Underwater mobile platform intelligent transmit-receive system based on self-state perception

Abstract

The invention discloses an underwater mobile platform intelligent transmit-receive system based on self-state perception, and belongs to the technical field of underwater mobile platform information communication. The method comprises the steps that a multi-mode sensing and monitoring module collects operation data of an underwater mobile platform in real time through a distributed sensor network and transmits the operation data to a central processing unit; the self-adaptive communication module adopts an in-band full-duplex communication architecture; the situation awareness module is used for analyzing the collected operation data in real time; the state monitoring module is used for classifying and monitoring the running state of the underwater mobile platform; the exception reproduction module is used for improving the recognition efficiency of the exception state; and the self-learning and communication optimization module dynamically adjusts a normal and abnormal judgment standard by analyzing a triggering rule of historical data and an abnormal state. The state monitoring module and the exception reproduction module are integrated, and through dynamic exception naming, recognition and reproduction mechanisms, the handling capacity for unknown problems is effectively improved, and the workload of repeated analysis is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater mobile platform information communication, and in particular to an underwater mobile platform intelligent transceiver system based on self-state perception. Background Art

[0002] As the complexity of underwater mobile platform tasks increases, internal communication systems need to achieve efficient and reliable information transmission, while monitoring the operating status of key equipment and systems in real time. However, due to the special environmental limitations of underwater mobile platforms underwater, existing communication systems are insufficient in signal transmission stability, data processing efficiency, and fault response capabilities. For example, the existing technology lacks a comprehensive solution that can simultaneously monitor the power system and environmental parameters and achieve efficient information interaction. How to combine situational awareness technology and real-time communication technology to improve the overall reliability of underwater mobile platform systems is a major challenge in the current technology field.

[0003] Therefore, it is necessary to provide an intelligent transceiver system for an underwater mobile platform based on self-state perception. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent transceiver system for an underwater mobile platform based on self-state perception, so as to solve the problems of deficiencies in signal transmission stability, data processing efficiency and fault response capability of existing communication systems.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An intelligent transceiver system for an underwater mobile platform based on self-state perception, comprising:

[0007] The multimodal sensing and monitoring module collects the operating data of the underwater mobile platform in real time through a distributed sensor network; the sensor nodes transmit the operating data to the central processing unit through wireless networking or wired connection;

[0008] An adaptive communication module, using an in-band full-duplex (IBFD) communication architecture, enables the system to simultaneously send and receive the operating data within the same frequency band;

[0009] A situation awareness module, used for real-time analysis of the collected operation data;

[0010] A status monitoring module is used to classify and monitor the operating status of the underwater mobile platform;

[0011] Abnormal reproduction module, used to improve the efficiency of identifying abnormal conditions;

[0012] The self-learning and communication optimization module dynamically adjusts the normal and abnormal judgment criteria by analyzing historical data and the triggering rules of abnormal conditions.

[0013] Furthermore, the operating data includes power system performance indicators, self-noise, environmental parameters, and platform position information and attitude data.

[0014] Furthermore, the adaptive communication module combines channel sensing technology and underwater acoustic channel adaptive coding algorithm to dynamically adjust coding and modulation parameters.

[0015] Furthermore, the adaptive communication module has a cross-domain communication function and can exchange data with other underwater platforms or command centers.

[0016] Furthermore, the situation awareness module predicts potential equipment failures and generates early warning information by introducing a random forest algorithm.

[0017] Furthermore, the situation awareness module locates the source of the fault in combination with the root cause analysis technology.

[0018] Furthermore, in the status monitoring module, when the operating data collected by the sensor matches the standard status model, the corresponding status name and description will be sent to the control terminal, the operating status will be fed back in an intuitive form, and the operation prompt will be triggered; when the operating data collected by the sensor fails to match any standard status model, it will be automatically identified as an abnormal state and named, and a detailed status description will be generated.

[0019] Furthermore, in the abnormal reproduction module, when the same or similar abnormal state as that in the historical record is detected again, it will be automatically identified through the state matching algorithm, and the named abnormal state information will be displayed, with additional detailed descriptions and processing suggestions; for the newly detected abnormal state, its characteristic parameters will be recorded and automatically classified and stored in the abnormal state library.

[0020] Furthermore, when the system detects an abnormal state or equipment failure, it will trigger an alarm mechanism and send an alarm to the operation and maintenance personnel through sound and light alarms, control terminals or remote communications. The system automatically generates a detailed analysis report, provides historical cases of similar abnormalities and treatment plans, and assists the operation and maintenance personnel in making decisions.

[0021] The present invention has the following beneficial effects:

[0022] 1. The multimodal sensing and monitoring module of the present invention has efficient monitoring and communication capabilities, can perceive the various operating states of the underwater mobile platform in real time, and realize low-latency data interaction through an efficient adaptive communication module.

[0023] 2. The present invention integrates a status monitoring module and an abnormality reproduction module, and through a dynamic abnormality naming, identification and reproduction mechanism, effectively improves the ability to handle unknown problems and reduces the workload of repeated analysis.

[0024] 3. The present invention relies on self-learning and communication optimization modules to enable the system to continuously optimize the state classification standard, enhance the adaptability and scalability to complex environments, and provide support for future application upgrades.

[0025] 4. By combining the situational awareness module with intelligent analysis, the system of the present invention significantly improves the operation and maintenance efficiency, helps operation and maintenance personnel to quickly respond to failures, and reduces unplanned downtime, thereby comprehensively improving the operational reliability of the underwater mobile platform. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of the core architecture of an embodiment of the present invention;

[0027] Figure 2 The figure is an operation flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0028] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0029] The embodiment of the present application provides an underwater mobile platform intelligent transceiver system based on self-state perception, which aims to solve the problems of low transmission efficiency and insufficient fault perception capability in the existing underwater mobile platform communication system. The system realizes comprehensive monitoring of the underwater platform operation status and efficient information interaction by introducing a multimodal sensing and monitoring module, an adaptive communication module, a situational awareness module, a state monitoring module, an abnormality reproduction module, and a self-learning and communication optimization module, thereby improving the operation efficiency and reliability of the underwater mobile platform.

[0030] The core architecture of an underwater mobile platform intelligent transceiver system based on self-state perception in the embodiment of the present application is as follows:

[0031] The multimodal sensing and monitoring module collects the operating data of the underwater mobile platform in real time through a distributed sensor network; the sensor nodes transmit the operating data to the central processing unit through wireless networking or wired connection.

[0032] Specifically, this module collects key operating data of underwater mobile platforms in real time through distributed sensor networks, including power system performance indicators (such as motor status, energy consumption rate, etc.), self-noise, environmental parameters (such as temperature, humidity, pressure), and platform location information and attitude data (such as pitch angle, roll angle). Among them, the attitude of the underwater platform directly affects the directivity of the communication equipment. When the directivity of the communication equipment deviates from the target direction, it will cause the scattering or attenuation of the signal energy, thereby reducing the quality of the communication link. Self-noise (such as motor vibration noise, fluid noise) may interfere with the communication signal, especially in a high-precision directional communication environment. This interference will significantly weaken the stability of signal reception. The sensor node transmits data to the central processing unit through wireless networking or wired connection to ensure the real-time and accuracy of the information. Multimodal sensing technology can capture multi-source information in complex underwater environments and provide reliable support for subsequent data processing and state analysis.

[0033] The adaptive communication module adopts the in-band full-duplex (IBFD) communication architecture, which enables the system to simultaneously send and receive the operating data in the same frequency band, thereby improving communication efficiency. In order to adapt to the complex underwater channel environment, the module combines channel sensing technology and underwater acoustic channel adaptive coding algorithm to dynamically adjust coding and modulation parameters (such as frequency, directivity, etc.) to ensure the stability and reliability of communication. The module also supports cross-domain communication functions, which can efficiently interact with other underwater platforms or command centers to form a stable underwater communication network.

[0034] The situation awareness module is used to analyze the collected operation data in real time. This module accurately predicts potential equipment failures by introducing the random forest algorithm, generates early warning information, and quickly locates the source of the failure by combining root cause analysis technology to provide decision support for operation and maintenance personnel.

[0035] The status monitoring module is used to classify and monitor the operating status of the underwater mobile platform, covering standard statuses such as "good status", "insufficient kinetic energy", and "insufficient fuel". When the monitored status data deviates significantly from the normal range but does not belong to a known classification, the system will identify it as an abnormal state and automatically name it (such as "abnormal state 1" and "abnormal state 2"). The system will generate a detailed description based on the status performance, including main parameters, timestamps, and impact analysis.

[0036] The abnormal recurrence module is used to improve the efficiency of identifying abnormal states. When the same or highly similar abnormal state is detected again, the system will automatically match the existing records, display the name and description of the abnormal state, and prompt the user to take corresponding measures. The module also records the frequency, duration and impact of the abnormal state, providing data support for subsequent optimization and further enhancing the system's abnormal management capabilities.

[0037] The self-learning and communication optimization module dynamically adjusts the normal and abnormal judgment criteria by analyzing historical data and the triggering rules of abnormal states. For example, if an abnormal state is triggered multiple times and behaves consistently, the system will suggest adding it to the standard state list to form a new normal state category, thereby improving the state classification system, improving the system's intelligence, and reducing the false alarm rate.

[0038] The multimodal sensing and monitoring module of the present application has efficient monitoring and communication capabilities, can perceive the various operating states of the underwater mobile platform in real time, and realize low-latency data interaction through an efficient adaptive communication module. The present application integrates a state monitoring module and an abnormality reproduction module, and effectively improves the ability to handle unknown problems and reduces the workload of repeated analysis through a dynamic abnormal naming, identification and reproduction mechanism. At the same time, the present application relies on self-learning and communication optimization modules to enable the system to continuously optimize the state classification standards, enhance the adaptability and scalability to complex environments, and provide support for future application upgrades. By combining the situational awareness module with intelligent analysis, the system of the present application significantly improves the operation and maintenance efficiency, helps operation and maintenance personnel to quickly respond to faults, and reduces unplanned downtime, thereby comprehensively improving the operational reliability of the underwater mobile platform, and solving the problems of the existing communication system in terms of signal transmission stability, data processing efficiency and fault response capabilities.

[0039] The operation process of an underwater mobile platform intelligent transceiver system based on self-state perception in the embodiment of the present application is as follows:

[0040] (1) Multimodal sensor nodes are installed in key components of the underwater mobile platform, such as the power system, navigation module, and environmental monitoring area, to collect data such as motor operating status, fuel remaining, temperature, and pressure. After deployment, the sensor's data collection range, resolution, and response time are adjusted through calibration software to ensure that the sensor can operate accurately in the actual environment. The system then starts a self-test program to confirm that the connection status and data transmission function of all sensor nodes are normal, and finally reports the sensor deployment information to the central processing unit (CPU).

[0041] (2) In the real-time data collection phase, the sensor node collects various operating status data at preset time intervals and has a triggered collection function. When specific conditions (such as abnormal temperature, sudden pressure changes, etc.) occur, the sensor automatically captures data. The collected data is transmitted to the CPU in real time through wireless networking technology. The CPU performs preliminary classification, screening and storage of the data for subsequent analysis and processing.

[0042] (3) The system’s built-in standard state model covers multiple categories such as “good condition”, “insufficient kinetic energy”, and “insufficient fuel”. When the data collected by the sensor matches the standard state model, the system sends the corresponding state name and description to the control terminal, provides intuitive feedback on the operating status, and triggers operation prompts, such as a “refuel” prompt when fuel is insufficient. When the data monitored by the system fails to match any standard state model, it is automatically identified as an abnormal state and named “abnormal state 1”, “abnormal state 2”, etc. At the same time, a detailed state description is generated, including the change range of the abnormal data and the potential impact range, providing a basis for subsequent analysis.

[0043] (4) When an abnormal state that is the same or similar to that in the historical record is detected again, the system will automatically identify it through the state matching algorithm and display the named abnormal state information, attaching the previous detailed description and processing suggestions, thereby reducing the workload of repeated analysis. For newly detected abnormal states, the system will record its characteristic parameters, automatically classify and store them in the abnormal state library, and form an abnormal file for subsequent reference.

[0044] (5) Dynamic updating and optimization are achieved through the system's self-learning function. The system will record the frequency, duration and impact of each abnormal state, and then optimize the threshold of abnormal detection, reduce the false alarm rate or classify high-frequency abnormal states as new standard states, continuously improving the system's monitoring accuracy and intelligence.

[0045] (6) In the alarm and decision-making support stage, when the system detects an abnormal state or equipment failure, it will immediately trigger the alarm mechanism and send an alarm to the operation and maintenance personnel through sound and light alarms, control terminals or remote communications. The system automatically generates a detailed analysis report, including a specific description of the abnormal state, changes in abnormal parameters, potential impact assessment and solution suggestions, such as recommendations to replace equipment, adjust parameters or perform maintenance operations. At the same time, the system provides historical cases of similar abnormalities and treatment plans to assist operation and maintenance personnel in making quick decisions, effectively shorten the time for fault diagnosis and processing, and ensure the continuous and stable operation of the underwater mobile platform.

[0046] The embodiments described above are only descriptions of the preferred modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An intelligent transceiver system for an underwater mobile platform based on self-state perception, characterized in that: include: The multimodal sensing and monitoring module collects the operating data of the underwater mobile platform in real time through a distributed sensor network; the sensor nodes transmit the operating data to the central processing unit through wireless networking or wired connection; An adaptive communication module, using an in-band full-duplex (IBFD) communication architecture, enables the system to simultaneously send and receive the operating data within the same frequency band; A situation awareness module, used for real-time analysis of the collected operation data; A status monitoring module is used to classify and monitor the operating status of the underwater mobile platform; Abnormal reproduction module, used to improve the efficiency of identifying abnormal conditions; The self-learning and communication optimization module dynamically adjusts the normal and abnormal judgment criteria by analyzing historical data and the triggering rules of abnormal conditions.

2. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 1 is characterized in that: The operating data includes power system performance indicators, self-noise, environmental parameters, and platform position information and attitude data.

3. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 1 is characterized in that: The adaptive communication module combines channel sensing technology and underwater acoustic channel adaptive coding algorithm to dynamically adjust coding and modulation parameters.

4. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 2 is characterized in that: The adaptive communication module has a cross-domain communication function and can exchange data with other underwater platforms or command centers.

5. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 1 is characterized in that: The situation awareness module predicts potential equipment failures and generates early warning information by introducing a random forest algorithm.

6. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 5 is characterized in that: The situation awareness module locates the source of the fault in conjunction with the root cause analysis technology.

7. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 1 is characterized in that: In the status monitoring module, when the operating data collected by the sensor matches the standard status model, the corresponding status name and description will be sent to the control terminal, the operating status will be fed back in an intuitive form, and operation prompts will be triggered; when the operating data collected by the sensor fails to match any standard status model, it will be automatically identified as an abnormal state and named, and a detailed status description will be generated.

8. The underwater mobile platform intelligent transceiver system based on self-state perception according to claim 7 is characterized in that: In the abnormal reproduction module, when the same or similar abnormal state as that in the historical record is detected again, it will be automatically identified through the state matching algorithm, and the named abnormal state information will be displayed, with a detailed description and processing suggestions attached; for the newly detected abnormal state, its characteristic parameters will be recorded, Automatically categorized and stored in the abnormal status library.

9. The underwater mobile platform intelligent transceiver system based on self-state perception according to any one of claims 1 to 8, characterized in that: When the system detects an abnormal condition or equipment failure, it will trigger an alarm mechanism and send an alarm to the operation and maintenance personnel through sound and light alarms, control terminals or remote communications. The system automatically generates a detailed analysis report, provides historical cases of similar abnormalities and treatment plans, and assists the operation and maintenance personnel in making decisions.

Images