A marine multi-platform cooperative communication fault prediction method and system based on an intelligent self-recovery mechanism
The marine multi-platform collaborative communication fault prediction system with intelligent self-healing mechanism solves the problems of large delay in multi-source heterogeneous data fusion, poor underwater acoustic communication quality, and unclear fault mechanisms in marine communication systems. It achieves efficient and real-time fault prediction and self-healing, and improves the stability and reliability of the system.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA SHIP DEV & DESIGN CENT
- Filing Date
- 2026-02-25
- Publication Date
- 2026-06-05
AI Technical Summary
Large delays in multi-source heterogeneous data fusion, poor underwater acoustic communication quality in complex marine environments, numerous communication coverage blind spots, and unclear fault mechanisms in marine communication systems make fault prediction and self-healing difficult.
The marine multi-platform collaborative communication fault prediction system, which adopts an intelligent self-healing mechanism, achieves real-time data acquisition, processing, and dynamic adjustment through multi-dimensional data monitoring, multi-dimensional data capture, data preprocessing, multi-dimensional data calculation, judgment, early warning, adjustment, and optimization modules, thereby improving fault prediction and self-healing capabilities.
It has achieved high efficiency, real-time performance, adaptability and reliability of marine communication systems, improved the accuracy of fault prediction and self-healing efficiency, reduced the probability of fault occurrence, and ensured communication quality and stability.
Smart Images
Figure CN122160232A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of communication resource management technology, specifically to a method and system for predicting collaborative communication faults among marine multi-platforms based on an intelligent self-healing mechanism. Background Technology
[0002] In the field of marine communications, building efficient fault prediction and self-healing systems faces numerous severe challenges. First, the marine environment is extremely complex and variable. Frequent changes in temperature, salinity, and pressure, along with the presence of suspended air bubbles in the water, cause underwater acoustic channels to exhibit bandwidth limitations, multipath delay spread, rapid channel time-varying speeds, and severe Doppler effects. These problems significantly impact communication quality and increase the difficulty of fault prediction and self-healing. Second, communication coverage has significant blind spots. The distribution of submarine optical cables globally is extremely uneven, with some sea areas experiencing coverage gaps. Although satellite communication systems offer wide-area coverage and low latency, and shore-based mobile communication systems possess mature infrastructure and low-latency advantages, they cannot directly penetrate seawater to achieve real-time communication between underwater nodes. This results in a lack of necessary data support or reliable communication guarantees for fault prediction and self-healing in these areas. Furthermore, marine communication equipment is diverse, and the fault mechanisms of different devices are complex and vary considerably. Due to a lack of sufficient research and data accumulation, the understanding of fault mechanisms is not yet fully clear, further affecting the accuracy of fault detection, feature extraction, and prediction models. In the fusion of multi-source heterogeneous data, it is necessary to collect and process information from multiple data sources. This process may introduce significant latency, making it difficult to meet the needs of real-time fault prediction.
[0003] Therefore, there is an urgent need for a method and system for predicting faults in marine multi-platform collaborative communication based on an intelligent self-healing mechanism, in order to overcome the above-mentioned technical challenges, improve the reliability and stability of marine communication systems, and ensure the efficient operation of marine communication. Summary of the Invention
[0004] This invention aims to address the challenges of fault prediction and self-healing in traditional marine communication systems, stemming from issues such as large delays in multi-source heterogeneous data fusion, poor underwater acoustic communication quality in complex marine environments, numerous communication coverage blind spots, and unclear fault mechanisms. By providing an intelligent collaborative communication fault prediction and self-healing method and system, it enhances the ability to promptly detect, classify, and effectively respond to faults.
[0005] In a first aspect, the present invention provides a marine multi-platform collaborative communication fault prediction system based on an intelligent self-healing mechanism, comprising: The multi-dimensional data monitoring module is used to collect marine environment and equipment status data in real time from underwater acoustic channel acquisition station, equipment status acquisition station, environmental parameter acquisition station and communication signal acquisition station; The multidimensional data capture module is used to perform preliminary summarization, classification, formatting and packaging of the collected data, and transmit it in real time; The data preprocessing module is used to perform noise removal, format conversion, and multi-source data fusion processing on the packaged data; The multi-dimensional data calculation module is used to calculate the data processing delay index, channel complex environment quality correction coefficient, and fault risk value based on the preprocessed data. The judgment module is used to compare the data processing delay index and fault risk value with the preset primary warning threshold, adjustment threshold and emergency threshold, and to classify and send the information to the warning module and / or adjustment module according to the comparison results. The early warning module is used to trigger early warnings of the corresponding level based on the received information; The adjustment module is used to adjust the transmission power, channel frequency, or data transmission rate of the communication equipment online according to preset rules based on the received information; The optimization module is used to continuously collect early warning and adjustment records, analyze fault patterns and adjustment effects, and dynamically update system parameters, thresholds and adjustment strategies. The execution module is used to receive and execute instructions from the early warning module, adjustment module and optimization module, and dynamically adjust the signal modulation mode and error correction coding strength of the communication equipment according to the channel complex environment quality correction coefficient.
[0006] In some instances, by Obtain the data processing latency index ,in, For the first i Data source processing time, For the first i Dynamic weights of each data source n This indicates the total number of data sources.
[0007] In some instances, by Obtain the channel complex environment quality correction coefficient ,in, Indicates environmental interference factors. , This refers to the bubble concentration. For pressure gradient, For Doppler frequency shift, For carrier frequency, For delay extension, The symbol period.
[0008] In some instances, by Obtain the fault risk value ,in, Based on the failure rate, The current data transmission load, For the rated data transmission load, For the intensity coefficient of collaborative interaction, Historical fault correlation factors This represents the system's self-healing capability coefficient.
[0009] In some instances, when and When this occurs, it is considered normal and no action is triggered; when or When the time is right, it is determined to be a primary warning, and information is sent to the warning module; when or When an issue is identified as requiring adjustment, a message is sent to the adjustment module. when or When an emergency is declared, information is simultaneously sent to the early warning module and the adjustment module.
[0010] In some instances, when At that time, conventional modulation methods and basic error correction coding are used; when When necessary, switch to anti-interference modulation mode and enhance error correction coding; when At this time, the highest level of anti-interference mode is activated and the number of signal retransmissions is increased.
[0011] In some instances, the analysis and update operations of the optimization module specifically include: By using association rule mining, temporal causal analysis, and clustering methods, we can extract the patterns of failure occurrence from historical failure data. Based on the A / B testing framework and hypothesis testing, the effects of different parameter adjustment strategies are quantitatively evaluated. Based on the analysis results, the thresholds at each level of the judgment module are dynamically updated. and The calculation model contains parameters, and the parameter adjustment rule library is located in the adjustment module.
[0012] In some examples, the underwater acoustic channel acquisition station collects parameters such as channel bandwidth, multipath delay, channel time-varying speed, Doppler shift, signal attenuation, and noise level using sonar detectors; the equipment status acquisition station collects equipment operating temperature, voltage, current, fault codes, operating time, and communication status data using sensors; the environmental parameter acquisition station collects information on temperature, salinity, pressure, and suspended bubble concentration using buoy devices; and the communication signal acquisition station collects indicators such as signal strength, delay, bit error rate, signal-to-noise ratio, spectral distribution, and coverage using a signal analyzer.
[0013] Secondly, this invention provides a method for predicting collaborative communication faults among marine multi-platforms based on an intelligent self-healing mechanism, comprising: Real-time data on marine environment and equipment status are collected from underwater acoustic channel acquisition stations, equipment status acquisition stations, environmental parameter acquisition stations, and communication signal acquisition stations. The collected data is initially summarized, classified, formatted, and packaged, and then transmitted in real time. The packaged data undergoes noise removal, format conversion, and multi-source data fusion processing. The data processing delay index, channel complex environment quality correction coefficient, and fault risk value are calculated based on the preprocessed data. The data processing delay index and fault risk value are compared with preset primary warning thresholds, adjustment thresholds and emergency thresholds, and the information is classified according to the comparison results. Trigger the corresponding level of alert based on the categorized information; Based on the classified information, the transmission power, channel frequency, or data transmission rate of the communication equipment are adjusted online according to preset rules; Continuously collect early warning and adjustment records, analyze fault patterns and adjustment effects, and dynamically update system parameters, thresholds, and adjustment strategies; The signal modulation method and error correction coding strength of the communication equipment are dynamically adjusted based on the channel complexity environment quality correction coefficient.
[0014] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.
[0015] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects: (1) Through the closed-loop linkage of four major links—multidimensional data monitoring, intelligent computing analysis, hierarchical judgment and response, and continuous optimization learning—these scattered solutions are integrated into an organic whole with adaptive, self-learning, and self-healing capabilities. This achieves a fundamental shift from "passively responding to faults" to "actively predicting and intelligently self-healing," comprehensively enhancing the reliability, stability, and operational efficiency of the marine multi-platform collaborative communication system.
[0016] (2) This invention calculates the data processing delay index Yc and uses it as a quantitative indicator to measure the real-time performance of data processing. It can intuitively reflect the delay in the multi-source data fusion process. When the calculated data processing delay index Yc exceeds the threshold, a re-acquisition mechanism is triggered to optimize the data processing process in a timely manner, thus solving the problem that the fusion of multi-source heterogeneous data has a large delay and is difficult to meet the real-time fault prediction requirements.
[0017] (3) The present invention calculates the channel complex environment quality correction coefficient Kx to quantify the impact of the complex marine environment on the underwater acoustic channel. It substitutes the coefficient into the adjustment strategy of the execution module. The execution module adjusts the signal modulation mode and error correction coding strength of the communication equipment according to the correction coefficient, so that the system can dynamically adapt to the complex and ever-changing marine environment, improve communication quality and stability, and solve the problem of poor communication quality caused by the complex marine environment in traditional systems.
[0018] (4) This invention calculates the fault risk value Gv and uses it as a key indicator to assess the possibility of equipment failure. It comprehensively considers factors such as marine environment, equipment load and collaborative interaction. When the calculated fault risk value Gv exceeds the corresponding threshold, the system will automatically trigger the early warning module and adjustment module and take measures to enable the system to accurately assess the equipment failure risk, improve the accuracy of fault prediction, and provide a basis for fault self-healing. It can solve the problem of difficulty in fault prediction caused by unclear fault mechanism in traditional systems.
[0019] (5) The present invention uses a judgment module to classify and judge the data processing delay index and fault risk value, providing a clear direction for subsequent processing; the early warning module issues different levels of early warning to the operation and maintenance personnel in a timely manner according to the judgment results, ensuring that the personnel can respond quickly; the adjustment module adjusts the equipment parameters online for the problem of exceeding the standard, thereby reducing the probability of fault occurrence; the execution module optimizes the communication mode according to the channel correction coefficient to ensure communication quality. After the four modules work together, the system can realize the timely detection, classified processing and effective response to faults, and ultimately improve the fault prediction and self-healing efficiency of the system, thereby enhancing the stability and reliability of the marine communication system. Attached Figure Description
[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0021] Figure 1 This is a schematic diagram of the system provided in an embodiment of the present invention. Detailed Implementation
[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0023] In the following description, specific embodiments of the invention will be illustrated with reference to steps and symbols performed by one or more computers, unless otherwise stated. Therefore, these steps and operations will be referred to several times as being performed by a computer, and computer execution as referred to herein includes operations by a computer processing unit representing electronic signals of data in a structured format. This operation transforms the data or maintains it at a location in the computer's memory system, which can be reconfigured or otherwise alter the operation of the computer in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the invention described above are not intended to be limiting, and those skilled in the art will understand that many of the following steps and operations can also be implemented in hardware.
[0024] The terms "module" or "unit" as used herein can be considered as software objects executing on the computing system. Different components, modules, engines, and services described herein can be considered as implementations on the computing system. The apparatus and methods described herein are preferably implemented in software, but can also be implemented in hardware, both of which are within the scope of this invention.
[0025] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.
[0026] This invention addresses the need for timely fault detection, graded processing, and effective response capabilities in communication systems. It proposes a marine multi-platform collaborative communication fault prediction system based on an intelligent self-healing mechanism. This system boasts advantages such as high efficiency, real-time performance, high adaptability, and high reliability. It solves the problems of large delays in multi-source heterogeneous data fusion, poor underwater acoustic communication quality in complex marine environments, numerous communication coverage blind spots, and difficulties in fault prediction and self-healing due to unclear fault mechanisms.
[0027] In this embodiment of the invention, a marine multi-platform collaborative communication fault prediction system based on an intelligent self-healing mechanism is provided. Specifically, it includes a multi-dimensional data monitoring module, a multi-dimensional data capture module, a data preprocessing module, a multi-dimensional data calculation module, a judgment module, an early warning module, an adjustment module, an optimization module, and an execution module. (1) Multidimensional data monitoring module The multidimensional data monitoring module establishes four acquisition stations to capture and monitor data in different ways, and installs monitoring equipment at the acquisition stations to monitor and capture the marine environment and equipment status in real time; there are a total of four acquisition stations, namely, underwater acoustic channel acquisition station, equipment status acquisition station, environmental parameter acquisition station and communication signal acquisition station.
[0028] Among them, the underwater acoustic channel acquisition station is equipped with sonar detectors to capture parameters such as channel bandwidth, multipath delay, channel time-varying speed, Doppler frequency shift, signal attenuation, and noise level in real time; the equipment status acquisition station is equipped with sensors to monitor equipment operating temperature, voltage, current, fault codes, operating time, and communication status data; the environmental parameter acquisition station collects information on temperature, salinity, pressure, and suspended bubble concentration through buoy devices; and the communication signal acquisition station is equipped with a signal analyzer to record indicators such as signal strength, delay, bit error rate, signal-to-noise ratio, spectral distribution, and coverage.
[0029] (2) Multidimensional data capture module The multidimensional data capture module performs preliminary aggregation and classification of the data captured by the multidimensional data monitoring module. The data includes data from the complex internal marine environment, data from the acquisition equipment itself, data from the seabed, and data from the monitoring area of the monitoring station. After being packaged according to a preset format, the data is divided into four units: underwater acoustic channel acquisition unit, equipment status acquisition unit, environmental parameter acquisition unit, and communication signal acquisition unit, and then transmitted to the data preprocessing module in real time.
[0030] (3) Data preprocessing module The data preprocessing module performs noise removal, format conversion, and data fusion on the collected data, and then transmits it to the multidimensional data calculation module for calculation.
[0031] (4) Multidimensional data calculation module The multidimensional data calculation module includes a real-time calculation unit, a channel correction unit, and a fault mechanism analysis unit. Based on the preprocessed data from the data preprocessing module, it calculates the data processing delay index Yc, the channel complex environment quality correction coefficient Kx, and the fault risk value Gv. The real-time computing unit calculates the data processing latency index Yc, and its calculation formula is as follows:
[0032] In the formula, This represents the data processing latency index. This represents the processing time of the i-th data source. This represents the weight of the data source, and n represents the total number of data sources.
[0033] The channel correction unit calculates the channel quality correction coefficient Kx for complex environments, and the calculation formula is as follows:
[0034]
[0035] In the formula, B represents the channel quality correction coefficient for complex environments, and B represents the environmental interference factor, which is determined by the bubble concentration C and the pressure gradient. The product is obtained as follows: Indicates Doppler frequency shift, Indicates the carrier frequency. Indicates delay spread, Indicates the symbol period.
[0036] The fault mechanism analysis unit calculates the fault risk value Gv using the following formula:
[0037] In the formula, Gv represents the fault risk value. B represents the base failure rate, and B represents the environmental disturbance factor, which is determined by the bubble concentration C and the pressure gradient. The product is obtained as follows: Indicates the current data transmission load. Indicates the rated data transmission load. O represents the collaborative interaction strength coefficient, O represents the system's built-in intelligent self-healing capability coefficient, and L represents the historical fault correlation factor.
[0038] (5) Judgment Module The judgment module receives the data processing delay index Yc and fault risk value Gv's numerical range from the multidimensional data calculation module. It sets three judgment thresholds: a primary warning threshold, an adjustment threshold, and an emergency threshold. When the calculation result falls within the primary warning threshold range, it is judged as a primary problem, and the information is sent to the warning module. When the result exceeds the adjustment threshold but does not reach the emergency threshold, it is judged as a problem requiring adjustment, and the information is sent to the adjustment module. When the result exceeds the emergency threshold, it is simultaneously sent to both the warning module and the adjustment module. This module enables rapid classification and judgment of the calculation results, providing direction for subsequent processing. Specifically, it is divided into: 1) When the data processing latency index Yc < 50ms, it is within the normal range and no module needs to be triggered; when 50ms ≤ data processing latency index Yc < 100ms, it is within the primary warning threshold range, is judged as a primary problem, and the information is sent to the warning module; when 100ms ≤ data processing latency index Yc < 150ms, it is judged as a problem requiring adjustment, and the information is sent to the adjustment module; when the data processing latency index Yc ≥ 150ms, it exceeds the emergency threshold, and the information is sent to both the warning module and the adjustment module simultaneously. 2) When the fault risk value Gv < 0.3, it is within the normal range and no module needs to be triggered; when 0.3 ≤ fault risk value Gv < 0.5, it is within the primary warning threshold range, is determined to be a primary problem, and the information is sent to the warning module; when 0.5 ≤ fault risk value Gv < 0.8, it is within the primary warning threshold range, is determined to be a problem that needs adjustment, and the information is sent to the adjustment module; when the fault risk value Gv ≥ 0.8, it exceeds the emergency threshold and is simultaneously sent to the warning module and the adjustment module.
[0039] (6) Early warning module The early warning module receives the initial problem information sent by the judgment module, triggers different levels of early warning according to the problem type, and alerts the operation and maintenance personnel to potential fault risks and take preliminary measures through audible and visual alarms and remote push.
[0040] (7) Adjustment module The adjustment module addresses the issue of transmission exceeding limits by the judgment module by adjusting the transmission power, channel frequency, data transmission rate, and other parameters of the communication equipment online according to preset parameter adjustment rules.
[0041] The preset parameter adjustment rules include: To address the issues requiring adjustment, dynamic rate adaptation is adopted, triggering a stepped rate reduction mechanism to gradually reduce the data transmission rate of non-core services according to preset gradients (10%, 20%, etc.) to release network bandwidth resources; channel avoidance and switching are adopted, and if the current channel has significant interference, automatic frequency hopping is triggered to switch the device's communication channel to a pre-configured backup clean channel; dynamic fine-tuning of transmit power is adopted, analyzing the signal strength indication at the receiver and implementing a power enhancement strategy (increasing by 1dBm each time, gradually approaching the optimal value) to combat link attenuation.
[0042] In response to urgent issues, an aggressive load reduction approach is adopted. When the measures in the "adjustment required" phase are ineffective, the data transmission mode is forcibly switched to "keep-alive mode," which significantly reduces the heartbeat packet frequency and data reporting granularity, maintaining only the basic connection. A rapid frequency avoidance approach is adopted, directly switching to the preset "emergency backup frequency" to quickly escape the current strong interference environment.
[0043] (8) Optimization module and execution module The optimization module collects early warning information from the early warning module and adjustment records from the adjustment module, analyzes fault occurrence patterns and parameter adjustment effects, such as fault spatiotemporal pattern mining, clustering analysis of historical fault timestamps to identify peak fault occurrence periods; and preceding event tracking, extracting all early warning information within a window preceding each fault occurrence and finding strong correlation rules through association rule mining. This continuously improves the system's fault prediction and self-healing capabilities. The execution module receives commands from the early warning module, adjustment module, and optimization module, and precisely executes early warning activation, parameter adjustment, and system setting update operations according to the instructions. Based on the channel complexity environment quality correction coefficient Kx, it adjusts the signal modulation method and error correction coding strength of the communication equipment. When the channel complexity environment quality correction coefficient Kx ≥ 0.8, it adopts a conventional modulation method (such as orthogonal amplitude modulation) and basic error correction coding (such as the basic code rate of LDPC low-density parity-check code). When 0.5 ≤ channel complexity environment quality correction coefficient Kx < 0.8, it switches to an anti-interference modulation method (such as BPSK in DSSS direct sequence spread spectrum) and enhances error correction coding (such as convolutional code + interleaving coding). When the channel complexity environment quality correction coefficient Kx < 0.5, it activates the highest level of anti-interference mode (such as Chirp spread spectrum) and increases the number of signal retransmissions.
[0044] Furthermore, such as Figure 1 As shown, specific embodiments of the present invention will be described in further detail.
[0045] Step 1: Establish four data acquisition stations: a sonar detector for the underwater acoustic channel acquisition station, sensors for the equipment status acquisition station, a buoy device for the environmental parameter acquisition station, and a signal analyzer for the communication signal acquisition station, forming a multi-dimensional data monitoring module. Step 2: The multidimensional data capture module will initially summarize and classify the data acquired by the collection station, package it according to the preset format, and transmit it to the data preprocessing module in real time. Step 3: The data preprocessing module performs noise removal, format conversion, and data fusion processing on the collected classification data and then transmits it to the multidimensional data calculation module. Step 4: The multi-dimensional data calculation module calculates the data processing delay index Yc, the channel complex environment quality correction coefficient Kx, and the fault risk value Gv, and outputs them to the judgment module; Step 5: The judgment module receives the output results from the multidimensional data calculation module, sets three judgment thresholds: primary warning threshold, adjustment threshold, and emergency threshold, and performs rapid classification judgment. Step 6: Maintenance personnel take relevant preliminary solutions based on the type of warning; Adjustment module: In response to the problem of transmission exceeding the standard by the judgment module, the transmission power, channel frequency, data transmission rate and other parameters of the communication equipment are adjusted online according to the preset parameter adjustment rules to optimize the communication status in real time; Step 7: The optimization module collects early warning information from the early warning module and adjustment records from the adjustment module, analyzes the fault occurrence patterns and parameter adjustment effects, and optimizes the system settings for data acquisition frequency, calculation unit thresholds, and parameter adjustment strategies. Step 8: The execution module receives commands from the early warning module, adjustment module, and optimization module, and executes early warning activation, parameter adjustment, and system setting update operations precisely according to the instructions.
[0046] In another embodiment of the present invention, a method for predicting collaborative communication faults among marine multi-platforms based on an intelligent self-healing mechanism is provided, comprising: Real-time data on marine environment and equipment status are collected from underwater acoustic channel acquisition stations, equipment status acquisition stations, environmental parameter acquisition stations, and communication signal acquisition stations. The collected data is initially summarized, classified, formatted, and packaged, and then transmitted in real time. The packaged data undergoes noise removal, format conversion, and multi-source data fusion processing. The data processing delay index, channel complex environment quality correction coefficient, and fault risk value are calculated based on the preprocessed data. The data processing delay index and fault risk value are compared with preset primary warning thresholds, adjustment thresholds and emergency thresholds, and the information is classified according to the comparison results. Trigger the corresponding level of alert based on the categorized information; Based on the classified information, the transmission power, channel frequency, or data transmission rate of the communication equipment are adjusted online according to preset rules; Continuously collect early warning and adjustment records, analyze fault patterns and adjustment effects, and dynamically update system parameters, thresholds, and adjustment strategies; The signal modulation method and error correction coding strength of the communication equipment are dynamically adjusted based on the channel complexity environment quality correction coefficient.
[0047] The specific implementation methods for each step can be found in the description of the above system embodiments, and will not be repeated in the embodiments of the present invention.
[0048] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.
[0049] Therefore, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to execute the steps of any method provided in the embodiments of the present invention.
[0050] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.
[0051] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.
[0052] Since the computer program stored in the computer-readable storage medium can execute the steps of any of the methods provided in the embodiments of the present invention, the beneficial effects that any of the methods provided in the embodiments of the present invention can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.
[0053] The above provides a detailed description of a marine multi-platform collaborative communication fault prediction method and system based on an intelligent self-healing mechanism, as provided in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A marine multi-platform collaborative communication fault prediction system based on an intelligent self-healing mechanism, characterized in that, include: The multi-dimensional data monitoring module is used to collect marine environment and equipment status data in real time from underwater acoustic channel acquisition station, equipment status acquisition station, environmental parameter acquisition station and communication signal acquisition station; The multidimensional data capture module is used to initially summarize, classify, format, and package the collected data, and transmit it in real time. The data preprocessing module is used to perform noise removal, format conversion, and multi-source data fusion processing on the packaged data; The multi-dimensional data calculation module is used to calculate the data processing delay index, channel complex environment quality correction coefficient, and fault risk value based on the preprocessed data. The judgment module is used to compare the data processing delay index and fault risk value with the preset primary warning threshold, adjustment threshold and emergency threshold, and to classify and send the information to the warning module and / or adjustment module according to the comparison results. The early warning module is used to trigger early warnings of the corresponding level based on the received information; The adjustment module is used to adjust the transmission power, channel frequency, or data transmission rate of the communication equipment online according to preset rules based on the received information; The optimization module is used to continuously collect early warning and adjustment records, analyze fault patterns and adjustment effects, and dynamically update system parameters, thresholds and adjustment strategies. The execution module is used to receive and execute instructions from the early warning module, adjustment module and optimization module, and dynamically adjust the signal modulation mode and error correction coding strength of the communication equipment according to the channel complex environment quality correction coefficient.
2. The system according to claim 1, characterized in that, Depend on Obtain the data processing latency index ,in, For the first i Data source processing time, For the first i Dynamic weights of each data source n This indicates the total number of data sources.
3. The system according to claim 2, characterized in that, Depend on Obtain the channel complex environment quality correction coefficient ,in, Indicates environmental interference factors. , This refers to the bubble concentration. For pressure gradient, For Doppler frequency shift, For carrier frequency, For delay extension, The symbol period.
4. The system according to claim 3, characterized in that, Depend on Obtain the fault risk value ,in, Based on the failure rate, The current data transmission load, For the rated data transmission load, For the intensity coefficient of collaborative interaction, Historical fault correlation factors This represents the system's self-healing capability coefficient.
5. The system according to claim 4, characterized in that, when and When this occurs, it is considered normal and no action is triggered; when or When the time is right, it is determined to be a primary warning, and information is sent to the warning module; when or When an issue is identified as requiring adjustment, a message is sent to the adjustment module. when or When an emergency is declared, information is simultaneously sent to the early warning module and the adjustment module.
6. The system according to claim 5, characterized in that, when At that time, conventional modulation methods and basic error correction coding are used; when When necessary, switch to anti-interference modulation mode and enhance error correction coding; when At this time, the highest level of anti-interference mode is activated and the number of signal retransmissions is increased.
7. The system according to claim 6, characterized in that, The analysis and update operations of the optimization module specifically include: By using association rule mining, temporal causal analysis, and clustering methods, we can extract the patterns of failure occurrence from historical failure data. Based on the A / B testing framework and hypothesis testing, the effects of different parameter adjustment strategies are quantitatively evaluated. Based on the analysis results, the thresholds at each level of the judgment module are dynamically updated. and The calculation model contains parameters, and the parameter adjustment rule library is located in the adjustment module.
8. The system according to claim 1, characterized in that, The underwater acoustic channel acquisition station collects parameters such as channel bandwidth, multipath delay, channel time-varying speed, Doppler frequency shift, signal attenuation, and noise level using sonar detectors; the equipment status acquisition station collects equipment operating temperature, voltage, current, fault codes, operating time, and communication status data using sensors. The environmental parameter acquisition station collects information on temperature, salinity, pressure, and suspended bubble concentration using a buoy device; the communication signal acquisition station collects indicators such as signal strength, delay, bit error rate, signal-to-noise ratio, spectral distribution, and coverage using a signal analyzer.
9. A method for predicting faults in marine multi-platform collaborative communication based on an intelligent self-healing mechanism, characterized in that, include: Real-time data on marine environment and equipment status are collected from underwater acoustic channel acquisition stations, equipment status acquisition stations, environmental parameter acquisition stations, and communication signal acquisition stations. The collected data is initially summarized, classified, formatted, and packaged, and then transmitted in real time. The packaged data undergoes noise removal, format conversion, and multi-source data fusion processing. The data processing delay index, channel complex environment quality correction coefficient, and fault risk value are calculated based on the preprocessed data. The data processing delay index and fault risk value are compared with preset primary warning thresholds, adjustment thresholds and emergency thresholds, and the information is classified according to the comparison results. Trigger the corresponding level of alert based on the categorized information; Based on the classified information, the transmission power, channel frequency, or data transmission rate of the communication equipment are adjusted online according to preset rules; Continuously collect early warning and adjustment records, analyze fault patterns and adjustment effects, and dynamically update system parameters, thresholds, and adjustment strategies; The signal modulation method and error correction coding strength of the communication equipment are dynamically adjusted based on the channel complexity environment quality correction coefficient.
10. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method of claim 9.