Long-distance cable attitude detection method for underwater robot

Abstract

The invention relates to the technical field of cable attitude detection, in particular to a long-distance cable attitude detection method for an underwater robot. Acceleration time sequence data of all monitoring positions on the long-distance cable of the underwater robot are obtained, and the posture change of the cable is analyzed. And determining the position posture change degree of each monitoring position by considering the acceleration difference and position distribution of different monitoring positions on the cable. When a specific task is executed, the posture changes of part of the monitoring positions on the cable are consistent, and the monitoring positions are divided based on the posture change degree. The more consistent the posture changes of the monitoring positions close to the underwater robot are, the greater the influence of the motion effect is, and the higher the abnormal probability is, so that the influence degree value of each monitoring position is calculated. The torsion change of the cable is a process of gradually accumulating stress, so that the influence degree value of each monitoring position at all moments is comprehensively analyzed, the attitude torsion degree value is obtained, and the attitude abnormal position is finally determined.

Description

Technical Field

[0001] The present invention relates to the technical field of cable attitude detection, and particularly relates to a long-distance cable attitude detection method for an underwater robot. Background Art

[0002] Underwater robots play a crucial role in deep-sea exploration, underwater operations, marine scientific research and other fields. They are connected to a ground control station through long-distance cables to achieve functions such as data transmission, energy supply, and command control. However, in a complex and changeable underwater environment, especially under adverse conditions such as strong deep-sea currents and undulating seabed topography, long-distance cables are extremely prone to problems such as attitude distortion, entanglement, and even breakage. This not only affects the normal operation of the underwater robot, but may also cause damage to it and even lead to mission failure. Therefore, it is usually necessary to detect the attitude of the long-distance cable of the underwater robot.

[0003] The attitude of the long-distance cable underwater is usually affected by the movement of the underwater robot and at the same time by buoyancy. Under the combined action of multiple forces, different degrees of distortion or entanglement will occur to the cables at different positions on the long-distance cable, resulting in changes in the movement speed. When the prior art detects the attitude of the long-distance cable, it usually screens out the positions where the movement speed on the long-distance cable is abnormal as the positions with attitude distortion. However, when a position on the cable moves under the action of an external force, the resulting stress effect will affect the surrounding monitoring positions. Therefore, simply analyzing the movement speed of each position on the long-distance cable individually will lead to a decrease in the screening accuracy of the final attitude abnormal positions. Summary of the Invention

[0004] In order to solve the technical problem that when a position on the cable moves under the action of an external force, the resulting stress effect will affect the surrounding monitoring positions, and simply analyzing the movement speed of each position on the long-distance cable individually will lead to a decrease in the screening accuracy of the final attitude abnormal positions, the purpose of the present invention is to provide a long-distance cable attitude detection method for an underwater robot, and the specific technical solution adopted is as follows: Obtain the acceleration time series data at all monitoring positions on the long-distance cable of the underwater robot; At each moment, determine the position attitude change degree at each monitoring position according to the difference situation between the acceleration values at different monitoring positions on the long-distance cable and the distribution of the monitoring positions; At each moment, all monitoring positions are divided based on the position and attitude change degrees at all monitoring positions on the long-distance cable to obtain a division set; based on the position distribution of the monitoring positions in the division set and their position distribution on the long-distance cable, the correlation relationship of the position and attitude change degrees at the monitoring positions is analyzed, so as to determine the influence degree value at each monitoring position. For any monitoring position, analyze the change of the influence degree value at this monitoring position at different moments to obtain the attitude distortion degree value of this monitoring position; based on the attitude torsion degree value, judge the attitude torsion conditions of all monitoring positions on the long-distance cable to determine the attitude abnormal positions.

[0005] Further, the method for obtaining the position and attitude change degree includes: On the long-distance cable, all monitoring positions are sorted in ascending order of the distance from the underwater robot to obtain a position sorting sequence; At each moment, for any monitoring position, the value obtained by normalizing the absolute value of the difference between the acceleration value of this monitoring position and the acceleration value of the next adjacent monitoring position in the position sorting sequence is used as the motion change index of this monitoring position; The ratio of the motion change index of this monitoring position to the serial number value of this monitoring position in the position sorting sequence is used as the position and attitude change degree of this monitoring position at each moment.

[0006] Further, the method for obtaining the division set includes: At each moment, in the position sorting sequence, the first monitoring position is used as the position to be measured and traversed, and the position to be measured corresponds to a position set. Judge whether the similarity degree value of the position and attitude change degree between the position to be measured and the next adjacent monitoring position meets the preset condition; If it meets the condition, the next adjacent monitoring position is incorporated into the position set corresponding to the position to be measured, and continue to judge whether the similarity degree value of the position and attitude change degree between the last monitoring position in the position set and the next adjacent monitoring position meets the preset condition. If it does not meet the condition, the next adjacent monitoring position is used as the new position to be measured for traversal; After the traversal is completed, all position sets are obtained as the division set; Wherein, the preset condition is that the similarity degree value is greater than or equal to the preset similarity threshold.

[0007] Further, the method for obtaining the similarity degree value includes: The value obtained by performing negative correlation mapping and normalization on the absolute value of the difference between the position and attitude change degrees between two monitoring positions is used as the similarity degree value between the two monitoring positions.

[0008] Further, the method for obtaining the degree of influence value includes: At each moment, for any monitoring position on the long-distance cable, calculate the average value of the position attitude change degrees of all monitoring positions in the partition set to which the monitoring position belongs as the first average value; According to the position distribution of the monitoring position on the long-distance cable, determine the comparison set corresponding to the monitoring position, and take the average value of the position attitude change degrees of all monitoring positions in the comparison set as the second average value; Take the value obtained by performing negative correlation mapping and normalization on the absolute value of the difference between the first average value and the second average value corresponding to the monitoring position as the degree of influence value at the monitoring position.

[0009] Further, the method for obtaining the comparison set includes: At each moment, for any monitoring position on the long-distance cable, form the comparison set of the monitoring position by including the monitoring position and all monitoring positions before it in the position sorting sequence.

[0010] Further, the method for obtaining the attitude distortion degree value includes: For any monitoring position, in terms of time sequence, calculate the absolute value of the difference between the degree of influence value of the monitoring position at each moment and the degree of influence value at the adjacent next moment as the change factor; Take the value obtained by normalizing the average value of all change factors corresponding to the monitoring position as the attitude distortion degree value of the monitoring position.

[0011] Further, the method for determining the attitude abnormal position includes: On the long-distance cable, take the monitoring positions greater than the preset abnormal threshold as the attitude abnormal positions.

[0012] Further, the value range of the abnormal threshold is [0.6, 1).

[0013] Further, the monitoring positions are evenly distributed on the long-distance cable at a fixed interval.

[0014] The present invention has the following beneficial effects: The underwater environment is complex and changeable, and the cable may change its attitude due to various factors. The acceleration time-series data can directly reflect the motion state of the cable at different times, including the change of speed, magnitude, etc., which is the basis for analyzing the attitude change of the cable. Therefore, first, the acceleration time-series data at all monitoring positions on the long-distance cable of the underwater robot are obtained as attitude data. The state change that occurs during the operation of the underwater robot will drive the movement of the cable, and the influence will gradually attenuate as the distance between the position on the cable and the underwater robot increases. In the present invention, considering the difference in acceleration at different monitoring positions on the long-distance cable at each moment and the distribution of the monitoring positions, the position attitude change degree at each monitoring position is determined to quantify the cable attitude change at each monitoring position. When the underwater robot performs a specific task, there is often a situation where the attitude changes at some monitoring positions on the long-distance cable are relatively consistent. Therefore, at each moment, the monitoring positions can be divided based on the attitude change degree at the monitoring positions to obtain a division set. At this time, the monitoring positions in each division set have relatively consistent attitude changes. When the underwater robot moves, it will drive the attitude changes of different monitoring points on the long-distance cable. Therefore, if the attitude change at a certain position is more consistent with the attitude change at a position closer to the underwater robot, it can be explained that the cable at this position is more affected by the movement of the underwater robot, and then the probability of occurrence of an abnormality is higher. Therefore, based on the foregoing logic, the influence degree value at each monitoring position is calculated. Further, since the torsional change of the cable is a process of gradual accumulation of stress in the long-distance cable, that is, as time goes by, the torsional stress of the cable may continue to accumulate. Therefore, for any monitoring position, the influence degree values of this monitoring position at all times are comprehensively analyzed to obtain the attitude distortion degree value of this monitoring position, and finally, the attitude abnormal position is determined among all the monitoring positions based on the attitude distortion degree value. In summary, the present invention comprehensively analyzes the attitude change situation at different positions on the long-distance cable and the gradual accumulation process of the torsional change, and more accurately judges the attitude distortion situation of each monitoring position, thereby improving the screening accuracy of the attitude abnormal position. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0016] Figure 1 It is a flowchart of a method for monitoring the attitude of a long-distance cable for an underwater robot provided by an embodiment of the present invention; Figure 2 The flowchart of a method for obtaining the hand influence degree value provided by an embodiment of the present invention; Figure 3 The system block diagram of a long-distance cable attitude detection system for an underwater robot provided by an embodiment of the present invention; Figure 4 The system structure schematic diagram of a long-distance cable attitude detection system for an underwater robot provided by an embodiment of the present invention. Detailed implementation manners

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and effects of a long-distance cable attitude detection method for an underwater robot proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following specifically describes the specific solution of a long-distance cable attitude detection method for an underwater robot provided by the present invention with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows the flowchart of a long-distance cable attitude detection method for an underwater robot provided by an embodiment of the present invention. The method includes the following steps: Step S1: Obtain the acceleration time series data at all monitoring positions on the long-distance cable of the underwater robot.

[0021] Cable power supply is a common power supply method for underwater robots (especially remotely operated vehicles, ROVs). Through the power source on the water surface, continuous power support is provided via a cable, enabling the robot to operate for a long time without returning to the water surface for charging. This cable is usually a composite cable, containing power lines and optical fibers, which can transmit electrical energy and high-speed data simultaneously, facilitating real-time data transmission and control instruction issuance, and is suitable for high-power tasks such as underwater construction, maintenance and inspection that require continuous power supply. The attitude detection of long-distance cables is very important, which can help monitor and manage the shape and position of the cable, prevent entanglement, excessive bending or damage, thus ensuring the smooth progress of the task and the reliable operation of the robot.

[0022] For the attitude analysis of a cable, generally, simulation objects such as the cable are discretized by mass points, that is, the cable is represented as a chain structure connected by a series of discrete mass points (or nodes). These mass points are distributed along the length of the cable. When a mass point moves under the action of an external force, the generated stress acts on other adjacent mass points, thereby transmitting the force to the surroundings and driving the adjacent mass points to move. Therefore, the deformation of the cable is caused by the movement of the mass points.

[0023] In the embodiment of the present invention, the mass points on the long-distance cable are distributed evenly at a fixed interval. The fixed interval is set to 10, and the mass points evenly distributed at the fixed interval on the long-distance cable are used as monitoring positions. An inertial measurement unit (IMU) is installed at each monitoring position to capture key motion patterns. During installation, ensure that the sensor direction is correct and set up a stable data transmission path by wired or wireless means.

[0024] For all monitoring positions on the long-distance cable, data acquisition is performed simultaneously to obtain the acceleration values at all monitoring positions at the same moment (the IMU can obtain acceleration values in three directions. In the embodiment of the present invention, mainly considering the intensity of the motion, the acceleration values in three directions at each monitoring position are synthesized to obtain a total acceleration value as the acceleration value at each monitoring position). The data acquisition time interval is set to 1 s, and the length of the acquisition time period is set to 1 minute, so as to obtain the acceleration time series data at all monitoring positions on the long-distance cable.

[0025] It should be noted that the interval of the distribution of the monitoring positions on the long-distance cable can be adjusted according to the actual length of the long-distance cable, and the data acquisition time interval and the length of the time period can also be adjusted according to the implementation scenario, which are not limited here.

[0026] Step S2: At each moment, according to the difference between the acceleration values at different monitoring positions on the long-distance cable and the distribution of the monitoring positions, determine the position attitude change degree at each monitoring position.

[0027] When the underwater robot is operating, the changed state will drive the cable to move. The motion resistance of the cable tends to maintain the stability of its attitude, and the dynamic stress caused by the movement of the underwater robot has a greater impact on the part of the long-distance cable close to the robot end. Therefore, the cable may be affected by multiple external forces in the complex underwater environment, resulting in a change in its attitude. Therefore, by monitoring the acceleration values at different positions on the cable and considering the differences between these acceleration values and the distribution of the monitoring positions, the position attitude change degree at each monitoring position at each moment can be determined, and the change situation of the attitude can be quantified.

[0028] Preferably, in an embodiment of the present invention, the method for obtaining the position and attitude change degree includes: For the side of the long-distance cable closer to the underwater robot, the attitude of the cable is more affected by the movement of the underwater robot during operation. Therefore, on the long-distance cable, all monitoring positions are first sorted in ascending order of their distances from the underwater robot to obtain a position sorting sequence, which establishes the relative position relationship between the monitoring positions and the underwater robot and is helpful for subsequent analysis processes.

[0029] At each moment, for any monitoring position, evaluate the acceleration change difference between this monitoring position and its adjacent monitoring position to reflect the motion state of the cable at this monitoring position: in the position sorting sequence, calculate the absolute value of the difference between the acceleration value of this monitoring position and the acceleration value of the next adjacent monitoring position. The larger the absolute value of the difference, the more prominent the influence of the force on this monitoring position. The value obtained by normalizing the absolute value of the difference is used as the motion change index of this monitoring position. Normalization is a well-known technical means to those skilled in the art. The choice of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0030] The closer the distance to the robot, the greater the influence of the robot on the attitude of the cable. Therefore, the ratio of the motion change index of this monitoring position to the serial number value of this monitoring position in the position sorting sequence is used as the position and attitude change degree of this monitoring position at each moment. The smaller the serial number value, the closer the distance between this monitoring position and the robot, and the greater the influence. Therefore, the greater the position and attitude change degree obtained by combining the serial number value and the motion change index, the greater the degree of position and attitude change of the monitoring position.

[0031] It should be noted that in this embodiment of the present invention, the serial number of the position sorting sequence starts from 1. In other embodiments, other positive numbers can also be used as the starting point; the motion change index at the last monitoring position is set to be the same as the motion change index at the previous adjacent monitoring position.

[0032] Step S3: At each moment, divide all the monitoring positions based on the position and attitude change degrees at all the monitoring positions on the long-distance cable to obtain a division set; based on the position distribution of the monitoring positions in the division set and the position distribution on the long-distance cable, analyze the correlation of the position and attitude change degrees at the monitoring positions to determine the influence degree value at each monitoring position.

[0033] Since the attitude change conditions on long-distance cables tend to be distributed in stages, and this stage distribution is affected by the movement conditions of the robot and the actual operation range. That is, when an underwater robot performs specific tasks such as inspecting pipelines, performing repairs, or collecting samples, often only the conditions at some monitoring positions show higher consistency. That is, this part of the cable will move within a certain spatial range and the monitoring data at each monitoring position will tend to be consistent. Therefore, by dividing the monitoring positions, a division set of different attitude change degrees can be obtained, which helps to more carefully understand the attitude change conditions at different positions on the cable.

[0034] When the underwater robot moves, it will drive the attitude changes of different monitoring points on the long-distance cable, such as twisting or winding. Therefore, if the attitude change at a certain position is more consistent with the attitude change at the position closer to the underwater robot, it can be explained that the cable at this position is more affected by the movement of the underwater robot due to its action, and then the probability of abnormality is higher. Therefore, the correlation between the attitude changes of different monitoring positions affected by the robot's operation is closely related to the position of the monitoring position and the underwater robot. Therefore, based on the above division set, the correlation of the position attitude change degree at the monitoring position can be analyzed based on the position distribution of the monitoring position in the division set and the position distribution on the long-distance cable, so as to determine the degree of influence value at each monitoring position.

[0035] Preferably, in an embodiment of the present invention, the method for obtaining the division set includes: At each moment, in the position sorting sequence, start traversing with the first monitoring position as the position to be measured. And the position to be measured corresponds to a position set. Judge whether the similarity degree value of the position attitude change degree between the position to be measured and the next adjacent monitoring position meets the preset conditions. The calculation method of the similarity degree value includes: calculating the absolute value of the difference in the position attitude change degree between two monitoring positions. The smaller the absolute value of the difference, the more similar the position attitude change situation between the adjacent two monitoring positions. Then perform a negative correlation mapping and normalization processing on the absolute value of the difference to realize logical relationship correction, so as to obtain the similarity degree value between the two monitoring positions. At this time, the larger the similarity degree value, the higher the similarity of the position attitude change situation between the two monitoring positions, and they should be divided into one division set. On the contrary, if the similarity degree value is smaller, it indicates that the similarity of the position attitude change situation between the two monitoring positions is lower and they should not be divided into one division set. Normalization is a well-known technical means in the art. The choice of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0036] Therefore, if the preset conditions are met, the next adjacent monitoring position will be incorporated into the position set corresponding to the position to be measured, and the similarity value of the position posture change between the last monitoring position in the position set and the next adjacent monitoring position will be judged whether it meets the preset conditions. If the preset conditions are not met, the next adjacent monitoring position will be taken as the new position to be measured and traversed.

[0037] After the traversal is completed, all position sets can be obtained, and each position set is used as a partition set.

[0038] The preset condition is that the similarity value is greater than or equal to a preset similarity threshold. It should be noted that the preset similarity threshold is set to 0.6, and the specific value can be adjusted according to the implementation scenario and is not limited here.

[0039] The acquisition process of the above partitioning set is illustrated with an example: for example, the total number of monitoring locations is 5. In the location sorting sequence, they are recorded in order as monitoring location 1-monitoring location 5. First, monitoring location 1 is taken as the location to be measured, and monitoring location 1 corresponds to a location set. Initially, the location set only contains monitoring location 1. The similarity value between monitoring location 1 and monitoring location 2 is calculated. If the similarity value is greater than or equal to the preset similarity threshold, monitoring location 2 is incorporated into the location set corresponding to monitoring location 1. Then, the similarity value between monitoring location 2 and monitoring location 3 is calculated. If the similarity value is less than the preset similarity threshold, monitoring location 3 is taken as a new location to be measured and corresponds to a location set (which only contains monitoring location 3 at this time), and the first partitioning set a (consisting of monitoring location 1 and monitoring location 2) is obtained. Next, the similarity value between monitoring position 3 and monitoring position 4 is calculated. If the similarity value is greater than or equal to the preset similarity threshold, monitoring position 4 is incorporated into the position set corresponding to monitoring position 3. Then, the similarity value between monitoring position 4 and monitoring position 5 is calculated. If the similarity value is still greater than or equal to the preset similarity threshold, monitoring position 5 is also incorporated into the position set corresponding to monitoring position 3. Thus, the second partition set b (including monitoring position 3, monitoring position 4 and monitoring position 5) is obtained. At this point, the traversal is completed and all the partition sets are obtained.

[0040] Based on the foregoing steps, all monitoring positions can be divided into different division sets, and the monitoring positions in each division set have relatively consistent attitude change conditions. When the underwater robot moves, it will drive the attitude changes of different monitoring points on the long-distance cable, such as twisting or winding. Moreover, the closer to the robot, the higher the possibility of attitude change. Therefore, if the attitude change of a certain position is more consistent with the attitude change of the position close to the underwater robot, it can indicate that the cable at this position is more affected by the movement of the underwater robot, and then the probability of abnormality is higher. Therefore, after obtaining the division set, based on the position distribution of the monitoring positions in the division set and the position distribution on the long-distance cable, analyze the correlation of the position attitude change degrees at the monitoring positions, so as to determine the influence degree value at each monitoring position.

[0041] Preferably, in an embodiment of the present invention, the method for obtaining the influence degree value includes: Please refer to Figure 2 , which shows the flowchart of the method for obtaining the influence degree value in an embodiment of the present invention. The method includes the following steps: Step S301: At each moment, in the division set to which any monitoring position on the long-distance cable belongs, determine the local average change condition of the attitude.

[0042] At each moment, for any monitoring position on the long-distance cable, calculate the mean value of the position attitude change degrees of all monitoring positions in the division set to which the monitoring position belongs as the first mean value. The first mean value reflects the average level of the position attitude changes of all monitoring positions in the division set.

[0043] Step S302: Based on the position distribution of any monitoring position on the long-distance cable, determine the corresponding comparison set.

[0044] In view of the need to analyze the change situation of the position attitude change value based on the distribution of the monitoring positions on the cable, so based on the position distribution of the monitoring position on the long-distance cable, determine the comparison set corresponding to the monitoring position: At each moment, for any monitoring position on the long-distance cable, form the comparison set of the monitoring position by including the monitoring position and all monitoring positions before the monitoring position in the position sorting sequence. The monitoring positions in the comparison set can be regarded as the monitoring positions relatively close to the underwater robot.

[0045] Step S303: At each moment, in the comparison set corresponding to any monitoring position on the long-distance cable, determine the overall average change condition of the attitude.

[0046] Similarly, calculate the mean value of the position and attitude change degrees of all monitoring positions in the comparison set as the second mean value, which reflects the average level of the attitude changes of all monitoring positions in the comparison set.

[0047] Step S304: At each moment, based on the correlation between the local average change condition and the overall average change condition of the attitude corresponding to any one monitoring position, determine the influence degree value at this monitoring position.

[0048] By comparing the first mean value with the second mean value, the correlation between the position and attitude changes in the division set to which the monitoring position belongs and the position and attitude changes in the corresponding comparison set can be evaluated, which is used to characterize the influence of the underwater robot on this monitoring position: calculate the absolute value of the difference between the first mean value and the second mean value corresponding to this monitoring position. The larger the absolute value of this difference, the lower the similarity between the position and attitude change conditions in the division set to which the monitoring position belongs and the position and attitude change conditions in the corresponding comparison set, and it is regarded as the weaker the influence of the underwater robot on it. On the contrary, the smaller the absolute value of this difference, the higher the similarity between the position and attitude change conditions in the division set to which the monitoring position belongs and the position and attitude change conditions in the corresponding comparison set, and it is regarded as the greater the influence of the underwater robot on it. Therefore, the absolute value of this difference can be subjected to negative correlation mapping and normalization processing to realize logical relationship correction, and the influence degree value at this monitoring position is obtained. At this time, the larger the influence degree value, the higher the influence degree of the underwater robot on this monitoring position. It should be noted that the negative correlation mapping and normalization processing here can adopt function, where represents the exponential function with the natural constant e as the base, and x represents the independent variable.

[0049] Step S4: For any one monitoring position, analyze the change situation of the influence degree value at this monitoring position at different moments to obtain the attitude distortion degree value of this monitoring position; based on the attitude torsion degree value, judge the attitude torsion situation of all monitoring positions on the long-distance cable to determine the attitude abnormal position.

[0050] When the underwater robot moves or changes direction, the cable rotates along its axis, resulting in an increase in the internal torsional stress. As the torsional angle increases, the outer layer material of the cable will bear greater shear stress and its posture will be distorted. The torsional change is a process of gradual accumulation in the long-distance cable, that is, during the operation of the underwater robot, the torsional stress of the long-distance cable keeps stacking up, and the posture of the cable will also show an obvious torsional tendency. Therefore, the change of the affected degree value at each monitoring position over time can be analyzed to obtain the posture distortion degree value of each monitoring position. Finally, based on the posture distortion degree value, the posture torsion conditions of all monitoring positions on the long-distance cable are judged to determine the posture abnormal positions. That is, the mutation status of the posture change at each position during the acquisition stage.

[0051] Preferably, in an embodiment of the present invention, the method for obtaining the posture distortion degree value includes: For any monitoring position, in terms of time sequence, calculate the absolute value of the difference between the affected degree value at each moment at this monitoring position and the affected degree value at the adjacent next moment as the change factor. The change factor can capture the dynamic change of the affected degree value at this monitoring position at different moments. The larger the change factor, the more significant the change in the posture at this monitoring position, and the higher the possibility of being regarded as distorted.

[0052] Finally, the normalized value of the mean of all change factors corresponding to this monitoring position is used as the posture distortion degree value of this monitoring position. The larger the posture distortion degree value, the higher the possibility that the cable at this monitoring position has a posture distortion. Among them, normalization is a technical means well-known to those skilled in the art. The choice of the normalization function can be linear normalization or standard normalization, etc. The specific normalization method is not limited here.

[0053] Based on the foregoing method, the posture distortion degree values of all monitoring positions on the long-distance cable of the underwater robot can be obtained. Subsequently, the posture abnormal positions can be screened out based on the posture distortion degree values.

[0054] Preferably, in an embodiment of the present invention, the method for obtaining the posture abnormal position includes: Because the larger the posture distortion degree value, the more likely that the cable at the corresponding monitoring position has undergone abnormal conditions such as distortion or entanglement. Therefore, on the long-distance cable, the monitoring positions greater than the preset abnormal threshold are used as the posture abnormal positions. Among them, the value of the abnormal threshold is set to [0.6, 1). In this embodiment of the present invention, it is set to 0.7. The specific value can be adjusted according to the implementation scenario and is not limited here.

[0055] In summary, the underwater environment is complex and variable, and the cable may change its attitude under the influence of various factors. The acceleration time-series data can directly reflect the motion state of the cable at different times, including the change and magnitude of speed, etc., which is the basis for analyzing the attitude change of the cable. Therefore, first, the acceleration time-series data at all monitoring positions on the long-distance cable of the underwater robot are obtained as attitude data. The state change that occurs during the operation of the underwater robot will drive the movement of the cable, and the influence will gradually attenuate as the distance between the position on the cable and the underwater robot increases. In the embodiment of the present invention, considering the difference in acceleration at different monitoring points on the long-distance cable at each moment and the distribution of the monitoring positions, the position attitude change degree at each monitoring position is determined to quantify the cable attitude change at each monitoring position. When the underwater robot performs a specific task, there are often cases where the attitude changes at some monitoring positions on the long-distance cable are relatively consistent. Therefore, at each moment, the monitoring positions can be divided based on the attitude change degree at the monitoring positions to obtain a division set. At this time, the monitoring positions in each division set have relatively consistent attitude changes. When the underwater robot moves, it will drive the attitude changes of different monitoring points on the long-distance cable. Therefore, if the attitude change at a certain position is more consistent with the attitude change at a position closer to the underwater robot, it can be explained that the cable at this position is more affected by the movement of the underwater robot, and then the probability of occurrence of an abnormality is higher. Therefore, based on the foregoing logic, the influence degree value at each monitoring position is calculated. Further, since the torsional change of the cable is a process of gradual accumulation of stress in the long-distance cable, that is, over time, the torsional stress of the cable may continue to accumulate. Therefore, for any monitoring position, the influence degree values of this monitoring position at all times are comprehensively analyzed to obtain the attitude distortion degree value of this monitoring position, and finally, the attitude abnormal position is determined among all the monitoring positions based on the attitude distortion degree value. In summary, the embodiment of the present invention comprehensively analyzes the attitude change situation at different positions on the long-distance cable and the gradual accumulation process of the torsional change, and more accurately judges the attitude distortion situation of each monitoring position, thereby improving the screening accuracy of the attitude abnormal position.

[0056] The embodiment of the present invention also provides a long-distance cable attitude detection system for an underwater robot. Please refer to Figure 3 , which shows a system block diagram, including a data acquisition module 401 for implementing step S1 in the above method; an attitude analysis module 402 for implementing step S2 in the above method; an influence degree analysis module 403 for implementing step S3 in the above method; and an abnormal position screening module 404 for implementing step S4 in the above method.

[0057] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the functions described above. In addition, an embodiment of a long-distance cable attitude detection system for an underwater robot and an embodiment of a long-distance cable attitude detection method for an underwater robot provided in the above embodiments belong to the same concept. The specific implementation process can be found in the method embodiment and will not be repeated here.

[0058] Please refer to Figure 4 , which shows a schematic structural diagram of a long-distance cable attitude detection system for an underwater robot provided by an embodiment of the present invention, including a processor 500, a memory 501, a bus 502, and a communication interface 503. The processor 500, the communication interface 503, and the memory 501 are connected through the bus 502. Among them, the memory 501 may include a high-speed random access memory. The bus 502 may be an ISA bus, a PCI bus, an EISA bus, etc. The processor 500 may be an integrated circuit chip with signal processing capabilities. At least one instruction, at least one program, a code set, or an instruction set is stored in the memory 501. When at least one instruction, at least one program, a code set, or an instruction set is loaded and executed by the processor, the steps in a long-distance cable attitude detection method for an underwater robot are implemented.

[0059] It should be noted that the above sequence of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0060] Each embodiment in this specification is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the differences between each embodiment and other embodiments are emphasized.

Claims

1. A long-distance cable attitude detection method for an underwater robot, characterized in that, The method includes: Obtaining the acceleration time series data at all monitoring positions on the long-distance cable of the underwater robot; At each moment, according to the difference between the acceleration values at different monitoring positions on the long-distance cable and the distribution of the monitoring positions, determining the position and attitude change degree at each monitoring position; At each moment, based on the position and attitude change degrees at all monitoring positions on the long-distance cable, dividing all the monitoring positions to obtain a division set; based on the position distribution of the monitoring positions in the division set and the position distribution on the long-distance cable, analyzing the correlation of the position and attitude change degrees at the monitoring positions, so as to determine the affected degree value at each monitoring position; For any monitoring position, analyzing the change of the affected degree value at this monitoring position at different moments to obtain the attitude distortion degree value of this monitoring position; based on the attitude torsion degree value, judging the attitude torsion conditions of all monitoring positions on the long-distance cable to determine the attitude abnormal positions.

2. The long-distance cable attitude detection method for an underwater robot according to claim 1, characterized in that The method for obtaining the position and attitude change degree includes: On the long-distance cable, sorting all the monitoring positions in ascending order of the distance from the underwater robot to obtain a position sorting sequence; At each moment, for any monitoring position, taking the value obtained by normalizing the absolute value of the difference between the acceleration value of this monitoring position and the acceleration value of the next adjacent monitoring position in the position sorting sequence as the motion change index of this monitoring position; Taking the ratio of the motion change index of this monitoring position to the serial number value of this monitoring position in the position sorting sequence as the position and attitude change degree of this monitoring position at each moment.

3. The long-distance cable attitude detection method for an underwater robot according to claim 2, characterized in that, The method for obtaining the division set includes: At each moment, in the position sorting sequence, starting from the first monitoring position as the position to be measured and traversing, and the position to be measured corresponds to a position set, judging whether the similarity degree value of the position and attitude change degree between the position to be measured and the next adjacent monitoring position meets the preset condition; If it meets the condition, incorporating the next adjacent monitoring position into the position set corresponding to the position to be measured, and continuing to judge whether the similarity degree value of the position and attitude change degree between the last monitoring position in the position set and the next adjacent monitoring position meets the preset condition. If it does not meet the condition, taking the next adjacent monitoring position as the new position to be measured for traversing; After the traversal is completed, obtaining all the position sets as the division set; Wherein, the preset condition is that the similarity degree value is greater than or equal to the preset similarity threshold.

4. A long-distance cable attitude detection method for an underwater robot according to claim 3, characterized in that, The method for obtaining the similarity degree value includes: Taking the value obtained by performing negative correlation mapping and normalization on the absolute value of the difference between the position and attitude change degrees between two monitoring positions as the similarity degree value between the two monitoring positions.

5. A long-distance cable attitude detection method for an underwater robot according to claim 2, characterized in that, The method for obtaining the affected degree value includes: At each moment, for any monitoring position on the long-distance cable, calculating the mean value of the position and attitude change degrees of all monitoring positions in the division set to which this monitoring position belongs as the first mean value; According to the position distribution of the monitoring positions on the long-distance cable, determine the comparison set corresponding to the monitoring position, and take the average value of the position attitude change degrees of all the monitoring positions in the comparison set as the second average value; Take the value obtained by performing negative correlation mapping and normalization on the absolute value of the difference between the first average value and the second average value corresponding to the monitoring position as the influence degree value at the monitoring position.

6. A long-distance cable attitude detection method for an underwater robot according to claim 5, characterized in that The method for obtaining the comparison set includes: At each moment, for any monitoring position on the long-distance cable, form the comparison set of the monitoring position by including the monitoring position and all the monitoring positions before it in the position sorting sequence.

7. A long-distance cable attitude detection method for an underwater robot according to claim 1, characterized in that The method for obtaining the attitude distortion degree value includes: For any monitoring position, calculate the absolute value of the difference between the influence degree value at the monitoring position at each moment and the influence degree value at the adjacent next moment in time sequence as the change factor; Take the value obtained by normalizing the average value of all the change factors corresponding to the monitoring position as the attitude distortion degree value of the monitoring position.

8. A long-distance cable attitude detection method for an underwater robot according to claim 1, characterized in that, The method for determining the attitude abnormal position includes: On the long-distance cable, take the monitoring positions greater than the preset abnormal threshold as the attitude abnormal positions.

9. A long-distance cable attitude detection method for an underwater robot according to claim 8, characterized in that The value range of the abnormal threshold is [0.6, 1).

10. A long-distance cable attitude detection method for an underwater robot according to claim 1, characterized in that, The monitoring positions are evenly distributed on the long-distance cable at a fixed interval.

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