Intelligent control system and method for navigational speed and navigational direction of surface ship

Abstract

The invention discloses an intelligent control system and method for the navigational speed and the navigational course of a surface ship, and relates to the technical field of ship navigation control, and the system comprises a data collection and mode construction unit, an integrated processing unit, a navigational speed and navigational course control mode selection unit and a real-time monitoring and optimization unit. The data collection and mode construction unit is used for collecting historical navigation data of a surface ship under different sea conditions, constructing a navigational speed and course control mode taking the sea condition data as parameters, and setting each threshold index in the navigational speed and course control mode; and the integrated processing unit is used for carrying out integrated processing on the set navigational speed and course control mode, the threshold value index and a naval vessel controller. By collecting and analyzing the historical navigation data, the navigation speed and course control mode taking the sea condition data as the parameters is constructed, so that the naval vessel can automatically select the optimal navigation mode and parameters according to the real-time sea condition and task requirements, intelligent navigation decision is realized, and the overall efficiency of the naval vessel is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ship navigation control, and in particular to an intelligent control system and method for the speed and heading of a surface ship. Background Art

[0002] Surface ships are ships that sail in the ocean or inland waters. They are usually equipped with propulsion systems and communication equipment to perform various tasks. With the increasing number of marine activities, the efficient and safe navigation of surface ships in various complex sea conditions has become a research focus. Traditional surface ship speed and heading control mainly relies on the captain's experience and manual operation. This method has great limitations when facing rapidly changing sea conditions.

[0003] In recent years, with the development of sensor technology, computer technology and artificial intelligence, intelligent control technology has gradually been applied to the speed and heading control of surface ships to improve the safety and economy of navigation. Although some intelligent control technologies have been applied to the speed and heading control of surface ships, the following problems still exist:

[0004] 1. Insufficient generalization ability: The existing intelligent control model has poor generalization ability when facing extreme sea conditions and is prone to misjudgment.

[0005] 2. Limited adaptability: The existing system has limited adaptability to different types of ships and sea conditions, making it difficult to achieve wide applicability.

[0006] 3. Low integration: Some intelligent control systems are not highly integrated with the existing navigation system of the ship, which affects the overall performance of the system.

[0007] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0008] In view of the problems in the related technology, the present invention proposes an intelligent control system and method for the speed and heading of a surface ship to overcome the above-mentioned technical problems existing in the existing related technology.

[0009] To this end, the specific technical solution adopted by the present invention is as follows:

[0010] According to one aspect of the present invention, there is provided an intelligent control system for the speed and heading of a surface ship, the system comprising: a data collection and pattern building unit, an integrated processing unit, a speed and heading control pattern selection unit and a real-time monitoring and optimization unit;

[0011] The data collection and mode construction unit is used to collect historical navigation data of surface ships under different sea conditions, construct a speed and heading control mode with sea condition data as parameters, and set various threshold indicators in the speed and heading control mode;

[0012] An integrated processing unit, used for integrating the set speed and heading control mode and threshold index with the ship controller;

[0013] The speed and heading control mode selection unit is used to select the corresponding speed and heading control mode using the integrated processed ship controller according to the mission requirements of the surface ship and the real-time sea condition data;

[0014] The real-time monitoring and optimization unit is used to monitor the speed and heading control mode and threshold indicators in real time, and optimize the speed and heading control mode and threshold indicators according to actual mission requirements and changes in sea conditions data.

[0015] Furthermore, the data collection and model building unit includes: a data collection and preprocessing module, a feature extraction and sample synthesis module, a model building and training module, a control mode formulation module and a threshold indicator setting module;

[0016] The data collection and preprocessing module is used to collect the historical navigation data of surface ships under different sea conditions and preprocess the historical navigation data;

[0017] The feature extraction and sample synthesis module is used to extract feature data from the preprocessed historical navigation data using the principal component analysis algorithm, and fuse the feature data with the corresponding sea state data to obtain sample data and establish a sample data set;

[0018] Model building and training module, which is used to build a speed and heading control mode prediction model using machine learning algorithms, and train the speed and heading control mode prediction model using sample data sets;

[0019] The control mode formulation module is used to input sample data into the trained speed and heading control mode prediction model, output the prediction results, and formulate several speed and heading control modes based on the prediction results;

[0020] The threshold index setting module is used to set various threshold indexes in the speed and heading control mode.

[0021] Furthermore, the data collection and preprocessing module includes: a data collection submodule, a data cleaning and interpolation processing submodule, and a data standardization and format conversion submodule;

[0022] The data collection submodule is used to deploy a sensor network and use the sensor network to collect historical navigation data of surface ships under different sea conditions from multiple data sources. The navigation data includes ship data, environmental data, and mission data.

[0023] The data cleaning and interpolation processing submodule is used to clean the historical navigation data and fill in the missing values ​​in the historical navigation data using the interpolation algorithm;

[0024] The data standardization and format conversion submodule is used to standardize the cleaned historical navigation data and convert the format of the standardized historical navigation data.

[0025] Furthermore, the feature extraction and sample synthesis module includes: a matrix calculation and feature decomposition submodule, a principal component selection submodule, a data projection and feature data extraction submodule, a feature data and sea state data fusion submodule and a sample data set establishment submodule;

[0026] The matrix calculation and eigendecomposition submodule is used to calculate the covariance matrix of the preprocessed historical navigation data using the principal component analysis algorithm, and to perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors;

[0027] The principal component selection submodule is used to sort the eigenvalues ​​according to a preset arrangement order and select several eigenvalues ​​as principal components;

[0028] The data projection and feature data extraction submodule is used to project the preprocessed historical navigation data into the subspace composed of the selected principal components to obtain feature data;

[0029] The feature data and sea state data fusion submodule is used to match the feature data with the corresponding sea state data, and fuse the feature data with the sea state data based on the matching result to obtain sample data;

[0030] The sample data set establishment submodule is used to establish a sample data set based on the obtained sample data.

[0031] Furthermore, the characteristic data is matched with the corresponding sea state data, and the characteristic data and the sea state data are fused based on the matching result, and the obtained sample data includes:

[0032] Collect sea state data and pre-process the sea state data;

[0033] According to the time series, the preprocessed sea state data is aligned with the characteristic data;

[0034] Based on the alignment results, the locality sensitive hashing technology is applied to map the preprocessed sea state data and feature data into the hash table;

[0035] Calculate the hash value of the feature data through a hash function, and select the sea state data matching the hash value of the feature data in the hash table as matching candidate data;

[0036] The cosine similarity algorithm is used to calculate the similarity between the feature data and the matching candidate data, and the sea state data point with the highest similarity is selected as the matching result according to the preset similarity threshold;

[0037] Based on the matching results, the feature data and the sea condition data are spliced ​​in a preset format to obtain sample data.

[0038] Furthermore, the formula of the hash function is:

[0039]

[0040] Where h(x) represents the hash value of feature data x;

[0041] a represents a randomly generated vector;

[0042] x represents feature data;

[0043] b represents a randomly generated bias;

[0044] ρ represents the width of the bucket.

[0045] Furthermore, the threshold indicators include speed threshold, heading threshold, dynamic performance threshold, environmental suitability threshold, safety protection threshold and control stability threshold.

[0046] Further, the integrated processing unit includes: an integration and signal acquisition module, a signal conversion and instruction generation module, a control frequency and quantity calculation module and a control and execution module;

[0047] The integration and signal acquisition module is used to integrate the set speed and heading control mode and threshold indicators into the ship controller, and collect corresponding control signals based on the integrated speed and heading control mode and threshold indicators;

[0048] The signal conversion and instruction generation module is used to convert the collected control signals into control data using the conversion algorithm built into the ship controller, and to generate control instructions based on the control data;

[0049] A control frequency and quantity calculation module is used to calculate the integrated control frequency of the ship controller according to the generated control instructions and determine the corresponding integrated control quantity;

[0050] The control and execution module is used to input the calculated integrated control frequency and the corresponding integrated control quantity into the ship controller after the integrated processing to perform the corresponding control processing.

[0051] Furthermore, the calculation formula of the conversion algorithm is:

[0052]

[0053] In the formula, R c Represents control data of a ship controller;

[0054] K p represents the proportional gain coefficient;

[0055] S represents the control signal of the ship controller;

[0056] K i represents the integral gain coefficient;

[0057] t represents time;

[0058] K d Represents the differential gain coefficient.

[0059] According to another aspect of the present invention, there is also provided an intelligent control method for the speed and heading of a surface ship, the method comprising:

[0060] S1. Collect historical navigation data of surface ships under different sea conditions, construct a speed and heading control mode with sea condition data as parameters, and set various threshold indicators in the speed and heading control mode;

[0061] S2. Integrate the set speed and heading control mode and threshold index with the ship controller;

[0062] S3, according to the mission requirements of the surface ship and the real-time sea condition data, the corresponding speed and heading control mode is selected by using the integrated processed ship controller;

[0063] S4. Monitor the speed and heading control mode and threshold indicators in real time, and optimize the speed and heading control mode and threshold indicators according to actual mission requirements and changes in sea conditions data.

[0064] The beneficial effects of the present invention are:

[0065] 1. The present invention collects and analyzes historical navigation data to construct a speed and heading control mode with sea condition data as parameters, so that the ship can automatically select the optimal navigation mode and parameters according to the real-time sea conditions and mission requirements, realize intelligent navigation decision-making, and significantly improve the overall efficiency of the ship.

[0066] 2. The present invention monitors the speed and heading control mode and threshold indicators in real time, and optimizes according to actual mission requirements and changes in sea conditions data. It can continuously improve and adjust navigation strategies, making ships more adaptable to complex and changeable marine environments, thereby significantly enhancing navigation safety.

[0067] 3. The present invention can continuously adjust and improve the navigation strategy by real-time monitoring and optimizing the speed and heading control mode and threshold indicators, making the navigation parameters of the ship more scientific and reasonable, which not only improves the navigation performance of the ship, but also further improves the automation level of the ship. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 It is a principle block diagram of an intelligent control system for the speed and heading of a surface ship according to an embodiment of the present invention;

[0070] Figure 2 The present invention is a flowchart of an intelligent control method for the speed and heading of a surface ship according to an embodiment of the present invention.

[0071] In the figure:

[0072] 1. Data collection and model building unit; 2. Integrated processing unit; 3. Speed ​​and heading control mode selection unit; 4. Real-time monitoring and optimization unit. DETAILED DESCRIPTION

[0073] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments. They can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in this field should be able to understand other possible implementation methods and advantages of the present invention.

[0074] According to an embodiment of the present invention, an intelligent control system and method for the speed and heading of a surface ship are provided.

[0075] The present invention will now be further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an intelligent control system for the speed and heading of a surface ship according to an embodiment of the present invention, the system comprises: a data collection and mode construction unit 1, an integrated processing unit 2, a speed and heading control mode selection unit 3 and a real-time monitoring and optimization unit 4;

[0076] The data collection and mode construction unit 1 is used to collect historical navigation data of surface ships under different sea conditions, construct a speed and heading control mode with sea condition data as parameters, and set various threshold indicators in the speed and heading control mode.

[0077] Specifically, the data collection and model building unit 1 includes: a data collection and preprocessing module, a feature extraction and sample synthesis module, a model building and training module, a control mode formulation module and a threshold indicator setting module;

[0078] The data collection and preprocessing module is used to collect the historical navigation data of surface ships under different sea conditions and preprocess the historical navigation data.

[0079] Specifically, the data collection and preprocessing module includes: a data collection submodule, a data cleaning and interpolation processing submodule, and a data standardization and format conversion submodule;

[0080] The data collection submodule is used to deploy a sensor network and use the sensor network to collect historical navigation data of surface ships under different sea conditions from multiple data sources. The navigation data includes ship data, environmental data, and mission data.

[0081] The data cleaning and interpolation processing submodule is used to clean the historical navigation data and fill in the missing values ​​in the historical navigation data using the interpolation algorithm;

[0082] The data standardization and format conversion submodule is used to standardize the cleaned historical navigation data and convert the format of the standardized historical navigation data.

[0083] It should be noted that navigation data includes the ship's speed, heading, position, time and the ship's response, which is the basis for building a speed and heading control mode.

[0084] The feature extraction and sample synthesis module is used to extract feature data from the preprocessed historical navigation data using the principal component analysis algorithm, and fuse the feature data with the corresponding sea condition data to obtain sample data and establish a sample data set.

[0085] Specifically, the feature extraction and sample synthesis module includes: matrix calculation and feature decomposition submodule, principal component selection submodule, data projection and feature data extraction submodule, feature data and sea state data fusion submodule and sample data set establishment submodule;

[0086] The matrix calculation and eigendecomposition submodule is used to calculate the covariance matrix of the preprocessed historical navigation data using the principal component analysis algorithm, and to perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors.

[0087] The principal component selection submodule is used to sort the eigenvalues ​​according to a preset arrangement order and select several eigenvalues ​​as principal components.

[0088] The data projection and feature data extraction submodule is used to project the preprocessed historical navigation data into the subspace composed of the selected principal components to obtain feature data.

[0089] The feature data and sea state data fusion submodule is used to match the feature data with the corresponding sea state data, and fuse the feature data with the sea state data based on the matching result to obtain sample data.

[0090] Specifically, the feature data is matched with the corresponding sea state data, and the feature data and the sea state data are fused based on the matching result, and the sample data obtained includes:

[0091] Collect sea state data and preprocess the sea state data.

[0092] The preprocessed sea state data are aligned with the feature data according to the time series.

[0093] Based on the alignment results, the local sensitive hashing technique is applied to map the preprocessed sea state data and feature data into the hash table.

[0094] The hash value of the feature data is calculated by a hash function, and the sea condition data matching the hash value of the feature data is screened out in the hash table as matching candidate data.

[0095] Specifically, the formula of the hash function is:

[0096]

[0097] Where h(x) represents the hash value of feature data x;

[0098] a represents a randomly generated vector;

[0099] x represents feature data;

[0100] b represents a randomly generated bias;

[0101] ρ represents the width of the bucket.

[0102] It should be noted that sea condition data includes wind speed, wave height, current, etc.

[0103] The cosine similarity algorithm is used to calculate the similarity between the feature data and the matching candidate data, and according to the preset similarity threshold, the sea state data point with the highest similarity is selected as the matching result.

[0104] Based on the matching results, the feature data and the sea condition data are spliced ​​in a preset format to obtain sample data.

[0105] The sample data set establishment submodule is used to establish a sample data set based on the obtained sample data.

[0106] The model building and training module is used to use machine learning algorithms to build a speed and heading control mode prediction model, and use sample data sets to train the speed and heading control mode prediction model.

[0107] The control mode formulation module is used to input sample data into the speed and heading control mode prediction model after training, output the prediction results, and formulate several speed and heading control modes based on the prediction results.

[0108] The threshold index setting module is used to set various threshold indexes in the speed and heading control mode.

[0109] Specifically, the threshold indicators include speed threshold, heading threshold, dynamic performance threshold, environmental suitability threshold, safety protection threshold and control stability threshold.

[0110] The integrated processing unit 2 is used to integrate the set speed and heading control mode and threshold indicators with the ship controller.

[0111] Specifically, the integrated processing unit 2 includes: an integration and signal acquisition module, a signal conversion and instruction generation module, a control frequency and quantity calculation module, and a control and execution module;

[0112] The integration and signal acquisition module is used to integrate the set speed and heading control mode and threshold indicators into the ship controller, and to collect corresponding control signals based on the integrated speed and heading control mode and threshold indicators.

[0113] The signal conversion and instruction generation module is used to convert the collected control signals into control data using the conversion algorithm built into the ship controller, and to generate control instructions based on the control data.

[0114] Specifically, the calculation formula of the conversion algorithm is:

[0115]

[0116] In the formula, R c Represents control data of a ship controller;

[0117] K p represents the proportional gain coefficient;

[0118] S represents the control signal of the ship controller;

[0119] K i represents the integral gain coefficient;

[0120] t represents time;

[0121] K d Represents the differential gain coefficient.

[0122] The control frequency and quantity calculation module is used to calculate the integrated control frequency of the ship controller according to the generated control instructions and determine the corresponding integrated control quantity.

[0123] The control and execution module is used to input the calculated integrated control frequency and the corresponding integrated control quantity into the ship controller after the integrated processing to perform the corresponding control processing.

[0124] The speed and heading control mode selection unit 3 is used to select the corresponding speed and heading control mode using the integrated processed ship controller according to the mission requirements of the surface ship and the real-time sea condition data.

[0125] It is necessary to clarify the type of mission, target location, time limit and other demand information currently performed by the surface ship, input the real-time sea condition data and mission requirement data into the integrated ship controller; pre-process the input data, including data cleaning, format conversion, normalization and other operations to ensure the accuracy and consistency of the data; based on the real-time sea condition data and mission requirement data, use the algorithm model in the ship controller for intelligent analysis, and select the most appropriate control mode from the preset speed and heading control modes based on the analysis results. For example, in severe sea conditions, a more robust and conservative control mode may be selected to ensure navigation safety; in urgent mission requirements, a faster and more flexible control mode may be selected to improve navigation efficiency.

[0126] According to the characteristics of the selected control mode and actual needs, set the corresponding speed and heading control threshold indicators; for example, set the maximum speed, minimum speed, safe speed range, maximum steering angle and other threshold indicators, which will be used to limit and control the ship's navigation speed and direction to ensure the stability and safety of the navigation process. The selected control mode and the set threshold indicators are transmitted to the ship's actuators (such as engines, steering gears, etc.), and the actuators perform corresponding operations and adjustments according to the received control instructions and threshold indicators to achieve the ship's speed and heading control.

[0127] The real-time monitoring and optimization unit 4 is used to monitor the speed and heading control mode and threshold indicators in real time, and optimize the speed and heading control mode and threshold indicators according to actual mission requirements and changes in sea state data.

[0128] It should be noted that the ship's speed and heading are tracked in real time and compared with the preset control mode. If a deviation is found between the actual navigation status and the control mode, an alarm is triggered immediately and optimization adjustments are prepared. According to the real-time monitored sea conditions and ship status, the threshold indicators of speed and heading are dynamically adjusted. For example, in the case of strong winds and waves, the speed threshold can be appropriately lowered to ensure the safety of the ship. If the current control mode cannot adapt to the real-time sea conditions or mission requirements, it will automatically switch to a more appropriate control mode; use machine learning algorithms, such as reinforcement learning and genetic algorithms, to continuously optimize the parameters of the control mode, and through iterative training, make the control mode more adaptable to the complex and changing marine environment and mission requirements.

[0129] like Figure 2 According to another embodiment of the present invention, there is also provided an intelligent control method for the speed and heading of a surface ship, the method comprising:

[0130] S1. Collect historical navigation data of surface ships under different sea conditions, construct a speed and heading control mode with sea condition data as parameters, and set various threshold indicators in the speed and heading control mode;

[0131] S2. Integrate the set speed and heading control mode and threshold index with the ship controller;

[0132] S3, according to the mission requirements of the surface ship and the real-time sea condition data, the corresponding speed and heading control mode is selected by using the integrated processed ship controller;

[0133] S4. Monitor the speed and heading control mode and threshold indicators in real time, and optimize the speed and heading control mode and threshold indicators according to actual mission requirements and changes in sea conditions data.

[0134] In summary, with the help of the above technical solution of the present invention, by collecting and analyzing historical navigation data, a speed and heading control mode with sea condition data as parameters is constructed, so that the ship can automatically select the optimal navigation mode and parameters according to the real-time sea conditions and mission requirements, realize intelligent navigation decision-making, and significantly improve the overall efficiency of the ship. By real-time monitoring of the speed and heading control mode and threshold indicators, and optimizing according to actual mission requirements and changes in sea condition data, the navigation strategy can be continuously improved and adjusted, so that the ship can better adapt to the complex and changeable marine environment, thereby significantly enhancing the safety of navigation. By real-time monitoring and optimizing the speed and heading control mode and threshold indicators, the navigation strategy can be continuously adjusted and improved, so that the navigation parameters of the ship are more scientific and reasonable, which not only improves the navigation performance of the ship, but also further improves the automation level of the ship.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control system for the speed and heading of a surface ship, characterized in that: The system includes: a data collection and mode building unit, an integrated processing unit, a speed and heading control mode selection unit, and a real-time monitoring and optimization unit; The data collection and mode construction unit is used to collect historical navigation data of surface ships under different sea conditions, construct a speed and heading control mode with sea condition data as parameters, and set various threshold indicators in the speed and heading control mode; The integrated processing unit is used to integrate the set speed and heading control mode and threshold index with the ship controller; The speed and heading control mode selection unit is used to select the corresponding speed and heading control mode using the integrated processed ship controller according to the mission requirements of the surface ship and the real-time sea condition data; The real-time monitoring and optimization unit is used to monitor the speed and heading control mode and threshold indicators in real time, and optimize the speed and heading control mode and threshold indicators according to actual mission requirements and changes in sea condition data.

2. The intelligent control system for speed and heading of a surface ship according to claim 1, characterized in that: The data collection and model building unit includes: a data collection and preprocessing module, a feature extraction and sample synthesis module, a model building and training module, a control mode formulation module and a threshold indicator setting module; The data collection and preprocessing module is used to collect historical navigation data of surface ships under different sea conditions and preprocess the historical navigation data; The feature extraction and sample synthesis module is used to extract feature data from the pre-processed historical navigation data using a principal component analysis algorithm, and fuse the feature data with the corresponding sea state data to obtain sample data and establish a sample data set; The model building and training module is used to construct a speed and heading control mode prediction model using a machine learning algorithm, and train the speed and heading control mode prediction model using a sample data set; The control mode formulation module is used to input sample data into the trained speed and heading control mode prediction model, output the prediction results, and formulate several speed and heading control modes based on the prediction results; The threshold index setting module is used to set various threshold indexes in the speed and heading control mode.

3. The intelligent control system for speed and heading of a surface ship according to claim 2, characterized in that: The data collection and preprocessing module includes: a data collection submodule, a data cleaning and interpolation processing submodule, and a data standardization and format conversion submodule; The data collection submodule is used to deploy a sensor network and use the sensor network to collect historical navigation data of surface ships under different sea conditions from multi-channel data sources, wherein the navigation data includes ship data, environmental data and mission data; The data cleaning and interpolation processing submodule is used to clean the historical navigation data and fill in the missing values ​​in the historical navigation data using an interpolation algorithm; The data standardization and format conversion submodule is used to standardize the cleaned historical navigation data and convert the format of the standardized historical navigation data.

4. The intelligent control system for speed and heading of a surface ship according to claim 3 is characterized in that: The feature extraction and sample synthesis module includes: a matrix calculation and feature decomposition submodule, a principal component selection submodule, a data projection and feature data extraction submodule, a feature data and sea state data fusion submodule and a sample data set establishment submodule; The matrix calculation and eigendecomposition submodule is used to calculate the covariance matrix of the preprocessed historical navigation data using the principal component analysis algorithm, and perform eigendecomposition on the covariance matrix to obtain eigenvalues ​​and eigenvectors; The principal component selection submodule is used to sort the eigenvalues ​​according to a preset arrangement order and select a number of eigenvalues ​​as principal components; The data projection and feature data extraction submodule is used to project the pre-processed historical navigation data into the subspace composed of the selected principal components to obtain feature data; The feature data and sea state data fusion submodule is used to match the feature data with the corresponding sea state data, and fuse the feature data with the sea state data based on the matching result to obtain sample data; The sample data set establishing submodule is used to establish a sample data set based on the obtained sample data.

5. The intelligent control system for speed and heading of a surface ship according to claim 4, characterized in that: The matching of the characteristic data with the corresponding sea condition data and fusing the characteristic data with the sea condition data based on the matching result to obtain the sample data includes: Collect sea state data and pre-process the sea state data; According to the time series, the preprocessed sea state data is aligned with the characteristic data; Based on the alignment results, the locality sensitive hashing technology is applied to map the preprocessed sea state data and feature data into the hash table; Calculate the hash value of the feature data through a hash function, and select the sea state data matching the hash value of the feature data in the hash table as matching candidate data; The cosine similarity algorithm is used to calculate the similarity between the feature data and the matching candidate data, and the sea state data point with the highest similarity is selected as the matching result according to the preset similarity threshold; Based on the matching results, the feature data and the sea condition data are spliced ​​in a preset format to obtain sample data.

6. The intelligent control system for speed and heading of a surface ship according to claim 5, characterized in that: The formula of the hash function is: Where h(x) represents the hash value of feature data x; a represents a randomly generated vector; x represents feature data; b represents a randomly generated bias; ρ represents the width of the bucket.

7. The intelligent control system for speed and heading of a surface ship according to claim 6, characterized in that: The threshold indicators include speed threshold, heading threshold, dynamic performance threshold, environmental suitability threshold, safety protection threshold and control stability threshold.

8. The intelligent control system for speed and heading of a surface ship according to claim 1, characterized in that: The integrated processing unit includes: an integration and signal acquisition module, a signal conversion and instruction generation module, a control frequency and quantity calculation module and a control and execution module; The integration and signal acquisition module is used to integrate the set speed and heading control mode and threshold index into the ship controller, and based on the integrated speed and heading control mode and threshold index, collect corresponding control signals; The signal conversion and instruction generation module is used to convert the collected control signal into control data using the conversion algorithm built into the ship controller, and generate control instructions based on the control data; The control frequency and quantity calculation module is used to calculate the integrated control frequency of the ship controller according to the generated control instructions and determine the corresponding integrated control quantity; The control and execution module is used to input the calculated integrated control frequency and the corresponding integrated control quantity into the ship controller after the integrated processing to execute the corresponding control processing.

9. The intelligent control system for speed and heading of a surface ship according to claim 8, characterized in that: The calculation formula of the conversion algorithm is: In the formula, R c Represents control data of a ship controller; K p represents the proportional gain coefficient; S represents the control signal of the ship controller; K i represents the integral gain coefficient; t represents time; K d Represents the differential gain coefficient.

10. An intelligent control method for the speed and heading of a surface ship, used to implement the intelligent control system for the speed and heading of a surface ship according to any one of claims 1 to 9, characterized in that: The method includes: S1. Collect historical navigation data of surface ships under different sea conditions, construct a speed and heading control mode with sea condition data as parameters, and set various threshold indicators in the speed and heading control mode; S2. Integrate the set speed and heading control mode and threshold index with the ship controller; S3, according to the mission requirements of the surface ship and the real-time sea condition data, the corresponding speed and heading control mode is selected by using the integrated processed ship controller; S4. Monitor the speed and heading control mode and threshold indicators in real time, and optimize the speed and heading control mode and threshold indicators according to actual mission requirements and changes in sea conditions data.

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