An AUV adaptive networking communication method and system

Abstract

The application provides an AUV adaptive networking communication method and system, which is suitable for the field of communication technology. The method comprises the following steps: obtaining AUV networking topology information according to AUV device position information, AUV device running state monitoring information, AUV routing channel occupation monitoring information, AP signal attenuation monitoring information and an AUV networking topology generation model; and generating target AUV networking communication parameter information according to the AUV device position information, the AUV device running state monitoring information, the AUV routing channel occupation monitoring information, the AP signal attenuation monitoring information, the AUV networking topology information, AUV networking communication transmission parameter benchmark value information, an initial AUV networking communication parameter generation model, historical AUV networking communication parameter information, AUV networking communication demand discrimination threshold value information and a target AUV networking communication parameter generation model, so as to perform AUV networking communication control processing. The application improves the communication abnormal problems caused by AUV device roaming switching lag, channel interference and the like in a complex industrial environment.

Description

Technical Field

[0001] This application belongs to the field of communication technology, and in particular relates to AUV adaptive networking communication methods and systems. Background Technology

[0002] With the large-scale implementation of automated material handling systems in smart manufacturing plants, clustered mobile autonomous transport equipment has become the core execution carrier for material transfer and warehousing sorting in workshops. The continuous cross-regional mobile operation of equipment clusters places stringent requirements on the real-time performance, roaming stability, and multi-node concurrent carrying capacity of the plant's wireless communication network.

[0003] In existing technologies, multiple industrial wireless APs are deployed throughout the factory area. All AP devices are connected to the factory's wired backbone network via switches. The AC controller establishes a communication link with the switches to complete the unified management of all AP devices. However, in scenarios where large-scale mobile equipment clusters move synchronously, phenomena such as channel occupancy conflicts, cross-AP roaming packet loss, and control command transmission delay fluctuations cannot be automatically suppressed by existing solutions. Summary of the Invention

[0004] In view of this, embodiments of this application provide an AUV adaptive networking communication method and system, which aims to solve the problems of low accuracy in network topology generation and matching, and the inability to automatically suppress roaming packet loss and latency fluctuations in large-scale cluster mobile scenarios in existing wireless networking schemes for mobile transportation equipment in factory areas.

[0005] The first aspect of this application provides an AUV adaptive networking communication method, including: Acquire location information of multiple AUV devices, operational status monitoring information of multiple AUV devices, channel occupancy monitoring information of multiple AUV devices, signal attenuation monitoring information of multiple AP devices, and historical AUV networking communication parameters. Based on the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the signal attenuation monitoring information of multiple APs, and the preset AUV networking topology generation model, multiple AUV networking topology information is calculated. Based on the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the signal attenuation monitoring information of multiple APs, the network topology information of multiple AUVs, the preset AUV network communication transmission parameter baseline information, and the preset initial AUV network communication parameter generation model, multiple initial AUV network communication parameter information are generated. Based on the multiple initial AUV networking communication parameter information, multiple historical AUV networking communication parameter information, multiple preset AUV networking communication demand discrimination threshold information, and a preset target AUV networking communication parameter generation model, multiple target AUV networking communication parameter information are generated to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

[0006] A second aspect of this application provides an AUV adaptive networking communication system, including: The information acquisition module is used to acquire multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and multiple historical AUV networking communication parameter information. The AUV network topology information generation module is used to calculate multiple AUV network topology information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and a preset AUV network topology generation model. The initial AUV network communication parameter information generation module is used to generate multiple initial AUV network communication parameter information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, multiple AUV network topology information, preset AUV network communication transmission parameter baseline value information, and preset initial AUV network communication parameter generation model. The target AUV networking communication parameter information generation module is used to generate multiple target AUV networking communication parameter information based on the multiple initial AUV networking communication parameter information, multiple historical AUV networking communication parameter information, multiple preset AUV networking communication requirement discrimination threshold information, and a preset target AUV networking communication parameter generation model, so as to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

[0007] A third aspect of this application provides a terminal device, the terminal device including a memory and a processor, the memory storing a computer program executable on the processor, and the processor executing the computer program to implement the steps of the AUV adaptive networking communication method described in the first aspect above.

[0008] A fourth aspect of this application provides a computer-readable storage medium, comprising: storing a computer program, wherein when executed by a processor, the computer program implements the steps of the AUV adaptive networking communication method as described in the first aspect above.

[0009] Compared with the prior art, the beneficial effects of this application are as follows: This application can complete intelligent topology generation and parameter iterative optimization based on multi-dimensional AUV network monitoring data in the factory area, effectively improving the matching accuracy of AUV cluster network topology and the control accuracy of network communication parameters in industrial plants, improving communication anomalies caused by AUV equipment roaming and switching lag, channel interference, and signal attenuation in complex industrial environments, and providing reliable technical support for stable, efficient, and adaptive network communication operations of clustered AUV equipment in the factory area. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment 1 of this application; Figure 2 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment 2 of this application; Figure 3 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment 4 of this application; Figure 5 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment 5 of this application; Figure 6 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment Six of this application; Figure 7 This is a schematic diagram illustrating the implementation process of the AUV adaptive networking communication method provided in Embodiment 7 of this application; Figure 8 This is a schematic diagram of the structure of the AUV adaptive networking communication system provided in the embodiments of this application; Figure 9 This is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed Implementation

[0012] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0013] To illustrate the technical solution described in this application, specific embodiments are provided below.

[0014] Figure 1 A flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment 1 of this application is shown below, and is described in detail below: Step S101: Obtain multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and multiple historical AUV networking communication parameter information.

[0015] In this embodiment, the AUV adaptive networking communication control task can be retrieved from the factory's industrial WiFi 6 networking operation and maintenance platform. The location information of multiple AUV devices refers to the spatial location data of multiple AUV devices collected in the AUV networking communication control task. Specifically, it can be the real-time positioning coordinate data of multiple AUV devices within the factory's operating area, which can be obtained by connecting to the factory's high-precision positioning system and vehicle-mounted positioning acquisition module. The operating status monitoring information of multiple AUV devices can be the operational data of multiple AUV devices collected in the AUV networking communication control task, specifically the operating load, movement speed, operating conditions, and online status of multiple AUV devices, which can be obtained through real-time reporting by the AUV vehicle-mounted status acquisition terminal. The routing channel occupancy monitoring information of multiple AUV devices can be the factory's wireless channel resource occupancy data collected in the AUV networking communication control task, specifically the location data of each Wi-Fi 6 device within the factory's operating area. Information such as the frequency band occupied by the Fi6 industrial routing channel, the duration of channel occupation, and the number of channel collisions can be obtained through the AC controller channel monitoring module and the cloud network operation and maintenance platform. Multiple AP signal attenuation monitoring information can be the signal loss data of the factory wireless access points collected in the AUV networking communication control task, specifically the signal transmission loss, signal coverage strength, long-distance attenuation, and other related information of each factory industrial AP, which can be obtained through the AP status monitoring system and the vehicle-mounted routing signal acquisition unit. Multiple historical AUV networking communication parameter information can be a set of networking parameter records obtained by the network system and arranged in chronological order throughout the entire process from the initial networking access of multiple AUV devices to the end of dynamic networking control in multiple factory AUV networking communication control tasks. This can be used to represent the past networking communication control situation of multiple AUV devices and can be stored in the form of a time sequence. Understandably, multiple historical AUV network communication parameter information can be included in the AUV network communication parameter data recorded by the network system in different time periods before the current network communication parameters of multiple AUV devices are adjusted and calculated. This data can include different parameter types such as normal network parameters, interference adaptation network parameters, and roaming switching network parameters, to indicate the AUV network communication status under different operating conditions in the past, and is stored in the form of time series.

[0016] Step S102: Based on the location information of the multiple AUV devices, the operating status monitoring information of the multiple AUV devices, the occupancy monitoring information of the multiple AUV routing channels, the signal attenuation monitoring information of the multiple APs, and the preset AUV network topology generation model, calculate the multiple AUV network topology information.

[0017] In this embodiment, the preset AUV network topology generation model can be manually preset and can employ a graph neural network (GNN) model. This model can be trained on a large amount of AUV cluster network sample data in a factory area and possesses the ability to perform topology deduction based on the spatial location association of multiple devices, channel status matching, and signal attenuation adaptation. First, the collected location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the channel occupancy monitoring information of multiple AUV routes, and the signal attenuation monitoring information of multiple APs can be aggregated. Then, the multi-dimensional monitoring data is uniformly input into the preset AUV network topology generation model. The model then performs in-depth analysis of the correlation between the spatial distribution, operating conditions, channel occupancy, and signal loss of multiple devices, thereby completing the association matching and topology architecture deduction of the AUV device cluster network nodes in the factory area, and calculating multiple AUV network topology information adapted to the current factory operating environment.

[0018] Step S103: Generate multiple initial AUV network communication parameter information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, multiple AUV network topology information, preset AUV network communication transmission parameter baseline value information, and preset initial AUV network communication parameter generation model.

[0019] In this embodiment, the preset AUV network communication transmission parameter baseline information can be preset manually. Specifically, the values ​​are: standard network transmission rate of 1000Mbps, channel bandwidth of 80MHz, signal transmission gain of 20dBi, and data transmission delay of 20ms. These are the standard parameter thresholds for AUV network communication under normal operating conditions in the factory. The preset initial AUV network communication parameter generation model can also be preset manually. It can adopt a CNN convolutional neural network model, which can be obtained by training on a large number of AUV network parameter sample data in the factory. It has the ability to fuse multi-dimensional network data and fit initial parameters. First, the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the signal attenuation monitoring information of multiple APs, and the network topology information of multiple AUVs can be fused together in a global data fusion process. Then, the fused network status data is compared and matched with the preset AUV network communication transmission parameter benchmark information. The matched dataset is then input into the preset initial AUV network communication parameter generation model for parameter fitting and preliminary correction, thereby completing the preliminary calculation of network parameters for multi-dimensional working condition adaptation and generating multiple initial AUV network communication parameter information.

[0020] Step S104: Based on the multiple initial AUV networking communication parameter information, multiple historical AUV networking communication parameter information, multiple preset AUV networking communication demand discrimination threshold information, and a preset target AUV networking communication parameter generation model, generate multiple target AUV networking communication parameter information, so as to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

[0021] In this embodiment, multiple preset AUV networking communication requirement discrimination thresholds can be manually preset. Specifically, their values ​​are: a networking transmission stability threshold of 95%, a channel interference tolerance threshold of 5%, a roaming handover delay threshold of 30ms, and a data packet loss rate threshold of 0.1%. These thresholds are used to accurately determine the adaptability and stability of the initial networking parameters. The preset target AUV networking communication parameter generation model can also be manually preset. It can adopt a Long Short-Term Memory (LSTM) network model, which can combine time-series historical data to complete dynamic iterative optimization of parameters and improve the environmental adaptability of networking parameters. First, multiple initial AUV networking communication parameters and multiple historical AUV networking communication parameters can be fused together in a time series to construct a multi-device networking parameter time series. Then, combined with multiple preset AUV networking communication requirement discrimination thresholds, the stability, adaptability, and anti-interference of the initial networking parameters can be verified and analyzed. The verified time series parameter dataset is then input into a preset target AUV networking communication parameter generation model for deep iterative optimization, thereby correcting the deviations and defects of the initial networking parameters. This generates multiple target AUV networking communication parameters that are adapted to the complex working conditions of the current plant area. Based on the accurate target parameters, the adaptive networking communication control processing of the plant AUV cluster can be completed.

[0022] The AUV adaptive networking communication method provided in this application can complete intelligent topology generation and parameter iterative optimization based on multi-dimensional AUV networking monitoring data in the factory area. It effectively improves the matching accuracy of the AUV cluster networking topology and the control accuracy of networking communication parameters in industrial plants, and improves the communication anomalies caused by AUV equipment roaming and switching lag, channel interference, and signal attenuation in complex industrial environments. It provides reliable technical support for stable, efficient, and adaptive networking communication operations of clustered AUV equipment in the factory area.

[0023] Figure 2 The flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment 2 of this application is shown. The difference between this method and Embodiment 1 described above is that: The location information of the multiple AUV devices includes the location information of a first AUV device and the location information of a second AUV device; The first AUV device location information includes the first AUV device access location coordinate information, the first AUV device update location coordinate information, the first AUV device location access time information, and the first AUV device location update time information; The second AUV device location information includes the second AUV device access location coordinate information, the second AUV device update location coordinate information, the second AUV device location access time information, and the second AUV device location update time information; Step S102 specifically includes: Step S201: Based on the access location coordinates of the first AUV device, the updated location coordinates of the first AUV device, the access location coordinates of the second AUV device, and the updated location coordinates of the second AUV device, calculate the spatial distance information of the first AUV device and the spatial distance information of the second AUV device.

[0024] In this embodiment, the location information of the first AUV device and the location information of the second AUV device can be two sets of location record data of different AUV devices collected in the AUV network communication control task. That is, two sets of typical AUV device location data obtained after the network control task starts and before the monitoring data collection ends. It can be understood that the plant network control task includes the location data of multiple AUV devices. This embodiment only selects two sets of data for illustrative purposes. Among them, the access location coordinate information of the first AUV device and the access location coordinate information of the second AUV device are used to represent the initial spatial coordinate positions of the two AUV devices when they access the plant network. The updated location coordinate information of the first AUV device and the updated location coordinate information of the second AUV device are used to represent the updated spatial coordinate positions of the two AUV devices during real-time operation. Then, by using the spatial coordinate difference calculation method, the difference between the access coordinates and the updated coordinates of the two AUV devices is calculated, and the spatial movement distance data of each device is obtained. This allows for the accurate quantification of the real-time movement amplitude of the two AUV devices, thereby calculating the spatial movement distance information of the first AUV device and the spatial movement distance information of the second AUV device.

[0025] Step S202: Based on the first AUV device location access time information, the first AUV device location update time information, the second AUV device location access time information, and the second AUV device location update time information, calculate the first AUV device location update time interval information and the second AUV device location update time interval information.

[0026] In this embodiment, the location access time information of the first AUV device and the location access time information of the second AUV device are used to represent the time nodes when the two AUV devices access the factory network and complete the location initialization data collection, respectively. The location update time information of the first AUV device and the location update time information of the second AUV device are used to represent the time nodes when the two AUV devices complete the latest location data collection and update, respectively. Then, by using the time difference calculation method, the difference between the access time and the update time of the two AUV devices is calculated, and the location data update cycle duration of each device is obtained. This allows for the precise quantification of the location update frequency of the two AUV devices, thereby calculating the location update time interval information of the first AUV device and the location update time interval information of the second AUV device.

[0027] Step S203: Calculate the AUV device position movement distance difference information based on the first AUV device position movement distance information and the second AUV device position movement distance information.

[0028] In this embodiment, the spatial distance information of the first AUV device and the spatial distance information of the second AUV device record the spatial movement amplitude data of the two AUV devices within the same monitoring period. Then, the difference between the two sets of spatial movement distance data is compared and calculated to obtain the movement amplitude difference data between the two AUV devices, thereby accurately reflecting the differentiated characteristics of the operation movement of different AUV devices, and thus calculating the spatial distance difference information of the AUV device position.

[0029] Step S204: Calculate the AUV device location update time interval difference information based on the first AUV device location update time interval information and the second AUV device location update time interval information.

[0030] In this embodiment, the first AUV device location update time interval information and the second AUV device location update time interval information respectively record the update cycle duration data of the location data of the two AUV devices. Then, the difference comparison calculation is performed on the two sets of time interval data to obtain the location update frequency difference data between the two AUV devices, thereby accurately reflecting the differentiated characteristics of the location data collection and update of different AUV devices, and thus calculating the AUV device location update time interval difference information.

[0031] Step S205: Generate multiple AUV device location information to be networked based on the updated location coordinate information of the first AUV device, the updated location coordinate information of the second AUV device, the spatial distance difference information of the AUV device location movement, the spatial distance difference information of the AUV device location update time interval, the preset AUV device location spatial distance difference threshold, and the preset AUV device location update time interval difference threshold.

[0032] In this embodiment, the preset threshold for the spatial distance difference of AUV equipment location movement can be manually preset, specifically set to 1.5m, and is used to determine the differentiated adaptation networking conditions for different AUV equipment movement amplitudes. The preset threshold for the AUV equipment location update time interval difference can also be manually preset, specifically set to 500ms, and is used to determine the differentiated adaptation networking conditions for different AUV equipment location update frequencies. First, the updated location coordinate information of the first AUV equipment and the updated location coordinate information of the second AUV equipment can be integrated to clarify the real-time spatial distribution of the two AUV equipment. Then, the spatial distance difference information of AUV equipment location movement and the AUV equipment location update time interval difference information are compared and verified with the two preset thresholds respectively. AUV equipment location data that meets the plant area networking adaptation conditions are then selected. Invalid location data is then eliminated, and valid location data is integrated and classified, thereby generating multiple AUV equipment location information to be networked to meet the networking control requirements.

[0033] Step S206: Based on the location information of the multiple AUV devices to be networked, the operating status monitoring information of the multiple AUV devices, the occupancy monitoring information of the multiple AUV routing channels, the signal attenuation monitoring information of the multiple APs, and the preset AUV network topology generation model, calculate the multiple AUV network topology information.

[0034] In this embodiment, the preset AUV network topology generation model can be manually preset and can adopt a graph neural network (GNN) model, which has the ability to perform multi-dimensional topology correlation and inference based on the spatial location, operating status, channel status, and signal status of multiple devices. First, the location information of multiple AUV devices to be networked can be used as the spatial basis data. Then, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, and the signal attenuation monitoring information of multiple APs in the corresponding time period can be matched to complete the accurate matching and integration of multi-dimensional network basis data. The integrated global network data is then input into the preset AUV network topology generation model to complete the association and pairing of multiple device network nodes, link construction, and topology optimization, thereby calculating multiple AUV network topology information adapted to the real-time operating conditions of the factory area.

[0035] The AUV adaptive networking communication method provided in this application effectively avoids interference from invalid location data in the generation of network topology, improves the matching degree between the AUV network topology architecture and the real-time operating conditions of equipment in the plant area, and ensures the accuracy and adaptability of subsequent network communication parameter adjustment, thereby enhancing the stability and reliability of AUV cluster adaptive networking communication in industrial plants.

[0036] Figure 3The flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment 3 of this application is shown. Its difference from Embodiment 2 described above lies in: The location information of the AUV device to be networked includes the updated location coordinates of the AUV device to be networked, the location access time of the AUV device to be networked, and the location update time of the AUV device to be networked. Step S206 specifically includes: Step S301: Based on the location access time information and location update time information of the multiple AUV devices to be networked, perform time-series filtering on the multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, and multiple AP signal attenuation monitoring information to generate multiple filtered AUV device operation status monitoring information, multiple filtered AUV routing channel occupancy monitoring information, and multiple filtered AP signal attenuation monitoring information.

[0037] In this embodiment, the location access time information and location update time information of multiple AUV devices to be networked can define the effective collection time sequence interval of each group of AUV device location data to be networked, which can be used to match the plant network and equipment operation monitoring data under the corresponding time sequence. First, based on the effective time sequence interval corresponding to each AUV device to be networked, time sequence matching and screening can be performed on the multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, and multiple AP signal attenuation monitoring information collected across the entire area. Monitoring data with mismatched time sequences, abnormal collection, and invalid redundancy can be eliminated. Then, the effective monitoring data that completely corresponds to the location time sequence of the AUV devices to be networked is retained, thereby completing the precise time sequence filtering of multi-dimensional monitoring data, and generating multiple filtered AUV device operation status monitoring information, multiple filtered AUV routing channel occupancy monitoring information, and multiple filtered AP signal attenuation monitoring information.

[0038] Step S302: Based on the updated location coordinate information of the multiple AUV devices to be networked, the location access time information of the multiple AUV devices to be networked, the location update time information of the multiple AUV devices to be networked, the operating status monitoring information of the multiple filtered AUV devices, the routing channel occupancy monitoring information of the multiple filtered AUV devices, the signal attenuation monitoring information of the multiple filtered AP devices, and the preset AUV network topology generation model, calculate multiple AUV network topology information.

[0039] In this embodiment, the preset AUV network topology generation model can be manually preset and can employ a graph neural network (GNN) model, capable of performing refined network topology deduction based on multi-dimensional temporal matching data. First, it integrates the update location coordinates of multiple AUV devices to be networked, the location access time information of multiple AUV devices to be networked, and the location update time information of multiple AUV devices to be networked, constructing a complete spatiotemporal location dataset of the devices to be networked. Then, it correlates and matches the time-series filtered AUV device operation status monitoring information, multiple filtered AUV routing channel occupancy monitoring information, and multiple filtered AP signal attenuation monitoring information. Finally, the integrated spatiotemporal multi-dimensional network data is input into the preset AUV network topology generation model, thereby completing a comprehensive correlation modeling of device spatial location, operating conditions, channel status, and signal loss, and calculating multiple AUV network topology information that accurately matches the device's temporal operating conditions.

[0040] The AUV adaptive networking communication method provided in this application effectively solves the problem of topology calculation deviation caused by time-series data misalignment and invalid data interference, significantly improves the matching degree between AUV networking topology information and equipment real-time operating time-series conditions, and provides a precise data foundation for the accurate generation and adaptive control of subsequent networking communication parameters, thereby improving the dynamic adaptation capability of AUV cluster networking communication in the plant area.

[0041] Figure 4 The flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment 4 of this application is shown. The difference between this method and Embodiment 3 is that step S103 specifically includes: Step S401: Perform topology spatial location matching processing based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AUV network topology information, and multiple filtered AP signal attenuation monitoring information to generate multiple AUV network topology spatial location signal attenuation information.

[0042] In this embodiment, the network node architecture and link distribution of the AUV equipment cluster in the factory area can be determined first by using multiple AUV network topology information. Then, the network topology architecture is spatially matched with multiple AUV equipment location information, multiple AUV equipment operation status monitoring information, and multiple AUV routing channel occupancy monitoring information. Then, combined with multiple filtered AP signal attenuation monitoring information, the signal attenuation loss of each network node at its corresponding spatial location is matched one by one. This quantifies the signal attenuation differences of different topology links and different equipment spatial locations, thereby generating multiple AUV network topology spatial location signal attenuation information.

[0043] Step S402: Based on the signal attenuation information of the multiple AUV network topology spatial location, select and process the preset AUV network communication transmission parameter reference value information to generate multiple selected AUV network communication transmission parameter reference value information.

[0044] In this embodiment, the preset AUV network communication transmission parameter reference values ​​can be manually preset, including reference parameters for weak attenuation scenarios, medium attenuation scenarios, and strong attenuation scenarios. Specifically, the reference transmission rate for weak attenuation scenarios is 1000Mbps and the channel bandwidth is 80MHz; for medium attenuation scenarios, the reference transmission rate is 800Mbps and the channel bandwidth is 40MHz; and for strong attenuation scenarios, the reference transmission rate is 500Mbps and the channel bandwidth is 20MHz. The signal attenuation level of each network link can be determined first based on the signal attenuation information of multiple AUV network topology spatial locations. Then, the corresponding reference values ​​for network communication transmission parameters corresponding to different attenuation levels can be matched. This allows for precise filtering and selection of the reference parameters, adapting to the signal loss characteristics of different topology locations, thereby generating multiple selected AUV network communication transmission parameter reference values.

[0045] Step S403: Based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, multiple AUV network topology information, and the preset initial AUV network communication parameter generation model, generate multiple AUV network communication parameter information to be corrected.

[0046] In this embodiment, the preset initial AUV network communication parameter generation model can be manually preset and can adopt a CNN convolutional neural network model, which can realize the fusion calculation and initial parameter generation of multi-dimensional network operating condition data. First, multiple AUV device location information, multiple AUV device operating status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and multiple AUV network topology information can be collected and integrated to construct a full-domain network operating condition dataset. Then, this full-domain network operating condition dataset is input into the preset initial AUV network communication parameter generation model. The model then completes the fusion analysis of multi-dimensional data and preliminary parameter fitting, thereby outputting network parameter data adapted to the basic operating conditions, thus generating multiple AUV network communication parameter information to be corrected.

[0047] Step S404: Based on the multiple selected AUV network communication transmission parameter reference values, the multiple AUV network communication parameter information to be corrected is modified to generate multiple initial AUV network communication parameter information.

[0048] In this embodiment, multiple selected AUV network communication transmission parameter reference values ​​can be used as parameter calibration standards for each topology link. Then, the deviation values ​​of multiple AUV network communication parameter information to be corrected are compared with the corresponding reference parameters one by one. Then, adaptive compensation and correction are performed for parameter deviations caused by signal attenuation, channel occupancy, and equipment operating conditions, thereby eliminating the operating condition adaptation deviation of the initial parameters and generating multiple initial AUV network communication parameter information with high precision adapted to the plant topology operating conditions.

[0049] The AUV adaptive networking communication method provided in this application effectively solves the problem that traditional fixed reference parameters cannot adapt to signal loss in different spatial locations within the factory area, significantly improving the accuracy and scenario adaptability of initial AUV networking communication parameter information, and laying a data foundation for subsequent iterative optimization of target networking parameters.

[0050] Figure 5 The flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment 5 of this application is shown. The difference between this method and Embodiment 1 is that step S104 specifically includes: Step S501: Based on multiple preset AUV networking communication requirement discrimination threshold information, the multiple initial AUV networking communication parameter information and multiple historical AUV networking communication parameter information are processed to obtain multiple discriminated AUV networking communication parameter information.

[0051] In this embodiment, multiple preset AUV networking communication requirement discrimination thresholds can be manually preset. Specifically, these thresholds are set to a network transmission stability threshold of 95%, a channel interference tolerance threshold of 5%, a roaming handover delay threshold of 30ms, and a data packet loss rate threshold of 0.1%. These thresholds are used to determine the network adaptability and stability of initial and historical parameters. First, multiple initial AUV networking communication parameters are compared with multiple historical AUV networking communication parameters using a time-series fusion method. Then, combined with the multiple preset AUV networking communication requirement discrimination thresholds, a comprehensive discrimination is performed on the transmission stability, anti-interference capability, roaming delay, and packet loss performance of the two sets of parameters. Valid parameters that meet the threshold standards are then selected, and parameters with deviations are marked for optimization. This completes the hierarchical discrimination and classification of networking parameters, resulting in multiple discriminated AUV networking communication parameter information.

[0052] Step S502: Based on the multiple discriminated AUV networking communication parameter information and the preset target AUV networking communication parameter generation model, generate multiple target AUV networking communication parameter information, so as to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

[0053] In this embodiment, the preset target AUV network communication parameter generation model can be manually preset and can adopt a Long Short-Term Memory (LSTM) network model, which has the ability to iteratively optimize time-series parameters and dynamically adapt and adjust them. Multiple discriminated AUV network communication parameter information can be completely input into the preset target AUV network communication parameter generation model. Then, by combining the model with historical time-series parameter patterns, the deviation of the initial parameters is iteratively optimized and adaptively adjusted. Finally, the optimal network parameters adapted to the current complex electromagnetic interference, dynamic equipment movement, and differential signal attenuation conditions in the plant area are output, thereby achieving precise iterative upgrades of the network parameters and generating multiple target AUV network communication parameter information. Based on the multiple target AUV network communication parameter information, the refined adaptive network communication control processing of the plant's AUV cluster is completed.

[0054] The AUV adaptive networking communication method provided in this application effectively compensates for the AUV operating condition adaptation defects of the initial networking parameters, improves the dynamic adaptability and stability of the target networking communication parameters, and thus accurately adapts to the complex operating scenarios of high-speed movement, multiple interferences, and differentiated signal attenuation of AUV equipment clusters in the factory area, significantly improving the overall performance and control accuracy of AUV adaptive networking communication in industrial plants.

[0055] Figure 6 The flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment Six of this application is shown. The difference between this method and Embodiment One is that, after step S104, the method further includes: Step S601: Based on the preset AUV networking communication parameter feature extraction model, feature extraction processing is performed on the multiple target AUV networking communication parameter information to generate multiple AUV networking communication parameter feature information.

[0056] In this embodiment, the preset AUV networking communication parameter feature extraction model can be manually preset and can employ a CNN convolutional neural network model, capable of finely extracting and classifying the multidimensional features of networking communication parameters. The feature extraction process can utilize network parameter signal processing techniques, such as extracting steady-state features of network transmission rate and communication delay through steady-state parameter analysis, and extracting dynamic fluctuation features of channel bandwidth and signal gain through dynamic parameter differential analysis; or capturing the anti-interference features, roaming adaptation features, and cluster concurrency features of target networking parameters through data feature clustering. Multiple AUV networking communication parameter feature information can include AUV networking communication steady-state parameter feature information, AUV networking communication dynamic fluctuation feature information, AUV networking channel adaptation feature information, and AUV cluster concurrency communication feature information, which can comprehensively characterize the operational characteristics of the current target AUV networking communication parameters.

[0057] Step S602: Based on the preset mapping relationship between AUV networking communication parameter characteristics and AP channel allocation control parameters, generate multiple AP channel allocation control parameter information according to the multiple AUV networking communication parameter characteristic information.

[0058] In this embodiment, the preset mapping relationship between AUV networking communication parameter characteristics and AP channel allocation control parameters can be pre-set manually. This is based on a multi-dimensional rule matrix library constructed from a large amount of actual plant networking test data, specifically covering multiple levels of corresponding parameter values: If the AUV networking communication parameter characteristics are high stability and low interference, then control parameters for fixed bandwidth channel allocation and static channel locking are generated, with a fixed channel bandwidth of 80MHz; if the AUV networking communication parameter characteristics are dynamic fluctuation and multi-device concurrency, then control parameters for dynamic bandwidth adaptation and channel rotation allocation are generated, with dynamic bandwidth adaptation ranging from 20MHz to 80MHz and a channel rotation period of 100ms. The preset mapping relationship can be obtained through multiple plant networking experiments. The specific construction process can be as follows: First, extract the AUV networking communication parameter characteristics under different operating conditions, and simultaneously set gradient combinations for the AP channel allocation control parameters. Channel bandwidth is set at three levels: 20MHz, 40MHz, and 80MHz; channel switching period is set at three levels: 50ms, 100ms, and 200ms. Subsequently, packet loss rate and latency fluctuation data of network communication under different combinations of characteristics and channel parameters were collected. With the optimization goal of network packet loss rate less than 0.1% and latency fluctuation less than 20 milliseconds, the final mapping rules were determined through statistical analysis. Then, parameter matching was completed based on the precise correspondence, thereby generating multiple AP channel allocation control parameter information adapted to the current network status.

[0059] Step S603: Based on the preset mapping relationship between AUV networking communication parameter characteristics and routing transmit power control parameters, generate multiple routing transmit power control parameter information according to the multiple AUV networking communication parameter characteristic information.

[0060] In this embodiment, the preset mapping relationship between AUV network communication parameters and routing transmit power control parameters can be manually preset. To adapt to the hierarchical linkage rule base for complex operating conditions in the factory area and to possess fine-grained power regulation capabilities, specific parameter values ​​include: 15 dBi for short-range, low-attenuation network scenarios; 20 dBi for medium-range, conventional-attenuation network scenarios; and 25 dBi for long-range, high-attenuation, interference-prone network scenarios. If the AUV network communication parameters show low signal attenuation and low channel occupancy, low-power voltage regulation routing transmit power control parameters are generated; if the AUV network communication parameters show high signal attenuation and strong channel interference, high-power gain compensation routing transmit power control parameters are generated. The routing transmit power control parameter information is sent to the AUV vehicle-mounted routing equipment and the factory AP routing equipment in the form of electrical signal commands, thereby achieving coordinated adaptation of the network transmit power with the current network communication status, and generating multiple routing transmit power control parameter information.

[0061] The AUV adaptive networking communication method provided in this application embodiment realizes multi-dimensional linkage control of target networking parameters, AP channel allocation, and route transmission power. By constructing a precise mapping mechanism between networking parameter characteristics and wireless resource regulation parameters, it improves the anti-interference capability and dynamic adaptation performance of AUV cluster networking in the factory area, and ensures the stability and accuracy of networking communication in complex industrial scenarios.

[0062] Figure 7 The flowchart illustrating the implementation of the AUV adaptive networking communication method provided in Embodiment Seven of this application is shown. The difference between this method and Embodiment Six is ​​that, after step S603, the method further includes: Step S701: Obtain the location information of multiple controlled AUV devices, the operation status monitoring information of multiple controlled AUV devices, and the occupancy monitoring information of multiple controlled AUV routing channels.

[0063] In this embodiment, the location information of multiple controlled AUV devices can be the real-time spatial positioning coordinates of the AUV cluster devices in the factory area after the AP channel allocation control and route transmit power control adjustment are completed. This data can be acquired in real time through the factory's high-precision positioning system and the AUV vehicle-mounted positioning acquisition module, reflecting the real-time spatial distribution status of each AUV device after adjustment. The operational status monitoring information of multiple controlled AUV devices can be the real-time operating load, movement speed, working conditions, and online status data of multiple AUV devices after the radio frequency parameter adjustment is completed. This data can be continuously reported by the AUV vehicle-mounted status acquisition terminal, characterizing the operational status of the devices after adjustment. The channel occupancy monitoring information of multiple controlled AUV devices can be the channel occupancy frequency band, channel occupancy duration, and channel collision count data of each wireless route in the factory area after the channel and power parameter adjustment is completed. This data can be collected in real time through the AC controller channel monitoring module and the cloud network operation and maintenance platform, accurately reflecting the wireless channel resource occupancy status after adjustment.

[0064] Step S702: Send the location information of the multiple controlled AUV devices, the operating status monitoring information of the multiple controlled AUV devices, and the routing channel occupancy monitoring information of the multiple controlled AUV devices to the terminal device.

[0065] In this embodiment, the terminal device can be an intelligent monitoring terminal held by the plant network maintenance personnel, a plant central control display screen, or a cloud-based maintenance management platform. It can process the collected AUV location information, AUV operating status monitoring information, and AUV routing channel occupancy monitoring information after control through data cleaning, format integration, and visualization conversion. Then, the integrated monitoring data is transmitted in real-time to the terminal device via the plant's industrial wireless communication module, allowing maintenance personnel to view the AUV network operation effect and channel resource adaptation status after RF parameter adjustment in real time.

[0066] In this embodiment, preferably, after the location information of multiple controlled AUV devices, the operational status monitoring information of multiple controlled AUV devices, and the routing channel occupancy monitoring information of multiple controlled AUV devices are sent to the terminal device, it is further possible to determine whether the current AUV network communication status has undergone optimization changes compared to before the adjustment, based on the location information of multiple controlled AUV devices, the operational status monitoring information of multiple controlled AUV devices, and the routing channel occupancy monitoring information of multiple controlled AUV devices. Based on the multi-dimensional monitoring data after adjustment, the current network adaptation status is judged, the target AUV network communication parameter information is regenerated iteratively, and the AP channel allocation control parameter information and the routing transmit power control parameter information are adjusted a second time based on the regenerated target AUV network communication parameter information. The stable state of the network communication after adjustment can be judged in real time using a preset network communication quality discrimination threshold and a random forest algorithm.

[0067] For example, if the network state before adjustment is determined to be high-interference and high-latency, and channel conflicts and excessive latency fluctuations still exist after adjustment, it indicates that the current network parameters and RF control parameters are poorly matched. Therefore, it is necessary to iteratively optimize the network parameters based on real-time equipment operation data and channel occupancy data to generate target AUV network communication parameters adapted to high-interference scenarios. Then, based on the new network parameters, the AP channel allocation control parameters and route transmit power control parameters are updated synchronously to suppress channel interference and reduce communication latency. Conversely, if the network state before adjustment is determined to be high-interference and high-latency, and the network state reaches a stable standard after adjustment, it indicates that the current control parameters are well-suited. Therefore, all current network parameters and RF control parameters can be kept unchanged, or the parameters can be fine-tuned to further optimize network performance, thereby maintaining a stable AUV cluster network communication state. For example, if the network status is determined to be stable before regulation, but the network status shows slight fluctuations after regulation, it indicates that the adaptability of the current radio frequency regulation parameters has decreased. Therefore, it is necessary to regenerate the target network parameters by combining real-time equipment location and channel occupancy data, and simultaneously correct the channel allocation and transmit power parameters to quickly restore the stable state of network communication.

[0068] The AUV adaptive networking communication method provided in this application embodiment not only facilitates operation and maintenance personnel to monitor the networking and control effect of the AUV cluster in the plant in real time, but also provides data support for the continuous dynamic iterative optimization of networking parameters and radio frequency resource parameters. It effectively improves the dynamic adaptability and long-term operational stability of the AUV adaptive networking communication system in industrial plants, and realizes the continuous optimization of networking communication effect under complex working conditions.

[0069] Corresponding to the method in the above embodiments, Figure 8 A structural block diagram of the AUV adaptive networking communication system provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown. Figure 8 The example AUV adaptive networking communication system can be the execution subject of the AUV adaptive networking communication method provided in the aforementioned embodiment 1.

[0070] Reference Figure 8 The AUV adaptive networking communication system includes: The information acquisition module 810 is used to acquire multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and multiple historical AUV networking communication parameter information. The AUV network topology information generation module 820 is used to calculate multiple AUV network topology information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and a preset AUV network topology generation model. The initial AUV network communication parameter information generation module 830 is used to generate multiple initial AUV network communication parameter information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, multiple AUV network topology information, preset AUV network communication transmission parameter reference value information, and preset initial AUV network communication parameter generation model. The target AUV networking communication parameter information generation module 840 is used to generate multiple target AUV networking communication parameter information based on the multiple initial AUV networking communication parameter information, multiple historical AUV networking communication parameter information, multiple preset AUV networking communication requirement discrimination threshold information, and a preset target AUV networking communication parameter generation model, so as to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

[0071] For details on how each module in the AUV adaptive networking communication system provided in this application implements its respective function, please refer to the foregoing. Figure 1 The description of Embodiment 1 shown will not be repeated here.

[0072] It should be understood that the sequence number of each step in the above embodiments does not imply 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 this application.

[0073] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0074] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0075] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0076] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance. It should also be understood that although the terms "first," "second," etc., are used in the text to describe various elements in some embodiments of this application, these elements should not be limited by these terms. These terms are merely used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. Both the first table and the second table are tables, but they are not the same table.

[0077] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0078] The AUV adaptive networking communication method provided in this application embodiment can be applied to terminal devices such as mobile phones, tablets, wearable devices, vehicle-mounted devices, augmented reality / virtual reality devices, laptops, super mobile personal computers, netbooks, and personal digital assistants. This application embodiment does not impose any restrictions on the specific type of terminal device.

[0079] For example, the terminal device may be a station in a WLAN, a cellular phone, a cordless phone, a session initiation protocol phone, a wireless local loop station, a personal digital processing device, a handheld device with wireless communication capabilities, a computing device or other processing device connected to a wireless modem, an in-vehicle device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a set-top box, a user premises equipment, and / or other devices for communication over a wireless system, as well as next-generation communication systems, such as mobile terminals in 5G networks or mobile terminals in future evolved public terrestrial mobile networks, etc.

[0080] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 9 As shown, the terminal device 9 of this embodiment includes: at least one processor 90 ( Figure 9 Only one is shown in the image), and a memory 91 is stored in which a computer program 92 that can run on the processor 90 is stored. When the processor 90 executes the computer program 92, it implements the steps in the various AUV adaptive networking communication method embodiments described above, for example... Figure 1 Steps S101 to S104 are shown. Alternatively, when the processor 90 executes the computer program 92, it implements the functions of each module / unit in the above system embodiments, for example... Figure 8 The functions of modules 810 to 840 are shown.

[0081] The terminal device 9 can be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor 90 and a memory 91. Those skilled in the art will understand that... Figure 9 This is merely an example of terminal device 9 and does not constitute a limitation on terminal device 9. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal device may also include input transmission devices, network access devices, buses, etc.

[0082] The processor 90 may be a central processing unit, or it may be other general-purpose processors, digital signal processors, application-specific integrated circuits, off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0083] In some embodiments, the memory 91 may be an internal storage unit of the terminal device 9, such as a hard disk or memory of the terminal device 9. The memory 91 may also be an external storage device of the terminal device 9, such as a plug-in hard disk, smart memory card, secure digital card, flash memory card, etc., equipped on the terminal device 9. Furthermore, the memory 91 may include both internal and external storage units of the terminal device 9. The memory 91 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 91 can also be used to temporarily store data that has been sent or will be sent.

[0084] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0085] This application also provides a terminal device, which includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, it causes the terminal device to implement the steps in any of the above method embodiments.

[0086] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0087] This application provides a computer program product that, when run on a terminal device, enables the terminal device to implement the steps described in the various method embodiments above.

[0088] If the integrated module / unit is implemented as 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, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0089] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0090] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0091] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0092] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. An AUV adaptive networking communication method, characterized in that, include: Acquire location information of multiple AUV devices, operational status monitoring information of multiple AUV devices, channel occupancy monitoring information of multiple AUV devices, signal attenuation monitoring information of multiple AP devices, and historical AUV networking communication parameters. Based on the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the signal attenuation monitoring information of multiple APs, and the preset AUV networking topology generation model, multiple AUV networking topology information is calculated. Based on the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the signal attenuation monitoring information of multiple APs, the network topology information of multiple AUVs, the preset AUV network communication transmission parameter baseline information, and the preset initial AUV network communication parameter generation model, multiple initial AUV network communication parameter information are generated. Based on the multiple initial AUV networking communication parameter information, multiple historical AUV networking communication parameter information, multiple preset AUV networking communication demand discrimination threshold information, and a preset target AUV networking communication parameter generation model, multiple target AUV networking communication parameter information are generated to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

2. The AUV adaptive networking communication method as described in claim 1, characterized in that, The location information of the multiple AUV devices includes the location information of a first AUV device and the location information of a second AUV device; The first AUV device location information includes the first AUV device access location coordinate information, the first AUV device update location coordinate information, the first AUV device location access time information, and the first AUV device location update time information; The second AUV device location information includes the second AUV device access location coordinate information, the second AUV device update location coordinate information, the second AUV device location access time information, and the second AUV device location update time information; The step of calculating multiple AUV network topology information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and a preset AUV network topology generation model specifically includes: Based on the access location coordinates of the first AUV device, the updated location coordinates of the first AUV device, the access location coordinates of the second AUV device, and the updated location coordinates of the second AUV device, the spatial distance information of the first AUV device and the spatial distance information of the second AUV device are calculated. Based on the location access time information of the first AUV device, the location update time information of the first AUV device, the location access time information of the second AUV device, and the location update time information of the second AUV device, the location update time interval information of the first AUV device and the location update time interval information of the second AUV device are calculated. Based on the spatial distance information of the first AUV device's location movement and the spatial distance information of the second AUV device's location movement, the spatial distance difference information of the AUV device's location movement is calculated. Based on the first AUV device location update time interval information and the second AUV device location update time interval information, the AUV device location update time interval difference information is calculated; Based on the updated location coordinates of the first AUV device, the updated location coordinates of the second AUV device, the spatial distance difference information of the AUV device location movement, the spatial time interval difference information of the AUV device location update, the preset spatial distance difference threshold of the AUV device location movement, and the preset spatial time interval difference threshold of the AUV device location update, multiple AUV device location information to be networked are generated. Based on the location information of the multiple AUV devices to be networked, the operating status monitoring information of the multiple AUV devices, the occupancy monitoring information of the multiple AUV routing channels, the signal attenuation monitoring information of the multiple APs, and the preset AUV network topology generation model, multiple AUV network topology information is calculated.

3. The AUV adaptive networking communication method as described in claim 2, characterized in that, The location information of the AUV device to be networked includes the updated location coordinates of the AUV device to be networked, the location access time of the AUV device to be networked, and the location update time of the AUV device to be networked. The step of calculating multiple AUV network topology information based on the location information of the multiple AUV devices to be networked, the operating status monitoring information of the multiple AUV devices, the occupancy monitoring information of the multiple AUV routing channels, the signal attenuation monitoring information of the multiple APs, and the preset AUV network topology generation model specifically includes: Based on the location access time information and location update time information of the multiple AUV devices to be networked, the time-series filtering processing is performed on the multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information and multiple AP signal attenuation monitoring information to generate multiple filtered AUV device operation status monitoring information, multiple filtered AUV routing channel occupancy monitoring information and multiple filtered AP signal attenuation monitoring information. Based on the updated location coordinates of the multiple AUV devices to be networked, the location access time information of the multiple AUV devices to be networked, the location update time information of the multiple AUV devices to be networked, the operation status monitoring information of the multiple filtered AUV devices, the routing channel occupancy monitoring information of the multiple filtered AUV devices, the signal attenuation monitoring information of the multiple filtered AP devices, and the preset AUV network topology generation model, multiple AUV network topology information is calculated.

4. The AUV adaptive networking communication method as described in claim 3, characterized in that, The step of generating multiple initial AUV network communication parameter information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, multiple AUV network topology information, preset AUV network communication transmission parameter baseline value information, and preset initial AUV network communication parameter generation model specifically includes: Based on the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the network topology information of multiple AUV devices, and the signal attenuation monitoring information of multiple filtered APs, topology spatial location matching processing is performed to generate multiple AUV network topology spatial location signal attenuation information. Based on the signal attenuation information of the multiple AUV network topology spatial location, the preset AUV network communication transmission parameter reference value information is selected and processed to generate multiple selected AUV network communication transmission parameter reference value information. Based on the location information of multiple AUV devices, the operating status monitoring information of multiple AUV devices, the occupancy monitoring information of multiple AUV routing channels, the signal attenuation monitoring information of multiple APs, the network topology information of multiple AUVs, and the preset initial AUV network communication parameter generation model, multiple AUV network communication parameter information to be corrected is generated. Based on the multiple selected AUV network communication transmission parameter reference values, the multiple AUV network communication parameter information to be corrected is processed to generate multiple initial AUV network communication parameter information.

5. The AUV adaptive networking communication method as described in claim 1, characterized in that, The step of generating multiple target AUV network communication parameters based on the multiple initial AUV network communication parameters, multiple historical AUV network communication parameters, multiple preset AUV network communication demand discrimination thresholds, and a preset target AUV network communication parameter generation model, and then performing AUV network communication control processing using the multiple target AUV network communication parameters, specifically includes: Based on multiple preset AUV networking communication requirement discrimination thresholds, the multiple initial AUV networking communication parameter information and multiple historical AUV networking communication parameter information are processed to obtain multiple discriminated AUV networking communication parameter information. Based on the multiple discriminated AUV networking communication parameter information and the preset target AUV networking communication parameter generation model, multiple target AUV networking communication parameter information is generated to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

6. The AUV adaptive networking communication method as described in claim 1, characterized in that, After the step of generating multiple target AUV network communication parameter information based on the multiple initial AUV network communication parameter information, multiple historical AUV network communication parameter information, multiple preset AUV network communication demand discrimination threshold information, and a preset target AUV network communication parameter generation model, and then performing AUV network communication control processing through the multiple target AUV network communication parameter information, the method further includes: Based on the preset AUV networking communication parameter feature extraction model, feature extraction processing is performed on the multiple target AUV networking communication parameter information to generate multiple AUV networking communication parameter feature information. Based on the preset mapping relationship between AUV networking communication parameter characteristics and AP channel allocation control parameters, multiple AP channel allocation control parameter information is generated according to the multiple AUV networking communication parameter characteristic information. Based on the preset mapping relationship between AUV networking communication parameter characteristics and routing transmit power control parameters, multiple routing transmit power control parameter information is generated according to the multiple AUV networking communication parameter characteristic information.

7. The AUV adaptive networking communication method as described in claim 6, characterized in that, After the step of generating multiple routing transmission power control parameter information based on the preset mapping relationship between AUV networking communication parameter characteristics and routing transmit power control parameters, the method further includes: Acquire location information of AUV devices after multiple controls, monitoring information on the operating status of AUV devices after multiple controls, and monitoring information on the occupancy of AUV routing channels after multiple controls; The location information of the multiple controlled AUV devices, the operating status monitoring information of the multiple controlled AUV devices, and the routing channel occupancy monitoring information of the multiple controlled AUV devices are sent to the terminal device.

8. An AUV adaptive networking communication system, characterized in that, include: The information acquisition module is used to acquire multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and multiple historical AUV networking communication parameter information. The AUV network topology information generation module is used to calculate multiple AUV network topology information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, and a preset AUV network topology generation model. The initial AUV network communication parameter information generation module is used to generate multiple initial AUV network communication parameter information based on the multiple AUV device location information, multiple AUV device operation status monitoring information, multiple AUV routing channel occupancy monitoring information, multiple AP signal attenuation monitoring information, multiple AUV network topology information, preset AUV network communication transmission parameter baseline value information, and preset initial AUV network communication parameter generation model. The target AUV networking communication parameter information generation module is used to generate multiple target AUV networking communication parameter information based on the multiple initial AUV networking communication parameter information, multiple historical AUV networking communication parameter information, multiple preset AUV networking communication requirement discrimination threshold information, and a preset target AUV networking communication parameter generation model, so as to perform AUV networking communication control processing through the multiple target AUV networking communication parameter information.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

Images