CN116861750B - Remote health diagnosis system for deep sea net cage - Google Patents
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Abstract
The invention relates to the technical field of deep sea net cage safety monitoring, in particular to a deep sea net cage remote health diagnosis system. The system comprises a physical entity layer, a data acquisition and transmission layer, a digital twin layer and a health diagnosis layer; the physical entity layer comprises a deep sea net cage, a marine environment, a sensor unit, a solar power supply unit and a storage battery power supply unit; the data acquisition and transmission layer comprises a data processing unit, a data storage unit, a satellite communication unit, a wireless communication unit and a remote control receiving unit; the digital twin layer comprises a tool box, deep sea net cage data, sea area environment data and a digital twin model; the health diagnosis layer comprises an algorithm library, a structural damage positioning unit, a remaining life assessment unit and a data visualization unit. The invention adopts the Beidou and wireless communication technology to ensure the reliability of the remote transmission of the deep sea cage data, adopts the digital twin and intelligent algorithm to analyze the monitoring data, and improves the accuracy of the damage positioning and service life assessment results of the deep sea cage.
Description
Technical Field
The invention relates to the technical field of deep sea net cage safety monitoring, in particular to a deep sea net cage remote health diagnosis system.
Background
With the rapid development of the deep sea cultivation industry, the important development directions of the offshore cultivation platform include large-scale and integrated, so that the structure of the offshore cultivation platform is more complex. The large-scale net cage culture is adopted in the deepwater environment, so that a wild growth environment and a natural ecological environment which are more similar to the growth of the fishes can be formed, and the high-quality fishes can be cultured. The main body structure of the culture net cage is positioned under water and is corroded by severe marine environment for a long time. Under the influence of marine environmental factors such as wind, waves, currents, sea ice, earthquakes, submarine scouring and the like, tiny damages such as structural cracks, defects, corrosion and the like are gradually accumulated, if the damage is not timely found and prevented, the self structure and the anchoring system of the net cage are damaged, the cultured fishes are extremely easy to injure and die or escape in a large amount, the personal safety is threatened, and huge property loss and ecological damage are caused. Although the field of safety monitoring in China is developed rapidly, the health diagnosis for the deep sea net cage is still blank. As the deep sea cage is far away from the coastline, the on-line safety monitoring of the cultivation platform structure is becoming urgent and important in order to further promote the continuous and stable development of offshore cultivation production.
As disclosed in the prior art patent CN 202120257811.5, an offshore culture platform deformation and vibration monitoring system adopts an attitude sensor and a vibration sensor to measure the attitude and vibration of the culture platform, and a microprocessor main board transmits acquired information to a data acquisition device through a GPS or 4G real-time transmission device; the method has the defects that monitoring information is incomplete, structural integrity cannot be evaluated, and the service life of the platform is predicted. As another prior art patent CN201810906237.4 discloses a method for monitoring the health of the net cage of the marine ranch by using an unmanned plane and a camera, and the monitoring result is sent to a remote receiving end by using LoRa spread spectrum communication; the unmanned aerial vehicle has the defects that the unmanned aerial vehicle is limited in cruising ability, and long-term on-site monitoring cannot be truly realized; the unmanned aerial vehicle is limited by the marine environment, and the survival ability of the unmanned aerial vehicle is obviously reduced under severe sea conditions; due to poor underwater visibility, the camera may not recognize the minute defect, resulting in low reliability of the monitoring result.
Because the deep sea net cage is far away from the coastline, the prior art cannot ensure that land operators can deeply understand the health condition of the deep sea net cage at the first time, the previous research is concentrated on aspects of data acquisition, transmission and the like, and the research on aspects of data post-processing analysis, structural integrity evaluation, structure service life estimation and the like is relatively lacking, so that the remote data transmission, structural damage positioning and service life evaluation of the deep sea net cage are urgently needed to be carried out by utilizing technologies such as new generation information technology, digital twin and the like, and the purpose of remote health diagnosis of the deep sea net cage is achieved. In view of this, we propose a deep sea cage remote health diagnosis system.
Disclosure of Invention
The invention aims to provide a deep sea net cage remote health diagnosis system for solving the problems in the background technology.
In order to solve the technical problems, one of the purposes of the invention is to provide a deep sea net cage remote health diagnosis system, which comprises a physical entity layer, a data acquisition and transmission layer and a digital twin layer which are sequentially connected in a communication way, wherein the data acquisition and transmission layer and the digital twin layer are simultaneously connected to a health diagnosis layer; wherein:
the physical entity layer comprises a deep sea net cage, a marine environment, a sensor unit, a solar power supply unit and a storage battery power supply unit; the sensor unit transmits the acquired data of the sensor unit to the data processing and transmitting layer through the data transmission interface; the solar power supply unit and the storage battery power supply unit supply power for the sensor unit and the data acquisition and transmission layer, and the solar power supply unit and the storage battery power supply unit are mutually backed up;
the data acquisition and transmission layer comprises a data processing unit, a data storage unit, a satellite communication unit, a wireless communication unit and a remote control receiving unit; the data processing unit transmits the processed data of the data processing unit to the data storage unit through the data transmission interface; the data of the data storage unit is transmitted to the remote control receiving unit through the satellite communication unit or the wireless communication unit; the remote control receiving unit transmits the received data to the health diagnosis layer through the data transmission interface;
the digital twin layer comprises a tool box, deep sea net cage data, sea area environment data and a digital twin model; the tool box comprises three-dimensional modeling software and finite element analysis software; the deep sea net cage data and sea area environment data are used as the input of a digital twin model, a three-dimensional modeling software and a finite element analysis software of a tool box are utilized to construct the digital twin model, a simulation result of the digital twin model is used as the output of the digital twin model, and the characteristic parameters of the digital twin model are iteratively optimized by comparing the difference between the input and the output;
the health diagnosis layer comprises an algorithm library, a structural damage positioning unit, a residual life assessment unit and a data visualization unit; the structural damage positioning unit analyzes the data provided by the remote control receiving unit through a corresponding algorithm library, finishes structural damage positioning, and transmits the data to the residual life assessment unit and the data visualization unit through a data transmission interface; the residual life assessment unit is used for calculating the residual life of the deep sea net cage by analyzing the data provided by the structural damage positioning unit based on the design life of the deep sea net cage, and transmitting the data to the data visualization unit through the data transmission interface; the data visualization unit comprises a display terminal, and displays analysis results of the structural damage positioning unit and the residual life assessment unit.
As a further improvement of the technical scheme, the sensor unit comprises an acceleration sensor, an inclination angle sensor, a displacement sensor, a wind speed and direction sensor, a wave instrument, an acoustic Doppler flow velocity profile instrument, a strain sensor, a fiber bragg grating demodulator and a signal acquisition instrument; wherein:
the acceleration sensor, the inclination angle sensor, the displacement sensor, the wind speed and direction sensor, the wave meter and the acoustic Doppler flow velocity profiler are powered on to acquire data and transmit the data to the signal acquisition instrument through the data transmission interface;
the strain sensor is electrified to collect data, and the data are transmitted to the signal collector after being processed by the fiber bragg grating demodulator;
the signal acquisition instrument transmits data to the data processing and transmission layer.
As a further improvement of the technical scheme, the satellite communication unit adopts a Beidou satellite communication system; the wireless communication unit adopts a wireless communication system; the remote control receiving unit adopts a Beidou satellite communication director.
As a further improvement of the technical scheme, the algorithm library comprises a modal analysis method, a linear discriminant analysis method, a statistical hypothesis test theory, a rain flow cycle counting method, a P-M linear damage theory, a Goodman formula and an S-N curve method.
As a further improvement of the present technical solution, in the physical entity layer, the step of performing data acquisition includes the following steps:
a1, arranging an acceleration sensor, an inclination angle sensor, a displacement sensor, a wind speed and direction sensor, a wave instrument, an acoustic Doppler flow velocity profile instrument, a strain sensor, a fiber bragg grating demodulator and a signal acquisition instrument on a deep sea cage;
a2, after the sensor unit is electrified, the environmental parameters and the deep sea net cage structure response parameters are collected at regular time;
a3, the data acquired by the sensor unit are transmitted to the data processing and transmitting layer through the data transmission interface.
As a further improvement of the technical scheme, in the data acquisition and transmission layer, the step of performing data processing includes the following steps:
b1, outliers, noise points and outliers are removed through a signal filtering method;
b2, marking the deep sea net cage and encoding;
b3, setting a latitude range of transmission, and completing latitude coordinate compression; setting a longitude range of transmission, and completing longitude coordinate compression;
b4, when the time reference is Beidou, adopting a week counting and intra-week second mode to count time, and completing lossless compression of time data;
b5, binary coding is carried out on the data of the acceleration sensor according to the transmission horizontal swing; binary coding is carried out on the data of the inclination sensor according to the transmission relative sedimentation monitoring quantity; binary coding is carried out on the displacement sensor; binary encoding is carried out on wind speed and direction sensor data; binary coding is carried out on the wave instrument data; binary encoding is carried out on acoustic Doppler current profiler data; the strain sensor data is binary encoded.
As a further improvement of the present technical solution, in the health diagnosis layer, the step of performing health diagnosis includes the following steps:
the method comprises the steps that C1, a structural damage positioning unit calculates the natural frequency or modal parameters such as the modal shape of the deep sea cage in a healthy state based on an established digital twin model of the deep sea cage; acquiring the natural frequency or modal shape and other modal parameters of the deep sea net cage in a real state by adopting a modal analysis method based on the acquired data of the sensor;
c2, if the modal parameter data meet Gaussian distribution, performing linear discriminant analysis to obtain an optimal projection vector of the modal parameter data in a health state and a real state, and extracting effective damage sensitive features from the modal parameters by using the vector;
if the Gaussian distribution is not met, converting the Gaussian distribution into Gaussian distribution data by using Gaussian transformation, and then performing linear discriminant analysis to extract effective damage sensitive characteristics;
c4, calculating the mean value and standard deviation of the damage sensitive characteristics of the deep sea net cage structure, calculating the hypothesis test statistics and the threshold value thereof, comparing the statistics and the threshold value thereof according to the statistical hypothesis test theory, further judging whether the deep sea net cage is damaged or not, and realizing accurate judgment of the structural damage under the influence of marine environmental factors;
and C5, adopting a rain flow cycle counting method for the stress of the measuring position, comprehensively using a P-M linear damage theory, a Goodman formula and an S-N curve method to obtain the fatigue damage level at the detecting position, and further obtaining the fatigue residual life of the detecting position.
The second object of the invention is to provide a deep sea net cage remote health diagnosis method, which comprises the deep sea net cage remote health diagnosis system, and specifically comprises the following steps:
and a data acquisition stage:
s1, arranging an acceleration sensor, an inclination angle sensor, a displacement sensor, a wind speed and direction sensor, a wave instrument, an acoustic Doppler flow velocity profile instrument, a strain sensor, a fiber bragg grating demodulation instrument and a signal acquisition instrument on a deep sea cage;
s2, powering up the sensor unit;
s3, the sensor unit collects environmental parameters and deep sea net cage structure response parameters at regular time;
s4, the sensor unit acquires data and transmits the data to the data processing and transmitting layer through the data transmission interface;
and a data processing stage:
s5, outliers, noise points and outliers are removed through a signal filtering method;
s6, identifying the deep sea net cages, and coding by using 8 bits according to the number of the current deep sea net cages;
s7, setting a latitude range of transmission, and completing latitude coordinate compression through 20 bits; setting a longitude range of transmission, and completing longitude coordinate compression through 20 bits;
s8, when the time reference is Beidou, adopting a week counting and intra-week second mode to count time, and completing lossless compression of time data through 33 bit numbers;
s9, binary coding is carried out on the data of the acceleration sensor according to the transmission horizontal swing; binary coding is carried out on the data of the inclination sensor according to the transmission relative sedimentation monitoring quantity; binary coding is carried out on the displacement sensor; binary encoding is carried out on wind speed and direction sensor data; binary coding is carried out on the wave instrument data; binary encoding is carried out on acoustic Doppler current profiler data; binary encoding the strain sensor data;
and a data transmission stage:
s10, the satellite communication unit or the wireless communication unit is used for remotely transmitting data, and the remote control receiving unit is used for receiving the data transmitted by the satellite communication unit or the wireless communication unit;
health diagnosis stage:
s11, deep sea cage data and sea area environment data are used as input of a digital twin model, a three-dimensional modeling software and a finite element analysis software of a tool box are utilized to construct the digital twin model, a simulation result of the digital twin model is used as output of the digital twin model, and characteristic parameters of the digital twin model are iteratively optimized by comparing the difference between the input and the output;
s12, based on a digital twin model, the structural damage positioning unit analyzes data provided by the remote control receiving unit through a corresponding algorithm library to finish structural damage positioning;
s13, a residual life assessment unit calculates the residual life of the deep sea net cage by analyzing data provided by the structural damage positioning unit based on the design life of the deep sea net cage;
s14, the data visualization unit displays analysis results of the structural damage positioning unit and the residual life assessment unit.
The third objective of the present invention is to provide a system carrying platform device, which includes a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the processor is configured to implement the steps of the deep sea cage remote health diagnosis method when executing the computer program.
The fourth object of the present invention is to provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the deep sea cage remote health diagnosis method described above.
Compared with the prior art, the invention has the beneficial effects that:
1. in the deep sea cage remote health diagnosis system, wireless communication is adopted as a preferable data transmission mode, and the system is automatically converted into Beidou satellite communication for data transmission when encountering a wireless communication blind area, so that the reliability of the deep sea cage data remote transmission is ensured;
2. in the deep sea net cage remote health diagnosis system, solar power supply is adopted as a preferable energy supply mode, and when the energy of a solar power supply unit is insufficient, a storage battery power supply mode is switched; meanwhile, the solar power supply unit can charge the storage battery, so that the cruising ability of the deep sea net cage remote health diagnosis system is improved;
3. in the deep sea cage remote health diagnosis system, environmental parameters and deep sea cage structure response parameters are collected by utilizing a plurality of groups of sensors, a digital twin model is established based on the data, the deep sea cage monitoring data is analyzed by adopting a health diagnosis algorithm, and the accuracy of the damage positioning and service life assessment results of the deep sea cage is improved, so that the purpose of deep sea cage health diagnosis is realized.
Drawings
FIG. 1 is a schematic diagram of an exemplary deep sea cage remote health diagnostic system of the present invention;
FIG. 2 is a schematic diagram of a sensor unit of an exemplary deep sea cage remote health diagnostic system of the present invention;
FIG. 3 is a health diagnostic flow chart of an exemplary deep sea cage remote health diagnostic system of the present invention;
fig. 4 is a schematic structural diagram of an exemplary system carrying platform electronic computer device according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the embodiment provides a deep sea cage remote health diagnosis system, which comprises a physical entity layer, a data acquisition and transmission layer and a digital twin layer which are sequentially connected in a communication way, wherein the data acquisition and transmission layer and the digital twin layer are simultaneously connected to a health diagnosis layer; wherein:
the physical entity layer comprises a deep sea net cage, a marine environment, a sensor unit, a solar power supply unit and a storage battery power supply unit; the sensor unit transmits the acquired data of the sensor unit to the data processing and transmitting layer through the data transmission interface; the solar power supply unit and the storage battery power supply unit supply power for the sensor unit and the data acquisition and transmission layer, and the solar power supply unit and the storage battery power supply unit are mutually backed up;
the data acquisition and transmission layer comprises a data processing unit, a data storage unit, a satellite communication unit, a wireless communication unit and a remote control receiving unit; the data processing unit transmits the processed data of the data processing unit to the data storage unit through the data transmission interface; the data of the data storage unit is transmitted to the remote control receiving unit through the satellite communication unit or the wireless communication unit; the remote control receiving unit transmits the received data to the health diagnosis layer through the data transmission interface;
the digital twin layer comprises a tool box, deep sea net cage data, sea area environment data and a digital twin model; the tool box comprises three-dimensional modeling software and finite element analysis software; the deep sea net cage data and sea area environment data are used as the input of a digital twin model, a three-dimensional modeling software and a finite element analysis software of a tool box are utilized to construct the digital twin model, a simulation result of the digital twin model is used as the output of the digital twin model, and the characteristic parameters of the digital twin model are iteratively optimized by comparing the difference between the input and the output;
the health diagnosis layer comprises an algorithm library, a structural damage positioning unit, a residual life assessment unit and a data visualization unit; the structural damage positioning unit analyzes the data provided by the remote control receiving unit through a corresponding algorithm library, finishes structural damage positioning, and transmits the data to the residual life assessment unit and the data visualization unit through a data transmission interface; the residual life assessment unit is used for calculating the residual life of the deep sea net cage by analyzing the data provided by the structural damage positioning unit based on the design life of the deep sea net cage, and transmitting the data to the data visualization unit through the data transmission interface; the data visualization unit comprises a display terminal, and displays analysis results of the structural damage positioning unit and the residual life assessment unit.
As shown in fig. 2, in this embodiment, the sensor unit includes an acceleration sensor, an inclination sensor, a displacement sensor, a wind speed and direction sensor, a wave meter, an acoustic doppler flow velocity profiler, a strain sensor, a fiber grating demodulator, and a signal acquisition instrument; wherein:
the acceleration sensor, the inclination angle sensor, the displacement sensor, the wind speed and direction sensor, the wave meter and the acoustic Doppler flow velocity profiler are powered on to acquire data and transmit the data to the signal acquisition instrument through the data transmission interface;
the strain sensor is electrified to collect data, and the data are transmitted to the signal collector after being processed by the fiber bragg grating demodulator;
the signal acquisition instrument transmits data to the data processing and transmission layer.
Further, the satellite communication unit adopts a Beidou satellite communication system; the wireless communication unit adopts a wireless communication system; the remote control receiving unit adopts a Beidou satellite communication director.
Further, the algorithm library comprises a modal analysis method, a linear discriminant analysis method, a statistical hypothesis test theory, a rain flow cycle counting method, a P-M linear damage theory, a Goodman formula and an S-N curve method.
In this embodiment, in the physical entity layer, the step of collecting data includes the following steps:
a1, arranging an acceleration sensor, an inclination angle sensor, a displacement sensor, a wind speed and direction sensor, a wave instrument, an acoustic Doppler flow velocity profile instrument, a strain sensor, a fiber bragg grating demodulator and a signal acquisition instrument on a deep sea cage;
a2, after the sensor unit is electrified, the environmental parameters and the deep sea net cage structure response parameters are collected at regular time;
a3, the data acquired by the sensor unit are transmitted to the data processing and transmitting layer through the data transmission interface.
Further, in the data acquisition and transmission layer, the step of performing data processing includes the following steps:
b1, outliers, noise points and outliers are removed through a signal filtering method;
b2, marking the deep sea net cage and encoding;
b3, setting a latitude range of transmission, and completing latitude coordinate compression; setting a longitude range of transmission, and completing longitude coordinate compression;
b4, when the time reference is Beidou, adopting a week counting and intra-week second mode to count time, and completing lossless compression of time data;
b5, binary coding (including range and precision) is carried out on the data of the acceleration sensor according to the transmission horizontal swing; binary encoding (including range and accuracy) is performed on the tilt sensor data according to the transmission relative sedimentation monitoring amount; binary encoding (including range and accuracy) the displacement sensor; binary encoding is carried out on wind speed and direction sensor data (including the range and the precision of wind speed and the range and the precision of wind direction); binary encoding is carried out on the wave instrument data (comprising the range and the precision of wave height, the range and the precision of wave period and the range and the precision of wave direction); binary encoding (including range and accuracy) of acoustic Doppler flow profiler data; the strain sensor data is binary coded (including range and accuracy).
Further, in the health diagnosis layer, the step of performing health diagnosis includes the steps of:
the method comprises the steps that C1, a structural damage positioning unit calculates the natural frequency or modal parameters such as the modal shape of the deep sea cage in a healthy state based on an established digital twin model of the deep sea cage; acquiring the natural frequency or modal shape and other modal parameters of the deep sea net cage in a real state by adopting a modal analysis method based on the acquired data of the sensor;
c2, if the modal parameter data meet Gaussian distribution, performing linear discriminant analysis to obtain an optimal projection vector of the modal parameter data in a health state and a real state, and extracting effective damage sensitive features from the modal parameters by using the vector;
if the Gaussian distribution is not met, converting the Gaussian distribution into Gaussian distribution data by using Gaussian transformation, and then performing linear discriminant analysis to extract effective damage sensitive characteristics;
c4, calculating the mean value and standard deviation of the damage sensitive characteristics of the deep sea net cage structure, calculating the hypothesis test statistics and the threshold value thereof, comparing the statistics and the threshold value thereof according to the statistical hypothesis test theory, further judging whether the deep sea net cage is damaged or not, and realizing accurate judgment of the structural damage under the influence of marine environmental factors;
and C5, adopting a rain flow cycle counting method for the stress of the measuring position, comprehensively using a P-M linear damage theory, a Goodman formula and an S-N curve method to obtain the fatigue damage level at the detecting position, and further obtaining the fatigue residual life of the detecting position.
In combination with the above, as shown in fig. 3, the embodiment further provides a deep sea cage remote health diagnosis method, which includes the foregoing deep sea cage remote health diagnosis system, and specifically includes the following steps:
and a data acquisition stage:
s1, arranging an acceleration sensor, an inclination angle sensor, a displacement sensor, a wind speed and direction sensor, a wave instrument, an acoustic Doppler flow velocity profile instrument, a strain sensor, a fiber bragg grating demodulation instrument and a signal acquisition instrument on a deep sea cage;
s2, powering up the sensor unit;
s3, the sensor unit collects environmental parameters and deep sea net cage structure response parameters at regular time;
s4, the sensor unit acquires data and transmits the data to the data processing and transmitting layer through the data transmission interface;
and a data processing stage:
s5, outliers, noise points and outliers are removed through a signal filtering method;
s6, marking the deep sea net cages, and coding by using 8 bits according to the number of the current deep sea net cages, wherein the number range can reach 250;
s7, setting the latitude range of transmission to be 10 degrees from north latitude to 55 degrees from north latitude, wherein the precision is 0.000057 degrees, about 5.72 meters, and completing latitude coordinate compression through 20 bit numbers; the longitude range of transmission is set to be 70 degrees to 135 degrees of east longitude, the precision is 0.000086 degrees, the longitude coordinate compression is completed through 20 bits, and the precision is about 9.56 meters;
s8, when the time reference is Beidou, adopting a week counting and intra-week second mode to count time, and completing lossless compression of time data through 33 bit numbers;
s9, binary coding is carried out on the data of the acceleration sensor according to the transmission horizontal swing, the range is-1 g, the precision is 0.12 mu g, and the data occupies 14 bits; binary coding is carried out on the data of the inclination sensor according to the transmission relative settlement monitoring quantity, the range is-5 degrees to 5 degrees, the precision is 0.0006 degrees, and the data occupies 14 bits; binary coding is carried out on the displacement sensor, the range is 100 mm-2000 mm, the precision is 0.49mm, and the displacement sensor occupies 12 bits; binary encoding is carried out on wind speed and wind direction sensor data, the wind speed range is 0 m/s-20 m/s, the precision is 0.1m/s, the wind speed range is 0-359 degrees, the precision is 1 degree, and the wind speed range is 10 bits; binary coding is carried out on the wave instrument data, the wave height range is 0-30 m, the precision is 0.1m, the wave height range occupies 10 bits, the wave period range is 0 s-25 s, the precision is 0.5s, the wave direction range is 0-359 degrees, the precision is 10 degrees, and the wave period range occupies 6 bits; binary coding is carried out on acoustic Doppler flow velocity profiler data, the range is 0.05 m/s-15 m/s, the accuracy is 0.1m/s, and the acoustic Doppler flow velocity profiler data occupies 8 bits; binary coding is carried out on the data of the strain sensor, the range is 3000 [ mu ] epsilon, the precision is 0.046 [ mu ] epsilon, and the data occupies 16 bits;
and a data transmission stage:
s10, the satellite communication unit or the wireless communication unit is used for remotely transmitting data, and the remote control receiving unit is used for receiving the data transmitted by the satellite communication unit or the wireless communication unit;
health diagnosis stage:
s11, deep sea cage data and sea area environment data are used as input of a digital twin model, a three-dimensional modeling software and a finite element analysis software of a tool box are utilized to construct the digital twin model, a simulation result of the digital twin model is used as output of the digital twin model, and characteristic parameters of the digital twin model are iteratively optimized by comparing the difference between the input and the output;
s12, based on a digital twin model, the structural damage positioning unit analyzes data provided by the remote control receiving unit through a corresponding algorithm library to finish structural damage positioning;
s13, a residual life assessment unit calculates the residual life of the deep sea net cage by analyzing data provided by the structural damage positioning unit based on the design life of the deep sea net cage;
s14, the data visualization unit displays analysis results of the structural damage positioning unit and the residual life assessment unit.
As shown in fig. 4, the present embodiment further provides a system carrying platform apparatus, which includes a processor, a memory, and a computer program stored in the memory and running on the processor.
The processor comprises one or more than one processing core, the processor is connected with the memory through a bus, the memory is used for storing program instructions, and the steps of the deep sea net cage remote health diagnosis method are realized when the processor executes the program instructions in the memory.
Alternatively, the memory may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.
In addition, the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the steps of the deep sea net cage remote health diagnosis method when being executed by a processor.
Optionally, the present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the steps of the deep sea cage remote health diagnosis method of the above aspects.
It will be appreciated by those of ordinary skill in the art that the processes for implementing all or part of the steps of the above embodiments may be implemented by hardware, or may be implemented by a program for instructing the relevant hardware, and the program may be stored in a computer readable storage medium, where the above storage medium may be a read-only memory, a magnetic disk or optical disk, etc.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The deep sea net cage remote health diagnosis system is characterized by comprising a physical entity layer, a data acquisition and transmission layer and a digital twin layer which are sequentially connected in a communication way, wherein the data acquisition and transmission layer and the digital twin layer are simultaneously connected into a health diagnosis layer; wherein:
the physical entity layer comprises a deep sea net cage, a marine environment, a sensor unit, a solar power supply unit and a storage battery power supply unit; the sensor unit transmits the acquired data of the sensor unit to the data processing and transmitting layer through the data transmission interface; the solar power supply unit and the storage battery power supply unit supply power for the sensor unit and the data acquisition and transmission layer, and the solar power supply unit and the storage battery power supply unit are mutually backed up;
the data acquisition and transmission layer comprises a data processing unit, a data storage unit, a satellite communication unit, a wireless communication unit and a remote control receiving unit; the data processing unit transmits the processed data of the data processing unit to the data storage unit through the data transmission interface; the data of the data storage unit is transmitted to the remote control receiving unit through the satellite communication unit or the wireless communication unit; the remote control receiving unit transmits the received data to the health diagnosis layer through the data transmission interface;
the digital twin layer comprises a tool box, deep sea net cage data, sea area environment data and a digital twin model; the tool box comprises three-dimensional modeling software and finite element analysis software; the deep sea net cage data and sea area environment data are used as the input of a digital twin model, a three-dimensional modeling software and a finite element analysis software of a tool box are utilized to construct the digital twin model, a simulation result of the digital twin model is used as the output of the digital twin model, and the characteristic parameters of the digital twin model are iteratively optimized by comparing the difference between the input and the output;
the health diagnosis layer comprises an algorithm library, a structural damage positioning unit, a residual life assessment unit and a data visualization unit; the structural damage positioning unit analyzes the data provided by the remote control receiving unit through a corresponding algorithm library, finishes structural damage positioning, and transmits the data to the residual life assessment unit and the data visualization unit through a data transmission interface; the residual life assessment unit is used for calculating the residual life of the deep sea net cage by analyzing the data provided by the structural damage positioning unit based on the design life of the deep sea net cage, and transmitting the data to the data visualization unit through the data transmission interface; the data visualization unit comprises a display terminal, and displays analysis results of the structural damage positioning unit and the residual life assessment unit;
in the health diagnosis layer, the health diagnosis step comprises the following steps:
the method comprises the steps that C1, a structural damage positioning unit calculates modal parameters of a deep sea cage in a healthy state based on an established digital twin model of the deep sea cage, wherein the modal parameters comprise natural frequencies or modal shapes; acquiring modal parameters of the deep sea cage in a real state by adopting a modal analysis method based on the sensor acquired data, wherein the modal parameters comprise natural frequencies or modal shapes;
c2, if the modal parameter data meet Gaussian distribution, performing linear discriminant analysis to obtain an optimal projection vector of the modal parameter data in a health state and a real state, and extracting effective damage sensitive features from the modal parameters by using the vector;
if the Gaussian distribution is not met, converting the Gaussian distribution into Gaussian distribution data by using Gaussian transformation, and then performing linear discriminant analysis to extract effective damage sensitive characteristics;
c4, calculating the mean value and standard deviation of the damage sensitive characteristics of the deep sea net cage structure, calculating the hypothesis test statistics and the threshold value thereof, comparing the statistics and the threshold value thereof according to the statistical hypothesis test theory, further judging whether the deep sea net cage is damaged or not, and realizing accurate judgment of the structural damage under the influence of marine environmental factors;
and C5, adopting a rain flow cycle counting method for stress of the detection position, comprehensively using a P-M linear damage theory, a Goodman formula and an S-N curve method to obtain the fatigue damage level at the detection position, and further obtaining the fatigue residual life of the fatigue damage level.
2. The deep sea cage remote health diagnosis system of claim 1, wherein the sensor unit comprises an acceleration sensor, an inclination sensor, a displacement sensor, a wind speed and direction sensor, a wave meter, an acoustic doppler flow profiler, a strain sensor, a fiber grating demodulator, a signal acquisition meter; wherein:
the acceleration sensor, the inclination angle sensor, the displacement sensor, the wind speed and direction sensor, the wave meter and the acoustic Doppler flow velocity profiler are powered on to acquire data and transmit the data to the signal acquisition instrument through the data transmission interface;
the strain sensor is electrified to collect data, and the data are transmitted to the signal collector after being processed by the fiber bragg grating demodulator;
the signal acquisition instrument transmits data to the data processing and transmission layer.
3. The deep sea cage remote health diagnosis system according to claim 2, wherein the satellite communication unit adopts a Beidou satellite communication system; the wireless communication unit adopts a wireless communication system; the remote control receiving unit adopts a Beidou satellite communication director.
4. The deep sea cage remote health diagnosis system of claim 3, wherein the algorithm library comprises a modal analysis method, a linear discriminant analysis method, a statistical hypothesis test theory, a rain flow cycle counting method, a P-M linear damage theory, a Goodman formula, and an S-N curve method.
5. The deep sea cage remote health diagnosis system of claim 4, wherein the step of collecting data in the physical layer comprises the steps of:
a1, arranging an acceleration sensor, an inclination angle sensor, a displacement sensor, a wind speed and direction sensor, a wave instrument, an acoustic Doppler flow velocity profile instrument, a strain sensor, a fiber bragg grating demodulator and a signal acquisition instrument on a deep sea cage;
a2, after the sensor unit is electrified, the environmental parameters and the deep sea net cage structure response parameters are collected at regular time;
a3, the data acquired by the sensor unit are transmitted to the data processing and transmitting layer through the data transmission interface.
6. The deep sea cage remote health diagnosis system of claim 5, wherein the step of performing data processing in the data acquisition and transmission layer comprises the steps of:
b1, outliers, noise points and outliers are removed through a signal filtering method;
b2, marking the deep sea net cage and encoding;
b3, setting a latitude range of transmission, and completing latitude coordinate compression; setting a longitude range of transmission, and completing longitude coordinate compression;
b4, when the time reference is Beidou, adopting a week counting and intra-week second mode to count time, and completing lossless compression of time data;
b5, binary coding is carried out on the data of the acceleration sensor according to the transmission horizontal swing; binary coding is carried out on the data of the inclination sensor according to the transmission relative sedimentation monitoring quantity; binary coding is carried out on the displacement sensor; binary encoding is carried out on wind speed and direction sensor data; binary coding is carried out on the wave instrument data; binary encoding is carried out on acoustic Doppler current profiler data; the strain sensor data is binary encoded.
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