A method and device for early warning of the operational status of marine engineering equipment.

Abstract

This invention relates to the field of marine engineering technology, and more particularly to a method and apparatus for early warning of the operational status of marine engineering equipment. The invention involves: acquiring the long-term service test response of the marine engineering equipment; determining a deterministic function of trend intercept and slope based on the long-term service test response; determining a uniformly distributed random function based on the deterministic function of trend intercept and slope; optimizing the uniformly distributed random function to obtain an optimized random function; updating the long-term service test response based on the optimized random function; and providing an early warning of the operational status of the marine engineering equipment based on the updated service test response. Thus, this invention can accurately provide early warning of the operational status of marine engineering equipment.

Description

Technical Field

[0001] This invention relates to the field of marine engineering technology, and in particular to a method and device for early warning of the operational status of marine engineering equipment. Background Technology

[0002] Marine engineering equipment is increasingly widely used in marine resource development. To ensure the safety and stability of such structures in complex marine environments, it is necessary to accurately analyze their dynamic response characteristics under complex coupled external loads. Current research mainly relies on numerical simulation, physical model tests, and field monitoring data analysis, and has made significant progress in dynamic response calculation and characteristic analysis. However, traditional analysis methods are generally based on steady-state motion assumptions and fail to fully consider the unsteady dynamic characteristics caused by the coupling effect between complex auxiliary structures and the marine environment in actual working conditions, thus failing to provide accurate early warning of the operating status of marine engineering equipment.

[0003] The unsteady dynamic behavior of marine engineering equipment exhibits multi-factor coupling characteristics: First, multi-modal composite motions such as rotation and impact may occur during operation; second, environmental loads (combined effects of wind, waves, and currents), structural coupled vibrations, and control system responses form multi-excitation couplings; third, the superposition of marine environmental noise and sensor signals makes feature extraction extremely difficult. The intensified vortex-induced vibration effect caused by increasing operating depth, and the nonlinear characteristics of fluid-structure interaction brought about by ultra-large structures, make the unsteady dynamic behavior more time-varying. Therefore, developing unsteady feature detection technologies that can effectively handle broadband noise and nonlinear modulated signals is of significant engineering value for revealing the dynamic response evolution mechanism of marine engineering equipment, assessing the health status of auxiliary structures, and ensuring the safety of deep-sea operations.

[0004] Based on this, the present invention proposes a method and device for early warning of the operating status of marine engineering equipment to solve the problem of how to accurately provide early warning of the operating status of marine engineering equipment. Summary of the Invention

[0005] To address the problem of how to accurately provide early warnings on the operational status of marine engineering equipment, embodiments of the present invention provide a method and apparatus for early warning of the operational status of marine engineering equipment.

[0006] In a first aspect, embodiments of the present invention provide a method for early warning of the operational status of marine engineering equipment, the method comprising:

[0007] Obtain long-term service test responses for marine engineering equipment;

[0008] Based on the long-term service test response, a deterministic function for the trend intercept and slope is determined;

[0009] Based on the deterministic function of the trend intercept and slope, determine the identically distributed random function;

[0010] The identically distributed random function is optimized to obtain an optimized random function;

[0011] The long-term service test response is updated based on the optimized random function to obtain the updated service test response;

[0012] Based on the updated service test response, early warnings are issued regarding the operational status of marine engineering equipment.

[0013] Secondly, embodiments of the present invention provide an early warning device for the operational status of marine engineering equipment, comprising:

[0014] The acquisition module is used to acquire the long-term service test response of marine engineering equipment;

[0015] The first data processing module is used to determine a deterministic function of the trend intercept and slope based on the long-term service test response.

[0016] The second data processing module is used to determine the identically distributed random function based on the deterministic function of the trend intercept and slope;

[0017] The third data processing module is used to optimize the same-distributed random function to obtain an optimized random function;

[0018] The fourth data processing module is used to update the long-term service test response based on the optimized random function to obtain the updated service test response;

[0019] The fifth data processing module is used to provide early warning of the operational status of marine engineering equipment based on the updated service test response.

[0020] Thirdly, embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the method described in any embodiment of the present invention.

[0021] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the methods described in any embodiment of the present invention.

[0022] This invention provides a method and apparatus for early warning of the operational status of marine engineering equipment. To accurately capture the unsteady dynamic characteristics of marine engineering equipment in complex marine environments, a distributed sensing system is first used to continuously collect long-term service test response data, covering multi-dimensional physical quantities such as vibration velocity, acceleration, and displacement of key structural components. Considering the influence of environmental loads and structural coupling vibrations during equipment operation, its dynamic response includes both quantifiable trend changes and random fluctuation components. Therefore, a deterministic function of trend intercept and slope is constructed based on time series analysis to characterize the regular evolution trend in the response data caused by structural aging, load accumulation, etc. Based on this, a random function with the same distribution characteristics as the original data is extracted using probability density matching technology to characterize the unsteady fluctuations caused by transient disturbances such as wind, waves, and currents, thus decoupling the deterministic trend from the random disturbance. To further improve data quality, an adaptive particle swarm optimization algorithm is used to iteratively optimize the random function. By dynamically adjusting the weighting coefficients, interference from broadband noise and nonlinear modulation signals is suppressed. This ultimately generates updated service test response data that integrates trend and randomness, making it more closely reflect the actual operating conditions of the equipment. Based on this updated data, combined with preset safety thresholds and a dynamic early warning model, the time-varying characteristics of the dynamic response can be analyzed in real time, accurately identifying the evolution trend of structural anomalies. This enables advanced early warning of the operational status of marine engineering equipment, effectively improving the accuracy and timeliness of equipment structural safety assessments in deep-sea operations. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart of an early warning method for the operational status of marine engineering equipment according to one embodiment is shown;

[0025] Figure 2 This is a hardware architecture diagram of an electronic device provided in an embodiment of the present invention;

[0026] Figure 3 A structural diagram of an early warning device for the operational status of marine engineering equipment according to one embodiment is shown. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0028] Please refer to Figure 1 This invention provides an early warning method for the operational status of marine engineering equipment, the method comprising:

[0029] Step 100: Obtain the long-term service test response of marine engineering equipment;

[0030] Step 102: Based on the long-term service test response, determine the deterministic functions of the trend intercept and slope;

[0031] Step 104: Determine the identically distributed random function based on the deterministic function of the trend intercept and slope;

[0032] Step 106: Optimize the identically distributed random function to obtain the optimized random function;

[0033] Step 108: Update the service test response by obtaining the updated service test response based on the optimized random function;

[0034] Step 110: Based on the updated service test response, issue an early warning on the operational status of marine engineering equipment.

[0035] In this embodiment, to accurately capture the unsteady dynamic characteristics of marine engineering equipment in complex marine environments, long-term service test response data is first continuously collected through a distributed sensing system, covering multi-dimensional physical quantities such as vibration velocity, acceleration, and displacement of key structural parts. Considering the influence of environmental loads and structural coupling vibrations during equipment operation, its dynamic response includes both quantifiable trend changes and random fluctuation components. Therefore, a deterministic function of trend intercept and slope is constructed based on time series analysis to characterize the regular evolution trend in the response data caused by structural aging, load accumulation, etc. On this basis, a random function with the same distribution characteristics as the original data is extracted using probability density matching technology to characterize the unsteady fluctuations caused by transient disturbances such as wind, waves, and currents, achieving decoupling between deterministic trends and random disturbances. To further improve data quality, an adaptive particle swarm optimization algorithm is used to iteratively optimize the random function. By dynamically adjusting the weight coefficients, interference from broadband noise and nonlinear modulation signals is suppressed, ultimately generating updated service test response data that integrates trends and randomness, making it more consistent with the actual operating conditions of the equipment. Based on the updated data, combined with preset safety thresholds and dynamic early warning models, the time-varying characteristics of dynamic response can be analyzed in real time, and the evolution trend of structural anomalies can be accurately identified. This enables early warning of the operating status of marine engineering equipment and effectively improves the accuracy and timeliness of equipment structural safety assessment in deep-sea operations.

[0036] In one embodiment of the present invention, optimizing a uniformly distributed random function to obtain an optimized random function includes:

[0037] The data is divided using a uniformly distributed random function to obtain a first preset number of sample blocks;

[0038] Randomly select from a first preset number of sample blocks to obtain a second preset number of sample blocks; wherein the first preset number is greater than the second preset number;

[0039] The second preset number of sample blocks are sorted according to the time series to obtain the optimized random function.

[0040] In this embodiment, the optimization process of the same-distributed random function aims to reduce the amount of data while retaining the core characteristics of non-steady-state fluctuations through a scientific data simplification strategy. The specific steps are as follows: First, based on the time window sliding method, the same-distributed random function is divided into a first preset number of sample blocks. Each sample block contains random fluctuation information within a continuous time period, ensuring that the data within the block retains the local non-steady-state characteristics of the original signal (such as the transient frequency of vortex-induced vibration and the amplitude change of the impact response). Then, a second preset number of sample blocks are extracted from the first preset number of sample blocks using a stratified random sampling method (where the first preset number is significantly larger than the second preset number, such as a ratio of 5:1 to 10:1, balancing data simplification and feature retention). By controlling the sampling probability, the extracted sample blocks cover both high-amplitude fluctuation ranges and stable fluctuation ranges, avoiding feature bias caused by over-sampling of a single range. Finally, the extracted second preset number of sample blocks are strictly reordered according to the original time series to ensure that the optimized random function still maintains temporal correlation and does not disrupt the dynamic evolution law of the non-steady-state signal. This process effectively reduces the amount of data to improve subsequent computational efficiency, while preserving key random fluctuation characteristics and reducing the interference of redundant information on the early warning model, thereby laying a data foundation for improving the accuracy of early warning of the operational status of marine engineering equipment.

[0041] In this embodiment, the test is only valid when the number of observations N is sufficiently large. Therefore, in this invention, a moving window b = b(N) is introduced to estimate the error. Divide the sample into N-b+1 blocks of length b for resampling, i.e.:

[0042]

[0043] Randomly select [N / b] blocks and replace the original time series in order. The first N samples in this time series are considered as bootstrap errors. Thus, the bootstrap pseudo-observations are obtained as follows:

[0044]

[0045] The innovation lies in the following: To address the issue of inaccurate test results in the ADF test when the sample size is insufficient, this step introduces the moving window bootstrap method. This method divides the error estimation data into multiple blocks, each containing a fixed number of continuous observations, preserving the short-term dependency structure in the error estimation data. Random sampling (with replacement) is then performed on these blocks to generate new samples. Without increasing the actual number of observations, this method can effectively increase the sample size, providing more data points for statistical analysis and enhancing the accuracy and robustness of the analysis.

[0046] In one embodiment of the present invention, an early warning of the operational status of marine engineering equipment is provided based on the updated service test response, including:

[0047] Based on the updated service test response, determine the test statistic;

[0048] Determine whether the test statistic is greater than the preset parameter;

[0049] If so, the marine engineering equipment is determined to be in an unsteady state, and an alarm message is sent to the preset terminal.

[0050] If not, the operating state of the marine engineering equipment is determined to be steady state.

[0051] In this embodiment, the process of providing early warning of the operational status of marine engineering equipment based on updated service test responses is as follows: First, key dynamic features (such as time-domain peak values, frequency-domain dominant frequency offset, and non-steady-state fluctuation variance) are extracted from the updated service test responses and fused to generate a verification statistic. This statistic can quantitatively characterize the time-varying severity of the equipment's structural response. Then, the verification statistic is compared with preset parameters. The preset parameters are determined based on historical safe operation data of similar equipment and structural load-bearing limit thresholds, covering the characteristic fluctuation range during steady-state operation. If the verification statistic is greater than the preset parameters, it indicates that the equipment response has exceeded the normal fluctuation range and is judged as a non-steady-state operation, potentially indicating structural fatigue accumulation or potential damage risk. In this case, an early warning mechanism is automatically triggered, sending alarm information to the remote monitoring center or maintenance terminal. If the verification statistic does not exceed the preset parameters, the operational status is judged to be steady-state, meeting safe operation standards, and continuous monitoring is sufficient. Through this logic, the quantitative judgment of equipment status is achieved, improving the objectivity and accuracy of early warnings.

[0052] In one embodiment of the present invention, the deterministic functions of the trend intercept and slope are determined by the following formula:

[0053]

[0054] In the formula, x n (t) represents the long-term service test response, and N is the length of the time series data. For trend intercept, Let be a deterministic function of the slope, and n be the number of segments in the time series data.

[0055] In one embodiment of the present invention, the identically distributed random function is determined by the following formula:

[0056]

[0057] In the formula, x n (t) represents the long-term service test response, and N is the length of the time series data. For trend intercept, It is a deterministic function of the slope. is a uniformly distributed random function, and n is the number of time series data segments.

[0058] In this embodiment, the long-term covariance function is used to analyze and model non-stationary time series from a statistical perspective, so that it can play a role under different data generation mechanisms and is independent of the specific data generation process.

[0059] In one embodiment of the present invention, the updated service test response is determined by the following formula:

[0060]

[0061] In the formula, To update the service test response, N is the length of the time series data. For trend intercept, It is a deterministic function of the slope. For the optimized random function, n is the number of time series data segments.

[0062] In one embodiment of the present invention, the test statistic is determined by the following formula:

[0063]

[0064] In the formula, To test the statistic, For the optimized random function, N is the length of the time series data, and n is the number of segments in the time series data.

[0065] In this embodiment, since the test statistic is equivalent to the test statistic in the case of a scalar... And error This constitutes the Bernoulli displacement, and therefore can be expressed as... Where g represents the observable function, η j Let be an independent and identically distributed random function in the measurable space S. In this case, the following weak convergence holds:

[0066]

[0067] In the formula, λ i Indicates error The eigenvalues ​​of the long-term covariance function c(t,s), where

[0068]

[0069] In the formula, yes The independent and identically distributed functions. Vi (t) represents a second-order Brownian process, defined as:

[0070]

[0071] Among them W i (t) represents the standard Wiener process.

[0072] By repeating the above steps, a series of... The bootstrap repeat value, used Let B represent the number of bootstrap repetitions. Based on this, the estimated... and To determine whether the dynamic response is unsteady, among other things... for The critical value of the confidence interval at the (1-α) confidence level, where α is the significance level in the hypothesis test. If the above conditions are met, the state is unstable; otherwise, it is stable.

[0073] In this embodiment, when simulating the limiting distribution of the test statistic, a standard Wiener process is used to simulate random fluctuations without boundary conditions, while a Brownian bridge is used to simulate random processes with specific boundary conditions. This allows for a better understanding of the behavior of the test statistic under different conditions and its distribution characteristics in the limiting case. The generated pseudo-feature distribution is quantitatively evaluated to address distribution characteristic errors caused by inappropriate selection of observation values. This leads to the inference of a pseudo-distribution that more closely approximates the actual situation, thus enabling the verification of the unsteady-state characteristics of the motion response of floating structures at sea.

[0074] like Figure 2 , Figure 3 As shown, this embodiment of the invention provides an early warning device for the operational status of marine engineering equipment. The device embodiment can be implemented through software, hardware, or a combination of both. From a hardware perspective, such as... Figure 2 The diagram shown is a hardware architecture diagram of an electronic device for monitoring the operational status of marine engineering equipment, provided in an embodiment of the present invention. (Except for...) Figure 2 In addition to the processor, memory, network interface, and non-volatile memory shown, the electronic device in the embodiment may also include other hardware, such as a forwarding chip responsible for processing packets. Taking software implementation as an example, such as... Figure 3 As shown, a device in a logical sense is formed by the CPU of the electronic device in which it is located reading the corresponding computer program from the non-volatile memory into the memory for execution.

[0075] like Figure 3 As shown in the figure, this embodiment provides an early warning device for the operational status of marine engineering equipment, comprising:

[0076] Acquisition module 300 is used to acquire the long-term service test response of marine engineering equipment;

[0077] The first data processing module 302 is used to determine a deterministic function of trend intercept and slope based on the long-term service test response;

[0078] The second data processing module 304 is used to determine the same-distributed random function based on the deterministic function of the trend intercept and slope;

[0079] The third data processing module 306 is used to optimize the same-distributed random function to obtain an optimized random function;

[0080] The fourth data processing module 308 is used to update the long-term service test response based on the optimized random function to obtain an updated service test response;

[0081] The fifth data processing module 310 is used to provide early warning of the operational status of marine engineering equipment based on the updated service test response.

[0082] In one embodiment of the present invention, the third data processing module 306 is configured to perform the following operations:

[0083] The data is divided using the same distributed random function to obtain a first preset number of sample blocks;

[0084] Randomly select from the first preset number of sample blocks to obtain a second preset number of sample blocks; wherein, the first preset number is greater than the second preset number;

[0085] The second preset number of sample blocks are sorted according to the time series to obtain the optimized random function.

[0086] In one embodiment of the present invention, the fifth data processing module 310 is configured to perform the following operations:

[0087] Based on the updated service test response, determine the test statistic;

[0088] Determine whether the test statistic is greater than a preset parameter;

[0089] If so, the marine engineering equipment is determined to be in an unsteady state, and an alarm message is sent to a preset terminal.

[0090] If not, the marine engineering equipment is determined to be in a steady state.

[0091] In one embodiment of the present invention, the deterministic functions of the trend intercept and the slope are determined by the following formula:

[0092]

[0093] In the formula, x n (t) represents the long-term service test response, and N is the length of the time series data. The trend intercept, Let be a deterministic function of the slope, and n be the number of time series data segments.

[0094] In one embodiment of the present invention, the identically distributed random function is determined by the following formula:

[0095]

[0096] In the formula, x n (t) represents the long-term service test response, and N is the length of the time series data. The trend intercept, Let be a deterministic function of the slope. Let n be the identically distributed random function, and n be the number of time series data segments.

[0097] In one embodiment of the present invention, the updated service test response is determined by the following formula:

[0098]

[0099] In the formula, For the updated service test response, N is the length of the time series data. The trend intercept, Let be a deterministic function of the slope. The optimized random function is denoted as n, where n is the number of time series data segments.

[0100] In one embodiment of the present invention, the test statistic is determined by the following formula:

[0101]

[0102] In the formula, Let be the test statistic. The optimized random function is defined as N, where N is the length of the time series data and n is the number of segments in the time series data.

[0103] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on an early warning device for the operational status of marine engineering equipment. In other embodiments of the present invention, an early warning device for the operational status of marine engineering equipment may include more or fewer components than illustrated, or combine some components, or split some components, or arrange different components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0104] The information interaction and execution process between the modules in the above-mentioned device are based on the same concept as the method embodiment of the present invention, and the specific details can be found in the description of the method embodiment of the present invention, and will not be repeated here.

[0105] This invention also provides an electronic device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements a method for early warning of the operating status of marine engineering equipment according to any embodiment of this invention.

[0106] This invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform an early warning method for the operational status of marine engineering equipment according to any embodiment of this invention.

[0107] Specifically, a system or apparatus equipped with a storage medium may be provided, on which software program code implementing the functions of any of the embodiments described above is stored, and the computer (or CPU or MPU) of the system or apparatus may read and execute the program code stored in the storage medium.

[0108] In this case, the program code read from the storage medium can itself implement the function of any of the above embodiments, and therefore the program code and the storage medium storing the program code constitute part of the present invention.

[0109] Examples of storage media used to provide program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, program code can be downloaded from a server computer via a communication network.

[0110] Furthermore, it should be clear that not only can the program code read by the computer be executed, but also the operating system or other components operating on the computer can be instructed based on the program code to perform some or all of the actual operations, thereby realizing the function of any of the embodiments described above.

[0111] Furthermore, it is understood that the program code read from the storage medium is written to the memory set in the expansion board inserted into the computer or to the memory set in the expansion module connected to the computer. Then, based on the instructions of the program code, the CPU or other components installed on the expansion board or expansion module execute some and all of the actual operations, thereby realizing the functions of any of the embodiments described above.

[0112] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus.

[0113] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as ROM, RAM, magnetic disk, or optical disk.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method of early warning of an operating condition of offshore engineering equipment, characterized in that, The method comprises: obtaining a long-term service test response of marine engineering equipment; determining a deterministic function of a trend intercept and a slope based on the long-term service test response; determining a homodistribution random function based on the deterministic function of the trend intercept and the slope; optimizing the homodistribution random function to obtain an optimized random function; updating the long-term service test response based on the optimized random function to obtain an updated service test response; warning an operating state of the marine engineering equipment based on the updated service test response.

2. The method of claim 1, wherein, The optimization of the homodistribution random function to obtain the optimized random function comprises: dividing the homodistribution random function into data to obtain a first preset number of sample blocks; randomly extracting a second preset number of sample blocks from the first preset number of sample blocks; wherein the first preset number is greater than the second preset number; sorting the second preset number of sample blocks in a time sequence to obtain the optimized random function.

3. The method of claim 1, wherein, The warning of the operating state of the marine engineering equipment based on the updated service test response comprises: determining a test statistic based on the updated service test response; determining whether the test statistic is greater than a preset parameter; if yes, determining that the operating state of the marine engineering equipment is unstable and sending an alarm information to a preset terminal; if no, determining that the operating state of the marine engineering equipment is stable.

4. The method of claim 1, wherein, The deterministic function of the trend intercept and the slope is determined by the following formula: where x n (t) is the long-term service test response, N is the length of the time series data, is the trend intercept, is the certainty function of the slope, n is the number of time series data segments.

5. The method of claim 1, wherein, The homodistribution random function is determined by the following formula: where x n (t) is the long-term service test response, N is the length of the time series data, is the trend intercept, is the certainty function of the slope, is the homogeneously distributed random function, n is the number of time series data segments.

6. The method of claim 1, wherein, The updated service test response is determined by the following formula: wherein is the update in-service test response, N is the length of the time series data, is the trend intercept, is the certainty function of the slope, is the random function of the optimization completion, n is the number of time series data segments.

7. The method of claim 3, wherein, The test statistic is determined by the following formula: In the formula, is the test statistic, is the random function of the optimization completion, N is the length of the time series data, and n is the number of time series data segments.

8. A device for early warning of an operating state of offshore engineering equipment, characterized in that comprises: an acquisition module configured to obtain a long-term service test response of marine engineering equipment; a first data processing module configured to determine a deterministic function of a trend intercept and a slope based on the long-term service test response; a second data processing module configured to determine a homodistribution random function based on the deterministic function of the trend intercept and the slope; a third data processing module configured to optimize the homodistribution random function to obtain an optimized random function; a fourth data processing module configured to update the long-term service test response based on the optimized random function to obtain an updated service test response; a fifth data processing module configured to warn an operating state of the marine engineering equipment based on the updated service test response.

9. An electronic device, comprising: comprises a memory and a processor, the memory stores a computer program, and the processor executes the computer program to implement the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, a computer program is stored thereon, and when the computer program is executed in a computer, the computer program causes the computer to execute the method of any one of claims 1-7.

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