Marine Communication Processing Method and System Based on Mobile Edge Computing

Abstract

The present invention relates to the technical field of mobile edge computing, specifically a method and system for ocean communication processing based on mobile edge computing, including the following steps: obtaining seabed terrain information and node capability parameters, calculating the node deployment adaptation index, extracting signal strength and angle changes, obtaining the link offset trend, identifying power and packet loss abnormal areas, generating path reconstruction parameters, adjusting the network structure in combination with tidal level changes, and obtaining the communication task scheduling result according to the entity behavior characteristics. In the present invention, by analyzing the terrain characteristics and node capabilities, the evaluation of the adaptability of the node location selection environment is realized. The joint use of signal and angle data is used to identify the offset trend, improving the prediction ability of link anomalies. The network structure is dynamically adjusted in combination with tidal level changes to optimize the stability of the communication network structure. The communication entity behavior characteristics are used to drive task scheduling classification, improving the accuracy of resource allocation and the regional regulation efficiency, and realizing the adaptive regulation ability under dynamic environments.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile edge computing, in particular to a marine communication processing method and system based on mobile edge computing. Background Art

[0002] The technical field of mobile edge computing includes technologies related to sinking computing and storage capabilities from remote data centers to the network edge. The core content is to deploy edge nodes at locations close to data sources or user sides, enabling data to be processed and responded locally, reducing transmission latency, relieving network pressure, and enhancing the real-time performance and reliability of services. This technical field involves multiple aspects such as edge node resource management, task offloading mechanisms, local computing scheduling, edge caching control, and distributed network architecture design, and is applied to the Internet of Things, vehicle-to-everything, intelligent manufacturing, remote healthcare, and various scenarios with high demands for low latency and high bandwidth. The technical system of mobile edge computing includes edge device deployment strategies, data flow and processing mechanisms, edge node collaborative operation modes, and communication protocol control between terminals and edges, aiming to build a distributed computing framework centered on the edge for computing.

[0003] Among them, the marine communication processing method based on mobile edge computing refers to deploying edge computing devices with local data processing capabilities in marine communication networks, including location nodes such as offshore platforms, ships, buoys, and shore base stations, and performing distributed management and scheduling of communication data in the marine environment through data interaction and local processing mechanisms between nodes. The technical matters covered include deploying low-power edge computing modules at marine communication nodes to achieve local preprocessing and compression coding of raw data, using short-range wireless transmission technology between nodes to perform multi-hop relay forwarding of data to enhance the continuity of data transmission, setting up a multi-channel communication structure to classify and manage different types of data and perform priority scheduling, and dynamically adjusting and selecting communication paths in combination with environmental status monitoring mechanisms to complete the effective transmission and real-time processing of data in the marine communication network. The method generally adopts a combination of fixed deployment and dynamic movement, and completes data communication processing in marine scenarios through low-power microcomputing modules, local caching mechanisms, multi-node coordinated transmission strategies, and communication link allocation rules.

[0004] In the traditional marine communication processing method technology, edge nodes are deployed at fixed positions. The deployment logic relies on static parameters or regional attributes for rough division, lacking a quantitative measurement of the matching relationship between terrain complexity and node capability differences. This results in frequent occurrences of communication blind spots or coverage redundancy after deployment. The path adjustment mechanism during the communication process usually only performs post - repair based on signal strength fluctuations or data transmission failures, lacking a procedural judgment on the trend of direction changes and abnormal angle offsets, which easily leads to short - term link interruptions. Path construction is mostly executed based on static topology maps or planned path tables, without introducing periodic tidal disturbances as the basis for adjusting the communication structure. As a result, the stability of the communication chain structure is limited in the marine environment. During the task scheduling process, priorities are set mostly with fixed weights or target values, lacking a sufficient response to the state differences between communication entities. The scheduling results are difficult to reflect the regional dynamic characteristics, causing uneven resource allocation or scheduling redundancy. In the offshore environment with multi - node dynamic deployment and dense mobile entities, it leads to risks of communication failure and scheduling mismatch. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, an embodiment of the present invention provides a marine communication processing method and system based on mobile edge computing. The technical solution is as follows:

[0006] To achieve the above object, the present invention adopts the following technical solution. The marine communication processing method based on mobile edge computing includes the following steps:

[0007] S1: Obtain the submarine terrain measurement information, divide the deployment area into multiple grid units, extract the transmission power levels, signal reception tolerance values, and anti - interference ability levels of multiple nodes, analyze the adaptation degree between node capabilities and the degree of regional communication interruption, and generate a node deployment adaptation index;

[0008] S2: Based on the node deployment adaptation index, extract the signal strength of transmitted and received signals in multiple consecutive cycles during the communication process, combine the node's heading angle and transmission direction angle, identify the abnormal change trend of the included angle, identify the communication links with stability risks, and obtain the link offset trend;

[0009] S3: Obtain the link offset trend, analyze the change value of the received power and the number of data packet losses of the node in the corresponding frequency band in consecutive cycles, identify the hole drift points in the deployment area, call the link path connection angle to re - sort, and generate path reconstruction parameters;

[0010] S4: Obtain the path reconstruction parameters, extract the tidal level change information of multiple grid units, extract the difference in the tidal level change trends of adjacent regions, and combine the change in the number of communication hops of the nodes within the unit to reorganize the node communication network and generate node reorganization parameters.

[0011] As a further solution of the present invention, the node deployment adaptation index includes a communication blocking level, a node coverage ability, and a deployment priority level. The link offset trend specifically refers to an obstacle avoidance connection angle, a frequency band switching index, and a path offset position identifier. The path reconstruction parameters include a connection angle offset amount, a void block identification label, and a path adjustment positioning coordinate. The node reorganization parameters specifically refer to a node hop count growth rate, a regional tide level change difference, and a reorganization deployment direction identifier.

[0012] As a further solution of the present invention, the steps of obtaining submarine terrain measurement information, dividing the deployment area into multiple grid cells, extracting the transmission power levels, signal reception tolerance values, and anti-interference ability levels of multiple nodes, and analyzing the adaptation degree between the node capabilities and the regional communication blocking degree to generate a node deployment adaptation index are specifically as follows:

[0013] S101: Obtain submarine terrain measurement information, including water depth gradient values, reef edge distribution values, and submarine undulation change amplitudes. Divide the deployment area into multiple grid cells and map the terrain parameter values to generate regional terrain distribution information;

[0014] S102: Based on the regional terrain distribution information, extract the transmission power levels, signal reception tolerance values, and anti-interference ability level information of multiple nodes, and calculate the node communication ability index;

[0015] S103: Invoke the node communication ability index, analyze the communication blocking degree of multiple grid cells according to the terrain parameters, compare and analyze with the communication ability of the nodes, calculate the adaptation degree of the nodes in multiple grid cells, and generate a node deployment adaptation index.

[0016] As a further solution of the present invention, based on the node deployment adaptation index, extract the transmission and reception signal intensities in multiple consecutive cycles during the communication process, combine the node heading angle and the transmission direction angle, identify the abnormal included angle change trend, identify the stable risk communication links, and the steps of obtaining the link offset trend are specifically as follows:

[0017] S201: Based on the node deployment adaptation index, extract the transmission signal intensity values and reception signal intensity values in multiple consecutive cycles during the communication process, extract the signal intensity differences between multiple nodes, and identify the change amplitude to generate a periodic transmission and reception intensity change value;

[0018] S202: Invoke the periodic transmission and reception intensity change value, collect the heading angle values and the corresponding transmission direction angle values of each node in the current cycle, calculate the included angle difference, and analyze the change trend of the included angle differences of multiple communication links to obtain the angle offset change trend value;

[0019] S203: Calculate the communication stability of multiple communication links based on the angle offset change trend value, in combination with the signal strength difference and the included angle difference, detect the communication links at risk of stability, and generate a link offset trend value.

[0020] As a further solution of the present invention, the steps of obtaining the link offset trend, analyzing the change value of the received power and the data packet loss number value of the corresponding frequency band of the node in consecutive periods, identifying the hole drift points in the deployment area, and calling the reordering of the link path connection angles to generate the path reconstruction parameters are specifically as follows:

[0021] S301: Obtain the link offset trend value, detect the signal abnormal area by analyzing the received signal power value and the data packet loss number value of multiple nodes in the corresponding frequency band in consecutive periods, and generate a signal drift abnormal area;

[0022] S302: Based on the signal drift abnormal area, extract the abnormal position set by analyzing the spatial continuity of multiple abnormal areas, mark the problem area of the signal hole, and obtain the coordinates of the hole drift segment;

[0023] S303: Call the coordinates of the hole drift segment, adjust the connection path between nodes according to the position information of the hole drift points, and generate a path reconstruction parameter value.

[0024] As a further solution of the present invention, the specific formula for detecting the signal abnormal area is:

[0025] ;

[0026] Calculate the signal drift fluctuation eigenvalue, detect the signal abnormal area, and generate a signal drift abnormal area;

[0027] Among them, represents the signal drift fluctuation eigenvalue of the th node in the current period, represents the received signal power value of the th node at the th sampling, represents the average value of all received signal power values of the th node in the current period, represents the data packet loss number of the th node at the th sampling, represents the average value of all packet loss numbers of the th node in the current period, represents the number of samplings in each period, is the node index, is the index of the number of samplings in the current period.

[0028] As a further solution of the present invention, the steps of obtaining the path reconstruction parameters, extracting the tidal level change information of multiple grid cells, extracting the difference in the tidal level change trends of adjacent regions, and combining the change in the communication hop count of the nodes within the cell to perform reorganization on the node communication network and generating node reorganization parameters are specifically as follows:

[0029] S401: Obtain the path reconstruction parameter values, collect the tidal level height change data of multiple grid cells within consecutive periods, extract the periodic tidal level fluctuation amplitude of each grid, calculate the change characteristics of the tidal level, and generate a tidal level change trend coefficient;

[0030] S402: Invoke the tidal level change trend coefficient, extract the tidal level trend difference between multiple grids and adjacent cells, analyze the stability of the tidal level, calculate the tidal level disturbance level, and obtain the tidal level trend difference distribution value;

[0031] The specific formula for analyzing the stability of the tidal level is:

[0032] ;

[0033] Calculate the tidal level disturbance difference index value and obtain the tidal level trend difference distribution value;

[0034] Among them, is the corrected tidal level disturbance difference index value between the cell with the current grid number and the adjacent grid with the grid number , is the tidal level change trend coefficient of the cell with the grid number , is the tidal level change trend coefficient of the adjacent cell with the grid number , is the standard deviation of the tidal level fluctuation amplitude between the cells numbered and , is the mean value of the tidal level fluctuation amplitude between the cells numbered and , is the tidal level height difference between the cells numbered and in the th observation period, is the average value of the tidal level height between the cells numbered and in the th observation period, is the total number of observation periods, is the current observation period number, is the current processed grid number, Is the number of an adjacent grid of the current grid;

[0035] S403: According to the tidal level trend difference distribution value, combined with the change of the communication hop count of each node in the unit, identify abnormal connection nodes and adjust the connection positions and associated objects of the nodes to generate node reorganization parameter values.

[0036] As a further solution of the present invention, the method further includes:

[0037] S5: Obtain the node reorganization parameters, extract the difference in the number of communication entities between adjacent grids by analyzing the number, mobile active state, and aggregation density level of communication entities in each grid unit, and calculate the task scheduling levels of multiple grid units in combination with the mobile active state difference value and density level span value corresponding to each grid to generate a communication task scheduling result;

[0038] The communication task scheduling result includes a task distribution level, a scheduling cycle control value, and a communication entity density interval.

[0039] As a further solution of the present invention, the steps of obtaining the node reorganization parameters, extracting the difference in the number of communication entities between adjacent grids by analyzing the number, mobile active state, and aggregation density level of communication entities in each grid unit, and calculating the task scheduling levels of multiple grid units in combination with the mobile active state difference value and density level span value corresponding to each grid to generate a communication task scheduling result are specifically as follows:

[0040] S501: Obtain the node reorganization parameter value, collect the number, mobile active state level, and aggregation density level of communication entities in each grid unit to generate a communication entity distribution feature set;

[0041] S502: According to the communication entity distribution feature set, compare the number of communication entities, mobile frequency level, and aggregation level between adjacent grids, analyze the entity behavior differences between regions, and obtain a regional behavior difference value;

[0042] S503: Invoke the regional behavior difference value, and calculate the task scheduling levels of multiple grid units according to the entity behavior characteristics to generate a communication task scheduling result value.

[0043] On the other hand, a marine communication processing system based on mobile edge computing is provided. This system is applied to the marine communication processing method based on mobile edge computing. The system includes:

[0044] The terrain adaptation module obtains the seabed terrain measurement information, divides the deployment area into grids, evaluates the communication adaptability of nodes and each area according to the transmission power level, signal reception tolerance value, and anti-interference ability level of multiple nodes, and establishes a node deployment adaptation index;

[0045] The link monitoring module monitors the signal strength of communication nodes based on the node deployment adaptation index, collects data on the node heading angle and the signal transmission direction angle, analyzes the angle change trend and the abnormal fluctuation of the communication link, and obtains the link deviation trend.

[0046] The path calibration module detects the received power of the node frequency band and the data packet loss situation based on the link deviation trend, identifies the signal hole drift area, reorders the node connection angles of the communication link, and generates path reconstruction parameters.

[0047] The network reconstruction module monitors the tide level fluctuation data of grid cells based on the path reconstruction parameters, analyzes the tide level trend difference between adjacent regions and the change of the node communication hop count, adjusts the node communication network structure, and generates node reorganization parameters.

[0048] The task allocation module analyzes the parameters of the number of communication entities, the mobile active state, and the aggregation density level within the grid cell based on the node reorganization parameters, identifies the differences in the behavior of communication entities between regions, calculates the task scheduling level, and obtains the communication task scheduling result.

[0049] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include:

[0050] By jointly matching the submarine terrain feature parameters with the node capability indicators, analyzing the communication adaptability of the deployment area, improving the coupling degree between the nodes and the environment before network construction, realizing the pre-identification of the abnormal trend of the communication state through the linkage extraction of the periodic signal strength and direction angle data, enhancing the sensitivity of the communication stability judgment, calibrating the signal hole drift range during the network operation by combining the periodic analysis of the received power and the packet loss amount, improving the dynamic recognition ability of abnormal paths, making the adjustment of the node structure have an adaptive response to geographical dynamics by reordering the path angles and combining the tide level change trend and the hop count fluctuation information, constructing a multi-dimensional hierarchical rule system by analyzing the behavior characteristics of communication entities at multiple locations, making the communication resource allocation fit the spatial distribution of the communication entity states, strengthening the adaptation performance of the edge communication network in the marine dynamic environment, and improving the stability of the communication link and the accuracy of task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0052] Figure 1 It is a schematic diagram of the working process of the present invention;

[0053] Figure 2 This is the system flow chart of the present invention. Detailed implementation manners

[0054] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0056] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same. To make the technical problems to be solved, technical solutions and advantages of the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0057] Please refer to Figure 1 , the present invention provides a technical solution, a marine communication processing method based on mobile edge computing, including the following steps:

[0058] S1: Obtain seabed terrain measurement information, divide the deployment area into multiple grid cells, extract the transmission power levels, signal reception tolerance values, and anti-interference ability levels of multiple nodes, analyze the adaptation degree between the node capabilities and the regional communication blocking degree, and generate a node deployment adaptation index;

[0059] S2: Based on the node deployment adaptation index, extract the signal strength of the received and transmitted signals in multiple consecutive cycles during the communication process, combine the node heading angle and the transmission direction angle, identify the abnormal angle change trend, identify the communication link with stability risk, and obtain the link offset trend;

[0060] S3: Obtain the link offset trend, analyze the received power change value and the data packet loss quantity value of the corresponding frequency band of the node in consecutive cycles, identify the hole drift points in the deployment area, call the link path connection angle to reorder, and generate path reconstruction parameters;

[0061] S4: Obtain path reconstruction parameters, extract the tidal level change information of multiple grid cells, extract the difference in the tidal level change trends of adjacent regions, and combine the change in the communication hop count of the nodes within the cell to reorganize the node communication network and generate node reorganization parameters;

[0062] S5: Obtain node reorganization parameters, extract the difference in the number of communication entities between adjacent grids by analyzing the number of communication entities, mobile active status, and aggregation density level in each grid cell, and combine the difference value of the mobile active status and the density level span value corresponding to each grid to calculate the task scheduling level of multiple grid cells and generate a communication task scheduling result.

[0063] The node deployment adaptation index includes a communication blocking level, node coverage ability, and deployment priority level. The link offset trend specifically refers to an obstacle avoidance connection angle, frequency band switching index, and path offset position identifier. The path reconstruction parameters include a connection angle offset amount, a void block identification label, and path adjustment positioning coordinates. The node reorganization parameters specifically refer to a node hop count growth rate, a regional tidal level change difference, and a reorganization deployment direction identifier. The communication task scheduling result includes a task distribution level, a scheduling cycle control value, and a communication entity density interval.

[0064] The steps of obtaining submarine terrain measurement information, dividing the deployment area into multiple grid cells, extracting the transmission power level, signal reception tolerance, and anti-interference ability level of multiple nodes, and analyzing the adaptation degree between the node capabilities and the regional communication blocking degree to generate a node deployment adaptation index are specifically as follows:

[0065] S101: Obtain submarine terrain measurement information, including water depth gradient values, reef edge distribution values, and submarine undulation change amplitudes, divide the deployment area into multiple grid cells, and map the terrain parameter values to generate regional terrain distribution information;

[0066] The submarine terrain measurement sub-module obtains the terrain element information within the deployment area through multi-source devices. First, it performs a spatial grid division operation on the target area, and the division accuracy is uniformly set according to the selected grid side length. For example, each grid cell is 1 km × 1 km, and then data collection is carried out within each cell. The water depth gradient value is collected by using a multi-beam sounding system to obtain the depth values of multiple points, and the gradient data is established through the relationship between the vertical difference and the horizontal distance between adjacent measurement points. The distribution value of the island reef edge is obtained by means of remote sensing image processing. Based on high-resolution satellite images, the pixel area of the island reef edge is identified, and the number of edge points is extracted through a boundary recognition algorithm and recorded in the corresponding grid. The amplitude of the submarine undulation is determined by the maximum and minimum water depth differences of the sampled measurement points within each grid. The number of measurement points within the same grid should be greater than 3 to ensure the accuracy of the undulation calculation. After each parameter value is calculated, the result needs to be mapped to the geographic information system to match the grid position, forming a mapping structure of terrain parameters and spatial indexes. When performing the standard level division on the parameter results, the continuous data needs to be converted into interval levels. For example, the gradient is level 1 with 0–2 m / km, 2–4 is level 2, and so on up to level 5, and a grid level code is generated. Using the formula:

[0067] ;

[0068] Calculate the water depth gradient value, where G is the water depth gradient value (m / km), is the maximum water depth value (m) among two adjacent points, is the minimum water depth value (m) among two adjacent points, and L is the horizontal distance (km) between the two measurement points. Set , , , and substitute the set values for calculation:

[0069] ;

[0070] The calculation results show that the gradient level of this area falls within the range of 2–4 m / km, corresponding to level 2. The level needs to be combined with the island reef edge level and the undulation level to generate a combined code. After each grid level code is completed, the code and its spatial index coordinates are jointly used to generate a terrain level distribution map, which serves as the basic data structure for subsequent deployment adaptation analysis. This process requires all grid information to be archived and managed consistently, supporting multiple reads and hierarchical retrievals.

[0071] S102: Based on the regional terrain distribution information, extract the transmit power level, signal reception tolerance, and anti-interference ability level information of multiple nodes, and calculate the node communication ability index;

[0072] The node capability calculation sub-module calls the communication performance metrics of each device in the node database, extracts three core element parameters: transmit power, signal reception tolerance, and anti-interference ability. The transmit power value is the maximum power generation of the node under open and unobstructed conditions, with the unit of dBm, usually marked by the device manufacturer at the time of factory shipment; the signal reception tolerance represents the lowest signal strength threshold that the device can receive without packet loss or error; the anti-interference ability level is a percentage score calculated based on parameters such as comprehensive noise resistance, modulation accuracy, and bit error rate test. The extracted original parameters are first normalized and then calculated according to the weight coefficient to obtain the comprehensive communication ability index. The normalization process uses a linear method, linearly mapping the maximum and minimum intervals of all data of this index to between 0 and 1 to ensure the comparability of each parameter. The weight ratio is set based on the experience of node communication stability testing, with the highest weight for transmit power being 0.4, followed by reception tolerance at 0.3, and anti-interference ability at 0.3. Using the formula: ;

[0073] Calculate the communication ability index, where is the communication ability index, , , are the transmit power, reception tolerance, and anti-interference ability score values of the node respectively, and are the minimum and maximum values of each parameter, , , are the weight coefficients of each parameter respectively. Set , , , , , , , , , , , , substitute the set values into the calculation: ;

[0074] The calculation result shows that the communication ability index of this node is 0.745. After all nodes are calculated, a mapping relationship is established between the communication ability index and the spatial position, which is used as the input for subsequent adaptation analysis.

[0075] S103: Call the node communication ability index, analyze the communication blocking degree of multiple grid cells according to the terrain parameters, compare and analyze with the communication ability of the node, calculate the adaptation degree of the node in multiple grid cells, and generate a node deployment adaptation index;

[0076] The adaptation analysis sub-module performs corresponding calculations using the communication blockage degree determined by the combination of the node communication capability index and the grid terrain level. The blockage degree is divided into three levels: high, medium, and low according to the combination of three levels through a rule table, and the blockage coefficients are set to 0.9, 0.6, and 0.3 respectively. If the combination of the water depth gradient level greater than 7, the reef edge level greater than 3, and the undulation level of 3 is defined as high blockage, and the rest are matched according to the rules. The adaptation degree is the difference between the relative distances of the communication capability index and the blockage coefficient, which is used to judge the deployment adaptation level of the node under the terrain limitation conditions. The formula is as follows:

[0077] ;

[0078] Calculate the deployment adaptation index, where is the adaptation degree, is the node communication capability index, is the grid terrain blockage coefficient.

[0079] Set , , and substitute the set values into the calculation:

[0080] ;

[0081] The calculation result shows that the deployment adaptation degree of the node in the current terrain grid is 0.855. After calculating the adaptation degree, it is necessary to match all node adaptation indexes with their spatial positions one by one to generate a node deployment adaptation layer, which is used as the basis for the initial deployment planning of the communication system.

[0082] Based on the node deployment adaptation index, extract the signal strength of transmission and reception in multiple consecutive cycles during the communication process, combine the node heading angle and the transmission direction angle, identify the abnormal change trend of the included angle, identify the communication link with stability risk, and the steps to obtain the link offset trend are as follows:

[0083] S201: Based on the node deployment adaptation index, extract the transmission signal strength value and the reception signal strength value in multiple consecutive cycles during the communication process, extract the signal strength difference between multiple nodes, and identify the change amplitude to generate the periodic transmission and reception strength change value;

[0084] Based on the node deployment adaptation index, continuously and periodically extract the signal strength during the communication process. First, obtain the transmitted signal strength values within a fixed time interval from the transmitting unit of each node, and synchronously collect the received signal strength values from the receiving unit of the target node. The two must be correspondingly matched within the same communication cycle. It is recommended that the collection cycle be once every 5 seconds, and continuously extract data sequences for no less than 3 cycles. After the data sequence is established, calculate the difference between the transmitted value and the received value within each communication cycle, and extract the amplitude change of the difference sequence. The amplitude change is achieved by calculating the difference between the differences between adjacent cycles. This process is performed in parallel among the many-to-many links between nodes to obtain the intensity fluctuation differences on different links. For example, if the transmitted powers of node A in three consecutive cycles are 20 dBm, 21 dBm, and 22 dBm respectively, and the received powers of node B are 18 dBm, 18.5 dBm, and 19 dBm respectively, then the differences within the cycles are 2 dBm, 2.5 dBm, and 3 dBm respectively. Analyze the amplitude change between cycles to obtain an amplitude change of 1 dBm. Using the formula:

[0085] ;

[0086] Calculate the change value of the transmitted and received signal strengths during the cycle. Among them, is the average amplitude change of the transmitted and received intensities, is the transmitted signal strength value in the cycle, is the cycle received signal strength value, is the number of cycles. Set ,, ,, ,, ,, ,, ,, Substitute the set values into the calculation:

[0087] ;

[0088] This amplitude change value indicates that the signal strength fluctuation between nodes is 0.5 dBm. If this value exceeds the system-set stability benchmark threshold of 1.0 dBm, it is classified as the normal fluctuation range, otherwise it is marked as the abnormal range. This process is executed one by one between all node pairs, and the results are stored as the change values of the transmitted and received intensities during the cycle of the communication link.

[0089] S202: Invoke the change value of the transmitted and received intensities during the cycle, collect the heading angle value and the corresponding transmission direction angle value of each node in the current cycle, calculate the included angle difference, and analyze the change trend of the included angle differences of multiple communication links to obtain the angle offset change trend value;

[0090] Call the transceiver intensity change value in the cycle, collect the heading angle and transmission direction angle of each node in the current cycle. The heading angle is the moving angle of the node relative to the geographic north direction, in degrees, with a range of 0° to 360°, obtained through an attitude sensor or an inertial navigation device; the transmission direction angle refers to the rotation angle of the antenna beam pointing in the horizontal direction, also recorded in degrees. When calculating the included angle for each communication link, it is necessary to obtain the included angle difference between the direction angle of the transmitting node and its moving direction (heading angle), use the absolute difference calculation formula to obtain the angle difference, and extract the trend of the angle difference change of the same link in multiple cycles. The trend extraction is obtained by calculating the average value of the difference between the included angle differences in adjacent cycles. For example, if the heading angles of a certain node in three cycles are 45°, 50°, and 55° respectively, and the transmission direction angles are 60°, 65°, and 70° respectively, then the included angle differences in each cycle are 15°, 15°, and 15° respectively, and the change trend is 0. Use the formula:

[0091] and ;

[0092] Calculate the angle offset change trend value, where is the angle difference change trend value, is the direction included angle difference in the cycle, is the transmission direction angle value (in degrees) in the cycle, is the heading angle value (in degrees) in the cycle, is the number of cycles. Set , , , , , , , and substitute the set values into the calculation:

[0093] , , ;

[0094] ;

[0095] The calculation result is 0, indicating that the included angle between the transmission direction and the heading angle of this node remains stable. Record this trend value under each communication link index to generate the angle offset change trend value.

[0096] S203: According to the angle offset change trend value, combined with the signal strength difference and the included angle difference, calculate the communication stability of multiple communication links, detect the communication links with stability risks, and generate the link offset trend value;

[0097] According to the obtained angle offset change trend value and combined with the periodic transceiver intensity change value, calculate the communication stability coefficient of each communication link. The communication stability coefficient represents the overall fluctuation performance of the current link in the spatial and energy states, and the normalized product of two indicators is used as the stability inverse index. It is necessary to define a stability risk threshold. When the stability coefficient is lower than this threshold, it is determined as a risk link. The stability coefficient is defined as 1 minus the product of the normalized means of two fluctuation terms, and the normalization process is normalized to between 0 and 1 using the maximum expected fluctuation threshold. The formula is as follows:

[0098] ;

[0099] Calculate the communication stability index of the link. Among them, is the communication stability coefficient, is the change value of the periodic transceiver signal intensity (dBm), is the maximum intensity fluctuation value set by the system (dBm), is the angle offset change trend value (degree), is the maximum angle fluctuation value set by the system (degree). Set , , , , and substitute the set values into the calculation:

[0100] ;

[0101] The result is 0.917. If the stability threshold is set to 0.85, the stability of this link is normal. If it is lower than this value, it is identified as a communication link with stability risk. After all links are detected, summarize each index, mark the risk level, and generate the link offset trend value.

[0102] The steps to obtain the link offset trend, analyze the received power change value and data packet loss number value of the corresponding frequency band of the node in consecutive cycles, identify the hole drift points in the deployment area, and call the link path connection angle to reorder and generate the path reconstruction parameters are specifically as follows:

[0103] S301: Obtain the link offset trend value. By analyzing the received signal power value and data packet loss number value of multiple nodes in the corresponding frequency band in consecutive cycles, detect the signal abnormal area and generate the signal drift abnormal area;

[0104] The specific formula for detecting the signal abnormal area is:

[0105] ;

[0106] Calculate the signal drift fluctuation characteristic value, detect the signal abnormal area, and generate the signal drift abnormal area;

[0107] Among them, Indicates the signal drift fluctuation eigenvalue of the th node in the current cycle, Indicates the received signal power value of the th node at the th sampling, Indicates the average value of all received signal power values of the th node in the current cycle, Indicates the number of data packet losses of the th node at the th sampling, Indicates the

[0108] formula:

[0109] ;

[0110] Detailed explanation of the formula and the derivation process of the formula calculation:

[0111] The formula is used to calculate the fluctuation amplitude of the received signal power and the number of packet losses of a certain node in the current communication cycle, and the result is used to evaluate whether there is a drift abnormal trend in the signal stability of the area where the node is located;

[0112] Parameter meaning and setting value:

[0113] is the number of samplings per cycle, set to 3;

[0114] is the received signal power value of the th node at the

[0115] average value of the signal power of the node in the cycle, ;

[0116] is the number of packet losses of the th node at the

[0117] average value of the number of packet losses, ;

[0118] Substitute the above values into the formula for calculation:

[0119] ;

[0120] ;

[0121] ;

[0122] The result shows that there are differential fluctuations in both the signal strength and the number of packet losses within the node cycle. The comprehensive fluctuation value is 2.23, indicating that the current node may be in an area with abnormal communication. It is necessary to further mark its location for identifying the signal drift abnormal area.

[0123] S302: Based on the signal drift abnormal area, by analyzing the spatial continuity of multiple abnormal areas, extract the set of abnormal positions, mark the problem areas of signal holes, and obtain the coordinates of the hole drift segments;

[0124] Based on the signal drift abnormal area, judge the adjacency of each abnormal area in the spatial dimension, extract the grid cells that meet the spatial connection characteristics in the continuous area, construct the set of abnormal areas. Taking the center coordinates of each abnormal cell as the reference, judge the status of its adjacent eight-neighborhood grids. If any one of the neighborhood grids meets both spatial proximity (distance less than 3 km) and the same signal abnormal type, it is included in the same set to form a spatially continuous abnormal block. After the continuous block is formed, calculate the distribution density and abnormal proportion of all nodes within the area. If the number of abnormal grids in the same area exceeds 60% of the total number, it is marked as a hole area. Use the formula:

[0125] ;

[0126] Among them, is the spatial abnormal proportion, is the number of abnormal cells, is the total number of cells in the set. Suppose there are abnormal grids and total number in a certain spatial set, substitute into the calculation:

[0127] ;

[0128] The abnormal proportion reaches 75%, higher than the 60% threshold. Therefore, this spatial set is marked as a signal hole area. The center coordinates of all grid cells in this segment are uniformly extracted as the set of abnormal positions, numbered, tagged, and grouped at the corresponding positions, and output as the coordinates of the hole drift segment.

[0129] S303: Call the coordinates of the hole drift segment, and adjust the connection path between nodes according to the position information of the hole drift points to generate the path reconstruction parameter value;

[0130] Call the coordinates of the hole drift segment to adjust the connection path between nodes in the current system. First, extract the paths that cross the abnormal area in all path segments. The extraction method is to determine whether the connection line between any two adjacent nodes in the path passes through the buffer area of the hole coordinate point. The buffer distance is set to 500 meters. The judgment criterion is that if the shortest distance between the connection line of the two nodes and the abnormal point is less than 500 meters, it is considered that the path crosses. For all crossing paths, it is necessary to recalculate the bypass path, select the alternative path with the smallest distance increment in the reachable paths for replacement. The alternative path needs to maintain connection integrity in the topological structure and avoid two consecutive hops crossing the hole area at the same time. Use the formula:

[0131] ;

[0132] Among them, is the total length of the new path, is the distance (in meters) between the th and the th nodes in the alternative path, is the number of nodes on the new path. Set the node sequence to 4, , , , substitute into the calculation:

[0133] ;

[0134] Take 2250 meters as the reconstructed path distance and compare it with the original path length. If the increment is within the allowable range (set not to exceed 30%), replace the original path and update the node routing table. Finally, after all affected paths are adjusted, output the path reconstruction parameter value.

[0135] Obtain the path reconstruction parameters, extract the tidal level change information of multiple grid cells, extract the difference in the tidal level change trend of adjacent areas, and combine the change in the communication hop count of the nodes within the cell to perform reorganization on the node communication network. The steps to generate the node reorganization parameters are specifically as follows:

[0136] S401: Obtain the path reconstruction parameter value, collect the tidal level height change data of multiple grid cells in consecutive periods, extract the periodic tidal level fluctuation amplitude of each grid, calculate the change characteristics of the tidal level, and generate the tidal level change trend coefficient;

[0137] After obtaining the path reconstruction parameter values, continuously collect the tidal level change data within consecutive cycles from multiple deployed communication node grid areas. Each cycle is set to 30 minutes, and the continuous collection time is 6 hours, forming 12 sampling cycles. Establish a tidal level time series for each grid cell, record the tidal level corresponding to each cycle, with the unit being meters. During the recording process, ensure that the sensor accuracy error does not exceed 5 centimeters. For the tidal level time series within each grid, calculate the difference between the maximum and minimum tidal levels in the cycle as the fluctuation amplitude within that cycle. Then, calculate the mean and standard deviation of all the fluctuation amplitudes within the 12 cycles as the tidal level change characteristics. The formula for calculating the tidal level fluctuation amplitude is the maximum tidal level in the cycle minus the minimum tidal level. If the maximum tidal levels in 12 cycles of a certain grid are 2.3 meters, 2.6 meters, and 2.1 meters respectively, and the minimum tidal levels are 1.2 meters, 1.4 meters, and 1.0 meters respectively, then the fluctuation amplitude sequence is 1.1 meters, 1.2 meters, and 1.1 meters. The mean operation on the fluctuation amplitude sequence is used for subsequent trend analysis.

[0138] Adopt the formula:

[0139] ;

[0140] Wherein, is the tidal level change trend coefficient (meter), is the maximum tidal level in the cycle (meter), is the minimum tidal level in the cycle (meter), is the number of cycles. Set , , , , , , , and substitute the set values into the calculation:

[0141] ;

[0142] After binding the tidal level change trend coefficient with the spatial coordinates, record it as the dynamic attribute value corresponding to the grid, and finally output the tidal level change trend coefficient.

[0143] S402: Invoke the tidal level change trend coefficient, extract the tidal level trend differences between multiple grids and adjacent cells, analyze the stability of the tidal level, and calculate the tidal level perturbation level to obtain the tidal level trend difference distribution value;

[0144] The specific formula for analyzing the stability of the tidal level is:

[0145] ;

[0146] Calculate the tidal level perturbation difference index value to obtain the tidal level trend difference distribution value;

[0147] Among them, is the correction tidal level perturbation difference index value between the cell with the current grid number and the cell with the adjacent grid number . is the tidal level change trend coefficient of the cell with the grid number . is the tidal level change trend coefficient of the adjacent cell with the grid number . is the standard deviation of the tidal level fluctuation amplitude between the cells numbered and . is the mean value of the tidal level fluctuation amplitude between the cells numbered and . is the tidal level height difference between the cells numbered and in the th observation period. is the average value of the tidal level height between the cells numbered and in the th observation period. is the total number of observation periods. is the current observation period number. is the current processed grid number. is an adjacent grid number of the current grid.

[0148] Formula:

[0149] ;

[0150] Detailed explanation of the formula and the derivation process of the formula calculation:

[0151] The formula is used to calculate the correction tidal level perturbation difference index value between the current grid cell and the adjacent grid, and the value is used to identify the difference in the tidal level trend change intensity between the two regions.

[0152] Parameter meaning and setting value:

[0153] is the tidal level change trend coefficient of the current grid cell, with the unit of meter, and is set to 1.24 meters;

[0154] is the tidal level change trend coefficient of the adjacent grid cell, and is set to 1.01 meters;

[0155] is the combined standard deviation of the tidal level fluctuations between the current grid and adjacent grids, with a set value of 0.18 m;

[0156] is the combined mean of the above fluctuations, with a set value of 1.11 m;

[0157] is the hourly tidal level difference between the two grids, with set values of 0.13 m, 0.25 m, 0.09 m, and 0.19 m respectively;

[0158] is the average of the tidal levels of the two grids at this hour, set to 1.28 m, 1.32 m, 1.21 m, and 1.34 m;

[0159] is the total number of cycles, set to 4 hours.

[0160] Substitute the parameters into the formula for calculation:

[0161] ;

[0162] ;

[0163] ;

[0164] ;

[0165] ;

[0166] ;

[0167] The result 0.2581 indicates that there is a moderately high difference between the current grid and adjacent grids in terms of trend changes and fluctuation intensity. This result will be used subsequently to compare with the set disturbance level classification intervals and determine its classification in the tidal level trend difference distribution.

[0168] S403: Based on the tidal level trend difference distribution value, combined with the change in the communication hop count of each node within the unit, identify abnormal connection nodes and adjust the connection positions and associated objects of the nodes to generate node reorganization parameter values;

[0169] Based on the differential distribution value of the tidal level trend, combined with the change in the communication hop count of each node within the grid, node structure adjustment analysis is carried out. For each node, the hop count change sequence within three consecutive sampling periods is extracted. If the hop count change shows an increasing trend and the average hop count growth exceeds 2 hops, it is determined as an unstable structure node. Through joint judgment with the disturbance level of the grid where the current node is located, when the node is within a grid with a disturbance level of 2 or 3 and the average hop count growth is greater than the set threshold of 2 hops, the system marks this node as an abnormally connected node. Scan the connection structures of all abnormal nodes, identify which communication objects they are connected to, and compare whether their original communication paths cross the trend disturbance area. If so, attempt to adjust their connection objects, and preferentially select nodes in adjacent grids with a disturbance level of 1 and stable hop counts (fluctuation less than 1 hop) of the target nodes as new connection objects. Use the formula:

[0170] ;

[0171] where, is the average growth amplitude of the node hop count, is the hop count value of the node in the period, is the number of periods. Set the node hop count as , , , , then:

[0172] ;

[0173] If this value does not exceed the set threshold of 2 hops, no adjustment is made. If the calculation result is 2.5, a reconstruction operation is performed. Finally, the connections of all eligible nodes are re-established and the connection targets, node indexes, and path numbers are recorded, and the output is the node reorganization parameter value.

[0174] To obtain the node reorganization parameters, by analyzing the number of communication entities, mobile active status, and aggregation density level in each grid cell, extracting the difference in the number of communication entities between adjacent grids, and combining the difference value of the mobile active status and the density level span value corresponding to each grid, the steps to calculate the task scheduling level of multiple grid cells and generate the communication task scheduling result are as follows:

[0175] S501: Obtain the node reorganization parameter value, collect the number of communication entities, mobile active status level, and aggregation density level in each grid cell, and generate a communication entity distribution feature set;

[0176] After obtaining the node recombination parameter values, entity sampling is performed on the area according to the grid division. Each grid cell has a range unit of 1 km × 1 km, and the quantity information of all recognizable communication entities is collected within this range. The entity types can include unmanned boats, surface sensing buoys, shore station nodes, etc. At the same time, the entity ID and spatial coordinates are called for deduplication and numbering operations to exclude duplicate sampling records and obtain the net entity quantity value. The mobile active state level is calculated based on the distance of entity position change and the number of change directions within a unit time. Entities that move more than 100 meters and change directions by more than 30 degrees within 10 minutes are determined as active entities. If the proportion of active entities exceeds 50%, the grid is marked as active level 3, otherwise it is sequentially reduced to level 1. The aggregation density level is divided according to the number of entities per square kilometer. It is set that 0 - 5 is level 1, 6 - 10 is level 2, and more than 11 is level 3. The entity distribution characteristics composed of three attributes are formed for each grid, and then it is bound to the grid number and output. Using the formula:

[0177] ;

[0178] Among them, is the communication entity density per unit area, is the number of communication entities within the grid, is the grid area (square kilometers). Suppose the number of entities in a certain grid is 12 and the area is 1 square kilometer. Substituting into the calculation gives:

[0179] ;

[0180] This density corresponds to level 3. The density level, active level, and entity quantity are formed into a structure to complete the organization of the entity data of this unit and output it as a communication entity distribution feature set.

[0181] S502: According to the communication entity distribution feature set, compare the number of communication entities, mobile frequency level, and aggregation level between adjacent grids, analyze the entity behavior differences between regions, and obtain the regional behavior difference value;

[0182] According to the communication entity distribution feature set, one by one select the current grid cell and its adjacent eight-neighborhood grids for attribute comparison operations. The comparison attribute items include the difference in the number of entities, the difference in the mobile active level, and the difference in the aggregation density level. Corresponding thresholds are respectively set to judge whether an entity behavior difference event is formed. A difference in the number of entities greater than 5 is an effective difference, a difference in the active level greater than 1 level is an effective difference, and a difference in the aggregation level greater than 1 level is an effective difference. Weights of 0.5, 0.3, and 0.2 are respectively set for each difference item. The three differences are weighted according to the difference level to obtain the behavior difference value. An evaluation interval is set for this value. 0 - 0.3 is difference level 1, 0.3 - 0.6 is difference level 2, and greater than 0.6 is difference level 3. Using the formula:

[0183] ;

[0184] Among them, is the regional behavior difference value, is the difference in the number of entities, is the difference in movement levels, is the difference in density levels, , , are the three weights, , , are the maximum possible differences for each item. Set , , , , , , , , substitute into the calculation to get:

[0185] ;

[0186] This difference value is in the range of level 2, indicating that the behavior difference between regions is at a medium level, and the final output is the regional behavior difference value.

[0187] S503: Invoke the regional behavior difference value, and calculate the task scheduling levels of multiple grid cells according to the entity behavior characteristics to generate a communication task scheduling result value;

[0188] Invoke the regional behavior difference value, calculate the grid task scheduling level according to the difference level and the characteristic value of the behavior index. The task scheduling level is scored in three dimensions: the entity density level score is the density level × 0.4, the movement activity level score is the activity level × 0.3, and the behavior difference level score is the difference level × 0.3. The sum of the three scores is the comprehensive scheduling score. Then, divide the scheduling level interval according to the score. The score of 0 - 1.5 is scheduling level 1, 1.5 - 2.5 is scheduling level 2, and greater than 2.5 is scheduling level 3. Use the formula:

[0189] ;

[0190] Among them, is the comprehensive task scheduling score, is the aggregation density level, is the movement activity level, is the regional behavior difference level. Set , , , substitute into the formula:

[0191] ;

[0192] This score is in the range of level 3, corresponding to the high-scheduling priority area. Mark the scheduling level result on the corresponding grid number and output it as the communication task scheduling result value.

[0193] Please refer to Figure 2 , the marine communication processing system based on mobile edge computing. The marine communication processing system based on mobile edge computing is used to execute the above-mentioned marine communication processing method based on mobile edge computing. The system includes:

[0194] The terrain adaptation module obtains the seabed terrain measurement information, divides the deployment area into grids, and evaluates the communication adaptability between the nodes and each area according to the transmission power levels, signal reception tolerance values, and anti-interference ability levels of multiple nodes, and establishes a node deployment adaptation index.

[0195] The link monitoring module monitors the signal strength of the communication nodes based on the node deployment adaptation index, collects the data of the node heading angle and the signal transmission direction angle, analyzes the angle change trend and the abnormal fluctuation of the communication link, and obtains the link offset trend.

[0196] The path calibration module detects the received power of the node frequency band and the data packet loss situation based on the link offset trend, identifies the signal hole drift area, reorders the node connection angles of the communication link, and generates path reconstruction parameters.

[0197] The network reconstruction module monitors the tidal level fluctuation data of the grid cells based on the path reconstruction parameters, analyzes the tidal level trend difference between adjacent areas and the change of the node communication hop count, adjusts the node communication network structure, and generates node reorganization parameters.

[0198] The task allocation module analyzes the parameters of the number of communication entities, the mobile active state, and the aggregation density level within the grid cell based on the node reorganization parameters, identifies the difference in the behavior of communication entities between regions, calculates the task scheduling level, and obtains the communication task scheduling result.

[0199] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0200] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0201] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0202] It should be understood that in various embodiments of the present invention, the magnitudes of the serial numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0203] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0204] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0205] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0206] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0207] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0208] If a function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0209] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A marine communication processing method based on mobile edge computing, characterized in that: The method comprises: S1: Obtain seabed topography measurement information, divide the deployment area into multiple grid units, extract the transmission power level, signal reception tolerance value, and anti-interference capability level of multiple nodes, analyze the degree of adaptation between node capabilities and regional communication blocking degree, and generate a node deployment adaptation index; S2: Based on the node deployment adaptation index, the strength of the receiving and transmitting signals in multiple consecutive cycles during the communication process is extracted, and the abnormal angle change trend is identified by combining the node heading angle and the transmission direction angle, and the stability risk communication link is identified to obtain the link deviation trend; S3: Obtain the link offset trend, analyze the received power change value and data packet loss value of the corresponding frequency band of the node in a continuous period, identify the hole drift point in the deployment area, call the link path connection angle reordering, and generate the path reconstruction parameter; S4: Obtain the path reconstruction parameters, extract the tidal level change information of multiple grid cells, extract the difference in tidal level change trends in adjacent areas, combine the change in the number of communication hops of the nodes in the cells, reorganize the node communication network, and generate node reorganization parameter values.

2. The marine communication processing method based on mobile edge computing according to claim 1 is characterized in that: The node deployment adaptation index includes the communication blocking level, node coverage capability, and deployment priority level. The link offset trend specifically includes the obstacle avoidance connection angle, the frequency band switching index, and the path offset position identifier. The path reconstruction parameters include the connection angle offset, the hole block identification label, and the path adjustment positioning coordinates. The node reorganization parameter value specifically refers to the node hop growth rate, the regional tidal level change difference, and the reorganization deployment direction identifier.

3. The marine communication processing method based on mobile edge computing according to claim 2 is characterized in that: Obtain seabed topographic measurement information, divide the deployment area into multiple grid units, extract the transmission power level, signal reception tolerance value, and anti-interference capability level of multiple nodes, analyze the degree of adaptation between node capabilities and regional communication blocking levels, and generate the node deployment adaptation index in the following steps: S101: Acquire seabed topography measurement information, including water depth gradient values, island and reef edge distribution values, and seabed undulation variation amplitude, divide the deployment area into multiple grid cells, and map the topographic parameter values ​​to generate regional topographic distribution information; S102: extracting the transmission power level, signal reception tolerance value, and anti-interference capability level information of multiple nodes based on the regional terrain distribution information, and calculating the node communication capability index; S103: calling the node communication capability index, analyzing the communication blocking degree of multiple grid units according to the terrain parameters, and comparing and analyzing with the communication capability of the node, calculating the adaptation degree of the nodes in the multiple grid units, and generating a node deployment adaptation index.

4. The marine communication processing method based on mobile edge computing according to claim 3 is characterized in that: Based on the node deployment adaptation index, the receiving and transmitting signal strengths in multiple consecutive cycles during the communication process are extracted, and the abnormal angle change trend is identified by combining the node heading angle and the transmission direction angle, and the stability risk communication link is identified. The specific steps of obtaining the link deviation trend are as follows: S201: extracting the transmission signal strength values ​​and the reception signal strength values ​​in a plurality of consecutive periods during the communication process based on the node deployment adaptation index, extracting the signal strength difference between a plurality of nodes, identifying the variation range, and generating a periodic transmission and reception strength variation value; S202: calling the periodic transmission and reception strength change value, collecting the heading angle value and the corresponding transmission direction angle value of each node in the current period, calculating the angle difference, and analyzing the change trend of the angle differences of multiple communication links to obtain the angle offset change trend value; S203: Calculate the communication stability of multiple communication links according to the angle deviation change trend value, combined with the signal strength difference and the angle difference, detect the communication links with stability risk, and generate a link deviation trend value.

5. The marine communication processing method based on mobile edge computing according to claim 4 is characterized in that: The steps of obtaining the link offset trend, analyzing the received power change value and the data packet loss value of the corresponding frequency band of the node in a continuous period, identifying the hole drift point in the deployment area, calling the link path connection angle reordering, and generating the path reconstruction parameter are as follows: S301: Acquire the link deviation trend value, detect the signal abnormal area by analyzing the received signal power values ​​and data packet loss quantity values ​​of multiple nodes in the corresponding frequency band in continuous periods, and generate the signal drift abnormal area; S302: Based on the signal drift abnormal area, by analyzing the continuity of multiple abnormal areas in space, extracting an abnormal position set, marking the problem area of ​​signal void, and obtaining the coordinates of the void drift fragment; S303: calling the hole drift fragment coordinates, adjusting the connection paths between nodes according to the position information of the hole drift points, and generating path reconstruction parameter values.

6. The marine communication processing method based on mobile edge computing according to claim 5 is characterized in that: The specific formula for detecting the abnormal signal area is: ； Calculate the signal drift fluctuation characteristic value, detect the signal abnormal area, and generate the signal drift abnormal area; in, Indicates The signal drift fluctuation characteristic value of the node in the current cycle, Indicates The node is The received signal power value at the sampling time is Indicates The average value of all received signal power values ​​of the node in the current cycle, Indicates The node is The number of data packets lost at sampling time, Indicates The average number of all packet losses of the node in the current cycle, Indicates the number of samples in each cycle, is the node index, The index of the sampling times in the current cycle.

7. The marine communication processing method based on mobile edge computing according to claim 5 is characterized in that: The steps of obtaining the path reconstruction parameters, extracting the tidal level change information of multiple grid cells, extracting the difference of the tidal level change trends of adjacent areas, and combining the change of the communication hop count of the nodes in the cells to reorganize the node communication network and generate the node reorganization parameter value are as follows: S401: Acquire the path reconstruction parameter value, collect tidal height change data of multiple grid units in continuous periods, extract the periodic tidal fluctuation amplitude of each grid, calculate the tidal change characteristics, and generate a tidal change trend coefficient; S402: calling the tide level change trend coefficient, extracting tide level trend differences between multiple grids and adjacent cells, analyzing the stability of the tide level, calculating the tide level disturbance level, and obtaining the tide level trend difference distribution value; The specific formula for analyzing the stability of tide level is: ； Calculate the tidal disturbance difference index value and obtain the tidal trend difference distribution value; in, The current grid number is The cells and adjacent grids are numbered The corrected tidal disturbance difference index value between the units, The grid number is The tidal level change trend coefficient of the unit, The grid number is The tidal level change trend coefficient of the adjacent unit, For the number With number The standard deviation of the tidal fluctuation amplitude between units, For the number With number The mean value of the tidal fluctuation amplitude between units, For the The observation period is numbered With number The tidal height difference of the unit, For the The observation period is numbered With number The average unit tidal height of is the total number of observation periods, is the number of the current observation period, is the grid number currently being processed, Number an adjacent grid of the current grid; S403: According to the tidal trend difference distribution value and in combination with the change of the communication hop count of each node in the unit, abnormal connection nodes are identified and the connection positions and associated objects of the nodes are adjusted to generate node reorganization parameter values.

8. The marine communication processing method based on mobile edge computing according to claim 7 is characterized in that: The method further comprises: S5: Obtain the node reorganization parameter value, extract the difference in the number of communication entities between adjacent grids by analyzing the number of communication entities, mobile active status, and aggregation density level in each grid unit, and calculate the task scheduling level of multiple grid units by combining the mobile active status difference value and density level span value corresponding to each grid, and generate a communication task scheduling result; The communication task scheduling result includes a task distribution level, a scheduling cycle control value, and a communication entity density interval.

9. The marine communication processing method based on mobile edge computing according to claim 8, characterized in that: The node reorganization parameter value is obtained, and the difference in the number of communication entities between adjacent grids is extracted by analyzing the number of communication entities, mobile active state, and aggregation density level in each grid unit. The task scheduling level of multiple grid units is calculated by combining the mobile active state difference value and density level span value corresponding to each grid. The steps of generating the communication task scheduling result are specifically as follows: S501: Acquire the node reorganization parameter value, collect the number of communication entities, mobile activity state level, and aggregation density level in each grid unit, and generate a communication entity distribution feature set; S502: comparing the number of communication entities, the level of mobile frequency, and the level of aggregation between adjacent grids according to the communication entity distribution feature set, analyzing the entity behavior differences between regions, and obtaining regional behavior difference values; S503: calling the regional behavior difference value, calculating the task scheduling levels of multiple grid units according to the entity behavior characteristics, and generating a communication task scheduling result value.

10. Marine communication processing system based on mobile edge computing, characterized in that: According to the marine communication processing method based on mobile edge computing according to any one of claims 1 to 9, the system comprises: The terrain adaptation module obtains seabed terrain measurement information, divides the deployment area into grids, evaluates the communication adaptability of the nodes and each area based on the transmission power levels, signal reception tolerance values, and anti-interference capability levels of multiple nodes, and establishes a node deployment adaptation index; The link monitoring module monitors the signal strength of the communication node based on the node deployment adaptation index, collects the node heading angle and signal transmission direction angle data, analyzes the angle change trend and abnormal fluctuation of the communication link, and obtains the link deviation trend; The path calibration module detects the node frequency band receiving power and data packet loss based on the link offset trend, identifies the signal hole drift area, reorders the node connection angles of the communication link, and generates path reconstruction parameters; The network reconstruction module monitors the grid unit tide fluctuation data based on the path reconstruction parameters, analyzes the tide trend differences between adjacent areas and the changes in the number of node communication hops, adjusts the node communication network structure, and generates node reorganization parameter values; The task dispatching module performs parameter analysis on the number of communication entities, mobile activity status, and aggregation density level within the grid unit based on the node reorganization parameter value, identifies the differences in the behaviors of communication entities between regions, calculates the task scheduling level, and obtains the communication task scheduling result.

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