Solution method and system for electromagnetic compatibility under ocean condition

Abstract

The invention provides a method and a system for solving electromagnetic compatibility under an ocean condition. The method comprises the following steps: deploying a distributed filter network with an adaptive tuning function to obtain electromagnetic environment data; carrying out simulation processing on the interaction influence of electromagnetic radiation between equipment under different sea conditions, and carrying out optimization processing on the optimal configuration strategy based on a particle swarm optimization algorithm to obtain a system layout scheme; implementing a cross-platform electromagnetic compatibility test scheme according to the system layout scheme, and performing risk assessment and prediction analysis on the potential interference source by using a random forest model to generate a safety guarantee strategy; and based on the security guarantee strategy, constructing a cloud collaborative management and control system, establishing a unified data exchange standard and protocol by using remote decision and instant adjustment processing, recording all operation logs, and generating an electromagnetic compatibility management process document. According to the technical scheme provided by the invention, the anti-interference capability and the stability of the system are remarkably improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of electromagnetic compatibility solution technology, and in particular, to an electromagnetic compatibility solution method and system under marine conditions. Background Art

[0002] In the marine environment, especially when it comes to communication and operation between fixed facilities (such as offshore oil platforms, wind power stations) and mobile platforms (such as ships, unmanned underwater vehicles), electromagnetic compatibility (EMC) is a key technical challenge. These scenarios usually require equipment to be able to operate stably in complex and changing electromagnetic environments while maintaining efficient signal transmission quality to ensure the security and reliability of data exchange. In addition, in extreme weather conditions or special sea conditions, it is also necessary to have strong anti-interference and adaptive adjustment capabilities to ensure the continuity and safety of the system.

[0003] At present, in order to deal with electromagnetic compatibility issues in the marine environment, the common practice is to use a fixed filtering network to reduce electromagnetic interference between devices and verify system performance through tests in a laboratory environment. In addition, some traditional simulation tools are also used to perform preliminary optimization of equipment layout, but these measures are mostly based on static analysis and lack a real-time response mechanism for dynamic changes in specific operating environments.

[0004] However, existing solutions have obvious limitations. Especially in the complex marine environment, fixed filtering networks are difficult to adapt to the ever-changing electromagnetic conditions, which limits their effectiveness. Traditional simulation and testing methods often ignore the unique challenges that may arise in actual operations, such as electromagnetic fluctuations under the influence of extreme weather and the uncertainty caused by interactions between different platforms. Therefore, they cannot provide sufficient safety guarantees and it is difficult to identify and avoid potential sources of interference in advance. Summary of the invention

[0005] The embodiments of the present application provide an electromagnetic compatibility solution and system under marine conditions, which are used to solve the problem in the prior art that it is difficult to identify and avoid potential interference sources in advance.

[0006] In a first aspect, an embodiment of the present application provides a solution to electromagnetic compatibility under marine conditions, including:

[0007] Deploy a distributed filter network with adaptive tuning capabilities to optimize electromagnetic compatibility between fixed facilities and mobile platforms, and perform global monitoring and local fine-tuning for extreme marine environmental conditions to obtain electromagnetic environment data;

[0008] The electromagnetic environment data is used to simulate the interaction of electromagnetic radiation between devices under different sea conditions, and a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to generate a variety of optimization configuration strategies. The optimization configuration strategies are optimized based on the particle swarm optimization algorithm to obtain a system layout plan;

[0009] According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify the system performance in the actual operating environment, and an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance. The random forest model is used to conduct risk assessment and predictive analysis on the potential interference sources to generate a safety assurance strategy;

[0010] Based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms, use remote decision-making and instant adjustment processing to establish unified data exchange standards and protocols, record all operation logs, and generate electromagnetic compatibility management process documents.

[0011] Optionally, the electromagnetic environment data is used to simulate the interactive effects of electromagnetic radiation between devices under different sea conditions, a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to generate a variety of optimization configuration strategies, and the optimization configuration strategies are further optimized based on a particle swarm optimization algorithm to obtain a system layout solution, including:

[0012] Using the electromagnetic environment data, the interaction effect of electromagnetic radiation between devices under different sea conditions is simulated to obtain an electromagnetic radiation interaction effect model;

[0013] According to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform combined with finite element analysis technology is used to simulate various possible equipment layouts and operating parameter settings to generate a variety of optimization configuration strategies;

[0014] Based on the multiple optimization configuration strategies, the particle swarm optimization algorithm is applied to evaluate and iteratively optimize the performance of each configuration scheme to obtain a system layout scheme.

[0015] Optionally, according to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform combined with finite element analysis technology is used to simulate various possible equipment layouts and operating parameter settings to generate multiple optimization configuration strategies, including:

[0016] According to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform is used to simulate the electromagnetic environment under different sea conditions to obtain a variety of electromagnetic scenarios;

[0017] Based on the various electromagnetic scenarios, combined with finite element analysis technology, detailed simulation processing is performed on various equipment layouts and operating parameter settings, taking into account the complex interactive effects of electromagnetic radiation between devices, and generating a preliminary optimization configuration solution set;

[0018] Using the preliminary optimized configuration scheme set, by introducing key performance indicators, the performance of each configuration scheme is comprehensively evaluated to obtain a performance evaluation report, wherein the key performance indicators include: signal integrity, anti-interference capability and communication reliability;

[0019] According to the performance evaluation report, multiple optimization configuration strategies that meet preset standards are screened out.

[0020] Optionally, based on the multiple electromagnetic scenarios, in combination with finite element analysis technology, various possible equipment layouts and operating parameter settings are simulated in detail, taking into account the complex interactive effects of electromagnetic radiation between devices, and generating a preliminary optimization configuration solution set, including:

[0021] Based on the various electromagnetic scenarios, combined with finite element analysis technology, detailed simulation processing is performed on various equipment layouts and operating parameter settings, considering the complex interactive effects of electromagnetic radiation between devices, and obtaining simulation results;

[0022] Using the simulation results, according to different equipment layouts and operating parameter settings, the electromagnetic field distribution, signal interference level and communication link stability under each configuration are deeply analyzed and processed to generate performance indicator data;

[0023] Based on the performance indicator data, a multi-criteria decision analysis method is applied to comprehensively evaluate the performance of each configuration scheme in terms of anti-interference capability, signal integrity and communication reliability, and a performance evaluation matrix is ​​constructed;

[0024] Based on the performance evaluation matrix, a preliminary optimized configuration solution set is generated by screening multiple configuration solutions that meet preset performance standards.

[0025] Optionally, according to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify the system performance in an actual operating environment, an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a random forest model is used to perform risk assessment and predictive analysis on the potential interference sources to generate a safety assurance strategy, including:

[0026] According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to fully verify the system performance in the actual operating environment to obtain performance verification data in the actual environment;

[0027] Using the performance verification data in the actual environment, an environment adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a list of potential interference sources is generated;

[0028] Based on the list of potential interference sources, a random forest model is applied to evaluate and predict the risk level and possibility of the potential interference sources to obtain a risk assessment report;

[0029] Develop a security strategy based on the risk assessment report.

[0030] Optionally, based on the list of potential interference sources, a random forest model is applied to evaluate and predict the risk level and possibility of the potential interference sources to obtain a risk assessment report, including:

[0031] Based on the list of potential interference sources, a random forest model is applied to construct an integrated learning model including multiple decision trees to comprehensively evaluate and predict the risk level and possibility of potential interference sources;

[0032] Using the results of the comprehensive assessment and predictive analysis, identify potential interference sources with high risk and high probability, and generate a list of key monitoring objects;

[0033] Based on the list of key monitoring objects, combined with historical data and real-time monitoring information, the development trend of potential interference sources is continuously tracked and processed to obtain dynamic risk warning information;

[0034] Based on the dynamic risk warning information, the assessment results of all potential interference sources are comprehensively considered to obtain a risk assessment report.

[0035] Optionally, based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms, support remote decision-making and instant adjustment, and generate electromagnetic compatibility management process documents by establishing unified data exchange standards and protocols, recording all operation logs, including:

[0036] Based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms to obtain a comprehensive electromagnetic environment data stream;

[0037] Utilizing the integrated electromagnetic environment data stream, utilizing remote decision making and instant adjustment processing, to generate dynamic dispatch instructions;

[0038] According to the dynamic scheduling instructions, a standardized data exchange process is obtained by establishing a unified data exchange standard and protocol;

[0039] Based on the standardized data interaction process, all operation logs are recorded to ensure that each step of the operation is traceable and generate electromagnetic compatibility management process documents.

[0040] In a second aspect, an embodiment of the present application provides an electromagnetic compatibility solution system under marine conditions, including:

[0041] The optimization module is used to deploy a distributed filter network with adaptive tuning function to optimize the electromagnetic compatibility between fixed facilities and mobile platforms, and to perform global monitoring and local fine-tuning for extreme marine environmental conditions to obtain electromagnetic environment data;

[0042] A simulation module is used to use the electromagnetic environment data to simulate the interaction between the electromagnetic radiation of the equipment under different sea conditions, and to generate a variety of optimization configuration strategies by using a multi-physics field coupling simulation platform combined with finite element analysis technology. The optimization configuration strategies are further optimized based on a particle swarm optimization algorithm to obtain a system layout plan;

[0043] A prediction module is used to implement a cross-platform electromagnetic compatibility test plan according to the system layout plan, verify the system performance in the actual operating environment, introduce an environmental adaptive algorithm to identify and avoid potential interference sources in advance, use a random forest model to conduct risk assessment and prediction analysis on the potential interference sources, and generate a safety assurance strategy;

[0044] The recording module is used to build a cloud-based collaborative management and control system based on the security assurance strategy, integrate real-time electromagnetic environment information from multiple mobile observation platforms, establish unified data exchange standards and protocols using remote decision-making and instant adjustment processing, record all operation logs, and generate electromagnetic compatibility management process documents.

[0045] In a third aspect, an embodiment of the present application provides a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an electromagnetic compatibility solution under marine conditions as described in the first aspect.

[0046] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements an electromagnetic compatibility solution under marine conditions as described in the first aspect.

[0047] In an embodiment of the present application, a distributed filtering network with an adaptive tuning function is deployed to optimize the electromagnetic compatibility between fixed facilities and mobile platforms, and global monitoring and local fine-tuning are performed for extreme marine environmental conditions to obtain electromagnetic environment data; the electromagnetic environment data is used to simulate the interactive effects of electromagnetic radiation between devices under different sea conditions, and a multi-physics field coupling simulation platform is combined with finite element analysis technology to generate a variety of optimization configuration strategies, and the optimization configuration strategies are optimized based on a particle swarm optimization algorithm to obtain a system layout plan; according to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify the system performance in an actual operating environment, an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a random forest model is used to perform risk assessment and predictive analysis on the potential interference sources to generate a security assurance strategy; based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms, and remote decision-making and instant adjustment processing are used to establish unified data exchange standards and protocols, record all operation logs, and generate electromagnetic compatibility management process documents.

[0048] The technical solution of this application has the following beneficial effects:

[0049] By deploying a distributed filtering network with adaptive tuning function, the electromagnetic compatibility between fixed facilities and mobile platforms can be effectively optimized. This helps to reduce electromagnetic interference between devices, improve signal transmission quality, and ensure the reliability of the communication system; this method performs global monitoring and local fine-tuning processing specifically for extreme marine environmental conditions to ensure that a good electromagnetic environment can be maintained even in harsh environments, thereby enhancing the stability and durability of the system under complex natural conditions; the use of a multi-physics field coupling simulation platform combined with finite element analysis technology can accurately simulate the interaction of electromagnetic radiation between devices under different sea conditions and generate a variety of optimization configuration strategies. Further optimization of these strategies based on the particle swarm optimization algorithm can obtain the best system layout solution, improve resource utilization and reduce costs; the introduction of an environmental adaptive algorithm can identify potential interference sources in advance in the actual operating environment and take corresponding avoidance measures. This method not only improves the security of the system, but also prevents service interruptions or failures caused by unforeseen interference; the use of a random forest model to conduct risk assessment and predictive analysis of potential interference sources can help decision makers formulate more scientific, reasonable and forward-looking security strategies and reduce operational risks; by integrating real-time electromagnetic environment information from multiple mobile observation platforms, remote decision-making and instant adjustment processing are supported, while establishing unified data exchange standards and protocols. This not only promotes information sharing and collaboration efficiency, but also ensures complete records of operation logs, facilitating subsequent audits and improvements.

[0050] Furthermore, by using electromagnetic environment data to construct an electromagnetic radiation interaction model, and combining the multi-physics field coupling simulation platform and finite element analysis technology to generate a variety of optimization configuration strategies, and then iteratively optimizing these strategies based on the particle swarm optimization algorithm, this method can accurately simulate the electromagnetic radiation interaction between equipment under different sea conditions, thereby ensuring that the selected system layout plan is not only optimal in theory, but also highly feasible and stable in practical applications, significantly improving the anti-interference capability and communication reliability of electronic systems in the marine environment.

[0051] Furthermore, cross-platform electromagnetic compatibility testing was implemented according to the system layout plan. While verifying the system performance in the actual operating environment, an environmental adaptive algorithm was introduced to identify and avoid potential interference sources in advance, and a random forest model was used for risk assessment and predictive analysis. This method not only improved the stability and safety of the system under real marine conditions, but also provided a scientific basis for dealing with unknown interference by generating detailed safety assurance strategies, greatly enhancing the system's predictive capabilities and emergency response efficiency.

[0052] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A flowchart of a method for solving electromagnetic compatibility problems under marine conditions provided in an embodiment of the present application;

[0055] Figure 2 A schematic diagram of the structure of an electromagnetic compatibility solution system under marine conditions provided in an embodiment of the present application;

[0056] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0058] In some of the processes described in the specification, claims, and the above-mentioned drawings of this application, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. Additionally, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as "first" and "second" in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit that "first" and "second" are of different types.

[0059] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present application.

[0060] Figure 1 The present application provides a flowchart of a method for solving electromagnetic compatibility under marine conditions. As Figure 1 shown, the method includes:

[0061] Deploy a distributed filtering network with an adaptive tuning function to optimize the electromagnetic compatibility between fixed facilities and mobile platforms, and perform global monitoring and local fine-tuning processing for extreme marine environmental conditions to obtain electromagnetic environment data;

[0062] In this step, the deployed adaptive tuning distributed filtering network includes a series of filters that can automatically adjust their parameters according to changes in the real-time electromagnetic environment, and is used to reduce electromagnetic interference between fixed facilities and mobile platforms. These filters collect electromagnetic environment data through sensors and use this data to optimize electromagnetic compatibility.

[0063] In the embodiments of the present application, in view of the variability of electromagnetic conditions under extreme marine environments, the system continuously monitors the global electromagnetic environment and performs fine-tuning processing on local areas to ensure that the filtering network is always in the best state, thereby guaranteeing the communication quality between devices.

[0064] Suppose a distributed filtering network with an adaptive tuning function is installed on an offshore oil platform. This network can dynamically adjust the filtering parameters according to changes in the marine environment (such as storms, tides, etc.) to maintain the stability and clarity of the communication link with nearby ships and other floating platforms.

[0065] The electromagnetic environment data is used to simulate the interaction of electromagnetic radiation between devices under different sea conditions, and a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to generate a variety of optimization configuration strategies. The optimization configuration strategies are optimized based on the particle swarm optimization algorithm to obtain a system layout plan;

[0066] In this step, electromagnetic environment data refers to information obtained from the deployed distributed filtering network and other monitoring equipment to describe the electromagnetic field characteristics existing in the current marine environment. This data is used to simulate the interaction of electromagnetic radiation between devices under different sea conditions, providing a basis for the subsequent generation of optimized configuration strategies.

[0067] In the embodiments of the present application, a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to accurately simulate the electromagnetic radiation conditions under various possible equipment layouts and operating parameter settings, and then a particle swarm optimization algorithm is applied to further optimize these configuration strategies, ultimately determining the most ideal system layout plan.

[0068] Suppose that when designing a new offshore wind power station, engineers used electromagnetic environment data and advanced simulation technology to simulate the electromagnetic radiation interaction between wind turbines and the control center under different weather conditions. After multiple iterative optimizations, they found an optimal layout plan that can both ensure signal transmission quality and minimize electromagnetic interference.

[0069] According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify the system performance in the actual operating environment, and an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance. The random forest model is used to conduct risk assessment and predictive analysis on the potential interference sources to generate a safety assurance strategy;

[0070] In this step, the cross-platform electromagnetic compatibility test solution refers to a set of methodologies for comprehensively verifying system performance, which covers not only simulation tests in the laboratory, but also actual tests in the actual working environment on site. Environmental adaptive algorithms and random forest models are used to identify potential interference sources in advance and assess their risk levels, based on which corresponding safety measures are formulated.

[0071] In the embodiment of the present application, after completing the theoretical optimization configuration, the system will be fully verified in the actual operating environment according to the proposed test plan. At the same time, intelligent algorithms will be introduced to detect and avoid potential interference sources in advance to ensure the safety and reliability of the system.

[0072] Suppose a research team is developing a new type of unmanned underwater vehicle. They first conduct a detailed cross-platform electromagnetic compatibility test based on the system layout plan obtained from the previous simulation. Then, they successfully predict and avoid some potential sources of electromagnetic interference through the environmental adaptive algorithm. Finally, they formulate a detailed safety assurance strategy based on the risk assessment of the random forest model to ensure that the unmanned underwater vehicle can operate safely and reliably in the complex and changeable marine environment.

[0073] 104 Based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms, use remote decision-making and instant adjustment processing to establish unified data exchange standards and protocols, record all operation logs, and generate electromagnetic compatibility management process documents.

[0074] In this step, the cloud-based collaborative management and control system is a system architecture that supports remote decision-making and instant adjustment processing by integrating real-time electromagnetic environment information from multiple mobile observation platforms. Unified data exchange standards and protocols ensure the consistency and accuracy of information transmission between all participants, while operation logs and management process documents record the operation details of each key step in the entire process for traceability and auditing.

[0075] In the embodiment of the present application, in order to achieve efficient management and resource sharing, the system constructs a cloud-based collaborative management and control platform that can receive and process real-time data from various mobile observation points, perform remote decision-making and instant adjustments, and establish strict data exchange standards to ensure that all operations are traceable and generate complete electromagnetic compatibility management documents.

[0076] Suppose a marine engineering company needs to manage multiple projects distributed in a vast sea area. It realizes centralized management and real-time monitoring of all projects by building a cloud-based collaborative management and control system. It can not only respond quickly to any electromagnetic compatibility issues, but also ensure that all operations follow a unified standard process, and all decisions and adjustments are recorded in detail, forming valuable electromagnetic compatibility management process documents.

[0077] In summary, the present invention covers a complete chain from hardware deployment to software algorithm application, to actual test verification and subsequent management and maintenance, aiming to provide a comprehensive electromagnetic compatibility solution under marine conditions to meet the growing demands of modern marine engineering projects for electromagnetic environment adaptability and system stability.

[0078] To address the problem that existing simulation methods lack in-depth analysis of the interactive effects of electromagnetic radiation, in some embodiments, the electromagnetic environment data is used to simulate the interactive effects of electromagnetic radiation between devices under different sea conditions. A multi-physics field coupling simulation platform combined with finite element analysis technology is adopted to generate a variety of optimized configuration strategies, and the particle swarm optimization algorithm is used to further optimize the optimized configuration strategies to obtain a system layout plan, including:

[0079] Using the electromagnetic environment data, simulate the interactive effects of electromagnetic radiation between devices under different sea conditions to obtain an electromagnetic radiation interactive effect model; according to the electromagnetic radiation interactive effect model, use a multi-physics field coupling simulation platform combined with finite element analysis technology to simulate various possible device layouts and operating parameter settings, and generate a variety of optimized configuration strategies; based on the various optimized configuration strategies, apply the particle swarm optimization algorithm to evaluate and iteratively optimize the performance of each configuration plan to obtain a system layout plan.

[0080] In this embodiment, the electromagnetic environment data includes real-time monitoring information from a distributed filtering network and other sensors, and is used to construct a mathematical model that accurately reflects the interactive effects of electromagnetic radiation between devices under different sea conditions.

[0081] In the embodiments of the present application, first, a detailed model describing the interactive effects of electromagnetic radiation is generated through simulation; second, a multi-physics field coupling simulation platform combined with finite element analysis technology is used to explore the possibilities of various device layouts; third, multiple optimized configuration strategies are proposed based on the simulation results; finally, these strategies are evaluated and optimized with the help of the particle swarm optimization algorithm, and the most suitable system layout plan is finally determined.

[0082] The following is a specific example:

[0083] Suppose in a newly built offshore wind power project, the engineering team first collects electromagnetic environment data covering the entire operation area and establishes a detailed electromagnetic radiation interactive effect model; second, they use advanced simulation tools to consider all possible device layouts under different weather conditions and conduct a detailed analysis of the electromagnetic field distribution; third, based on these analysis results, multiple preliminary optimized configuration plans are proposed; finally, through repeated iterative optimization with the particle swarm optimization algorithm, an optimal layout plan that can not only ensure the signal transmission quality but also minimize electromagnetic interference is found. Through the above steps, the team not only ensures the electromagnetic compatibility of the project but also significantly improves the stability and reliability of the system.

[0084] In order to solve the problem that the existing simulation methods are insufficient in optimizing the device layout in a complex electromagnetic environment, in some embodiments, according to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to simulate various possible device layouts and operating parameter settings, and generate a variety of optimization configuration strategies, including:

[0085] According to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform is used to simulate the electromagnetic environment under different sea conditions to obtain a variety of electromagnetic scenarios; based on the various electromagnetic scenarios, combined with finite element analysis technology, various equipment layouts and operating parameter settings are simulated in detail, and the complex interactive effects of electromagnetic radiation between devices are considered to generate a preliminary optimization configuration scheme set; using the preliminary optimization configuration scheme set, the performance of each configuration scheme is comprehensively evaluated by introducing key performance indicators to obtain a performance evaluation report, wherein the key performance indicators include: signal integrity, anti-interference capability and communication reliability; according to the performance evaluation report, multiple optimization configuration strategies that meet preset standards are screened out.

[0086] In this embodiment, the electromagnetic radiation interaction model is constructed based on the electromagnetic environment data collected in the early stage, and is used to describe how the electromagnetic radiation between devices interact under different sea conditions. These models provide a basis for subsequent simulations and ensure the authenticity and accuracy of the simulation results.

[0087] In the embodiments of the present application, a multi-physics field coupling simulation platform is first used to simulate the electromagnetic environment under different sea conditions to generate a series of possible electromagnetic scenarios; secondly, for each electromagnetic scenario, the finite element analysis technology is combined to deeply study the electromagnetic characteristics under various equipment layouts and operating parameter settings; thirdly, through a comprehensive evaluation of all preliminary optimization configuration schemes, key performance indicators such as signal integrity, anti-interference capability and communication reliability are introduced to form a detailed performance evaluation report; finally, based on the results of the evaluation report, the optimization configuration strategy that meets specific standards is selected to ensure that the final selected scheme can perform well in practical applications.

[0088] Here is a specific example:

[0089] Suppose in a deep-sea exploration project, the engineering team first simulated the electromagnetic environment that may occur under different ocean conditions from calm to severe based on the existing electromagnetic radiation interaction model, using the multi-physics field coupling simulation platform, and generated multiple electromagnetic scenarios; secondly, they combined finite element analysis technology to simulate the electromagnetic characteristics of different equipment layouts and parameter settings in these scenarios in detail, taking into account the complex electromagnetic interaction, and formed a preliminary set of optimized configuration solutions; thirdly, the team introduced signal integrity, anti-interference capability and communication reliability as evaluation criteria, conducted a comprehensive evaluation of each solution, and compiled a detailed performance evaluation report; finally, based on the evaluation in the report, several optimized configuration strategies that best meet the project needs were selected. Through the above steps, engineers can not only predict potential electromagnetic compatibility issues, but also take measures in advance to ensure the stable operation of the system.

[0090] In order to solve the problem that the existing simulation methods do not adequately consider the complex electromagnetic interaction effects between devices, in some embodiments, based on the multiple electromagnetic scenarios, in combination with finite element analysis technology, various possible device layouts and operating parameter settings are simulated in detail, considering the complex interaction effects of electromagnetic radiation between devices, and generating a preliminary optimization configuration solution set, including:

[0091] Based on the multiple electromagnetic scenarios, combined with finite element analysis technology, detailed simulation processing is performed on various equipment layouts and operating parameter settings, and the complex interactive effects of electromagnetic radiation between devices are considered to obtain simulation results; using the simulation results, according to different equipment layouts and operating parameter settings, the electromagnetic field distribution, signal interference level and communication link stability under each configuration are deeply analyzed and processed to generate performance indicator data; based on the performance indicator data, a multi-criteria decision analysis method is applied to comprehensively evaluate the performance of each configuration scheme in terms of anti-interference capability, signal integrity and communication reliability, and a performance evaluation matrix is ​​constructed; based on the performance evaluation matrix, a preliminary optimized configuration scheme set is generated by screening multiple configuration schemes that meet the preset performance standards.

[0092] In this embodiment, multiple electromagnetic scenarios refer to a series of simulated conditions generated according to the changes in the electromagnetic environment under different sea conditions (such as calm, storm, etc.). These scenarios are used to test the electromagnetic compatibility under different equipment layouts and operating parameter settings. The simulation results include specific values ​​of the electromagnetic field distribution map, signal interference level, and communication link stability, which are the basis for evaluating the performance of each configuration scheme.

[0093] In the embodiments of the present application, firstly, the finite element analysis technology is combined to carry out a detailed simulation of the equipment layout and operating parameters under each electromagnetic scenario; secondly, the obtained simulation results are used to deeply analyze the electromagnetic characteristics under each configuration, and the data of key performance indicators are recorded; thirdly, a multi-criteria decision analysis method is used to comprehensively evaluate all configuration schemes, forming a performance evaluation matrix including anti-interference capability, signal integrity and communication reliability; finally, the optimal configuration scheme is screened out according to the pre-set standards, thereby generating a preliminary set of optimized configuration schemes.

[0094] Here is a specific example: suppose that in an offshore oil platform project, the engineering team first used finite element analysis technology to simulate a variety of possible equipment layouts in detail based on electromagnetic scenarios generated under different marine conditions, and obtained a series of results reflecting electromagnetic characteristics; secondly, based on these simulation results, they analyzed the electromagnetic field distribution, signal interference level and communication link stability under each configuration, and generated detailed performance indicator data; thirdly, the team used a multi-criteria decision analysis method to comprehensively evaluate all configuration schemes from multiple dimensions such as anti-interference capability, signal integrity and communication reliability, and constructed a performance evaluation matrix; finally, based on the evaluation results in this matrix, several optimized configuration schemes that best meet the project requirements were selected. Through the above steps, engineers can ensure that the final selected equipment layout can not only work properly in the expected environment, but also provide the best electromagnetic compatibility.

[0095] This application takes into account that in the prior art, due to the problem of insufficient evaluation of electromagnetic compatibility optimization configuration strategies in complex marine environments, the invention embodiment proposes this optional solution. Traditional methods usually rely on static analysis and laboratory testing, lacking a real-time response mechanism to dynamic changes in the actual operating environment, which makes it difficult for the system to maintain optimal performance when facing a complex and changeable electromagnetic environment. In order to meet this challenge, the present application further provides an iterative optimization processing solution based on a particle swarm optimization algorithm, which aims to ensure that the anti-interference ability of the system is further improved while meeting all constraints, thereby solving the technical problem that the optimization configuration solution in the prior art is not flexible enough and has poor adaptability.

[0096] Optionally, based on the multiple optimization configuration strategies, a particle swarm optimization algorithm is applied to evaluate and iteratively optimize the performance of each configuration scheme to ensure that the anti-interference ability of the system is further improved while satisfying all constraints, and a system layout scheme is obtained, including:

[0097] In calculating the fitness score F iPreviously, we simulated the interaction of electromagnetic radiation through multi-physics coupling simulation and finite element analysis technology to generate detailed simulation results. Then, we used the environmental adaptive algorithm to identify potential interference sources, and applied the random forest model to assess their risks and build a variety of optimization configuration strategies;

[0098]

[0099] Among them, F i represents the fitness score of the i-th configuration scheme; N represents the total number of devices; w j represents the importance weight coefficient of the jth device; P ij represents the availability score of the jth device under the i-th configuration scheme; λ represents the distance attenuation factor, which is used to adjust the impact intensity of the distance from the resource to the task location; D ij represents the distance from the jth device to the task location in the i-th configuration scheme; C i represents the cost of the i-th configuration scheme; α represents the uncertainty impact coefficient, reflecting the impact of environmental changes and unknown factors; V i represents the variability score of the i-th configuration scheme, reflecting the uncertainty and risk of the path in the current environment; η represents the interference influence coefficient, which is used to adjust the influence of interference factors on the fitness score; I i Represents the interference impact score of the i-th configuration scheme.

[0100] After calculating the fitness score F i Finally, the particle swarm optimization algorithm is used to perform iterative optimization in combination with system constraints to evaluate the long-term stability and reliability of each configuration scheme. The dynamic priority scoring mechanism is introduced to comprehensively consider the influence of success rate deviation, task uncertainty and external factors, and the dynamic priority score G of each configuration scheme is calculated. i ;

[0101]

[0102] Among them, G i represents the dynamic priority score of the i-th configuration scheme; β represents the probability enhancement factor, emphasizing the impact of fitness score on path selection probability; F i Calculated by the previous formula, it represents the fitness score of the i-th configuration scheme; γ represents the sensitivity coefficient, which reflects the degree of influence of the fitness score deviating from the average value; ΔS i represents the success rate deviation of the i-th configuration scheme relative to the average success rate score of all configuration schemes; δ represents the uncertainty coefficient, which reflects the influence of unknown factors in the path; μ represents the average success rate score of all configuration schemes; U irepresents the task uncertainty score of the i-th configuration scheme; θ represents the external factor influence coefficient, emphasizing the impact of external factors on the prediction results; M represents the number of external factors; R ik It indicates the impact score of the kth external factor on the i-th configuration scheme.

[0103] Based on the dynamic priority score G i , sort all configuration schemes and select the best candidate scheme. Verify its feasibility through virtual environment testing, and fine-tune and optimize it in combination with real-time monitoring data and historical experience, and finally determine the most suitable system layout scheme to ensure its efficient electromagnetic compatibility and stability in complex marine environments.

[0104] This formula aims to accurately quantify the fitness score F of each configuration solution. i and dynamic priority score G i , so that iterative optimization can be performed through the particle swarm optimization algorithm. These formulas comprehensively consider multiple factors such as the importance, availability, cost, distance attenuation, uncertainty impact, interference impact, and external factors of the equipment to ensure that the final system layout solution is not only optimal in theory, but also highly feasible and stable in practical applications.

[0105] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0106]

[0107] Weighted score of equipment availability; reflects the contribution of each equipment in a specific configuration; C i : Configuration plan cost; directly reflects the economic efficiency of the plan; α·V i : Uncertainty impact adjustment; Consider the impact of environmental changes and unknown factors on path selection; η·I i : Interference impact adjustment; adjust the impact of interference factors on fitness scores;

[0108] The following is a brief introduction to how to obtain the parameters of the formula:

[0109] w j : The importance weight coefficient of the jth device, which is set according to the device function and task requirements; P ij : The availability score of the jth device under the i-th configuration scheme, obtained through simulation; λ: Distance attenuation factor, which adjusts the impact intensity of the distance from the resource to the task location and is set according to the empirical value; D ij : The distance from the jth device to the mission location in the i-th configuration scheme, obtained through geographic information system (GIS) data; C i: The cost of the i-th configuration plan, which is the budget estimate provided by the finance department; α: Uncertainty impact coefficient, obtained based on historical data analysis; V i : The variability score of the i-th configuration plan, obtained through statistical analysis of historical data; η: Interference impact coefficient, set according to industry standards or empirical values; I i : The interference impact score of the i-th configuration plan, calculated after evaluating the risks of potential interference sources through a random forest model;

[0110] The following briefly introduces the design reasons for each item of this formula:

[0111]

[0112] β·F i : The product of the probability enhancement factor and the fitness score; emphasizing the impact of the fitness score on the path selection probability; γ·ΔS i : Success rate deviation adjustment; reflecting the degree of influence of the fitness score deviating from the average value; δ·(μ - U i ): Uncertainty coefficient adjustment; reflecting the impact of unknown factors in the path; External factor influence adjustment; emphasizing the impact of external factors on the prediction result;

[0113] The following briefly introduces the acquisition methods of each parameter of this formula:

[0114] β: Probability enhancement factor, set according to historical successful cases; F i : Calculated by the previous formula, representing the fitness score of the i-th configuration plan; γ: Sensitivity coefficient, obtained based on historical data analysis; ΔS i : The success rate deviation of the i-th configuration plan relative to the average success rate score of all configuration plans, obtained through statistical analysis of historical data; δ: Uncertainty coefficient, obtained based on historical data analysis; μ: The average value of the success rate scores of all configuration plans, obtained through statistical analysis of historical data; U i : The task uncertainty score of the i-th configuration plan, obtained through expert evaluation or simulation; θ: External factor influence coefficient, set according to industry standards or empirical values; R ik : The influence score of the i-th configuration plan affected by the k-th external factor, calculated through a risk assessment model;

[0115] Suppose in an offshore wind power station project, the engineer team first simulated the electromagnetic radiation interaction effects under different sea conditions through multi-physics field coupling simulation and finite element analysis technology, generating detailed simulation results. Subsequently, the environmental adaptive algorithm was used to identify potential interference sources, and the random forest model was applied to evaluate their risks, constructing a variety of optimization configuration strategies. For one of the configuration plans:

[0116]

[0117] Among them, N = 10 represents the total number of devices, and w j = [0.8, 0.7, …, 0.6] are the importance weight coefficients of each device, and P ij is the device availability score obtained from simulation, λ = 0.2 is the distance attenuation factor, and D ij is the distance information obtained from GIS data, and C i = 500 is the cost, α = 0.5, and V i = 0.4 is the variability score obtained from statistical analysis, η = 0.3, and I i = 0.6 is the risk score evaluated by the random forest model. After calculation, F i = 0.78 is obtained. Next, the particle swarm optimization algorithm is used to perform iterative optimization in combination with the system constraint conditions to calculate the dynamic priority score G i of each configuration plan:

[0118]

[0119] Among them, β = 1.2, γ = 0.3, and ΔS i = 0.05 is the success rate deviation, δ = 0.4, μ = 0.8, and U i = 0.2, θ = 0.7, M = 3, and R ik is the impact score calculated by the risk assessment model. After calculation, G i = 1.92 is obtained. Assuming that the set threshold is 1.5, since the calculated dynamic priority score G i = 1.92 is greater than the set threshold, it indicates that this configuration plan has high feasibility and reliability and can provide high-efficiency electromagnetic compatibility and stability in a complex marine environment. This shows that the optimized system layout plan not only performs excellently theoretically but also can maintain good performance and anti-interference ability in practical applications. Based on G i , all configuration plans are sorted, and the optimal candidate plan is selected. Its feasibility is verified through virtual environment testing, and fine-tuning optimization is carried out in combination with real-time monitoring data and historical experience to finally determine the most suitable system layout plan to ensure its high-efficiency electromagnetic compatibility and stability in a complex marine environment.

[0120] In summary, through the above steps, engineers can not only accurately evaluate the performance of each configuration plan but also ensure that the selected plan performs excellently in practical applications, significantly improving the anti-interference ability and long-term stability of the system.

[0121] In order to solve the problem that existing methods are insufficient in identifying and processing potential interference sources in actual operating environments, in some embodiments, according to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify system performance in an actual operating environment, an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a random forest model is used to perform risk assessment and predictive analysis on the potential interference sources to generate a safety assurance strategy, including:

[0122] According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented, and the system performance is comprehensively verified in the actual operating environment to obtain performance verification data in the actual environment; using the performance verification data in the actual environment, an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a list of potential interference sources is generated; based on the list of potential interference sources, a random forest model is applied to evaluate and predict the risk level and possibility of potential interference sources to obtain a risk assessment report; based on the risk assessment report, a safety assurance strategy is formulated.

[0123] In this embodiment, the system layout plan is based on the optimal equipment configuration determined after preliminary simulation optimization, which is used to guide actual testing; the performance verification data under actual conditions refers to various indicators of system performance obtained through testing in a real marine environment, such as signal strength, communication stability and anti-interference capability; the list of potential interference sources lists all sources of electromagnetic interference and their characteristics that may affect the normal operation of the system.

[0124] In the embodiments of the present application, firstly, a detailed electromagnetic compatibility test is implemented in the actual operating environment according to the system layout plan, and comprehensive performance verification data is collected; secondly, these data are combined with the environment adaptive algorithm to achieve early identification of potential interference sources, and corresponding avoidance measures are taken to compile a list of potential interference sources; thirdly, based on the list of potential interference sources, a random forest model is used to conduct in-depth risk assessment and predictive analysis to form a detailed risk assessment report; finally, a specific security assurance strategy is formulated based on the evaluation results to ensure the safety and reliability of the system.

[0125] The following is a specific example: Suppose in an offshore wind power station project, the engineer team first conducted a comprehensive electromagnetic compatibility test in the actual marine environment according to the pre-optimized system layout plan, and obtained a number of performance verification data including signal transmission quality, anti-interference ability, and communication stability; Secondly, they used this data combined with the environmental adaptive algorithm to successfully identify and record a series of potential electromagnetic interference sources, forming a list of potential interference sources; Thirdly, based on this list, the team applied the random forest model to conduct a detailed assessment of the risk level and occurrence probability of each potential interference source, generating a risk assessment report; Finally, according to the evaluation results, targeted safety assurance strategies were formulated to ensure that the wind power station can operate continuously and stably in the complex and changeable marine environment. Through the above steps, the engineers not only improved the electromagnetic compatibility of the system, but also enhanced the ability to cope with unknown interference.

[0126] In order to further improve the accuracy and forward-looking of the risk assessment of potential interference sources, in some embodiments, based on the list of potential interference sources, a random forest model is applied to evaluate and predict the risk level and possibility of potential interference sources, and a risk assessment report is obtained, including:

[0127] Based on the list of potential interference sources, a random forest model is applied to construct an ensemble learning model containing multiple decision trees to comprehensively evaluate and predict the risk level and possibility of potential interference sources; Using the results of the comprehensive evaluation and prediction analysis, identify potential interference sources with high risk and high possibility, and generate a list of key monitoring objects; According to the list of key monitoring objects, combined with historical data and real-time monitoring information, continuously track the development trend of potential interference sources to obtain dynamic risk warning information; Based on the dynamic risk warning information, comprehensively consider the evaluation results of all potential interference sources to obtain a risk assessment report.

[0128] In this embodiment, the list of potential interference sources is a set of electromagnetic interference sources that may affect the system operation identified through preliminary tests and environmental adaptive algorithms. These data are used to train the random forest model to help identify which interference sources are most likely to pose high risks. The list of key monitoring objects is the items that need special attention selected from the potential interference sources, and the dynamic risk warning information provides a trend prediction of the future behavior of these interference sources.

[0129] In the embodiment of the present application, firstly, an integrated learning model consisting of multiple decision trees is constructed using a random forest model to evaluate the risk level and possibility of potential interference sources; secondly, based on the results of the model output, it is determined which potential interference sources have the highest risk and possibility, and a list of key monitoring objects is generated; thirdly, the team combines historical data and real-time monitoring information to continuously track these key monitoring objects and form dynamic risk warning information; finally, based on the evaluation results of all potential interference sources, a detailed risk assessment report is compiled to provide a scientific basis for subsequent security assurance strategies.

[0130] Here is a specific example:

[0131] Suppose in an offshore oil platform project, the engineering team first built an integrated learning model of multiple decision trees based on the list of potential interference sources using the random forest model, and comprehensively evaluated the risk level and possibility of each potential interference source; secondly, based on the output of the model, they identified several key monitoring objects with high risk and high possibility, and listed them in detail; thirdly, the team combined historical interference event data and real-time monitoring information to track these key monitoring objects for a long time, discovered the behavior patterns of interference sources under certain specific conditions, and obtained dynamic risk warning information; finally, based on the evaluation results of all potential interference sources, the team compiled a risk assessment report covering all key findings. Through the above steps, engineers can not only predict potential threats in advance, but also take timely measures to prevent risks and ensure the stable operation of the system.

[0132] In this step, in the prior art, because there is a problem of insufficient timeliness and accuracy in identifying and avoiding potential interference sources, the embodiment of the invention proposes this optional solution. Traditional methods usually rely on static analysis and laboratory testing, lacking a real-time response mechanism to dynamic changes in the actual operating environment, which makes it difficult for the system to maintain optimal performance when facing a complex and changeable electromagnetic environment. In order to meet this challenge, the present application further provides an early identification and avoidance processing solution based on an environmental adaptive algorithm and multi-source data fusion technology, which aims to solve the technical problems of untimely identification and inaccurate evaluation of potential interference sources in the prior art, thereby improving the electromagnetic compatibility and stability of the system.

[0133] Optionally, utilizing the performance verification data in the actual environment, introducing an environment adaptive algorithm, identifying and avoiding potential interference sources in advance, and generating a list of potential interference sources includes:

[0134] In calculating the identification score R of potential interference sources kPreviously, we collected and processed real-time electromagnetic environment information from multiple monitoring points and different types of sensors through multi-source data fusion technology, applied environmental adaptive algorithms to identify potential interference signals and evaluate their detection probability, and built a preliminary list of potential interference sources by referring to the impact of similar historical events;

[0135]

[0136] Among them, R k represents the identification score of the kth potential interference source; M represents the total number of monitoring points; A i represents the signal strength weight of the ith monitoring point; b ik represents the detection probability of the kth potential interference source at the i-th monitoring point; L represents the number of sensor types; T l Represents the sensitivity coefficient of the lth sensor type; S lk represents the probability that the kth potential interference source is detected by the lth sensor type; N represents the number of similar events in the historical records; C j represents the impact coefficient of the jth historical similar event; P represents the number of known interference sources in the current environment; Q p represents the impact strength of the pth known interference source; D k represents the historical occurrence frequency of the kth potential interference source;

[0137] After calculating the recognition score R k After that, the avoidance processing priority evaluation is performed. The recognition score is normalized, and the impact attenuation factor is introduced to adjust the score based on the system response speed, key task impact, external factors, etc., to generate the avoidance processing priority score P k , to assess the urgency and importance of each potential source of interference;

[0138]

[0139] Among them, P k represents the avoidance priority score of the kth potential interference source; E represents the system response speed factor; R k Calculated by the previous formula, it represents the identification score of the kth potential interference source; F represents the score adjustment index; G represents the impact attenuation factor; H k represents the impact of the kth potential interference source on the critical task; Q represents the number of external factors; Z q represents the importance coefficient of the qth external factor; W kq Indicates the degree to which the kth potential interference source is affected by the qth external factor;

[0140] Based on the avoidance priority score P k, sort and screen potential interference sources, and select the candidate interference sources that need the most attention. Verify its actual performance through virtual environment testing, and dynamically update the avoidance strategy based on real-time monitoring data and historical experience, and finally generate a detailed list of potential interference sources to ensure the electromagnetic compatibility and stability of the system.

[0141] This formula aims to accurately quantify the identification score R of each potential interference source. k and avoidance priority score R k , so that they can be identified and avoided in advance through environmental adaptive algorithms. These formulas take into account multiple factors such as the signal strength of the monitoring point, detection probability, sensor sensitivity, the impact of historical events, and known interference sources, ensuring that the final list of potential interference sources is not only optimal in theory, but also highly feasible and reliable in practical applications.

[0142] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0143]

[0144] The weighted detection probability of the signal strength of the monitoring point; reflects the probability of each monitoring point detecting a potential interference source;

[0145] Sensor type sensitivity weighted detection probability; considers the detection capabilities of different types of sensors for potential interference sources;

[0146] Adjustment of the impact of historical similar events and known interference sources: Considering the impact of historical events and known interference sources makes the scoring more comprehensive;

[0147] ln(1+D k ): Adjustment of historical occurrence frequency; increase the score of potential interference sources that have appeared multiple times;

[0148] The following is a brief introduction to how to obtain the parameters of the formula:

[0149] A i : The signal strength weight of the i-th monitoring point, which is set according to the importance of the monitoring point; B ik : The detection probability of the jth potential interference source at the i-th monitoring point is calculated by the environment adaptive algorithm; T l : Sensitivity coefficient of the first sensor type, set according to sensor performance; S lk : The probability that the kth potential interference source is detected by the first sensor type, calculated by the environment adaptive algorithm; C j : The impact coefficient of the jth historical similar event, obtained based on historical data analysis; Q p: The impact strength of the pth known interference source, set according to experience or historical records; D k : The historical occurrence frequency of the kth potential interference source, obtained through historical data analysis;

[0150] The following is a brief introduction to the design reasons of each sub-item of the formula:

[0151]

[0152] Normalized recognition score adjustment: Normalize and adjust the recognition score to highlight potential interference sources with high scores; Adjust the impact of key tasks; consider the impact of potential interference sources on key tasks; Adjustment of the influence of external factors; Emphasis on the impact of external factors on avoidance processing priorities;

[0153] The following is a brief introduction to how to obtain the parameters of the formula:

[0154] E: System response speed factor, set according to the system response time; R k : Calculated by the previous formula, it represents the recognition score of the kth potential interference source; F: score adjustment index, set according to the score distribution characteristics; G: impact attenuation factor, set according to the importance of the task; H k : The impact of the kth potential interference source on the critical task, obtained through expert evaluation or simulation; Z q : The importance coefficient of the qth external factor, set according to industry standards or experience values; W kq : The degree to which the kth potential interference source is affected by the qth external factor is calculated by the risk assessment model;

[0155] Assume that in an offshore oil platform project, the engineering team first collects and processes real-time electromagnetic environment information from multiple monitoring points and different types of sensors through multi-source data fusion technology, applies environmental adaptive algorithms to identify potential interference signals and evaluate their detection probability, and builds a preliminary list of potential interference sources by referring to the impact of similar historical events. For one of the potential interference sources:

[0156]

[0157] Where, M = 5, A i =0.8,B ik =0.9,L=3,T l =0.7,S lk =0.8,N=10,C j =0.6,P=4,Q p =0.5, D k =0.2. After calculation, we get Rk = 1.5.

[0158] Next, calculate the avoidance processing priority score P k :

[0159]

[0160] where E = 1.2, R k = 1.5, max(R) = 1.8, F = 1.5, G = 0.3, H k = 0.9, Q = 2, Z q = 0.6, W kq = 0.7. After calculation, P k = 1.68.

[0161] Based on P k , all potential interference sources are sorted and screened, and the candidate interference sources that need the most attention are selected. Verify their actual performance through virtual environment testing, and dynamically update the avoidance strategy by combining real-time monitoring data and historical experience. Finally, generate a detailed list of potential interference sources to ensure the electromagnetic compatibility and stability of the system.

[0162] Assume that the set threshold is 1.5. Since the calculated avoidance processing priority score P k = 1.68 is greater than the set threshold, it indicates that this potential interference source has a high level of urgency and importance, and avoidance measures need to be taken immediately. This shows that the optimized potential interference source identification and avoidance processing scheme not only performs excellently in theory, but also can effectively improve the anti-interference ability and stability of the system in practical applications, ensuring its efficient operation in a complex marine environment.

[0163] To solve the problems of information silos and decision-making lag in the electromagnetic compatibility management of existing systems, in some embodiments, based on the security guarantee strategy, a cloud-based collaborative control system is constructed, integrating real-time electromagnetic environment information from multiple mobile observation platforms, supporting remote decision-making and immediate adjustment. By establishing a unified data exchange standard and protocol, recording all operation logs, and generating electromagnetic compatibility management process documents, including:

[0164] Based on the security guarantee strategy, construct a cloud-based collaborative control system, integrate real-time electromagnetic environment information from multiple mobile observation platforms to obtain a comprehensive electromagnetic environment data stream; use the comprehensive electromagnetic environment data stream for remote decision-making and immediate adjustment processing to generate dynamic scheduling instructions; according to the dynamic scheduling instructions, establish a unified data exchange standard and protocol to obtain a standardized data interaction process; based on the standardized data interaction process, record all operation logs to ensure that the operations of each step are traceable, and generate electromagnetic compatibility management process documents.

[0165] In this embodiment, the security strategy refers to a series of measures formulated based on the results of the previous risk assessment to guide the safe operation of the system. The comprehensive electromagnetic environment data stream is composed of electromagnetic environment monitoring data collected in real time by multiple mobile observation platforms and uploaded to the cloud. These data provide a basis for subsequent remote decision-making. The standardized data interaction process is to ensure the consistency and accuracy of information transmission between different platforms by establishing unified data exchange standards and protocols.

[0166] In the embodiments of the present application, firstly, according to the existing security assurance strategy, a cloud-based collaborative management and control system is built, which can receive and integrate real-time electromagnetic environment information from multiple mobile observation platforms to form a comprehensive integrated electromagnetic environment data stream; secondly, using this data stream, the system can execute remote decisions, and make instant adjustments to cope with the changing electromagnetic environment, and generate corresponding dynamic scheduling instructions; thirdly, in order to ensure the effectiveness and consistency of information transmission, the team established a standardized data interaction process and defined unified data exchange standards and protocols; finally, all operations are recorded in detail to form a complete operation log to ensure that each step is traceable, and finally a detailed electromagnetic compatibility management process document is generated.

[0167] Here is a specific example:

[0168] Suppose that in a large offshore wind farm project, the engineering team first built a cloud-based collaborative management and control platform based on the previously formulated security strategy. The platform integrates real-time electromagnetic environment data from multiple wind turbines and monitoring ships to form a comprehensive electromagnetic environment data stream; secondly, they use this data stream to make remote decisions, adjust equipment parameters in time for different electromagnetic conditions, and generate dynamic scheduling instructions; thirdly, the team has formulated a standardized data interaction process to ensure that all participants can follow the unified data exchange standards and protocols to achieve efficient information sharing; finally, all operations are recorded in detail, from the initial decision to the final adjustment, every step is traceable, and finally a detailed electromagnetic compatibility management process document is compiled. Through the above steps, not only the system's response speed and decision-making efficiency are improved, but also the transparency and traceability of the entire management process are ensured.

[0169] In addition, the EMC characteristics of the equipment itself, especially the radiation disturbance and anti-interference capabilities, will be supplemented below.

[0170] In practical applications, ensuring that the equipment does not generate excessive electromagnetic radiation is essential to maintaining a good electromagnetic environment. In order to control the radiated disturbance of the equipment:

[0171] At the early stage of product design, we should consider adopting low-noise architecture, selecting materials and technologies with good shielding performance, and planning a reasonable circuit layout to reduce unnecessary electromagnetic emissions.

[0172] Using metal casings or coatings provides physical shielding to prevent internal electronic components from emitting harmful radiation to the outside; it also protects internal components from external interference.

[0173] Install appropriate input and output port filters to filter out high-frequency components and reduce electromagnetic pollution to the outside world.

[0174] Ensure that the equipment has a good and consistent grounding scheme to avoid additional radiation issues caused by poor grounding.

[0175] In order to enable the equipment to work stably in a complex electromagnetic environment, its anti-interference ability must be enhanced:

[0176] Take necessary protective measures for the power lines and signal lines entering the equipment, such as using common-mode chokes, differential-mode capacitors and other components to suppress conducted interference.

[0177] For key circuit parts that are susceptible to electromagnetic interference, such as microprocessors, memories, etc., additional layers of protection can be added, such as a Faraday cage or dedicated EMI absorbing materials.

[0178] Write robust program code that can tolerate a certain degree of data errors and quickly restore normal operations; in addition, you can introduce redundancy mechanisms to improve the fault tolerance of the system.

[0179] In summary, when deploying electronic equipment in a marine environment, in addition to macro-level electromagnetic compatibility management according to the method described in the patent, it is also necessary to pay attention to the EMC design of each individual device to ensure that they will not become new interference sources and can maintain efficient and stable operation in a complex and changing electromagnetic environment. This will not only help improve the reliability of the entire system, but also provide users with a safer and more reliable service experience.

[0180] Specifically, robust program code should be written to tolerate a certain degree of data errors and quickly restore normal operations. In addition, redundancy mechanisms can be introduced to improve the fault tolerance of the system, including:

[0181] Error detection and correction (EDAC): Implements hardware or software level error detection and correction mechanisms such as parity check, cyclic redundancy check (CRC), Hamming code, etc. These techniques can help identify and repair data transmission errors caused by electromagnetic interference.

[0182] Triple Modular Redundancy (TMR): For critical tasks, triple modular redundancy can be used, that is, running three identical program copies at the same time and determining the final result through a voting algorithm. Even if one of the copies is disturbed, the other two copies can still guarantee correct output.

[0183] Input validation: Ensure that all external input data undergoes a strict validation process to prevent illegal or abnormal data from entering the system and causing unpredictable behavior.

[0184] Status monitoring and recovery: Continuously monitor the operating status of the system and take immediate action if an abnormality is found, such as restarting the affected service or rolling back to the nearest safe point.

[0185] Current limiting and throttling: Limit the request rate or the number of service calls to avoid resource exhaustion due to sudden traffic or malicious attacks.

[0186] Real-time operating system (RTOS): Use a real-time operating system to manage task scheduling, ensuring that high-priority tasks can be executed within the specified time and reducing the impact of delays.

[0187] Preemptive scheduling: Allows higher priority tasks to interrupt lower priority tasks, thereby quickly responding to emergency events and improving system response speed.

[0188] Dynamic parameter adjustment: Automatically adjust algorithm parameters according to current workload or electromagnetic environment conditions to achieve optimal performance. For example, reduce the communication rate or increase the number of retransmissions in a strong electromagnetic interference environment.

[0189] Intelligent fault prediction: Use machine learning models to analyze historical data, predict possible faults in advance, and take preventive measures in time.

[0190] Distributed database replication: Save the same data copies on multiple nodes. When a node has a problem, the latest data can be obtained from other nodes to maintain business continuity.

[0191] Transaction logging: Detailed log information is recorded before and after each operation so that the cause can be traced and effective recovery operations can be performed after a problem occurs.

[0192] Data encryption: Encrypted storage and transmission of sensitive data to prevent eavesdropping or tampering, which is especially important in wireless communications.

[0193] Authentication and authorization: Strictly control access rights to ensure that only legitimate users and services can access specific resources, reducing potential security threats.

[0194] Through the above software-level optimization strategy, the ability of the equipment to resist electromagnetic interference can be greatly improved, ensuring that it can still work stably and reliably in complex marine environments. This not only helps to protect hardware facilities from damage, but also significantly improves the robustness and service quality of the entire system.

[0195] Figure 2 A schematic diagram of the structure of an electromagnetic compatibility solution system under marine conditions is provided for an embodiment of the present application. Figure 2 As shown, the device comprises:

[0196] The optimization module 21 is used to deploy a distributed filter network with adaptive tuning function, optimize the electromagnetic compatibility between fixed facilities and mobile platforms, and perform global monitoring and local fine-tuning for extreme marine environmental conditions to obtain electromagnetic environment data;

[0197] The simulation module 22 is used to use the electromagnetic environment data to simulate the interaction of electromagnetic radiation between devices under different sea conditions, use a multi-physics field coupling simulation platform combined with finite element analysis technology to generate a variety of optimization configuration strategies, and further optimize the optimization configuration strategies based on a particle swarm optimization algorithm to obtain a system layout plan;

[0198] Prediction module 23, used to implement a cross-platform electromagnetic compatibility test plan according to the system layout plan, verify system performance in an actual operating environment, introduce an environment adaptive algorithm to identify and avoid potential interference sources in advance, use a random forest model to perform risk assessment and prediction analysis on the potential interference sources, and generate a safety assurance strategy;

[0199] The recording module 24 is used to build a cloud-based collaborative management and control system based on the security assurance strategy, integrate real-time electromagnetic environment information from multiple mobile observation platforms, establish unified data exchange standards and protocols using remote decision-making and instant adjustment processing, record all operation logs, and generate electromagnetic compatibility management process documents.

[0200] Figure 2 The electromagnetic compatibility solution system under marine conditions can be implemented Figure 1 The implementation principle and technical effect of the electromagnetic compatibility solution method under marine conditions described in the embodiment shown are not repeated here. The specific way in which each module and unit performs operations in the electromagnetic compatibility solution system under marine conditions in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0201] In one possible design, Figure 2 The electromagnetic compatibility solution system under marine conditions of the embodiment shown can be implemented as a computing device, such as Figure 3As shown, the computing device may include a storage component 31 and a processing component 32;

[0202] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0203] The processing component 32 is used to: deploy a distributed filtering network with adaptive tuning function, optimize the electromagnetic compatibility between fixed facilities and mobile platforms, and perform global monitoring and local fine-tuning for extreme marine environmental conditions to obtain electromagnetic environment data; use the electromagnetic environment data to simulate the interactive impact of electromagnetic radiation between devices under different sea conditions, use a multi-physics field coupling simulation platform combined with finite element analysis technology to generate a variety of optimization configuration strategies, optimize the optimization configuration strategies based on the particle swarm optimization algorithm to obtain a system layout plan; according to the system layout plan, implement a cross-platform electromagnetic compatibility test plan, verify the system performance in the actual operating environment, introduce an environmental adaptive algorithm to identify and avoid potential interference sources in advance, use a random forest model to perform risk assessment and predictive analysis on the potential interference sources, and generate a safety assurance strategy; based on the safety assurance strategy, build a cloud-based collaborative management and control system, integrate real-time electromagnetic environment information from multiple mobile observation platforms, use remote decision-making and instant adjustment processing to establish a unified data exchange standard and protocol, record all operation logs, and generate an electromagnetic compatibility management process document.

[0204] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0205] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0206] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0207] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0208] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0209] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0210] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a solution to electromagnetic compatibility under marine conditions.

[0211] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0212] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0213] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for solving electromagnetic compatibility problems under marine conditions, characterized in that: include: Deploy a distributed filter network with adaptive tuning capabilities to optimize electromagnetic compatibility between fixed facilities and mobile platforms, and perform global monitoring and local fine-tuning for extreme marine environmental conditions to obtain electromagnetic environment data; The electromagnetic environment data is used to simulate the interaction of electromagnetic radiation between devices under different sea conditions, and a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to generate a variety of optimization configuration strategies. The optimization configuration strategies are optimized based on the particle swarm optimization algorithm to obtain a system layout plan; According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify the system performance in the actual operating environment, and an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance. The random forest model is used to conduct risk assessment and predictive analysis on the potential interference sources to generate a safety assurance strategy; Based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms, use remote decision-making and instant adjustment processing to establish unified data exchange standards and protocols, record all operation logs, and generate electromagnetic compatibility management process documents.

2. The method according to claim 1, characterized in that The electromagnetic environment data is used to simulate the interaction between the electromagnetic radiation of the equipment under different sea conditions, and a multi-physics field coupling simulation platform is used in combination with finite element analysis technology to generate a variety of optimization configuration strategies. The optimization configuration strategies are further optimized based on the particle swarm optimization algorithm to obtain a system layout plan, including: Using the electromagnetic environment data, the interaction effect of electromagnetic radiation between devices under different sea conditions is simulated to obtain an electromagnetic radiation interaction effect model; According to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform combined with finite element analysis technology is used to simulate various possible equipment layouts and operating parameter settings to generate a variety of optimization configuration strategies; Based on the multiple optimization configuration strategies, the particle swarm optimization algorithm is applied to evaluate and iteratively optimize the performance of each configuration scheme to obtain a system layout scheme.

3. The method according to claim 2, characterized in that According to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform combined with finite element analysis technology is used to simulate various possible equipment layouts and operating parameter settings to generate a variety of optimization configuration strategies, including: According to the electromagnetic radiation interaction model, a multi-physics field coupling simulation platform is used to simulate the electromagnetic environment under different sea conditions to obtain a variety of electromagnetic scenarios; Based on the various electromagnetic scenarios, combined with finite element analysis technology, detailed simulation processing is performed on various equipment layouts and operating parameter settings, taking into account the complex interactive effects of electromagnetic radiation between devices, and generating a preliminary optimization configuration solution set; Using the preliminary optimized configuration scheme set, by introducing key performance indicators, the performance of each configuration scheme is comprehensively evaluated to obtain a performance evaluation report, wherein the key performance indicators include: signal integrity, anti-interference capability and communication reliability; According to the performance evaluation report, multiple optimization configuration strategies that meet preset standards are screened out.

4. The method according to claim 3, characterized in that Based on the multiple electromagnetic scenarios, combined with finite element analysis technology, various possible equipment layouts and operating parameter settings are simulated in detail, considering the complex interactive effects of electromagnetic radiation between devices, and generating a preliminary optimization configuration solution set, including: Based on the various electromagnetic scenarios, combined with finite element analysis technology, detailed simulation processing is performed on various equipment layouts and operating parameter settings, considering the complex interactive effects of electromagnetic radiation between devices, and obtaining simulation results; Using the simulation results, according to different equipment layouts and operating parameter settings, the electromagnetic field distribution, signal interference level and communication link stability under each configuration are deeply analyzed and processed to generate performance indicator data; Based on the performance indicator data, a multi-criteria decision analysis method is applied to comprehensively evaluate the performance of each configuration scheme in terms of anti-interference capability, signal integrity and communication reliability, and a performance evaluation matrix is ​​constructed; Based on the performance evaluation matrix, a preliminary optimized configuration solution set is generated by screening multiple configuration solutions that meet preset performance standards.

5. The method according to claim 1, characterized in that According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to verify the system performance in the actual operating environment, an environmental adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a random forest model is used to conduct risk assessment and predictive analysis on the potential interference sources to generate a safety assurance strategy, including: According to the system layout plan, a cross-platform electromagnetic compatibility test plan is implemented to fully verify the system performance in the actual operating environment to obtain performance verification data in the actual environment; Using the performance verification data in the actual environment, an environment adaptive algorithm is introduced to identify and avoid potential interference sources in advance, and a list of potential interference sources is generated; Based on the list of potential interference sources, a random forest model is applied to evaluate and predict the risk level and possibility of the potential interference sources to obtain a risk assessment report; Develop a security strategy based on the risk assessment report.

6. The method according to claim 5, characterized in that Based on the list of potential interference sources, the random forest model is applied to evaluate and predict the risk level and possibility of the potential interference sources to obtain a risk assessment report, including: Based on the list of potential interference sources, a random forest model is applied to construct an integrated learning model including multiple decision trees to comprehensively evaluate and predict the risk level and possibility of potential interference sources; Using the results of the comprehensive assessment and predictive analysis, identify potential interference sources with high risk and high probability, and generate a list of key monitoring objects; Based on the list of key monitoring objects, combined with historical data and real-time monitoring information, the development trend of potential interference sources is continuously tracked and processed to obtain dynamic risk warning information; Based on the dynamic risk warning information, the assessment results of all potential interference sources are comprehensively considered to obtain a risk assessment report.

7. The method according to claim 1, characterized in that Based on the security strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms, support remote decision-making and instant adjustment, and generate electromagnetic compatibility management process documents by establishing unified data exchange standards and protocols, recording all operation logs, including: Based on the security assurance strategy, a cloud-based collaborative management and control system is constructed to integrate real-time electromagnetic environment information from multiple mobile observation platforms to obtain a comprehensive electromagnetic environment data stream; Utilizing the integrated electromagnetic environment data stream, utilizing remote decision making and instant adjustment processing, to generate dynamic dispatch instructions; According to the dynamic scheduling instructions, a standardized data exchange process is obtained by establishing a unified data exchange standard and protocol; Based on the standardized data interaction process, all operation logs are recorded to ensure that each step of the operation is traceable and generate electromagnetic compatibility management process documents.

8. An electromagnetic compatibility solution system under marine conditions, characterized in that: include: The optimization module is used to deploy a distributed filter network with adaptive tuning function to optimize the electromagnetic compatibility between fixed facilities and mobile platforms, and to perform global monitoring and local fine-tuning for extreme marine environmental conditions to obtain electromagnetic environment data; A simulation module is used to use the electromagnetic environment data to simulate the interaction between the electromagnetic radiation of the equipment under different sea conditions, and to generate a variety of optimization configuration strategies by using a multi-physics field coupling simulation platform combined with finite element analysis technology. The optimization configuration strategies are further optimized based on a particle swarm optimization algorithm to obtain a system layout plan; A prediction module is used to implement a cross-platform electromagnetic compatibility test plan according to the system layout plan, verify the system performance in the actual operating environment, introduce an environmental adaptive algorithm to identify and avoid potential interference sources in advance, use a random forest model to conduct risk assessment and prediction analysis on the potential interference sources, and generate a safety assurance strategy; The recording module is used to build a cloud-based collaborative management and control system based on the security assurance strategy, integrate real-time electromagnetic environment information from multiple mobile observation platforms, establish unified data exchange standards and protocols using remote decision-making and instant adjustment processing, record all operation logs, and generate electromagnetic compatibility management process documents.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement an electromagnetic compatibility solution method under marine conditions as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, an electromagnetic compatibility solution method under marine conditions as claimed in any one of claims 1 to 7 is implemented.

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