Corrosion degree monitoring device, method and equipment for multiple detection data of cable for unmanned ship
By setting up multiple sensors on the cables for unmanned boats to obtain detection data, and using the autoregressive integral sliding average model to predict corrosion trends, the problem of difficult monitoring of the corrosion status of unmanned boats is solved, and accurate monitoring and prediction of cable corrosion conditions is achieved, and the operation safety of unmanned boats is improved.
Patent Information
- Application Number
- CN202510197897.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-21
- Publication Date
- 2025-07-18
AI Technical Summary
The existing technology is difficult to achieve accurate monitoring and prediction of the corrosion status of cables for unmanned boats, resulting in the lack of scientific basis for operation and maintenance personnel and the inability to take maintenance measures in a timely manner, affecting the operation safety of unmanned boats.
The monitoring position determination module obtains the cable distribution information for boats, and uses temperature sensors, humidity sensors, gas sensors, salt spray sensors, PH value sensors and strain sensors to obtain multiple detection data. Combined with the autoregressive integral sliding average model and visual display module, real-time monitoring and prediction of cable corrosion status is achieved.
Accurate monitoring and prediction of the corrosion of cables for unmanned boats has been achieved, helping operation and maintenance personnel to take maintenance measures in a timely manner and improve the operation safety of unmanned boats.
Smart Images
Figure CN120334106A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the technical field of power facilities, and particularly relates to a corrosion degree monitoring device, method and equipment for multi-detection data of cables for unmanned boats. Background Art
[0002] Cables for boats are specially designed for various boats to transmit electric energy and signals. Since boats mainly operate in waters such as the ocean and inland rivers, the high humidity and salt spray environment where the boats are located, as well as the hull vibration generated when the boats are sailing, are likely to cause corrosion of the cables for boats. Corrosion of the cables for boats will increase the conductor resistance and reduce the insulation performance, thus triggering short-circuit faults and affecting the operation of boat equipment. Therefore, it is very necessary to monitor the corrosion state of the cables for boats in real time.
[0003] However, the current monitoring methods mainly rely on regular resistance and insulation performance tests, but these data can often only reflect problems after the corrosion of the cables for boats has had a significant impact on their electrical performance, and early warning cannot be achieved. In addition, due to the lack of comprehensive monitoring data, it is difficult to accurately reflect the actual corrosion state of the cables for boats, and it is even more difficult to accurately predict its corrosion development trend, resulting in a lack of scientific basis for maintenance personnel when formulating maintenance plans.
[0004] Therefore, how to achieve accurate monitoring and prediction of the corrosion state of the cables for boats is an urgent problem to be solved by those skilled in the art. Summary of the Invention
[0005] Embodiments of this application provide a corrosion degree monitoring device, method and equipment for multi-detection data of cables for unmanned boats, aiming to achieve accurate monitoring and prediction of the corrosion situation of the cables for unmanned boats, facilitate maintenance personnel to take maintenance measures in time, and thus improve the operation safety of unmanned boats.
[0006] In a first aspect, embodiments of this application provide a corrosion degree monitoring device for multi-detection data of cables for unmanned boats, and the device includes:
[0007] A monitoring position determination module, configured to obtain the distribution information of the cables for boats and determine the key corrosion monitoring positions according to the distribution information of the cables for boats;
[0008] A detection data acquisition module, configured to obtain multi-detection data through monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor and a strain sensor, and the gas sensor is used to obtain corrosive gas concentration data;
[0009] The corrosion trend prediction module is used to determine the real-time corrosion state and the corrosion development trend of the submarine cable according to the multi-detection data;
[0010] The visualization display module is used to visually display the real-time corrosion state and the corrosion development trend.
[0011] In a second aspect, an embodiment of the present application provides a method for monitoring the corrosion degree of multi-detection data of a submarine cable. The method includes:
[0012] Obtain the distribution information of the submarine cable through the monitoring position determination module, and determine the key corrosion monitoring positions according to the distribution information of the submarine cable;
[0013] Obtain multi-detection data through the detection data acquisition module by using the monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain corrosive gas concentration data;
[0014] Determine the real-time corrosion state and the corrosion development trend of the submarine cable according to the multi-detection data through the corrosion trend prediction module;
[0015] Visually display the real-time corrosion state and the corrosion development trend through the visualization display module.
[0016] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.
[0017] In the embodiment of the present application, a monitoring position determination module is configured to obtain the distribution information of the boat cable and determine the key corrosion monitoring positions according to the distribution information of the boat cable; a detection data acquisition module is configured to obtain multi-detection data through monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain the corrosive gas concentration data; a corrosion trend prediction module is configured to determine the real-time corrosion state and the corrosion development trend of the boat cable according to the multi-detection data; a visualization display module is configured to visually display the real-time corrosion state and the corrosion development trend. The above corrosion degree monitoring device for the multi-detection data of the unmanned boat cable can realize the accurate monitoring and prediction of the corrosion situation of the unmanned boat cable by determining the real-time corrosion state and the corrosion development trend of the boat cable according to the multi-detection data and visually displaying them, which is convenient for the operation and maintenance personnel to take maintenance measures in time, thereby improving the operation safety of the unmanned boat. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 FIG. 6 is a schematic structural diagram of a corrosion degree monitoring device for multi-detection data of an unmanned boat cable provided in Embodiment 1 of the present application;
[0019] Figure 2 FIG. 10 is a schematic structural diagram of a corrosion degree monitoring device for multi-detection data of an unmanned boat cable provided in Embodiment 2 of the present application;
[0020] Figure 3 FIG. 14 is a schematic structural diagram of a corrosion degree monitoring device for multi-detection data of an unmanned boat cable provided in Embodiment 3 of the present application;
[0021] Figure 4 FIG. 18 is a schematic flowchart of a corrosion degree monitoring method for multi-detection data of an unmanned boat cable provided in Embodiment 4 of the present application;
[0022] Figure 5 FIG. 22 is a schematic structural diagram of an electronic device provided in Embodiment 5 of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0023] To make the objectives, technical solutions, and advantages of this application more clear, the following further describes the specific embodiments of this application in detail with reference to the accompanying drawings. It can be understood that the specific embodiments described herein are only used to explain this application, rather than limiting this application. Additionally, it should be noted that for ease of description, only the parts related to this application rather than all of the content are shown in the drawings. Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of the operations can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operations are completed, but it can also have additional steps not included in the drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, and so on.
[0024] Next, the technical solutions in the embodiments of this application will be clearly described with reference to the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are some, rather than all, of the embodiments of this application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this application belong to the scope of protection of this application.
[0025] The terms "first", "second", etc. in the specification and claims of this application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are usually of the same category, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / ", generally represents an "or" relationship between the associated objects before and after.
[0026] Next, with reference to the accompanying drawings, through specific embodiments and their application scenarios, the corrosion degree monitoring device, method, and equipment for multi-detection data of cables for unmanned boats provided in the embodiments of this application will be described in detail.
[0027] Embodiment 1
[0028] Figure 1 is a schematic structural diagram of the corrosion degree monitoring device for multi-detection data of cables for unmanned boats provided in Embodiment 1 of this application. As Figure 1 shown, the device includes:
[0029] A monitoring position determination module 110, configured to obtain the distribution information of the cables for the boat and determine the key corrosion monitoring positions according to the distribution information of the cables for the boat;
[0030] The detection data acquisition module 120 is configured to acquire multiple detection data through monitoring sensors arranged at the critical corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is configured to acquire corrosive gas concentration data;
[0031] The corrosion trend prediction module 130 is configured to determine the real-time corrosion state and the corrosion development trend of the cable for unmanned boats according to the multiple detection data;
[0032] The visualization display module 140 is configured to visually display the real-time corrosion state and the corrosion development trend.
[0033] This application is applicable to scenarios where cables are installed on unmanned boats. Specifically, the determination of the critical corrosion monitoring positions, the determination and visualization display of the real-time corrosion state and corrosion development trend monitoring data of the cables for unmanned boats, etc. can be executed by intelligent terminal devices. Staff members view the visualized display information and take corresponding maintenance measures for the cables for unmanned boats with corrosion hazards according to the real-time corrosion state and corrosion development trend to ensure the normal and safe operation of the unmanned boats.
[0034] Based on the above usage scenarios, it can be understood that the execution subject of this application can be an intelligent terminal device, such as a desktop computer, a laptop computer, a mobile phone, a tablet computer, and an interactive multimedia, etc., and no excessive limitation is made here.
[0035] The monitoring position determination module 110 is configured to acquire the cable distribution information for unmanned boats and determine the critical corrosion monitoring positions according to the cable distribution information for unmanned boats.
[0036] A cable is a device for transmitting electricity or signals. The cable for unmanned boats is the cable installed on an unmanned boat. Among them, an unmanned boat can be a watercraft with a certain degree of intelligence that can autonomously or semi-autonomously complete a series of tasks.
[0037] The cable distribution information for unmanned boats may refer to relevant information such as the specific laying path, installation position of the cable for unmanned boats on the unmanned boat, and the connection relationship with other devices. The cable distribution information for unmanned boats can be obtained by referring to instruction documents such as the usage instructions and production reports of the unmanned boat.
[0038] The corrosion key monitoring positions can refer to the positions of the boat cables that are prone to corrosion and may have a significant impact on the operation of the unmanned boat once corrosion occurs. The method of determining the corrosion key monitoring positions according to the boat cable distribution information can adopt determining the positions of the boat cables in the areas near the water line, the bottom vortex area, and the equipment compartment area as the corrosion key monitoring positions based on the boat cable distribution information. It can also adopt obtaining the environmental parameters of each distribution position of the boat cable, such as temperature parameters, humidity parameters, salt spray concentration parameters, and mechanical vibration parameters, etc., and determining the corrosion key monitoring positions according to the boat cable distribution information and the environmental parameters.
[0039] The detection data acquisition module 120 is used to acquire multi-detection data through the monitoring sensors arranged at the corrosion key monitoring positions.
[0040] The multi-detection data can be various types of data required to determine the real-time corrosion state and corrosion development trend of the boat cable, and can include temperature data, humidity data, corrosive gas concentration data, salt spray concentration data, pH value data, and strain data, etc. Correspondingly, the monitoring sensors are the sensors used to acquire multi-detection data, and can include temperature sensors, humidity sensors, gas sensors, salt spray sensors, pH value sensors, and strain sensors, etc.
[0041] Specifically, a temperature sensor is a sensor that can sense temperature and convert it into an available output signal; a humidity sensor is a sensor used to measure the water vapor content in air or other gases; a gas sensor is used to acquire corrosive gas concentration data, and the corrosive gas refers to a gas that can chemically react with substances such as metals and non-metals under certain conditions, thereby corroding these substances; a salt spray sensor is a sensor used to detect the salt spray concentration or salt spray corrosion degree in the environment; a pH value sensor is a sensor used to measure the acidity and alkalinity of substances; a strain sensor is a sensor that can sense the strain generated by an object due to force and convert this strain into a measurable electrical signal or other signal.
[0042] The corrosion trend prediction module 130 is used to determine the real-time corrosion state and corrosion development trend of the boat cable according to the multi-detection data.
[0043] The real-time corrosion state can refer to the corrosion degree and specific conditions presented by the boat cable at the current moment. The corrosion development trend can be the prediction and inference of the change in the corrosion situation of the boat cable in the future for a period of time.
[0044] The method for determining the real-time corrosion state and corrosion development trend of submarine cables based on multi-detection data can be to perform compensation processing and normalization processing on the multi-detection data according to the temperature data obtained by the temperature sensor, calculate the real-time corrosion index as the real-time corrosion state based on the multi-detection data after the compensation processing and normalization processing, and input the real-time corrosion index into the pre-constructed autoregressive integrated moving average model to obtain the corrosion development trend output by the autoregressive integrated moving average model.
[0045] The visualization display module 140 is used to visually display the real-time corrosion state and the corrosion development trend.
[0046] Visualization display is a method of presenting data, information, processes, etc. in an intuitive visual form so that users can understand and analyze relevant content more quickly and clearly.
[0047] The method for visually displaying the real-time corrosion state and the corrosion development trend can be to obtain the hull structure model of the unmanned boat, determine the display color according to the real-time corrosion state and the corrosion development trend, and perform rendering display in the hull structure model according to the display color.
[0048] In the example of this application, the monitoring position determination module is used to obtain the distribution information of submarine cables and determine the key corrosion monitoring positions according to the distribution information of the submarine cables; the detection data acquisition module is used to obtain multi-detection data through the monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain corrosive gas concentration data; the corrosion trend prediction module is used to determine the real-time corrosion state and the corrosion development trend of the submarine cables according to the multi-detection data; the visualization display module is used to visually display the real-time corrosion state and the corrosion development trend. With this technical solution, by determining the real-time corrosion state and the corrosion development trend of the submarine cables based on multi-detection data and visually displaying them, accurate monitoring and prediction of the corrosion of the submarine cables of the unmanned boat can be achieved, which is convenient for the operation and maintenance personnel to take maintenance measures in a timely manner, thereby improving the operation safety of the unmanned boat.
[0049] Embodiment 2
[0050] Figure 2It is a schematic structural diagram of a corrosion degree monitoring device for multi-detection data of a cable for an unmanned boat provided in Embodiment 2 of the present application. This solution makes a better improvement on the basis of the above embodiment. The specific improvement is as follows: The detection data acquisition module includes: a positioning compensation unit for acquiring the positioning information of a monitoring sensor arranged at the key corrosion monitoring position; a data acquisition unit for acquiring multi-detection data through the monitoring sensor; wherein, the monitoring sensor includes at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used for acquiring corrosive gas concentration data; a data filtering unit for filtering out abnormal data from the multi-detection data.
[0051] As Figure 2 shown, the device includes:
[0052] A monitoring position determination module 210 for acquiring the distribution information of the boat cable and determining the key corrosion monitoring position according to the distribution information of the boat cable;
[0053] A detection data acquisition module 220 for acquiring multi-detection data through a monitoring sensor arranged at the key corrosion monitoring position; wherein, the monitoring sensor includes at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used for acquiring corrosive gas concentration data;
[0054] A corrosion trend prediction module 230 for determining the real-time corrosion state and corrosion development trend of the boat cable according to the multi-detection data;
[0055] A visualization display module 240 for visually displaying the real-time corrosion state and the corrosion development trend.
[0056] Among them, the detection data acquisition module 220 includes:
[0057] A positioning compensation unit 2201 for acquiring the positioning information of a monitoring sensor arranged at the key corrosion monitoring position;
[0058] A data acquisition unit 2202 for acquiring multi-detection data through the monitoring sensor; wherein, the monitoring sensor includes at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used for acquiring corrosive gas concentration data;
[0059] A data filtering unit 2203 for filtering out abnormal data from the multi-detection data.
[0060] The positioning information of the monitoring sensor may refer to the position information of the monitoring sensor relative to the hull structure. To obtain the positioning information of the monitoring sensor set at the key corrosion monitoring position, the monitoring sensor can be embedded with a positioning tag with a unique identifier, and based on the radio frequency identification technology, the positioning information of the monitoring sensor set at the key corrosion monitoring position can be obtained.
[0061] Abnormal data may refer to data points that are significantly different from other data in the dataset, do not conform to the general pattern or law of the data. To filter out the abnormal data in the multi-detection data, statistical methods such as the set threshold method, box plot method, etc., or model-based methods such as the fitting curve method, or machine learning methods such as the isolation forest algorithm, local outlier factor algorithm, etc. can be used to filter out the abnormal data in the multi-detection data.
[0062] In this technical solution, optionally, the data filtering unit is specifically used for:
[0063] In the case where the monitoring sensor includes a strain sensor, the strain data obtained by the strain sensor is filtered based on the sliding window algorithm, and the strain abnormal data caused by the large movement of the boat is filtered out.
[0064] Strain data refers to the degree of deformation generated when an object is subjected to an external force. Strain abnormal data may refer to strain data points that exceed the strain data range under the normal operating state of the boat due to factors such as waves and wind forces acting on the boat, resulting in large shaking movements of the boat, and are significantly different from the characteristics of the strain data during the smooth navigation of the boat.
[0065] The sliding window algorithm is an algorithm strategy on linear data structures such as arrays and strings. By setting two pointers to define a sub-interval (i.e., window) with a dynamic range, and expanding or contracting the window according to specific conditions. It can be understood that if the boat movement is relatively stable, the window size can be relatively small; if the boat movement changes frequently and has a large amplitude, the window size can be relatively large.
[0066] The following is an example code for filtering out the strain abnormal data caused by the large movement of the boat in the strain data:
[0067] import numpy as np
[0068] def sliding_window_filter(strain_data,window_size,threshold_factor=3):
[0069] "″"
[0070] Use the sliding window algorithm to filter out the outliers in the strain data
[0071] :param strain_data: List of strain data
[0072] :param window_size: Sliding window size
[0073] :param threshold_factor: Threshold factor used to determine the range of outliers
[0074] :return: Filtered list of strain data
[0075] "″"
[0076] filtered_data = []
[0077] n = len(strain_data)
[0078] for i in range(n):
[0079] # Determine the start and end positions of the window
[0080] start = max(0, i - window_size / / 2)
[0081] end = min(n, i + window_size / / 2 + 1)
[0082] window = strain_data[start:end]
[0083] # Calculate the mean and standard deviation of the data within the window
[0084] mean = np.mean(window)
[0085] std = np.std(window)
[0086] # Set the threshold
[0087] lower_threshold = mean - threshold_factor * std
[0088] upper_threshold = mean + threshold_factor * std
[0089] # Determine if the current data point is an outlier
[0090] if lower_threshold <= strain_data[i] <= upper_threshold:
[0091] filtered_data.append(strain_data[i])
[0092] else:
[0093] # If it is an outlier, replace it with the window mean
[0094] filtered_data.append(mean)
[0095] return filtered_data
[0096] # Example data # Assume this is the strain data obtained by the strain sensor
[0097] strain_data = [10, 12, 11, 100, 13, 14, 12, 11, 10, 13, 15, 1000, 14, 13] # Select an appropriate window size according to the boat's motion situation # Here, assume that the boat's motion changes frequently and greatly, and a larger window size is selected
[0098] window_size = 5
[0099] # Perform data filtering
[0100] filtered_strain_data = sliding_window_filter(strain_data, window_size)
[0101] print("Original strain data:", strain_data) print("Filtered strain data:", filtered_strain_data)
[0102] The advantage of this solution is that by filtering the strain data obtained by the strain sensor based on the sliding window algorithm, the abnormal strain data caused by the large movement of the boat is filtered out, which can reduce the strain data error caused by the boat's movement, thereby improving the accuracy of the subsequent real-time corrosion state and corrosion development trend.
[0103] The advantage of this solution is that during the navigation of the boat, it will be affected by various factors such as waves, ocean currents, and wind, resulting in various forms of movement. During the use of the boat cable, due to the influence of factors such as its own weight, tension, and temperature change, it may deform and move. By obtaining the positioning information of the monitoring sensor set at the key corrosion monitoring position, the situation where the monitoring sensor is displaced and causes an error in the determined corrosion position can be avoided.
[0104] In this technical solution, optionally, the visualization display module is specifically configured to:
[0105] Obtain the hull structure model of the unmanned boat;
[0106] Determine the display color of each positioning information according to the real-time corrosion state and the corrosion development trend corresponding to each positioning information;
[0107] Render and display in the hull structure model according to the positioning information and its display color.
[0108] The hull structure model of the unmanned boat may refer to the digital three-dimensional model of the actual hull structure of the unmanned boat. The method of obtaining the hull structure model of the unmanned boat can be to consult the instruction manuals, production reports and other description documents of the unmanned boat to obtain the hull structure parameters of the unmanned boat, and perform three-dimensional modeling according to the hull structure parameters to obtain the hull structure model of the unmanned boat.
[0109] The display color may be a color identifier used to intuitively display the corrosion conditions of the positions of each boat cable in the hull structure model. Different display colors represent different real-time corrosion states and / or corrosion development trends. For example, red represents a serious real-time corrosion state and / or a fast corrosion development trend, yellow represents a medium real-time corrosion state and / or a general corrosion development trend, and green represents a light real-time corrosion state and / or a controllable corrosion development trend.
[0110] The method of rendering and displaying in the hull structure model according to the positioning information and its display color can be to determine the rendering object in the hull structure model according to the positioning information and render the rendering object as the display color.
[0111] In this technical solution, optionally, the visualization display module is further configured to:
[0112] Obtain the historical corrosion data of each positioning information;
[0113] Generate a corrosion rate change line graph for at least one time span according to the historical corrosion data, the real-time corrosion state and the corrosion development trend.
[0114] The historical corrosion data may refer to the corrosion states of the positioning information at each time point before the current time point.
[0115] The corrosion rate change line graph may be a line graph with time as the horizontal axis and corrosion state as the vertical axis, representing the corrosion states corresponding to different time points with data points and connecting these data points in sequence. The corrosion rate change line graph can intuitively display the change of the corrosion rate.
[0116] The time span can refer to the range of time for the expression information of the corrosion rate change line graph, which can be weeks, months, years, etc. The method of generating a corrosion rate change line graph for a time span can be to determine the corrosion state corresponding to each time point within the time span based on historical corrosion data, real-time corrosion status, and corrosion development trend, represent it with data points, and connect these data points in sequence to obtain the corrosion rate change line graph for the time span.
[0117] The advantage of this solution is that by obtaining the historical corrosion data of each positioning information and generating at least one corrosion rate change line graph for a time span based on the historical corrosion data, real-time corrosion status, and corrosion development trend, it can help to intuitively and comprehensively grasp the corrosion status and change trend of the positions of each submarine cable at different time periods, providing a strong data basis for in-depth analysis of the corrosion law of submarine cables.
[0118] The advantage of this solution is that by determining the display color according to the real-time corrosion status and corrosion development trend, and performing rendering display in the hull structure model according to the positioning information and its display color, it can help the staff quickly identify the areas with severe corrosion or dangerous development trends, so as to timely formulate scientific and reasonable maintenance plans and emergency plans, effectively reduce the risk of accidents caused by submarine cable failures, and ensure the safe and stable operation of the unmanned submarine.
[0119] Embodiment III
[0120] Figure 3 It is a schematic structural diagram of a corrosion degree monitoring device for multi-detection data of submarine cables for an unmanned submarine provided in Embodiment III of the present application. This solution makes a better improvement on the basis of the above-mentioned embodiments. The specific improvement is as follows: The corrosion trend prediction module includes: a data preprocessing unit for performing compensation processing and normalization processing on the multi-detection data according to the temperature data obtained by the temperature sensor; a corrosion index calculation unit for calculating a real-time corrosion index as the real-time corrosion status according to the multi-detection data after compensation processing and normalization processing; a corrosion trend prediction unit for inputting the real-time corrosion index into a pre-constructed autoregressive integrated moving average model to obtain the corrosion development trend output by the autoregressive integrated moving average model.
[0121] As Figure 3 shown, the device includes:
[0122] A monitoring position determination module 310 for obtaining the distribution information of submarine cables and determining the key corrosion monitoring positions according to the distribution information of submarine cables;
[0123] The detection data acquisition module 320 is configured to obtain multiple detection data through monitoring sensors arranged at the critical corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is configured to obtain corrosive gas concentration data;
[0124] The corrosion trend prediction module 330 is configured to determine the real-time corrosion state and the corrosion development trend of the marine cable according to the multiple detection data;
[0125] The visualization display module 340 is configured to visually display the real-time corrosion state and the corrosion development trend.
[0126] Wherein, the corrosion trend prediction module 330 includes:
[0127] The data preprocessing unit 3301 is configured to perform compensation processing and normalization processing on the multiple detection data according to the temperature data obtained by the temperature sensor;
[0128] The corrosion index calculation unit 3302 is configured to calculate a real-time corrosion index as the real-time corrosion state according to the multiple detection data after compensation processing and normalization processing;
[0129] The corrosion trend prediction unit 3303 is configured to input the real-time corrosion index into a pre-constructed autoregressive integrated moving average model to obtain the corrosion development trend output by the autoregressive integrated moving average model.
[0130] Temperature data is quantitative information about the degree of hotness or coldness of an object or environment. Temperature changes will affect the solubility and evaporation rate of salts in salt spray, temperature changes will also affect the degree of ionization in a solution, and temperature changes will also affect the corrosive gas concentration. Therefore, it is necessary to perform compensation processing on other data in the multiple detection data according to the temperature data. The method of performing compensation processing on the corrosive gas concentration data, salt spray concentration data, and pH value data according to the temperature data can be to pre-construct a function relationship model between the temperature data and the corrosive gas concentration data, salt spray concentration data, and pH value data respectively, and then calculate the compensation values of the current corrosive gas concentration data, salt spray concentration data, and pH value data at the standard temperature according to the current temperature data and the function relationship model.
[0131] Normalization processing is a data preprocessing technique aimed at converting data with different ranges and different dimensions into a specific standard range or scale to eliminate the influence brought by the differences in dimensions and numerical ranges between data, making the data comparable and consistent.
[0132] As a method for calculating the real-time corrosion index based on the compensated and normalized multi-detection data as the real-time corrosion state, it is possible to perform weighted summation calculation on the compensated and normalized multi-detection data according to the weight values preset for each detection data, obtain the real-time corrosion index and use it as the real-time corrosion state.
[0133] The autoregressive integrated moving average model is a statistical model used for time series analysis and prediction. The autoregressive integrated moving average model transforms a non-stationary time series into a stationary series through differencing operations, and combines the characteristics of autoregressive using past observations and moving average using past prediction errors.
[0134] In this technical solution, optionally, the corrosion trend prediction module further includes:
[0135] A confidence level calculation unit for calculating the confidence level of the real-time corrosion index based on the Monte Carlo simulation method.
[0136] The Monte Carlo simulation method is a numerical calculation method based on probability and statistics theory that uses random numbers to solve complex problems.
[0137] Confidence level refers to the probability that the population parameter falls within a certain interval in statistical inference. The confidence level of the real-time corrosion index reflects the reliability degree of the currently calculated real-time corrosion index.
[0138] As a method for calculating the confidence level of the real-time corrosion index based on the Monte Carlo simulation method, it is possible to quantify the multi-detection data acquisition error, compensation processing error, and autoregressive integrated moving average model error through random sampling, generate a large number of simulation results and statistically analyze their distribution, thereby calculating the confidence interval of the corrosion index, and further obtaining the confidence level of the current real-time corrosion index.
[0139] The following is an example code for calculating the confidence level of the real-time corrosion index based on the Monte Carlo simulation method:
[0140] import numpy as npfrom statsmodels.tsa.arima.model import ARIMA
[0141] # Initialize parameters
[0142] N_simulations = 10000
[0143] confidence_level = 0.95
[0144] alpha = 1 - confidence_level
[0145] # Store results
[0146] predictions = []
[0147] for _ in range(N_simulations):
[0148] # 1. Generate input data with errors
[0149] T_sim = T_raw + np.random.normal(0, sigma_T)
[0150] # 2. Perturb the preprocessing parameters and calculate the corrosion index
[0151] C_history = compute_corrosion_index(T_sim, perturbed_params)
[0152] # 3. ARIMA prediction (assuming the model is already fitted)
[0153] model = ARIMA(C_history, order=(p, d, q))
[0154] results = model.fit()
[0155] # 4. Generate future residuals and make predictions
[0156] forecast_steps = 10
[0157] epsilon_sim = np.random.normal(0, results.resid.std(), forecast_steps)
[0158] C_pred = results.forecast(steps = forecast_steps) + epsilon_sim
[0159] predictions.append(C_pred)
[0160] # Calculate the confidence interval
[0161] lower = np.percentile(predictions, alpha / 2 * 100, axis = 0)
[0162] upper = np.percentile(predictions, (1 - alpha / 2) * 100, axis = 0)
[0163] The advantage of this solution is that by calculating the confidence level of the real-time corrosion index based on the Monte Carlo simulation method, the confidence level reflects the reliability of the real-time corrosion index, enabling the staff to clearly understand the uncertainty range of the real-time corrosion index calculation result.
[0164] The advantage of this solution is that by compensating and normalizing the multi-detection data according to the temperature data obtained by the temperature sensor, the interference of temperature changes on each detection data can be effectively eliminated, enabling detection data of different types and magnitudes to be under a unified standard, significantly improving the accuracy and comparability of the detection data, and providing a solid and reliable data basis for subsequent analysis and decision-making; by using the autoregressive integrated moving average model, the accurate prediction of the corrosion development trend can be realized.
[0165] Embodiment 4
[0166] Figure 4 is a schematic flowchart of a method for monitoring the corrosion degree of multi-detection data of a cable for an unmanned boat provided in Embodiment 4 of this application. As Figure 4 shown, it specifically includes the following steps:
[0167] S401. Obtain the distribution information of the boat cable through the monitoring position determination module, and determine the key corrosion monitoring positions according to the distribution information of the boat cable;
[0168] S402. Obtain multi-detection data through the detection data acquisition module by using the monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain corrosive gas concentration data;
[0169] S403. Determine the real-time corrosion state and corrosion development trend of the boat cable according to the multi-detection data through the corrosion trend prediction module;
[0170] S404. Visually display the real-time corrosion state and the corrosion development trend through the visualization display module.
[0171] In the embodiments of the present application, the distribution information of the boat cable is obtained through the monitoring position determination module, and the key corrosion monitoring positions are determined according to the distribution information of the boat cable; the multi-detection data is obtained through the detection data acquisition module by using the monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain the corrosive gas concentration data; the real-time corrosion state and the corrosion development trend of the boat cable are determined through the corrosion trend prediction module according to the multi-detection data; and the real-time corrosion state and the corrosion development trend are visually displayed through the visualization display module. For the above method for monitoring the corrosion degree of the multi-detection data of the unmanned boat cable, by determining the real-time corrosion state and the corrosion development trend of the boat cable according to the multi-detection data and visually displaying them, accurate monitoring and prediction of the corrosion situation of the unmanned boat cable can be realized, which is convenient for the operation and maintenance personnel to take maintenance measures in time, thereby improving the operation safety of the unmanned boat.
[0172] The method for monitoring the corrosion degree of the multi-detection data of the unmanned boat cable provided in the embodiments of the present application corresponds to the device for monitoring the corrosion degree of the multi-detection data of the unmanned boat cable provided in the above embodiments, and has the same functional modules and beneficial effects. To avoid repetition, it will not be elaborated here.
[0173] Embodiment Five
[0174] As Figure 5 shown, the embodiments of the present application also provide an electronic device 500, including a processor 501, a memory 502, and a program or instruction stored on the memory 502 and executable on the processor 501. When the program or instruction is executed by the processor 501, it realizes each process of the embodiment of the device for monitoring the corrosion degree of the multi-detection data of the unmanned boat cable described above, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.
[0175] It should be noted that the electronic device in the embodiments of the present application includes the above-mentioned mobile electronic devices and non-mobile electronic devices.
[0176] Embodiment Six
[0177] The embodiments of the present application also provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, it realizes each process of the embodiment of the device for monitoring the corrosion degree of the multi-detection data of the unmanned boat cable described above, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.
[0178] Among them, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.
[0179] Embodiment Seven
[0180] Another embodiment of the present application provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement each process of the corrosion degree monitoring device for multi-detection data of the cable for unmanned boats in the above embodiments, and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.
[0181] It should be understood that the chip mentioned in the embodiments of the present application can also be referred to as a system-on-chip, system chip, chip system, or system-on-chip, etc.
[0182] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article, or device. Without more limitations, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article, or device including that element. In addition, it should be pointed out that the methods and devices in the embodiments of the present application are not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in a reverse order according to the functions involved. For example, the methods described can be performed in an order different from that described, and various steps can be added, omitted, or combined. Additionally, the features described with reference to certain examples can be combined in other examples.
[0183] Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases, the former is a better implementation method. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.
[0184] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.
[0185] The above is only the preferred embodiment of the present application and the technical principles applied. The present application is not limited to the specific embodiments described herein. Various obvious changes, re-adjustments and substitutions that can be made by those skilled in the art will not depart from the protection scope of the present application. Therefore, although the present application has been described in more detail through the above embodiments, the present application is not limited to the above embodiments. Without departing from the concept of the present application, it can also include more other equivalent embodiments, and the scope of the present application is determined by the scope of the claims.
Claims
1. A corrosion degree monitoring device for multi-detection data of a cable for an unmanned boat, characterized in that, The device includes: A monitoring position determination module, configured to obtain the distribution information of the boat cables and determine the critical corrosion monitoring positions according to the distribution information of the boat cables; A detection data acquisition module, configured to obtain multiple detection data through the monitoring sensors arranged at the critical corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is configured to obtain the corrosive gas concentration data; A corrosion trend prediction module, configured to determine the real-time corrosion state and the corrosion development trend of the boat cables according to the multiple detection data; A visualization display module, configured to visually display the real-time corrosion state and the corrosion development trend.
2. The corrosion degree monitoring device for multi-detection data of the cable for unmanned boats according to claim 1, characterized in that The detection data acquisition module includes: A positioning compensation unit, configured to obtain the positioning information of the monitoring sensors arranged at the critical corrosion monitoring positions; A data acquisition unit, configured to obtain multiple detection data through the monitoring sensors; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is configured to obtain the corrosive gas concentration data; A data filtering unit, configured to filter out the abnormal data in the multiple detection data.
3. The corrosion degree monitoring device for multi-detection data of the cable for the unmanned boat according to claim 2, characterized in that The visualization display module is specifically configured to: Obtain the hull structure model of the unmanned boat; Determine the display colors of each positioning information according to the real-time corrosion state and the corrosion development trend corresponding to each positioning information; Perform rendering display in the hull structure model according to the positioning information and its display color.
4. The corrosion degree monitoring device for multi-detection data of the cable for unmanned boats according to claim 3, characterized in that, The visualization display module is further configured to: Obtain the historical corrosion data of each positioning information; Generate a broken line graph of the corrosion rate change for at least one time span according to the historical corrosion data, the real-time corrosion state, and the corrosion development trend.
5. The corrosion degree monitoring device for multi-detection data of the cable for unmanned boat according to claim 2, characterized in that, The data filtering unit is specifically configured to: When the monitoring sensors include strain sensors, filter the strain data obtained by the strain sensors based on the sliding window algorithm, and filter out the strain abnormal data caused by the large movement of the boat.
6. The corrosion degree monitoring device for multi-detection data of the cable for the unmanned boat according to claim 1, wherein, The corrosion trend prediction module includes: A data preprocessing unit, configured to perform compensation processing and normalization processing on the multiple detection data according to the temperature data obtained by the temperature sensor; A corrosion index calculation unit, configured to calculate the real-time corrosion index as the real-time corrosion state according to the multiple detection data after compensation processing and normalization processing; A corrosion trend prediction unit, configured to input the real-time corrosion index into a pre-constructed autoregressive integrated moving average model to obtain the corrosion development trend output by the autoregressive integrated moving average model.
7. The corrosion degree monitoring device for multi-detection data of the cable for unmanned boats according to claim 6, characterized in that, The corrosion trend prediction module further includes: A confidence level calculation unit, configured to calculate the confidence level of the real-time corrosion index based on the Monte Carlo simulation method.
8. A method for monitoring the corrosion degree of multi-detection data of a cable for an unmanned boat, characterized in that, The method includes: Obtain the distribution information of the boat cables through the monitoring position determination module, and determine the critical corrosion monitoring positions according to the distribution information of the boat cables; The detection data acquisition module obtains multiple detection data through monitoring sensors arranged at the key corrosion monitoring positions; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain corrosive gas concentration data; The corrosion trend prediction module determines the real-time corrosion state and the corrosion development trend of the cable for the unmanned boat according to the multiple detection data; The visualization display module visually displays the real-time corrosion state and the corrosion development trend.
9. The method for monitoring the corrosion degree of multi-detection data of the cable for unmanned boats according to claim 8, characterized in that, The detection data acquisition module obtains multiple detection data through monitoring sensors arranged at the key corrosion monitoring positions, including: The positioning compensation unit obtains the positioning information of the monitoring sensors arranged at the key corrosion monitoring positions; The data acquisition unit obtains multiple detection data through the monitoring sensors; wherein, the monitoring sensors include at least one of a temperature sensor, a humidity sensor, a gas sensor, a salt spray sensor, a pH value sensor, and a strain sensor, and the gas sensor is used to obtain corrosive gas concentration data; The data filtering unit filters out abnormal data in the multiple detection data.
10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements the steps of the method for monitoring the corrosion degree of the multiple detection data of the cable for the unmanned boat as described in any one of claims 8-9.