SVM-based small sample navigational speed optimization method and system

Abstract

The invention provides a small sample navigational speed optimization method and system based on an SVM (Support Vector Machine). The method comprises the following steps: collecting historical navigation data of a ship, and carrying out data cleaning; in the historical navigation data subjected to data cleaning, navigational speed features are extracted, and dimension reduction processing is carried out; dividing the navigational speed features after dimension reduction into a training set and a test set; training a support vector machine by using the training set; evaluating the trained support vector machine by using the test set; deploying the trained support vector machine in practical application, and performing real-time or batch prediction; formulating a navigational speed optimization strategy according to a prediction result; analyzing a prediction result, and identifying an error source; and adjusting parameters and an optimization strategy of the support vector machine according to the error source feedback. The scheme provided by the invention is high in generalization ability; and the nonlinear processing capability is high. The relation between the factors and the navigational speed can be well captured; and the over-fitting risk can be well reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of machine learning, and particularly relates to a small-sample ship speed optimization method, system, electronic device and storage medium based on SVM. Background Art

[0002] Ship speed optimization algorithms are important tools for improving energy efficiency, reducing costs and minimizing environmental pollution in maritime transportation. Ship speed optimization may involve dozens or even hundreds of variables, such as wind speed, wind direction, wave height, wave direction, etc. Since ship speed optimization tasks often require processing time series data, each time point may contain multiple related measurement values, so these data have high-dimensional characteristics. However, due to the vastness and variability of the marine environment, some data points may be very sparse, especially in some remote or harsh sea areas. At the same time, the actual collected data may contain noise, which may be caused by sensor errors, data transmission problems or other random factors. High-dimensional data increases the computational complexity of method training and optimization, resulting in the method being more prone to overfitting, that is, learning the noise in the data rather than the true ship speed optimization pattern.

[0003] Therefore, how to provide a small-sample ship speed optimization method, system, electronic device and storage medium based on SVM has become a technical problem urgently to be solved in this field. Summary of the Invention

[0004] The object of the present invention is to provide a small-sample ship speed optimization method and system based on SVM.

[0005] According to the first aspect of the present invention, there is provided a small-sample ship speed optimization method based on SVM, including,

[0006] Step S1: Collect historical navigation data of the ship and perform data cleaning; extract ship speed features from the cleaned historical navigation data and perform dimensionality reduction processing;

[0007] Step S2: Divide the ship speed features after dimensionality reduction processing into a training set and a test set; use the training set to train a support vector machine; use the test set to evaluate the trained support vector machine;

[0008] Step S3: Deploy the trained support vector machine into actual applications for real-time or batch prediction; formulate a ship speed optimization strategy according to the prediction results;

[0009] Step S4: Analyze the prediction results, identify the error sources; adjust the parameters and optimization strategy of the support vector machine according to the feedback of the error sources.

[0010] According to the method of the first aspect of the present invention, in the step S1, the historical navigation data includes:

[0011] Ship speed, course, wind speed, wind direction, wave height, wave period, and sailing time.

[0012] According to the method of the first aspect of the present invention, in the step S1, in addition to data cleaning, it further includes:

[0013] Processing the missing values of the historical sailing data and performing data normalization or standardization processing.

[0014] According to the method of the first aspect of the present invention, in the step S1, the ship speed features include:

[0015] Average wind speed, average wave height, and sailing time.

[0016] According to the method of the first aspect of the present invention, in the step S1, the dimensionality reduction processing includes:

[0017] Using the principal component analysis method to perform dimensionality reduction processing on the ship speed features.

[0018] According to the method of the first aspect of the present invention, in the step S2, before applying the training set to train the support vector machine, it further includes:

[0019] Selecting the kernel function and parameters of the support vector machine;

[0020] The metrics for evaluating the trained support vector machine include:

[0021] Mean squared error and coefficient of determination.

[0022] According to the method of the first aspect of the present invention, in the step S2, before deploying the trained support vector machine to the actual application, it further includes:

[0023] According to the evaluation results of the test, adjusting the parameters of the support vector machine to optimize the performance of the support vector machine.

[0024] The second aspect of the present invention discloses a small sample ship speed optimization system based on SVM; the system includes:

[0025] The first processing module is configured to collect the historical sailing data of the ship and perform data cleaning; extract the ship speed features from the historical sailing data after data cleaning and perform dimensionality reduction processing;

[0026] The second processing module is configured to divide the ship speed features after dimensionality reduction processing into a training set and a test set; use the training set to train the support vector machine; use the test set to evaluate the trained support vector machine;

[0027] The third processing module is configured to deploy the trained support vector machine to the actual application for real-time or batch prediction; formulate a ship speed optimization strategy according to the prediction results;

[0028] The fourth processing module is configured to analyze the prediction results and identify the error sources; and feedback and adjust the parameters and optimization strategies of the support vector machine according to the error sources.

[0029] A third aspect of the present invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps in any one of the methods for optimizing small-sample ship speeds based on SVM in the first aspect of the present disclosure are implemented.

[0030] A fourth aspect of the present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in any one of the methods for optimizing small-sample ship speeds based on SVM in the first aspect of the present disclosure are implemented.

[0031] The beneficial effects brought by the present invention are as follows:

[0032] As can be seen from the above solutions, the embodiments of the present invention provide a method and system for optimizing small-sample ship speeds based on SVM, having the following beneficial effects: strong generalization ability: it can handle high-dimensional data well, so that good results can still be maintained in the case of small samples; strong non-linear processing ability: it can effectively handle non-linear problems by using kernel functions. It can well capture the relationship between these factors and the ship speed; it can well reduce the risk of overfitting: in the training process, the complexity of the model is controlled by the principle of structural risk minimization, thereby effectively reducing the risk of overfitting and ensuring the prediction accuracy of the method on unknown data. Description of the Drawings

[0033] Figure 1 It is a flowchart of a method for optimizing small-sample ship speeds based on SVM provided according to an embodiment;

[0034] Figure 2 It is a structural diagram of a system for optimizing small-sample ship speeds based on SVM according to an embodiment of the present invention;

[0035] Figure 3 It is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Embodiments

[0036] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0037] Example 1:

[0038] According to the first aspect of the present invention, a small - sample ship speed optimization method based on SVM is disclosed. Figure 1 As shown in the flowchart of a small - sample ship speed optimization method based on SVM according to an embodiment of the present invention, Figure 1 the method includes:

[0039] Step S1: Collect the historical navigation data of the ship and perform data cleaning; extract the ship speed features from the historically navigated data after data cleaning and perform dimensionality reduction processing;

[0040] Step S2: Divide the ship speed features after dimensionality reduction processing into a training set and a test set; use the training set to train a support vector machine; use the test set to evaluate the trained support vector machine;

[0041] Step S3: Deploy the trained support vector machine to actual applications for real - time or batch prediction; formulate a ship speed optimization strategy according to the prediction results;

[0042] Step S4: Analyze the prediction results to identify the error sources; adjust the parameters of the support vector machine and the optimization strategy according to the feedback of the error sources.

[0043] In step S1, collect the historical navigation data of the ship and perform data cleaning; extract the ship speed features from the historically navigated data after data cleaning and perform dimensionality reduction processing.

[0044] In some embodiments, in step S1, the historical navigation data includes:

[0045] Ship speed, course, wind speed, wind direction, wave height, wave period, and navigation time.

[0046] In addition to data cleaning, it also includes:

[0047] Process the missing values of the historical navigation data and perform data normalization or standardization processing.

[0048] The ship speed features include:

[0049] Average wind speed, average wave height, and navigation time.

[0050] The dimensionality reduction processing includes:

[0051] Use the principal component analysis method to perform dimensionality reduction processing on the ship speed features.

[0052] In step S2, divide the ship speed features after dimensionality reduction processing into a training set and a test set; use the training set to train a support vector machine; use the test set to evaluate the trained support vector machine.

[0053] In some embodiments, before training the support vector machine with the training set in step S2, the following steps are further included:

[0054] Select the kernel function and parameters of the support vector machine;

[0055] The metrics for evaluating the trained support vector machine include:

[0056] Mean squared error and coefficient of determination.

[0057] Before deploying the trained support vector machine into practical applications, the following steps are further included:

[0058] According to the evaluation results of the test, adjust the parameters of the support vector machine to optimize its performance.

[0059] In summary, the solution proposed by the present invention has strong generalization ability: it can handle high-dimensional data well, so that good results can still be maintained in the case of small samples; it has strong non-linear processing ability: by using the kernel function, non-linear problems can be effectively processed. It can well capture the relationship between these factors and the ship speed; it can well reduce the risk of overfitting: in the training process, the complexity of the model is controlled by the principle of structural risk minimization, thereby effectively reducing the risk of overfitting and ensuring the prediction accuracy of the method on unknown data.

[0060] Embodiment 2:

[0061] The present invention discloses a small-sample ship speed optimization system based on SVM. Figure 2 As shown in the structural diagram of a small-sample ship speed optimization system based on an embodiment of the present invention; Figure 2 As shown, the system 100 includes:

[0062] A first processing module 101, configured to collect historical navigation data of a ship and perform data cleaning; extract ship speed features from the historical navigation data after data cleaning and perform dimensionality reduction processing;

[0063] A second processing module 102, configured to divide the ship speed features after dimensionality reduction processing into a training set and a test set; train a support vector machine with the training set; evaluate the trained support vector machine with the test set;

[0064] A third processing module 103, configured to deploy the trained support vector machine into practical applications for real-time or batch prediction; formulate a ship speed optimization strategy according to the prediction results;

[0065] A fourth processing module 104, configured to analyze the prediction results, identify the error sources; feedback and adjust the parameters of the support vector machine and the optimization strategy according to the error sources.

[0066] For the system according to the second aspect of the present invention, the first processing module 101 is specifically configured such that the historical navigation data includes:

[0067] Ship speed, course, wind speed, wind direction, wave height, wave period, and navigation time.

[0068] In addition to data cleaning, it further includes:

[0069] Processing the missing values of the historical navigation data and performing data normalization or standardization processing.

[0070] The ship speed features include:

[0071] Average wind speed, average wave height, and navigation time.

[0072] The dimensionality reduction processing includes:

[0073] Using the principal component analysis method to perform dimensionality reduction processing on the ship speed features.

[0074] For the system according to the second aspect of the present invention, the second processing module 102 is specifically configured such that before applying the training set to train the support vector machine, it further includes:

[0075] Selecting the kernel function and parameters of the support vector machine;

[0076] The metrics for evaluating the trained support vector machine include:

[0077] Mean square error and coefficient of determination.

[0078] Before deploying the trained support vector machine to actual applications, it further includes:

[0079] According to the evaluation results of the test, adjusting the parameters of the support vector machine to optimize the performance of the support vector machine.

[0080] Example 3:

[0081] This application discloses an electronic device. The electronic device includes a memory and a processor. When the processor executes the computer program stored in the memory, it implements the steps in any one of the small-sample ship speed optimization methods based on SVM in the disclosed embodiment 1 of the present invention.

[0082] Figure 3 FIG. is a structural diagram of an electronic device according to an embodiment of the present invention, as Figure 3As shown, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WI-FI, a carrier network, near field communication (NFC), or other technologies. The display screen of the electronic device can be a liquid crystal display screen or an electronic ink display screen. The input device of the electronic device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the electronic device, or an external keyboard, touchpad, or mouse, etc.

[0083] Those skilled in the art can understand that Figure 3 the structure shown in is only a structural diagram of a part related to the technical solution of the present disclosure, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have a different component arrangement.

[0084] Embodiment 4:

[0085] The present invention discloses a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the computer program is executed by a processor, the steps in any one of Embodiment 1 of the present invention, a small-sample ship speed optimization method based on SVM, are implemented.

[0086] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, all possible combinations of the technical features in the above embodiments are not described. However, as long as the combinations of these technical features do not conflict, they should be considered as falling within the scope described in this specification. The above embodiments only represent several implementation manners of the present application, and their descriptions are relatively specific and detailed, but they should not be construed as a limitation on the scope of the invention patent. It should be pointed out that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0087] The embodiments of the subject matter and the functional operations described in this specification can be implemented in the following: digital electronic circuits, tangible computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. The embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible non-transitory program carrier to be executed by a data processing apparatus or to control the operation of a data processing apparatus. Alternatively or additionally, the program instructions can be encoded on a machine-generated propagated signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode and transmit information to a suitable receiver apparatus for execution by the data processing apparatus. A computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0088] The processes and logical flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logical flows can also be performed by special-purpose logic circuitry, such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit), and the apparatus can also be implemented as special-purpose logic circuitry.

[0089] Computers suitable for executing a computer program include, for example, general and / or special-purpose microprocessors, or any other type of central processing unit. Generally, the central processing unit will receive instructions and data from a read-only memory and / or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, etc., or the computer will be operatively coupled to such mass storage devices to receive data therefrom or to transfer data thereto, or both. However, a computer is not necessarily required to have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name just a few.

[0090] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. The processor and the memory may be supplemented by, or incorporated in, special purpose logic circuitry.

[0091] Although this specification contains many specific implementation details, these should not be construed as limiting the scope of any invention or the scope of what is claimed, but rather as mainly being used to describe the features of specific embodiments of a particular invention. Certain features that are described in multiple embodiments in this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. Additionally, although features may operate in certain combinations as described above and even be claimed as such initially, one or more features from a claimed combination may in some cases be removed from that combination, and the claimed combination may be directed to a sub-combination or a variation of a sub-combination.

[0092] Similarly, although operations are depicted in the drawings in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or sequentially, or that all illustrated operations be performed, to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Additionally, the separation of the various system modules and components in the above embodiments should not be understood as being required in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0093] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the acts recited in the claims may be performed in a different order and still achieve the desired result. Additionally, the processes depicted in the drawings are not necessarily in the particular order or sequential order shown to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0094] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the principle of the present invention, several improvements and refinements can still be made, and these improvements and refinements should also be regarded as within the protection scope of the present invention.

Claims

1. A small sample speed optimization method based on SVM, characterized in that: include: Step S1, collecting historical navigation data of the ship and performing data cleaning; Extract speed features from historical navigation data in data cleaning and perform dimensionality reduction processing; Step S2, dividing the speed features after dimensionality reduction processing into a training set and a test set; using the training set to train a support vector machine; and using the test set to evaluate the trained support vector machine; Step S3: deploy the trained support vector machine to actual applications for real-time or batch prediction; Develop speed optimization strategies based on the prediction results; Step S4, analyzing the prediction results and identifying the source of errors; Adjust the parameters and optimization strategy of the support vector machine based on feedback from the error source.

2. The small sample speed optimization method based on SVM according to claim 1 is characterized in that: In step S1, the historical navigation data includes: Speed, course, wind speed, wind direction, wave height, wave period and sailing time.

3. The small sample speed optimization method based on SVM according to claim 1 is characterized in that: In step S1, in addition to data cleaning, the following steps are also included: The missing values ​​of the historical navigation data are processed, and data normalization or standardization is performed.

4. The small sample speed optimization method based on SVM according to claim 1 is characterized in that: In step S1, the speed characteristics include: Average wind speed, average wave height and sailing time.

5. The small sample speed optimization method based on SVM according to claim 1 is characterized in that: In the step S1, the dimensionality reduction process includes: The principal component analysis method is used to reduce the dimension of speed characteristics.

6. The small sample speed optimization method based on SVM according to claim 1 is characterized in that: In the step S2, before using the training set to train the support vector machine, the method further includes: Select the kernel function and parameters of the support vector machine; The indicators for evaluating the trained support vector machine include: Mean square error and coefficient of determination.

7. The small sample speed optimization method based on SVM according to claim 1 is characterized in that: In the step S2, before the trained support vector machine is deployed in a practical application, the following steps are further included: According to the evaluation results of the test, the parameters of the support vector machine are adjusted to optimize the performance of the support vector machine.

8. A small sample speed optimization system based on SVM, characterized in that: The system comprises: The first processing module is configured to collect historical navigation data of the ship and perform data cleaning; extract speed features from the cleaned historical navigation data and perform dimensionality reduction processing; The second processing module is configured to divide the speed features after dimensionality reduction processing into a training set and a test set; use the training set to train a support vector machine; and use the test set to evaluate the trained support vector machine; The third processing module is configured to deploy the trained support vector machine to actual applications to perform real-time or batch predictions; and formulate a speed optimization strategy based on the prediction results; The fourth processing module is configured to analyze the prediction results and identify the source of errors; and adjust the parameters and optimization strategy of the support vector machine according to the feedback of the error source.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the small sample speed optimization method based on SVM described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, the steps of the small sample speed optimization method based on SVM described in any one of claims 1 to 7 are implemented.

Images