the minimum average task completion delay of the allocated tasks under the delay requirement condition. The allocation of the power system business resources is achieved through a particle swarm algorithm, which divides the business resources into particles for allocation calculation. The specific implementation formula is as follows: GS best =(g1,g2,...,g D ) Among them, Y represents the particle population size, S represents the power system business resource size, l represents the D-dimensional position vector of the lth particle, represents the optimal position searched by the lth particle so far, GS best It represents the global optimal value of the optimal position searched by the entire particle swarm so far. represents the two collaborative nodes selected by the i-th terminal node, Indicates that the jth subtask of the i-th terminal node is Execute on.

4. The intelligent multi-node online monitoring method based on edge computing as claimed in claim 3, characterized in that: The allocation of the power system business resources is achieved by evaluating the particle quality of the particle swarm algorithm using the Fitness function. The specific implementation formula is as follows: Where G(S) represents the sum of the violation degrees of computing resource constraints of all edge nodes, and satisfies the formula G(S)>0, γ∈(0,1) represents the constraint violation factor, Represents the constraint target, Cross() and Mutate() represent crossover and mutation operations respectively. represents the optimal position searched by the lth particle so far, GS best represents the global optimal value of the optimal position searched by the entire particle swarm so far, and c represents the data propagation delay of the wired channel.

5. The intelligent multi-node online monitoring method based on edge computing according to claim 4, characterized in that: The minimum average task completion delay of the assigned tasks under the delay requirement condition is calculated by calculating the transmission delay of the detection data between the two cooperation points. The specific calculation process is as follows: When there are N terminal nodes UE and M edge nodes EN in the network, and the terminal nodes UE and edge nodes EN satisfy the sets N = {1, 2, 3, ..., N} and M = {1, 2, 3, ..., M} respectively, then: Among them, x ijk , represents the constraints on data transmission between collaboration points, v i Indicates the data transmission rate, c ij represents the resource requirements of the calculation, d ij Indicates the amount of input data, e ij represents the storage resource requirement, C k Represents the data of the virtual computing unit, y i represents the signal-to-noise ratio of two data access points, P h represents the transmission power of the data access point, represents the channel gain of two data access points, σ 2 represents the additive white Gaussian noise power, In particular, is the physical distance between two data access points, c represents the data propagation delay of the wired channel, Indicates the transmission delay of detection data between two cooperation points.

6. The intelligent multi-node online monitoring method based on edge computing according to claim 5, characterized in that: The online monitoring of the intelligent node by the magnetic induction principle is to analyze the working principle of the energy-taking coil through the self-inductance model, establish the equivalent circuit model of the energy-taking coil, derive the expression of the output power of the energy-taking coil in the linear state, establish the relationship between the output power, the primary side current, the number of turns on the secondary side and the magnetic core, and analyze the change trend of the output power of the energy-taking coil in the saturated state, thereby realizing the online monitoring of the intelligent node. The magnetic induction principle includes a coil equivalent model and an energy extraction coil equivalent circuit; the coil equivalent model includes a primary winding and a secondary winding, and the primary winding and the secondary winding are both wound on the same magnetic core and maintain a coupling relationship between the two coils, which is specifically implemented as follows: When the equivalent coils are wound on the same magnetic core, the number of turns of the primary winding of the coil is N1, the number of turns of the secondary winding is N2, the secondary winding is open circuit, and the secondary winding has no effect on the magnetic flux in the primary winding; When a sinusoidal current I passes through the primary winding 10 When the sinusoidal current generates a magnetic flux φ in the magnetic core 10 The N1 turns of coil are hinged, then: φ 10 =BS in, represents magnetic flux, B represents magnetic induction intensity in the core, S represents cross-sectional area of ​​the core, and the magnetic field intensity H in the core magnetic circuit l is equal everywhere. According to the total current law, we have: Among them, R m is the magnetic resistance of the core, m is the magnetic permeability of the core, I 10 Represents the sinusoidal current in the primary winding, L s10 It represents leakage inductance, which is the ratio of leakage flux to primary current. L1 represents the self-inductance of the primary winding, L2 represents the self-inductance of the secondary winding, and l represents the magnetic circuit of the core.

7. The intelligent multi-node online monitoring method based on edge computing according to claim 6, characterized in that: The equivalent circuit model of the energy extraction coil is established by fully hinged the equivalent circuits of the two windings and satisfying the complete coupling relationship. The specific implementation is as follows: When the secondary side of the energy harvesting coil is connected to the load resistor R, an excitation signal is applied across the primary winding, a current I1 flows through the primary winding, the mutual inductance between the two coils is M, and a current I2 flows through the secondary winding, then: Among them, L s1 Represents the leakage inductance of the primary winding, L s2 represents the leakage inductance of the secondary winding, r1 represents the internal resistance of the secondary winding itself, Represents the primary winding input voltage, Represents the voltage at the load resistance terminal of the secondary winding, It represents the voltage across the primary winding self-inductance. It represents the voltage across the secondary winding self-inductance; When the primary winding leakage impedance satisfies: Z s1 =r1+jwL s1 , and the secondary winding leakage impedance satisfies: Z s2 =r2+jwL s2 When , it means that the magnetic core passes through the primary winding and the secondary winding at the same time, then: Where M represents the mutual inductance between the two coils, represents the equivalent current passing through the primary side, represents the equivalent current through the secondary side, It represents the voltage across the primary winding self-inductance. It represents the voltage across the secondary winding self-inductance, P represents the output power of the energy-taking coil, q represents the angle of the equivalent current, S represents the cross-sectional area of ​​the magnetic core, and R represents the resistance value of the load resistor.

8. A system using the intelligent multi-node online monitoring method based on edge computing as claimed in any one of claims 1 to 7, characterized in that: Including data acquisition module, edge computing and data processing and analysis module and intelligent node online monitoring module; The data acquisition module is used to collect data from network nodes; The edge computing and data processing module is used to process and analyze the collected data using edge computing technology; The smart node online monitoring module is used to perform online monitoring on the smart node.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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