CN117970820A - Digital buoy automatic mooring control system based on signal transmission - Google Patents
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Abstract
The invention belongs to the technical field of intelligent ocean, in particular to a digital buoy automatic mooring control system based on signal transmission, which comprises an intelligent control system, wherein the intelligent control system comprises a buoy sensing unit, a signal transmission unit, a data processing unit, a control instruction unit, a control execution unit and a system monitoring unit, the data processing unit is improved through arrangement, the data processing unit comprises a data acquisition unit, a filtering and calibrating unit, a data fusion unit, a gesture algorithm unit and a parameter model unit, and the design of the data processing unit has the advantages of improving the data processing precision, the accuracy and the robustness, optimizing the precision and the stability of gesture resolving, improving the adaptability and the intelligent degree of the system and the like in the digital buoy automatic mooring control system based on the signal transmission. These beneficial effects can promote the performance and the reliability of system to improve the operating efficiency and the security of flotation pontoon automatic mooring control system.
Description
Technical Field
The invention relates to the technical field of intelligent ocean, in particular to a digital buoy automatic mooring control system based on signal transmission.
Background
Digital buoy automatic mooring is a system for buoy mooring control by using digital technology. The system acquires and processes information such as the position, the attitude and the environmental parameters of the pontoon in real time through the sensor and the computer control unit, and automatically adjusts and controls the pontoon according to a preset control algorithm so as to keep the pontoon at a stable position and attitude; the existing digital buoy automatic mooring control system has the following defects:
The data processing precision is not high: although the data processing unit is designed to improve the accuracy and precision of data processing, there may be problems of data acquisition errors, imperfect filtering and calibration, etc., resulting in low accuracy of data processing results, which may affect the accuracy and stability of the pose calculation;
The accuracy of the pose solution is limited: in a system based on signal transmission, the accuracy of gesture resolving is possibly limited by factors such as sensor resolution, noise and the like, which may lead to limited accuracy of gesture resolving results and can not meet the high-accuracy requirements of certain specific application scenes;
the system adaptability is to be improved: the existing data processing unit may lack adaptability to different environmental conditions and working condition changes, and the system may not be capable of accurately processing data in time when facing complex sea conditions or other abnormal conditions, thereby affecting the reliability and stability of the system;
the existing digital buoy automatic mooring control system is inconvenient to use due to the defects.
Disclosure of Invention
Based on the technical problem that the existing digital buoy automatic mooring control system is inconvenient to use, the invention provides a digital buoy automatic mooring control system based on signal transmission.
The invention provides a digital buoy automatic mooring control system based on signal transmission, which comprises an intelligent control system, wherein the intelligent control system comprises a buoy sensing unit, a signal transmission unit, a data processing unit, a control instruction unit, a control execution unit and a system monitoring unit, the buoy sensing unit is used for sensing information of the position, the posture and the surrounding environment of a buoy, the information comprises the horizontal inclination angle, the inclination direction, the motion state and the water depth of the buoy, and the signal transmission unit is used for transmitting signals acquired by the buoy sensing unit to a next-stage unit so as to realize information transmission and sharing.
Preferably, the data processing unit processes and analyzes the data received from the signal transmission unit, and calculates the position, the posture and the surrounding environment information of the pontoon according to a preset algorithm, and the data processing unit comprises a data acquisition unit, a filtering and calibrating unit, a data fusion unit, a posture algorithm unit and a parameter model unit.
Preferably, the data acquisition unit adopts various sensors and shooting equipment to acquire the position, the posture and the surrounding environment information of the pontoon in multiple angles and multiple directions, so that the accuracy and the accuracy of data processing are improved, the filtering and calibrating unit filters and corrects the acquired data, the accuracy and the stability of the data are improved, and the data fusion unit is responsible for integrating the data information from different sensors, and the accuracy and the robustness of the data processing are improved.
Preferably, the attitude algorithm unit is responsible for improving the accuracy and stability of the attitude calculation of the pontoon through an improved attitude estimation algorithm, and the parameter model unit is responsible for establishing an environment parameter model, modeling and predicting the environment parameter in real time, and improving the adaptability and the intelligent degree of the system.
Preferably, a quaternion formula is introduced into the data processing unit, and the formula is as follows:
q = (w, x, y, z)
where w is the real part, (x, y, z) is the imaginary part,
And combining with an attitude algorithm unit, in the process of estimating the attitude, using a quaternion to represent the attitude change of the pontoon, and if the quaternion at the current moment is q_cur and the quaternion at the next moment is q_next, updating by using the following formula:
q_next = q_cur * dq
wherein dq represents the posture change from the current time to the next time, obtained by converting the angle information obtained by the sensor,
In the data fusion process, when a plurality of sensors provide gesture information, the sensors are integrated by using a weighted average method, quaternions provided by n sensors are respectively q1, q2, qn, the corresponding weights are w1, w2, qn, and wn, and the integrated quaternions are expressed as:
q_fusion = normalize(w1q1 + w2q2 + ... + wn*qn)
wherein normalize denotes that the result is normalized to ensure the unit length of the quaternion.
Through the technical scheme, the quaternion formula is used for conveniently carrying out attitude estimation and data fusion, and the accuracy and the robustness of the data processing unit are improved.
Preferably, the quaternion formula is improved through a multi-sensor fusion algorithm, a Kalman filter is introduced, and the quaternion formula is combined with various data sources of an accelerometer, a magnetometer and a vision sensor to perform attitude estimation, wherein the formula is as follows:
step one, a quaternion updating formula in the attitude estimation process:
q_next = q_cur * dq
Wherein dq is the gesture change from the current moment to the next moment, and is obtained by converting the angle information obtained by the sensor;
step two, a posture estimation formula after multi-sensor data fusion is as follows:
q_fusion = normalize(w1q1 + w2q2 + ... + wn*qn)
Wherein q1, q2,..qn is the quaternion provided by the plurality of sensors, w1, w2,..wn is the corresponding weight, normalize denotes the normalization of the results to ensure the unit length of the quaternion,
Thirdly, using an attitude estimation formula of a Kalman filter:
The equation of state: x (k) =f (x (k-1), u (k-1)) +w (k-1)
Observation equation: z (k) =h (x (k)) +v (k)
Wherein x (k) is an attitude state vector, u (k-1) is a control vector, w (k-1) is system noise, z (k) is a measurement vector, h (x (k)) is an observation function, and v (k) is measurement noise, unlike EKF, KF uniformly represents measurement data of all sensors as one measurement vector, and multi-sensor data fusion is realized by estimating a measurement noise covariance matrix and a system noise covariance matrix.
Through the technical scheme, the multi-sensor fusion algorithm is combined with various data sources such as the accelerometer, the magnetometer and the vision sensor to perform gesture estimation, and the Kalman filter can effectively fuse data of different sensors and adaptively estimate gesture states, so that accuracy and robustness of gesture estimation are improved.
Preferably, the data fusion unit is provided with a fault-tolerant mechanism, the fault-tolerant mechanism comprises a redundancy design unit, an abnormality detection unit, a data verification unit, a state reset unit and a disaster recovery scheme unit, and the redundancy design unit automatically switches to the standby sensor and the equipment when partial sensor data are abnormal by adding the standby sensor and the equipment, so that the continuity and the reliability of the system are ensured.
Preferably, the abnormality detection unit monitors the running state and the data change of the system in real time, when an abnormality occurs, the abnormality detection unit can give an alarm in time and take corresponding processing measures, the data verification unit performs verification and error correction processing on the data acquired from the sensor to ensure the integrity and accuracy of the data, the state reset unit automatically resets the system state to an initial state and performs state recovery operation when the abnormality occurs in the system, and the disaster recovery scheme unit formulates a disaster recovery scheme and performs periodic exercise and test, and when a disaster occurs, the system is quickly recovered and operation stability is ensured.
Through the technical scheme, through the design of the fault-tolerant mechanism, the digital buoy automatic mooring control system can respond in time and take corresponding treatment measures when faults or abnormal conditions occur, so that the stability and reliability of the system are improved.
Preferably, the improvement of the parameter model unit includes data source diversity, model component complexity, high-requirement real-time property and decision optimization importance, the data source diversity uses different sensors and devices to collect more kinds and comprehensive data, the model component complexity models and predicts environmental parameters in real time, the construction of the models uses advanced mathematical and statistical methods, including machine learning, regression analysis and support vector machines, the high-requirement real-time property is convenient for a control instruction generating unit to generate corresponding control instructions according to the latest environmental parameters, therefore, the parameter model unit timely acquires the latest environmental parameter data, and can quickly update and predict the model, the decision optimization importance is used for decision and optimization through intelligent algorithms, and more accurate, stable and highly adaptable control instructions are generated.
Through the technical scheme, the improvement of the parameter model unit needs to be focused on the importance of multi-source data acquisition, complex model construction, high real-time requirements and decision optimization.
Preferably, the control instruction unit generates a control instruction according to the result calculated by the data processing unit so as to control the movement and the posture of the pontoon, and the specific control instruction comprises: the steering engine is controlled to rotate, the anchor chain is controlled to stretch out and draw back, the motor rotating speed is controlled, the control executing unit executes a control instruction and controls the pontoon to an expected position and an expected gesture, the system monitoring unit monitors the running state and performance of the whole system and generates alarm information according to the requirement, and meanwhile, the system operation data is recorded, so that reference is provided for subsequent optimization and management.
The beneficial effects of the invention are as follows:
1. The data processing unit is improved by setting, and comprises a data acquisition unit, a filtering and calibrating unit, a data fusion unit, a gesture algorithm unit and a parameter model unit, and the design of the data processing unit has the advantages of improving the data processing precision, accuracy and robustness, optimizing the precision and stability of gesture calculation, improving the adaptability and intelligent degree of the system and the like in the digital buoy automatic mooring control system based on signal transmission. These beneficial effects can promote the performance and the reliability of system to improve the operating efficiency and the security of flotation pontoon automatic mooring control system.
2. By introducing the quaternion formula into the data processing unit and combining with the multi-sensor fusion algorithm and the attitude estimation method of the Kalman filter, the accurate sensing and stable control of the digital buoy automatic mooring control system on the buoy attitude based on signal transmission can be improved, so that the automatic mooring control with higher efficiency is realized, the operation efficiency and the safety are improved, the system performance and the reliability are effectively improved, and the obvious advantage is brought to the application of the buoy automatic mooring control system.
3. The fault tolerance mechanism comprises a redundancy design unit, an anomaly detection unit, a data verification unit, a state reset unit and a disaster recovery scheme unit, the design of the fault tolerance mechanism can improve the reliability, stability and data accuracy of the digital buoy automatic mooring control system, and meanwhile, the system state can be quickly recovered and disaster can be prevented, the beneficial effects can obviously improve the performance and reliability of the system, and better guarantee is provided for the application of the buoy automatic mooring control system.
Drawings
FIG. 1 is a schematic diagram of a digital buoy automatic mooring control system based on signal transmission according to the present invention;
FIG. 2 is a block diagram of a data processing unit of a digital buoy automatic mooring control system based on signal transmission according to the present invention;
FIG. 3 is a block diagram of a data fusion unit of a digital buoy automatic mooring control system based on signal transmission;
fig. 4 is a block diagram of a parametric model unit of a digital buoy automatic mooring control system based on signal transmission.
In the figure: 1. an intelligent control system; 11. a pontoon sensing unit; 12. a signal transmission unit; 13. a data processing unit; 131. a data acquisition unit; 132. a filtering and calibrating unit; 133. a data fusion unit; 1331. a redundancy design unit; 1332. an abnormality detection unit; 1333. a data verification unit; 1334. a state reset unit; 1335. a disaster recovery scheme unit; 134. a gesture algorithm unit; 135. a parameter model unit; 1351. data source diversity; 1352. model component complexity; 1353. high-requirement real-time performance; 1354. decision optimization importance; 14. a control instruction unit; 15. a control execution unit; 16. and a system monitoring unit.
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.
Referring to fig. 1-4, a digital buoy automatic mooring control system based on signal transmission comprises an intelligent control system 1, wherein the intelligent control system 1 comprises a buoy sensing unit 11, a signal transmission unit 12, a data processing unit 13, a control instruction unit 14, a control execution unit 15 and a system monitoring unit 16, the buoy sensing unit 11 is responsible for sensing information of the position, the posture and the surrounding environment of a buoy, including the horizontal inclination angle, the inclination direction of the buoy, the motion state and the water depth of a ship, and the signal transmission unit 12 is responsible for transmitting signals acquired by the buoy sensing unit 11 to a next-stage unit so as to realize information transmission and sharing.
In order to improve the data processing unit 13, the data processing unit 13 processes and analyzes the data received from the signal transmission unit 12, and calculates the position, the posture and the surrounding environment information of the pontoon according to a preset algorithm, the data processing unit 13 comprises a data acquisition unit 131, a filtering and calibrating unit 132, a data fusion unit 133, a posture algorithm unit 134 and a parameter model unit 135, the data acquisition unit 131 adopts various sensors and shooting equipment to acquire the position, the posture and the surrounding environment information of the pontoon in multiple angles and multiple directions, the accuracy and the accuracy of data processing are further improved, the filtering and calibrating unit 132 filters and corrects the acquired data, the accuracy and the stability of the data are improved, the data fusion unit 133 is responsible for integrating the data information from different sensors, the accuracy and the robustness of the data processing are improved, the posture algorithm unit 134 is responsible for establishing an environment parameter model through the improved posture estimation algorithm, the parameter model unit 135 is responsible for establishing an environment parameter model, modeling and predicting the environment parameter in real time, and the adaptability and the intelligent degree of the system are improved.
The data processing unit 13 is improved through setting, the data processing unit 13 comprises a data acquisition unit 131, a filtering and calibrating unit 132, a data fusion unit 133, a gesture algorithm unit 134 and a parameter model unit 135, and the design of the data processing unit 13 has the advantages of improving the data processing precision, accuracy and robustness, optimizing the precision and stability of gesture resolving, improving the adaptability and intelligent degree of the system and the like in the digital buoy automatic mooring control system based on signal transmission. These beneficial effects can promote the performance and the reliability of system to improve the operating efficiency and the security of flotation pontoon automatic mooring control system.
The quaternion formula is introduced into the setting data processing unit 13, and is as follows:
q = (w, x, y, z)
where w is the real part, (x, y, z) is the imaginary part,
And in combination with the attitude algorithm unit 134, in the process of attitude estimation, the quaternion is used to represent the attitude change of the pontoon, and if the quaternion at the current moment is q_cur and the quaternion at the next moment is q_next, the following formula is used for updating:
q_next = q_cur * dq
Wherein dq represents the posture change from the current moment to the next moment, the posture information is obtained by converting angle information obtained by the sensors, when a plurality of sensors provide posture information in the data fusion process, the sensors are integrated by using a weighted average method, quaternions provided by n sensors are respectively q1, q2, & gt, qn, corresponding weights are w1, w2, & gt, and wn, and the integrated quaternions are expressed as:
q_fusion = normalize(w1q1 + w2q2 + ... + wn*qn)
Wherein normalize represents that normalization processing is performed on the result to ensure the unit length of the quaternion, the quaternion formula is used for conveniently performing attitude estimation and data fusion, the accuracy and the robustness of the data processing unit 13 are improved, the quaternion formula is improved through a multi-sensor fusion algorithm, a Kalman filter is introduced, and the accelerometer, the magnetometer and the vision sensor are combined for performing attitude estimation, wherein the formula is as follows:
step one, a quaternion updating formula in the attitude estimation process:
q_next = q_cur * dq
Wherein dq is the gesture change from the current moment to the next moment, and is obtained by converting the angle information obtained by the sensor;
step two, a posture estimation formula after multi-sensor data fusion is as follows:
q_fusion = normalize(w1q1 + w2q2 + ... + wn*qn)
Wherein q1, q2,..qn is the quaternion provided by the plurality of sensors, w1, w2,..wn is the corresponding weight, normalize denotes the normalization of the results to ensure the unit length of the quaternion,
Thirdly, using an attitude estimation formula of a Kalman filter:
The equation of state: x (k) =f (x (k-1), u (k-1)) +w (k-1)
Observation equation: z (k) =h (x (k)) +v (k)
Wherein x (k) is a gesture state vector, u (k-1) is a control vector, w (k-1) is system noise, z (k) is a measurement vector, h (x (k)) is an observation function, v (k) is measurement noise, and unlike EKF, KF uniformly represents measurement data of all sensors as one measurement vector, multi-sensor data fusion is realized by estimating a measurement noise covariance matrix and a system noise covariance matrix, a multi-sensor fusion algorithm combines various data sources such as an accelerometer, a magnetometer and a vision sensor to perform gesture estimation, and a kalman filter can effectively fuse data of different sensors and adaptively estimate gesture states, thereby improving accuracy and robustness of gesture estimation.
By introducing the quaternion formula into the data processing unit 13 and combining with the multi-sensor fusion algorithm and the attitude estimation method of the Kalman filter, the accurate sensing and stable control of the digital buoy automatic mooring control system on the buoy attitude based on signal transmission can be improved, so that the automatic mooring control is realized more efficiently, the operation efficiency and the safety are improved, the system performance and the reliability are effectively improved, and the obvious advantage is brought to the application of the buoy automatic mooring control system.
In order to improve the data fusion unit 133, the data fusion unit 133 is provided with a fault-tolerant mechanism, the fault-tolerant mechanism comprises a redundancy design unit 1331, an anomaly detection unit 1332, a data verification unit 1333, a state reset unit 1334 and a disaster recovery scheme unit 1335, the redundancy design unit 1331 automatically switches to the standby sensor and the device by adding the standby sensor and the device when partial sensor data are abnormal, the continuity and the reliability of the system are ensured, the anomaly detection unit 1332 monitors the operation state and the data change of the system in real time, when the abnormal condition occurs, an alarm can be sent out timely and corresponding processing measures are adopted, the data verification unit 1333 performs verification and error correction processing on the data acquired from the sensor, the integrity and the accuracy of the data are ensured, the state reset unit 1334 automatically resets the system state to an initial state when the abnormal condition occurs in the system, performs state recovery operation, the disaster recovery scheme unit 1335 makes periodic exercise and test, the system is quickly recovered and ensured to operate stably when the disaster occurs, the digital automatic buoy mooring control can respond to the abnormal condition or the corresponding measures and the reliability of the system can be improved timely and the stability are adopted when the abnormal condition occurs.
By arranging the data fusion unit 133 to have a fault-tolerant mechanism, the fault-tolerant mechanism comprises a redundancy design unit 1331, an anomaly detection unit 1332, a data verification unit 1333, a state reset unit 1334 and a disaster recovery scheme unit 1335, the design of the fault-tolerant mechanism can improve the reliability, stability and data accuracy of the digital buoy automatic mooring control system, and meanwhile, the system state can be quickly recovered and disaster can be prevented, the beneficial effects can obviously improve the performance and reliability of the system, and better guarantee is provided for the application of the buoy automatic mooring control system.
And the improvement of the parameter model unit 135 is set to include data source diversity 1351, model component complexity 1352, high-requirement real-time 1353 and decision optimization importance 1354, the data source diversity 1351 uses different sensors and devices to collect more kinds of and comprehensive data, the model component complexity 1352 models and predicts environmental parameters in real time, the construction of the models uses advanced mathematical and statistical methods, including machine learning, regression analysis and support vector machines, the high-requirement real-time 1353 facilitates the control instruction generating unit to generate corresponding control instructions according to the latest environmental parameters, therefore, the parameter model unit 135 timely obtains the latest environmental parameter data and can quickly update and predict the model, the decision optimization importance 1354 makes decisions and optimizations through intelligent algorithms to generate more accurate, stable and adaptive control instructions, the improvement of the parameter model unit 135 needs to consider the collection of multi-source data, the construction of complex models, the high-real-time requirement and the importance of optimization, and the importance of the control instruction generating unit 14 generates control instructions according to the results calculated by the data processing unit 13, and the control instructions include the specific motion control instructions and the pontoon: the steering engine is controlled to rotate, the anchor chain is controlled to stretch out and draw back, the motor rotating speed is controlled, the control executing unit 15 executes control instructions, the pontoon is controlled to an expected position and an expected attitude, the system monitoring unit 16 monitors the running state and performance of the whole system, generates alarm information according to requirements, and meanwhile records running data of the system to provide reference for subsequent optimization and management.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical scheme of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (10)
1. The utility model provides a digital flotation pontoon automatic mooring control system based on signal transmission, includes intelligent control system (1), its characterized in that: the intelligent control system (1) comprises a buoy sensing unit (11), a signal transmission unit (12), a data processing unit (13), a control instruction unit (14), a control execution unit (15) and a system monitoring unit (16), wherein the buoy sensing unit (11) is used for sensing the position, the posture and the surrounding environment information of the buoy, the information comprises the horizontal inclination angle, the inclination direction and the motion state and the water depth of the ship, and the signal transmission unit (12) is used for transmitting the signals acquired by the buoy sensing unit (11) to a next-stage unit so as to realize information transmission and sharing.
2. A digital buoy automatic mooring control system based on signal transmission according to claim 1, characterized in that: the data processing unit (13) processes and analyzes the data received from the signal transmission unit (12) and calculates the position, the posture and the surrounding environment information of the pontoon according to a preset algorithm, and the data processing unit (13) comprises a data acquisition unit (131), a filtering and calibrating unit (132), a data fusion unit (133), a posture algorithm unit (134) and a parameter model unit (135).
3. A digital buoy automatic mooring control system based on signal transmission according to claim 2, characterized in that: the data acquisition unit (131) adopts various sensors and shooting equipment to acquire the position, the posture and the surrounding environment information of the pontoon in multiple angles and multiple directions, so that the accuracy and the accuracy of data processing are improved, the filtering and calibrating unit (132) filters and corrects the acquired data, the accuracy and the stability of the data are improved, and the data fusion unit (133) is responsible for integrating the data information from different sensors, and the accuracy and the robustness of the data processing are improved.
4. A digital buoy automatic mooring control system based on signal transmission according to claim 3, characterized in that: the attitude algorithm unit (134) is responsible for improving the accuracy and stability of the attitude calculation of the pontoon through an improved attitude estimation algorithm, the parameter model unit (135) is responsible for establishing an environment parameter model, modeling and predicting environment parameters in real time, and improving the adaptability and the intelligent degree of the system.
5. The digital buoy automatic mooring control system based on signal transmission according to claim 4, wherein: the data processing unit (13) is internally introduced with a quaternion formula, which is as follows:
q = (w, x, y, z)
where w is the real part, (x, y, z) is the imaginary part,
And combining with a gesture algorithm unit (134), in the gesture estimation process, using quaternion to represent gesture change of the pontoon, and assuming that the quaternion at the current moment is q_cur and the quaternion at the next moment is q_next, updating by using the following formula:
q_next = q_cur * dq
wherein dq represents the posture change from the current time to the next time, obtained by converting the angle information obtained by the sensor,
In the data fusion process, when a plurality of sensors provide gesture information, the sensors are integrated by using a weighted average method, quaternions provided by n sensors are respectively q1, q2, qn, the corresponding weights are w1, w2, qn, and wn, and the integrated quaternions are expressed as:
q_fusion = normalize(w1q1 + w2q2 + ... + wn*qn)
wherein normalize denotes that the result is normalized to ensure the unit length of the quaternion.
6. A digital buoy automatic mooring control system based on signal transmission according to claim 5, characterized in that: the quaternion formula is improved through a multi-sensor fusion algorithm, a Kalman filter is introduced, and the quaternion formula is combined with various data sources of an accelerometer, a magnetometer and a vision sensor to perform attitude estimation, wherein the formula is as follows:
step one, a quaternion updating formula in the attitude estimation process:
q_next = q_cur * dq
Wherein dq is the gesture change from the current moment to the next moment, and is obtained by converting the angle information obtained by the sensor;
step two, a posture estimation formula after multi-sensor data fusion is as follows:
q_fusion = normalize(w1q1 + w2q2 + ... + wn*qn)
Wherein q1, q2,..qn is the quaternion provided by the plurality of sensors, w1, w2,..wn is the corresponding weight, normalize denotes the normalization of the results to ensure the unit length of the quaternion,
Thirdly, using an attitude estimation formula of a Kalman filter:
The equation of state: x (k) =f (x (k-1), u (k-1)) +w (k-1)
Observation equation: z (k) =h (x (k)) +v (k)
Wherein x (k) is an attitude state vector, u (k-1) is a control vector, w (k-1) is system noise, z (k) is a measurement vector, h (x (k)) is an observation function, and v (k) is measurement noise, unlike EKF, KF uniformly represents measurement data of all sensors as one measurement vector, and multi-sensor data fusion is realized by estimating a measurement noise covariance matrix and a system noise covariance matrix.
7. The digital buoy automatic mooring control system based on signal transmission according to claim 6, wherein: the data fusion unit (133) is provided with a fault-tolerant mechanism, the fault-tolerant mechanism comprises a redundancy design unit (1331), an anomaly detection unit (1332), a data verification unit (1333), a state reset unit (1334) and a disaster recovery scheme unit (1335), and the redundancy design unit (1331) automatically switches to the standby sensor and the standby device when partial sensor data are abnormal by adding the standby sensor and the standby device, so that the continuity and the reliability of the system are ensured.
8. A digital buoy automatic mooring control system based on signal transmission according to claim 7, characterized in that: the system comprises a data verification unit (1333) and a state reset unit (1334), wherein the data verification unit (1332) is used for verifying and correcting data acquired from a sensor through monitoring the running state and data change of the system in real time, when an abnormal condition occurs, the data verification unit (1333) can timely send out an alarm and take corresponding processing measures, the integrity and accuracy of the data are ensured, the state reset unit (1334) is used for automatically resetting the system state to an initial state and performing state recovery operation when the abnormal condition occurs in the system, the disaster recovery scheme unit (1335) is used for making a disaster recovery scheme and performing periodic exercise and test, and the system is quickly recovered and running stability is ensured when the disaster occurs.
9. The digital buoy automatic mooring control system based on signal transmission according to claim 8, wherein: the improvement of the parameter model unit (135) comprises data source diversity (1351), model component complexity (1352), high-requirement real-time performance (1353) and decision optimization importance (1354), wherein the data source diversity (1351) uses different sensors and devices to collect more kinds and comprehensive data, the model component complexity (1352) models and predicts environmental parameters in real time, advanced mathematical and statistical methods are used for constructing the models, including machine learning, regression analysis and support vector machines, the high-requirement real-time performance (1353) is convenient for a control instruction generating unit to generate corresponding control instructions according to the latest environmental parameters, the parameter model unit (135) can timely acquire the latest environmental parameter data, model updating and prediction can be rapidly carried out, and the decision optimization importance (1354) carries out decision and optimization through intelligent algorithms to generate more accurate, stable and adaptive control instructions.
10. A digital buoy automatic mooring control system based on signal transmission according to claim 9, characterized in that: the control instruction unit (14) generates a control instruction according to the result calculated by the data processing unit (13) so as to control the movement and the gesture of the pontoon, and the specific control instruction comprises: the steering engine is controlled to rotate, the anchor chain is controlled to stretch out and draw back, the motor rotating speed is controlled, the control executing unit (15) executes control instructions, the pontoon is controlled to an expected position and an expected gesture, the system monitoring unit (16) monitors the running state and performance of the whole system, generates alarm information according to requirements, and meanwhile records running data of the system to provide reference for subsequent optimization and management.
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