Wharf steel pipe pile corrosion rate monitoring method, device and equipment and storage medium

Abstract

The invention provides a wharf steel pipe pile corrosion rate monitoring method and device, equipment and a storage medium, and the method comprises the steps: firstly collecting target area environment data and steel pipe pile thickness data, and preprocessing the data to obtain an environment average physical and chemical parameter and an average steel pipe pile thickness; calculating a risk index and a corrosion rate; and finally, in the digital twinborn model, dynamically updating according to the risk index, conventionally updating in low or medium risk, and emergently updating the model thickness in high risk. Through real-time data acquisition and processing, risk index and corrosion rate calculation and dynamic updating of the digital twinborn model, real-time sensing and accurate early warning of the corrosion state are realized, and the monitoring efficiency and accuracy are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of steel pipe pile monitoring, and particularly relates to a wharf steel pipe pile corrosion rate monitoring method, device, equipment and storage medium. BACKGROUND

[0002] As an important hub of marine transportation and logistics, the safety and stability of the infrastructure of the wharf are directly related to the efficiency and safety of the overall operation. Among them, the steel pipe pile, as the core load-bearing component of the wharf structure, is exposed to the complex and harsh marine environment for a long time, and is subjected to the superimposed influence of multiple factors such as high salinity, high humidity, dry-wet alternation and wave scour. These environmental factors together make the steel pipe pile face continuous and severe corrosion threat. The influence of corrosion on the steel pipe pile cannot be ignored. With the continuous development of corrosion, the thickness of the steel pipe pile gradually thins, and the structural strength significantly decreases, which not only reduces the bearing capacity of the steel pipe pile, but also may cause pile body fracture, wharf collapse and other major safety accidents, causing immeasurable loss to personnel safety, property safety and the environment. Therefore, accurate monitoring and risk warning of the corrosion state of the steel pipe pile have become a key link to ensure the safe and stable operation of the wharf.

[0003] However, the traditional steel pipe pile corrosion monitoring method has many limitations. At present, manual inspection is one of the main monitoring methods, but this method highly depends on divers to carry out underwater operations, which is greatly restricted by natural factors such as tides and weather, and the daily effective operation time is limited, resulting in low inspection frequency and limited coverage. In addition, the data collected by manual inspection often shows discrete characteristics, making it difficult to form continuous monitoring records, so as to fully and accurately reflect the corrosion state of the steel pipe pile. On the other hand, as an important means to evaluate the corrosion state of the steel pipe pile, the electrochemical monitoring technology can provide certain corrosion information, but still generally adopts the way of regular testing by manual handheld universal meter.

[0004] This way not only has low efficiency and long data collection interval, but also in special areas such as splash zone and tidal zone, due to the environmental characteristics of dry-wet alternation, it is difficult to maintain stable circuit connection, resulting in that the traditional electrochemical method is difficult to apply and there is obvious monitoring blind area. More importantly, the existing monitoring system generally has the phenomenon of data island, that is, the environmental parameters and equipment state data are collected separately, and there is a lack of effective fusion analysis mechanism. This leads to the difficulty of management personnel in extracting valuable information from massive data for accurate corrosion state evaluation and risk warning. At the same time, the output of the existing monitoring means is mostly discrete numerical values or text reports, which lack dynamic display and intuitive presentation, making it difficult for management personnel to quickly locate high-risk areas and take effective maintenance measures in time. SUMMARY

[0005] The application aims to overcome the defects in the prior art and provide a wharf steel pipe pile corrosion rate monitoring method, device, equipment and storage medium.

[0006] The application also provides a wharf steel pipe pile corrosion rate monitoring method, which comprises the following steps:

[0007] Collecting environment data and steel pipe pile thickness data of a target wharf steel pipe pile detection sub-region;

[0008] Pretreating the environment data and steel pipe pile thickness data to obtain environment average physicochemical parameters and average steel pipe pile thickness;

[0009] Calculating a risk index based on the environment average physicochemical parameters;

[0010] Calculating a corrosion rate based on the average steel pipe pile thickness and initial thickness;

[0011] In the digital twin model, the model is dynamically updated according to the risk index, including: when the risk index indicates low risk or medium risk, performing regular updating to update the model thickness and corrosion rate at a preset frequency; when the risk index indicates high risk, triggering emergency updating to update the model thickness of the corresponding high-risk area in the digital twin model, and the high-risk area is determined based on the risk index reaching a preset threshold.

[0012] Optionally, the pretreatment of the environment data and steel pipe pile thickness data comprises:

[0013] Identifying abnormal environment data, wherein the abnormal environment data satisfies any one of the following conditions:

[0014] The pH value fluctuation exceeds the pH value fluctuation threshold within a continuous preset number of collection periods, or the temperature fluctuation exceeds the temperature change threshold within a short time;

[0015] The thickness data deviates from the initial thickness value by a range exceeding the proportion tolerance threshold;

[0016] When the abnormal environment data is identified, the abnormal data points are replaced by the sliding average of adjacent valid period data.

[0017] Optionally, the calculation of the risk index based on the environment average physicochemical parameters comprises:

[0018] Extracting salinity, pH value, temperature and dissolved oxygen as four sub-parameters from the environment average physicochemical parameters;

[0019] Mapping each of the sub-parameters into a standardized contribution value through an independent nonlinear conversion function;

[0020] Weighted fusion of the standardized contribution values, wherein the weight coefficient of the salinity sub-parameter is higher than that of other sub-parameters.

[0021] outputting a discretized risk level signal according to the weighted fusion result.

[0022] Optionally, the triggering the emergency update when the risk index indicates high risk comprises:

[0023] locating a cluster of spatial coordinates of the high-risk area in the digital twin model;

[0024] performing a multi-level resolution enhancement operation on the high-risk area based on the cluster of spatial coordinates;

[0025] generating a dynamic color-changing warning boundary layer based on the display effect after the enhancement operation in the three-dimensional view of the digital twin model.

[0026] Optionally, after the triggering the emergency update, the method further comprises:

[0027] adjusting a time interval for collecting the environmental data and the steel pipe pile thickness data to a first period value, the first period value being less than an original time interval;

[0028] obtaining monitoring data of the high-risk area based on the first period value;

[0029] updating the risk index based on the monitoring data;

[0030] when the risk index continuously falls below a degradation threshold value: restoring the time interval for collecting to the original time interval; and updating a dynamic warning identifier of the high-risk area in the digital twin model.

[0031] Optionally, the updating a model thickness of the corresponding high-risk area in the digital twin model comprises:

[0032] monitoring a change amplitude of the risk index, and increasing an update frequency of the digital twin model when the change amplitude exceeds a preset sensitivity threshold value;

[0033] synchronously recording a spatiotemporal evolution trajectory of the steel pipe pile thickness at each update of the digital twin model;

[0034] calculating a corrosion acceleration based on the recorded spatiotemporal evolution trajectory, and when the corrosion acceleration exceeds a critical threshold value: generating an embedded reinforcement scheme instruction and updating a visual marker state of the high-risk area in the digital twin model.

[0035] Optionally, the method further comprises:

[0036] converting the corrosion rate into a thickness change rate parameter of the steel pipe pile in the digital twin model;

[0037] predicting a remaining service life of the structure based on the thickness change rate parameter.

[0038] trigger a forced update of the digital twin model when the remaining life of the structure is less than a maintenance safety threshold;

[0039] calibrate the prediction algorithm parameters dynamically according to the deviation rate of the measured corrosion rate after the forced update and the predicted value.

[0040] The application also provides a wharf steel pipe pile corrosion rate monitoring device, comprising:

[0041] The acquisition module acquires environment data and steel pipe pile thickness data of a target wharf steel pipe pile detection sub-region.

[0042] The preprocessing module pre-processes the environment data and the steel pipe pile thickness data to obtain average physicochemical parameters of the environment and average steel pipe pile thickness.

[0043] The index module calculates a risk index based on the average physicochemical parameters of the environment.

[0044] The corrosion module calculates a corrosion rate based on the average steel pipe pile thickness and the initial thickness.

[0045] The update module dynamically updates a model in the digital twin model according to the risk index, including: when the risk index indicates a low risk or a medium risk, performing a regular update to update the model thickness and the corrosion rate at a preset frequency; and when the risk index indicates a high risk, triggering an emergency update to update the model thickness of a corresponding high-risk area in the digital twin model, the high-risk area being determined based on the risk index reaching a preset threshold.

[0046] The application also provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0047] The application also provides a computer-readable storage medium storing a computer program, which makes the computer execute the above method when the computer program is executed in the computer.

[0048] The application has the following beneficial effects:

[0049] The application also provides a wharf steel pipe pile corrosion rate monitoring method, comprising: collecting environment data and steel pipe pile thickness data of a target wharf steel pipe pile detection sub-region; preprocessing the environment data and steel pipe pile thickness data to obtain average physicochemical parameters of the environment and average steel pipe pile thickness; calculating a risk index based on the average physicochemical parameters of the environment; calculating a corrosion rate based on the average steel pipe pile thickness and initial thickness; in a digital twin model, dynamically updating the model according to the risk index, comprising: when the risk index indicates a low risk or a medium risk, performing a regular update to update the model thickness and the corrosion rate at a preset frequency; when the risk index indicates a high risk, triggering an emergency update to update the model thickness of a corresponding high-risk area in the digital twin model, the high-risk area being determined based on the risk index reaching a preset threshold. Through real-time data collection and processing, risk index and corrosion rate calculation, and dynamic updating of the digital twin model, the application realizes real-time perception and accurate early warning of the corrosion state, and improves the monitoring efficiency and accuracy. BRIEF DESCRIPTION OF DRAWINGS

[0050] Fig. 1 is a wharf steel pipe pile corrosion rate monitoring process schematic diagram in the application;

[0051] Fig. 2 is a wharf steel pipe pile corrosion rate monitoring system schematic diagram in the application;

[0052] Fig. 3 is a data preprocessing process schematic diagram in the application. DETAILED DESCRIPTION

[0053] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it is understood that various forms implement the present disclosure and should not be limited by the embodiments set forth herein. On the contrary, the embodiments are provided to enable a more thorough understanding of the present disclosure and to convey the scope of the present disclosure to those skilled in the art.

[0054] Please refer to Figs. 1-3 The application provides a wharf steel pipe pile corrosion rate monitoring method, which realizes closed-loop monitoring through data collection, preprocessing, risk calculation, corrosion rate calculation and model updating.

[0055] S101, collecting environment data and steel pipe pile thickness data of a target wharf steel pipe pile detection sub-region;

[0056] The application relates to collecting environment data and steel pipe pile thickness data of a target wharf steel pipe pile detection sub-region.

[0057] The target wharf steel pipe pile detection sub-region is a one-meter circular region centered on the target wharf steel pipe pile. The environmental data includes the measured conductivity value of seawater in the target wharf steel pipe pile detection region, the measured pH value of seawater in the target wharf steel pipe pile detection region, the measured temperature of seawater in the target wharf steel pipe pile detection region, and the measured dissolved oxygen content of seawater in the target wharf steel pipe pile detection region. The steel pipe pile thickness data is the measured thickness obtained by the ultrasonic thickness gauge collecting the thickness of each steel pipe pile at multiple places.

[0058] The environmental data is collected by the conductivity sensor arranged on the side of the steel pipe pile to collect the conductivity value of the surrounding seawater to obtain the measured conductivity value, by the glass electrode sensor arranged on the side of the steel pipe pile to collect the pH value of the surrounding seawater to obtain the measured pH value, by the platinum resistance temperature sensor arranged on the side of the steel pipe pile to collect the temperature of the surrounding seawater to obtain the measured temperature, and by the electrochemical dissolved oxygen sensor arranged on the side of the steel pipe pile to collect the dissolved oxygen content of the surrounding seawater to obtain the measured dissolved oxygen content.

[0059] The optional model of the conductivity sensor is TDS-8002 of Lianji, with a measurement range of 0.1 ms / cm-500 ms / cm; the optional model of the glass electrode sensor is MIK-PH-5013A of Mikro Sens, with a measurement range of 0-14 pH; the optional model of the platinum resistance temperature sensor is HSTL-WZP of Huakangxingye, with a measurement range of -50-200℃; the optional model of the electrochemical dissolved oxygen sensor is a smart digital electrode of Mikro Sens. The optional model of the ultrasonic thickness gauge is AS860 of SMART-SENSOR, with a measurement range of 1.0-300 mm.

[0060] The conductivity sensor, the glass electrode sensor, the platinum resistance temperature sensor, and the electrochemical dissolved oxygen sensor have three on each side of the steel pipe pile, distributed at different depths of the steel pipe pile, 1 m, 5 m, and 10 m away from the sea surface; the system is preset to collect data once every 6 hours, triggering the sensor to collect data synchronously.

[0061] The shell of the sensor 1 m away from the sea surface is made of PVDF anti-ultraviolet material, and the cable has a reserved length of 1.5 m to cope with the slight shaking of the pile caused by sea waves; the sensor 10 m away from the sea surface needs to be additionally equipped with a stainless steel anti-collision net with a mesh diameter of 5 cm. The sensor surface needs to be sprayed with anti-fouling paint, and cleaned regularly by underwater robots every 3 months, especially the probes of the dissolved oxygen and conductivity sensors need to be kept clean to avoid the influence of shellfish and algae coverage on measurement accuracy. The conductivity value, pH value, temperature, and dissolved oxygen content collected by each sensor are transmitted to the temporary storage unit of the data preprocessing module through waterproof cables, and the sensor number is added, which can be used to distinguish different depth positions. The thickness of the steel pipe pile collected by the ultrasonic thickness gauge is transmitted to the temporary storage unit of the data preprocessing module through waterproof cables, and the collection time stamp and sensor number accurate to seconds are added, and the sensor number can be used to distinguish different depth positions.

[0062] S102, preprocessing the environmental data and the steel pipe pile thickness data to obtain average environmental physicochemical parameters and average steel pipe pile thickness;

[0063] The present application relates to preprocessing of environmental data and steel pipe pile thickness data to obtain average environmental physicochemical parameters and average steel pipe pile thickness. The preprocessing process includes marking processing of collected environmental data and steel pipe pile data, identifying data outside the reasonable range and marking as invalid value; after marking processing, the data collected by the sensor is averaged to obtain the average environmental physicochemical parameters in the target wharf steel pipe pile detection sub-region, and the average thickness of the steel pipe pile; the conductivity is converted into salinity by a conversion formula.

[0064] The marking processing is to exclude abnormal values, and the pH value exceeding the conventional range or mutation exceeding ±2 units, the temperature value less than-6℃ or greater than 40℃ or short-term fluctuation exceeding ±5℃, the salinity value being negative or greater than 50%, the dissolved oxygen content greater than 12 mg / L or exceeding the saturation value by 120% at high temperature, and the steel pipe pile thickness with negative value or exceeding the initial thickness by ±20% are all deleted.

[0065] Under ideal conditions, the calculation formula of the average thickness of the steel pipe pile is:

[0066]

[0067] Among them, is the average steel pipe pile thickness, , , represents the steel pipe pile thickness measured by the three thickness gauges.

[0068] The average environmental physicochemical parameters include average conductivity, average pH value, average temperature, and average dissolved oxygen content; the calculation formula of the average conductivity is:

[0069]

[0070] where C(t) is the average conductivity, 、 、 represents the conductivity measured by the three conductivity sensors; the calculation formula of the average pH value is:

[0071]

[0072] where PH is the average pH value, 、 、 represents the pH value measured by the three glass electrode sensors; the calculation formula of the average temperature is:

[0073]

[0074] where T is the average temperature, 、 、 represents the temperature measured by the three platinum resistance temperature sensors; the calculation formula of the average dissolved oxygen content is:

[0075]

[0076] where DO is the average dissolved oxygen content, 、 、 represents the dissolved oxygen content measured by the three electrochemical dissolved oxygen sensors.

[0077] The conductivity is converted into salinity by a conversion formula, including the following steps:

[0078] Step 1: Calculate the temperature correction coefficient:

[0079]

[0080] where K(t, 15) is the temperature correction coefficient, which is a function of temperature t, and a is an empirical constant;

[0081] Step 2: Correct to the equivalent conductivity at temperature 15:

[0082]

[0083] where C(15) is the equivalent conductivity at temperature 15, and C(t) is the average conductivity obtained at temperature t;

[0084] Step 3: Obtain the salinity by the conversion formula of conductivity and salinity:

[0085]

[0086] wherein S is salinity, is the ratio of the conductivity of the seawater sample to that of a standard KCl solution, and the coefficients , , , , , are fixed values. The coefficients , , , , , have the specific values = 0.0080, = -0.1692, = 25.3851, = 14.0941, = -7.0261, = 2.7081.

[0087] identifying abnormal environmental data, wherein the abnormal environmental data satisfies any one of the following conditions:

[0088] the pH value fluctuates more than the pH fluctuation threshold in a continuous preset number of collection periods, or the temperature fluctuates more than the temperature change threshold in a short time; the thickness data deviates from the initial thickness value by more than the proportional tolerance threshold;

[0089] when the abnormal environmental data is identified, replacing the abnormal data point with a sliding average of adjacent valid period data.

[0090] identifying abnormal environmental data in preprocessing, and replacing the abnormal data point.

[0091] The abnormal environmental data is defined as the pH value fluctuating more than ±2 units of the pH fluctuation threshold in three consecutive collection periods, or the temperature fluctuating more than ±5°C of the temperature change threshold in a short time; the thickness data deviating from the initial thickness value by more than ±20% of the proportional tolerance threshold.

[0092] when the abnormal environmental data is identified, replacing the abnormal data point with a sliding average of adjacent valid period data. For example, if the pH value fluctuates more than ±2 units in three consecutive periods, the system calculates a sliding average (i.e. arithmetic mean) of the pH values of the previous two valid periods to replace the current abnormal point; similarly, when the thickness data deviates from the initial thickness by ±20%, the sliding average of the thickness of the adjacent two valid periods is used to replace it.

[0093] S103, calculating a risk index based on the average physicochemical parameters of the environment;

[0094] The formula for calculating the risk index is:

[0095]

[0096] wherein W is the risk index, S is the average salinity, is the safe salinity threshold, PH is the average pH value, T is the average temperature, is the safe temperature threshold, DO is the average dissolved oxygen content, is the safe dissolved oxygen threshold, , , , is a weight coefficient, and = 1.

[0097] The value of S is 40%, The value of T is 40℃, The value of DO is 8 mg / L.

[0098] = 0.30, salinity is the most important factor affecting corrosion in the sea, and a higher weight is assigned to salinity, = 0.20, pH value has little effect on corrosion if no extreme acid or alkaline environment occurs, and pH is assigned a lower weight, temperature and dissolved oxygen are assigned a certain weight, = 0.25.

[0099] Four sub-parameters of salinity, pH, temperature and dissolved oxygen are extracted from the average physicochemical parameters of the environment; each of the sub-parameters is mapped to a standardized contribution value by an independent nonlinear conversion function; the standardized contribution values are fused by weighting, wherein the weight coefficient of the salinity sub-parameter is higher than that of other sub-parameters; and a discretized risk level signal is output according to the weighted fusion result.

[0100] Parameter extraction, conversion and fusion when calculating the risk index.

[0101] Four sub-parameters of salinity, pH, temperature and dissolved oxygen are extracted from the average physicochemical parameters of the environment, wherein the salinity is converted from conductivity, the pH is the average pH value, the temperature is the average temperature, and the dissolved oxygen is the average dissolved oxygen content. Each of the sub-parameters is mapped to a standardized contribution value by an independent nonlinear conversion function, specifically, the salinity sub-parameter is mapped by the formula , the pH sub-parameter is mapped by the formula , the temperature sub-parameter is mapped by the formula , and the dissolved oxygen sub-parameter is mapped by the formula ​​​​mapping, each mapping function converts the original value into a normalized contribution value in the range of 0-1.

[0102] The normalized contribution values are weighted and fused, and the weight coefficient of the salinity sub-parameter is 0.30, which is higher than the weight of the pH value 0.20, the weight of the temperature 0.25, and the weight of the dissolved oxygen 0.25. A discretized risk level signal is output according to the weighted fusion result, and the risk level signal includes a low risk warning (risk index W is less than 0.6), a medium risk warning (risk index W is greater than or equal to 0.6 and less than 0.8), and a high risk warning (risk index W is greater than or equal to 0.8).

[0103] S104, calculating a corrosion rate based on the average steel pipe pile thickness and the initial thickness;

[0104] The formula used by the corrosion rate calculation module is:

[0105]

[0106] wherein v is the corrosion rate, is the initial thickness of the steel pipe pile, is the average thickness of the steel pipe pile, and t is the corrosion time. is the measured thickness at the time of installation; t is the cumulative time from the time the steel pipe pile is put into use to the present time.

[0107] S105, in the digital twin model, dynamically updating the model according to the risk index, including: when the risk index indicates a low risk or a medium risk, performing a regular update to update the model thickness and the corrosion rate at a preset frequency; when the risk index indicates a high risk, triggering an emergency update to update the model thickness of a corresponding high-risk area in the digital twin model, the high-risk area being determined based on the risk index reaching a preset threshold.

[0108] The present application relates to dynamically updating a model in a digital twin model according to a risk index.

[0109] The digital twin modeling module is used to realize bidirectional mapping of the model and the physical entity, dynamically update the thickness of the steel pipe pile and the corrosion rate of the steel pipe pile in the model according to the average thickness of the steel pipe pile, and mark the model with three colors respectively according to the risk index.

[0110] The initial model of the digital twin modeling module is constructed based on the material parameters and the specification parameters of the steel pipe pile; the average thickness of each steel pipe pile and the corrosion rate of the steel pipe pile are dynamically updated in the dynamic update model; the model is marked with three colors respectively according to the risk index, the risk index is displayed, and the high-risk area is located.

[0111] From the perspective of basic modeling, Unity three-dimensional engine can be used to construct the initial geometric model based on the parameters of the material, diameter, length, and wall thickness of the steel pipe pile. The size accuracy of the model and the physical entity is ensured by importing CAD drawings. For dynamic updating function, Python programming language is used to develop data interface to receive the average thickness from the data preprocessing module, the rate value from the corrosion rate calculation module, and the index from the risk warning module in real time. The model parameter changes such as thickness thinning and color marking dynamic changes are driven by the script system of the engine.

[0112] On the data interaction level, WebSocket lightweight communication protocol is used to realize real-time bidirectional mapping of physical sensor data and virtual model. When the sensor uploads the thickness data, the geometric size of the corresponding position of the model is updated synchronously. At the same time, the time series database InfluxDB is integrated to store historical data, supporting model backtracking to display the corrosion state at different time periods.

[0113] On the visualization presentation, WebGL technology is used to develop the browser-side interactive interface. The corresponding risk level is mapped by blue, yellow, and red colors. The corrosion rate distribution is displayed through a heat map, allowing users to intuitively view the three-dimensional form, corrosion trend, and high-risk areas of the steel pipe pile, and ultimately achieve the deep integration from physical entity to virtual model.

[0114] The trigger condition for model dynamic updating is usually regular updating. The interval length of regular updating is consistent with the collection frequency. The thickness and corrosion rate of the steel pipe pile are updated based on the measured data every 6 hours. When a red warning is triggered in a certain area, the model undergoes emergency updating. The model immediately changes the color marking of that area from yellow to red and enlarges the three-dimensional details of that position.

[0115] When the risk index indicates low risk or medium risk, regular updating is performed to update the model thickness and corrosion rate every 6 hours at the preset frequency. When the risk index indicates high risk, emergency updating is triggered to update the model thickness of the corresponding high-risk area in the digital twin model. High risk is determined based on the risk index reaching the preset threshold of 0.8.

[0116] The multi-level warning signal includes low risk warning:

[0117] When the risk index W is less than the first preset threshold of 0.6, a low-risk warning signal is output, prompting normal monitoring. When the risk index W is greater than or equal to the first preset threshold of 0.6 and less than the second preset threshold of 0.8, a medium-risk warning signal is output, prompting to strengthen the data collection frequency in key areas. When the risk index W is greater than or equal to the second preset threshold of 0.8, a high-risk warning signal is output, which is immediately pushed to the management personnel and marked in the corresponding high-risk area of the digital twin model.

[0118] When W=0.6, the corresponding environmental data is in the slight influence interval, at this time the salinity is about 60% of the safety threshold equal to 24‰, the pH value deviates from the neutral about 1.2 units, the temperature is about 60% of the safety threshold equal to 24℃, the dissolved oxygen is about 60% of the safety threshold equal to 4.8mg / L, at this time the corrosion rate is at a slow level, no additional intervention is needed; when W=0.8, the corresponding environmental data enters the significant influence interval, at this time the salinity reaches 80% of the safety threshold equal to 32‰, the pH value deviates from the neutral about 1.6 units, the temperature reaches 80% of the safety threshold equal to 32℃, the dissolved oxygen reaches 80% of the safety threshold equal to 6.4mg / L, at this time the corrosion rate is significantly accelerated, emergency intervention is needed to avoid structural damage.

[0119] locating a spatial coordinate cluster of a high-risk area in the digital twin model;

[0120] performing a multi-level resolution enhancement operation on the high-risk area based on the spatial coordinate cluster;

[0121] generating a dynamic color-changing warning boundary layer in the three-dimensional view of the digital twin model based on the display effect after the enhancement operation.

[0122] locating a spatial coordinate cluster of a high-risk area in the digital twin model, the spatial coordinate cluster being determined based on sensor numbers and steel pipe pile positions; performing a multi-level resolution enhancement operation on the high-risk area based on the spatial coordinate cluster, the multi-level resolution enhancement operation including gradually magnifying geometric details of the high-risk area from a basic resolution to a high resolution; generating a dynamic color-changing warning boundary layer in the three-dimensional view of the digital twin model based on the display effect after the enhancement operation, the dynamic color-changing warning boundary layer being a flashing red boundary around the high-risk area, the color gradually changing from yellow to red.

[0123] adjusting a collection time interval of the environmental data and the steel pipe pile thickness data to a first period value, the first period value being less than an original time interval; obtaining monitoring data of the high-risk area based on the first period value; updating a risk index based on the monitoring data; when the risk index continuously falls below a degradation threshold value: restoring the collection time interval to the original time interval; updating a dynamic warning identifier of the high-risk area in the digital twin model.

[0124] After triggering the emergency update, the collection time interval of the environmental data and the steel pipe pile thickness data is adjusted to a first period value, which is every 1 hour, less than the original time interval every 6 hours. The monitoring data of the high-risk area is obtained based on the first period value, and the monitoring data includes real-time environmental data and thickness data of the high-risk area. The risk index is updated based on the monitoring data, and the updated risk index is calculated using the same formula. When the risk index continues to be lower than the degradation threshold 0.6: the collection time interval is restored to the original time interval every 6 hours; the dynamic warning mark of the high-risk area in the digital twin model is updated, and the dynamic warning mark changes from red to yellow.

[0125] The change amplitude of the risk index is monitored, and when the change amplitude exceeds a preset sensitivity threshold, the update frequency of the digital twin model is increased; the spatiotemporal evolution trajectory of the steel pipe pile thickness is recorded synchronously at each update of the digital twin model; the corrosion acceleration is calculated based on the recorded spatiotemporal evolution trajectory, and when the corrosion acceleration exceeds a critical threshold: an embedded reinforcement scheme instruction is generated and the visualization marker state of the high-risk area in the digital twin model is updated.

[0126] The change amplitude of the risk index is monitored, and when the change amplitude exceeds a preset sensitivity threshold 0.1, the update frequency of the digital twin model is increased from every 6 hours to every 2 hours. The spatiotemporal evolution trajectory of the steel pipe pile thickness is recorded synchronously at each update of the digital twin model, and the spatiotemporal evolution trajectory includes thickness data and position data at different time points. The corrosion acceleration is calculated based on the recorded spatiotemporal evolution trajectory, and the corrosion acceleration calculation formula is:

[0127]

[0128] When the corrosion acceleration exceeds a critical threshold 0.05 mm / h²: an embedded reinforcement scheme instruction is generated, the embedded reinforcement scheme instruction is to send a maintenance suggestion to increase the corrosion protection coating; and the visualization marker state of the high-risk area in the digital twin model is updated, the visualization marker state changes from a static marker to a dynamic flashing.

[0129] The corrosion rate is converted into a thickness change rate parameter of the steel pipe pile in the digital twin model; the remaining life of the structure is predicted based on the thickness change rate parameter; when the remaining life of the structure is less than a maintenance safety threshold, a forced update of the digital twin model is triggered; and the prediction algorithm parameters are dynamically calibrated according to the deviation rate of the measured corrosion rate after the forced update and the predicted value.

[0130] The corrosion rate is converted into a thickness change rate parameter of the steel pipe pile in the digital twin model, and the thickness change rate parameter is a thickness thinning speed variable in the model. The remaining life of the structure is predicted based on the thickness change rate parameter, and the remaining life of the structure is calculated according to the formula:

[0131]

[0132] The minimum safety thickness is 50% of the initial thickness. When the remaining life of the structure is less than the maintenance safety threshold for 1 year, a forced update of the digital twin model is triggered, the forced update being an immediate update of the model data regardless of the regular frequency. According to the deviation rate of the measured corrosion rate after the forced update from the predicted value, the prediction algorithm parameters are dynamically calibrated, the deviation rate being calculated as |measured value-predicted value| / predicted value, and the dynamic calibration being an adjustment of the weight coefficient 、 、 、 .

[0133] The application also provides a wharf steel pipe pile corrosion rate monitoring device, comprising:

[0134] The acquisition module acquires environmental data and steel pipe pile thickness data of a target wharf steel pipe pile detection sub-region;

[0135] The preprocessing module pre-processes the environmental data and the steel pipe pile thickness data to obtain average physicochemical parameters of the environment and average steel pipe pile thickness;

[0136] The index module calculates a risk index based on the average physicochemical parameters of the environment;

[0137] The corrosion module calculates a corrosion rate based on the average steel pipe pile thickness and the initial thickness;

[0138] The update module dynamically updates a model in a digital twin model according to the risk index, including: when the risk index indicates a low risk or a medium risk, performing a regular update to update the model thickness and the corrosion rate at a preset frequency; and when the risk index indicates a high risk, triggering an emergency update to update a model thickness of a corresponding high-risk area in the digital twin model, the high-risk area being determined based on the risk index reaching a preset threshold.

[0139] The application also provides an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above method when executing the computer program.

[0140] The application also provides a computer-readable storage medium storing a computer program, which, when executed in a computer, causes the computer to execute the above method.

[0141] The above description of the embodiments is for the purpose of enabling one of ordinary skill in the art to make and use the application. Various modifications to the embodiments described above will be readily apparent to those of ordinary skill in the art, and the generic principles described herein can be applied to other embodiments without the use of the inventive faculty. Thus, the present application is not intended to be limited to the embodiments described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the corrosion rate of dock steel pipe piles, characterized in that: include: Collect environmental data and steel pipe pile thickness data of the target wharf steel pipe pile detection sub-area; Preprocessing the environmental data and the steel pipe pile thickness data to obtain average environmental physical and chemical parameters and average steel pipe pile thickness; Calculating a risk index based on the average physical and chemical parameters of the environment; calculating a corrosion rate based on the average steel pipe pile thickness and the initial thickness; In the digital twin model, dynamically updating the model according to the risk index includes: when the risk index indicates low risk or medium risk, performing a regular update to update the model thickness and corrosion rate at a preset frequency; When the risk index indicates a high risk, an emergency update is triggered to update the model thickness of the corresponding high-risk area in the digital twin model, and the high-risk area is determined based on the risk index reaching a preset threshold.

2. The method according to claim 1, characterized in that The preprocessing of the environmental data and the steel pipe pile thickness data includes: Identify abnormal environmental data, where the abnormal environmental data meets any of the following conditions: The pH value fluctuates beyond the pH fluctuation threshold within a preset number of consecutive collection cycles, or the temperature fluctuates beyond the temperature change threshold for a short period of time; The thickness data deviates from the initial thickness value by more than the proportional tolerance threshold; When the abnormal environmental data is identified, the abnormal data point is replaced by a sliding average of adjacent valid cycle data.

3. The method according to claim 1, characterized in that The calculating of the risk index based on the average physical and chemical parameters of the environment includes: Extracting four sub-parameters of salinity, pH, temperature and dissolved oxygen from the average physical and chemical parameters of the environment; Mapping each of the sub-parameters to a standardized contribution value through an independent nonlinear conversion function; Performing weighted fusion on the standardized contribution values, wherein the weight coefficient of the salinity sub-parameter is higher than that of other sub-parameters; The discretized risk level signal is output according to the weighted fusion result.

4. The method according to claim 1, wherein When the risk index indicates a high risk, triggering an emergency update includes: locating spatial coordinate clusters of high-risk areas in the digital twin model; performing a multi-level resolution enhancement operation on the high-risk area based on the spatial coordinate cluster; In the three-dimensional view of the digital twin model, a dynamic color-changing warning boundary layer is generated based on the display effect after the enhancement operation.

5. The method according to claim 1, wherein After triggering the emergency update, the following steps are also included: Adjusting the time interval for collecting the environmental data and the steel pipe pile thickness data to a first period value, wherein the first period value is smaller than the original time interval; Acquire monitoring data of the high-risk area based on the first period value; updating a risk index based on the monitoring data; When the risk index continues to be lower than the degradation threshold: the collection time interval is restored to the original time interval; and the dynamic warning sign of the high-risk area is updated in the digital twin model.

6. The method according to claim 1, characterized in that The updating of the model thickness corresponding to the high-risk area in the digital twin model includes: Monitoring the magnitude of the change in the risk index, and increasing the update frequency of the digital twin model when the magnitude of the change exceeds a preset sensitivity threshold; Synchronously recording the spatiotemporal evolution trajectory of the thickness of the steel pipe pile each time the digital twin model is updated; The corrosion acceleration is calculated based on the recorded spatiotemporal evolution trajectory. When the corrosion acceleration exceeds a critical threshold, an embedded reinforcement solution instruction is generated and the visual marking status of the high-risk area in the digital twin model is updated.

7. The method according to claim 1, characterized in that Also includes: Converting the corrosion rate into a thickness change rate parameter of the steel pipe pile in the digital twin model; predicting the remaining life of the structure based on the thickness change rate parameter; When the remaining life of the structure is less than a maintenance safety threshold, triggering a mandatory update of the digital twin model; The prediction algorithm parameters are dynamically calibrated based on the deviation rate between the measured corrosion rate and the predicted value after forced updating.

8. A device for monitoring the corrosion rate of steel pipe piles at a dock, characterized in that: include: The acquisition module collects environmental data and steel pipe pile thickness data of the target wharf steel pipe pile detection sub-area; A preprocessing module preprocesses the environmental data and the steel pipe pile thickness data to obtain average environmental physical and chemical parameters and average steel pipe pile thickness; An index module, for calculating a risk index based on the average physical and chemical parameters of the environment; a corrosion module, calculating a corrosion rate based on the average steel pipe pile thickness and an initial thickness; An updating module, in the digital twin model, dynamically updates the model according to the risk index, including: when the risk index indicates low risk or medium risk, performing a regular update to update the model thickness and corrosion rate at a preset frequency; When the risk index indicates a high risk, an emergency update is triggered to update the model thickness of the corresponding high-risk area in the digital twin model, and the high-risk area is determined based on the risk index reaching a preset threshold.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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 is caused to execute the method according to any one of claims 1 to 7.

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