Low latency communication routing method and system based on power network

Abstract

The application provides a low-latency communication routing method and system based on a power network, relates to the technical field of communication routing control, and considers electromagnetic interference levels and electromagnetic compatibility parameters when path selection is performed, and further utilizes electromagnetic field simulation software to perform real-time prediction on the electromagnetic interference levels in different states, to generate a second electromagnetic interference distribution map, so that the accuracy of path selection and the optimization effect are improved; in addition, by monitoring and analyzing the service transmission requirements and electromagnetic interference environment data of the power internet of things in real time, a comprehensive evaluation index is generated, and the comprehensive priority index of the optimal path is dynamically adjusted in combination with the transmission delay index, so that the adaptive ability and response speed of the system are further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication routing control, in particular to a low-latency communication routing method and system based on a power network. BACKGROUND

[0002] Power Internet of Things (IoT) enables intelligent interconnection and data exchange between devices by integrating sensors, controllers, and communication modules into power equipment. However, with the increasing complexity of power networks and the growing number of devices, efficient and reliable data transmission has become a pressing problem. Traditional communication routing methods have certain limitations in dealing with the high transmission demand and low latency requirements of power IoT. Especially in the electromagnetic interference (EMI) environment, electromagnetic compatibility (EMC) problems are more prominent, affecting the stability and reliability of communication. Therefore, the research on low-latency communication routing method based on power network has important practical significance.

[0003] In the prior art, a low-latency communication routing method and system based on power are disclosed in CN117857423A. The method includes: determining the business transmission demand and network resource demand according to the application demand of power IoT; establishing a broadband resource allocation model using a nonlinear programming method; determining the best routing path of latency-sensitive business according to the network topology under the power IoT scenario.

[0004] The existing technology mainly relies on fixed path selection and simple interference avoidance strategies in the communication routing of power IoT; these methods can meet the basic communication demand in a relatively ideal environment, but in actual application, the electromagnetic interference environment faced by power IoT is complex and variable, and the fixed path selection method is difficult to adapt to such dynamic changes. In addition, traditional routing methods usually lack consideration of electromagnetic compatibility parameters, resulting in suboptimal selection of communication paths in high-interference environments, high transmission latency, and affecting the performance and reliability of the overall system.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a low-latency communication routing method and system based on a power network to solve the problems raised in the background.

[0007] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0008] The low-latency communication routing method based on a power network includes the following specific steps:

[0009] Step S1: Collect the service transmission demand of the current device corresponding to the power internet of things and the electromagnetic interference environment data, the electromagnetic interference environment data including electromagnetic interference level and electromagnetic compatibility parameters of the device; and based on the collected electromagnetic interference environment data, a first electromagnetic interference distribution map is constructed;

[0010] Step S2: Introducing electromagnetic compatibility as a constraint condition, creating an optimization objective function according to the first electromagnetic interference distribution map, the optimization objective function being that the ratio of resource utilization rate and electromagnetic interference level is less than a preset threshold, to determine a plurality of low-interference transmission paths, finally constructing a multi-level routing optimization model, and comprehensively calculating a transmission delay index according to the output of the multi-level routing optimization model;

[0011] Step S3: Based on the multi-level routing optimization model, the electromagnetic interference level is predicted by using electromagnetic field simulation software to simulate the electromagnetic interference level of the current device in different operating states in real time, to obtain a second electromagnetic interference distribution map, and based on the simulation results, the path with the minimum interference and the optimal bandwidth resource utilization is selected from the plurality of low-interference transmission paths determined in step S2;

[0012] Step S4: Real-time monitoring and analyzing the service transmission demand of the current device corresponding to the power internet of things and the electromagnetic interference environment data, generating a comprehensive evaluation index, combining and analyzing the transmission delay index and the comprehensive evaluation index, and generating a comprehensive priority index for dynamically adjusting the optimal path in step S3.

[0013] Further, the comprehensive evaluation index is generated, specifically including:

[0014] The maximum amount of data transmission is marked as CSM, and the allowed delay of data transmission is marked as CSY;

[0015] The conducted emission, radiated emission, conducted immunity and radiated immunity are marked as CF1, CF2, CF3 and CF4 in turn;

[0016] The normalized output values of each parameter contained in the service transmission demand and the electromagnetic interference environment data are adjusted to the range of (0, 1) through scaling and offset;

[0017] x j The adjusted parameter value is represented by x, and x e {CSM, CSY, CF1, CF2, CF3, CF4};

[0018] When j takes values 1, 2, 3, 4, 5 and 6 in turn, it corresponds to CSM, CSY, CF1, CF2, CF3 and CF4;

[0019] The comprehensive evaluation index is defined as CEI, and the calculation formula is as follows:

[0020]

[0021] wherein, x j is the jth parameter value, e is the base of natural logarithm, a is an adjustment parameter, 0.12≤a≤0.68, a is used to control the decay rate of the exponential function; η1 is a normal number term; 0.02≤η1≤0.11;

[0022] The effective value range of the comprehensive evaluation index CEI is set to (0, 1);

[0023] When 0.5≤CEI<1, it is a high priority interval, indicating that the device has high transmission demand and excellent electromagnetic compatibility;

[0024] When 0.2≤CEI<0.5, it is a medium priority interval, indicating that the device has medium transmission demand and electromagnetic compatibility;

[0025] When 0<CEI<0.2, it is a low priority interval, indicating that the device has low transmission demand or poor electromagnetic compatibility.

[0026] Further, the transmission delay index and the comprehensive evaluation index are combined for analysis to generate a comprehensive priority index for dynamically adjusting the optimal path in step S3, specifically including:

[0027] The transmission delay index and the comprehensive evaluation index are combined, and the generated comprehensive priority index is defined as CPI, and the calculation formula is as follows:

[0028]

[0029] Wherein, 0.01≤η2≤0.14; when CPI>1, it indicates that the currently selected optimal path is a low priority path, which is avoided for reselection from several low-interference transmission paths;

[0030] When 0.5≤CPI≤1, it indicates that the currently selected optimal path is a medium priority path, which is a suboptimal selection for reselection of the optimal path from several low-interference transmission paths;

[0031] When CPI<0.5, it indicates that the currently selected optimal path is a high priority path, which is a priority selection.

[0032] A low-latency communication routing system based on a power network, the system is used to execute the low-latency communication routing method based on the power network, comprising:

[0033] A data acquisition module: for acquiring the business transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data, the electromagnetic interference environment data including electromagnetic interference level and electromagnetic compatibility parameters of the device; and based on the collected electromagnetic interference environment data, a first electromagnetic interference distribution map is constructed;

[0034] The optimization model construction module is used for introducing electromagnetic compatibility as a constraint condition, creating an optimization objective function according to the first electromagnetic interference distribution map, the optimization objective function being a ratio of resource utilization and electromagnetic interference level being less than a preset threshold value, determining a plurality of low-interference transmission paths, and finally constructing a multi-level routing optimization model, and comprehensively calculating a transmission delay index according to an output of the multi-level routing optimization model;

[0035] The simulation prediction module is used for predicting the electromagnetic interference level by using electromagnetic field simulation software on the basis of the multi-level routing optimization model, so as to simulate the electromagnetic interference level of the current device under different operating states in real time, obtain a second electromagnetic interference distribution map, and select a path with minimum interference and optimal bandwidth resource utilization from the plurality of low-interference transmission paths based on the simulation result;

[0036] The dynamic adjustment module is used for monitoring and analyzing the business transmission demand of the current device corresponding to the power internet of things and the electromagnetic interference environment data in real time, generating a comprehensive evaluation index, combining and analyzing the transmission delay index and the comprehensive evaluation index, and generating a comprehensive priority index for dynamically adjusting the optimal path.

[0037] Compared with the prior art, the beneficial effects of the present application are that the electromagnetic interference level and electromagnetic compatibility parameters are considered in path selection, and the electromagnetic field simulation software is used to predict the electromagnetic interference level under different states in real time, a second electromagnetic interference distribution map is generated, the accuracy and optimization effect of path selection are improved, and the adaptive ability and response speed of the system are further improved by monitoring and analyzing the business transmission demand of the power internet of things and the electromagnetic interference environment data in real time, generating a comprehensive evaluation index, combining the transmission delay index, and dynamically adjusting the comprehensive priority index of the optimal path. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The figure is a schematic diagram of the overall method of the present application;

[0039] Figure 2 The figure is a system module block diagram of the present application. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below with specific examples.

[0041] It should be noted that the technical terms or scientific terms used in the present application should be understood as the general meaning understood by those skilled in the art to which the present application belongs, unless otherwise defined. The "first", "second" and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Include" or "contain" and similar words mean that the elements or objects before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connected" or "connected" and similar words are not limited to physical or mechanical connection, but can include electrical connection, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to represent the relative positional relationship, when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0042] Embodiment one:

[0043] Please refer to Figure 1 The present application provides a technical solution:

[0044] A low-latency communication routing method based on a power network, comprising the following steps:

[0045] Step S1: Collecting the service transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data, the electromagnetic interference environment data including the electromagnetic interference level and the electromagnetic compatibility parameters of the device; and based on the collected electromagnetic interference environment data, constructing a first electromagnetic interference distribution map;

[0046] Step S2: Introducing electromagnetic compatibility as a constraint condition, creating an optimization objective function according to the first electromagnetic interference distribution map, the optimization objective function being that the ratio of resource utilization rate and electromagnetic interference level is less than a preset threshold, to determine a number of low-interference transmission paths, finally constructing a multi-level routing optimization model, and comprehensively calculating a transmission delay index according to the output of the multi-level routing optimization model;

[0047] Step S3: Based on the multi-level routing optimization model, using electromagnetic field simulation software to predict the electromagnetic interference level, to simulate the electromagnetic interference level of the current device under different operating states in real time, to obtain a second electromagnetic interference distribution map, and based on the simulation results, selecting the path with the smallest interference and the optimal bandwidth resource utilization from the number of low-interference transmission paths determined in step S2;

[0048] Step S4: Real-time monitoring and analyzing the service transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data, generating a comprehensive evaluation index, combining and analyzing the transmission delay index and the comprehensive evaluation index, and generating a comprehensive priority index for dynamically adjusting the optimal path in step S3.

[0049] Further, based on the collected electromagnetic interference environment data, a first electromagnetic interference distribution map is constructed, specifically including:

[0050] 1.1) Extract the service transmission demand of the current device in the power Internet of Things to clarify the information transmission requirements of delay-sensitive services;

[0051] The information transmission requirements include the maximum amount of data transmission per unit time, the allowed delay of data transmission, and the quality requirements of the communication link;

[0052] 1.2) The electromagnetic interference level includes the frequency bandwidth and intensity of the interference source, and the electromagnetic interference level is used as an input parameter for subsequent optimization; These parameters are collected by existing electromagnetic compatibility test equipment;

[0053] Electromagnetic compatibility parameters include conducted emission, radiated emission, conducted immunity, and radiated immunity;

[0054] Conducted emission: The electromagnetic energy emitted by the device through the cable, expressed in voltage or current;

[0055] Radiated emission: The electromagnetic energy radiated by the device through space, expressed in field strength;

[0056] Conducted immunity: The ability of the device to resist electromagnetic interference transmitted through the cable;

[0057] Radiated immunity: The ability of the device to resist electromagnetic interference transmitted through space;

[0058] 1.3) Based on the collected electromagnetic interference environment data, use electromagnetic compatibility analysis tools to visually present the potential interference area and degree in the power Internet of Things corresponding to the current device, and then construct a first electromagnetic interference distribution map.

[0059] The electromagnetic compatibility analysis tool uses any of the following:

[0060] EMC Studio: Used for modeling and simulation of electromagnetic compatibility;

[0061] CST Studio Suite: Widely used in antenna design, signal integrity, and electromagnetic interference analysis;

[0062] Ansys HFSS: Focuses on high-frequency electromagnetic field simulation, suitable for antenna, RF, and microwave device analysis.

[0063] Using electromagnetic compatibility analysis tools to visually present the potential interference area and degree in the power Internet of Things, specifically including:

[0064] Using electromagnetic compatibility analysis tools to process data to generate a frequency bandwidth and intensity model of electromagnetic interference sources;

[0065] The collected electromagnetic interference level and electromagnetic compatibility parameters are input into the electromagnetic compatibility analysis tool.

[0066] The electromagnetic signal is subjected to spectral analysis using the electromagnetic compatibility analysis tool to identify the interference sources at each frequency band.

[0067] The frequency and intensity of the main interference sources are determined;

[0068] Based on the spectral analysis results, a three-dimensional space model is established to mark the positions of different interference sources in the network, obtaining a 3D topology structure;

[0069] The interference intensity is identified using color coding or other visualization means;

[0070] The influence of different interference sources on each node is simulated, and the specific influence degree on signal quality and communication delay is calculated.

[0071] The compatibility parameters of each node are evaluated to identify the areas most susceptible to interference.

[0072] The analysis results are integrated into an electromagnetic interference influence map to show the positions of the interference sources and their influence ranges.

[0073] For constructing the first electromagnetic interference distribution map:

[0074] The spectral and spatial distribution data obtained from the analysis are ensured to contain the frequency, intensity, position, and influence range of the interference sources;

[0075] Color coding of the three-dimensional space model is performed using MATLAB software tools;

[0076] The color and identification rules are defined:

[0077] Interference intensity levels: low (0-33%) green, medium (34%-66%) yellow, and high (67%-100%) red.

[0078] A distribution map is generated based on the model to identify the key interference areas and their intensity levels;

[0079] The distribution map is exported in PDF format, and the map is used for evaluating and optimizing the communication path.

[0080] Further, a number of low-interference transmission paths are determined, and finally a multi-level routing optimization model is constructed, and a transmission delay index is calculated based on the output of the multi-level routing optimization model, specifically including:

[0081] 2.1) The creation of the optimization objective function is based on the existing bandwidth resource allocation model, and the objective function is in the following form:

[0082] Based on experimental verification and expert analysis, it is determined that the ratio of resource utilization rate and electromagnetic interference level less than the preset threshold is BY1;

[0083]

[0084] Wherein, B represents a bandwidth allocation vector, EMI represents an overall electromagnetic interference environment; the objective of this optimization model is to meet business needs while avoiding high interference paths and ensuring low latency communication; f represents the objective function; according to the target and variable relationship, it is selected from linear and nonlinear mathematical expressions, and no further description is made;

[0085] When , it is a certain low-interference transmission path; when , the value of the objective function is infinite, and this transmission path needs to be excluded;

[0086] RUR represents resource utilization rate, ESL represents electromagnetic interference level, S i is the spectral bandwidth of the ith interference source, I i is the intensity of the ith interference source; n is the number of interference sources, and i represents the index of the ith interference source;

[0087] When allocating bandwidth, paths with RUR less than 30% are selected as priority paths;

[0088] If RUR decreases by 10% and ESL remains unchanged, f(B, EMI) will decrease by 10%;

[0089] 2.2) Divide the power internet of things into at least four levels, and optimize different network levels;

[0090] The four levels are access network layer, core network layer, edge computing layer, and cloud service layer; each level is optimized separately based on electromagnetic interference environment data to minimize interference between levels;

[0091] For the access network layer:

[0092] In the access network layer, the electromagnetic environment near the user access end is focused on, and the main task is to evaluate and reduce the interference caused by adjacent power equipment; the optimization strategy of this level includes adjusting the frequency allocation of the equipment and strengthening the electromagnetic shielding of the equipment, to ensure that users can stably and efficiently connect to the power internet of things;

[0093] For the core network layer:

[0094] The core network layer is responsible for large-scale data transmission and processing, and its optimization focuses on identifying and reducing electromagnetic interference from external environments in cross-regional transmission; by using advanced signal processing technology and anti-interference algorithms, it ensures that data is not disturbed during long-distance transmission, maintaining the efficiency and reliability of the network;

[0095] For the edge computing layer:

[0096] The edge computing layer aims to reduce the delay of data transmission and improve real-time processing capability, and the optimization of this layer focuses on reducing interference between local devices and terminals, ensuring efficient use of computing resources, and optimizing processing performance through dynamic resource allocation and local caching technology;

[0097] For the cloud service layer:

[0098] The cloud service layer provides data storage and advanced analysis capabilities, and the optimization focus is to protect the cloud infrastructure from large-scale electromagnetic interference, ensure the integrity and security of data, and ensure the continuity and stability of services through the use of advanced cloud security measures and redundancy design, even in a high-interference environment. It can maintain normal operation.

[0099] When determining bandwidth allocation, paths with RUR below 30% are selected as several low-interference transmission paths, and the baseline values of electromagnetic shielding coverage, signal processing capability, local caching technology indicators, and redundancy design proportion are determined in turn according to expert groups or experimental verification;

[0100] If RUR is reduced by 10% and ESL remains unchanged, f(B, EMI) will be reduced by 10%;

[0101] For access network layer optimization, evaluate the electromagnetic environment of user access end and adjust frequency allocation;

[0102] If the electromagnetic shielding coverage exceeds the corresponding baseline value by 15%, the ESL is reduced by 15% to improve connection stability;

[0103] Define the ESL of the access network layer after adjustment as ESL access , and ESL access = ESL(1-0.15);

[0104] For core network layer optimization, use signal processing technology to reduce cross-region interference;

[0105] When the signal processing capability value exceeds the corresponding baseline value by 20%, the ESL is reduced by 20% to improve transmission efficiency;

[0106] Define the ESL of the core network layer after adjustment as ESL core , and ESL core = ESL(1-0.2);

[0107] For edge computing layer optimization, dynamically allocate computing resources to reduce delay;

[0108] If the local caching technology indicator exceeds the corresponding baseline value by 25%, reduce the data transmission delay by 25%;

[0109] The ESL adjusted by the edge computing layer is defined as ESL edge , and ESL edge =ESL(1-0.25);

[0110] For cloud service layer optimization, a redundant design is adopted to enhance the anti-interference capability.

[0111] When the proportion of the redundant design exceeds 30% of the corresponding benchmark value, the ESL is reduced by 30% to ensure data security.

[0112] The ESL adjusted by the cloud service layer is defined as ESL cloud , and ESL cloud =ESL(1-0.3);

[0113] The transmission delay index is defined as Delay, and the calculation formula is as follows:

[0114]

[0115] Where TD is the total data transmission time, P is the path priority, and ESL total is the adjusted comprehensive average value of ESL.

[0116] Further, electromagnetic field simulation software is used to predict the electromagnetic interference level to simulate the electromagnetic interference level of the current device in different operating states in real time, obtain a second electromagnetic interference distribution map, and based on the simulation results, select the path with the smallest interference and the most optimal bandwidth resource utilization from the several low-interference transmission paths determined in step S2. Specifically, it includes:

[0117] Different operating states include different business transmission requirements and electromagnetic interference environment data.

[0118] 3.1) The electromagnetic interference level prediction is based on the multi-level routing optimization model, and the electromagnetic field simulation software selects COMSOL or CST; the electromagnetic field simulation software is also used to predict the influence of electromagnetic interference level on different communication links;

[0119] Draw the network topology diagram of the power Internet of Things, mark the device nodes and their connection paths with each other; at the same time, superimpose the second electromagnetic interference distribution map obtained by prediction on the network topology diagram; specifically, it includes:

[0120] The input parameters of the simulation are set as follows:

[0121] The information transmission requirement includes the maximum amount of data transmission per unit time, the allowed time delay of data transmission, and the quality requirement of the communication link;

[0122] Electromagnetic compatibility parameters include conducted emission, radiated emission, conducted immunity and radiated immunity;

[0123] Conducted emission is represented as conducted voltage; radiated emission is represented as field strength; conducted immunity is represented as immunity to cable-borne interference; radiated immunity is represented as immunity to space-borne interference;

[0124] Select COMSOL Multiphysics: This software has the ability of multi-physical field coupling, suitable for simulation of complex electromagnetic environment;

[0125] Obtain the 3D topology structure drawn in step S1: According to the actual device layout of the power Internet of Things, establish a 3D model of the device and the connection path;

[0126] Set the material parameters and boundary conditions of the current device; Set the open boundary condition to simulate the electromagnetic radiation of open space, and set the absorbing boundary condition to reduce the reflection effect;

[0127] Start COMSOL simulation: According to the set input parameters and physical model, run the simulation calculation of electromagnetic interference level;

[0128] Generate a second electromagnetic interference distribution map to show the electromagnetic interference distribution of the interference source under different frequency bandwidth and intensity;

[0129] Mark the area where the interference intensity exceeds 85%, which is used for subsequent routing optimization;

[0130] Identify key interference nodes: Combine the network topology graph of the power Internet of Things to identify high-interference nodes and areas;

[0131] According to the data of high-interference areas, adjust the communication routing to avoid transmitting delay-sensitive services in these areas;

[0132] Ensure that the communication link quality reaches more than 95%, and reduce the data transmission delay to 80% of the original;

[0133] 3.2) Based on the simulation results, superimpose the network topology graph of the power Internet of Things and the second electromagnetic interference distribution map to determine the transmission path with the minimum interference and optimal bandwidth resource utilization; Specifically including:

[0134] Regarding the power Internet of Things as a graph model, on the basis of a number of low-interference transmission paths determined in step S2, each communication node is a vertex in the graph, and the link between nodes is the edge of the graph; For each link, the following two weights are assigned:

[0135] Interference weight: Based on the simulation results, the higher the interference intensity, the greater the weight;

[0136] Bandwidth weight: represents the bandwidth resource utilization of the path; the smaller the weight, the better the bandwidth resource utilization;

[0137] Combine the two factors into a comprehensive weight to define the transmission cost of each path; the total transmission cost of the path is the sum of the interference and bandwidth weight of each link on the path;

[0138] Use the dynamic programming algorithm to make the optimal path selection, which is implemented as follows:

[0139] Define the problem state as the minimum transmission cost path from the starting node to the target node, define an array, and use the array to record the minimum transmission cost of each node;

[0140] For each node, calculate the minimum transmission cost to the next node and update the array;

[0141] By iteratively calculating the minimum transmission cost of each node, the optimal path from the starting node to the target node is finally determined step by step;

[0142] The optimal path from the starting node to the target node is obtained by dynamic programming, i.e. the transmission path with the minimum electromagnetic interference and the optimal bandwidth resource utilization; this path is directly applied to the routing planning of the power internet of things through an automated script or management system.

[0143] Further explanation, generate a comprehensive evaluation index, combine the transmission delay index with the comprehensive evaluation index for combined analysis, generate a comprehensive priority index for dynamic adjustment of the optimal path in step S3, which specifically includes:

[0144] Mark the maximum amount of data transmission currently monitored as CSM, and mark the data transmission allowed delay as CSY;

[0145] Mark the current monitored conducted emission, radiated emission, conducted immunity, and radiated immunity as CF1, CF2, CF3, and CF4, respectively;

[0146] Normalize the output values of each parameter contained in the business transmission demand and electromagnetic interference environment data to the range of (0, 1) through scaling and offset adjustment;

[0147] x j ∈{CSM, CSY, CF1, CF2, CF3, CF4} is the adjusted parameter value;

[0148] When j takes values 1, 2, 3, 4, 5, and 6 in turn, it corresponds to CSM, CSY, CF1, CF2, CF3, and CF4;

[0149] Define the comprehensive evaluation index as CEI, and the calculation formula is as follows:

[0150]

[0151] wherein x j is the jth parameter value, e is the base of the natural logarithm, a is an adjustment parameter, 0.12≤a≤0.68, a is used to control the decay speed of the exponential function; η1is a normal number term; 0.02≤η1≤0.11; the specific values of a and η1are determined by the expert group through experimental data;

[0152] The effective value range of the comprehensive evaluation index CEI is set to (0, 1);

[0153] For high transmission requirements and excellent electromagnetic compatibility:

[0154] High transmission requirements:

[0155] Data transmission maximum (CSM)≥80Mbps;

[0156] Data transmission allowed latency (CSY)≤30ms;

[0157] Excellent electromagnetic compatibility:

[0158] Conducted emission (CF1)≤10dB;

[0159] Radiated emission (CF2)≤20dB;

[0160] Conducted immunity (CF3)≥80dB;

[0161] Radiated immunity (CF4)≥60dB;

[0162] For medium transmission requirements and electromagnetic compatibility:

[0163] Medium transmission requirements:

[0164] Data transmission maximum (CSM) is between 40 and 80Mbps;

[0165] Data transmission allowed latency (CSY) is between 30 and 70ms;

[0166] Medium electromagnetic compatibility:

[0167] Conducted emission (CF1) is between 10 and 20dB;

[0168] Radiated emission (CF2) is between 20 and 40dB;

[0169] Conducted immunity (CF3) is between 60 and 80dB;

[0170] Radiated immunity (CF4) is between 40 and 60dB;

[0171] Low equipment transmission requirements or poor electromagnetic compatibility:

[0172] Low equipment transmission requirements:

[0173] Data transmission maximum (CSM) < 40 Mbps;

[0174] Data transmission allowed latency (CSY) > 70 ms;

[0175] Poor electromagnetic compatibility:

[0176] Conducted emission (CF1) > 20 dB;

[0177] Radiated emission (CF2) > 40 dB;

[0178] Conducted immunity (CF3) < 60 dB;

[0179] Radiated immunity (CF4) < 40 dB;

[0180] When 0.5≤ CE I < 1, it is a high priority interval, indicating that the equipment has high transmission requirements and excellent electromagnetic compatibility;

[0181] When 0.2≤ CE I < 0.5, it is a medium priority interval, indicating that the equipment has medium transmission requirements and electromagnetic compatibility;

[0182] When 0 < CE I < 0.2, it is a low priority interval, indicating that it has low equipment transmission requirements or poor electromagnetic compatibility;

[0183] Combining the transmission latency index and the comprehensive evaluation index, and defining the generated comprehensive priority index as CPI, the calculation formula is as follows:

[0184]

[0185] Wherein, 0.01≤η2≤0.14; η2 is determined according to the experimental data of the expert group;

[0186] When CPI > 1, it indicates that the currently selected optimal path is a low priority path, which is avoided to be used, and is reselected from several low interference transmission paths;

[0187] When 0.5≤ CPI ≤ 1, it indicates that the currently selected optimal path is a medium priority path, which is a suboptimal selection, and is reselected from several low interference transmission paths;

[0188] When CPI < 0.5, it indicates that the currently selected optimal path is a high priority path, which is a priority selection.

[0189] Example two:

[0190] Please refer toFigure 2 A low-latency communication routing system based on a power network, the system being used to perform the low-latency communication routing method based on the power network, comprising:

[0191] A data collection module: configured to collect the service transmission demand of the current device corresponding to the power Internet of Things and electromagnetic interference environment data, the electromagnetic interference environment data including electromagnetic interference level and electromagnetic compatibility parameters of the device; and based on the collected electromagnetic interference environment data, a first electromagnetic interference distribution map is constructed;

[0192] An optimization model construction module: configured to introduce electromagnetic compatibility as a constraint condition, create an optimization objective function according to the first electromagnetic interference distribution map, and determine a plurality of low-interference transmission paths, wherein the optimization objective function is a ratio of resource utilization rate and electromagnetic interference level being less than a preset threshold, and finally construct a multi-level routing optimization model, and calculate a transmission latency index according to the output of the multi-level routing optimization model;

[0193] A simulation prediction module: configured to use electromagnetic field simulation software to predict the electromagnetic interference level based on the multi-level routing optimization model, to simulate the electromagnetic interference level of the current device in different operating states in real time, obtain a second electromagnetic interference distribution map, and based on the simulation results, select a path with the minimum interference and the optimal bandwidth resource utilization from the plurality of low-interference transmission paths;

[0194] A dynamic adjustment module: configured to monitor and analyze the service transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data in real time, generate a comprehensive evaluation index, combine the transmission latency index and the comprehensive evaluation index, and generate a comprehensive priority index for dynamically adjusting the optimal path.

[0195] The above formulas are all dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the most recent real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0196] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially. Those skilled in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0197] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, and may be located in one place, or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment of the present application according to actual needs.

[0198] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for low latency communication routing based on power network, characterized in that, The specific steps include: Step S1: Collecting the service transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data, the electromagnetic interference environment data including electromagnetic interference level and electromagnetic compatibility parameters of the device; and based on the collected electromagnetic interference environment data, constructing a first electromagnetic interference distribution map; Step S2: Introducing electromagnetic compatibility as a constraint condition, creating an optimization objective function according to the first electromagnetic interference distribution map, the optimization objective function being that the ratio of resource utilization rate and electromagnetic interference level is less than a preset threshold, so as to determine a plurality of low-interference transmission paths, and finally constructing a multi-level routing optimization model, and comprehensively calculating a transmission delay index according to the output of the multi-level routing optimization model; Step S3: On the basis of the multi-level routing optimization model, using electromagnetic field simulation software to predict the electromagnetic interference level, so as to simulate the electromagnetic interference level of the current device in different operating states in real time, obtain a second electromagnetic interference distribution map, and based on the simulation results, select the path with the minimum interference and the optimal bandwidth resource utilization from the plurality of low-interference transmission paths determined in step S2; Step S4: Real-time monitoring and analyzing the service transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data, generating a comprehensive evaluation index, combining the transmission delay index with the comprehensive evaluation index for combined analysis, and generating a comprehensive priority index for dynamically adjusting the optimal path in step S3; Based on the collected electromagnetic interference environment data, a first electromagnetic interference distribution map is constructed, specifically including: 1.1) Information transmission requirements include maximum data transmission amount per unit time and data transmission allowed delay; 1.2) The electromagnetic interference level includes the frequency bandwidth and intensity of the interference source, and the electromagnetic interference level is taken as an input parameter for subsequent optimization; The electromagnetic compatibility parameters include conducted emission, radiated emission, conducted immunity and radiated immunity; 1.3) Based on the collected electromagnetic interference environment data, the electromagnetic compatibility analysis tool is used to visually present the potential interference area and degree in the current device corresponding to the power Internet of Things, and then the first electromagnetic interference distribution map is constructed. 2.The power grid based low latency communication routing method of claim 1, wherein: A plurality of low-interference transmission paths are determined, and finally a multi-level routing optimization model is constructed, and a transmission delay index is comprehensively calculated according to the output of the multi-level routing optimization model, specifically including: 2.1) The objective function is as follows: Based on experimental verification and expert analysis, it is determined that the ratio of resource utilization rate and electromagnetic interference level is less than a preset threshold BY1; ； Wherein, B represents bandwidth allocation vector, EMI represents overall electromagnetic interference environment; the objective of this optimization model is to avoid high interference path and ensure low delay communication while meeting business requirements; represents the objective function; When , the transmission path is of certain low interference; when , the value of the objective function is infinite, and this transmission path should be excluded. RUR represents resource utilization, ESL represents electromagnetic interference level, is the spectral bandwidth of the ith interferer, is the intensity of the ith interferer; n is the number of interferers, i represents the index of the ith interferer; When allocating bandwidth, paths with RUR less than 30% are selected as priority paths; If RUR is reduced by 10% and ESL remains unchanged, f(B, EMI) will be reduced by 10%; 2.2) The power Internet of Things is divided into at least four levels, and optimization is performed for different network levels; The four levels are access network layer, core network layer, edge computing layer and cloud service layer; each level is optimized based on electromagnetic interference environment data to minimize the interference between levels; When determining the bandwidth allocation, paths with RUR below 30% are selected as several low-interference transmission paths, and the benchmark values of electromagnetic shielding coverage, signal processing capability value, local cache technology index, and redundancy design proportion are determined in turn according to expert groups or experimental verification; If RUR decreases by 10% while keeping ESL unchanged, f(B, EMI) will decrease by 10%; For access network layer optimization, the user access end electromagnetic environment is evaluated, and the frequency allocation is adjusted; If the electromagnetic shielding coverage exceeds the corresponding benchmark value by 15%, ESL decreases by 15% to improve connection stability; The ESL adjusted by the access network layer is defined as , and ; For core network layer optimization, signal processing technology is used to reduce cross-region interference; When the signal processing capability value exceeds the corresponding benchmark value by 20%, ESL decreases by 20% to improve transmission efficiency; The ESL adjusted by the core network layer is defined as , and ; For edge computing layer optimization, dynamic allocation of computing resources is used to reduce delay; If the local cache technology index exceeds the corresponding benchmark value by 25%, ESL decreases by 25%; The ESL adjusted by the edge computing layer is defined as , and ; For cloud service layer optimization, redundancy design is adopted to enhance anti-interference capability; When the redundancy design proportion exceeds the corresponding benchmark value by 30%, ESL decreases by 30% to ensure data security; The ESL adjusted for the cloud service tier is defined as , and ; The transmission delay index is defined as and is calculated as follows: ； where TD is the total data transfer time, P is the path priority, is the ESL-adjusted overall average. 3.The power grid based low latency communication routing method of claim 2, wherein: The electromagnetic interference level prediction is performed using electromagnetic field simulation software to simulate the electromagnetic interference level of the current device in different operating states in real time, obtain a second electromagnetic interference distribution map, and based on the simulation results, select the path with the smallest interference and the most optimal bandwidth resource utilization from the several low-interference transmission paths determined in step S2, which specifically includes: Different operating states include different business transmission demands and electromagnetic interference environment data; 3.1) The electromagnetic interference level prediction is based on the multi-level routing optimization model to draw the network topology map of the power Internet of Things, indicating the device nodes and their connection paths with each other; at the same time, the second electromagnetic interference distribution map obtained by prediction is superimposed on the network topology map; 3.2) Based on the simulation results, the network topology map of the power Internet of Things is superimposed with the second electromagnetic interference distribution map to determine the transmission path with the smallest interference and the most optimal bandwidth resource utilization.

4. The power grid based low latency communication routing method of claim 3, wherein: A comprehensive evaluation index is generated, which specifically includes: Mark the maximum amount of data transmission currently monitored as CSM, and mark the data transmission allowed delay as CSY; Mark the conducted emission, radiated emission, conducted immunity, and radiated immunity currently monitored as CF1, CF2, CF3, and CF4, respectively; The normalized output values of each parameter contained in the business transmission demand and electromagnetic interference environment data are adjusted to the range of (0, 1) through scaling and offset; ∈ {CSM, CSY, CF1, CF2, CF3, CF4} is the adjusted parameter value; When j takes values of 1, 2, 3, 4, 5, and 6 in turn, it corresponds to CSM, CSY, CF1, CF2, CF3, and CF4, respectively; Define the comprehensive evaluation index as CEI, and the calculation formula is as follows: ； wherein, is the jth parameter value, e is the base of the natural logarithm, is an adjustment parameter, , for controlling the decay speed of the exponential function; is a normal number term; ; Set the effective value range of the comprehensive evaluation index CEI to (0, 1); When 0.5 ≤ CEI < 1, it is a high priority interval, indicating that the device has high transmission demand and excellent electromagnetic compatibility; When 0.2 ≤ CEI < 0.5, it is a medium priority interval, indicating that the device has medium transmission demand and electromagnetic compatibility; When 0 < CEI < 0.2, it is a low priority interval, indicating that the device has low device transmission demand or poor electromagnetic compatibility.

5. The power network based low latency communication routing method of claim 4, wherein: The transmission delay index is combined with the comprehensive evaluation index to generate a comprehensive priority index for dynamically adjusting the optimal path in step S3, specifically including: The transmission delay index is combined with the comprehensive evaluation index, and the generated comprehensive priority index is defined as CPI, and the calculation formula is as follows: ； wherein, ; when CPI > 1, it indicates that the currently selected optimal path is a low-priority path, and reselection is performed from the remaining several low-interference transmission paths. When the current selected optimal path is a medium-priority path, as a suboptimal selection, reselect the optimal path from the remaining several low-interference transmission paths. When the current selected optimal path is a high priority path, as a priority selection.

6. A low latency communication routing system based on a power network, characterized by: The system is used to execute the low-latency communication routing method based on the power network in any one of claims 1-5, comprising: The data acquisition module is used to acquire the business transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data, and the electromagnetic interference environment data includes electromagnetic interference level and electromagnetic compatibility parameter of the device; and based on the collected electromagnetic interference environment data, a first electromagnetic interference distribution map is constructed; The optimization model construction module is used to introduce electromagnetic compatibility as a constraint condition, create an optimization objective function according to the first electromagnetic interference distribution map, and the optimization objective function is that the ratio of resource utilization rate and electromagnetic interference level is less than a preset threshold, to determine a plurality of low-interference transmission paths, finally construct a multi-level routing optimization model, and comprehensively calculate the transmission delay index according to the output of the multi-level routing optimization model; The simulation prediction module is used to predict the electromagnetic interference level by using electromagnetic field simulation software based on the multi-level routing optimization model, to simulate the electromagnetic interference level of the current device in different operating states in real time, obtain a second electromagnetic interference distribution map, and based on the simulation results, select the path with the minimum interference and the optimal bandwidth resource utilization from the determined plurality of low-interference transmission paths; The dynamic adjustment module is used to monitor and analyze the business transmission demand of the current device corresponding to the power Internet of Things and the electromagnetic interference environment data in real time, generate a comprehensive evaluation index, combine the transmission delay index with the comprehensive evaluation index, and generate a comprehensive priority index for dynamically adjusting the optimal path.

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