A kind of sail monitoring and automatic control method, device, equipment and medium

Abstract

The present application relates to a kind of sail monitoring and automatic control method, device, equipment and medium, belong to ship navigation control technical field, sail monitoring and automatic control method is based on the wind pressure data and deformation data of real-time acquisition sail of wind sail sensor array spaced distribution along sail transverse and longitudinal;Based on wind pressure data and deformation data draw wind pressure curve and deformation curve, respectively on wind pressure curve and deformation curve are carried out smooth detection, determine wind pressure abnormal point and deformation abnormal point, based on wind pressure abnormal point and deformation abnormal point determine the abnormal detection point of sail, the wind pressure value of the transverse and longitudinal of the sensing node in sail sensor array is carried out consistency detection, to determine the abnormal area and abnormal state of sail;Based on the abnormal detection point of sail, abnormal area and wind pressure data of sail longitudinal and transverse, determine the pressure distribution of sail, based on pressure distribution to sail is automatically controlled, improve the safety and stability of sailboat navigation.

Description

Technical Field

[0001] This invention relates to the field of ship navigation control technology, and in particular to a method, device, equipment and medium for sail monitoring and automatic control. Background Technology

[0002] Sailing aids can effectively reduce fuel consumption and carbon emissions from ships, meeting energy efficiency and environmental protection requirements. Traditional sailboat navigation control mainly relies on the crew's experience, real-time observation of wind speed, wind direction and ship attitude, and manual adjustment of sail angle and rudder direction to maintain the predetermined course and speed. However, manual operation has significant drawbacks in complex wind conditions: human judgment is highly subjective, slow to respond to changes in wind field and ship dynamics, and insufficient adjustment precision, which can easily lead to course deviation and reduced navigation efficiency. In extreme or sudden wind conditions, the lag in manual operation can easily cause sail overload and excessive hull rolling, threatening navigation safety and even causing accidents.

[0003] To improve sail control performance, existing technologies have introduced sail condition monitoring methods, mostly using single-point / limited-point sensors (such as pressure sensors, strain sensors, and angle sensors) to obtain local wind pressure, deformation, or angle information. However, these methods have inherent technical bottlenecks: insufficient measurement point coverage, making it impossible to obtain information on the force distribution and deformation of the entire sail area, and thus difficult to reflect the true aerodynamic load state; weak ability to identify early damage and abnormal operating conditions such as sail tearing, wrinkling, and local overload, resulting in low damage detection accuracy and a high rate of missed detection; and incomplete and incomplete monitoring data, which cannot provide accurate feedback for intelligent sail control, thus limiting control accuracy and response speed.

[0004] Existing technologies have significant shortcomings in terms of full-range force sensing of sails, high-precision damage detection, and rapid adaptive control, making it difficult to simultaneously ensure the safety and stability of ship navigation. Summary of the Invention

[0005] In view of this, it is necessary to provide a method, device, equipment and medium for wind turbine monitoring and automatic control to solve the technical problem of low accuracy in wind turbine damage detection.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a method for monitoring and automatically controlling a sail, comprising: Construct a sail sensor array, and collect real-time wind pressure and deformation data of the sail based on the sail sensor array that is distributed at intervals along the lateral and longitudinal directions of the sail; Based on the wind pressure data and deformation data, wind pressure curves and deformation curves are plotted. Smoothing detection is performed on the wind pressure curves and deformation curves respectively to determine wind pressure anomaly points and deformation anomaly points. Based on the wind pressure anomaly points and deformation anomaly points, the abnormal detection points of the sail are determined. Consistency detection is performed on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine the abnormal area and abnormal state of the sail. The pressure distribution of the sail is determined based on the abnormal detection points and abnormal areas of the sail, as well as the longitudinal and lateral wind pressure data of the sail, and the sail is automatically controlled based on the pressure distribution.

[0007] In one possible implementation, the sail sensor is a fiber pressure sensor, which includes one or more of the following: fiber Bragg grating, distributed fiber scattering sensor, conductive polymer fiber, printed flexible thin film sensor, and carbonized leaf vein functional fiber.

[0008] In one possible implementation, the smoothing detection of the wind pressure curve and deformation curve to determine wind pressure anomalies and deformation anomalies, and the determination of sail anomaly detection points based on the wind pressure anomalies and deformation anomalies, includes: The wind pressure curve is smoothed to obtain a smoothed wind pressure curve. The smoothed wind pressure curve is compared with the original wind pressure curve point by point to calculate the wind pressure deviation at each detection point. Based on a preset wind pressure smoothing threshold and the wind pressure deviation, the wind pressure anomaly points in the wind sail sensor array are determined. When the wind pressure deviation of a detection point is greater than the wind pressure smoothing threshold, the detection point is a wind pressure anomaly point in the wind sail sensor array. When the wind pressure deviation of a detection point is less than or equal to the wind pressure smoothing threshold, the detection point is a normal wind pressure point in the wind sail sensor array. The deformation curve is smoothed to obtain a smoothed deformation curve. The smoothed deformation curve is compared with the original deformation curve point by point to calculate the deformation deviation of each detection point. Based on a preset deformation smoothing threshold and the deformation deviation, abnormal deformation points in the sail sensor array are determined. When the deformation deviation of a detection point is greater than the deformation smoothing threshold, the detection point is an abnormal deformation point in the sail sensor array. When the deformation deviation of a detection point is less than or equal to the deformation smoothing threshold, the detection point is a normal deformation point in the sail sensor array. The abnormal detection points of the sail are determined based on the abnormal wind pressure points and abnormal deformation points.

[0009] In one possible implementation, the consistency detection of the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine abnormal areas and abnormal states of the sail includes: Based on the wind pressure data in the lateral direction and the wind pressure data in the longitudinal direction of the sail, the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array are determined. Based on the lateral and longitudinal wind pressure values, the lateral and longitudinal wind pressure difference of the sensing node is calculated, wherein the sensing node is the intersection of the lateral and longitudinal directions. The preset wind pressure threshold is compared with the wind pressure difference. When the wind pressure difference is greater than the wind pressure threshold, the location of the sensing node is an abnormal area of ​​the sail, and the sensing node is marked as abnormal. When the wind pressure difference is less than or equal to the wind pressure threshold, the location of the sensing node is a normal area of ​​the sail, and the sensing node is marked as normal.

[0010] In one possible implementation, the abnormal state includes an undamaged state, a slightly damaged state, and a clearly abnormal state.

[0011] In one possible implementation, the preset wind pressure threshold includes a first wind pressure threshold, a second wind pressure threshold, and a third wind pressure threshold; marking the sensing node as an abnormal state includes: When the wind pressure difference is greater than the first wind pressure threshold and the wind pressure difference is less than or equal to the second wind pressure threshold, the abnormal state of the sensing node is determined to be an undamaged state. When the wind pressure difference is greater than the second wind pressure threshold and the wind pressure difference is less than or equal to the third wind pressure threshold, the abnormal state of the sensing node is determined to be a slightly damaged state. When the wind pressure difference is greater than the third wind pressure threshold, the abnormal state of the sensing node is determined to be an obvious abnormal state.

[0012] In one possible implementation, the automatic control of the sail based on the pressure distribution includes: The attitude of the sails and the direction of the rudder are adjusted in real time based on the pressure distribution.

[0013] Secondly, the present invention also provides a sail monitoring and automatic control device, comprising: The data acquisition module is used to collect wind pressure data and deformation data of the sail in real time based on the sail sensor array that is distributed at intervals along the lateral and longitudinal directions of the sail. An anomaly detection module is used to draw wind pressure curves and deformation curves based on the wind pressure data and deformation data, perform smoothing detection on the wind pressure curves and deformation curves respectively, determine wind pressure anomaly points and deformation anomaly points, determine anomaly detection points of the sail based on the wind pressure anomaly points and deformation anomaly points, and perform consistency detection on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine the abnormal area and abnormal state of the sail. The control module is used to determine the pressure distribution of the sail based on the abnormal detection points and abnormal areas of the sail, as well as the longitudinal and lateral wind pressure data of the sail, and to automatically control the sail based on the pressure distribution.

[0014] Thirdly, the present invention also provides a sailboat monitoring and automatic control device, comprising: a processor and a memory; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps in the sail monitoring and automatic control method described above.

[0015] Fourthly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instructions, which, when executed by a processor, can implement the steps of the sail monitoring and automatic control method described in any one of the above-mentioned method items.

[0016] The beneficial effects of this invention are as follows: It collects wind pressure and deformation data of the sail in real time based on a sail sensor array spaced laterally and longitudinally along the sail; by collecting data in both the lateral and longitudinal directions of the sail through the sail sensor array, high-resolution monitoring of the force across the entire sail surface is achieved; wind pressure and deformation curves are plotted based on the wind pressure and deformation data; smoothing detection is performed on the wind pressure and deformation curves respectively to identify abnormal points in wind pressure and deformation; abnormal detection points of the sail are determined based on these abnormal points; consistency detection is performed on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to identify abnormal areas and abnormal states of the sail; through smoothing and consistency detection, the accuracy of sail damage detection is improved; the pressure distribution of the sail is determined based on the abnormal detection points, abnormal areas, and longitudinal and lateral wind pressure data; automatic control of the sail is performed based on the pressure distribution; and the sail's attitude and rudder direction are adjusted in real time according to the sail's pressure distribution, ensuring the safety and stability of ship navigation. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating an embodiment of the sail monitoring and automatic control method provided by the present invention; Figure 2 A schematic diagram of the fiber pressure sensor grid array layout for the sail monitoring and automatic control method provided by the present invention; Figure 3 A schematic diagram of the layout of transverse and longitudinal sensor detection points in the sail monitoring and automatic control method provided by the present invention; Figure 4 A schematic diagram of sail pressure in the sail monitoring and automatic control method provided by the present invention; Figure 5 A schematic diagram of an embodiment of the sail monitoring and automatic control device provided by the present invention; Figure 6 This is a schematic diagram of an embodiment of the sailboat monitoring and automatic control device provided by the present invention. Detailed Implementation

[0019] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0020] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0021] This invention discloses a method, apparatus, device, and medium for wind turbine monitoring and automatic control, which can be used in a computer. The method, apparatus, or computer-readable storage medium involved in this invention can be integrated with the aforementioned apparatus or can be relatively independent.

[0022] One specific embodiment of the present invention discloses a sail monitoring and automatic control method, which can be executed by a computer, specifically by one or more processors of the computer. For example... Figure 1 As shown, the sail monitoring and automatic control method includes: S101. Real-time acquisition of wind pressure and deformation data of the sail based on a sail sensor array distributed at intervals along the lateral and longitudinal directions of the sail; It should be noted that by collecting data on the lateral and longitudinal directions of the sail using a sail sensor array, high-resolution monitoring of the force across the entire sail surface is achieved.

[0023] S102. Based on wind pressure data and deformation data, draw wind pressure curves and deformation curves, perform smoothing detection on wind pressure curves and deformation curves respectively, determine wind pressure anomaly points and deformation anomaly points, determine the abnormal detection points of the sail based on wind pressure anomaly points and deformation anomaly points, and perform consistency detection on the horizontal and vertical wind pressure values ​​of the sensing nodes in the sail sensor array to determine the abnormal area and abnormal state of the sail. It should be noted that smoothness detection and consistency detection have improved the accuracy of sail damage detection.

[0024] S103. Determine the pressure distribution of the sail based on the abnormal detection points and abnormal areas of the sail, as well as the longitudinal and lateral wind pressure data of the sail, and automatically control the sail based on the pressure distribution.

[0025] It should be noted that by adjusting the sail's attitude and the rudder's direction in real time according to the pressure distribution of the sail, the safety and stability of the ship's navigation are ensured.

[0026] In some embodiments, in step S101, wind pressure data and deformation data of the sail are collected in real time based on a sail sensor array distributed at intervals along the transverse and longitudinal directions of the sail. The sail sensors are fiber pressure sensors. The sail sensor array is constructed by arranging fiber pressure sensors along the longitudinal and transverse directions of the sail. For a schematic diagram of the fiber pressure sensor grid array layout, please refer to [link to schematic diagram]. Figure 2 ,like Figure 2 As shown, fiber pressure sensors are arranged longitudinally and laterally inside the sail, forming a grid matrix structure, i.e., a sail sensor array. Each grid is an independent monitoring unit. The fiber pressure sensors include one or more of the following: fiber Bragg gratings, distributed fiber scattering sensors, conductive polymer fibers, printed flexible thin-film sensors, and carbonized leaf vein functional fibers. The fiber pressure sensors can be integrated through weaving, strip stitching, lamination, or modular connection methods, and are embedded into the sail through a waterproof coating and reinforcement layer to ensure the strength and weather resistance of the canvas. The grid serves as the monitoring unit for real-time collection of wind pressure and deformation data in both the longitudinal and lateral directions of the sail. For a schematic diagram of the layout of the transverse and longitudinal sensor detection points, please refer to [link to schematic diagram]. Figure 3 For a schematic diagram of its sail pressure, please refer to [link / reference]. Figure 4 ,like Figure 4 As shown, the tension on the sail is detected by the transverse or longitudinal fiber pressure sensors, and the sail surface pressure data, i.e., wind pressure data, is calculated by the tension of adjacent fiber pressure sensors.

[0027] In some embodiments, in step S102, wind pressure curves and deformation curves are plotted based on wind pressure data and deformation data. After obtaining the wind pressure data and deformation data, the data is preprocessed, i.e., amplified, filtered, and synchronizedly corrected to ensure consistency of the data detected by the sensors. After data processing, wind pressure curves are plotted based on the lateral and longitudinal wind pressure data, and deformation curves are plotted based on the lateral and longitudinal deformation data. Smoothing detection is performed on the wind pressure curves and deformation curves respectively to determine wind pressure anomalies and deformation anomalies. Based on the wind pressure anomalies and deformation anomalies, abnormal detection points of the sail are determined. The wind pressure curves are smoothed to obtain smoothed wind pressure curves. The smoothed wind pressure curves are compared point by point with the original wind pressure curves to calculate the wind pressure deviation at each detection point. Based on a preset wind pressure smoothing threshold and wind pressure deviation, wind pressure anomalies in the sail sensor array are determined. When the wind pressure deviation at a detection point is greater than the wind pressure smoothing threshold, the detection is performed. The measuring points are the wind pressure anomaly points in the sail sensor array. When the wind pressure deviation of a measuring point is less than or equal to the wind pressure smoothing threshold, the measuring point is considered a normal wind pressure point in the sail sensor array. The deformation curve is smoothed to obtain a smoothed deformation curve. The smoothed deformation curve is compared point by point with the normal deformation curve to calculate the deformation deviation of each measuring point. Based on the preset deformation smoothing threshold and deformation deviation, the deformation anomaly points in the sail sensor array are determined. Specifically, when the deformation deviation of a measuring point is greater than the deformation smoothing threshold, the measuring point is considered a deformation anomaly point in the sail sensor array. When the deformation deviation of a measuring point is less than or equal to the deformation smoothing threshold, the measuring point is considered a normal deformation point in the sail sensor array. Based on the wind pressure anomaly points and deformation anomaly points, the abnormal detection points of the sail are determined. By smoothing the wind pressure curve and deformation curve, the sudden change anomaly points of the data are identified. If the curve shows a sudden change or is not smooth, it is judged as an anomaly, thus detecting the location of the sail anomaly. Consistency detection is performed on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to identify abnormal areas and states of the sail. Based on the lateral and longitudinal wind pressure data of the sail, the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array are calculated. The lateral and longitudinal wind pressure difference of the sensing nodes is then calculated, where the sensing node is the intersection of the lateral and longitudinal directions. A preset wind pressure threshold is compared with the wind pressure difference. When the wind pressure difference is greater than the wind pressure threshold, the location of the sensing node is considered an abnormal area of ​​the sail, and the sensing node is marked as abnormal. When the wind pressure difference is less than or equal to the wind pressure threshold, the location of the sensing node is considered normal. The system identifies the area and marks the sensor nodes as being in a normal state. Abnormal states include undamaged, slightly damaged, and obviously abnormal states. Preset wind pressure thresholds include a first wind pressure threshold, a second wind pressure threshold, and a third wind pressure threshold. When the wind pressure difference is greater than the first wind pressure threshold and less than or equal to the second wind pressure threshold, the sensor node is determined to be in an undamaged state. When the wind pressure difference is greater than the second wind pressure threshold and less than or equal to the third wind pressure threshold, the sensor node is determined to be in a slightly damaged state. When the wind pressure difference is greater than the third wind pressure threshold, the sensor node is determined to be in an obviously abnormal state, which is indicated by a broken fiber pressure sensor, requiring repair. By comparing the pressure values ​​in the transverse and longitudinal directions at the intersection of the transverse and longitudinal sensors, it can be determined whether the middle part of the mesh formed by the sensor fibers has been damaged.

[0028] Anomaly detection of sails can also be achieved using an anomaly detection model. This model detects anomalies in the wind pressure data of the sails to identify abnormal regions and states. The model includes a convolutional module and a temporal recursive module. The convolutional module extracts the spatial local force features of the wind pressure data, while the temporal recursive module extracts the temporal features. Based on these features, the abnormal regions and states of the sails are determined. A CNN-LSTM deep learning model is the preferred choice for this anomaly detection model. Data collected from the sail sensor array was used to train the anomaly detection model. Long-term historical data from the sail sensor array and experimental data from wind tunnel tests were collected, including pressure distribution records under undamaged, slightly anomalous, and significantly damaged conditions. To ensure data comparability in the model, all collected pressure matrices were normalized, ensuring that pressure values ​​from different measuring points, ranges, and environmental conditions were analyzed on a uniform scale. Subsequently, the normalized pressure sequence was segmented using a sliding window according to a set time window and step size, so that each sample included both the sail surface and other parameters under specific conditions. The force distribution at a given moment also includes the pressure change trend within a short period before and after that moment, thus constructing time-series data representing the short-term dynamic response of the sail. A pressure matrix is ​​constructed using the time-series data, and a CNN convolutional structure is used to extract the spatial feature sequence of the pressure matrix in the spatial dimension, including peak positions, pressure change regions, strip-shaped structures, and overall distribution patterns. The extracted spatial feature sequence is fed into an LSTM (time recursion module) to learn the delayed relationship, continuous deviation trend, and sudden anomalies of force changes over time. Through joint training, the model acquires the feature representation ability to distinguish between normal states and damaged, fatigued, or other abnormal states, thus obtaining a fully trained anomaly detection model. The real-time collected data is preprocessed to obtain a window-level feature sequence, which is then input into the fully trained anomaly detection model. Local force features are extracted from the current window-level feature sequence, and combined with the time-series change trend within the window, it is determined whether the force in each region deviates from the normal pattern. Since the model can simultaneously focus on the spatial force structure and temporal dynamic changes, it can still maintain relatively stable recognition accuracy when the wind direction fluctuates rapidly, local pressure peaks drift, or the sail is slightly twisted. When the system detects an abnormal deviation between the pressure pattern and the time trend in a local area, it can output an abnormal score and the corresponding spatial location. Under normal sailing conditions, the sailboat continuously records the pressure distribution and deformation of the sail, forming an internal reference for the normal or abnormal stress pattern of the sail surface. When the real-time pressure deviates from the reference pattern, or the direction of local pressure change is inconsistent with the surrounding area, it can be determined that there may be structural abnormalities, material fatigue, or initial damage. The model continuously learns to improve the accuracy of the judgment.

[0029] When the sensor node is in an abnormal state, an alarm signal is issued. That is, when the sail is abnormal, damage and abnormality warnings can be issued through sound alarm, interface prompts or remote signals.

[0030] In some embodiments, in step S103, the pressure distribution of the sail is determined based on the abnormal detection points and abnormal areas of the sail, as well as the longitudinal and transverse wind pressure data of the sail. The sail is automatically controlled based on the pressure distribution. After determining the abnormal detection points and abnormal areas of the sail, the overall pressure distribution of the sail can be determined based on the real-time collected longitudinal and transverse wind pressure data of the sail. The direction and magnitude of the sail thrust are determined based on the pressure distribution. The attitude of the sail and the direction of the rudder are adjusted in real time based on the direction and magnitude of the sail thrust, thereby ensuring the safety and stability of the sailboat's navigation.

[0031] Fiber pressure sensors are installed inside the sail along the longitudinal and transverse directions. The pressure distribution curve of each sensor is plotted, and the pressure values ​​in the longitudinal and transverse directions are compared at the intersection. When the curve changes abruptly or the pressure difference at the intersection exceeds the set threshold, the sail is determined to be damaged and an alarm is triggered. The sail angle and rudder direction are adjusted according to the overall wind pressure distribution to achieve automatic course maintenance.

[0032] In summary, the sail monitoring and automatic control method provided by this invention collects wind pressure and deformation data of the sail in real time based on a sail sensor array spaced laterally and longitudinally along the sail. Based on the wind pressure and deformation data, wind pressure and deformation curves are plotted, and smoothing detection is performed on both curves to identify abnormal wind pressure and deformation points. Based on these abnormal points, abnormal detection points on the sail are determined. Consistency detection is performed on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to identify abnormal areas and states of the sail. Based on the abnormal detection points, abnormal areas, and longitudinal and lateral wind pressure data, the pressure distribution of the sail is determined. Automatic control of the sail is then performed based on this pressure distribution to achieve automatic course stabilization, thereby improving the safety and stability of sailboat navigation.

[0033] To better implement the sail monitoring and automatic control method in the embodiments of the present invention, based on the sail monitoring and automatic control method, correspondingly, as follows: Figure 5 As shown, this embodiment of the invention also provides a sail monitoring and automatic control device, the sail monitoring and automatic control device 500 comprising: The data acquisition module 501 is used to acquire wind pressure data and deformation data of the sail in real time based on the sail sensor array that is distributed at intervals along the lateral and longitudinal directions of the sail. The anomaly detection module 502 is used to draw wind pressure curves and deformation curves based on wind pressure data and deformation data, perform smoothing detection on the wind pressure curves and deformation curves respectively, determine wind pressure anomaly points and deformation anomaly points, determine the anomaly detection points of the sail based on the wind pressure anomaly points and deformation anomaly points, and perform consistency detection on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine the abnormal area and abnormal state of the sail. The control module 503 is used to determine the pressure distribution of the sail based on the abnormal detection points and abnormal areas of the sail, as well as the longitudinal and lateral wind pressure data of the sail, and to automatically control the sail based on the pressure distribution.

[0034] like Figure 6 As shown, the present invention also provides a sailboat monitoring and automatic control device 600, which can be a mobile terminal, desktop computer, laptop, handheld computer, server, or other computing device. The sailboat monitoring and automatic control device 600 includes a processor 601, a memory 602, and a display 603. Figure 6 Only some components of the sailboat monitoring and automatic control device 600 are shown; however, it should be understood that implementation of all shown components is not required, and more or fewer components may be implemented instead.

[0035] In some embodiments, memory 602 can be an internal storage unit of the sailboat monitoring and automatic control device 600, such as a hard disk or memory of the sailboat monitoring and automatic control device 600. In other embodiments, memory 602 can also be an external storage device of the sailboat monitoring and automatic control device 600, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the sailboat monitoring and automatic control device 600. Furthermore, memory 602 can include both internal storage units and external storage devices of the sailboat monitoring and automatic control device 600. Memory 602 is used to store application software and various types of data installed on the sailboat monitoring and automatic control device 600, such as the program code installed on the sailboat monitoring and automatic control device 600. Memory 602 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 602 stores a sail monitoring and automatic control program, which can be executed by the processor 601 to implement the sail monitoring and automatic control method of the various embodiments of the present invention.

[0036] In some embodiments, processor 601 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 602 or process data, such as a sail monitoring and automatic control method.

[0037] In some embodiments, display 603 may be an LED display, a liquid crystal display, a touch-screen liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 603 is used to display identification information of the sail monitoring and automatic control program and to display a visual user interface. Components 601-603 of the sailboat monitoring and automatic control device 600 communicate with each other via a system bus.

[0038] In some embodiments, when the processor 601 executes the sail monitoring and automatic control program in the memory 602, it implements each step of the sail monitoring and automatic control method as described in the above embodiments. Since the sail monitoring and automatic control method has been described in detail above, it will not be repeated here.

[0039] Accordingly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps or functions of the sail monitoring and automatic control methods provided in the above-described method embodiments.

[0040] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0041] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring and automatically controlling a sail, characterized in that, include: The wind pressure and deformation data of the sail are collected in real time by a sail sensor array that is spaced along the lateral and longitudinal sides of the sail. Based on the wind pressure data and deformation data, wind pressure curves and deformation curves are plotted. Smoothing detection is performed on the wind pressure curves and deformation curves respectively to determine wind pressure anomaly points and deformation anomaly points. Based on the wind pressure anomaly points and deformation anomaly points, the abnormal detection points of the sail are determined. Consistency detection is performed on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine the abnormal area and abnormal state of the sail. The pressure distribution of the sail is determined based on the abnormal detection points and abnormal areas of the sail and the wind pressure data of the sail, and the sail is automatically controlled based on the pressure distribution.

2. The sail monitoring and automatic control method according to claim 1, characterized in that, The sail sensor is a fiber pressure sensor, which includes one or more of the following: fiber Bragg grating, distributed fiber scattering sensor, conductive polymer fiber, printed flexible thin film sensor, and carbonized leaf vein functional fiber.

3. The sail monitoring and automatic control method according to claim 2, characterized in that, The process of smoothing and detecting the wind pressure curve and deformation curve to identify wind pressure anomalies and deformation anomalies, and then identifying sail anomaly detection points based on these anomalies, includes: The wind pressure curve is smoothed to obtain a smoothed wind pressure curve. The smoothed wind pressure curve is compared with the original wind pressure curve point by point to calculate the wind pressure deviation at each detection point. Based on a preset wind pressure smoothing threshold and the wind pressure deviation, the wind pressure anomaly points in the wind sail sensor array are determined. When the wind pressure deviation of a detection point is greater than the wind pressure smoothing threshold, the detection point is a wind pressure anomaly point in the wind sail sensor array. When the wind pressure deviation of a detection point is less than or equal to the wind pressure smoothing threshold, the detection point is a normal wind pressure point in the wind sail sensor array. The deformation curve is smoothed to obtain a smoothed deformation curve. The smoothed deformation curve is compared with the original deformation curve point by point to calculate the deformation deviation of each detection point. Based on a preset deformation smoothing threshold and the deformation deviation, abnormal deformation points in the sail sensor array are determined. When the deformation deviation of a detection point is greater than the deformation smoothing threshold, the detection point is an abnormal deformation point in the sail sensor array. When the deformation deviation of a detection point is less than or equal to the deformation smoothing threshold, the detection point is a normal deformation point in the sail sensor array. The abnormal detection points of the sail are determined based on the abnormal wind pressure points and abnormal deformation points.

4. The sail monitoring and automatic control method according to claim 3, characterized in that, The process of performing consistency detection on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine abnormal areas and states of the sail includes: Based on the wind pressure data in the lateral direction and the wind pressure data in the longitudinal direction of the sail, the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array are determined. Based on the lateral and longitudinal wind pressure values, the lateral and longitudinal wind pressure difference of the sensing node is calculated, wherein the sensing node is the intersection of the lateral and longitudinal directions. The preset wind pressure threshold is compared with the wind pressure difference. When the wind pressure difference is greater than the wind pressure threshold, the location of the sensing node is an abnormal area of ​​the sail, and the sensing node is marked as abnormal. When the wind pressure difference is less than or equal to the wind pressure threshold, the location of the sensing node is a normal area of ​​the sail, and the sensing node is marked as normal.

5. The sail monitoring and automatic control method according to claim 4, characterized in that, The abnormal states include undamaged state, slightly damaged state, and obviously abnormal state.

6. The sail monitoring and automatic control method according to claim 5, characterized in that, The preset wind pressure thresholds include a first wind pressure threshold, a second wind pressure threshold, and a third wind pressure threshold; marking the sensing node as an abnormal state includes: When the wind pressure difference is greater than the first wind pressure threshold and the wind pressure difference is less than or equal to the second wind pressure threshold, the abnormal state of the sensing node is determined to be an undamaged state. When the wind pressure difference is greater than the second wind pressure threshold and the wind pressure difference is less than or equal to the third wind pressure threshold, the abnormal state of the sensing node is determined to be a slightly damaged state. When the wind pressure difference is greater than the third wind pressure threshold, the abnormal state of the sensing node is determined to be an obvious abnormal state.

7. The sail monitoring and automatic control method according to claim 4, characterized in that, The automatic control of the sail based on the pressure distribution includes: The attitude of the sails and the direction of the rudder are adjusted in real time based on the pressure distribution.

8. A sail monitoring and automatic control device, characterized in that, include: The data acquisition module is used to collect wind pressure data and deformation data of the sail in real time based on the sail sensor array that is distributed at intervals along the lateral and longitudinal directions of the sail. An anomaly detection module is used to draw wind pressure curves and deformation curves based on the wind pressure data and deformation data, perform smoothing detection on the wind pressure curves and deformation curves respectively, determine wind pressure anomaly points and deformation anomaly points, determine anomaly detection points of the sail based on the wind pressure anomaly points and deformation anomaly points, and perform consistency detection on the lateral and longitudinal wind pressure values ​​of the sensing nodes in the sail sensor array to determine the abnormal area and abnormal state of the sail. The control module is used to determine the pressure distribution of the sail based on the abnormal detection points and abnormal areas of the sail, as well as the longitudinal and lateral wind pressure data of the sail, and to automatically control the sail based on the pressure distribution.

9. A sailboat monitoring and automatic control device, characterized in that, Including memory and processor; The memory stores a computer-readable program that can be executed by the processor; When the processor executes the computer-readable program, it implements the steps of the sail monitoring and automatic control method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps of the sail monitoring and automatic control method according to any one of claims 1-7.

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