Internet of Things (IoT) networking communication method and system applied to marine observation buoys

Abstract

This invention relates to the field of wireless communication technology, specifically to an Internet of Things (IoT) networking communication method and system applied to marine observation buoys. The method includes: deploying multiple buoys in a marine environment and installing observation sensors to collect monitoring data; forming several links for transmitting monitoring data based on gateways and buoys; selecting the optimal link for the current transmission; comprehensively analyzing the buoys and the optimal link to determine the curvature constraint of the buoys; obtaining the attitude stability of the buoys based on the monitoring data; assessing the impact of the marine environment on the buoys, determining the sea surface smoothness trend of the buoys, and adjusting the attitude stability to obtain the expected attitude stability of the buoys; integrating the marine environment, buoys, and corresponding curvature constraint to draw a sea surface buoy grid map; constructing candidate links for the next transmission based on the sea surface buoy grid map; comprehensively considering the expected attitude stability and path cost to select the optimal link for the next transmission from the candidate links; and compensating for the deficiencies in buoy feedback.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a networking communication method and system of Internet of Things applied to ocean observation buoy. BACKGROUND

[0002] The development, management, comprehensive utilization and territory protection of ocean resources all need the support of ocean communication technology. Ocean buoy has the characteristics of system simplification, strong speciality and high real-time performance, and is suitable for being used as a carrier of satellite relay communication system on the sea, and is widely used in the fields of ocean engineering equipment such as communication between ocean ships, ocean environment monitoring station and underwater robot control. However, due to the environmental problems in the deep sea, it also faces challenges such as poor communication efficiency and low communication link reliability.

[0003] The ocean monitoring buoy operates on the sea surface, and can only rely on self-power generation to obtain energy supply, and needs to cope with unpredictable climate and complex ocean conditions, so it has very high requirements for low-power operation. In the Internet of Things communication technology, the LoRa (Long Range) communication technology has the advantages of long-distance communication and low power consumption in the ocean monitoring system, and has flexibility in networking, so it is an ideal and reliable stable networking communication method.

[0004] LoRa is a physical layer modulation technology of linear frequency spread, which occupies a wider frequency band to obtain better signal sensitivity, so that it approaches the limit specified by Shannon's theorem. In the application scenario of ocean observation buoy, it uses Internet of Things technology for networking, constructs a propagation link in a step-by-step upload manner for the data of the buoy which needs to transmit data, and transmits it to the gateway, and then the gateway is connected with the satellite to realize data upload. However, in this process, each ocean observation buoy needs to broadcast signals to the surrounding buoys, so an omnidirectional antenna is installed on the buoy, which sacrifices the beam gain value, causing the ocean observation buoy to be more easily affected by the attitude stability and marine interference factors in the marine environment, reducing the reliability of data transmission, and thus affecting the communication accuracy. SUMMARY

[0005] In order to solve the technical problem that the existing buoy is easily affected by the attitude stability and marine interference factors when transmitting data, the purpose of the present application is to provide a networking communication method of Internet of Things applied to ocean observation buoy, and the technical scheme adopted is as follows: A plurality of buoys are arranged in the marine environment, and observation sensors are installed to collect monitoring data; Based on the gateway, a plurality of links for transmitting monitoring data are formed in combination with the buoys, the optimal link for the current transmission is screened, the buoys and the optimal link are comprehensively analyzed, and the curvature limitation degree of the buoys is determined; The attitude stability degree of the buoy is obtained according to the monitoring data; Assess the impact of the marine environment on the buoy, determine the buoy's sea surface smoothness trend, and adjust the attitude stability to obtain the buoy's expected attitude stability. A grid map of sea surface buoys was created by integrating marine environment, buoys, and corresponding curvature constraints. Based on the sea surface buoy grid map, candidate links for the next transmission are constructed, and the optimal link for the next transmission is selected from the candidate links by comprehensively considering the expected attitude stability and path cost.

[0006] Preferably, the observation sensors include a conductivity sensor, an illuminance sensor, a Stevenson screen sensor, a water temperature sensor, an acoustic Doppler current meter, and an inertial wave sensor.

[0007] Preferably, based on the gateway and buoys, several links are formed for transmitting monitoring data. The optimal link for the current transmission is selected, and the buoys and the optimal link are comprehensively analyzed to determine the curvature constraint degree of the buoys, including: Analyze the monitoring data collected by the buoys on each link to determine the corresponding path cost, and select the link with the lowest path cost as the optimal link for the current transmission. Based on the distance between buoys in the optimal link and the height of the buoys, the curvature constraint degree of each buoy in the optimal link can be obtained; Based on each buoy, the buoy closest to the optimal link is identified, the distance between the two buoys is analyzed, and the curvature constraint of each buoy is determined by combining the curvature constraint of the buoys in the optimal link.

[0008] Preferably, the path cost includes link quality and link cost, wherein the link quality includes, but is not limited to, evaluation metrics such as the buoy's signal-to-noise ratio, latency, and packet loss rate; and the link cost is the interference intensity of the buoy.

[0009] Preferably, the curvature restriction degree for each buoy is determined as follows: Define any buoy other than the buoy in the optimal link as the target buoy, obtain the distance between the target buoy and the nearest buoy in the optimal link, filter the maximum distance based on the distance between each pair of buoys in the optimal link, and obtain the curvature constraint degree corresponding to the nearest buoy in the optimal link. Comprehensively determine the curvature constraint degree of the target buoy.

[0010] Preferably, the attitude stability of the buoy is obtained based on monitoring data, specifically as follows: By analyzing monitoring data collected by inertial wave sensors, the wave height of the buoy in the marine environment is determined, and the attitude stability of the buoy is obtained by combining the acquisition time corresponding to the monitoring data.

[0011] Preferably, assessing the impact of the marine environment on the buoy, determining the buoy's sea surface smoothness trend, and adjusting its attitude stability to obtain the buoy's expected attitude stability includes: Based on the analysis of the marine environment using monitoring data, the direction of ocean currents is determined and the wave height of the buoys within the corresponding collection time is obtained. In the corresponding ocean current direction, the buoy's wave height is combined with the collection time to assess the buoy's wave changes, and the trend of the buoy's sea surface smoothness is determined by the wave changes. The attitude stability of the buoy is adjusted by the trend of sea surface smoothness to obtain the expected attitude stability of the buoy.

[0012] Preferably, a sea surface buoy grid map is drawn by integrating the marine environment, buoys, and corresponding curvature constraints, including: A scaled-down sea surface grid map is drawn based on the latitude and longitude of the marine environment. The distance between each grid and all the buoys around the corresponding grid is determined. The corrected distance between the grid and the buoy is obtained by using the curvature constraint of the buoys. By correcting the distance, the buoys are divided into corresponding grids, and the expected attitude stability of the buoys and the ocean current velocity determined based on monitoring data are assigned to the corresponding grids to obtain a sea surface buoy grid map.

[0013] Preferably, a candidate link for the next transmission is constructed based on the sea surface buoy grid map, and the optimal link for the next transmission is selected from the candidate links by comprehensively considering the expected attitude stability and path cost, including: The time interval is determined based on the time corresponding to the current transmission and the next transmission; The sea surface buoy grid map is adjusted based on the time interval and monitoring data to obtain a new sea surface buoy grid map. The candidate link for the next transmission is obtained from the new sea surface buoy grid map. Analyze the pairwise buoys in the candidate links, obtain the selection weights of the candidate links based on the expected attitude stability, and determine the path cost of the candidate links; The optimal link for the next transmission is obtained by comprehensively selecting weights and path costs.

[0014] To address the aforementioned problems, the present invention also provides an Internet of Things (IoT) networking communication system for marine observation buoys, the system comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus, and the processor calls logical instructions in the memory to execute the IoT networking communication method for marine observation buoys described in any of the preceding claims.

[0015] The present invention has the following beneficial effects: 1. Based on the observation sensors installed on the buoys, corresponding monitoring data is collected to provide stable data support and ensure the comprehensiveness of monitoring data collection. The optimal link for the current transmission is selected from the links, and the buoy performance in the optimal link is analyzed to determine the curvature limitation of each buoy, so as to assess the impact of the Earth's curvature in the marine environment on buoy data transmission. Then, the buoy's attitude stability is analyzed by the wave height of the ocean activity, and the expected attitude stability is determined by judging the changes in the ocean waves. Finally, a sea surface buoy grid map is drawn, and candidate links for the next transmission are established and selected to achieve dynamic optimization of the links and precise adaptation of buoy monitoring data transmission. That is, the influence of buoy attitude stability and marine environment is fully considered. Through multi-path selection and optimal selection of links, the path cost is controlled under the premise of meeting the requirements of low power consumption operation. At the same time, the deficiency of insufficient beam gain of omnidirectional antennas in buoys is effectively compensated, the anti-interference capability of data transmission is improved, and the reliability of communication links is ensured. This provides reliable communication support for the stable operation of marine engineering equipment such as marine resource development, environmental monitoring, and ship communication.

[0016] 2. The Internet of Things (IoT) networking communication system for marine observation buoys provided by this invention has the same beneficial effects as the IoT networking communication method for marine observation buoys provided by this invention, and will not be described in detail here. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This invention provides a schematic diagram of an Internet of Things (IoT) networking communication method for marine observation buoys, as an embodiment of the present invention. Figure 1 ; Figure 2 This invention provides a schematic diagram of an Internet of Things (IoT) networking communication method for marine observation buoys, as an embodiment of the present invention. Figure 2 ; Figure 3 This invention provides a schematic diagram of an Internet of Things (IoT) networking communication method for marine observation buoys, as an embodiment of the present invention. Figure 3 ; Figure 4 This is a flowchart illustrating the steps of an Internet of Things (IoT) networking communication method for marine observation buoys, provided as an embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an Internet of Things (IoT) networking communication method and system for marine observation buoys proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details a specific scheme for an Internet of Things (IoT) networking communication method and system for marine observation buoys provided by this invention.

[0022] To better illustrate this, let's explain the initial scenario of ocean observation buoys. LoRa technology can be used to build various network structures. Since ocean observation buoys are distributed in a grid pattern, LoRa communication technology in this scenario involves constructing a LoRaMesh protocol stack to form a topology network. Each ocean observation buoy acts as a node. When a terminal node needs to transmit data, it broadcasts the data to surrounding terminal nodes. These terminal nodes relay the signal and broadcast it further until it reaches the gateway. At this point, one or more links are formed from the node needing data transmission to the gateway. The gateway selects the optimal link from these links to form a transmission path and sends the data back to the terminal node. The terminal node then transmits information using the optimal link.

[0023] It can be explained that a gateway refers to the central equipment or system in an ocean observation network responsible for receiving monitoring data transmitted from various terminal nodes, i.e., ocean observation buoys. It possesses strong communication and data processing capabilities, undertaking crucial functions such as data aggregation, preliminary processing, format conversion, and forwarding data to higher-level data processing centers or user terminals. In specific marine environments, the LoRaMesh protocol stack can dynamically adjust the selection of relay nodes based on factors such as the distance between buoys, signal strength, and interference, ensuring the stability and reliability of data transmission. Furthermore, by dynamically selecting buoys for signal relay, it can effectively extend the communication coverage, enabling long-distance buoys that were previously unable to be covered by single-hop communication to transmit data to the gateway through multi-level relays. This avoids the problem of limited single-point communication. In cases where the buoy transmission distance is long or interference exists, the transmitting buoy can be replaced to establish a corresponding transmission path, providing stable and reliable communication support.

[0024] Please see Figure 1Figure (a) represents a star network; Figure (b) represents a mesh network. Specifically, the LoRaMesh protocol stack mainly includes star networks and mesh networks. In a star network, the bolded circle represents the gateway, and the remaining rings represent nodes. All nodes communicate directly with the gateway. This network is simple to deploy and suitable for scenarios with a relatively small number of nodes and a concentrated coverage area. In a mesh network, it is a distributed network structure. The bolded circle represents the gateway, and the remaining rings or circles can act as terminal nodes for receiving broadcast signals and transmitting data, or as signal relay nodes for transmitting information. They can change according to the data transmission requirements and the sending and receiving of broadcast signals. In this network, each node can act as a forwarding node to achieve the purpose of transmitting data to the gateway, which can effectively expand the network coverage and improve the network reliability and fault tolerance. That is, in this network, nodes can jump to each other to achieve data transmission, which can meet the needs of IoT applications in different scenarios. Preferably, in this embodiment, a detailed analysis is based on the mesh network.

[0025] Please combine Figure 2 and Figure 3 The explanation is that the hierarchical long-distance data transmission method is the primary way for the host computer system to acquire marine monitoring data. The host computer system refers to the central control system located in the entire marine observation network, which can monitor the working status of each node in real time. Specifically, when the network is in Mesh network mode, communication is always initiated by the last node in the network, i.e., terminal node n. When terminal node n reaches the system's preset communication time, it will send the latest monitoring data it stores to the upper-level node n-1 according to the specified data frame structure, i.e., the transmission link. In other words, data transmission is initiated by node n, which sends the data packet to node n-1. In node n-1, the data packet is parsed, its own node's data is added to the data packet, and the address and channel information of the upper-level node in the data packet frame header are updated. Finally, the new data packet is uploaded, i.e., it continues to be transmitted to the next node n-2. That is, the identification information and related data of its own node are added as the payload to the corresponding data packet to complete the data encapsulation. The wake-up code is used to wake up the receiving node in the low-power network, so that it enters the working state from the sleep state to receive data. The check is used to verify whether the data has been corrupted during transmission and to determine the integrity of the data. Nodes in the network send data sequentially until the data packet reaches the aggregation node, i.e., the gateway. The Beidou module on the aggregation node uploads the data of the entire Mesh network to the host computer system.

[0026] Therefore, in this context, an IoT networking communication method for marine observation buoys is proposed to effectively avoid the communication interference problems caused by attitude instability and ocean interference that existing marine buoys are susceptible to. An IoT networking communication system for marine observation buoys is also proposed. When operating, it needs to utilize the IoT networking communication method for marine observation buoys. Therefore, whether the system and program data are integrated or different hardware is configured to produce functions with similar effects to those achieved by this invention, they all fall within the protection scope of this invention.

[0027] Please see Figure 4 The diagram illustrates a flowchart of the steps of an Internet of Things (IoT) networking communication method for marine observation buoys provided in the first embodiment of the present invention. The method includes: Step S1: Deploy multiple buoys in the marine environment and install observation sensors to collect monitoring data; Step S2: Based on the gateway and buoy, form several links for transmitting monitoring data, select the optimal link for the current transmission, comprehensively analyze the buoy and the optimal link, and determine the curvature limitation of the buoy; Step S3: Obtain the buoy's attitude stability based on the monitoring data; Step S4: Assess the impact of the marine environment on the buoy, determine the trend of the buoy's sea surface smoothness, and adjust the attitude stability to obtain the expected attitude stability of the buoy. Step S5: Integrate marine environment, buoys, and corresponding curvature constraints to create a surface buoy grid map; Step S6: Construct candidate links for the next transmission based on the sea surface buoy grid map, and select the optimal link for the next transmission from the candidate links by comprehensively considering the expected attitude stability and path cost.

[0028] As explained in this embodiment, the observation sensors on the buoy are used to collect monitoring data and upload it to the gateway through a link formed by the buoy. After the gateway uploads the data to the satellite, it is stored in the database. The gateway also transmits data back by issuing commands through the link formed by the buoy, so as to form a closed-loop communication link and ensure the real-time performance and reliability of IoT communication in the entire marine monitoring environment.

[0029] It can be explained that in step S1, multiple marine observation buoys are deployed in the marine environment after manual selection of locations, which is aimed at marine environmental surveys and site selection, making the collection of monitoring data more representative. The buoy has a gourd-shaped structure to effectively disperse the impact force of ocean waves and reduce the violent swaying of the buoy in the waves. Several observation sensors are installed below the buoy, that is, at the end closest to the ocean, to collect the buoy's monitoring data.

[0030] Furthermore, the observation sensors include conductivity sensors, illuminance sensors, Stevenson screen sensors, water temperature sensors, acoustic Doppler current meters, and inertial wave sensors.

[0031] The explanation is as follows: conductivity sensors are used to measure the conductivity of water bodies, which can be used to estimate the salinity of the water; illuminance sensors are used to detect the light intensity in the environment; Stevenson screen sensors are used to measure meteorological elements such as air temperature, air pressure, and relative humidity; water temperature sensors are used to monitor the temperature changes of water bodies in real time; acoustic Doppler current meters use the Doppler effect of sound waves to measure the velocity and direction of seawater flow; and inertial wave sensors determine parameters such as the height, period, and direction of wave activity by measuring the acceleration or displacement caused by wave activity in the marine environment.

[0032] Understandably, a gateway combined with buoys forms several links for transmitting monitoring data. In practice, after each buoy collects monitoring data, it broadcasts a signal to all other buoys. Once a buoy receives the broadcast signal, it relays it until it reaches the gateway, forming several links. In particular, during the entire data transmission process, based on the hierarchical uploading method, not all buoys need to transmit monitoring data. Although all buoys can relay the signal, during the transmission of different monitoring data, due to factors such as path cost and signal quality, only some buoys that need to transmit monitoring data can receive and relay the signal. Therefore, the number of links is limited, and the topology network formed between the gateway and the links is not a fully connected graph.

[0033] Further, step S2 includes: Step S21: Analyze the monitoring data collected by the buoy on each link to determine the corresponding path cost, and select the link with the lowest path cost as the optimal link for the current transmission.

[0034] The gateway selects the link with the lowest path cost and sends a transmission permission signal to the buoy that initially needs to transmit data, i.e., the last terminal node on the entire optimal link. This terminal node then uploads the collected monitoring data step by step to the corresponding aggregation node buoy of the gateway according to the optimal link. This buoy connects to the satellite and establishes a communication loop to upload and download monitoring data and store it in the database to support efficient storage, rapid retrieval, and multi-dimensional analysis of monitoring data.

[0035] Furthermore, path cost includes link quality and link cost. Link quality includes, but is not limited to, evaluation metrics such as the buoy's signal-to-noise ratio, latency, and packet loss rate; link cost is the buoy's interference intensity.

[0036] It can be explained that link quality is used to measure the reliability and efficiency of link transmission of monitoring data. Among them, the higher the signal-to-noise ratio, the less noise interference the signal on the corresponding link is subjected to during transmission; latency reflects the time required from the sending end to the receiving end, and the lower the latency, the stronger the real-time performance; packet loss rate reflects the proportion of data packets lost during the transmission of monitoring data to the total number of data packets sent, and the lower the packet loss rate, the higher the integrity of data transmission; buoy interference intensity refers to the degree of electromagnetic interference generated by other buoys or external factors in the marine environment on the current buoy communication link, and the greater the interference intensity, the greater the negative impact on communication quality.

[0037] Furthermore, considering multiple factors, the link with the lowest path cost is selected as the optimal link. For example, based on the marine environment, the scores of each evaluation indicator in the link quality are determined and summarized to form the overall quality score of each link. The interference intensity corresponding to the link cost is also considered. Then, the weights corresponding to the link quality and link cost are determined separately. The path cost corresponding to each link is determined by multiplying the inverse of the overall quality score and the interference intensity by their respective weights. The link with the lowest result is the optimal link, which controls the efficiency of monitoring data transmission while ensuring better economic efficiency of link transmission.

[0038] Understandably, since buoys propagate radio waves in a straight line, the curvature of the Earth on the sea surface limits the propagation distance of radio waves. That is, when the distance between two buoys is too large, the curvature of the Earth's surface causes radio waves to be unable to reach the receiving end directly, and the communication distance is limited. Even if LoRa signals can diffract slightly, the transmission distance is basically limited to the signal line of sight, resulting in signal attenuation or termination. In the buoy structure, the omnidirectional antenna is placed at the top of the buoy, that is, at the end furthest from the observation sensor. The higher the omnidirectional antenna is above the sea level, the higher the altitude at which it can transmit signals. At this point, the two buoys are less affected by the curvature of the Earth, and the line of sight of the communication link increases accordingly, effectively extending the communication range. For example, under standard atmospheric conditions, when the height of the omnidirectional antenna is 10 meters, its theoretical maximum communication distance is about 20 kilometers. If the antenna height is increased to 20 meters, the communication distance can be extended to about 30 kilometers. That is, the higher antenna position can reduce the obstruction of the straight propagation path of electromagnetic waves by the curvature of the Earth, so that the signal can propagate further and be received by the other buoy. Therefore, the curvature restriction degree of each buoy on the optimal link is determined according to the curvature restriction degree of all buoys.

[0039] Step S22: Based on the distance between buoys in the optimal link and the height of the buoys, obtain the curvature constraint degree of each buoy in the optimal link.

[0040] The formula for calculating the curvature constraint of the buoy in the optimal link is as follows: in, Indicates the current curvature constraint of the buoy; Represents the normalization function; This represents the distance from the previous buoy to the current buoy in the optimal link; This represents the distance from the current buoy to the next adjacent buoy in the optimal link; This indicates the current height of the buoy.

[0041] It should be noted that, for the sake of explaining the subsequent steps, the current buoy will be designated as the [number]th [unit] in the optimal link. If there are 1 buoy, then the curvature constraint level corresponding to that buoy is: The higher the altitude, the less it is restricted by the curvature of the Earth.

[0042] To better illustrate, The Euclidean norm, representing the sum of straight-line distances between the current buoy and its two adjacent buoys in the optimal link, is an approximation of the length of the broken-line path from the previous buoy to the current buoy and then to the next buoy. It is used to assess the spatial correlation between buoys in the marine environment. Next, the Euclidean norm of the sum of straight-line distances between the current buoy and its two adjacent buoys in the optimal link is divided by the buoy's own height to quantify the impact of the curvature of the Earth's surface on buoy communication. Since the Earth's surface is a curved surface approximating a sphere, rather than a plane, the current buoy and its two adjacent buoys form a communication link in the marine environment. In theory, if we disregard the Earth's curvature, the communication path between the three buoys should be a straight line in a plane. However, due to the curvature of the Earth's surface, the spatial geometry of the three buoys' positions deviates from the ideal planar state. This means that due to curvature, a longer path is required or there is a risk of obstruction. Furthermore, considering the buoy's own height, which reflects the height of the buoy antenna—the reference height at which the communication signal can be effectively transmitted—the ratio of these two data points is calculated to visually represent the degree of communication path deviation caused by the Earth's curvature. Preferably, since the distance between the buoys and their heights are known, the normalization function uses the minimum-maximum normalization method to linearly transform the data to... Within this range, we can more intuitively analyze the impact of the Earth's curvature on each buoy.

[0043] Step S23: Based on each buoy, determine the buoy closest to the optimal link, analyze the distance between the two buoys, and combine the curvature constraint degree of the buoys in the optimal link to determine the curvature constraint degree of each buoy.

[0044] The explanation is as follows: During implementation, the optimal link only includes a portion of all buoys. Other buoys are either not currently transmitting data or were filtered out during the selection of the optimal link. This indicates that the path cost shared by buoys other than those on the optimal link is higher than that of the optimal link. Therefore, by using the distance between other buoys and the buoys on the optimal link, nearest neighbor interpolation is performed on the buoys other than those on the optimal link to determine the curvature constraint degree of each buoy. This allows for a comprehensive evaluation of the curvature constraint degree of all buoys based on the buoys on the optimal link, providing data support for subsequent data transmission.

[0045] Further, in step S23, the curvature restriction degree of each buoy is determined, specifically as follows: Define any buoy other than the buoy in the optimal link as the target buoy, obtain the distance between the target buoy and the nearest buoy in the optimal link, filter the maximum distance based on the distance between each pair of buoys in the optimal link, and obtain the curvature constraint degree corresponding to the nearest buoy in the optimal link. Comprehensively determine the curvature constraint degree of the target buoy.

[0046] Specifically, the target buoy is designated as the first in the marine environment. The nth buoy, assuming it is on the optimal link... The nearest buoy determines the curvature constraint of the target buoy, and the corresponding calculation formula is: in, Indicates the first The degree of curvature restriction of each buoy; Indicates the th on the optimal link The degree of curvature restriction of each buoy; Indicates the first The buoy and the first The distance between the buoys; This represents the maximum distance between any two buoys on the optimal link.

[0047] It can be explained that when the first When the curvature constraint of a buoy is large, it indicates that there is a difference between the buoy and other buoys in the selection of the optimal link. This means that there are fewer buoys around the buoy that can be used for communication, resulting in the buoy being farther away from other buoys in the current transmission's optimal link. Therefore, the buoy should be avoided as much as possible in the next transmission. Similarly, the curvature constraint of each buoy other than the buoy of the optimal link should be determined.

[0048] Understandably, in actual marine environments, wave activity such as wind and waves or swells causes buoys to experience periodic pitch and roll movements. Since the buoy's antenna is located at the top of the buoy, when the buoy is in a wave-prone area, the buoy will sway, causing the antenna to deviate from its ideal operating state. Because omnidirectional antennas mainly transmit signals to other buoys, the swaying limits the longitudinal signal transmission range of the omnidirectional antenna, meaning its transmission range is related to the horizontal angle of the buoy. When wave activity exhibits a clear pattern, it can cause the antenna to sway at a large angle, resulting in the longitudinal signal transmission range forming an angle with the horizontal plane. This reduces the antenna's signal transmission distance, causing severe signal fluctuations or attenuation, or even temporary interruptions, affecting the reliability of monitoring data transmission.

[0049] Inertial wave sensors can acquire wave height during the acquisition time. The higher the wave and the longer its period, the greater the range of sway angle of the buoy's antenna. The wave height trend during the acquisition time is approximately a normal distribution, that is, it gradually rises to the peak and then gradually decreases. Therefore, if the wave height is in a continuous rising phase and the higher the wave height, the more unstable the attitude of the antenna on the buoy is caused by the waves.

[0050] Furthermore, in step S3, specifically: By analyzing monitoring data collected by inertial wave sensors, the wave height of the buoy in the marine environment is determined, and the attitude stability of the buoy is obtained by combining the acquisition time corresponding to the monitoring data.

[0051] To clarify, for the step-by-step upload technology corresponding to the optimal link, the monitoring data collected by each buoy is the wave height within a time period. Optionally, in this embodiment, the wave height data during wave activity is smoothed data fitted using the least squares method. By eliminating small fluctuations, the dispersion of the wave height data is reduced, making the overall data more continuous and stable, and making the wave height data closer to a normal distribution. Since each buoy on the optimal link has a data transmission requirement, the time range corresponding to the wave height of the buoys on the optimal link is the same. Therefore, the wave height data of each buoy can be analyzed to obtain the buoy's attitude stability. The corresponding calculation formula is as follows: in, Indicates the degree of stability of the buoy's attitude; This indicates the wave height corresponding to the last data collection moment of the buoy within the data collection period; This represents the maximum wave height of the buoy at all sampling moments within the sampling period; Represents the normalization function; This represents the time interval from the maximum wave height to the last acquisition time.

[0052] It can be explained that wave height refers to the vertical distance from the wave crest to the adjacent wave trough, used to describe the wave pattern in the marine environment. Under ideal conditions, i.e., calm or light winds, the wave height may be 0. However, in actual marine environments, this ideal condition almost never exists. Therefore, based on natural conditions, the wave height of a buoy is... It cannot be 0. For example, the wave height of a 10m buoy is generally 0.1m-20m. The larger the value of , the greater the time interval between the last acquisition moment and the acquisition moment corresponding to the maximum wave height within the acquisition time. This indicates that the buoy is more likely to be in the wave height decreasing phase of wave activity, at which point the buoy's attitude stability is higher. Furthermore, the larger the time interval between the maximum wave height and the last acquisition moment... The larger the wave height, the higher the wave height at the last acquisition time within the acquisition period. The smaller the wave height, the more stable the current wave height of the buoy, and thus the higher the buoy's attitude stability. Preferably, since the wave height and time interval are known, the normalization function adopts the minimum-maximum normalization method. Data linear transformation to Within its range, it objectively reflects the rate characteristics of wave change during wave propagation.

[0053] Understandably, buoys are scattered across the sea surface, and ocean currents drive the movement of waves, causing the area of ​​fluctuation to shift with the currents. This, in turn, affects the buoys in stages, such as altering the wave activity environment and stress conditions they experience. Since the electromagnetic waves emitted by LoRa communication technology propagate in a straight line, their signal propagation path is theoretically relatively stable in open sea environments. However, if they encounter high waves, their straight-line propagation path will be blocked and weakened by the waves, resulting in attenuation and reflection from the sea surface, ultimately reducing their transmission efficiency.

[0054] Since the next communication can only occur when any buoy has the capability to send monitoring data after the current communication, the timing of the two communications differs. This causes the waves to move towards the ocean current over time, making it impossible to guarantee the timeliness of the current transmission. Therefore, in this embodiment, based on the macroscopic invariance of the same wave moving with the ocean current and its wave height and fluctuations, the influence of upstream wave activity on each buoy is determined according to the ocean current direction and the wave height of the wave activity during the collection period. This yields the sea surface smoothness trend for each buoy, which is used to analyze the expected attitude stability of each buoy during the next communication after the buoy fluctuates due to wave activity.

[0055] Further, step S4 includes: Step S41: Analyze the marine environment based on monitoring data, determine the direction of ocean currents, and obtain the wave height of the buoy within the corresponding collection time of the monitoring data.

[0056] It is explained that if the buoy is located in the ocean current zone, it will generate more small ripples. In the ocean current zone, due to the fast flow of seawater, there are often complex eddies and wave phenomena, which will continuously disturb the sea surface. This will cause a large number of tiny undulations and ripples to appear on the originally relatively calm sea surface, resulting in an excessively large contact area between the sea surface and electromagnetic waves. When the small waves increase, the mirror surface formed by the sea surface increases, making it easier to reflect electromagnetic waves during signal transmission, thus affecting the quality of signal transmission. Therefore, by analyzing the wave height fluctuation of each buoy during its collection period, the trend of sea surface smoothness of each buoy can be obtained.

[0057] Step S42: In the corresponding ocean current direction, assess the buoy's wave height in combination with the collection time to determine the buoy's sea surface smoothness trend through wave changes.

[0058] Specifically, from a macroscopic perspective, ocean wave activity causes the wave height of buoys to exhibit an approximately normal distribution. This is illustrated by constructing a window with 21 data collection times centered at each time point, obtaining the standard deviation of the wave height at each time point. This standard deviation is used to assess whether the wave height fluctuation of each buoy gradually increases or decreases. In other words, the trend of the standard deviation of the wave height of each buoy at each time point over time is analyzed. The standard deviation of the wave height corresponding to the nearest extreme value of the wave height to the last data collection time is denoted as... Where the extreme values ​​are either maximum or minimum values; and the standard deviation of the wave height corresponding to the last acquisition time is calculated and denoted as . This allows us to determine the trend of sea surface smoothness for the buoy, and the corresponding calculation formula is: in, Indicates the trend of sea surface smoothness for the buoy; Indicated by An exponential function with base 0; This refers to the Mann-Kendall trend test algorithm; This represents the standard deviation of the wave height corresponding to the nearest extreme value of the buoy's wave height to the last acquisition time during the acquisition period; This represents the standard deviation of the wave height corresponding to the last data collection moment of the buoy during the data collection period.

[0059] It should be noted that the Mann-Kendall trend test algorithm is a non-parametric statistical test method used to detect whether there is an upward or downward trend in time series data. The result ranges from [value missing]. A value greater than 0 indicates that the standard deviation of the wave height increases over time, a value less than 0 indicates that the standard deviation of the wave height decreases over time, and a value equal to 0 indicates that the trend is stable; furthermore This represents the trend of the standard deviation of wave height at extreme moments relative to the standard deviation of wave height at the last acquisition moment. Specifically, it involves calculating the standard deviation by constructing a window for each moment, and then analyzing the changes from... and The trend of the standard deviation of all times within this time period.

[0060] Specifically, as the wave height increases, the fluctuation also increases, indicating that the buoy currently being analyzed is in a phase of increasing wave height fluctuation, that is... The value of is greater than 0, and the range of values ​​is . The current sea surface smoothness trend is relatively small, indicating frequent wave activity and fluctuating sea surface conditions. This suggests that the area where the buoy is located is unsuitable for signal transmission, so a new buoy for transmitting monitoring data needs to be selected. Conversely, when... The value of is less than 0, and the range of values ​​is . The greater the current sea surface smoothness trend, the calmer the current sea waves and the smaller the environmental changes. At this time, the sea area where the buoy is located is more suitable for communication. Similarly, the sea surface smoothness trend of each buoy can be determined.

[0061] Step S43: Adjust the attitude stability using the sea surface smoothness trend to obtain the expected attitude stability of the buoy.

[0062] Specifically, the formula for calculating the expected attitude stability of the buoy is as follows: in, Indicates the expected stability of the buoy's attitude; Indicates the trend of sea surface smoothness for the buoy; This indicates the degree of stability of the buoy's attitude.

[0063] It can be seen that the higher the trend of sea surface smoothness and the higher the stability of the buoy's attitude, the higher the expected attitude stability during the next communication transmission.

[0064] Further, step S5 includes: Step S51: Draw a scaled-down sea surface grid map based on the latitude and longitude corresponding to the marine environment. Determine the distance between each grid in the sea surface grid map and all the buoys around the corresponding grid. Use the curvature constraint of the buoys to obtain the corrected distance between the grid and the buoys.

[0065] Specifically, a scaled-down sea surface grid is drawn based on latitude and longitude. The position of each buoy is projected onto the grid, and the distance between each grid and all surrounding buoys is calculated. The distance is then corrected using curvature constraints to determine the corrected distance between the grid and the buoy. The corresponding calculation formula is as follows: in, Indicates the first The grid and the first Corrected distance between buoys; Indicates the first The degree of curvature restriction of each buoy; Indicates the first The grid and the first The Euclidean distance between the buoys.

[0066] Step S52: Divide the buoys and grids by correcting the distance, and assign the expected attitude stability of the buoys and the ocean current velocity determined based on the monitoring data to the corresponding grids to obtain the sea surface buoy grid map.

[0067] The explanation is as follows: based on the corrected distance, each grid is assigned to each buoy according to the nearest distance. Then, the expected attitude stability of each buoy and the average value determined by integrating the ocean current velocity at all acquisition times during the acquisition time are assigned to the corresponding grid of the buoy to obtain the sea surface buoy grid map. That is, in the sea surface grid map, the grid that can be represented by the average ocean current velocity and expected attitude stability of each buoy during the acquisition time is determined and assigned values ​​to form the sea surface buoy grid map.

[0068] Furthermore, step S6 includes: Step S61: Determine the time interval based on the time corresponding to the current transmission and the next transmission; record the time interval determined by the two transmissions as... .

[0069] Step S62: Adjust the sea surface buoy grid map based on the time interval and monitoring data to obtain a new sea surface buoy grid map, and obtain the candidate link for the next transmission from the new sea surface buoy grid map.

[0070] It can be explained that in the buoy grid map, as the buoy moves, the grid also changes accordingly. That is, each grid moves according to the ocean current speed and direction. The direction of movement is the ocean current direction, and the distance moved is the result of multiplying the ocean current speed by the time interval between two transmissions, thus obtaining a new buoy grid map. If two grids in the new buoy grid map move to the same grid position, the grid with the highest expected attitude stability between the two grids needs to be reassigned, and the average ocean current speed at the current grid position is confirmed as the value assigned to that grid. In particular, as movement occurs, some grids may move out without new grids being added, resulting in gaps. Therefore, Gaussian filtering is used to smooth the new buoy grid map. That is, a Gaussian function is used as the weight kernel to perform a weighted average of the grids to achieve smoothness, effectively suppressing the interference caused by gaps, and obtaining a more continuous and smooth buoy grid map.

[0071] During the next transmission, the buoy will broadcast a signal according to the hierarchical upload protocol. After receiving the broadcast signal from the final buoy, other buoys will record the buoys that have already propagated and broadcast it, until the gateway receives the broadcast and uses the link through which the broadcast signal passes as the candidate link.

[0072] Step S63: Analyze the pairwise buoys in the candidate links, obtain the selection weights corresponding to the candidate links based on the expected attitude stability, and determine the path cost of the candidate links.

[0073] Specifically, in the new surface buoy grid map, the mean value of the expected attitude stability of the grid through which the line connecting each pair of adjacent buoys in the candidate links passes is determined, and this value is used as the link priority between the two buoys; similarly, the link priority between all pairs of adjacent buoys in each candidate link is determined, and the link priorities are averaged to obtain the selection weight of each candidate link; and the path cost of each candidate link is determined in the same way according to the aforementioned steps.

[0074] Step S64: Select the optimal link for the next transmission by combining the weight and path cost.

[0075] The explanation is as follows: the product of the path cost and the inversely proportional normalized value of the selection weight is used as the corrected path cost of each candidate link. That is, the value obtained by taking the reciprocal of the normalized selection weight is multiplied by the path cost to obtain the corrected path cost of each candidate link. Similarly, the candidate link with the lowest corrected path cost is selected as the optimal link for the next transmission, and the data is transmitted in reverse to the final buoy according to the optimal link. The buoy receiving the optimal link transmits monitoring data according to the link, thus realizing the communication of the ocean observation buoy.

[0076] Understandably, monitoring data is collected by observation sensors installed on buoys to provide stable data support and ensure the comprehensiveness of monitoring data collection. The optimal link for the current transmission is selected from the available links, and the buoy performance within the optimal link is analyzed to determine the curvature limitation of each buoy, thus assessing the impact of the Earth's curvature on buoy data transmission in the marine environment. Then, the buoy's attitude stability is analyzed by examining wave heights during ocean activity, and the expected attitude stability is determined by assessing wave changes. Finally, a buoy grid map is drawn on the sea surface, and candidate links for the next transmission are established and selected. This achieves dynamic optimization of the links and precise adaptation of buoy monitoring data transmission. In other words, it fully considers the impact of buoy attitude stability and the marine environment, and through multi-path selection and optimal selection of links, it controls path costs while meeting low-power operation requirements. Simultaneously, it effectively compensates for the insufficient beam gain of the omnidirectional antenna in the buoy, improving the anti-interference capability of data transmission and ensuring the reliability of the communication link. This provides reliable communication support for the stable operation of marine engineering equipment such as marine resource development, environmental monitoring, and ship communication.

[0077] The second embodiment of the present invention provides an Internet of Things (IoT) networking communication system for marine observation buoys. The system includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The processor calls logical instructions in the memory to execute the IoT networking communication method for marine observation buoys described in any embodiment of the present invention. This system has the same beneficial effects as the aforementioned IoT networking communication method for marine observation buoys, and will not be described in detail here.

[0078] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0079] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An Internet of Things (IoT) networking communication method applied to marine observation buoys, characterized in that, The method includes: Multiple buoys were deployed in the marine environment and equipped with observation sensors to collect monitoring data; Based on the gateway and buoys, several links are formed for transmitting monitoring data. The optimal link for the current transmission is selected, and the buoys and the optimal link are comprehensively analyzed to determine the curvature limitation of the buoys. The attitude stability of the buoy is determined based on the monitoring data; Assess the impact of the marine environment on the buoy, determine the buoy's sea surface smoothness trend, and adjust the attitude stability to obtain the buoy's expected attitude stability. A grid map of sea surface buoys was created by integrating marine environment, buoys, and corresponding curvature constraints. Based on the sea surface buoy grid map, candidate links for the next transmission are constructed, and the optimal link for the next transmission is selected from the candidate links by comprehensively considering the expected attitude stability and path cost.

2. The IoT networking communication method for marine observation buoys according to claim 1, characterized in that, The observation sensors include conductivity sensors, illuminance sensors, Stevenson screen sensors, water temperature sensors, acoustic Doppler current meters, and inertial wave sensors.

3. The IoT networking communication method for marine observation buoys according to claim 1, characterized in that, Based on the gateway and buoys, several links are formed for transmitting monitoring data. The optimal link for the current transmission is selected, and the buoys and the optimal link are comprehensively analyzed to determine the curvature constraint of the buoys, including: Analyze the monitoring data collected by the buoys on each link to determine the corresponding path cost, and select the link with the lowest path cost as the optimal link for the current transmission. Based on the distance between buoys in the optimal link and the height of the buoys, the curvature constraint degree of each buoy in the optimal link can be obtained; Based on each buoy, the buoy closest to the optimal link is identified, the distance between the two buoys is analyzed, and the curvature constraint of each buoy is determined by combining the curvature constraint of the buoys in the optimal link.

4. The IoT networking communication method for marine observation buoys according to claim 3, characterized in that, The path cost includes link quality and link cost. The link quality includes, but is not limited to, evaluation metrics such as the buoy's signal-to-noise ratio, latency, and packet loss rate. The link cost is the interference intensity of the buoy.

5. The IoT networking communication method for marine observation buoys according to claim 3, characterized in that, The curvature constraint for each buoy is determined as follows: Define any buoy other than the buoy in the optimal link as the target buoy, obtain the distance between the target buoy and the nearest buoy in the optimal link, filter the maximum distance based on the distance between each pair of buoys in the optimal link, and obtain the curvature constraint degree corresponding to the nearest buoy in the optimal link. Comprehensively determine the curvature constraint degree of the target buoy.

6. The IoT networking communication method for marine observation buoys according to claim 2, characterized in that, The attitude stability of the buoy was determined based on the monitoring data, specifically as follows: By analyzing monitoring data collected by inertial wave sensors, the wave height of the buoy in the marine environment is determined, and the attitude stability of the buoy is obtained by combining the acquisition time corresponding to the monitoring data.

7. The IoT networking communication method for marine observation buoys according to claim 6, characterized in that, Assess the impact of the marine environment on the buoy, determine the buoy's sea surface smoothness trend, and adjust its attitude stability to obtain the buoy's expected attitude stability, including: Based on the analysis of the marine environment using monitoring data, the direction of ocean currents is determined and the wave height of the buoys within the corresponding collection time is obtained. In the corresponding ocean current direction, the buoy's wave height is combined with the collection time to assess the buoy's wave changes, and the trend of the buoy's sea surface smoothness is determined by the wave changes. The attitude stability of the buoy is adjusted by the trend of sea surface smoothness to obtain the expected attitude stability of the buoy.

8. The Internet of Things (IoT) networking communication method for marine observation buoys according to claim 1, characterized in that, A grid map of sea surface buoys was created by integrating the marine environment, buoys, and corresponding curvature constraints, including: A scaled-down sea surface grid map is drawn based on the latitude and longitude of the marine environment. The distance between each grid and all the buoys around the corresponding grid is determined. The corrected distance between the grid and the buoy is obtained by using the curvature constraint of the buoys. By correcting the distance, the buoys are divided into corresponding grids, and the expected attitude stability of the buoys and the ocean current velocity determined based on monitoring data are assigned to the corresponding grids to obtain a sea surface buoy grid map.

9. The Internet of Things (IoT) networking communication method for marine observation buoys according to claim 4, characterized in that, Based on the sea surface buoy grid map, candidate links for the next transmission are constructed. The optimal link for the next transmission is selected from these candidate links, considering both expected attitude stability and path cost. This includes: The time interval is determined based on the time corresponding to the current transmission and the next transmission; The sea surface buoy grid map is adjusted based on the time interval and monitoring data to obtain a new sea surface buoy grid map. The candidate link for the next transmission is obtained from the new sea surface buoy grid map. Analyze the pairwise buoys in the candidate links, obtain the selection weights of the candidate links based on the expected attitude stability, and determine the path cost of the candidate links; The optimal link for the next transmission is obtained by comprehensively selecting weights and path costs.

10. An Internet of Things (IoT) networking communication system applied to marine observation buoys, characterized in that, The system includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other through the communication bus. The processor calls logical instructions in the memory to execute the Internet of Things networking communication method for marine observation buoys as described in any one of claims 1 to 9.

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