A method, device and storage medium for detecting the strength of a nautical antenna support structure

Abstract

The present invention discloses a method, device and storage medium for detecting the strength of a marine antenna support structure. The method includes: when a ship is sailing on the sea area, respectively reading the attitude signal collected by an inertial measurement unit, the vibration signal collected by a vibration sensor and the resistance signal collected by a resistance sensor; inputting the attitude signal into a first encoder to extract a target attitude feature; inputting the vibration signal into a second encoder to extract a first target vibration feature; inputting the resistance signal into a third encoder to extract a target resistance feature; inputting the target attitude feature, the first target vibration feature and the target resistance feature into a decoder to decode them into target multimodal features; inputting the target multimodal features into a head structure to classify the strength of the support structure of the marine antenna; and performing an alarm operation on the support structure of the marine antenna according to the strength. This embodiment reduces the risk of the marine antenna being damaged due to the breakage of the support structure of the marine antenna during the ship's navigation, and ensures the safety of the ship's navigation.

Description

Technical Field

[0001] The present invention relates to the technical field of deep learning, and in particular to a method, device, and storage medium for detecting the strength of a supporting structure of a marine antenna. Background Art

[0002] A marine antenna is configured on a ship, which is one of the components of the ship's communication and navigation system, and realizes functions such as communication and navigation in marine activities. When the ship sails on the ocean, environmental factors such as seawater and salt spray cause certain corrosion to each structure of the marine antenna.

[0003] Currently, small ships such as yachts and fishing boats lack professionals. Their shipowners usually arrange professionals to overhaul the marine antenna at intervals, and the lengths of the overhaul intervals vary, resulting in low overhaul accuracy and a risk that the marine antenna is damaged due to structural corrosion and breakage during navigation. Summary of the Invention

[0004] In view of this, the present invention provides a method, device, and storage medium for detecting the strength of a supporting structure of a marine antenna, so as to improve the accuracy of overhauling the radome structure on a ship.

[0005] The first aspect of the present invention provides a method for detecting the strength of a supporting structure of a marine antenna, including:

[0006] When the ship is sailing in the sea area, an inertial measurement unit, a vibration sensor, and a resistance sensor are respectively started; the inertial measurement unit is deployed on the hull of the ship, and both the vibration sensor and the resistance sensor are deployed on the supporting structure of the marine antenna on the ship;

[0007] The attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor are respectively read;

[0008] A structure classification network is determined; the structure classification network includes a first encoder, a second encoder, a third encoder, a decoder, and a head structure;

[0009] The attitude signal is input into the first encoder to extract target attitude features;

[0010] The vibration signal is input into the second encoder to extract first target vibration features;

[0011] The resistance signal is input into the third encoder to extract target resistance features;

[0012] The target attitude features, the first target vibration features, and the target resistance features are input into the decoder to be decoded into target multimodal features;

[0013] Input the target multimodal feature into the head structure to classify the strength of the support structure of the maritime antenna;

[0014] Perform an alarm operation on the support structure of the maritime antenna according to the strength.

[0015] The second aspect of the present invention provides a device for detecting the strength of the support structure of a marine antenna, including:

[0016] A sensor activation module, configured to respectively activate an inertial measurement unit, a vibration sensor, and a resistance sensor when the ship is sailing in the sea area; the inertial measurement unit is deployed on the hull of the ship, and both the vibration sensor and the resistance sensor are deployed on the support structure of the marine antenna of the ship;

[0017] A signal reading module, configured to respectively read the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor;

[0018] A structure classification network determination module, configured to determine a structure classification network; the structure classification network includes a first encoder, a second encoder, a third encoder, a decoder, and a head structure;

[0019] A target attitude feature extraction module, configured to input the attitude signal into the first encoder to extract a target attitude feature;

[0020] A target vibration feature extraction module, configured to input the vibration signal into the second encoder to extract a first target vibration feature;

[0021] A target resistance feature extraction module, configured to input the resistance signal into the third encoder to extract a target resistance feature;

[0022] A target multimodal feature decoding module, configured to input the target attitude feature, the first target vibration feature, and the target resistance feature into the decoder to decode them into a target multimodal feature;

[0023] A strength classification module, configured to input the target multimodal feature into the head structure to classify the strength of the support structure of the maritime antenna;

[0024] An alarm operation execution module, configured to perform an alarm operation on the support structure of the maritime antenna according to the strength.

[0025] The third aspect of the present invention provides an electronic device, and the electronic device includes:

[0026] At least one processor; and

[0027] A memory communicatively connected to the at least one processor; wherein,

[0028] The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the method for detecting the strength of a marine antenna support structure as described in the first aspect above.

[0029] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, it implements the method for detecting the strength of a marine antenna support structure as described in the first aspect above.

[0030] A fifth aspect of the present invention provides a computer program product including a computer program, and when the computer program is executed by a processor, it implements the method for detecting the strength of a marine antenna support structure as described in the first aspect above.

[0031] In this embodiment, when the ship is sailing on the sea area, the inertial measurement unit, the vibration sensor and the resistance sensor are respectively started; the inertial measurement unit is deployed on the hull of the ship, and both the vibration sensor and the resistance sensor are deployed on the support structure of the marine antenna on the ship; the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor and the resistance signal collected by the resistance sensor are respectively read; a structure classification network is determined; the structure classification network includes a first encoder, a second encoder, a third encoder, a decoder and a head structure; the attitude signal is input into the first encoder to extract the target attitude feature; the vibration signal is input into the second encoder to extract the first target vibration feature; the resistance signal is input into the third encoder to extract the target resistance feature; the target attitude feature, the first target vibration feature and the target resistance feature are input into the decoder to be decoded into the target multi-modal feature; the target multi-modal feature is input into the head structure to classify the strength of the support structure of the marine antenna; an alarm operation is performed on the support structure of the marine antenna according to the strength. This embodiment mines the features of the support structure of the marine antenna in terms of vibration, resistance, etc. to evaluate the strength of the support structure of the marine antenna, so as to perform an alarm operation, realizing the self-inspection of the support structure of the marine antenna, improving the accuracy of overhauling the support structure of the marine antenna on the ship, reducing the risk of damage to the marine antenna caused by the damage of the support structure of the marine antenna during the navigation of the ship, and ensuring the safety of ship navigation.

[0032] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0034] Figure 1 It is a flowchart of a method for detecting the strength of a marine antenna support structure provided in Embodiment 1 of the present invention.

[0035] Figure 2 It is a schematic diagram of a ship and a marine antenna provided in Embodiment 1 of the present invention.

[0036] Figure 3 It is a schematic diagram of a structure classification network provided in Embodiment 1 of the present invention.

[0037] Figure 4 It is a schematic structural diagram of a device for detecting the strength of a marine antenna support structure provided in Embodiment 2 of the present invention.

[0038] Figure 5 It is a schematic structural diagram of an electronic device provided in Embodiment 3 of the present invention. Detailed implementation manners

[0039] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0040] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can cover sequences other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units does not necessarily need to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0041] Embodiment 1

[0042] See Figure 1, which shows a flowchart of a method for detecting the strength of a nautical antenna support structure provided in the first embodiment of the present invention. This method can be executed by a device for detecting the strength of a nautical antenna support structure, which can be implemented in the form of hardware and / or software, and the device for detecting the strength of a nautical antenna support structure can be configured in an electronic device. As Figure 1 shown, the method includes:

[0043] Step 101: When the ship is sailing on the sea area, start the inertial measurement unit, vibration sensor, and resistance sensor respectively.

[0044] As Figure 2 shown, install one or more maritime antennas 201 on the ship 200, and the manufacturer of the maritime antenna provides various maintenance services for the maritime antenna 201 on the ship 200.

[0045] In this embodiment, when the ship is sailing on the sea area, the inertial measurement unit (Inertial Measurement Unit, IMU), vibration sensor, and resistance sensor can be started respectively.

[0046] Among them, the inertial measurement unit is deployed on the hull of the ship, and the vibration sensor and the resistance sensor are both deployed on the support structure of the maritime antenna on the ship.

[0047] Step 102: Read the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor respectively.

[0048] In practical applications, the inertial measurement unit collects the attitude signal of the ship's hull at a preset first frequency, the vibration sensor collects the vibration signal of the support structure of the maritime antenna at a preset second frequency, and the resistance sensor collects the resistance signal of the support structure of the maritime antenna at a preset third frequency.

[0049] At this time, the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor can be continuously read respectively.

[0050] Step 103: Determine the structure classification network.

[0051] In this embodiment, the structure classification network can be pre-constructed and trained based on deep learning. Among them, as Figure 3 shown, the structure classification network includes a first encoder Encoder1, a second encoder Encoder2, a third encoder Encoder3, a decoder Decoder, and a head structure Head.

[0052] The first encoder, Encoder1, is used to extract features from the attitude signal. The second encoder, Encoder2, is used to extract features from the vibration signal. The third encoder, Encoder3, is used to extract features from the resistance signal. The decoder, Decoder, is used to decode the features of the attitude signal, the vibration signal, and the resistance signal into new features. The head structure, Head, is used to perform a multi-classification task based on the decoded features and output the strength of the support structure of the marine antenna.

[0053] When training the structure classification network, the attitude signal collected by the historical inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor are selected as samples. Technicians label the strength of the corresponding support structure of the marine antenna as the label, Label, and choose cross-entropy as the loss function to perform supervised training on the structure classification network.

[0054] Step 104: Input the attitude signal into the first encoder to extract the target attitude features.

[0055] In this embodiment, as Figure 3 shown, the sequence composed of the attitude signal, Pose, of the ship's hull is input into the first encoder, Encoder1, for encoding, and the target attitude features are extracted from the sequence composed of the attitude signal of the ship's hull.

[0056] In one design, as Figure 3 shown, the first encoder, Encoder1, includes a first convolutional layer, Conv1, a second convolutional layer, Conv2, a third convolutional layer, Conv3, and a first fully connected layer, FC1.

[0057] In this design, the transpose of the attitude signal, Pose, is multiplied by the attitude signal to obtain the attitude matrix. Then, the attitude matrix can be expressed as P T *P, where P is the attitude signal and T is the transpose.

[0058] The attitude matrix is input into the first fully connected layer, FC1, to be mapped into the first candidate attitude features, realizing dimensionality reduction and alignment.

[0059] The first candidate attitude features are input into the first convolutional layer, Conv1, to perform a convolution operation to obtain the second candidate attitude features.

[0060] The second candidate attitude features are input into the second convolutional layer, Conv2, to perform a convolution operation to obtain the third candidate attitude features.

[0061] The first candidate attitude features and the third candidate attitude features are concatenated, Concat, into the fourth candidate attitude features.

[0062] The fourth candidate posture feature is input into the third convolutional layer Conv3 to perform a convolution operation to obtain the target posture feature.

[0063] Step 105: Input the vibration signal into a second encoder to extract the first target vibration feature.

[0064] In this embodiment, if Figure 3 As shown, the sequence composed of the vibration signal Vibration of the support structure of the maritime antenna is input into the second encoder Encoder2 for encoding, and the first target vibration feature is extracted from the sequence composed of the vibration signal Vibration of the support structure of the maritime antenna.

[0065] In practical applications, maritime antennas generate vibration signals as ships drift on the sea.

[0066] When the supporting structure of the maritime antenna is damaged, the stiffness and mass distribution of the supporting structure of the maritime antenna change, causing the natural frequency of the vibration signal to change. Some originally suppressed vibration modes may be excited, resulting in an increase in the amplitude at a specific frequency.

[0067] When corrosion occurs in the support structure of a maritime antenna, its material properties degrade, the effective cross-sectional area decreases, and the overall structural stiffness decreases. Furthermore, corrosion is often localized, leading to uneven mass distribution and the appearance of additional low-frequency vibration components. This results in a larger vibration response and increased amplitude under the same excitation. This increase in amplitude is particularly pronounced when corrosion progresses to a certain extent and the structure becomes loose in certain areas.

[0068] Therefore, there is a significant difference between the vibration signal generated when the supporting structure of the maritime antenna is intact and the vibration signal generated when the supporting structure of the maritime antenna is damaged or corroded. The vibration signal of the supporting structure of the maritime antenna can be used to detect the strength of the supporting structure of the maritime antenna.

[0069] In one design, Figure 3 As shown, the second encoder Encoder2 includes a fourth convolutional layer Conv4, a fifth convolutional layer Conv5, a sixth convolutional layer Conv6 and a second fully connected layer FC2.

[0070] In this design, we use algorithms like the Discrete Wavelet Transform (DWT) to convert the vibration signal into a time-frequency graph. Converting the vibration signal into a time-frequency graph helps us identify the signal's characteristics in terms of frequency and amplitude.

[0071] The vibration time-frequency map is input into the second fully connected layer FC2 and mapped into the first candidate vibration feature to achieve dimensionality reduction and alignment.

[0072] Input the first candidate vibration feature into the fourth convolutional layer Conv4 to perform a convolutional operation, obtaining a second candidate vibration feature.

[0073] Input the second candidate vibration feature into the fifth convolutional layer Conv5 to perform a convolutional operation, obtaining a third candidate vibration feature.

[0074] Concatenate the first candidate vibration feature and the third candidate vibration feature by Concat to obtain a fourth candidate vibration feature.

[0075] Input the fourth candidate vibration feature into the sixth convolutional layer Conv6 to perform a convolutional operation, obtaining the target vibration feature.

[0076] Step 106: Input the resistance signal into the third encoder to extract the target resistance feature.

[0077] In this embodiment, as Figure 3 shown, input the sequence composed of the resistance signal Resistance of the support structure of the maritime antenna into the third encoder Encoder3 for encoding, and extract the target resistance feature from the sequence composed of the resistance signal Resistance of the support structure of the maritime antenna.

[0078] When the support structure of the maritime antenna is damaged, the current path becomes longer or there is an open circuit, resulting in an increase in resistance. The damage to the support structure of the maritime antenna may cause the contact points to become loose, and under the influence of vibration or other external factors, the contact resistance will change, leading to large fluctuations in the resistance signal.

[0079] When the support structure of the maritime antenna is corroded, an oxide film or other corrosion products will form on the surface. The conductivity of these products is usually worse than that of the metal itself, which is equivalent to adding a resistance element in the current path, increasing the overall resistance.

[0080] Therefore, there are obvious differences between the resistance signals generated when the support structure of the maritime antenna is intact and those generated when the support structure of the maritime antenna is damaged or corroded. The resistance signal of the support structure of the maritime antenna can be selected to detect the strength of the support structure of the maritime antenna.

[0081] In one design, as Figure 3 shown, the third encoder Encoder3 includes a seventh convolutional layer Conv7, an eighth convolutional layer Conv8, a ninth convolutional layer Conv9, and a third fully connected layer FC3.

[0082] In this design, multiply the transpose of the resistance signal by the resistance signal to obtain a resistance matrix. Then, the resistance matrix can be expressed as R T*R, where R is the resistance signal and T is the transpose. Converting the resistance signal into a resistance matrix helps to extract the characteristics when the resistance signal fluctuates.

[0083] Input the resistance matrix into the third fully connected layer FC3 to map it into the first candidate resistance feature, achieving dimensionality reduction and alignment.

[0084] Input the first candidate resistance feature into the seventh convolutional layer Conv7 to perform a convolution operation, obtaining the second candidate resistance feature.

[0085] Input the second candidate resistance feature into the eighth convolutional layer Conv8 to perform a convolution operation, obtaining the third candidate resistance feature.

[0086] Concatenate the first candidate resistance feature and the third candidate resistance feature Concat into the fourth candidate resistance feature.

[0087] Input the fourth candidate pose feature into the ninth convolutional layer Conv9 to perform a convolution operation, obtaining the target resistance feature.

[0088] Step 107: Input the target pose feature, the first target vibration feature, and the target resistance feature into the decoder to decode them into the target multimodal feature.

[0089] As Figure 3 shown, input the target pose feature, the first target vibration feature, and the target resistance feature into the decoder Decoder to decode them into the target multimodal feature.

[0090] In an embodiment of the present invention, as Figure 3 shown, the decoder includes a modulator Modulator and a fusion module (Fussion Module, FM). Then, in this embodiment, step 107 may include the following steps:

[0091] Step 1071: In the modulator, adjust the first target vibration feature according to the target pose feature to obtain the second target vibration feature.

[0092] In practical applications, as Figure 3 shown, there is a non - linear relationship between the sway of the ship and the vibration of the support structure of the marine antenna. Therefore, input the target pose feature and the first target vibration feature into the modulator Modulator, and adjust the first target vibration feature according to the target pose feature to filter out the interference of the ship's sway and obtain a relatively pure second target vibration feature.

[0093] In one design, as Figure 3As shown, the modulator includes a tenth convolutional layer Conv10. Then, in this design, in the tenth convolutional layer Conv10, a convolution operation is performed on the first target vibration feature using the target pose feature as the convolution kernel to obtain a second target vibration feature.

[0094] Step 1072: In the fusion module, fuse the second target vibration feature and the target resistance feature into a target multimodal feature.

[0095] In this embodiment, as Figure 3 shown, input the second target vibration feature and the target resistance feature into the fusion module FM, and interact and fuse the second target vibration feature and the target resistance feature into a target multimodal feature.

[0096] In one design, as Figure 3 shown, the fusion module FM includes a first convolutional module ConvModule1, a second convolutional module ConvModule2, a neural network Transformer, a first atrous spatial pyramid pooling module ASPP1, and a second atrous spatial pyramid pooling module ASPP2.

[0097] Among them, both the first convolutional module ConvModule1 and the second convolutional module ConvModule2 sequentially include a convolutional layer (Convolutional Layer) and a pooling layer Pooling. The convolutional layer provides a convolution operation, and the pooling layer provides a pooling operation, such as a max pooling operation or an average pooling operation, etc.

[0098] In this design, input the second target vibration feature into the first convolutional module ConvModule1 to sequentially perform a convolution operation and a pooling operation to extract a first vibration modal feature.

[0099] Input the first vibration modal feature into the neural network Transformer to extract a second vibration modal feature.

[0100] Input the target resistance feature into the second convolutional module ConvModule2 to extract a first resistance modal feature.

[0101] Input the first resistance modal feature into the neural network Transformer to extract a second resistance modal feature.

[0102] Concatenate Concat the first vibration modal feature and the second resistance modal feature into a first candidate multimodal feature.

[0103] Concatenate Concat the first resistance modal feature and the second vibration modal feature into a second candidate multimodal feature.

[0104] Input the first candidate multi-modal feature into the first Atrous Spatial Pyramid Pooling module ASPP1 to extract the third candidate multi-modal feature.

[0105] Input the second candidate multi-modal feature into the second Atrous Spatial Pyramid Pooling module ASPP2 to extract the fourth candidate multi-modal feature.

[0106] Concatenate the third candidate multi-modal feature and the fourth candidate multi-modal feature by Concat to obtain the target multi-modal feature.

[0107] In practical applications, there are high-density fluctuations in vibration and resistance. In this scenario, this embodiment relies on the attention mechanism of the Transformer and the receptive fields of the Atrous Spatial Pyramid Pooling module (ASPP) at multiple scales to capture global context features, effectively improving the quality of the features fused in the multi-modal situation.

[0108] Step 108: Input the target multi-modal feature into the head structure to classify the strength of the support structure of the maritime antenna.

[0109] In this embodiment, as Figure 3 shown, input the target multi-modal feature into the head structure Head. The head structure Head performs a multi-classification task and outputs the strength of the support structure of the maritime antenna.

[0110] In one design, as Figure 3 shown, the head structure Head includes a fourth fully connected layer FC4. Input the target multi-modal feature into the fourth fully connected layer FC4 to map it into a structural strength feature, and use an activation function such as sigmoid to activate the structural strength feature to obtain the probabilities at various strengths, and output the strength Intensity with the highest probability.

[0111] The structure classification network of this embodiment has a simple structure, low computational complexity, and low resource consumption, and is suitable for running on edge computing devices on ships. It can still detect the strength of the support structure of the maritime antenna in real time in a weak network environment during the ship's navigation.

[0112] Step 109: Perform an alarm operation on the support structure of the maritime antenna according to the strength.

[0113] In this embodiment, statistical analysis can be performed on the strength of the support structure of the maritime antenna. When there are risks such as damage and corrosion to the support structure of the maritime antenna, an alarm operation is performed on the support structure of the maritime antenna. At this time, a prompt message is sent to the accounts of relevant users (such as the owner or lessee of the ship, etc.), prompting them to pay attention to the support structure of the maritime antenna and contact the manufacturer that produces or sells the maritime antenna in time to arrange professionals to repair the support structure of the maritime antenna.

[0114] In addition, relevant users (such as the owner or lessee of the ship, etc.) can also use the application provided by the manufacturer of marine antennas to actively detect the strength of the support structure of the marine antenna, contact the manufacturer that produces or sells marine antennas as needed, and arrange for professionals to overhaul the support structure of the marine antenna.

[0115] In a statistical analysis method, the detection strength of the support structure of the marine antenna can be continuously measured, and multiple strengths can be smoothed to obtain a strength sequence.

[0116] A slidable window is added to the strength sequence. Each time the window slides, the average value of the strengths in the window is calculated to obtain the average strength.

[0117] If the average strength is less than or equal to a preset strength threshold, indicating that there are risks such as damage and corrosion in the support structure of the marine antenna, an alarm operation is performed on the support structure of the marine antenna using instant messaging, emails, text messages, etc.

[0118] In this embodiment, when the ship is sailing on the sea, the inertial measurement unit, vibration sensor, and resistance sensor are respectively activated; the inertial measurement unit is deployed on the hull of the ship, and both the vibration sensor and the resistance sensor are deployed on the support structure of the marine antenna on the ship; the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor are respectively read; a structure classification network is determined; the structure classification network includes a first encoder, a second encoder, a third encoder, a decoder, and a head structure; the attitude signal is input into the first encoder to extract the target attitude feature; the vibration signal is input into the second encoder to extract the first target vibration feature; the resistance signal is input into the third encoder to extract the target resistance feature; the target attitude feature, the first target vibration feature, and the target resistance feature are input into the decoder to be decoded into target multimodal features; the target multimodal features are input into the head structure to classify the strength of the support structure of the marine antenna; an alarm operation is performed on the support structure of the marine antenna according to the strength. This embodiment excavates the features of the support structure of the marine antenna in terms of vibration, resistance, etc. to evaluate the strength of the support structure of the marine antenna, thereby performing an alarm operation, realizing the self-inspection of the support structure of the marine antenna, improving the accuracy of overhauling the support structure of the marine antenna in the ship, reducing the risk of the marine antenna being damaged due to the damage of the support structure of the marine antenna during the navigation of the ship, and ensuring the safety of ship navigation.

[0119] Embodiment 2

[0120] See Figure 4 , which shows a schematic structural diagram of a device for detecting the strength of the support structure of a marine antenna provided by the second embodiment of the present invention. As Figure 4 shown, the device includes:

[0121] A sensor activation module 401, which is used to activate an inertial measurement unit, a vibration sensor, and a resistance sensor respectively when the ship is sailing on the sea; the inertial measurement unit is deployed on the hull of the ship, and both the vibration sensor and the resistance sensor are deployed on the support structure of the ship's marine antenna;

[0122] A signal reading module 402, which is used to read the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor, and the resistance signal collected by the resistance sensor respectively;

[0123] A structure classification network determination module 403, which is used to determine a structure classification network; the structure classification network includes a first encoder, a second encoder, a third encoder, a decoder, and a head structure;

[0124] A target attitude feature extraction module 404, which is used to input the attitude signal into the first encoder to extract target attitude features;

[0125] A target vibration feature extraction module 405, which is used to input the vibration signal into the second encoder to extract first target vibration features;

[0126] A target resistance feature extraction module 406, which is used to input the resistance signal into the third encoder to extract target resistance features;

[0127] A target multimodal feature decoding module 407, which is used to input the target attitude features, the first target vibration features, and the target resistance features into the decoder to decode them into target multimodal features;

[0128] An intensity classification module 408, which is used to input the target multimodal features into the head structure to classify the intensity of the support structure of the marine antenna;

[0129] An alarm operation execution module 409, which is used to execute an alarm operation on the support structure of the marine antenna according to the intensity.

[0130] In an embodiment of the present invention, the first encoder includes a first convolutional layer, a second convolutional layer, a third convolutional layer, and a first fully connected layer, and the target attitude feature extraction module 404 is further used for:

[0131] Multiplying the transpose of the attitude signal by the attitude signal to obtain an attitude matrix;

[0132] Inputting the attitude matrix into the first fully connected layer to map it into a first candidate attitude feature;

[0133] Inputting the first candidate attitude feature into the first convolutional layer to perform a convolutional operation to obtain a second candidate attitude feature;

[0134] Input the second candidate pose feature into the second convolutional layer to perform a convolutional operation, obtaining a third candidate pose feature;

[0135] Concatenate the first candidate pose feature and the third candidate pose feature into a fourth candidate pose feature;

[0136] Input the fourth candidate pose feature into the third convolutional layer to perform a convolutional operation, obtaining a target pose feature.

[0137] In an embodiment of the present invention, the second encoder includes a fourth convolutional layer, a fifth convolutional layer, a sixth convolutional layer, and a second fully connected layer. The target vibration feature extraction module 405 is further configured to:

[0138] Perform time-frequency conversion on the vibration signal to obtain a vibration time-frequency diagram;

[0139] Input the vibration time-frequency diagram into the second fully connected layer to map it into a first candidate vibration feature;

[0140] Input the first candidate vibration feature into the fourth convolutional layer to perform a convolutional operation, obtaining a second candidate vibration feature;

[0141] Input the second candidate vibration feature into the fifth convolutional layer to perform a convolutional operation, obtaining a third candidate vibration feature;

[0142] Concatenate the first candidate vibration feature and the third candidate vibration feature into a fourth candidate vibration feature;

[0143] Input the fourth candidate vibration feature into the sixth convolutional layer to perform a convolutional operation, obtaining a target vibration feature.

[0144] In an embodiment of the present invention, the third encoder includes a seventh convolutional layer, an eighth convolutional layer, a ninth convolutional layer, and a third fully connected layer. The target resistance feature extraction module 406 is further configured to:

[0145] Multiply the transpose of the resistance signal by the resistance signal to obtain a resistance matrix;

[0146] Input the resistance matrix into the third fully connected layer to map it into a first candidate resistance feature;

[0147] Input the first candidate resistance feature into the seventh convolutional layer to perform a convolutional operation, obtaining a second candidate resistance feature;

[0148] Input the second candidate resistance feature into the eighth convolutional layer to perform a convolutional operation, obtaining a third candidate resistance feature;

[0149] Splice the first candidate resistance feature and the third candidate resistance feature into a fourth candidate resistance feature;

[0150] Input the fourth candidate pose feature into the ninth convolutional layer to perform a convolutional operation, obtaining a target resistance feature.

[0151] In an embodiment of the present invention, the decoder includes a mediator and a fusion module, and the target multi-modal feature decoding module 407 includes:

[0152] A signal conditioning module, configured to condition the first target vibration feature according to the target pose feature in the mediator, obtaining a second target vibration feature;

[0153] A signal fusion module, configured to fuse the second target vibration feature and the target resistance feature into a target multi-modal feature in the fusion module.

[0154] In an embodiment of the present invention, the mediator includes a tenth convolutional layer; the signal conditioning module is further configured to:

[0155] In the tenth convolutional layer, perform a convolutional operation on the first target vibration feature with the target pose feature as the convolutional kernel, obtaining a second target vibration feature.

[0156] In an embodiment of the present invention, the fusion module includes a first convolutional module, a second convolutional module, a neural network Transformer, a first atrous spatial pyramid pooling module, and a second atrous spatial pyramid pooling module; both the first convolutional module and the second convolutional module sequentially include a convolutional layer and a pooling layer;

[0157] The signal fusion module is further configured to:

[0158] Input the second target vibration feature into the first convolutional module to extract a first vibration modal feature;

[0159] Input the first vibration modal feature into the neural network Transformer to extract a second vibration modal feature;

[0160] Input the target resistance feature into the second convolutional module to extract a first resistance modal feature;

[0161] Input the first resistance modal feature into the neural network Transformer to extract a second resistance modal feature;

[0162] Splice the first vibration modal feature and the second resistance modal feature into a first candidate multi-modal feature;

[0163] Concatenate the first resistance modal feature and the second vibration modal feature into a second candidate multi-modal feature;

[0164] Input the first candidate multi-modal feature into the first atrous spatial pyramid pooling module to extract a third candidate multi-modal feature;

[0165] Input the second candidate multi-modal feature into the second atrous spatial pyramid pooling module to extract a fourth candidate multi-modal feature;

[0166] Concatenate the third candidate multi-modal feature and the fourth candidate multi-modal feature into a target multi-modal feature.

[0167] In an embodiment of the present invention, the alarm operation execution module 409 is further configured to:

[0168] Smooth the multiple intensities to obtain an intensity sequence;

[0169] Add a slidable window to the intensity sequence;

[0170] Calculate the average value of the intensities in the window to obtain an average intensity;

[0171] If the average intensity is less than or equal to a preset intensity threshold, perform an alarm operation on the support structure of the maritime antenna.

[0172] The device for detecting the strength of the support structure of a marine antenna provided by the embodiments of the present invention can execute the method for detecting the strength of the support structure of a marine antenna provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method for detecting the strength of the support structure of a marine antenna.

[0173] Embodiment III

[0174] Refer to Figure 5 , which shows a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0175] As Figure 5As shown, the electronic device 10 includes at least one processor 11 and a memory communicatively connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc. Among them, the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0176] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disc, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0177] The processor 11 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include but are not limited to a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for detecting the strength of the marine antenna support structure.

[0178] In some embodiments, the method for detecting the strength of the marine antenna support structure can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for detecting the strength of the marine antenna support structure described above can be executed. Alternatively, in other embodiments, the processor 11 can be configured to execute the method for detecting the strength of the marine antenna support structure in any other appropriate way (for example, by means of firmware).

[0179] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0180] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, a special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions / operations specified in the flowchart and / or block diagram to be implemented. The computer programs can be executed entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0181] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0182] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the electronic device. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0183] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by digital data communication in any form or medium (e.g., a communication network). Examples of communication networks include: local area network (LAN), wide area network (WAN), blockchain network, and the Internet.

[0184] A computing system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The relationship between the client and the server is created by computer programs running on respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0185] Embodiment 4

[0186] The embodiment of the present invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the method for detecting the strength of a marine antenna support structure provided in any embodiment of the present invention.

[0187] In the process of implementing the computer program product, computer program code for performing the operations of the present invention can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, alternatively, can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0188] It should be understood that the various forms of the processes shown above can be used, steps can be reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0189] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting the strength of a nautical antenna support structure, characterized in that, Including: When the ship is sailing in the sea area, start the inertial measurement unit, vibration sensor and resistance sensor respectively; The inertial measurement unit is deployed on the hull of the ship, and both the vibration sensor and the resistance sensor are deployed on the support structure of the ship's maritime antenna; Read the attitude signal collected by the inertial measurement unit, the vibration signal collected by the vibration sensor and the resistance signal collected by the resistance sensor respectively; Determine the structure classification network; the structure classification network includes a first encoder, a second encoder, a third encoder, a decoder and a head structure; Input the attitude signal into the first encoder to extract the target attitude feature; Input the vibration signal into the second encoder to extract the first target vibration feature; Input the resistance signal into the third encoder to extract the target resistance feature; Input the target attitude feature, the first target vibration feature and the target resistance feature into the decoder to decode them into target multi-modal features; Input the target multi-modal features into the head structure to classify the strength of the support structure of the maritime antenna; Perform an alarm operation on the support structure of the maritime antenna according to the strength.

2. The method according to claim 1, characterized in that The first encoder includes a first convolutional layer, a second convolutional layer, a third convolutional layer and a first fully connected layer. The step of inputting the attitude signal into the first encoder to extract the target attitude feature includes: Multiply the transpose of the attitude signal by the attitude signal to obtain an attitude matrix; Input the attitude matrix into the first fully connected layer to map it into a first candidate attitude feature; Input the first candidate attitude feature into the first convolutional layer to perform a convolutional operation to obtain a second candidate attitude feature; Input the second candidate attitude feature into the second convolutional layer to perform a convolutional operation to obtain a third candidate attitude feature; Concatenate the first candidate attitude feature and the third candidate attitude feature into a fourth candidate attitude feature; Input the fourth candidate attitude feature into the third convolutional layer to perform a convolutional operation to obtain the target attitude feature.

3. The method according to claim 2, wherein The second encoder includes a fourth convolutional layer, a fifth convolutional layer, a sixth convolutional layer and a second fully connected layer. The step of inputting the vibration signal into the second encoder to extract the first target vibration feature includes: Perform time-frequency conversion on the vibration signal to obtain a vibration time-frequency diagram; Input the vibration time-frequency diagram into the second fully connected layer to map it into a first candidate vibration feature; Input the first candidate vibration feature into the fourth convolutional layer to perform a convolutional operation to obtain a second candidate vibration feature; Input the second candidate vibration feature into the fifth convolutional layer to perform a convolutional operation to obtain a third candidate vibration feature; Concatenate the first candidate vibration feature and the third candidate vibration feature into a fourth candidate vibration feature; Input the fourth candidate vibration feature into the sixth convolutional layer to perform a convolutional operation to obtain the target vibration feature.

4. The method according to claim 3, characterized in that, The third encoder includes a seventh convolutional layer, an eighth convolutional layer, a ninth convolutional layer and a third fully connected layer. The step of inputting the resistance signal into the third encoder to extract the target resistance feature includes: Multiply the transpose of the resistance signal by the resistance signal to obtain a resistance matrix; Input the resistance matrix into the third fully connected layer to map it into a first candidate resistance feature; Input the first candidate resistance feature into the seventh convolutional layer to perform a convolutional operation to obtain a second candidate resistance feature; Input the second candidate resistance feature into the eighth convolutional layer to perform a convolutional operation to obtain a third candidate resistance feature; Concatenate the first candidate resistance feature and the third candidate resistance feature into a fourth candidate resistance feature; Input the fourth candidate pose feature into the ninth convolutional layer to perform a convolutional operation to obtain a target resistance feature.

5. The method according to claim 4, wherein The decoder includes a mediator and a fusion module. Inputting the target pose feature, the first target vibration feature, and the target resistance feature into the decoder to decode them into a target multimodal feature includes: In the mediator, mediate the first target vibration feature according to the target pose feature to obtain a second target vibration feature; In the fusion module, fuse the second target vibration feature and the target resistance feature into a target multimodal feature.

6. The method according to claim 5, wherein The mediator includes a tenth convolutional layer. In the mediator, mediating the first target vibration feature according to the target pose feature to obtain a second target vibration feature includes: In the tenth convolutional layer, use the target pose feature as a convolution kernel to perform a convolutional operation on the first target vibration feature to obtain a second target vibration feature.

7. The method according to claim 6, wherein The fusion module includes a first convolutional module, a second convolutional module, a neural network Transformer, a first atrous spatial pyramid pooling module, and a second atrous spatial pyramid pooling module; both the first convolutional module and the second convolutional module sequentially include a convolutional layer and a pooling layer; In the fusion module, fusing the second target vibration feature and the target resistance feature into a target multimodal feature includes: Input the second target vibration feature into the first convolutional module to extract a first vibration modal feature; Input the first vibration modal feature into the neural network Transformer to extract a second vibration modal feature; Input the target resistance feature into the second convolutional module to extract a first resistance modal feature; Input the first resistance modal feature into the neural network Transformer to extract a second resistance modal feature; Concatenate the first vibration modal feature and the second resistance modal feature into a first candidate multimodal feature; Concatenate the first resistance modal feature and the second vibration modal feature into a second candidate multimodal feature; Input the first candidate multimodal feature into the first atrous spatial pyramid pooling module to extract a third candidate multimodal feature; Input the second candidate multimodal feature into the second atrous spatial pyramid pooling module to extract a fourth candidate multimodal feature; Concatenate the third candidate multimodal feature and the fourth candidate multimodal feature into a target multimodal feature.

8. The method according to any one of claims 1-7, characterized in that, Performing an alarm operation on the support structure of the maritime antenna according to the intensity includes: Smooth multiple of the said intensities to obtain an intensity sequence; Add a slidable window to the intensity sequence; Calculate the average value of the intensities in the window to obtain an average intensity; If the average intensity is less than or equal to a preset intensity threshold, perform an alarm operation on the support structure of the maritime antenna.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for detecting the strength of the support structure of a marine antenna as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for detecting the strength of the support structure of a marine antenna as described in any one of claims 1-8 is implemented.

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