Offshore wind power safety monitoring information visual dynamic management system

Abstract

The present application relates to the technical field of wind power safety monitoring, and particularly relates to a kind of offshore wind power safety monitoring information visual dynamic management system, comprising: data acquisition module;With to store and three-dimensional dynamic display the data visualization module of monitoring parameter;With to determine directly repair or to carry out sensitivity analysis according to based on monitoring abnormality and fluctuation characteristic value determination data analysis module;With in sensitivity analysis, based on alternating hysteresis coefficient and photosensitive damage coefficient determines sensitive representation value, according to sensitive representation value determines sensitive level, and according to sensitive level determines analysis mode, the analysis mode includes direct repair, whether to carry out repair based on repair frequency determination, according to abnormality comparison value determines whether to adjust for collection interval sensitive analysis module.The present application can improve the accuracy and reliability of offshore wind power pile foundation damage monitoring.

Description

Technical Field

[0001] This invention relates to the field of wind power safety monitoring technology, and in particular to a visual dynamic management system for offshore wind power safety monitoring information. Background Technology

[0002] Offshore wind power is a crucial component of the new energy sector, but its structural safety operation faces severe challenges from the marine environment. Wind turbine foundations, as supporting structures, are constantly exposed to a corrosive environment resulting from the coupling of multiple factors, including tides, waves, salt spray, and microorganisms. The coupling effect of the failure of the anti-corrosion system (coatings, cathodic protection) and structural fatigue damage is a major factor leading to reduced structural lifespan. However, due to the complex and variable conditions of the marine environment, existing monitoring methods have limitations, making it difficult to accurately perceive and provide early warnings of foundation damage. Therefore, improving the accuracy and reliability of offshore wind turbine foundation damage monitoring is a pressing technical problem that needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN120947733A discloses a multi-dimensional parameter health monitoring system and method for offshore wind turbine towers, including: multi-source sensing, data preprocessing, feature extraction, state assessment, and early warning output modules. The multi-source sensing module integrates devices such as a triaxial accelerometer, fiber optic strain gauge, and corrosion rate probe to collect real-time data on structural response, environmental load, and corrosion. The data preprocessing module uses adaptive Kalman filtering and multi-mode standardization to improve data quality. The feature extraction module extracts 28-dimensional feature vectors through time-domain statistics, frequency-domain power spectrum, and wavelet transform. The state assessment module outputs a Health Status Index (SHI) based on a bidirectional LSTM-Attention model, combined with Monte Carlo Dropout to quantify uncertainty. The early warning output module triggers a three-level graded early warning based on the SHI threshold. However, the above technical solution suffers from the following problems: it only focuses on structural vibration strain and conventional corrosion monitoring and early warning, neglecting the analysis of latent anomalies when conventional monitoring parameters are relatively good. Consequently, it cannot achieve in-depth identification and graded treatment of potential damage, resulting in poor decision-making accuracy and poor adaptability to operation and maintenance resources. Summary of the Invention

[0004] To address this, the present invention provides a visualized dynamic management system for offshore wind power safety monitoring information, which overcomes the problem in existing technologies that do not consider the analysis of hidden anomalies when conventional monitoring parameters are good, thus failing to achieve in-depth identification and graded handling of potential damage, resulting in poor decision-making accuracy and adaptability to operation and maintenance resources.

[0005] To achieve the above objectives, the present invention provides a visual dynamic management system for offshore wind power safety monitoring information, comprising: The data acquisition module is used to collect monitoring parameters corresponding to each wind turbine pile foundation in the target area; A data visualization module, which is connected to the data acquisition module, is used to store and dynamically display the monitoring parameters in three dimensions; The data analysis module is connected to the data acquisition module and the data visualization module respectively, and is used to determine whether to perform direct maintenance or sensitivity analysis based on the monitoring anomaly degree and fluctuation characteristic value. The sensitivity analysis module is connected to the data analysis module and the data visualization module respectively. In the sensitivity analysis, it determines the sensitivity characterization value based on the alternating hysteresis coefficient and the photosensitive damage coefficient, determines the sensitivity level based on the sensitivity characterization value, and determines the analysis method based on the sensitivity level. The analysis method includes direct inspection, determining whether to carry out inspection based on the inspection frequency, and determining whether to adjust the acquisition interval based on the abnormal comparison value. The maintenance frequency is determined based on the anomaly degree of the rate of change or the influence parameter, wherein the influence parameter is the stray current interference degree, and whether the influence parameter includes the apparent influence coefficient is determined based on the stress anomaly degree. The apparent influence coefficient is determined by selecting either the pitting step coefficient or the adhesion damage coefficient based on the thickness damage correlation.

[0006] Furthermore, the data analysis module responds to conditions where the monitoring anomaly degree is greater than or equal to a preset monitoring anomaly degree or the fluctuation characteristic value is greater than or equal to a preset fluctuation characteristic value, and performs direct maintenance. The data analysis module performs sensitivity analysis when the detected anomaly degree is less than the preset detected anomaly degree and the fluctuation characteristic value is less than the preset fluctuation characteristic value.

[0007] Furthermore, the sensitivity analysis module determines a sensitivity level of one when the sensitivity characterization value is greater than or equal to the first preset sensitivity characterization value. The sensitivity analysis module determines a level 2 sensitivity level when the sensitivity characterization value is less than the first preset sensitivity characterization value and greater than or equal to the second preset sensitivity characterization value. The sensitivity analysis module determines the sensitivity level to be level three if the sensitivity characterization value is less than the second preset sensitivity characterization value. The sensitivity characterization value is positively correlated with both the alternation hysteresis coefficient and the photosensitive damage coefficient, and the first preset sensitivity characterization value is greater than the second preset sensitivity characterization value.

[0008] Furthermore, the method for confirming the alternating hysteresis coefficient includes: Plot the water level-impedance curve within the current pile foundation maintenance analysis cycle, with tidal water level as the x-axis and coating impedance as the y-axis. The maximum hysteresis width is determined based on the maximum value of the coating impedance difference at the same water level during the high tide and low tide stages in the water level-impedance curve. The ratio of the maximum hysteresis width to the preset maximum hysteresis width is denoted as the alternating hysteresis coefficient.

[0009] Furthermore, for the first-level sensitivity level, the sensitivity analysis module determines the analysis method as direct repair.

[0010] Furthermore, for the second-level sensitivity level, the sensitivity analysis module determines whether maintenance should be performed based on the maintenance frequency. If the maintenance frequency is met, then maintenance will be carried out.

[0011] Furthermore, the sensitivity analysis module responds to the condition that the rate of change anomaly is greater than or equal to the preset rate of change anomaly, and determines the maintenance frequency based on the rate of change anomaly. The sensitivity analysis module determines the maintenance frequency based on the influence coefficient determined by the influence parameters when the response rate of change anomaly is less than the preset rate of change anomaly.

[0012] Furthermore, the sensitivity analysis module determines the influencing parameters, including the apparent influence coefficient, based on the condition that the stress anomaly degree is greater than or equal to the preset stress anomaly degree.

[0013] Furthermore, the method for determining the apparent influence coefficient is as follows: If the thickness damage correlation degree is less than the preset thickness damage correlation degree, the apparent influence coefficient is determined according to the pitting step coefficient. If the thickness damage correlation degree is greater than or equal to the preset thickness damage correlation degree, the apparent influence coefficient is determined based on the adhesion damage coefficient.

[0014] Furthermore, for the three-level sensitivity level, the sensitivity analysis module determines whether to adjust the acquisition interval based on the abnormal comparison value. If the abnormal comparison value is greater than or equal to the preset abnormal comparison value, the collection interval will be reduced.

[0015] Compared with the prior art, the beneficial effects of the present invention are that, in the technical solution of the present invention, by monitoring the degree of anomaly and the fluctuation characteristic value, the comprehensive risk level currently faced by the pile foundation is effectively reflected. Then, based on the monitored degree of anomaly and the fluctuation characteristic value, direct maintenance or sensitivity analysis is selected, making the risk identification and maintenance decision-making initiation mechanism more in line with the actual application scenario. This is conducive to accurately identifying potential risks and hidden damage, thereby effectively improving the utilization efficiency of operation and maintenance resources and the safety of the structure throughout its entire life cycle.

[0016] Furthermore, this invention effectively reflects the degree of latent damage and dynamic response characteristics of the coating under the coupled effects of alternating wet and dry conditions and diurnal temperature variation by using the alternating hysteresis coefficient and photosensitive damage coefficient. Then, the sensitivity level is determined based on the sensitivity characterization value, and the analysis method is determined based on the sensitivity level. This is conducive to accurately identifying early failure signs of the coating, achieving the matching of differentiated operation and maintenance strategies, and thus effectively extending the service life of the anti-corrosion coating and reducing the risk of unplanned downtime of the pile foundation structure due to corrosion hazards.

[0017] Furthermore, this invention effectively reflects the dynamic changing trend of sensitive characterization values ​​and the rate of structural deterioration through the rate of change anomaly. The maintenance frequency is then determined based on the rate of change anomaly or influencing parameters, facilitating dynamic matching and differentiated management of maintenance strategies. This avoids over-maintenance or missed maintenance while ensuring structural safety, optimizes the allocation efficiency of maintenance resources, and determines whether maintenance is necessary based on the maintenance frequency. This facilitates a refined shift from fixed-cycle maintenance to state-driven maintenance.

[0018] Furthermore, this invention effectively reflects the synergistic relationship between the growth of the deposit and the metal corrosion process by using the thickness damage correlation degree. Then, the pitting corrosion step coefficient or the deposit damage coefficient is selected to determine the apparent influence coefficient, which is conducive to accurately identifying the dominant corrosion factors and realizing the adaptive calculation of the apparent influence coefficient. This improves the accuracy and pertinence of the health status assessment of the pile foundation structure and provides a reliable basis for differentiated operation and maintenance decisions. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the offshore wind power safety monitoring information visualization and dynamic management system of the present invention; Figure 2 This is a flowchart illustrating the process of determining whether to perform direct maintenance or sensitivity analysis based on monitoring anomalies and fluctuation characteristics in this invention. Figure 3 This is a flowchart illustrating how the analysis method is determined based on the sensitivity level in this invention. Figure 4 This is a flowchart illustrating the process of determining the maintenance frequency based on the rate of change anomaly or influencing parameters according to the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] Please see Figures 1 to 4 As shown, the present invention provides a visualized dynamic management system for offshore wind power safety monitoring information, comprising: The data acquisition module is used to collect monitoring parameters corresponding to each wind turbine pile foundation in the target area; A data visualization module, which is connected to the data acquisition module, is used to store and dynamically display the monitoring parameters in three dimensions; The data analysis module is connected to the data acquisition module and the data visualization module respectively, and is used to determine whether to perform direct maintenance or sensitivity analysis based on the monitoring anomaly degree and fluctuation characteristic value. The sensitivity analysis module is connected to the data analysis module and the data visualization module respectively. In the sensitivity analysis, it determines the sensitivity characterization value based on the alternating hysteresis coefficient and the photosensitive damage coefficient, determines the sensitivity level based on the sensitivity characterization value, and determines the analysis method based on the sensitivity level. The analysis method includes direct inspection, determining whether to carry out inspection based on the inspection frequency, and determining whether to adjust the acquisition interval based on the abnormal comparison value. The maintenance frequency is determined based on the anomaly degree of the rate of change or the influence parameter, wherein the influence parameter is the stray current interference degree, and whether the influence parameter includes the apparent influence coefficient is determined based on the stress anomaly degree. The apparent influence coefficient is determined by selecting either the pitting step coefficient or the adhesion damage coefficient based on the thickness damage correlation.

[0023] This invention is applied to intelligent safety monitoring of the health and corrosion protection of offshore wind turbine foundation structures. The wind turbine foundation is the basic support structure of offshore wind turbine generators. Its surface is coated with an anti-corrosion coating and equipped with an impressed current cathodic protection system.

[0024] The monitoring parameters include electrochemical monitoring parameters and basic monitoring parameters. The electrochemical monitoring parameters are coating impedance, metal corrosion rate, and cathodic protection potential. The basic monitoring parameters are tidal level, deposit thickness, and stress. Coating impedance, metal corrosion rate, and cathodic protection potential are monitored using an electrochemical impedance spectroscopy (EIS) sensor, an LPR linear polarization resistance sensor, and a reference electrode, with units of Ω, mm / a, and V, respectively. Tidal level is detected using an ultrasonic water level sensor installed in the tidal zone of the wind turbine pile foundation. Deposits thickness is monitored using an ultrasonic thickness sensor installed in the tidal zone of the wind turbine pile foundation. Stress is monitored using a fiber optic grating strain sensor deployed in the tidal zone of the wind turbine pile foundation.

[0025] In this embodiment, the average high tide level (+2.0m) and the average low tide level (-1.0m) are both elevation values ​​relative to the theoretical depth reference surface; the tidal zone is the pile foundation section located between the average high tide level and the average low tide level; the water level data collected by the ultrasonic water level sensor takes the theoretical depth reference surface as the starting point, and a positive water level value indicates that it is higher than the theoretical depth reference surface, and a negative value indicates that it is lower than the theoretical depth reference surface.

[0026] This invention employs a continuously cyclical pile foundation maintenance analysis cycle to determine the monitoring anomaly degree and fluctuation characteristic value at the end of each pile foundation maintenance analysis cycle. The greater the need for real-time risk identification of wind power pile foundation structures, the shorter the duration of the pile foundation maintenance analysis cycle. In this embodiment, the duration of a single pile foundation maintenance analysis cycle is 24 hours, and a collection point is set every 5 minutes in a single pile foundation maintenance analysis cycle to collect monitoring parameters.

[0027] The data visualization module is built on WebGL technology and presents a three-dimensional model of the wind power pile foundation to the user through a browser or client application, and spatially associates and dynamically maps the monitoring parameters with the three-dimensional model.

[0028] The specific implementation of the three-dimensional dynamic display of the monitoring parameters includes: (1) 3D scene construction: Based on the design drawings or laser scanning point cloud data of the wind turbine pile foundation, a 3D geometric model of the pile foundation, transition section, tower, nacelle and impeller is constructed, and the surface of the pile foundation is colored and marked according to the division of tidal zone, splash zone, fully immersed zone and marine mud zone. The 3D model supports users to rotate, zoom, translate and switch perspectives by mouse or touch.

[0029] (2) Sensor location mapping: The actual installation location of each sensor is visualized and marked in the 3D model. Each sensor location is presented in the form of an icon and supports click interaction to view the detailed information and historical data curves of the sensor.

[0030] (3) Real-time data-driven: The three-dimensional dynamic display unit establishes a real-time data connection with the backend server through WebSocket or HTTP long polling protocol to obtain the monitoring parameters of each collection point within the current pile foundation maintenance analysis cycle, and dynamically displays the monitoring parameters on the three-dimensional model in the following ways: Numerical labels: Display the current monitored values ​​and their units in real time near the sensor location, and indicate the percentage deviation from the preset threshold; Dynamic Curves: Displays real-time trend curves of selected parameters on the side or in a floating window of the 3D model. The horizontal axis of the curve represents time, and the vertical axis represents the monitored value. It supports displaying historical data and predicted trends within the current period. Cloud map rendering: For monitoring parameters that are continuously distributed in space (such as the thickness of the attachment and the coating impedance), an interpolation algorithm is used to generate a three-dimensional cloud map to show the spatial distribution characteristics of the parameters on the pile foundation surface.

[0031] (4) Timeline backtracking: The three-dimensional dynamic display unit provides a timeline control. Users can select historical time points or time periods by dragging the timeline. The system loads the historical data of the corresponding time period and plays it back synchronously on the three-dimensional model, supporting users to observe the dynamic evolution of monitoring parameters over time.

[0032] (5) Warning and notification display: When the data analysis module or sensitivity analysis module triggers maintenance, the three-dimensional dynamic display unit issues a warning to the user in the three-dimensional model by means of highlighting flashing, pop-up prompts, sound alarms or push notifications.

[0033] Specifically, the data analysis module performs direct maintenance when the monitoring anomaly degree is greater than or equal to the preset monitoring anomaly degree or the fluctuation characteristic value is greater than or equal to the preset fluctuation characteristic value. The data analysis module performs sensitivity analysis when the detected anomaly degree is less than the preset detected anomaly degree and the fluctuation characteristic value is less than the preset fluctuation characteristic value.

[0034] Specifically, the monitoring anomaly is the maximum value among the sub-anomalies corresponding to each electrochemical monitoring parameter. The sub-anomaly corresponding to a single electrochemical monitoring parameter = the average value of the electrochemical monitoring parameter at each collection point in the current pile foundation maintenance analysis cycle / the parameter threshold corresponding to the electrochemical monitoring parameter. In this embodiment, the parameter thresholds for each electrochemical monitoring parameter are as follows: the parameter threshold for coating impedance is... The threshold value for the metal corrosion rate is 0.1 mm / a; the threshold value for the cathodic protection potential is -0.80V (relative to the silver / silver chloride reference electrode). However, the above values ​​are not limited to these values. Users can set them themselves based on engineering experience and structural safety requirements.

[0035] The fluctuation characteristic value is the maximum value among the sub-fluctuation characteristic values ​​corresponding to each electrochemical monitoring parameter; The sub-fluctuation characteristic value corresponding to a single electrochemical monitoring parameter is the standard deviation of the comparison value of the electrochemical monitoring parameter corresponding to each collection point in the current pile foundation maintenance analysis cycle. The comparison value of the electrochemical monitoring parameter corresponding to a single collection point is the ratio of the value of the electrochemical monitoring parameter corresponding to the collection point to the parameter threshold corresponding to the electrochemical monitoring parameter. Users can determine the preset monitoring anomaly degree and preset fluctuation characteristic value based on their tolerance for operation and maintenance risks. The monitoring anomaly degree and fluctuation characteristic value effectively reflect the comprehensive risk level currently faced by the pile foundation. The greater the user's need for real-time risk identification, the smaller the preset monitoring anomaly degree and preset fluctuation characteristic value will be. In this embodiment, the preset monitoring anomaly degree is 0.9 and the preset fluctuation characteristic value is 0.3.

[0036] Specifically, the sensitivity analysis module determines a level 1 sensitivity level when the sensitivity characterization value is greater than or equal to the first preset sensitivity characterization value. The sensitivity analysis module determines a level 2 sensitivity level when the sensitivity characterization value is less than the first preset sensitivity characterization value and greater than or equal to the second preset sensitivity characterization value. The sensitivity analysis module determines the sensitivity level to be level three if the sensitivity characterization value is less than the second preset sensitivity characterization value. The sensitivity characterization value is positively correlated with both the alternation hysteresis coefficient and the photosensitive damage coefficient, and the first preset sensitivity characterization value is greater than the second preset sensitivity characterization value.

[0037] Specifically, the sensitivity characterization value = alternating hysteresis coefficient / preset alternating hysteresis coefficient × first weighting coefficient + photosensitive damage coefficient / preset photosensitive damage coefficient × second weighting coefficient; Both the first and second weighting coefficients are 0.5; The photosensitive damage coefficient is the ratio of the daytime average impedance to the nighttime average impedance. The daytime average impedance is the average value of the coating impedance at each collection point from 6:00 to 18:00 within the current pile foundation maintenance analysis cycle, and the nighttime average impedance is the average value of the coating impedance at each collection point from 18:00 to 6:00 the next day within the current pile foundation maintenance analysis cycle. The values ​​of the preset alternating hysteresis coefficient and the preset photosensitive damage coefficient can be determined according to the actual application scenario. It can be understood that the alternating hysteresis coefficient and the photosensitive damage coefficient effectively reflect the degree of latent damage and dynamic response characteristics of the coating under the action of alternating wet and dry conditions and day and night temperature differences. The greater the user's demand for real-time risk identification or monitoring sensitivity, the smaller the values ​​of the preset alternating hysteresis coefficient and the preset photosensitive damage coefficient. In this embodiment, the preset alternating hysteresis coefficient is 0.25 and the preset photosensitive damage coefficient is 0.95.

[0038] The values ​​of the first and second preset sensitivity characterization values ​​can be determined according to the accuracy requirements of operation and maintenance monitoring. The sensitivity characterization values ​​effectively reflect the degree of hidden damage and dynamic response characteristics of the coating under the coupled effects of alternating wet and dry conditions and day and night temperature differences. The greater the requirement for real-time risk identification or monitoring sensitivity, the smaller the values ​​of the first and second preset sensitivity characterization values. In this embodiment, the first preset sensitivity characterization value is 0.7 and the second preset sensitivity characterization value is 0.3.

[0039] Specifically, the methods for confirming the alternating hysteresis coefficient include: Plot the water level-impedance curve within the current pile foundation maintenance analysis cycle, with tidal water level as the x-axis and coating impedance as the y-axis. The maximum hysteresis width is determined based on the maximum value of the coating impedance difference at the same water level during the high tide and low tide stages in the water level-impedance curve. The ratio of the maximum hysteresis width to the preset maximum hysteresis width is denoted as the alternating hysteresis coefficient.

[0040] Specifically, when determining the maximum hysteresis width based on the maximum value of the coating impedance difference at the same water level during the high tide and low tide stages in the water level-impedance curve, for any water level value within the water level range, the coating impedance value Zflood(h) at that water level during the high tide stage and the coating impedance value Zebb(h) at the same water level during the low tide stage are obtained respectively. The difference ΔZ(h) = |Zflood(h) - Zebb(h)| is calculated. The maximum value ΔZmax = maxΔZ(h) is taken by traversing the entire water level range. This is the maximum hysteresis width. High tide phase: In the water level-resistance curve, this corresponds to the period when the water level rises from low to high; Low tide phase: In the water level-resistance curve, this corresponds to the period when the water level falls from high to low. The preset maximum hysteresis width can be determined by the user based on the real-time requirements of risk identification. The maximum hysteresis width effectively reflects the degree of irreversible damage to the coating caused by alternating wet and dry conditions. The greater the need for real-time risk identification or monitoring sensitivity, the smaller the preset maximum hysteresis width should be. In this embodiment, the preset maximum hysteresis width is [value missing]. ; It should be noted that if there are no tidal water levels at the same level, the ratio of the change in coating impedance within the current cycle to the preset impedance change threshold is used as an alternative criterion for determining the alternating hysteresis coefficient. The specific calculation formula is: H = (maximum value of coating impedance within the current cycle - minimum value of coating impedance within the current cycle) / preset impedance change threshold. The preset impedance change threshold is determined based on the normal impedance fluctuation range under normal operating conditions during the initial 30 days of pile foundation commissioning. The normal impedance fluctuation range is... In this embodiment, the preferred value is... .

[0041] Specifically, the sensitivity analysis module targets the first-level sensitivity level and determines the analysis method as direct repair.

[0042] Specifically, the sensitivity analysis module targets the second-level sensitivity level and determines whether maintenance should be performed based on the maintenance frequency. If the maintenance frequency is met, then maintenance will be carried out.

[0043] The maintenance frequency is satisfied when the interval between the end of the current cycle and the end of the previous maintenance cycle is greater than or equal to 1 / maintenance frequency. If the maintenance frequency is not met, then no maintenance is required.

[0044] Specifically, the sensitivity analysis module responds to a condition where the rate of change anomaly is greater than or equal to a preset rate of change anomaly, and determines the maintenance frequency based on the rate of change anomaly. The sensitivity analysis module determines the maintenance frequency based on the influence coefficient determined by the influence parameters when the response rate of change anomaly is less than the preset rate of change anomaly.

[0045] Anomaly of rate of change = Sensitivity value of the current pile foundation maintenance analysis cycle - Sensitivity value of the pile foundation maintenance analysis cycle that is adjacent to and precedes the current pile foundation maintenance analysis cycle. The preset value of the rate of change anomaly can be determined according to the actual application scenario. The rate of change anomaly can effectively reflect the dynamic change trend of sensitive characterization values ​​and the rate of structural state deterioration. The greater the user's demand for real-time risk identification or monitoring sensitivity, the smaller the preset value of the rate of change anomaly. In this embodiment, the preset rate of change anomaly is 0.10.

[0046] When determining the maintenance frequency based on the rate of change anomaly, the maintenance frequency = maintenance frequency threshold × rate of change anomaly / preset rate of change anomaly. The maintenance frequency threshold is 1 time / 60 days.

[0047] If the influencing parameter is stray current interference degree, then the influence coefficient is determined based on the stray current interference degree. In this case, the influence coefficient = stray current interference degree / preset stray current interference degree; If the influencing parameters are stray current interference degree and apparent influence coefficient, then the influence coefficient is determined based on stray current interference degree and apparent influence coefficient. In this case, the influence coefficient = max(stray current interference degree / preset stray current interference degree, apparent influence coefficient / preset apparent influence coefficient). The values ​​of the preset stray current interference level and the preset apparent influence coefficient can be determined by the user based on the stray current intensity in the sea area, the corrosion sensitivity of the coating, and the safety level of pile foundation operation and maintenance. The stray current interference level and the apparent influence coefficient effectively reflect the stability of the cathodic protection system and the sensitivity of the pile foundation coating to latent damage. The greater the user's demand for real-time risk identification and monitoring sensitivity, the smaller the values ​​of the preset stray current interference level and the preset apparent influence coefficient. In this embodiment, the preset stray current interference level is 0.3 and the preset apparent influence coefficient is 0.2. If the influence coefficient is greater than or equal to the preset influence coefficient, the maintenance frequency is set to the product of the maintenance frequency threshold and the first proportional coefficient. If the impact coefficient is less than the preset impact coefficient, the maintenance frequency will be set as the product of the maintenance frequency threshold and the second proportional coefficient. The value of the preset influence coefficient can be determined by the user based on the risk tolerance of pile foundation operation and maintenance, the anti-interference capability of the cathodic protection system, and the accuracy of coating damage early warning. The influence coefficient effectively reflects the urgency of pile foundation maintenance needs and the comprehensive impact level of stray current and apparent damage. The greater the user's need for the accuracy of maintenance decisions and the timeliness of risk prevention and control, the smaller the value of the preset influence coefficient. In this embodiment, the preset influence coefficient is 0.5. The first proportional coefficient is greater than the second proportional coefficient. In this embodiment, the preferred value ranges for the first proportional coefficient and the second proportional coefficient are [1.2, 2.5] and [0.5, 1.0], respectively. The first proportional coefficient is 1.5 and the second proportional coefficient is 0.8.

[0048] Stray current interference level = (difference between the maximum and minimum cathodic protection potential during the current pile foundation maintenance analysis cycle / potential fluctuation threshold) × (number of potential pulses / pulse number threshold). The potential fluctuation threshold is 50mV. The number of potential pulses is the number of sampling points where the absolute value of the change in cathodic protection potential relative to the previous adjacent sampling point exceeds 50mV during the current pile foundation maintenance analysis cycle. The pulse number threshold is 5 times.

[0049] Specifically, the sensitivity analysis module determines the influencing parameters, including the apparent influence coefficient, when the response stress anomaly degree is greater than or equal to the preset stress anomaly degree.

[0050] Specifically, the stress anomaly degree = (average value of stress values ​​at each collection point within the current pile foundation maintenance analysis cycle - preset stress value) / preset stress value. The preset stress value is the average stress value under normal operating conditions during the initial stage of pile foundation commissioning (first 30 days), which is 120MPa in this embodiment. The value of the preset stress anomaly can be determined by the user according to the actual application scenario. The stress anomaly can effectively reflect whether the mechanical load borne by the pile foundation structure exceeds the normal operation window, which may be due to coating damage or foreign object adhesion. The greater the user's demand for real-time risk identification or monitoring sensitivity, the smaller the value of the preset stress anomaly. In this embodiment, the preset stress anomaly is 1.0. Specifically, the apparent influence coefficient is determined as follows: If the thickness damage correlation degree is less than the preset thickness damage correlation degree, the apparent influence coefficient is determined according to the pitting step coefficient. If the thickness damage correlation degree is greater than or equal to the preset thickness damage correlation degree, the apparent influence coefficient is determined based on the adhesion damage coefficient.

[0051] Specifically, the thickness damage correlation is the Pearson correlation coefficient between the thickness of the attached material and the metal corrosion rate at each sampling point within the current pile foundation maintenance analysis cycle. The value of the preset thickness damage correlation can be determined by the user according to the actual application scenario. The thickness damage correlation effectively reflects the sensitivity of pitting corrosion risk. The greater the user's need for the timeliness of pitting corrosion early warning, the smaller the value of the preset thickness damage correlation. In this embodiment, the preset thickness damage correlation is 0.7. Pitting step coefficient = (average value of metal corrosion rate at each sampling point within the current pile foundation maintenance analysis cycle - average value of metal corrosion rate at each sampling point within the pile foundation maintenance analysis cycle adjacent to and prior to the current pile foundation maintenance analysis cycle) / parameter threshold corresponding to metal corrosion rate. The adhesion damage coefficient = the average thickness of the attachment at each collection point during the current pile foundation maintenance analysis cycle / the reference attachment thickness × thickness weight coefficient + thickness damage correlation / preset thickness damage correlation × correlation weight coefficient. The reference attachment thickness is 1000μm, which is the upper limit of the attachment thickness allowed by the pile foundation design. The thickness weight coefficient is 0.8, and the correlation weight coefficient is 0.2. When determining the apparent influence coefficient based on the pitting step coefficient, the apparent influence coefficient = pitting step coefficient; when determining the apparent influence coefficient based on the adhesion damage coefficient, the apparent influence coefficient = adhesion damage coefficient.

[0052] It is understandable that when the thickness damage correlation is less than the preset thickness damage correlation, it indicates that the local pitting corrosion characteristics are more significant in the current corrosion process, and the correlation between the thickness of the deposit and the corrosion rate is weak. Therefore, the apparent influence coefficient is determined based on the pitting step coefficient. When the thickness damage correlation degree is greater than or equal to the preset thickness damage correlation degree, it indicates that the current corrosion process is mainly caused by uniform damage from the deposits, and the deposit thickness and corrosion rate have a strong synergistic change trend. Therefore, the apparent influence coefficient is determined based on the deposit damage coefficient.

[0053] Specifically, the sensitivity analysis module targets the three-level sensitivity level and determines whether to adjust the sampling interval based on the abnormal comparison value. If the abnormal comparison value is greater than or equal to the preset abnormal comparison value, the collection interval will be reduced.

[0054] Specifically, the anomaly assessment value = (monitoring anomaly degree / preset monitoring anomaly degree × 0.4) + (fluctuation characteristic value / preset fluctuation characteristic value × 0.3) + (sensitivity characterization value / second preset sensitivity characterization value × 0.3); Anomaly comparison value = Anomaly assessment value of the current pile foundation maintenance analysis cycle - Anomaly assessment value of the pile foundation maintenance analysis cycle adjacent to and preceding the current pile foundation maintenance analysis cycle. The user can determine the preset anomaly comparison value according to the actual application scenario. The anomaly comparison value effectively reflects the comprehensive degree of anomaly of the monitoring parameters. The greater the user's demand for real-time risk identification or monitoring sensitivity, the smaller the preset anomaly comparison value should be. In this embodiment, the preset anomaly comparison value is 0.3.

[0055] When adjusting the acquisition interval to decrease, the acquisition interval = 5min - the decrease in acquisition interval, and the decrease in acquisition interval = min (abnormal comparison value / preset abnormal comparison value × 2min, 2min). The acquisition interval is the time interval between two adjacent acquisition points. Understandably, by quantifying the overall anomaly level of monitoring parameters through anomaly comparison values, and dynamically encrypting the collection interval when the anomaly level increases, an intelligent transformation from "fixed-period collection" to "on-demand encrypted collection" is achieved, effectively improving the timeliness and accuracy of monitoring data.

[0056] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A visualized dynamic management system for offshore wind power safety monitoring information, characterized in that, include: The data acquisition module is used to collect monitoring parameters corresponding to each wind turbine pile foundation in the target area; A data visualization module, which is connected to the data acquisition module, is used to store and dynamically display the monitoring parameters in three dimensions; The data analysis module is connected to the data acquisition module and the data visualization module respectively, and is used to determine whether to perform direct maintenance or sensitivity analysis based on the monitoring anomaly degree and fluctuation characteristic value. The sensitivity analysis module is connected to the data analysis module and the data visualization module respectively. In the sensitivity analysis, it determines the sensitivity characterization value based on the alternating hysteresis coefficient and the photosensitive damage coefficient, determines the sensitivity level based on the sensitivity characterization value, and determines the analysis method based on the sensitivity level. The analysis method includes direct inspection, determining whether to carry out inspection based on the inspection frequency, and determining whether to adjust the acquisition interval based on the abnormal comparison value. The maintenance frequency is determined based on the anomaly degree of the rate of change or the influence parameter, wherein the influence parameter is the stray current interference degree, and whether the influence parameter includes the apparent influence coefficient is determined based on the stress anomaly degree. The apparent influence coefficient is determined by selecting either the pitting step coefficient or the adhesion damage coefficient based on the thickness damage correlation.

2. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 1, characterized in that, The data analysis module responds to conditions where the monitoring anomaly degree is greater than or equal to the preset monitoring anomaly degree or the fluctuation characteristic value is greater than or equal to the preset fluctuation characteristic value, and performs direct maintenance. The data analysis module performs sensitivity analysis when the detected anomaly degree is less than the preset detected anomaly degree and the fluctuation characteristic value is less than the preset fluctuation characteristic value.

3. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 2, characterized in that, The sensitivity analysis module determines a level 1 sensitivity level when the sensitivity characterization value is greater than or equal to the first preset sensitivity characterization value. The sensitivity analysis module determines a level 2 sensitivity level when the sensitivity characterization value is less than the first preset sensitivity characterization value and greater than or equal to the second preset sensitivity characterization value. The sensitivity analysis module determines the sensitivity level to be level three if the sensitivity characterization value is less than the second preset sensitivity characterization value. The sensitivity characterization value is positively correlated with both the alternation hysteresis coefficient and the photosensitive damage coefficient, and the first preset sensitivity characterization value is greater than the second preset sensitivity characterization value.

4. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 3, characterized in that, The methods for confirming the alternating hysteresis coefficient include: Plot the water level-impedance curve within the current pile foundation maintenance analysis cycle, with tidal water level as the x-axis and coating impedance as the y-axis. The maximum hysteresis width is determined based on the maximum value of the coating impedance difference at the same water level during the high tide and low tide stages in the water level-impedance curve. The ratio of the maximum hysteresis width to the preset maximum hysteresis width is denoted as the alternating hysteresis coefficient.

5. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 3, characterized in that, The sensitivity analysis module targets the first-level sensitivity level, and the determination and analysis method is direct inspection.

6. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 3, characterized in that, The sensitivity analysis module targets the second-level sensitivity level and determines whether maintenance should be performed based on the maintenance frequency. If the maintenance frequency is met, then maintenance will be carried out.

7. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 6, characterized in that, The sensitivity analysis module responds to a change rate anomaly degree that is greater than or equal to a preset change rate anomaly degree, and determines the maintenance frequency based on the change rate anomaly degree. The sensitivity analysis module determines the maintenance frequency based on the influence coefficient determined by the influence parameters when the response rate of change anomaly is less than the preset rate of change anomaly.

8. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 7, characterized in that, The sensitivity analysis module determines the influencing parameters, including the apparent influence coefficient, based on the condition that the stress anomaly degree is greater than or equal to the preset stress anomaly degree.

9. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 8, characterized in that, The method for determining the apparent influence coefficient is as follows: If the thickness damage correlation degree is less than the preset thickness damage correlation degree, the apparent influence coefficient is determined according to the pitting step coefficient. If the thickness damage correlation degree is greater than or equal to the preset thickness damage correlation degree, the apparent influence coefficient is determined based on the adhesion damage coefficient.

10. The offshore wind power safety monitoring information visualization and dynamic management system according to claim 3, characterized in that, The sensitivity analysis module targets the three sensitivity levels and determines whether to adjust the sampling interval based on the anomaly comparison value. If the abnormal comparison value is greater than or equal to the preset abnormal comparison value, the collection interval will be reduced.

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