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 particularly to an intelligent transceiver system for underwater mobile platforms based on self-state perception. Background Art

[0002] With the increasing complexity of the tasks of underwater mobile platforms, the internal communication system needs to achieve efficient and reliable information transmission, and at the same time, monitor the operating status of key devices and systems in real time. However, due to the special environmental limitations of underwater mobile platforms underwater, the existing communication systems have deficiencies in signal transmission stability, data processing efficiency, and fault response capabilities. For example, there is a lack of an integrated solution in the prior art that can simultaneously monitor the power system, environmental parameters, and achieve efficient information interaction. How to combine situation awareness technology and real-time communication technology to improve the overall reliability of underwater mobile platform systems is a major challenge in the current technical field.

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

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

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

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

[0007] A multi-modal sensing and monitoring module that 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 that adopts an in-band full-duplex (IBFD) communication architecture to enable the system to simultaneously transmit and receive the operating data in the same frequency band;

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

[0010] A status monitoring module for classifying and monitoring the operating status of the underwater mobile platform;

[0011] An anomaly reproduction module for improving the recognition efficiency of abnormal states;

[0012] A self-learning and communication optimization module that dynamically adjusts the determination criteria for normal and abnormal states by analyzing the historical data and the triggering rules of abnormal states.

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

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

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

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

[0017] Further, the situation awareness module locates the fault source by combining root cause analysis technology.

[0018] Further, in the status monitoring module, when the operation data collected by the sensor matches the standard status model, the corresponding status name and description will be sent to the control terminal to feedback the operation status in an intuitive form and trigger an operation prompt; when the operation data collected by the sensor fails to match any standard status model, it will be automatically identified as an abnormal status and named, and at the same time, a detailed status description will be generated.

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

[0020] Further, when the system detects an abnormal status or equipment failure, it will trigger an alarm mechanism, send an alarm to the operation and maintenance personnel through audible and visual alarms, the control terminal or remote communication, and the system will automatically generate a detailed analysis report, providing cases and treatment solutions of similar historical abnormalities to assist the operation and maintenance personnel in making decisions.

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

[0022] 1. The multi-modal sensing and monitoring module of the present invention has efficient monitoring and communication capabilities, can real-time sense various operation states of the underwater mobile platform, and realizes low-latency data interaction through an efficient adaptive communication module.

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

[0024] 3. By relying on the self-learning and communication optimization module, the present invention enables the system to continuously optimize the state classification criteria, enhances the adaptability and scalability to complex environments, and provides support for the upgrade of future applications.

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

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

[0027] Figure 2 It is a flowchart of the operation of an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0029] The embodiment of the present application provides an intelligent transceiver system for an underwater mobile platform based on self-state awareness, aiming to solve problems such as low transmission efficiency and insufficient fault perception ability in the existing underwater mobile platform communication system. The system realizes the comprehensive monitoring of the operation state of the underwater platform and efficient information interaction by introducing a multi-modal sensing and monitoring module, an adaptive communication module, a situation awareness module, a state monitoring module, an anomaly 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 intelligent transceiver system for an underwater mobile platform based on self-state awareness in the embodiment of the present application is as follows:

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

[0032] Specifically, this module collects key operation data of the underwater mobile platform in real time through a distributed sensor network, including power system performance indicators (such as motor status, energy consumption rate, etc.), self-noise, environmental parameters (such as temperature, humidity, pressure), and the position information and attitude data of the platform (such as pitch angle, roll angle). Among them, the attitude of the underwater platform directly affects the directivity of the communication device. When the directivity of the communication device deviates from the target direction, it will cause scattering or attenuation of 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 directivity communication environment, this kind of interference will significantly weaken the stability of signal reception. The sensor nodes transmit the data to the central processing unit through wireless networking or wired connection to ensure the timeliness and accuracy of the information. The multi-modal sensing technology can capture multi-source information in the complex underwater environment and provide reliable support for subsequent data processing and status analysis.

[0033] The adaptive communication module adopts an in-band full-duplex (IBFD) communication architecture, enabling the system to simultaneously transmit and receive the operation data within the same frequency band, thereby improving communication efficiency. To adapt to the complex underwater channel environment, this module combines channel sensing technology and an 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. This module also supports cross-domain communication functions and can perform efficient data interaction with other underwater platforms or command centers to form a stable underwater communication network.

[0034] The situation awareness module is used to perform real-time analysis on the collected operation data. By introducing the random forest algorithm, this module accurately predicts potential equipment failures and generates warning information. By combining root cause analysis technology, it quickly locates the source of the failure and provides decision-making support for operation and maintenance personnel.

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

[0036] The anomaly reproduction module is used to improve the recognition efficiency of abnormal statuses. When the same or highly similar abnormal status is detected again, the system will automatically match the existing records, display the abnormal status name and description information, and at the same time prompt the user to take corresponding measures. This module also records the occurrence frequency, duration, and impact degree of the abnormal status, providing data support for subsequent optimization and further enhancing the system's anomaly management ability.

[0037] The self-learning and communication optimization module dynamically adjusts the criteria for determining normal and abnormal states by analyzing historical data and the triggering patterns of abnormal states. For example, if an abnormal state is triggered multiple times and shows consistent behavior, the system will recommend adding it to the standard state list to form a new regular state category, thereby improving the state classification system, enhancing the intelligence of the system, and reducing the false alarm rate.

[0038] The multi-modal sensing and monitoring module of this application has efficient monitoring and communication capabilities. It can real-time sense various operating states of the underwater mobile platform and achieve low-latency data interaction through an efficient adaptive communication module. This application integrates a state monitoring module and an abnormal reproduction module. Through a dynamic abnormal naming, identification, and reproduction mechanism, it effectively improves the ability to handle unknown problems and reduces the workload of repeated analysis. At the same time, by relying on the self-learning and communication optimization module, this application continuously optimizes the state classification criteria of the system, enhances the adaptability and scalability to complex environments, and provides support for future application upgrades. By combining the situation awareness module with intelligent analysis, this application significantly improves the operation and maintenance efficiency, helps operation and maintenance personnel quickly respond to faults, reduces the unplanned downtime, and thus comprehensively improves the operation reliability of the underwater mobile platform, solving the problems existing in the existing communication system in terms of signal transmission stability, data processing efficiency, and fault response ability.

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

[0040] (1) Install multi-modal sensor nodes at key components of the underwater mobile platform, such as the power system, navigation module, and environmental monitoring areas, to collect data such as motor operating status, fuel reserve, temperature, and pressure. After the deployment is completed, use calibration software to adjust the data collection range, resolution, and response time of the sensors to ensure that the sensors can operate accurately in the actual environment. Subsequently, the system starts a self-check 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 link, the sensor nodes collect various operating state data at preset time intervals and also have a triggered collection function. When specific conditions (such as abnormal temperature, rapid pressure change, etc.) occur, the sensors automatically capture data. The collected data is transmitted to the CPU in real time through wireless networking technology. The CPU preliminarily classifies, filters, and stores the data for subsequent analysis and processing.

[0042] (3) The built-in standard state model of the system covers multiple categories such as "good state", "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, feedbacks the operating state in an intuitive form, and triggers operation prompts. For example, when the fuel is insufficient, a prompt of "refuel" is issued. When the data monitored by the system fails to match any standard state model, it is automatically identified as an abnormal state, named "abnormal state 1", "abnormal state 2", etc., and at the same time, a detailed state description is generated, including the change range of abnormal data and the potential impact range, providing a basis for subsequent analysis.

[0043] (4) When the same or similar abnormal state as 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, thus reducing the workload of repeated analysis. For newly detected abnormal states, the system records their characteristic parameters and automatically classifies and stores them in the abnormal state library to form an abnormal file for subsequent reference.

[0044] (5) Through the system's self-learning function for dynamic update and optimization, the system records the occurrence frequency, duration, and impact degree of each abnormal state, and then optimizes the threshold of abnormal detection, reduces the false alarm rate, or classifies frequently occurring abnormal states as new standard states, continuously improving the monitoring accuracy and intelligence level of the system.

[0045] (6) In the alarm and auxiliary decision-making 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, the control terminal, or remote communication. The system automatically generates a detailed analysis report, including the specific description of the abnormal state, the change of abnormal parameters, the potential impact assessment, and solutions such as suggesting equipment replacement, parameter adjustment, or maintenance operations. At the same time, the system provides cases and treatment plans of similar historical abnormalities to assist the operation and maintenance personnel in making decisions quickly, effectively shortening the fault diagnosis and treatment time, and ensuring the continuous and stable operation of the underwater mobile platform.

[0046] The embodiments described above are only descriptions of the preferred embodiments of the present invention and do not limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations and improvements made by those of ordinary skill in the art to the technical solutions of the present invention shall 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, Including: A multi-modal sensing and monitoring module that collects the operation data of the underwater mobile platform in real time through a distributed sensor network; the sensor nodes transmit the operation data to the central processing unit through wireless networking or wired connection; An adaptive communication module that uses an in-band full-duplex (IBFD) communication architecture to enable the system to send and receive the operation data simultaneously in the same frequency band; A situation awareness module for real-time analysis of the collected operation data; A status monitoring module for classifying and monitoring the operation status of the underwater mobile platform; An anomaly reproduction module for improving the recognition efficiency of abnormal states; A self-learning and communication optimization module that dynamically adjusts the judgment criteria for normal and abnormal states by analyzing the historical data and the triggering rules of abnormal states.

2. The intelligent transceiver system for an underwater mobile platform based on self-state perception according to claim 1, characterized in that, The operation data includes power system performance indicators, self-noise, environmental parameters, as well as the position information and attitude data of the platform.

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

4. The intelligent transceiver system for an underwater mobile platform based on self-state perception according to claim 2, wherein The adaptive communication module has a cross-domain communication function and can perform data interaction with other underwater platforms or command centers.

5. The intelligent transceiver system for an underwater mobile platform based on self-state perception according to claim 1, wherein The situation awareness module predicts potential equipment failures and generates warning information by introducing a random forest algorithm.

6. The intelligent transceiver system for an underwater mobile platform based on self-state perception according to claim 5, characterized in that, The situation awareness module locates the source of the fault by combining root cause analysis technology.

7. The intelligent transceiver system for an underwater mobile platform based on self-state perception according to claim 1, characterized in that In the status monitoring module, when the operation data collected by the sensor matches the standard state model, the corresponding state name and description will be sent to the control terminal to visually feedback the operation status and trigger an operation prompt; when the operation data collected by the sensor fails to match any standard state model, it will be automatically identified as an abnormal state and named, and at the same time, a detailed state description will be generated.

8. The intelligent transceiver system of an underwater mobile platform based on self-state perception according to claim 7, characterized in that In the anomaly reproduction module, when the same or similar abnormal state as in the historical record is detected again, it will be automatically identified through a state matching algorithm, and the named abnormal state information will be displayed, with additional detailed descriptions and handling suggestions; for newly detected abnormal states, their characteristic parameters will be recorded, Automatically classified and stored in the abnormal state library.

9. The intelligent transceiver system for an underwater mobile platform based on self-state perception according to any one of claims 1-8, characterized in that, When the system detects an abnormal state or equipment failure, it will trigger an alarm mechanism to send an alarm to the operation and maintenance personnel through audible and visual alarms, the control terminal, or remote communication. The system will automatically generate a detailed analysis report, providing cases and handling solutions for historical similar anomalies to assist the operation and maintenance personnel in making decisions.

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