Ship operation man-hour measuring and calculating method, system, equipment and medium

Abstract

The invention discloses a ship operation man-hour measuring and calculating method, system and device and a medium, and belongs to the technical field of ship measuring and calculating. According to the method, the problem of low precision of existing working hour measurement and calculation is solved, the operation time data and the original signal are collected and processed respectively, the three-axis acceleration information is obtained through the original signal, the standard deviation is calculated to obtain the three-axis acceleration standard deviation signal, the vibration frequency information is obtained and extracted, and the accuracy of working hour measurement and calculation is improved. A neural network is used to establish a model, and the model is trained through vibration frequency information and operation time data, so that the vibration frequency information can be predicted through the model, and the operation time of multiple engines or generators of a ship can be measured and calculated by comparing the time interval between the actual vibration frequency and the predicted vibration frequency. The problems of false reporting and missing reporting caused by manual reporting are avoided, and accurate measurement and calculation of ship operation working hours are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of ship calculation, and in particular to a method, system, equipment and medium for calculating ship operation hours. Background Art

[0002] In the shipping industry, the operating status of ships directly affects cargo loading and unloading, navigation safety and fleet economic benefits. However, traditional ship operation time calculation methods usually rely on manual operation, which is time-consuming and labor-intensive and easily affected by human factors. In addition, the operating status of a ship is a dynamically changing process, and traditional ship operation time calculation methods are difficult to meet the real-time and accurate calculation requirements of its operation time; therefore, it does not meet the existing needs. For this reason, we propose a ship operation time calculation method, system, equipment and medium. Summary of the invention

[0003] The object of the present invention is to provide a method, system, equipment and medium for calculating the operating hours of a ship, by collecting operating time data and original signals and processing them separately, and at the same time, obtaining a three-axis acceleration standard deviation signal by calculating the standard deviation, thereby obtaining the extracted vibration frequency information, using a neural network to establish a model, and training the model with the vibration frequency information and operating time data, so that the vibration frequency information can be predicted by the model, and by comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated, thereby solving the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solution: a method for calculating ship operation hours, comprising the following steps:

[0005] Install timing sensors and acceleration sensors on multiple engines or generators of ships;

[0006] Collect the running time data of multiple engines or generators through timing sensors and perform data cleaning;

[0007] The original signals of multiple engines or generators in three-axis directions within a period of time are collected by acceleration sensors and processed;

[0008] After the original signal is processed, the standard deviation of each axis of the multi-engine or generator is calculated, and the standard deviation of each axis is integrated to form a three-axis acceleration standard deviation signal;

[0009] Extract vibration frequency information from the three-axis acceleration standard deviation signal;

[0010] Use a neural network to build a model and use the vibration frequency information and running time data as input and output to train the neural network model;

[0011] Input the new running time data into the trained neural network model to obtain the predicted vibration frequency information;

[0012] By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

[0013] Furthermore, the acceleration sensor adopts a three-axis acceleration sensor, and the collection time is set in advance. According to the set collection time, the three-axis acceleration sensor detects the vibration of multiple engines or generators in three-axis directions within a period of time in real time, and then collects the original signals of the multiple engines or generators in three-axis directions.

[0014] Furthermore, the collected original signal is processed, including:

[0015] Filtering: Use a low-pass filter to remove high-frequency noise;

[0016] Denoising: The median method is used to calculate the median of the original signal to remove outliers, thereby achieving the purpose of removing noise;

[0017] Large vibration signal extraction: The threshold method is used to obtain large vibrations.

[0018] Furthermore, the large vibration signal extraction specifically includes the following process:

[0019] Using the threshold method, the threshold is set in advance;

[0020] Compare the raw signals on each axis;

[0021] If the acceleration value of a certain axis exceeds the threshold, it is determined to be a large vibration signal.

[0022] Furthermore, before establishing the neural network model, the vibration frequency information and the running time data are organized into a data frame, and the processed raw data are organized into a data frame. The data frame includes multiple data records, each of which should contain at least one data item and a timestamp. The data records in the data frame are sorted in chronological order to ensure that the timestamp of each data item corresponds to its corresponding time point.

[0023] Furthermore, the establishment of the neural network model specifically includes the following steps:

[0024] Use a neural network to build a model and train the neural network model using vibration frequency information and running time data as input and output;

[0025] After the neural network model training is completed, the cross-validation technique is used to evaluate the neural network model;

[0026] If the neural network model performs well, deploy the neural network model and apply it in practice;

[0027] Input the new running time data into the trained neural network model to obtain the predicted vibration frequency;

[0028] Compare the predicted vibration frequency with the actual vibration frequency to determine the accuracy of the neural network model;

[0029] At the same time, by comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

[0030] A ship operation time calculation system is used to implement a ship operation time calculation method, comprising:

[0031] Data acquisition unit for:

[0032] The operating time data of multiple engines or generators are collected by the installed timing sensors, and the original signals of multiple engines or generators in three-axis directions within a period of time are collected and processed by the installed acceleration sensors;

[0033] Data processing unit for:

[0034] Perform data cleaning on the collected runtime data;

[0035] Filter and denoise the collected original signal, and use the threshold method to extract the large vibration signal;

[0036] Calculate the processed data to obtain the standard deviation signal of the three-axis acceleration;

[0037] Working time measurement unit, used for:

[0038] A neural network is used to establish a model, and the vibration frequency information and running time data are used to train the neural network model;

[0039] After training, the neural network model is evaluated using cross-validation techniques;

[0040] Deploy the evaluated neural network models into practical applications;

[0041] Input the new running time data into the trained neural network model to obtain the predicted vibration frequency;

[0042] By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

[0043] Furthermore, the data processing unit comprises:

[0044] Data processing module for:

[0045] Perform data cleaning on the collected runtime data, including deduplication, outlier processing, and missing value processing;

[0046] Filter and denoise the collected original signal;

[0047] Signal extraction module for:

[0048] Set a threshold in advance and compare the original signal on each axis. If the acceleration value of a certain axis exceeds the threshold, it is determined to be a large vibration signal and extracted.

[0049] Data calculation module, used for:

[0050] Calculate the standard deviation of each axis of multiple engines or generators, integrate the standard deviation of each axis, and finally obtain the three-axis acceleration standard deviation signal.

[0051] An electronic device comprises: a memory and a processor, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, a method for calculating the operating hours of a ship is implemented.

[0052] A storage medium stores a computer program, which, when read and executed, implements a method for calculating ship operation hours.

[0053] Compared with the prior art, the present invention has the following beneficial effects:

[0054] The present invention collects running time data and original signals and processes them respectively. After the processing is completed, three-axis acceleration information is obtained through the original signals, so that the standard deviation on each axis of multiple engines or generators can be calculated, thereby obtaining three-axis acceleration standard deviation signals, thereby obtaining extracted vibration frequency information, and then, a neural network is used to establish a model, and the model is trained through vibration frequency information and running time data, so that the vibration frequency information can be predicted through the model, and the time interval between the actual vibration frequency and the predicted vibration frequency can be compared, so that the running hours of multiple engines or generators of the ship can be calculated, thereby solving the problem of low accuracy of manually reported working hours, avoiding false reporting and missed reporting, and ensuring accurate calculation of the ship's running hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the ship operation man-hour calculation method of the present invention;

[0056] Figure 2 It is a structural diagram of the ship operation man-hour calculation system of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In order to solve the problem that the existing ship operation time calculation method usually relies on manual operation, is time-consuming and labor-intensive, and is easily affected by human factors, and it is difficult to meet the real-time and accurate calculation requirements of its operation time, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0059] A method for calculating ship operation hours comprises the following steps:

[0060] Install timing sensors and acceleration sensors on multiple engines or generators of ships;

[0061] Collect the running time data of multiple engines or generators through timing sensors and perform data cleaning;

[0062] The original signals of multiple engines or generators in three-axis directions within a period of time are collected by acceleration sensors and processed;

[0063] After the original signal is processed, the standard deviation of each axis of the multi-engine or generator is calculated, and the standard deviation of each axis is integrated to form a three-axis acceleration standard deviation signal;

[0064] Extract vibration frequency information from the three-axis acceleration standard deviation signal;

[0065] Use a neural network to build a model and use the vibration frequency information and running time data as input and output to train the neural network model;

[0066] Input the new running time data into the trained neural network model to obtain the predicted vibration frequency information;

[0067] By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

[0068] The technical effect of the above content is: by collecting the running time data and the original signal and processing them separately, at the same time, by calculating the standard deviation, the three-axis acceleration standard deviation signal is obtained to obtain the extracted vibration frequency information, and a neural network is used to establish a model. The model is trained with the vibration frequency information and the running time data, so that the vibration frequency information can be predicted by the model, and by comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated, thereby avoiding the problems of false reporting and underreporting due to manual reporting.

[0069] The acceleration sensor adopts a three-axis acceleration sensor and sets the acquisition time in advance. According to the set acquisition time, the three-axis acceleration sensor detects the vibration of multiple engines or generators in three-axis directions within a period of time in real time, and then collects the original signals of the multiple engines or generators in three-axis directions.

[0070] The technical effect of the above content is: when the three-axis acceleration sensor monitors multiple engines or generators in real time, it will detect their vibration conditions within a set time period. In this way, the original signals of multiple engines or generators in three-axis directions within a specific time can be collected, and real-time and accurate vibration information can be provided.

[0071] Process the collected raw signals, including:

[0072] Filtering: Use a low-pass filter to remove high-frequency noise;

[0073] Denoising: The median method is used to calculate the median of the original signal to remove outliers, thereby achieving the purpose of removing noise;

[0074] Large vibration signal extraction: The threshold method is used to obtain large vibrations.

[0075] Among them, the large vibration signal extraction specifically includes the following process:

[0076] Using the threshold method, the threshold is set in advance;

[0077] Compare the raw signals on each axis;

[0078] If the acceleration value of a certain axis exceeds the threshold, it is determined to be a large vibration signal.

[0079] The technical effect of the above content is: after the original signal is collected, it needs to be processed, including filtering and denoising, as well as the extraction of large vibration signals. The filtering process is to remove high-frequency noise by using a low-pass filter to achieve the purpose of filtering. The denoising process is to remove outliers by calculating the median in the original signal using the median method to achieve the purpose of removing noise. The quality of the original signal can be improved through filtering and denoising. After the above processing, the threshold method is used to obtain large vibrations, so that the large vibration signal can be used to solve the problem of high-power mutual interference. High-power mutual interference will cause the received signal to be stronger, but the frequency range is narrower. Therefore, when the large vibration signal is obtained, a low-pass filter can be used to filter out the high-frequency noise and retain the lower-frequency signal. In addition, other preprocessing methods can also be considered, such as de-averaging and de-trending, to improve the accuracy of subsequent recognition.

[0080] Before establishing the neural network model, the vibration frequency information and running time data are organized into a data frame, and the processed raw data are organized into a data frame. The data frame includes multiple data records, each of which should contain at least one data item and a timestamp. The data records in the data frame are sorted in chronological order to ensure that the timestamp of each data item corresponds to its corresponding time point.

[0081] The technical effect of the above content is: organizing the vibration frequency information and operating time data into a data frame can ensure that the timestamps of the two are one-to-one corresponding, and can realize real-time and accurate processing of data from different sources and in different formats, and ensure that the timestamp of the data corresponds to its corresponding time point one-to-one, thereby improving the comparability and traceability of the data, and facilitating the analysis and utilization of the data.

[0082] Establishing a neural network model includes the following steps:

[0083] Use a neural network to build a model and train the neural network model using vibration frequency information and running time data as input and output;

[0084] After the neural network model training is completed, the cross-validation technique is used to evaluate the neural network model;

[0085] If the neural network model performs well, deploy the neural network model and apply it in practice;

[0086] Input the new running time data into the trained neural network model to obtain the predicted vibration frequency;

[0087] Compare the predicted vibration frequency with the actual vibration frequency to determine the accuracy of the neural network model;

[0088] At the same time, by comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

[0089] The technical effect of the above content is: by using a neural network, a neural network model is established, and the neural network model is trained by using vibration frequency information and operating time data as input and output. After the training is completed, the neural network model is evaluated using cross-validation technology. If the neural network model performs well, the neural network model is deployed and actually applied. The neural network model can autonomously learn complex nonlinear relationships without relying on prior knowledge, so as to better adapt to the needs of ship operation time measurement. At the same time, through cross-validation and other technologies, evaluation and optimization are performed to ensure the reliability and generalization ability of the model, so that the neural network model can predict and analyze the vibration frequency and operating time in real time, thereby realizing real-time and smooth calculation of the ship's operating hours. By comparing the predicted vibration frequency with the actual vibration frequency and time interval, the operating hours of multiple engines or generators of the ship can be calculated, thereby more accurately reflecting the actual operation of the ship. The operating hours calculation method of multiple engines or generators of ships based on neural networks can realize real-time and accurate calculation of ship operating hours, avoiding false reporting and omission problems due to manual reporting.

[0090] Specifically, the present invention further proposes a ship operation time calculation system, which is used to implement a ship operation time calculation method, including:

[0091] Data acquisition unit for:

[0092] The operating time data of multiple engines or generators are collected by the installed timing sensors, and the original signals of multiple engines or generators in three-axis directions within a period of time are collected and processed by the installed acceleration sensors;

[0093] Data processing unit for:

[0094] Perform data cleaning on the collected runtime data;

[0095] Filter and denoise the collected original signal, and use the threshold method to extract the large vibration signal;

[0096] Calculate the processed data to obtain the standard deviation signal of the three-axis acceleration;

[0097] Working time measurement unit, used for:

[0098] A neural network is used to establish a model, and the vibration frequency information and running time data are used to train the neural network model;

[0099] After training, the neural network model is evaluated using cross-validation techniques;

[0100] Deploy the evaluated neural network models into practical applications;

[0101] Input the new running time data into the trained neural network model to obtain the predicted vibration frequency;

[0102] By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

[0103] The technical effect of the above content is: the operating time data and the original signal can be collected by the data acquisition unit, and then the collected operating time data and the original signal are processed respectively by the data processing unit, and the large vibration signal is extracted by the threshold method, so that the large vibration signal can be used to solve the problem of high-power mutual interference. After the processing is completed, the three-axis acceleration information is obtained through the original signal, and the standard deviation is calculated to obtain the three-axis acceleration standard deviation signal, so that the vibration frequency information can be extracted from the three-axis acceleration standard deviation signal. Then, the working hour measurement unit establishes a model by using a neural network, and trains the model with vibration frequency information and operating time data, so that the vibration frequency information can be predicted through the model. By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated, avoiding false reporting and omissions due to manual reporting, and ensuring accurate measurement of the ship's operating hours.

[0104] The data processing unit includes:

[0105] Data processing module for:

[0106] Perform data cleaning on the collected runtime data, including deduplication, outlier processing, and missing value processing;

[0107] Filter and denoise the collected original signal;

[0108] Signal extraction module for:

[0109] Set a threshold in advance and compare the original signal on each axis. If the acceleration value of a certain axis exceeds the threshold, it is determined to be a large vibration signal and extracted.

[0110] Data calculation module, used for:

[0111] Calculate the standard deviation of each axis of multiple engines or generators, integrate the standard deviation of each axis, and finally obtain the three-axis acceleration standard deviation signal.

[0112] The technical effect of the above content is: the collected running time data can be cleaned through the data processing module, and deduplication is to use a deduplication algorithm (such as the Hashes algorithm) to deduplicate the collected running time data, remove duplicate records, and ensure that each running time is calculated only once. Outlier processing is performed on the running time data by using an anomaly detection algorithm (such as the box plot method), and abnormal data points that obviously deviate from the normal distribution are identified and removed. This can avoid the adverse effects of outliers on data analysis, and use the mean or median to fill in the missing values ​​of the running time data. The data processing module uses technical means such as deduplication, outlier processing and missing value processing to clean the collected running time data, improve data quality, and provide accurate and reliable data support for subsequent data processing and use. At the same time, the collected original signal can also be filtered through the data processing module. And denoising processing, the main purpose of filtering and denoising processing is to reduce or eliminate the noise components in the original signal, so as to improve the quality of the original signal for subsequent processing and use. Then, the signal extraction module can extract the large vibration signal by setting the threshold in advance and comparing it with the original signal on each axis. When the large vibration signal is obtained, the high-frequency noise is filtered out and the lower-frequency signal is retained, thereby solving the problem of high-power mutual interference to improve the accuracy of subsequent identification. Finally, through the data calculation module, the standard deviation on each axis of multiple engines or generators can be calculated, and the standard deviation of each axis can be integrated to finally obtain the three-axis acceleration standard deviation signal. The three-axis acceleration standard deviation signal reflects the vibration state of the engine and generator, so that the vibration frequency information can be extracted from the three-axis acceleration standard deviation signal. The extracted vibration frequency information can be used to train the established model for subsequent model prediction.

[0113] Specifically, the present invention further proposes an electronic device, comprising: a memory and a processor, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, a method for calculating ship operation hours is implemented.

[0114] Specifically, the present invention also proposes a storage medium on which a computer program is stored. When the computer program is read and executed, a method for calculating the operating hours of a ship is implemented.

[0115] Working principle: By collecting the running time data and the original signal and processing them separately, after the processing is completed, the three-axis acceleration information is obtained through the original signal, so that the standard deviation on each axis of the multi-engine or generator can be calculated, so as to obtain the three-axis acceleration standard deviation signal, so as to obtain the extracted vibration frequency information, and then, the neural network is used to establish a model, and the model is trained with the vibration frequency information and the running time data, so that the vibration frequency information can be predicted through the model. By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of the ship's multiple engines or generators can be calculated, thereby avoiding the problems of false reporting and underreporting due to manual reporting, and ensuring the accurate calculation of the ship's operating hours.

[0116] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0117] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for calculating ship operation hours, characterized in that: The following steps are involved: Install timing sensors and acceleration sensors on multiple engines or generators of ships; Collect the running time data of multiple engines or generators through timing sensors and perform data cleaning; The original signals of multiple engines or generators in three-axis directions within a period of time are collected by acceleration sensors and processed; After the original signal is processed, the standard deviation of each axis of the multi-engine or generator is calculated, and the standard deviation of each axis is integrated to form a three-axis acceleration standard deviation signal, from which the vibration frequency information is extracted; Use a neural network to build a model and use the vibration frequency information and running time data as input and output to train the neural network model; Input the new running time data into the trained neural network model to obtain the predicted vibration frequency information; By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

2. A method for calculating ship operation hours according to claim 1, characterized in that: The acceleration sensor adopts a three-axis acceleration sensor, and the collection time is set in advance. According to the set collection time, the three-axis acceleration sensor detects the vibration of multiple engines or generators in three-axis directions within a period of time in real time, and collects the original signals of the multiple engines or generators in three-axis directions.

3. A method for calculating ship operation hours according to claim 1, characterized in that: Process the collected raw signals, including: Filtering: Use a low-pass filter to remove high-frequency noise; Denoising: The median method is used to calculate the median of the original signal to remove outliers, thereby achieving the purpose of removing noise; Large vibration signal extraction: The threshold method is used to obtain large vibrations.

4. A method for calculating ship operation hours according to claim 3, characterized in that: The large vibration signal extraction specifically includes the following process: Using the threshold method, the threshold is set in advance; Compare the raw signals on each axis; If the acceleration value of a certain axis exceeds the threshold, it is determined to be a large vibration signal.

5. A method for calculating ship operation hours according to claim 1, characterized in that: Before establishing the neural network model, the vibration frequency information and running time data are organized into a data frame, and the processed raw data are organized into a data frame. The data frame includes multiple data records, each of which should contain at least one data item and a timestamp. The data records in the data frame are sorted in chronological order to ensure that the timestamp of each data item corresponds to its corresponding time point.

6. A method for calculating ship operation hours according to claim 1, characterized in that: The establishment of the neural network model specifically includes the following steps: Use a neural network to build a model and train the neural network model using vibration frequency information and running time data as input and output; After the neural network model training is completed, the cross-validation technique is used to evaluate the neural network model; If the neural network model performs well, deploy the neural network model and apply it in practice; Input the new running time data into the trained neural network model to obtain the predicted vibration frequency; Compare the predicted vibration frequency with the actual vibration frequency to determine the accuracy of the neural network model; At the same time, by comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

7. A system for calculating ship operation hours, used to implement the method for calculating ship operation hours according to any one of claims 1 to 6, characterized in that: include: Data acquisition unit for: The operating time data of multiple engines or generators are collected by the installed timing sensors, and the original signals of multiple engines or generators in three-axis directions within a period of time are collected and processed by the installed three-axis acceleration sensors; Data processing unit for: Perform data cleaning on the collected runtime data; Filter and denoise the collected original signal, and use the threshold method to extract the large vibration signal; Calculate the processed data to obtain the standard deviation signal of the three-axis acceleration; Working time measurement unit, used for: A neural network is used to establish a model, and the vibration frequency information and running time data are used to train the neural network model; After training, the neural network model is evaluated using cross-validation techniques; Deploy the evaluated neural network models into practical applications; Input the new running time data into the trained neural network model to obtain the predicted vibration frequency; By comparing the time interval between the actual vibration frequency and the predicted vibration frequency, the operating hours of multiple engines or generators of the ship can be calculated.

8. A system for calculating ship operation hours according to claim 7, characterized in that: The data processing unit comprises: Data processing module for: Perform data cleaning on the collected runtime data, including deduplication, outlier processing, and missing value processing; Filter and denoise the collected original signal; Signal extraction module for: Set a threshold in advance and compare the original signal on each axis. If the acceleration value of a certain axis exceeds the threshold, it is determined to be a large vibration signal and extracted. Data calculation module, used for: Calculate the standard deviation of each axis of multiple engines or generators, integrate the standard deviation of each axis, and finally obtain the three-axis acceleration standard deviation signal.

9. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program executable by the processor, and when the processor executes the computer program, the method for calculating the ship operation hours described in any one of claims 1 to 6 is implemented.

10. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is read and executed, the method for calculating the ship operation hours described in any one of claims 1 to 6 is implemented.

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