CN117273197A - Ship operation state prediction method and system based on track and production information fusion - Google Patents

Ship operation state prediction method and system based on track and production information fusion Download PDF

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Publication number
CN117273197A
CN117273197A CN202311100187.8A CN202311100187A CN117273197A CN 117273197 A CN117273197 A CN 117273197A CN 202311100187 A CN202311100187 A CN 202311100187A CN 117273197 A CN117273197 A CN 117273197A
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ship
operation state
time step
influence
state prediction
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付晓坤
芦伟
肖文卓
邓燕
张博
施雨倩
廖婧
袁小平
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Yangtze River Water Traffic Monitoring And Emergency Response Center
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Yangtze River Water Traffic Monitoring And Emergency Response Center
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Priority to CN202311100187.8A priority Critical patent/CN117273197A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Abstract

The invention discloses a ship operation state prediction method and a system based on track and production information fusion, wherein the method comprises the following steps: acquiring historical track information and historical production information, and segmenting the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps; setting a ship operation state prediction model, training the ship operation state prediction model by taking the historical track information and the historical production information in each time step as training data, acquiring real-time track information and real-time production information, and calculating an operation state prediction value through the trained ship operation state prediction model.

Description

Ship operation state prediction method and system based on track and production information fusion
Technical Field
The invention belongs to the technical field of ship operation state prediction, and particularly relates to a ship operation state prediction method and system based on track and production information fusion.
Background
Monitoring the operational status of a ship is an important step in ensuring safe operation and efficient management of the ship. The following are some methods and techniques for monitoring the operational status of a ship:
sensor technology: sensors, such as a temperature sensor, a pressure sensor, a vibration sensor and the like, are installed at different parts of the ship so as to monitor physical parameters of the ship in real time. These sensors may provide data regarding the status of the engine, hull, mechanical components, etc., helping to detect potential problems.
GPS and navigation system: global Positioning System (GPS) and advanced navigation techniques are used to track the position, heading and speed of a vessel. This is important to ensure that the vessel is sailing on a predetermined course and to avoid collisions with other vessels or obstacles.
However, in the prior art, no technical scheme is available for predicting the operation state of the ship according to the historical shipping information.
Disclosure of Invention
In order to solve the technical characteristics, the invention provides a ship operation state prediction method based on track and production information fusion, which comprises the following steps:
Acquiring historical track information and historical production information, and segmenting the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
setting a ship operation state prediction model, taking the historical track information and the historical production information in each time step as training data, training the ship operation state prediction model to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
the ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
Further, the ship operation state prediction model is as follows:
S Prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
Further, the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor and σ is a specific vessel position impact adjustment factor.
Further, the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max The ship load sensitivity adjustment factor is calculated according to the ship load sensitivity adjustment factor, and the ship load influence adjustment factor is calculated according to the ship load sensitivity adjustment factor.
Further, at time step T, the ship operation influence function F of the environmental factor E in which the ship is located E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor and β is a specific environmental factor influence adjustment factor.
The invention also provides a ship operation state prediction system based on track and production information fusion, which comprises:
the data acquisition module is used for acquiring historical track information and historical production information, segmenting the historical track information and the historical production information according to time steps, and acquiring the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
a prediction module for setting a ship operation state prediction model, taking the historical track information and the historical production information in each time step as training data, training the ship operation state prediction model to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
the ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
Further, the ship operation state prediction model is as follows:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (E(T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
Further, the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor and σ is a specific vessel position impact adjustment factor.
Further, the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max Is the rated maximum cargo capacity of the ship, and gamma is the shipAnd the sensitivity adjustment factor of the ship load, wherein theta is the adjustment factor of the ship load influence.
Further, at time step T, the ship operation influence function F of the environmental factor E in which the ship is located E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor and β is a specific environmental factor influence adjustment factor.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the invention acquires historical track information and historical production information, and segments the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps; setting a ship operation state prediction model, training the ship operation state prediction model by taking the historical track information and the historical production information in each time step as training data, acquiring real-time track information and real-time production information, and calculating an operation state prediction value through the trained ship operation state prediction model, wherein the ship operation state prediction model consists of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating the influence of ship positions, shipping environment factors and ship cargo capacity on the ship operation state. According to the technical scheme, the ship operation state can be accurately predicted according to the historical information and through the prediction model.
Drawings
FIG. 1 is a flow chart of embodiment 1 of the present invention;
fig. 2 is a block diagram of a system of embodiment 2 of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
All subscripts in the formula of the invention are only used for distinguishing parameters and have no practical meaning.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a ship operation state prediction method based on track and production information fusion, including:
step 101, acquiring historical track information and historical production information, and segmenting the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
102, setting a ship operation state prediction model, training the ship operation state prediction model by taking the historical track information and the historical production information in each time step as training data to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
specifically, the ship operation state prediction model is as follows:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (E(T))
Wherein S is Prediction (T) is an operation state prediction value, W P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
The ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
Specifically, the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor, μ controls the slope of the function, a larger μ value results in steeper rise and fall of the function, making the function more sensitive to changes in vessel position, a smaller μ value results in flatter slope of the function, making the response of the function more gradual, in practice, a suitable μ value is selected according to the change in data, if the magnitude of the change in data is larger, a larger μ value is set to better capture these changes, conversely, if the change in data is smaller, a smaller μ value is more suitable, σ is a particular vessel position influencing adjustment factor, σ is a horizontal offset parameter of the function, controlling the translation of the function on the vessel position axis, i.e. where the function starts to rise or fall, by adjusting σ value, the response of the function can be enhanced or reduced near a particular position, e.g. if the influence of the change in position near that particular position on the operating state exceeds a set threshold, the function is made more sensitive to changes near that position by adjustment.
The present embodiment is based on a ship operation influence function F set at a ship position P at a time step T P (P (T)) can achieve the following technical effects:
1. richer nonlinear response: using ship operation influencing functions F P (P (T)) can create a richer nonlinear response. Ship operation influence function F P The shape of (P (T)) resembles an S-curve, such that the slope of the function varies at different locations, which allows more accurate capture of the effect of the ship 'S position on the ship' S operating state, especially when the position is close to one side, the variation of the function is more sensitive, providing finer effect modeling.
2. Better data fitting ability: due to the ship operation influencing function F P (P (T)) which better fits the non-linear relationship, wherein the influence over different ranges of vessel positions may be different. For example, during voyages, a ship may be subjected to different conditionsInfluence of geographical conditions, ship operation influence function F P (P (T)) can better characterize such complex relationships.
3. Flexibility of modeling is enhanced: using ship operation influencing functions F P The model is more flexible, different types of data can be adapted by adjusting parameters, the model can be better adapted to various ship operation state prediction problems, and accordingly prediction accuracy and reliability are improved.
4. Improving the sensitivity of the prediction: ship operation influence function F P (P (T)) is very sensitive to changes in certain intervals and therefore the effect of small changes in the position of the vessel on the operating conditions can be better captured, which is very advantageous for predictive problems requiring responses to small changes.
Specifically, the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max For maximum cargo capacity of ship rating, D in this embodiment max For standardizing D (T) to a range, i.e. [0,1 ]][0，1]In order to ensure that the influence of the ship cargo capacity on the ship operation state is within a controllable range, gamma is a ship cargo capacity sensitivity adjustment factor, and the parameter gamma influences the slope of the function, so that the change rate of the ship cargo capacity in the function is adjusted. The larger gamma value can lead to steeper functions, the change of the ship cargo capacity when affecting the ship operation state is more sensitive, the smaller value can lead to flatter functions, the influence of the ship cargo capacity on the ship operation state is smoother, theta is a ship cargo capacity influence adjusting factor, theta can be regarded as a threshold value, the central position of the ship cargo capacity in the functions is determined, when D (T) approaches theta, the value of the functions approaches 0.5, namely the middle value, and when D (T) deviates from theta, the value of the functions rapidly approaches 0 or 1, and therefore, the starting point and the change amplitude of the functions in the ship cargo capacity value range are adjusted through theta.
The present embodiment is based on a ship operation influence function F of the ship cargo capacity D set at time step T D (D (T)) can achieve the following technical effects:
1. nonlinear effects are enhanced: setting a ship operation influence function F D And (D (T)) the influence of the ship cargo capacity on the ship operation state is not linear any more, and the influence of the ship cargo capacity is different in different ranges by the S-shaped curve of the function, so that the influence change of the ship cargo capacity in different ranges can be better captured, and the nonlinear expression capacity of the model is enhanced.
2. Interval-specific effects: the parameters gamma and theta control the shape and position of the function, which enables the magnitude and direction of the influence to be adjusted within a specific interval for the ship cargo capacity, and this flexibility helps the model to more accurately reflect the influence of the ship cargo capacity within different ranges on the ship operation state.
3. Saturation and suppression are considered: ship operation influence function F D The characteristic of (D (T)) is to introduce saturation and dampening effects in a certain range, which means that the ship load can have a greater influence on the ship's operating conditions in a low or high range, while the influence in the middle range is diminished, which may better reflect the actual situation, as some ship load levels may lead to significant changes in conditions, while other levels may not.
Specifically, at time step T, the ship operation influence function F of the environmental factor E in which the ship is located E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor, a larger α value causes the function to rise or fall steeper in a change interval, shows greater sensitivity, a smaller α value causes the function to change more gradually, β is a specific environmental factor influence adjustment factor by adjusting a change rate of the α adjustment function in a change range of the environmental factor (for example, a specific environmental factor refers to that in historical data, when an influence of a certain weather condition on a ship running state exceeds a preset threshold, the weather condition is considered as a specific environmental factor), β is a horizontal offset parameter of the function, influences a position of the function on an environmental factor axis, and is used for controlling translation of the function on the environmental factor axis by adjusting β value, so that the function starts to rise or fall in the specific environmental factor range, thereby helping to capture the change of the influence factor better.
Example 2
As shown in fig. 2, the embodiment of the present invention further provides a ship operation state prediction system based on track and production information fusion, including:
The data acquisition module is used for acquiring historical track information and historical production information, segmenting the historical track information and the historical production information according to time steps, and acquiring the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
a prediction module for setting a ship operation state prediction model, taking the historical track information and the historical production information in each time step as training data, training the ship operation state prediction model to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
specifically, the ship operation state prediction model is as follows:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (E(T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
The ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
Specifically, the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor, μ controls the slope of the function, a larger μ value results in steeper rise and fall of the function, making the function more sensitive to changes in vessel position, a smaller μ value results in flatter slope of the function, making the response of the function more gradual, in practice, a suitable μ value is selected according to the change in data, if the magnitude of the change in data is larger, a larger μ value is set to better capture these changes, conversely, if the change in data is smaller, a smaller μ value is more suitable, σ is a particular vessel position influencing adjustment factor, σ is a horizontal offset parameter of the function, controlling the translation of the function on the vessel position axis, i.e. where the function starts to rise or fall, by adjusting σ value, the response of the function can be enhanced or reduced near a particular position, e.g. if the influence of the change in position near that particular position on the operating state exceeds a set threshold, the function is made more sensitive to changes near that position by adjustment.
The present embodiment is based on a ship operation influence function F set at a ship position P at a time step T P (P (T)) can achieve the following technical effects:
1. richer nonlinear response: using ship operation influencing functions F P (P (T)) can create a richer nonlinear response. Ship operation influence function F P The shape of (P (T)) resembles an S-curve, such that the slope of the function varies at different locations, which allows more accurate capture of the effect of the ship 'S position on the ship' S operating state, especially when the position is close to one side, the variation of the function is more sensitive, providing finer effect modeling.
2. Better data fitting ability: due to the ship operation influencing function F P (P (T)) which better fits the non-linear relationship, wherein the influence over different ranges of vessel positions may be different. For example, during voyage, the vessel may be affected by different geographical conditions, the vessel operation affecting the function F P (P (T)) can better characterize such complex relationships.
3. Flexibility of modeling is enhanced: using ship operation influencing functions F P The model is more flexible, different types of data can be adapted by adjusting parameters, the model can be better adapted to various ship operation state prediction problems, and accordingly prediction accuracy and reliability are improved.
4. Improving the sensitivity of the prediction: ship operation influence function F P (P (T)) is very sensitive to changes in certain intervals and therefore the effect of small changes in the position of the vessel on the operating conditions can be better captured, which is very advantageous for predictive problems requiring responses to small changes.
Specifically, the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max For maximum cargo capacity of ship rating, D in this embodiment max For standardizing D (T) to a range, i.e. [0,1 ]][0，1]To ensure the ship carrying capacity to the shipThe influence of the operation state is in a controllable range, gamma is a ship cargo capacity sensitivity adjustment factor, and the parameter gamma influences the slope of the function, so that the change rate of the ship cargo capacity in the function is adjusted. The larger gamma value can lead to steeper functions, the change of the ship cargo capacity when affecting the ship operation state is more sensitive, the smaller value can lead to flatter functions, the influence of the ship cargo capacity on the ship operation state is smoother, theta is a ship cargo capacity influence adjusting factor, theta can be regarded as a threshold value, the central position of the ship cargo capacity in the functions is determined, when D (T) approaches theta, the value of the functions approaches 0.5, namely the middle value, and when D (T) deviates from theta, the value of the functions rapidly approaches 0 or 1, and therefore, the starting point and the change amplitude of the functions in the ship cargo capacity value range are adjusted through theta.
The present embodiment is based on a ship operation influence function F of the ship cargo capacity D set at time step T D (D (T)) can achieve the following technical effects:
1. nonlinear effects are enhanced: setting a ship operation influence function F D And (D (T)) the influence of the ship cargo capacity on the ship operation state is not linear any more, and the influence of the ship cargo capacity is different in different ranges by the S-shaped curve of the function, so that the influence change of the ship cargo capacity in different ranges can be better captured, and the nonlinear expression capacity of the model is enhanced.
2. Interval-specific effects: the parameters gamma and theta control the shape and position of the function, which enables the magnitude and direction of the influence to be adjusted within a specific interval for the ship cargo capacity, and this flexibility helps the model to more accurately reflect the influence of the ship cargo capacity within different ranges on the ship operation state.
3. Saturation and suppression are considered: ship operation influence function F D The characteristic of (D (T)) is to introduce saturation and dampening effects in a certain range, which means that the ship load can have a greater influence on the ship's operating conditions in a low or high range, while the influence in the middle range is diminished, which may better reflect the actual situation, as some ship load levels may lead to significant changes in conditions, while other levels may not.
Specifically, at time step T, the ship operation influence function F of the environmental factor E in which the ship is located E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor, a larger α value causes the function to rise or fall steeper in a change interval, shows greater sensitivity, a smaller α value causes the function to change more gradually, β is a specific environmental factor influence adjustment factor by adjusting a change rate of the α adjustment function in a change range of the environmental factor (for example, a specific environmental factor refers to that in historical data, when an influence of a certain weather condition on a ship running state exceeds a preset threshold, the weather condition is considered as a specific environmental factor), β is a horizontal offset parameter of the function, influences a position of the function on an environmental factor axis, and is used for controlling translation of the function on the environmental factor axis by adjusting β value, so that the function starts to rise or fall in the specific environmental factor range, thereby helping to capture the change of the influence factor better.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the ship operation state prediction method based on the track and production information fusion.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: step 101, acquiring historical track information and historical production information, and segmenting the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
102, setting a ship operation state prediction model, training the ship operation state prediction model by taking the historical track information and the historical production information in each time step as training data to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
Specifically, the ship operation state prediction model is as follows:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (E(T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
The ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
Specifically, the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor, μ controls the slope of the function, a larger μ value results in steeper rise and fall of the function, making the function more sensitive to changes in vessel position, a smaller μ value results in flatter slope of the function, making the response of the function more gradual, in practice, a suitable μ value is selected according to the change in data, if the magnitude of the change in data is larger, a larger μ value is set to better capture these changes, conversely, if the change in data is smaller, a smaller μ value is more suitable, σ is a particular vessel position influencing adjustment factor, σ is a horizontal offset parameter of the function, controlling the translation of the function on the vessel position axis, i.e. where the function starts to rise or fall, by adjusting σ value, the response of the function can be enhanced or reduced near a particular position, e.g. if the influence of the change in position near that particular position on the operating state exceeds a set threshold, the function is made more sensitive to changes near that position by adjustment.
The present embodiment is based on a ship operation influence function F set at a ship position P at a time step T P (P (T)) can achieve the following technical effects:
1. richer nonlinear response: using ship operation influencing functions F P (P (T)) can create a richer nonlinear response. Ship operation influence function F P The shape of (P (T)) resembles an S-curve, such that the slope of the function varies at different locations, which allows more accurate capture of the effect of the ship 'S position on the ship' S operating state, especially when the position is close to one side, the variation of the function is more sensitive, providing finer effect modeling.
2. Better data fitting ability: due to the ship operation influencing function F P (P (T)) which better fits the non-linear relationship, wherein the influence over different ranges of vessel positions may be different. For example, during voyage, the vessel may be affected by different geographical conditions, the vessel operation affecting the function F P (P (T)) can better characterize such complex relationships.
3.Flexibility of modeling is enhanced: using ship operation influencing functions F P The model is more flexible, different types of data can be adapted by adjusting parameters, the model can be better adapted to various ship operation state prediction problems, and accordingly prediction accuracy and reliability are improved.
4. Improving the sensitivity of the prediction: ship operation influence function F P (P (T)) is very sensitive to changes in certain intervals and therefore the effect of small changes in the position of the vessel on the operating conditions can be better captured, which is very advantageous for predictive problems requiring responses to small changes.
Specifically, the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max For maximum cargo capacity of ship rating, D in this embodiment max For standardizing D (T) to a range, i.e. [0,1 ]][0，1]In order to ensure that the influence of the ship cargo capacity on the ship operation state is within a controllable range, gamma is a ship cargo capacity sensitivity adjustment factor, and the parameter gamma influences the slope of the function, so that the change rate of the ship cargo capacity in the function is adjusted. The larger gamma value can lead to steeper functions, the change of the ship cargo capacity when affecting the ship operation state is more sensitive, the smaller value can lead to flatter functions, the influence of the ship cargo capacity on the ship operation state is smoother, theta is a ship cargo capacity influence adjusting factor, theta can be regarded as a threshold value, the central position of the ship cargo capacity in the functions is determined, when D (T) approaches theta, the value of the functions approaches 0.5, namely the middle value, and when D (T) deviates from theta, the value of the functions rapidly approaches 0 or 1, and therefore, the starting point and the change amplitude of the functions in the ship cargo capacity value range are adjusted through theta.
The present embodiment is based on a ship operation influence function F of the ship cargo capacity D set at time step T D (D (T)) can achieve the following technical effects:
1. nonlinear effects are enhanced: setting a ship operation influence function F D And (D (T)) the influence of the ship cargo capacity on the ship operation state is not linear any more, and the influence of the ship cargo capacity is different in different ranges by the S-shaped curve of the function, so that the influence change of the ship cargo capacity in different ranges can be better captured, and the nonlinear expression capacity of the model is enhanced.
2. Interval-specific effects: the parameters gamma and theta control the shape and position of the function, which enables the magnitude and direction of the influence to be adjusted within a specific interval for the ship cargo capacity, and this flexibility helps the model to more accurately reflect the influence of the ship cargo capacity within different ranges on the ship operation state.
3. Saturation and suppression are considered: ship operation influence function F D The characteristic of (D (T)) is to introduce saturation and dampening effects in a certain range, which means that the ship load can have a greater influence on the ship's operating conditions in a low or high range, while the influence in the middle range is diminished, which may better reflect the actual situation, as some ship load levels may lead to significant changes in conditions, while other levels may not.
Specifically, at time step T, the ship operation influence function F of the environmental factor E in which the ship is located E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor, a larger α value causes the function to rise or fall steeper in a change interval, shows greater sensitivity, a smaller α value causes the function to change more gradually, β is a specific environmental factor influence adjustment factor by adjusting a change rate of the α adjustment function in a change range of the environmental factor (for example, a specific environmental factor refers to that in historical data, when an influence of a certain weather condition on a ship running state exceeds a preset threshold, the weather condition is considered as a specific environmental factor), β is a horizontal offset parameter of the function, influences a position of the function on an environmental factor axis, and is used for controlling translation of the function on the environmental factor axis by adjusting β value, so that the function starts to rise or fall in the specific environmental factor range, thereby helping to capture the change of the influence factor better.
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a ship operation state prediction method based on track and production information fusion.
Specifically, the electronic device of the present embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
The storage medium can be used for storing software programs and modules, such as a ship operation state prediction method based on track and production information fusion in the embodiment of the invention, and the processor executes various functional applications and data processing by running the software programs and the modules stored in the storage medium, namely the ship operation state prediction method based on track and production information fusion. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may invoke the information stored in the storage medium and the application program via the transmission system to perform the following steps: step 101, acquiring historical track information and historical production information, and segmenting the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
102, setting a ship operation state prediction model, training the ship operation state prediction model by taking the historical track information and the historical production information in each time step as training data to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
specifically, the ship operation state prediction model is as follows:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (E(T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
The ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
Specifically, the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor, μ controls the slope of the function, a larger μ value results in steeper rise and fall of the function, making the function more sensitive to changes in vessel position, a smaller μ value results in flatter slope of the function, making the response of the function more gradual, in practice, a suitable μ value is selected according to the change in data, if the magnitude of the change in data is larger, a larger μ value is set to better capture these changes, conversely, if the change in data is smaller, a smaller μ value is more suitable, σ is a particular vessel position influencing adjustment factor, σ is a horizontal offset parameter of the function, controlling the translation of the function on the vessel position axis, i.e. where the function starts to rise or fall, by adjusting σ value, the response of the function can be enhanced or reduced near a particular position, e.g. if the influence of the change in position near that particular position on the operating state exceeds a set threshold, the function is made more sensitive to changes near that position by adjustment.
The present embodiment is based on a ship operation influence function F set at a ship position P at a time step T P (P (T)) can achieve the following technical effects:
1. richer nonlinear response: using ship operation influencing functions F P (P (T)) can create a richer nonlinear response. Ship operation influence function F P The shape of (P (T)) resembles an S-curve, such that the slope of the function varies at different locations, which allows more accurate capture of the effect of the ship 'S position on the ship' S operating state, especially when the position is close to one side, the variation of the function is more sensitive, providing finer effect modeling.
2. Better data fitting ability: due to the ship operation influencing function F P (P (T)) which better fits the non-linear relationship, wherein the influence over different ranges of vessel positions may be different. For example, during voyage, the vessel may be affected by different geographical conditions, the vessel operation affecting the function F P (P (T)) canThis complex relationship is better characterized.
3. Flexibility of modeling is enhanced: using ship operation influencing functions F P The model is more flexible, different types of data can be adapted by adjusting parameters, the model can be better adapted to various ship operation state prediction problems, and accordingly prediction accuracy and reliability are improved.
4. Improving the sensitivity of the prediction: ship operation influence function F P (P (T)) is very sensitive to changes in certain intervals and therefore the effect of small changes in the position of the vessel on the operating conditions can be better captured, which is very advantageous for predictive problems requiring responses to small changes.
Specifically, the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
/>
wherein D is max For maximum cargo capacity of ship rating, D in this embodiment max For standardizing D (T) to a range, i.e. [0,1 ]][0，1]In order to ensure that the influence of the ship cargo capacity on the ship operation state is within a controllable range, gamma is a ship cargo capacity sensitivity adjustment factor, and the parameter gamma influences the slope of the function, so that the change rate of the ship cargo capacity in the function is adjusted. The larger gamma value can lead to steeper functions, the change of the ship cargo capacity when affecting the ship operation state is more sensitive, the smaller value can lead to flatter functions, the influence of the ship cargo capacity on the ship operation state is smoother, theta is a ship cargo capacity influence adjusting factor, theta can be regarded as a threshold value, the central position of the ship cargo capacity in the functions is determined, when D (T) approaches theta, the value of the functions approaches 0.5, namely the middle value, and when D (T) deviates from theta, the value of the functions rapidly approaches 0 or 1, and therefore, the starting point and the change amplitude of the functions in the ship cargo capacity value range are adjusted through theta.
The present embodiment is carried by a ship with the ship load D set at time step TCamp influence function F D (D (T)) can achieve the following technical effects:
1. nonlinear effects are enhanced: setting a ship operation influence function F D And (D (T)) the influence of the ship cargo capacity on the ship operation state is not linear any more, and the influence of the ship cargo capacity is different in different ranges by the S-shaped curve of the function, so that the influence change of the ship cargo capacity in different ranges can be better captured, and the nonlinear expression capacity of the model is enhanced.
2. Interval-specific effects: the parameters gamma and theta control the shape and position of the function, which enables the magnitude and direction of the influence to be adjusted within a specific interval for the ship cargo capacity, and this flexibility helps the model to more accurately reflect the influence of the ship cargo capacity within different ranges on the ship operation state.
3. Saturation and suppression are considered: ship operation influence function F D The characteristic of (D (T)) is to introduce saturation and dampening effects in a certain range, which means that the ship load can have a greater influence on the ship's operating conditions in a low or high range, while the influence in the middle range is diminished, which may better reflect the actual situation, as some ship load levels may lead to significant changes in conditions, while other levels may not.
Specifically, at time step T, the ship operation influence function F of the environmental factor E in which the ship is located E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor, a larger α value causes the function to rise or fall steeper in a change interval, shows greater sensitivity, a smaller θ value causes the function to change more gradually, β is a specific environmental factor influence adjustment factor by adjusting a change rate of the α adjustment function in a change range of the environmental factor (for example, a specific environmental factor refers to that in historical data, when an influence of a certain weather condition on a ship running state exceeds a preset threshold, the weather condition is considered as a specific environmental factor), β is a horizontal offset parameter of the function, influences a position of the function on an environmental factor axis, and is used for controlling translation of the function on the environmental factor axis by adjusting β value, so that the function starts to rise or fall in the specific environmental factor range, thereby helping to capture the change of the influence factor better.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random-access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or the like, which can store program codes.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.

Claims (10)

1. A ship operation state prediction method based on track and production information fusion is characterized by comprising the following steps:
acquiring historical track information and historical production information, and segmenting the historical track information and the historical production information according to time steps to acquire the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
setting a ship operation state prediction model, taking the historical track information and the historical production information in each time step as training data, training the ship operation state prediction model to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
the ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
2. The ship operation state prediction method based on track and production information fusion as claimed in claim 1, wherein the ship operation state prediction model is:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (e(T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
3. A ship operation state prediction method based on track and production information fusion as claimed in claim 2, characterized in that the ship operation influence function F of the ship position P at time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor and σ is a specific vessel position impact adjustment factor.
4. A ship operation state prediction method based on track and production information fusion as claimed in claim 2, wherein the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max The ship load sensitivity adjustment factor is calculated according to the ship load sensitivity adjustment factor, and the ship load influence adjustment factor is calculated according to the ship load sensitivity adjustment factor.
5. The ship operation state prediction method based on track and production information fusion as claimed in claim 2, wherein the ship operation influence function F of the environmental factor E in which the ship is located at the time step T E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor and β is a specific environmental factor influence adjustment factor.
6. A ship operation state prediction system based on track and production information fusion is characterized by comprising:
the data acquisition module is used for acquiring historical track information and historical production information, segmenting the historical track information and the historical production information according to time steps, and acquiring the historical track information and the historical production information in each time step, wherein the historical track information in each time step comprises: the ship location, shipping environmental factors in time steps, the historical production information in each time step including: ship cargo capacity in time steps;
a prediction module for setting a ship operation state prediction model, taking the historical track information and the historical production information in each time step as training data, training the ship operation state prediction model to obtain real-time track information and real-time production information, calculating an operation state prediction value by using the trained ship operation state prediction model,
The ship operation state prediction model is composed of a plurality of set ship operation influence functions, and the ship operation influence functions are used for calculating influences of ship positions, shipping environment factors and ship cargo capacity on the ship operation state.
7. The ship operation state prediction system based on track and production information fusion as claimed in claim 6, wherein the ship operation state prediction model is:
S prediction (T)＝w P *F P (P(T))+w D *F D (D(T))+w E *F E (E(T))
Wherein S is Prediction (T) is an operation state prediction value, w P For the ship position influence weight, P (T) is the ship position P, F at time step T P (P (T)) is a ship operation influence function of the ship position P at the time step T, w D Weight for influencing ship cargo capacity, F D (D (T)) is a ship operation influence function of the ship cargo capacity D at time step T, and D (T) is the ship cargo capacity D at time step T, w E For the environmental factor influence weight, E (T) is the environmental factor E, F of the ship at the time step T E (E (T)) is a ship operation influence function of the environmental factor E in which the ship is located at time step T.
8. A ship operation state prediction system based on track and production information fusion as claimed in claim 7, wherein the ship operation influence function F of the ship position P at the time step T P (P (T)) is:
where μ is a vessel position sensitivity adjustment factor and σ is a specific vessel position impact adjustment factor.
9. A ship operation state prediction system based on track and production information fusion as claimed in claim 7, wherein the ship operation influence function F of the ship cargo capacity D at time step T D (D (T)) is:
wherein D is max The ship load sensitivity adjustment factor is calculated according to the ship load sensitivity adjustment factor, and the ship load influence adjustment factor is calculated according to the ship load sensitivity adjustment factor.
10. The ship operation state prediction system based on track and production information fusion according to claim 7, wherein the ship operation influence function F of the environmental factor E in which the ship is located at the time step T E (E (T)) is:
where α is an environmental factor sensitivity adjustment factor and β is a specific environmental factor influence adjustment factor.
CN202311100187.8A 2023-08-28 2023-08-28 Ship operation state prediction method and system based on track and production information fusion Pending CN117273197A (en)

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