CN115871901A - Sturgeon-imitated robot and submarine cable fault detection method - Google Patents
Sturgeon-imitated robot and submarine cable fault detection method Download PDFInfo
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Abstract
The invention discloses a sturgeon-imitated robot and a submarine cable fault detection method, and relates to the technical field of ocean cable detection. The fish head part comprises a head shell, two pectoral fins are symmetrically arranged on two sides of the head shell, and dorsal fins are arranged at the top of the head shell. The fishtail portion is connected to the head shell by a plurality of torso units, including a tail shell and tail fins. The trunk unit comprises a trunk shell and a steering driving mechanism, and the trunk units are sequentially connected end to end. Each trunk unit is matched with the fish head part to realize the integral swing of the sturgeon-imitating robot and provide forward power for the sturgeon-imitating robot. Control system includes controller, sonar, image acquisition module and signal emission module, and image acquisition module establishes at head shell front end. The invention adopts sonar to find the position of the submarine cable, identifies the fault type of the submarine cable through image acquisition and a convolutional neural network, has high searching speed and accurate position judgment, and is suitable for the complex submarine environment.
Description
Technical Field
The invention relates to the technical field of marine cable fault detection, in particular to an sturgeon-imitated robot and a submarine cable fault detection method.
Background
Submarine cable is a cable which is wrapped by insulating materials and laid on the seabed and is mainly used for telecommunication transmission. Submarine cables are classified into underwater communication cables and power cables. The application fields of the method comprise international communication, electric power and communication between coastal islands and cities, offshore wind power generation, offshore oil platform electric power and information transmission and the like. The underwater communication cable is a main mode of international information transmission and accounts for 97% of world data transmission; submarine cables are an important transmission means for power and communication between coastal islands and cities, and China has a wide sea area, telecommunication transmission among islands, coastal cities and lands and islands needs huge amount of submarine cables; in the field of energy development, submarine cables have wide application prospects in offshore platforms and offshore wind power generation and transmission. Therefore, the submarine cable has important values in social life, economy, military and the like.
However, even with shielding and burial, over 200 underwater cable failures occur each year. Damage to the submarine cable not only causes significant economic losses, but can also lead to a series of problems in the resource exploitation process, further causing marine environmental pollution. The damage of the submarine cable mainly includes natural disasters and man-made damages. Natural disasters include ocean bottom earthquakes, landslides, ocean currents and waves, tsunamis, billows, sea level rises, extreme weather (hurricanes), volcanic activity, and the like. These natural disasters may eventually lead to submarine cable wear, stress fatigue and failure, breakage, etc. Man-made vandalism includes both accidental and deliberate vandalism. Unexpected human threats include accidental human behaviors of the cable, such as trawling or anchoring in fishing, and submarine operations such as clam dredger and scallop dredger are all likely to damage the submarine cable. The intentional damage to the cable is mainly reflected in the theft of the submarine cable. The artificial damage mainly causes damage and breakage of the submarine cable.
In summary, submarine cables present a number of threats that may cause damage to varying degrees. Many related researches have been carried out to find out the fault of the submarine cable and to maintain the submarine cable in time, and the existing submarine cable detection can be divided into methods based on optical, electrical, acoustic, magnetic and multi-sensor in terms of detection technology. Visual detection based on optical detection has the characteristics of insensitivity to noise data and capability of obtaining good environmental information, but is easily influenced by light and water quality; the detection based on electricity does not need short-distance detection, but needs to load an electric signal on the submarine cable, and is generally used for detecting electric faults such as short circuit and open circuit of a copper core; the detection technology based on acoustics has the characteristic of long detection distance, but is easily interfered by external noise, and the detection precision is limited; based on the magnetic detection technology, the method has the advantage of long detection distance, but the active detection needs to inject current into the cable, and the passive detection is easily interfered by the surrounding magnetic field; the multi-sensor fusion technology based on the combination of optical, electrical, acoustic, magnetic and other sensors has the characteristic of high reliability of detection results, but has the disadvantages of complex data processing, large calculation amount and long time consumption. From the above analysis, it can be known that various submarine cable fault detection methods in the prior art are limited by corresponding disadvantages, and the submarine cable detection problem in the actual engineering can not be effectively solved. Accordingly, the prior art is subject to further improvements and enhancements.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides the sturgeon-imitating robot, and aims to solve the problems that a submarine cable fault detection means in the prior art is influenced by a submarine environment, so that the corresponding defects exist, the detection result is low in accuracy and reliability, and a submarine cable fault point and fault type cannot be actually and effectively determined.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the utility model provides an imitative sturgeon robot, includes fish head part, truck unit, fish tail part and control system, the fish head part is including the head shell of class sturgeon form, and the left and right sides symmetry of head shell is equipped with two pectoral fins, and its top is provided with the dorsal fin.
The fish tail part is located the rear side of fish head part to through a plurality of arranging in proper order the truck unit links to each other with the rear side of head shell, and the fish tail part includes sturgeon-like afterbody shell, and the rear end of afterbody shell is provided with the tail fin.
The trunk unit comprises a trunk shell and a steering driving mechanism arranged in the trunk shell, each trunk unit is sequentially connected from head to tail, the front end of the first trunk unit is fixedly connected with the rear end of the head shell, and the rear end of the last trunk unit is fixedly connected with the tail shell.
Each trunk unit and fish head part cooperate and realize the whole swing of imitative sturgeon robot, provide its power that gos forward.
Control system includes controller, sonar, image acquisition module and signal emission module, and image acquisition module sets up the front end at the head shell, and sonar, image acquisition module and signal emission module all are connected with the controller signal.
Further, the trunk shell is a section of annular shell, and gaps are formed between any two adjacent trunk shells and between the trunk shell and the head shell for the first time.
The steering driving mechanism comprises a double-shaft servo motor and a steering support, the double-shaft servo motor is fixed in the trunk shell, the steering support is located on the front side of the double-shaft servo motor and fixedly connected with an output shaft of the double-shaft servo motor, and a connecting frame is fixed on the rear side of each double-shaft servo motor.
Furthermore, the front end of the steering support in the first trunk unit is fixedly connected with the rear end of the head shell, and the front ends of the steering supports in the remaining trunk units are fixedly connected with the rear end of the connecting frame in the trunk unit adjacent to the front side.
The rear end of the connecting frame in the last-level trunk unit is fixedly connected with the front end of the tail shell.
Further, each the inboard of truck shell all is fixed and is equipped with motor support, and biax servo motor fixes on motor support, and its two output shafts are vertical to be arranged.
The left side and the right side of the double-shaft servo motor are symmetrically provided with two float blocks made of buoyancy materials, and the two float blocks are fixedly connected with the inner side wall of the trunk shell.
Further, each trunk shell comprises two shell monomers which are oppositely arranged, the upper end of each shell monomer is fixed with a hinged block, and the two hinged blocks of the same shell monomer are arranged in a staggered manner and hinged through a rotating shaft.
Bolt blocks are fixed on the adjacent sides of the bottoms of the two shell monomers, and the two bolt blocks at the bottom of the same shell monomer are opposite and fixedly connected through bolts.
Further, the motor support comprises two support monomers, each support monomer comprises a T-shaped structure formed by fixedly connecting a cross rod and a longitudinal rod, and the two support monomers are arranged in an axisymmetric mode.
The one end that two horizontal poles kept away from each other all links to each other with the inside wall of truck shell is fixed, and the other end is pegged graft with corresponding support monomer vertical pole is fixed, encloses into the closed region that can place biax servo motor.
Furthermore, every the top and the bottom of link all are provided with connecting terminal, and each connecting terminal all links to each other with the rear end of head shell through a muscle line that elastic material made.
One side of each pectoral fin, which is close to the head shell, is fixedly connected with a cross shaft, the cross shafts are in rotating sealing fit with the side wall of the head shell, and one end, which is far away from the pectoral fins, of each cross shaft is connected with a first servo motor.
The dorsal fin is vertically arranged, the bottom of the dorsal fin is fixedly connected with a vertical shaft, the vertical shaft is in rotating sealing fit with the top wall of the head shell, and the lower end of the vertical shaft is connected with the output end of a second servo motor fixed in the head shell.
The invention also aims to provide a submarine cable fault detection method adopting the sturgeon-imitating robot.
The submarine cable fault detection method adopts the sturgeon-imitating robot and comprises the following steps:
step one, a convolutional neural network prediction model is established, the convolutional neural network comprises an input layer, a convolutional layer, a pooling layer, a full-connection layer and an output layer, and a submarine cable fault detection convolutional neural network mathematical model with the optimal performance is obtained through learning and training.
And step two, placing the sturgeon-imitating robot in water and submerging the sturgeon-imitating robot to a deep water area, sending a signal to detect the position of the submarine cable by the sonar in the swimming process, after determining the position of the submarine cable, sending the signal to the controller by the sonar, and enabling the sturgeon-imitating robot to reach the position of the submarine cable.
And thirdly, the sturgeon-imitating robot swims along the extending direction of the submarine cable, the image acquisition module acquires image information of the submarine cable in real time and sends the image information to the controller, the state and the fault type of the submarine cable are judged through the convolutional neural network, and data information is stored.
And step four, after the sturgeon-imitating robot floats upwards out of the water surface, the data information is sent to a receiving terminal on a ship or on the ground through a signal transmitting module, and the position sent by the signal is positioned through a base station so as to determine the position coordinate of the fault point.
And step five, repeating the process of the step four, sequentially determining each fault point and fault type of the submarine cable, and making a fault processing scheme according to the data information by the staff.
Further, the building of the submarine cable detection prediction model comprises the following steps:
s1: acquiring images, horizontally turning, randomly deducting, carrying out scale transformation and rotating, realizing submarine cable data image expansion, and establishing a data set containing normal submarine cables and suspended and electrical faults caused by external force.
The data set of the convolutional neural network is randomly and averagely divided into a training subset, a verification subset and a test subset, the training set is used for learning model parameters of the convolutional neural network, the verification subset selects hyper-parameters through evaluating network performance, and the test subset measures the network performance through generalization errors.
S2: and training the hyper-parameters of the convolutional neural network, namely the hyper-parameters of the number of convolutional layers, the size of convolutional cores and the number of convolutional cores by using a particle swarm optimization algorithm, and optimizing.
S3: training the convolutional neural network by using the optimized number of convolutional layers, the size of convolutional cores and the number of convolutions, and leading data into an input layer, a pooling layer and a full-connection layer, wherein the convolutional neural network can extract the training fault characteristics.
S4: and calculating the output result loss function, updating the weight value by a gradient descent method, and realizing multiple iterations of the convolutional neural network so that the training model is converged.
S5: and after multiple iterations, judging the classification performance of the convolutional neural network based on particle swarm optimization by adopting a test set.
S6: and adjusting parameters of the node number, the iteration times and the loss function value in the convolutional neural network according to the performance evaluation result.
S7: and repeating the steps to finally obtain the submarine cable fault detection convolutional neural network mathematical model with the optimal performance.
Furthermore, the image acquisition module comprises a camera provided with a light source, in the first step, the input layer is a submarine cable image, the input layer expands the submarine cable data image, and the image is further enhanced.
The convolution layer is formed by convolution of the feature vector and a convolution kernel and realizes the feature extraction of the submarine image through activating function response.
The convolutional layer neuron mathematical expression is:
wherein the content of the first and second substances,
convolutional neural network convolutional layer->
In a first or second section>
The output of each channel; />
Convolutional neural network convolutional layer->
Is based on the fifth->
The output of each channel; />
For calculating a ^ th->
A subset of input profiles for which there are net activations of channels; />
Is a convolution operation symbol; />
Is a convolutional layer>
Input vector->
And neuron->
A connected weight matrix; />
Is a convolution layer>
A fifth or fifth letter>
Deviation values for individual characteristic maps>
Is an activation function.
The chemical expression of the pool layers is as follows:
wherein, the first and the second end of the pipe are connected with each other,
pooling layer for convolutional neural network>
Is/are>
The weight coefficient of each channel; />
Is a pooling function; />
For pooled layers>
A fifth or fifth letter>
The offset term of each channel.
The mathematical expression of the full connection layer is as follows:
wherein, the first and the second end of the pipe are connected with each other,
is a fully connected layer->
An output of (d); />
Is a fully connected layer->
The network weight coefficient of (a); />
Is a full connection layer
An output of (d); />
Is a fully connected layer->
The bias term of (1).
The output layer gives out a specific classification result and judges the normal state of the submarine cable and specific types of suspended and electric faults caused by external force.
By adopting the technical scheme, the invention has the beneficial technical effects that: the sturgeon-imitating robot has long sonar detection distance, can accurately measure the target distance, quickly find the position of a submarine cable and reach the vicinity of the submarine cable, the camera acquires image information of the submarine cable in real time, the condition of the submarine cable is detected through the convolutional neural network and a fault point is judged, after the sturgeon-imitating robot floats out of the water surface, data information is sent to a receiving terminal of a ship or the ground, and a base station on the bottom surface determines the fault position coordinate through a signal source. The method has the advantages of high fault point searching speed and accurate position judgment, the convolutional neural network prediction model has strong generalization capability, can adapt to the complex submarine environment, has good identification and classification capability, effectively identifies the fault type of the submarine cable, and provides accurate basis for formulating a fault treatment scheme.
Drawings
Fig. 1 is a schematic structural diagram of an imitated sturgeon robot.
Fig. 2 is a schematic structural view of the present invention in fig. 1 with one side of the housing removed.
Fig. 3 is a schematic view of the invention of fig. 1 with the torso shell removed in its entirety.
Fig. 4 is a schematic diagram of the structure of fig. 3 after all the float blocks are further removed.
Fig. 5 is a schematic diagram of the configuration of the pectoral fins and associated components of the present invention of fig. 1.
Fig. 6 is a partial assembled view of the present invention showing the fishtail portion, steering drive and tendons.
Fig. 7 is a schematic diagram of a portion of the invention of fig. 1 showing the torso unit.
Fig. 8 is a schematic view of a portion of the structure of fig. 7 showing the torso housing and motor support.
Fig. 9 is a flow chart of the submarine cable fault detection based on the convolutional neural network according to the present invention.
Detailed Description
The embodiments of the present invention will be described in further detail with reference to the drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
In the description of the present invention, "a plurality" means two or more unless otherwise specified; the terms "upper", "lower", "left", "right", "inner", "outer", "front", "rear", "head", "tail", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely for convenience in describing the present invention and to simplify the description, and do not indicate or imply that the referenced mechanism or element must have a particular orientation, be constructed in a particular orientation, and be operated, and therefore, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "connected" and "connected" are to be interpreted broadly, e.g., as being fixed or detachable or integrally connected; can be mechanically or electrically connected; may be directly connected or indirectly connected through an intermediate. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Embodiment 1 combines fig. 1 to 8, an imitative sturgeon robot, includes fish head part, trunk unit, fish tail part and control system, the fish head part includes sturgeon-like head shell 1, and the left and right sides symmetry of head shell 1 is equipped with two pectoral fins 11, and its top is provided with dorsal fin 12.
Specifically, a lateral shaft 14 is fixedly connected to one side of each pectoral fin 11 close to the head shell 1, the lateral shaft 14 is in rotating sealing fit with the side wall of the head shell 1, and a first servo motor 13 is connected to one end of each lateral shaft far from the pectoral fin 11.
The dorsal fins 12 are vertically arranged, the bottom of the dorsal fins is fixedly connected with a vertical shaft, the vertical shaft is in rotary sealing fit with the top wall of the head shell 1, and the lower end of the vertical shaft is connected with the output end of a second servo motor fixed in the head shell 1.
The fish tail part is located the rear side of fish head part to through a plurality of arranging in proper order the truck unit links to each other with the rear side of head shell 1, and the fish tail part includes sturgeon-like afterbody shell 2, and the rear end of afterbody shell 2 is provided with tail fin 21.
The trunk unit includes trunk shell 3 and sets up the actuating mechanism 4 that turns to inside trunk shell 3, trunk shell 3 is one section annular casing, and each trunk shell 3 includes two shell monomers 31 of mutual disposition, and the upper end of each shell monomer 31 is fixed with articulated piece 32, and two articulated piece 32 dislocation arrangements of same shell monomer 31, and articulated through the pivot.
Bolt blocks 33 are fixed on the adjacent sides of the bottoms of the two shell single bodies 31, and the two bolt blocks 33 at the bottom of the same shell single body 31 are opposite and fixedly connected through bolts.
There are gaps between any two adjacent torso shells 3 and between the first torso shell 3 and the head shell 1. Each trunk unit is connected end to end in sequence, the front end of the first trunk unit is fixedly connected with the rear end of the head shell 1, and the rear end of the last trunk unit is fixedly connected with the tail shell 2.
Specifically, the steering driving mechanism 4 includes a double-shaft servo motor 41 and a steering support 42, the double-shaft servo motor 41 is fixed inside the trunk shell 3, specifically, a motor support 5 is fixedly arranged inside each trunk shell 3, the double-shaft servo motor 41 is fixed on the motor support 5, and two output shafts thereof are vertically arranged.
Preferably, the motor bracket 5 includes two bracket units 51, the bracket units 51 include a T-shaped structure formed by fixedly connecting a cross bar and a longitudinal bar, and the two bracket units 51 are arranged in an axisymmetrical manner. The one end that two horizontal poles kept away from each other all links to each other with the inside wall of trunk shell 3 is fixed, and the other end and the fixed grafting of corresponding support monomer 51 vertical pole enclose into the closed region that can place biax servo motor 41.
The left and right sides symmetry of biax servo motor 41 is equipped with two float pieces 6 that the buoyancy material made, and two float pieces 6 all link to each other with the inside wall of trunk shell 3 is fixed.
The steering support 42 is positioned at the front side of the double-shaft servo motor 41 and is fixedly connected with the output shaft of the double-shaft servo motor, and the connecting frame 7 is fixed at the rear side of each double-shaft servo motor 41. The top and the bottom of each connecting frame 7 are provided with connecting terminals 71, and each connecting terminal 71 is connected with the rear end of the head housing 1 through a rib 72 made of an elastic material.
The front end of the steering support 42 in the first trunk unit is fixedly connected with the rear end of the head shell 1, and the front ends of the steering supports 42 in the remaining trunk units are fixedly connected with the rear ends of the connecting frames 7 in the trunk units adjacent to the front sides. The rear end of the connecting frame 7 in the last secondary trunk unit is fixedly connected with the front end of the tail shell 2. Each trunk unit and fish head part cooperate and realize imitating sturgeon robot's whole swing, provide its power that gos forward.
Control system includes controller, sonar, image acquisition module and signal emission module, and image acquisition module sets up the front end at head shell 1, and sonar, image acquisition module and signal emission module all are connected with the controller signal. The sonar adopts active sonar echo to survey submarine cable's general position, and this instrument will be sent the sound wave to the sea, and the echo passes back the time that spends on the machine fish, can be used for figuring out the shape and the position of imitative sturgeon robot submarine cable. The active sonar can accurately measure the target distance and can detect a fixed target.
The invention is designed by simulating sturgeon in nature based on the bionics principle and starting from the practical engineering application of functional bionics. The shape of the sturgeon in the nature is simulated, the head is sharp and slightly tilted, and obstacle crossing of the sturgeon-simulated robot is facilitated; the sturgeon tail is long at the upper end and short at the lower end and can move forward along the surface of the cable in a close range; sturgeon-like tendon 72 is similar to the sturgeon's keel so that the mechanical structure has a stronger connection mechanism. The position of the submarine cable is remotely sensed by adopting an active sonar, sturgeon eyes are simulated by a camera at a short distance, and the fault detection of the submarine cable is realized based on a particle swarm optimization convolutional neural network aiming at the problem of image blurring caused by uneven submarine light, turbid water quality and the like. The submarine cable detection method adopts a bionic principle, effectively overcomes the defects of the traditional method, adopts active sonar detection to simulate sturgeon whiskers, detects the position of the submarine cable and is further used for detecting the fault type of the submarine cable. Therefore, on one hand, the active sonar reduces the influence of external noise on the active sonar in principle, on the other hand, the position of the submarine cable can be determined through multiple detections, and the requirements are reduced in the aspect of realized functions.
In embodiment 2, submarine cable detection mainly depends on data collected by a camera to carry out corresponding work.
There are various classification methods of submarine cables, and the submarine cables are classified into 3 types according to different fault processing methods for subsequent processing: (1) Due to external force, the ship body anchor is hung up, the fishing net is dragged, fish bites and the like, and the faults of the type need to be connected with the line again; (2) The pipeline is suspended, the pipeline can be suspended along with the flushing of seabed underflow and the like, and the faults are solved by adding a sleeve; (3) Electric type trouble, the cable dispatches from the factory and does not standardize phenomenons such as bulge, deformation that appear, and this type trouble needs further maintenance to it, avoids causing further damage. Therefore, the invention mainly judges which type of the submarine cable belongs to the normal state and the types of the electrical faults, suspension faults and the like caused by external force through the sturgeon-imitating robot.
A submarine cable fault detection method is based on the sturgeon-imitating robot and comprises the following steps:
step one, a convolutional neural network prediction model is established, the convolutional neural network comprises an input layer, a convolutional layer, a pooling layer, a full-connection layer and an output layer, and a submarine cable fault detection convolutional neural network mathematical model with the best performance is obtained through learning and training. In the first step, the submarine cable detection prediction model is established by adopting the following steps:
s1: acquiring images, horizontally turning, randomly deducting, carrying out scale transformation and rotating, realizing submarine cable data image expansion, and establishing a data set containing normal submarine cables and suspended and electrical faults caused by external force.
The data set of the convolutional neural network is randomly and averagely divided into a training subset, a verification subset and a test subset, the training set is used for learning model parameters of the convolutional neural network, the verification subset selects hyper-parameters through evaluating network performance, and the test subset measures the network performance through generalization errors.
S2: and training the hyper-parameters of the convolutional neural network, namely the hyper-parameters of the number of convolutional layers, the size of convolutional cores and the number of convolutional cores by using a particle swarm optimization algorithm, and optimizing.
S3: training the convolutional neural network by using the optimized number of convolutional layers, the size of convolutional cores and the number of convolutional layers, and leading data into an input layer, a pooling layer and a full connection layer, wherein the convolutional neural network can extract the training fault characteristics.
S4: and calculating the output result loss function, updating the weight value by a gradient descent method, and realizing multiple iterations of the convolutional neural network so that the training model is converged.
S5: and after multiple iterations, judging the classification performance of the convolutional neural network based on particle swarm optimization by adopting a test set.
S6: and adjusting parameters of the node number, the iteration times and the loss function value in the convolutional neural network according to the performance evaluation result.
S7: and repeating the steps to finally obtain the submarine cable fault detection convolutional neural network mathematical model with the optimal performance.
Specifically, the image acquisition module comprises a camera provided with a light source, in the first step, the input layer is a submarine cable image, the input layer expands the submarine cable data image, and the image is further enhanced.
The convolution layer is formed by convolution of the feature vectors and convolution kernels, and submarine image feature extraction is achieved through activating function response.
The convolutional layer neuron mathematical expression is:
wherein, the first and the second end of the pipe are connected with each other,
convolutional neural network convolutional layer->
In a first or second section>
The output of each channel; />
Convolutional neural network convolutional layer>
In a first or second section>
The output of each channel; />
For calculating the ^ h>
A subset of input profiles for which there are net activations of channels; />
Is a convolution operation symbol; />
Is a convolution layer>
Input vector->
And neuron->
A connected weight matrix; />
Is a convolutional layer>
Is/are>
Deviation values for individual characteristic maps>
Is an activation function.
The chemical expression of the pond layers is as follows:
wherein, the first and the second end of the pipe are connected with each other,
pooling layer for convolutional neural network>
Is/are>
The weight coefficient of each channel; />
Is a pooling function; />
For pooled layers>
Is/are>
The offset terms of the individual channels.
The mathematical expression of the full connection layer is as follows:
wherein the content of the first and second substances,
is a fully connected layer->
An output of (d); />
Is a full connection layer/>
The network weight coefficient of (a); />
Is a full connection layer
An output of (d); />
Is a fully connected layer->
The bias term of (c).
The output layer gives out a specific classification result and judges the normal state of the submarine cable and specific types of suspended and electric faults caused by external force.
And step two, placing the sturgeon-imitating robot in water and submerging the sturgeon-imitating robot to a deep water area, sending a signal to a controller by a sonar in the swimming process to detect the position of the submarine cable, and after the position of the submarine cable is determined, sending the signal to the controller by the sonar, so that the sturgeon-imitating robot reaches the position of the submarine cable.
And thirdly, the sturgeon-imitating robot swims along the extending direction of the submarine cable, the image acquisition module acquires image information of the submarine cable in real time and sends the image information to the controller, the state and the fault type of the submarine cable are judged through the convolutional neural network, and data information is stored.
And step four, after the sturgeon-imitating robot floats upwards out of the water surface, the data information is sent to a receiving terminal on a ship or on the ground through a signal transmitting module, and the position sent by the signal is positioned through a base station so as to determine the position coordinate of the fault point. The fault position of the submarine cable is just below the floating water level position of the sturgeon-imitating robot, the type and the state of the fault can be known through the received data information, and a maintenance scheme is further formulated.
And step five, repeating the process of the step four, sequentially determining each fault point of the submarine cable and the fault type of each fault point, and making a fault processing scheme according to the data information by the staff.
Compared with the traditional optical-based visual detection, the deep learning algorithm based on the convolutional neural network has good model generalization capability, can adapt to the complex submarine environment, has good identification and classification capabilities, and can effectively identify various faults of the submarine cable.
Parts which are not described in the invention can be realized by adopting or referring to the prior art.
The embodiments of the present invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiment was chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make various changes, modifications, additions and substitutions within the spirit and scope of the present invention.
Claims (10)
1. A sturgeon-imitating robot comprises a fish head part, a trunk unit, a fish tail part and a control system, and is characterized in that the fish head part comprises a sturgeon-like head shell, two pectoral fins are symmetrically arranged on the left side and the right side of the head shell, and dorsal fins are arranged at the top of the head shell;
the tail part is positioned at the rear side of the fish head part and is connected with the rear side of the head shell through a plurality of trunk units which are sequentially arranged, the tail part comprises a sturgeon-like tail shell, and a tail fin is arranged at the rear end of the tail shell;
the trunk unit comprises a trunk shell and a steering driving mechanism arranged in the trunk shell, the trunk units are sequentially connected end to end, the front end of the first secondary trunk unit is fixedly connected with the rear end of the head shell, and the rear end of the last secondary trunk unit is fixedly connected with the tail shell;
each trunk unit is matched with the fish head part to realize the integral swing of the sturgeon-imitating robot and provide forward power for the sturgeon-imitating robot;
control system includes controller, sonar, image acquisition module and signal emission module, and image acquisition module sets up the front end at the head shell, and sonar, image acquisition module and signal emission module all are connected with controller signal.
2. The sturgeon-imitating robot according to claim 1, wherein the trunk shells are a section of annular shell, and gaps are reserved between any two adjacent trunk shells and between the first trunk shell and the head shell;
the steering driving mechanism comprises a double-shaft servo motor and a steering support, the double-shaft servo motor is fixed in the trunk shell, the steering support is located on the front side of the double-shaft servo motor and fixedly connected with an output shaft of the double-shaft servo motor, and a connecting frame is fixed on the rear side of each double-shaft servo motor.
3. The sturgeon-imitating robot according to claim 2, wherein the front end of the turning support in the first trunk unit is fixedly connected with the rear end of the head shell, and the front ends of the turning supports in the remaining trunk units are fixedly connected with the rear ends of the connecting frames in the trunk units adjacent to the front side;
the rear end of the connecting frame in the last trunk unit is fixedly connected with the front end of the tail shell.
4. The sturgeon-imitating robot according to claim 2, wherein a motor bracket is fixedly arranged on the inner side of each trunk shell, a double-shaft servo motor is fixed on the motor bracket, and two output shafts of the double-shaft servo motor are vertically arranged;
the left side and the right side of the double-shaft servo motor are symmetrically provided with two float blocks made of buoyancy materials, and the two float blocks are fixedly connected with the inner side wall of the trunk shell.
5. The sturgeon-imitating robot according to claim 4, wherein each trunk shell comprises two shell single bodies which are oppositely arranged, a hinge block is fixed at the upper end of each shell single body, and the two hinge blocks of the same shell single body are arranged in a staggered manner and hinged through a rotating shaft;
bolt blocks are fixed on the adjacent sides of the bottoms of the two shell monomers, and the two bolt blocks at the bottom of the same shell monomer are opposite and fixedly connected through bolts.
6. The sturgeon-imitating robot according to claim 4, wherein the motor support comprises two support single bodies, each support single body comprises a T-shaped structure formed by fixedly connecting a cross rod and a longitudinal rod, and the two support single bodies are arranged in an axisymmetric manner;
the one end that two horizontal poles kept away from each other all links to each other with the inside wall of truck shell is fixed, and the other end and the fixed grafting of corresponding support monomer vertical pole enclose into the closed region that can place biax servo motor.
7. The sturgeon-imitating robot according to claim 3, wherein the top and the bottom of each connecting rack are provided with connecting terminals, and each connecting terminal is connected with the rear end of the head shell through a muscle wire made of elastic material;
one side of each pectoral fin, which is close to the head shell, is fixedly connected with a cross shaft, the cross shafts are in rotating sealing fit with the side wall of the head shell, and one end of each cross shaft, which is far away from the pectoral fins, is connected with a first servo motor;
the dorsal fins are vertically arranged, the bottom of the dorsal fins is fixedly connected with a vertical shaft, the vertical shaft is matched with the top wall of the head shell in a rotating and sealing mode, and the lower end of the vertical shaft is connected with the output end of a second servo motor fixed in the head shell.
8. The submarine cable fault detection method using the sturgeon-imitating robot according to any one of claims 1 to 7, comprising the steps of:
step one, establishing a convolutional neural network prediction model, wherein the convolutional neural network comprises an input layer, a convolutional layer, a pooling layer, a full-connection layer and an output layer, and learning and training to obtain a submarine cable fault detection convolutional neural network mathematical model with optimal performance;
placing the sturgeon-imitating robot in water and submerging the sturgeon-imitating robot to a deep water area, sending a signal to a controller by a sonar in the swimming process to detect the position of the submarine cable, and after the position of the submarine cable is determined, sending the signal to the controller by the sonar, and enabling the sturgeon-imitating robot to reach the position of the submarine cable;
thirdly, the sturgeon-imitating robot moves along the extending direction of the submarine cable, the image acquisition module acquires image information of the submarine cable in real time and sends the image information to the controller, the state and the fault type of the submarine cable are judged through the convolutional neural network, and data information is stored;
after the sturgeon-imitating robot floats upwards out of the water surface, data information is sent to a receiving terminal on a ship or the ground through a signal transmitting module, and the position sent by a signal is positioned through a base station so as to determine the position coordinate of a fault point;
and step five, repeating the process of the step four, sequentially determining each fault point and fault type of the submarine cable, and working personnel formulating a fault processing scheme according to the data information.
9. The submarine cable fault detection method according to claim 8, wherein the building of a submarine cable detection prediction model comprises the following steps:
s1: acquiring an image, carrying out horizontal overturning, random deduction, scale transformation and rotation, realizing submarine cable data image expansion, and establishing a data set containing normal submarine cables and suspended and electric faults caused by external force;
the data set of the convolutional neural network is randomly and averagely divided into a training subset, a verification subset and a test subset, wherein the training set is used for learning model parameters of the convolutional neural network, the verification subset selects a hyper-parameter by evaluating network performance, and the test subset measures the network performance by generalization errors;
s2: training hyper-parameters of the convolutional neural network, namely hyper-parameters of the number of convolutional layers, the size of convolutional kernels and the number of convolutional kernels by using a particle swarm optimization algorithm, and optimizing;
s3: training the convolutional neural network by using the optimized number of convolutional layers, the size of convolutional cores and the number of convolutions, and leading data into an input layer, a pooling layer and a full-connection layer, wherein the convolutional neural network can extract the training fault characteristics;
s4: calculating an output result loss function, updating the weight value through a gradient descent method, and realizing multiple iterations of the convolutional neural network so that the training model is converged;
s5: after multiple iterations, judging the classification performance of the convolutional neural network based on particle swarm optimization by adopting a test set;
s6: adjusting parameters of the number of nodes, the iteration times and the loss function value in the convolutional neural network according to the performance evaluation result;
s7: and repeating the steps to finally obtain the submarine cable fault detection convolutional neural network mathematical model with the optimal performance.
10. The submarine cable fault detection method according to claim 9, wherein the image acquisition module comprises a camera equipped with a light source, and in step one, the input layer is a submarine cable image, and the input layer expands the submarine cable data image and further performs an enhancement operation on the image;
the convolution layer is formed by convolution of the feature vector and a convolution kernel and realizes the feature extraction of the submarine image through activating function response;
the convolutional layer neuron mathematical expression is:
wherein, the first and the second end of the pipe are connected with each other,
convolutional neural network convolutional layer>
In a first or second section>
The output of each channel; />
For convolutional neural network convolutional layer
In a first or second section>
The output of each channel; />
For calculating a ^ th->
A subset of input profiles for which there are net activations of channels; />
Is a convolution operation symbol; />
Is a convolution layer>
Input vector->
And neurons>
A weight matrix of the connection; />
Is a convolution layer>
A fifth or fifth letter>
Deviation values for individual characteristic maps>
Is an activation function;
the chemical expression of the pond layers is as follows:
wherein, the first and the second end of the pipe are connected with each other,
pooling layers for convolutional neural networks>
A fifth or fifth letter>
The weight coefficient of each channel; />
Is a pooling function; />
For a pooling layer
A fifth or fifth letter>
An offset term for each channel;
the mathematical expression of the full connection layer is as follows:
wherein the content of the first and second substances,
is a fully connected layer>
An output of (d); />
Is a fully connected layer>
The network weight coefficient of (a); />
Is a fully connected layer->
An output of (d); />
Is a fully connected layer>
The bias term of (a);
and the output layer gives a specific classification result and judges the normal state of the submarine cable and specific categories of suspended faults and electrical faults caused by external force.
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Application publication date: 20230331 Assignee: Qingdao Zhongshi Chuanzhi Technology Co.,Ltd. Assignor: SHANDONG University OF SCIENCE AND TECHNOLOGY Contract record no.: X2024980005552 Denomination of invention: A Sturgeon like Robot and Submarine Cable Fault Detection Method Granted publication date: 20230516 License type: Common License Record date: 20240510 |