Ocean communication processing method and system based on mobile edge computing

Abstract

The invention relates to the technical field of mobile edge computing, in particular to an ocean communication processing method and system based on mobile edge computing, and the method comprises the following steps: obtaining submarine topography information and node capability parameters, computing node deployment adaptation indexes, extracting signal intensity and angle changes, obtaining a link offset trend, and identifying power and packet loss abnormal regions. Generating path reconstruction parameters, adjusting a network structure in combination with tide level changes, and obtaining a communication task scheduling result according to entity behavior characteristics. According to the method, the adaptability of the node site selection environment is evaluated by analyzing the topographic features and the node capacity, the deviation trend is jointly recognized through signals and angle data, the pre-judgment capacity of link abnormity is improved, the network structure is dynamically adjusted in combination with the tide level change, and the stability of the communication network structure is optimized; and task scheduling grading is driven by adopting communication entity behavior characteristics, so that the accuracy of resource allocation and the regional regulation and control efficiency are improved, and the self-adaptive regulation and control capability in a dynamic environment is realized.

Description

Technical Field

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

[0002] The field of mobile edge computing technology includes technologies that sink computing and storage capabilities from remote data centers to the edge of the network. The core content is to deploy edge nodes close to the data source or user side so that data can be processed and responded to locally, reducing transmission delays, reducing network pressure and improving the real-time and reliability of services. This technical field involves edge node resource management, task offloading mechanism, local computing scheduling, edge cache control, and distributed network architecture design. It is applied to the Internet of Things, Internet of Vehicles, intelligent manufacturing, telemedicine 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. It aims to build a distributed computing framework with the edge as the computing center.

[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 the marine communication network, including location nodes such as offshore platforms, ships, buoys and shore base stations, and distributing and scheduling 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 realize local preprocessing and compression encoding of original data, using short-distance wireless transmission technology between nodes to perform multi-hop relay forwarding of data to improve the continuity of data transmission, and setting up a multi-channel communication structure to classify and manage different types of data and prioritize them. In addition, the communication path is dynamically adjusted and selected in combination with the environmental status monitoring mechanism 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 micro-computing modules, local caching mechanisms, multi-node coordinated transmission strategies, and communication link allocation rules.

[0004] Traditional marine communication processing methods and technologies deploy edge nodes at fixed locations. The deployment logic relies on static parameters or regional attributes for rough division. There is a lack of quantitative measurement of the matching relationship between terrain complexity and node capability differences, resulting in frequent communication blind spots or coverage redundancy after deployment. The path adjustment mechanism in the communication process usually only performs post-repair based on signal strength fluctuations or data transmission failures. There is a lack of procedural judgment on direction change trends and angle offset anomalies, which can easily cause short-term link interruptions. Path construction is mostly based on static topology maps or planned path tables. Periodic tidal disturbances are not introduced into the adjustment basis of the communication structure, resulting in limited stability of the communication chain structure in the marine environment. Priorities are mostly set with fixed weights or target values ​​during task scheduling, and there is insufficient response to state differences between communication entities. The scheduling results are difficult to reflect regional dynamic characteristics, resulting in uneven resource allocation or scheduling redundancy. In the offshore environment with dynamic deployment of multiple nodes and dense mobile entities, there is a risk of communication failure and scheduling mismatch. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art, the embodiments of the present invention provide a method and system for marine communication processing based on mobile edge computing. The technical solution is as follows: In order to achieve the above object, the present invention adopts the following technical solution, a marine communication processing method based on mobile edge computing, comprising the following steps: 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, the stability risk communication link is identified, and the link deviation trend is obtained; 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 parameters.

[0006] As a further solution of the present invention, the node deployment adaptation index includes the communication blockage 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 parameters specifically refer to the node hop growth rate, the regional tidal level change difference, and the reorganization deployment direction identifier.

[0007] As a further solution of the present invention, the steps of obtaining seabed topography measurement information, dividing the deployment area into multiple grid units, extracting the transmission power level, signal reception tolerance value, and anti-interference capability level of multiple nodes, analyzing the degree of adaptation between the node capability and the regional communication blocking degree, and generating the node deployment adaptation index are specifically as follows: 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.

[0008] As a further solution of the present invention, 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. The steps of obtaining the link deviation trend are specifically 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.

[0009] As a further solution of the present invention, the link offset trend is obtained, the received power change value and the data packet loss value of the corresponding frequency band of the node in the continuous period are analyzed, the hole drift point of the deployment area is identified, the link path connection angle is called to reorder, and the path reconstruction parameter is generated. Specifically, the steps are: 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.

[0010] As a further solution of the present invention, the specific formula for detecting the abnormal area of ​​the signal 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.

[0011] As a further solution of the present invention, the path reconstruction parameters are obtained, the tidal level change information of multiple grid cells is extracted, the difference of the tidal level change trend of adjacent areas is extracted, and the node communication network is reorganized in combination with the change of the number of communication hops of the nodes in the unit. The steps of generating the node reorganization parameters are specifically 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.

[0012] As a further embodiment of the present invention, the method further comprises: S5: Obtain the 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 unit, and calculate the task scheduling levels 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.

[0013] As a further solution of the present invention, the node reorganization parameters are 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 levels of multiple grid units are calculated by combining the mobile active state difference value and the 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.

[0014] On the other hand, a marine communication processing system based on mobile edge computing is provided, the system is applied to a marine communication processing method based on mobile edge computing, and the system includes: 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 parameters; The task allocation 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 parameters, identifies differences in the behaviors of communication entities between regions, calculates the task scheduling level, and obtains the communication task scheduling result.

[0015] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least: By jointly matching the seabed terrain feature parameters with the node capability indicators, analyzing the communication adaptability of the deployment area, and improving the coupling degree between the node and the environment before the network is built, the pre-identification of abnormal communication status trends is achieved through the linkage extraction of periodic signal strength and directional angle data, and the sensitivity of communication stability judgment is enhanced. Combined with the periodic analysis of received power and packet loss, the signal hole drift range is calibrated during network operation to improve the dynamic identification capability of abnormal paths. By reordering the path angles and combining the tide level change trend and hop number fluctuation information, the node structure adjustment has an adaptive response to geographical dynamics. By analyzing the behavioral characteristics of communication entities at multiple locations, a multi-dimensional hierarchical rule system is constructed to make the communication resource allocation fit the spatial distribution of the communication entity status, strengthen the adaptability of the edge communication network in the dynamic marine environment, and improve the stability of the communication link and the accuracy of task allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0018] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0019] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0020] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the distinction between them is not emphasized, the meanings they intend to express are consistent. In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript form such as W1. When the distinction between them is not emphasized, the meanings they intend to express are consistent. In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0021] See also Figure 1 The present invention provides a technical solution, a marine communication processing method based on mobile edge computing, comprising the following steps: 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. Combined with the node heading angle and the transmission direction angle, the abnormal angle change trend is identified, the stability risk communication link is identified, and the link deviation trend is obtained; 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 path reconstruction parameters, extract tide level change information of multiple grid cells, extract the difference of tide level change trends in adjacent areas, combine the change of communication hop count of nodes in the cell, reorganize the node communication network, and generate node reorganization parameters; 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 unit, and calculate the task scheduling levels of multiple grid units by combining the mobile active status difference value and density level span value corresponding to each grid, and generate the communication task scheduling results.

[0022] The node deployment adaptation index includes the communication blocking level, node coverage capability, and deployment priority level. The link deviation trend specifically refers to the obstacle avoidance connection angle, frequency band switching index, and path deviation 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 parameters specifically refer to the node hop growth rate, the regional tidal level change difference, and the reorganization deployment direction identifier. The communication task scheduling results include the task distribution level, the scheduling cycle control value, and the communication entity density range.

[0023] 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; The submarine topography measurement submodule obtains terrain feature information in the deployment area through multi-source equipment. First, the target area is spatially gridded. The division accuracy is uniformly set according to the selected grid side length, such as each grid unit is 1 km × 1 km, and then data is collected in each cell. The water depth gradient value acquisition uses a multi-beam bathymetric system to obtain the depth values ​​of multiple points, and the gradient data is established through the vertical difference and horizontal spacing relationship between adjacent measuring points. The distribution value of the edge of the island and reef adopts remote sensing image processing. The pixel area of ​​the edge of the island and reef is identified based on high-resolution satellite images. The number of edge points is extracted through the boundary recognition algorithm and recorded in the corresponding grid. The amplitude of the seabed undulation is determined by the maximum and minimum water depth difference of the sampled measuring points in each grid. The number of measuring points in the same grid must be greater than 3 points to ensure the accuracy of the undulation calculation. After the calculation of each parameter value is completed, the result needs to be mapped to the geographic information system to match the grid position to form a mapping structure of terrain parameters and spatial indexes. When the parameter results are classified into standard levels, the continuous data needs to be converted into interval levels, for example, the gradient is 0-2 meters / km for level 1, 2-4 for level 2, and so on to level 5, and a grid level code is generated. The formula is: ;

[0024] Calculate the water depth gradient value, where G is the water depth gradient value (meters / kilometers), is the maximum water depth between two adjacent points (meters), is the minimum water depth between two adjacent points (meters), and L is the horizontal distance between the two measuring points (kilometers). , , , substitute the set value into the calculation: ;

[0025] The calculation results show that the gradient level of the area falls within the range of 2-4 meters / km, corresponding to level 2. The level needs to be combined with the island edge level and the undulation level to generate a combined code. After each grid level is coded, the code and its spatial index coordinates are used together to generate a terrain level distribution map, which serves as the basic data structure for subsequent deployment and adaptation analysis. This process requires that all grid information be archived and managed consistently, supporting multiple reads and hierarchical retrieval.

[0026] 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; The node capability calculation submodule calls the communication performance index of each device in the node database, extracts the three core parameters of transmission power, signal reception tolerance value, and anti-interference ability. The transmission power value is the maximum power generated by the node under open and barrier-free conditions, in dBm, usually marked by the device at the factory; the signal reception tolerance value indicates the minimum signal strength threshold that the device can receive without packet loss or bit error. The anti-interference ability level is a percentage score calculated by comprehensive anti-noise ability, modulation accuracy, bit error rate test and other parameters. The extracted original parameters are first normalized, and then the comprehensive communication capability index is calculated according to the weight coefficient. The normalization process uses a linear method to linearly map the maximum and minimum intervals of all data of the indicator to between 0 and 1 to ensure the comparability of various parameters. The weight ratio is set based on the experience of node communication stability testing. The highest transmission power weight is 0.4, followed by the reception tolerance of 0.3, and the anti-interference ability is 0.3. The formula used is: ;

[0027] Calculate the communication capability index, where is the communication capability indicator, , , are the node’s transmit power, receive margin, and anti-interference capability scores, respectively. and are the minimum and maximum values ​​of each parameter, , , are the weight coefficients of each parameter. , , , , , , , , , , , , substitute the set value into the calculation: ;

[0028] The calculation results show that the communication capability index of the node is 0.745. After all nodes have completed the calculation, the communication capability index and the spatial position are mapped as the input for subsequent adaptation analysis.

[0029] 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 nodes, calculating the adaptation degree of the nodes in the multiple grid units, and generating the node deployment adaptation index; The adaptation analysis submodule uses the node communication capability index and the grid terrain level combination to determine the communication blocking degree. The blocking degree is divided into high, medium and low levels according to the three-level combination in the rule table, and the blocking coefficients are set to 0.9, 0.6 and 0.3 respectively. If the water depth gradient level is greater than 7, the island edge level is greater than 3, and the undulation level is 3, it is defined as high blocking, and the rest are matched according to the rules. The adaptability is the difference in relative distance between the communication capability index and the blocking coefficient, which is used to judge the deployment adaptability level of the node under terrain restrictions. The formula is used: ;

[0030] Calculate the deployment adaptation index, where For the degree of adaptation, is the node communication capability indicator, is the grid terrain blocking coefficient.

[0031] set up , , substitute the set value into the calculation: ;

[0032] The calculation results show that the deployment adaptation degree of the node in the current terrain grid is 0.855. After completing the calculation of 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.

[0033] 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. Combined with the node heading angle and the transmission direction angle, the abnormal angle change trend is identified, and the stability risk communication link is identified. The specific steps for 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; Based on the node deployment adaptation index, the signal strength in the communication process is extracted in a continuous cycle. First, the transmission signal strength value in a fixed time interval is obtained from the transmitting unit of each node, and the receiving signal strength value is synchronously collected from the receiving unit of the target node. The two must be matched in the same communication cycle. The collection cycle is recommended to be once every 5 seconds, and the data sequence of no less than 3 cycles is continuously extracted. After the data sequence is established, the difference between the transmission value and the reception value is calculated in each communication cycle, and the amplitude change of the difference sequence is extracted. The amplitude change is realized by calculating the difference between the differences between adjacent cycles. The process is performed in parallel between multiple links between nodes to obtain the strength fluctuation difference on different links. For example, the transmission power of node A in three consecutive cycles is 20dBm, 21dBm, and 22dBm, respectively, and the receiving power of node B is 18dBm, 18.5dBm, and 19dBm, respectively. The difference in the cycle is 2dBm, 2.5dBm, and 3dBm, respectively. The amplitude change between cycles is analyzed and the amplitude change is 1dBm. The formula is used: ;

[0034] Calculate the periodic transmit and receive signal strength change value, where: is the average transmit / receive strength variation, For the Periodically send signal strength values, For the Periodic received signal strength value, is the number of cycles. Set , , , , , , , substitute the set value into the calculation: ;

[0035] The change amplitude value indicates that the signal strength fluctuation between nodes is 0.5dBm. If the value exceeds the stability benchmark threshold of 1.0dBm set by the system, it is classified as a normal fluctuation interval, otherwise it is marked as an abnormal interval. This process is performed one by one between all node pairs, and the results are stored as the periodic transmission and reception strength change value of the communication link.

[0036] 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 difference of multiple communication links to obtain the angle offset change trend value; Call the periodic transmission and reception strength change value to collect the heading angle and transmission direction angle of each node in the current cycle. The heading angle is the movement angle of the node itself relative to the geographic north direction, in degrees, ranging from 0° to 360°, obtained through attitude sensors or inertial navigation devices; 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 angle of each communication link, it is necessary to obtain the 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 angle difference of adjacent cycles. For example, the heading angles of a node in three cycles are 45°, 50°, and 55°, respectively, and the transmission direction angles are 60°, 65°, and 70°, respectively. The angle difference in each cycle is 15°, 15°, and 15°, respectively, and the change trend is 0. Use the formula: and ;

[0037] Calculate the angle offset change trend value, where: is the angle difference change trend value, For the The difference in the direction angle of the cycle, For the Periodic transmission direction angle value (degrees), For the Cycle heading angle value (degrees), is the number of cycles. Set , , , , , , , substitute the set value into the calculation: , , ; ;

[0038] The calculation result is 0, indicating that the angle between the node's transmission direction and the heading angle remains stable. The trend value is recorded under each communication link index to generate an angle offset change trend value.

[0039] 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 risks, and generate a link deviation trend value; According to the obtained angle offset change trend value, combined with the periodic transmission and reception intensity change value, the communication stability coefficient of each communication link is calculated. The communication stability coefficient represents the overall fluctuation performance of the current link under the space and energy state. The normalized product of the two indicators is used as the inverse indicator of stability. A stability risk threshold needs to be defined. When the stability coefficient is lower than the threshold, it is judged as a risky link. The stability coefficient is defined as 1 minus the product of the normalized mean of the two fluctuation terms. The normalization process uses the maximum expected fluctuation threshold to normalize to between 0 and 1. The formula is used: ;

[0040] Calculate the link communication stability index, where is the communication stability coefficient, is the periodic transmit and receive signal strength change value (dBm), Set the maximum intensity fluctuation value (dBm) for the system. is the angle deviation change trend value (degrees), Set the maximum angle fluctuation value (degrees) for the system. , , , , substitute the set value into the calculation: ;

[0041] The result is 0.917. If the stability threshold is set to 0.85, the link stability is normal. If it is lower than this value, it is identified as a communication link with stability risk. After all links are detected, the indicators are summarized, the risk level is marked, and the link deviation trend value is generated.

[0042] The steps to 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 continuous cycles, identify the hole drift points in the deployment area, call the link path connection angle reordering, and generate the path reconstruction parameters are as follows: S301: Obtain a link deviation trend value, detect a signal abnormal area by analyzing received signal power values ​​and data packet loss quantity values ​​of multiple nodes in corresponding frequency bands in continuous periods, and generate a signal drift abnormal area; The specific formula for detecting abnormal signal areas is: ;

[0043] 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.

[0044] formula: ;

[0045] Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the fluctuation range of the received signal power and the number of packet losses of a certain node in the current communication cycle. The result is used to evaluate whether there is an abnormal drift trend in the signal stability of the area where the node is located. Parameter meaning and setting value: is the number of samples per cycle, set to 3; For the The node is The received signal power value at the sampling time, in dBm, is set to -82, -85, -81; is the average signal power within the node period, ;

[0046] For the The node is The number of packet losses during sampling is set to 1, 2, or 3; is the mean number of packet losses, ;

[0047] Substitute the above values ​​into the formula to calculate: ; ; ;

[0048] The results show that there are differential fluctuations in both signal strength and packet loss within the node cycle, with a comprehensive fluctuation value of 2.23, reflecting that the current node may be in an abnormal communication area and needs to be further marked for the identification of abnormal signal drift areas.

[0049] S302: Based on the signal drift abnormal area, by analyzing the continuity of multiple abnormal areas in space, extracting the abnormal position set, marking the problem area of ​​signal void, and obtaining the coordinates of the void drift fragment; Based on the signal drift anomaly area, the proximity of each anomaly area in the spatial dimension is judged, and the grid units that meet the spatial connection characteristics in the continuous area are extracted to construct an abnormal area set. Each abnormal unit uses the central coordinate as the reference to judge the status of its adjacent eight neighborhood grids. If any neighborhood grid satisfies both spatial proximity (distance less than 3 kilometers) and the same signal anomaly type, it is included in the same set to form a spatial continuous anomaly block. After the continuous block is formed, the distribution density and anomaly proportion of all nodes in the area are calculated. If the number of abnormal grids in the same area exceeds 60% of the total number, it is marked as a void area. The formula is used: ;

[0050] in, is the proportion of spatial anomalies, is the number of abnormal units, is the total number of cells in the collection. Set the number of abnormal grids in a certain space collection , total number , substitute into the calculation: ;

[0051] The anomaly ratio reaches 75%, which is higher than the 60% threshold. Therefore, the spatial set is marked as a signal hole area, and the center coordinates of all grid cells in the segment are uniformly extracted as an abnormal position set. The corresponding positions are numbered, labeled, grouped and recorded, and output as the coordinates of the hole drift segment.

[0052] S303: calling the coordinates of the hole drift fragment, adjusting the connection path between the nodes according to the position information of the hole drift point, and generating a path reconstruction parameter value; Call the coordinates of the void drift fragment to adjust the structure of the connection path between the 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 between any two adjacent nodes in the path passes through the buffer of the void coordinate point. The buffer distance is set to 500 meters. The judgment standard is that the shortest distance between the two-node connection and the abnormal point is less than 500 meters, which is considered as path crossing. For all crossing paths, the avoidance path needs to be recalculated, and the alternative path with the smallest distance increment in the reachable path is selected for replacement. The alternative path needs to maintain connection integrity in the topological structure and avoid crossing the void area for two consecutive hops at the same time. Use the formula: ;

[0053] in, is the total length of the new path, For the alternative path With The distance between nodes (meters), is the number of nodes on the new path. Set the node sequence to 4, , , , substitute into the calculation: ;

[0054] 2250 meters is used as the reconstructed path distance and compared with the original path length. If the increment is within the allowable range (set to not exceed 30%), the original path is replaced and the node routing table is updated. Finally, after all affected paths are adjusted, the output is the path reconstruction parameter value.

[0055] Obtain path reconstruction parameters, extract tidal level change information of multiple grid cells, extract the difference of tidal level change trends in adjacent areas, and reorganize the node communication network in combination with the change of communication hop count of nodes in the cell. The specific steps for generating node reorganization parameters are as follows: S401: Obtain path reconstruction parameter values, collect tidal height change data of multiple grid cells in continuous periods, extract the periodic tidal fluctuation amplitude of each grid, calculate the tidal change characteristics, and generate a tidal change trend coefficient; After obtaining the path reconstruction parameter values, the tide height change data in continuous cycles are synchronously collected 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. A tide time series is established for each grid unit, and the tide height corresponding to each cycle is recorded in meters. During the recording process, the sensor accuracy error must not exceed 5 cm. For the tide time series in each grid, the difference between the maximum and minimum tide levels of the cycle is calculated as the fluctuation amplitude in the cycle, and then the mean and standard deviation of all fluctuation amplitudes in 12 cycles are calculated as the tide change characteristics. The tide fluctuation amplitude calculation formula is the maximum tide level of the cycle minus the minimum tide level. If the maximum tide levels of 12 cycles in a grid are 2.3 meters, 2.6 meters, and 2.1 meters, respectively, and the minimum tide levels are 1.2 meters, 1.4 meters, and 1.0 meters, then the fluctuation amplitude sequence is 1.1 meters, 1.2 meters, and 1.1 meters. The fluctuation amplitude sequence is averaged and used for subsequent trend analysis.

[0056] Using the formula: ;

[0057] in, is the tidal level change trend coefficient (m), For the Maximum periodic tidal level (m), For the Minimum periodic tide level (m), is the number of cycles. Set , , , , , , , substitute the set value into the calculation: ;

[0058] After binding the tide level change trend coefficient with the spatial coordinates, it is recorded as the dynamic attribute value corresponding to the grid and finally output as the tide level change trend coefficient.

[0059] S402: calling the tide level change trend coefficient, extracting the tide level trend difference 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 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, The number of an adjacent grid of the current grid.

[0060] formula: ;

[0061] Detailed explanation of the formula and the process of formula calculation and derivation: The formula is used to calculate the modified tidal disturbance difference index value between the current grid cell and the adjacent grid, and the value is used to identify the difference in the intensity of tidal trend changes between the two areas.

[0062] Parameter meaning and setting value: is the tide level change trend coefficient of the current grid cell, in meters, and is set to 1.24 meters; is the tidal level change trend coefficient of adjacent grid cells, which is set to 1.01 m; is the joint standard deviation of the tidal fluctuation amplitude of the current grid and the adjacent grids, and the set value is 0.18 meters; is the joint mean of the above fluctuation amplitudes, and the set value is 1.11 meters; For the The hourly tidal height difference between the two grids is set to 0.13 m, 0.25 m, 0.09 m, and 0.19 m respectively; The average of the tide heights of the two grids for that hour is set to 1.28 m, 1.32 m, 1.21 m, and 1.34 m; is the total number of cycles, set to 4 hours.

[0063] Substitute the parameters into the formula for calculation: ; ; ; ; ; ;

[0064] The result of 0.2581 indicates that there is a moderate to high difference between the current grid and the adjacent grids in terms of trend change and fluctuation intensity. This result will be used in the future to compare the set disturbance level division interval and determine its level classification in the tidal trend difference distribution.

[0065] S403: According to the tide trend difference distribution value and the change of the communication hop count of each node in the unit, the abnormal connection node is identified and the connection position and the associated object of the node are adjusted to generate the node reorganization parameter value; According to the difference distribution value of tide trend, the node structure adjustment analysis is performed in combination with the change of communication hop count of each node in the grid. The hop count change sequence within three consecutive sampling periods is extracted for each node. If the hop count change shows an increasing trend and the average hop count growth rate exceeds 2 hops, it is determined to be a structurally unstable node. By making a joint judgment with the disturbance level of the grid where the current node is located, when the node is located in a grid with a disturbance level of 2 or 3 and the average hop count growth value is greater than the set threshold of 2 hops, the system marks the node as an abnormal connection node. The connection structure of all abnormal nodes is scanned to identify which communication objects they have established connections with, and to compare whether their original communication paths cross the trend disturbance area. If so, try to adjust their connection objects, and give priority to nodes in adjacent grids with a disturbance level of 1 and a stable target node hop count (fluctuation less than 1 hop) as new connection objects. Use the formula: ;

[0066] in, is the average increase of node hop count, For the Periodic node hop value, is the number of cycles. Set the number of node hops to , , , ,but: ;

[0067] If the value does not exceed the set threshold of 2 hops, no adjustment is made. If the calculated result is 2.5, a reconstruction operation is performed. Finally, all eligible node connections are reestablished and the connection target, node index and path number are recorded and output as the node reorganization parameter value.

[0068] Obtain node reorganization parameters, analyze the number of communication entities, mobile active status, and aggregation density level in each grid unit, extract the difference in the number of communication entities between adjacent grids, combine the mobile active status difference value and density level span value corresponding to each grid, calculate the task scheduling level of multiple grid units, and generate the communication task scheduling results in the following steps: S501: Obtain node reorganization parameter values, collect the number of communication entities, mobile activity status level, and aggregation density level in each grid unit, and generate a communication entity distribution feature set; After obtaining the node reorganization parameter value, the area is sampled by grid division. Each grid unit is 1 km × 1 km in range. The number information of all identifiable communication entities is collected within this range. The entity types may include unmanned boats, surface sensor buoys, shore station nodes, etc. At the same time, the entity ID and spatial coordinates are called for deduplication and numbering operations to exclude repeated sampling records and obtain the net entity quantity value; the mobile active state level is calculated based on the distance and direction of entity position change per unit time. It is set that the movement of more than 100 meters and the direction change of more than 30 degrees within 10 minutes is determined as an active entity. If the number of active entities accounts for more than 50%, the grid is marked as active level 3, otherwise it is reduced to level 1 in turn; the aggregation density level is divided by the number of entities per square kilometer, and 0-5 is set as level 1, 6-10 is set as level 2, and 11 or more is set as level 3. The entity distribution characteristics composed of three attributes are formed for each grid, which are then bound to the grid number and output. The formula is used: ;

[0069] in, is the communication entity density per unit area, is the number of communication entities in the grid, is the grid area (square kilometers). Assuming the number of entities in a grid is 12 and the area is 1 square kilometer, we can calculate: ;

[0070] The density corresponds to level 3. The density level, activity level, and number of entities form a structure to complete the unit entity data organization and output it as a communication entity distribution feature set.

[0071] 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; According to the communication entity distribution feature set, the current grid unit and its eight adjacent neighborhood grids are selected one by one for attribute comparison operations. The comparison attribute items include the difference in the number of entities, the difference in mobile activity level, and the difference in aggregation density level. The corresponding thresholds are set to determine whether it constitutes an entity behavior difference event. The difference in the number of entities is greater than 5 for a valid difference, the difference in activity level is greater than 1 for a valid difference, and the difference in aggregation level is greater than 1 for a valid difference. The weights of each difference item are set to 0.5, 0.3, and 0.2 respectively. The three differences are weighted according to the difference level to obtain the behavior difference value. The evaluation interval is set for the value. 0-0.3 is the difference level 1, 0.3-0.6 is the difference level 2, and greater than 0.6 is the difference level 3. The formula is used: ;

[0072] in, is the regional behavior difference value, is the difference in the number of entities, is the mobile level difference, is the density level difference, , , are the three weights, , , is the maximum possible difference of each item. Set , , , , , , , , substitute into the calculation and get: ;

[0073] The difference value is in the level 2 interval, indicating that the behavior difference between regions is at a medium level, and the final output is the regional behavior difference value.

[0074] 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; The regional behavior difference value is called, and the grid task scheduling level is calculated according to the difference level and the behavior indicator characteristic value. The task scheduling level is scored according to three dimensions: the entity density level score is density level × 0.4, the mobile activity level score is activity level × 0.3, and the behavior difference level score is difference level × 0.3. The sum of the three scores is the comprehensive scheduling score, and then the scheduling level interval is divided according to the score. The score 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: ;

[0075] in, Provide a comprehensive score for task scheduling. is the aggregation density level, is the mobile activity level, is the level of regional behavior difference. Set , , , substitute into the formula: ;

[0076] The score is in the level 3 interval, corresponding to the high scheduling priority area. The scheduling level result is marked on the corresponding grid number and output as the communication task scheduling result value.

[0077] See also Figure 2 , a 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: 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 communication nodes based on the node deployment adaptation index, collects 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 tidal fluctuation data of grid units based on the path reconstruction parameters, analyzes the tidal 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 parameters; The task scheduling 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 parameters, identifies the differences in the behaviors of communication entities between regions, calculates the task scheduling level, and obtains the communication task scheduling results.

[0078] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, 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.). 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 data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0079] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0080] In the present invention, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers 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 mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0081] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0084] In the 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 only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0085] 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 distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] If the 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, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0088] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on 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, the stability risk communication link is identified, and the link deviation trend is obtained; 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 parameters.

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 parameters specifically refer 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 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 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; wherein, 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 path reconstruction parameters are obtained, the tidal level change information of multiple grid cells is extracted, the difference of tidal level change trends in adjacent areas is extracted, and the node communication network is reorganized in combination with the change of the number of communication hops of the nodes in the cells. The steps of generating the node reorganization parameters are specifically 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, The current grid number is 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 1, characterized in that: The method further comprises: S5: Obtain the 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 unit, and calculate the task scheduling levels 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 parameters are obtained, and the difference in the number of communication entities between adjacent grids is extracted by analyzing the number of communication entities, mobile active status, and aggregation density level in each grid unit. The task scheduling levels of multiple grid units are calculated by combining the mobile active status 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 parameters; The task allocation 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 parameters, identifies 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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