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CN119228005A - Ship driver navigation task planning method, system, device and storage medium - Google Patents

Ship driver navigation task planning method, system, device and storage medium Download PDF

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CN119228005A
CN119228005A CN202411122323.8A CN202411122323A CN119228005A CN 119228005 A CN119228005 A CN 119228005A CN 202411122323 A CN202411122323 A CN 202411122323A CN 119228005 A CN119228005 A CN 119228005A
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毛喆
吕文晨
陈德山
万程鹏
李治宏
周嘉鹭
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Wuhan University of Technology WUT
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Abstract

The embodiment of the application provides a method, a system, equipment and a storage medium for planning navigation tasks of ship drivers, and belongs to the technical field of artificial intelligence. According to the method, driver monitoring data of a ship driver under a current sailing task are obtained through a data acquisition device, the driver monitoring data comprise monitoring index data of heart rate variability, skin electric activity, skin temperature and activity state, a comprehensive psychological load index is determined according to the driver monitoring data through a trained psychological load analysis model, then whether the comprehensive psychological load index of the sailing task is larger than a load threshold value is judged, and when the comprehensive psychological load index is larger than the load threshold value, the sailing task is planned again. According to the application, psychological load of a driver in the navigation process is analyzed in real time, and navigation tasks are adjusted when the psychological load is overlarge, so that the navigation tasks are reasonably planned according to the psychological load, the psychological load of the driver is reduced, and the navigation safety is improved.

Description

Ship pilot sailing task planning method, system, equipment and storage medium
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a ship driver sailing task planning method, a system, equipment and a storage medium.
Background
In an actual sailing process, the states of the ship driver facing different sailing tasks are different, for example, when the ship driver faces a highly concentrated monitoring task, compared with a physical task, the risk of sailing is possibly increased due to poor states, for example, when the ship driver faces a driving scene with higher requirements on the sailing speed, the driving process is tense due to unskilled driving at a high speed, and the error rate is increased. Thus, the ship pilot's status is different when facing different sailing tasks, and tasks with too bad status may increase the accident rate.
Disclosure of Invention
The embodiment of the application mainly aims to provide a method, a system, equipment and a storage medium for planning navigation tasks of ship drivers, and aims to reasonably plan the navigation tasks of the ship drivers and reduce risk accidents.
In order to achieve the above object, an aspect of the embodiments of the present application provides a method for planning a sailing mission of a ship driver, including the following steps:
Acquiring driver monitoring data of a ship driver under a current sailing task through a data acquisition device, wherein the driver monitoring data comprise monitoring index data of heart rate variability, skin electric activity, skin temperature and activity state;
determining a comprehensive psychological load index according to the driver monitoring data through a trained psychological load analysis model;
judging whether the comprehensive psychological load index of the navigation task is larger than a load threshold value or not;
and when the comprehensive psychological load index is larger than a load threshold value, re-planning the sailing task.
In some embodiments, the data acquisition device is a portable wearable device that includes a photoplethysmography sensor, a picosensor, a body temperature sensor, and an acceleration sensor.
In some embodiments, the acquiring, by the data acquisition device, the pilot monitoring data of the ship pilot under the current sailing task includes the following steps:
collecting heart rate data through the photoplethysmogram sensor, and performing feature analysis on the heart rate data by using a fast Fourier transform and a time domain analysis method to obtain heart rate variability in the driver monitoring data, wherein the heart rate variability comprises time domain features and frequency domain features;
Collecting skin conductivity through the skin electric sensor, and performing feature analysis on the skin conductivity to obtain skin electric activity in the driver monitoring data, wherein the skin electric activity comprises skin conductivity and skin electric reaction;
The skin surface temperature is acquired through the body temperature sensor, and the average value of the skin temperature in a preset time interval is calculated according to the skin surface temperature to obtain the skin temperature in the monitoring data of the driver;
And acquiring triaxial acceleration data through the acceleration sensor, integrating the triaxial acceleration data to obtain total acceleration, and classifying the total acceleration by using a machine learning algorithm to obtain an activity state in the driver monitoring data.
In some embodiments, the determining the integrated psychological load index from the driver monitoring data by the trained psychological load analysis model comprises the steps of:
After data preprocessing is carried out on each monitoring index data in the driver monitoring data, a feature vector is constructed according to each monitoring index data;
Inputting the feature vector into a trained psychological load analysis model to obtain a basic psychological load index;
And determining a comprehensive psychological load index according to the basic psychological load index.
In some embodiments, the psychological load analysis model is trained by:
acquiring a plurality of sample data, wherein the plurality of sample data comprise feature vectors and psychological load index labels of historical monitoring data of different ship drivers;
Dividing the plurality of sample data into a training data set and a test data set;
optimizing a psychological load analysis model based on a support vector machine algorithm by using the training data set, and evaluating the optimized psychological load analysis model by using a test data set to obtain a model evaluation result, wherein the evaluation result comprises a mean square error and a decision coefficient;
and outputting a trained psychological load analysis model when the evaluation result meets the preset requirement.
In some embodiments, the determining the composite psychological load index from the base psychological load index comprises the steps of:
Determining individualized reference values of all monitoring indexes according to the user history monitoring data of the ship driver;
determining a personalized adjustment coefficient according to the currently collected driver monitoring data and the personalized reference value;
inputting the personalized adjustment coefficient and the basic psychological load index into a psychological load correction model to obtain a comprehensive psychological load index.
In some embodiments, the re-planning the voyage task includes the steps of:
Determining a plurality of first candidate voyages according to position information of a voyage driver;
inquiring comprehensive psychological load indexes of ship drivers corresponding to the first candidate navigation sections from a database;
and re-determining the sailing task according to the first candidate voyage segment of which the comprehensive psychological load index of the ship driver is smaller than the load threshold value.
To achieve the above object, another aspect of the embodiments of the present application provides a ship pilot sailing mission planning system, including:
The system comprises a first module, a second module and a third module, wherein the first module is used for acquiring driver monitoring data of a ship driver under the current sailing task through a data acquisition device, wherein the driver monitoring data comprise monitoring index data of heart rate variability, skin electric activity, skin temperature and activity state;
the second module is used for determining a comprehensive psychological load index according to the driver monitoring data through the trained psychological load analysis model;
The third module is used for judging whether the comprehensive psychological load index of the sailing task is larger than a load threshold value or not;
And a fourth module, configured to reprogram the voyage task when the comprehensive psychological load index is greater than a load threshold.
To achieve the above object, another aspect of the embodiments of the present application proposes an electronic device including a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, the program implementing the method of the above embodiments when executed by the processor.
To achieve the above object, another aspect of the embodiments of the present application provides a storage medium, which is a computer-readable storage medium, for computer-readable storage, the storage medium storing one or more programs executable by one or more processors to implement the method described in the above embodiments.
The application provides a ship driver sailing task planning method, a system, equipment and a storage medium, which are characterized in that driver monitoring data of a ship driver under the current sailing task are obtained through a data acquisition device, the driver monitoring data comprise monitoring index data of heart rate variability, skin electric activity, skin temperature and activity state, a comprehensive psychological load index is determined according to the driver monitoring data through a trained psychological load analysis model, then whether the comprehensive psychological load index of the sailing task is larger than a load threshold value is judged, and when the comprehensive psychological load index is larger than the load threshold value, the sailing task is planned again. According to the application, psychological load of a driver in the navigation process is analyzed in real time, and navigation tasks are adjusted when the psychological load is overlarge, so that the navigation tasks are reasonably planned according to the psychological load, the psychological load of the driver is reduced, and the navigation safety is improved.
Drawings
FIG. 1 is a flow chart of a method for planning a sailing mission of a ship pilot provided by an embodiment of the application;
fig. 2 is a schematic hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that although a logical order of blocks is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in a different order than the blocks in the system or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
The embodiment of the application provides a method, a system, equipment and a storage medium for planning navigation tasks of ship drivers, which aim to reasonably plan the navigation tasks of the ship drivers and reduce risk accidents.
The method, the system, the equipment and the storage medium for planning the navigation task of the ship driver provided by the embodiment of the application are specifically described through the following embodiment, and the method for planning the navigation task of the ship driver in the embodiment of the application is described first.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The embodiment of the application provides a ship driver sailing task planning method, and relates to the technical field of artificial intelligence. The ship driver navigation task planning method provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc., the server may be configured as an independent physical server, may be configured as a server cluster or a distributed system formed by a plurality of physical servers, and may be configured as a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and basic cloud computing services such as big data and artificial intelligent platforms, and the software may be an application for implementing a navigation task planning method for a ship driver, etc., but is not limited to the above form.
The application is operational with numerous general purpose or special purpose computer system environments or configurations. Such as a personal computer, a server computer, a hand-held or portable device, a tablet device, a multiprocessor system, a microprocessor-based system, a set top box, a programmable consumer electronics, a network PC, a minicomputer, a mainframe computer, a distributed computing environment that includes any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Fig. 1 is an optional flowchart of a method for planning a sailing mission of a ship driver according to an embodiment of the present application, where the method in fig. 1 may include, but is not limited to, steps S101 to S104.
Step S101, acquiring driver monitoring data of a ship driver under a current sailing task through a data acquisition device, wherein the driver monitoring data comprise monitoring index data of heart rate variability, skin electric activity, skin temperature and activity state;
step S102, determining a comprehensive psychological load index according to the driver monitoring data through a trained psychological load analysis model;
Step S103, judging whether the comprehensive psychological load index of the sailing task is larger than a load threshold value or not;
and step S104, when the comprehensive psychological load index is larger than the load threshold, the sailing task is re-planned.
In step S101 of some embodiments, the data acquisition device is provided with various sensors for acquiring physiological state data required for analyzing the psychological load of the driver. The data acquisition device may be a portable wearable device, which may be in the form of a wristband, including a wristband, a sensor assembly, a display screen, and a processing chip, which may be of a comfortable material for long wear.
The sensor assembly comprises a photo-volume pulse wave sensor, a piezoelectric sensor, a body temperature sensor and an acceleration sensor. The system comprises an acceleration sensor, a skin temperature sensor, a photoelectric volume pulse wave (PPG) sensor, a heart rate and Heart Rate Variability (HRV) and other related indexes, wherein the acceleration sensor is used for monitoring the activity state of a driver, the tension degree and the fatigue level of the driver in work can be estimated by analyzing the movement change, the skin temperature sensor can reflect the stress response and the physiological state of the driver by measuring the body surface temperature change and help identify the psychological pressure, the photoelectric volume pulse wave (PPG) sensor is used for measuring the blood flow change, and the psychological load condition of the driver is estimated by calculating the heart rate and Heart Rate Variability (HRV) and other related indexes.
The processing chip is responsible for the acquisition, processing and analysis of the sensor data to obtain relevant monitoring index data, each of which forms driver monitoring data including, but not limited to, heart rate variability, galvanic skin activity, skin temperature and activity status.
It can be understood that, according to the monitoring index requirement of the psychological load index, more sensors can be arranged on the data acquisition device to acquire more kinds of monitoring index data, and illustratively, an oxygen sensor and a blood pressure sensor can also be arranged on the data acquisition device to further improve the psychological load detection precision. The blood oxygen sensor can monitor index data of blood oxygen saturation of a driver in real time to identify physiological changes caused by fatigue or pressure, and the blood pressure sensor can detect index data of blood pressure changes of the driver and can provide comprehensive and accurate psychological load assessment by combining the index data with other physiological data.
The data acquisition device is also provided with a wireless communication module which can support Bluetooth and Wi-Fi communication and transmit data to a central monitoring system (namely a remote server) or mobile equipment in real time. Furthermore, the data acquisition device is also internally provided with a storage chip for locally storing sensor data and processing results, so that the data can still be recorded and stored when the network connection is interrupted.
The central monitoring system is provided with a database for centralized management and analysis of the received data. In view of the real-time performance, high concurrency and subsequent analysis requirements of the data, the central monitoring system of the embodiment of the application uses the following two databases:
A timing Database (TIME SERIES Database, TSDB) for storing sensor data, the timing Database being adept at processing time-series data, supporting efficient read and write operations and queries, such as InfluxDB.
A relational database (Relational Database, RDBMS) for storing user information, device information, and comprehensive analysis results (e.g., comprehensive psychological load index). Relational databases support complex queries and transactions, such as PostgreSQL.
Illustratively, the timing database (InfluxDB) structure is designed as follows:
the relational database (PostgreSQL) structure is designed as follows:
The data acquisition device (such as a bracelet) transmits data to the central monitoring system through the wireless communication module. The central monitoring system uses InfluxDB to store real-time sensor data and PostgreSQL to store user information, device information and comprehensive analysis results. The central monitoring system reads real-time data from the InfluxDB, and calculates a comprehensive psychological load index in combination with user and device information, and stores the result in PostgreSQL.
In some embodiments, step S101 may include, but is not limited to including, step S201 to step S204:
Step S201, heart rate data are collected through a photoelectric volume pulse wave sensor, and characteristic analysis is carried out on the heart rate data by using a fast Fourier transform and a time domain analysis method to obtain heart rate variability in driver monitoring data, wherein the heart rate variability comprises time domain characteristics and frequency domain characteristics;
Step S202, acquiring skin conductivity through a skin electric sensor, and performing feature analysis on the skin conductivity to obtain skin electric activity in monitoring data of a driver, wherein the skin electric activity comprises skin conductivity and skin electric reaction;
Step S203, the skin surface temperature is acquired through a body temperature sensor, and the average value of the skin temperature in a preset time interval is calculated according to the skin surface temperature to obtain the skin temperature in the monitoring data of the driver;
Step S204, three-axis acceleration data are collected through an acceleration sensor, the three-axis acceleration data are integrated to obtain total acceleration, and the total acceleration is classified by using a machine learning algorithm to obtain the activity state in the monitoring data of the driver.
In this embodiment, after the data acquisition device acquires heart rate data, skin conductivity, skin surface temperature and triaxial acceleration data through the sensor assembly, the data acquired by the sensor can be locally analyzed to obtain monitoring index data such as heart rate variability, skin electrical activity, skin temperature and activity state, and each monitoring index data forms driver monitoring and is sent to a remote service for psychological load analysis. The data acquisition device can also acquire sensor data and then send the data to a remote server, and the remote server analyzes and monitors index data and calculates psychological load. After the sensor data is collected according to a certain sampling frequency, the missing value and the abnormal value can be processed by utilizing an interpolation method, and then the monitoring index data is analyzed based on the processed sensor data.
In step S201 of some embodiments, heart rate data is acquired by a PPG sensor, with a sampling rate of 250Hz. Using a Fast Fourier Transform (FFT) and a time domain analysis method, a heartbeat interval (RR) is calculated, and then a Standard Deviation (SDNN) and a root mean square deviation (RMSSD) of a time domain index and a ratio (LF/HF) of a Low Frequency (LF) and a High Frequency (HF) of a frequency domain index are calculated according to the heartbeat interval, and monitored index data of the SDNN, the RMSSD and the LF/HF collectively represent Heart Rate Variability (HRV), specifically as shown in the following formula:
Where P represents the power of the frequency and N represents the number of samples.
In step S202 of some embodiments, skin electrical signals (EDA), i.e. skin conductivity, are detected by a skin sensor with a sampling rate of 50Hz. Analyzing the amplitude and frequency of the EDA signal determines amplitude and frequency variations, skin Conductivity (SCL) and skin electrical response (SCR), the monitored indicator data of Skin Conductivity (SCL) and skin electrical response (SCR) collectively representing skin electrical activity.
Wherein Skin Conductivity (SCL) represents the average value of skin conductivity over a period of time, reflecting the baseline level, and the calculation formula is as follows:
the skin electrical response (SCR) represents the instantaneous change in skin conductivity at the occurrence of a stress event, calculated as follows:
ΔSCR=EDApeak-EDAbaseline;
Where EDA peak represents the peak skin conductivity and EDA baseline represents the baseline skin conductivity.
In step S203 of some embodiments, the skin surface temperature is detected by the body temperature sensor, the sampling rate is 1Hz, the variation trend of the skin temperature is analyzed, the average skin temperature is calculated, and the calculation formula is as follows:
Where T represents skin temperature, N represents the total number of skin temperature measurements (number of data points), and T i represents the skin surface temperature of the ith measurement. The skin temperature may be collectively represented by a skin average temperature value and a skin temperature change rate.
In step S204 of some embodiments, the three-dimensional acceleration sensor continuously collects X, Y, Z acceleration data in three directions, with a sampling rate of 100Hz. The expression is as follows:
a(t)=[ax(t),ay(t),az(t)];
Where a x(t),ay(t),az (t) represents the acceleration value in the direction X, Y, Z at time t, respectively.
Then denoising the original acceleration data, and removing high-frequency noise by using a low-pass filter, wherein the low-pass filter is expressed as follows:
afiltered(t)=LowPassFilter(a(t));
The magnitude of the total acceleration is calculated as follows:
Acceleration data is classified using a machine learning algorithm (e.g., K-means, support vector machine, etc.), and activity states are classified into different categories (e.g., stationary, walking, running, etc.), activity states ACTIVITYSTATE are represented as follows.
ActivityState=Classifier(amagnitude(t))
Wherein, classifier represents a Classifier obtained based on a machine learning algorithm.
In step S102 of some embodiments, the psychological load analysis model may be trained based on a machine learning algorithm or a deep learning algorithm, where the machine learning algorithm may be a decision tree, a random forest, a Support Vector Machine (SVM), etc., and the deep learning algorithm may be a neural network, and embodiments of the present application are not limited in particular. After training by using the historical sample data to obtain the psychological load model, the psychological load model can be used for calculating the monitoring data of the driver so as to obtain the psychological load index of the driver. Specifically, the psychological load index output can be obtained by inputting the driver monitoring data into the psychological load analysis model, and the psychological load index is the comprehensive psychological load index, or the psychological load index output can be obtained by inputting the driver monitoring data into the psychological load analysis model. And (5) performing personalized adjustment on the psychological load index to obtain the comprehensive psychological load index.
In some embodiments, step S102 may include, but is not limited to including, step S301 to step S303:
Step S301, after data preprocessing is carried out on each monitoring index data in the monitoring data of the driver, a feature vector is constructed according to each monitoring index data;
step S302, inputting the feature vector into a trained psychological load analysis model to obtain a basic psychological load index;
Step S303, determining the comprehensive psychological load index according to the basic psychological load index.
In this embodiment, before the driver monitoring data is input into the psychological load analysis model, the driver monitoring data may be preprocessed to form the feature vector, and then the feature vector is input into the psychological load analysis model for analysis and calculation, specifically as follows:
And (3) carrying out standardized processing on each monitoring index data, so that the monitoring index data is convenient to fuse and calculate under the same dimension, and the formula is as follows:
Wherein, X standard represents the standardized monitoring index data, X represents the original monitoring index data, mu represents the mean value, and sigma represents the standard deviation.
From SDNN, RMSSD, LF/HF in heart rate variability, SCL and SCR of skin electrical activity, skin temperature average ST mean of skin temperature and skin temperature change rate ST delta, the active state ACC activitysituation jointly constructs a feature vector, which is expressed as follows:
F＝[HRVSDNN,HRVRMSSD,HRVLF/HF,EDASCL,EDASCR,STmean,STdelta,ACCactivitysituation];
Inputting the feature vector into a trained psychological load analysis model to obtain a basic psychological load index, and then carrying out personalized correction on the basic psychological load index to obtain the current comprehensive psychological load index of the ship driver.
In some embodiments, the psychological load analysis model in step S302 may be obtained through, but is not limited to, steps S401 to S404:
Step S401, a plurality of sample data are obtained, wherein the plurality of sample data comprise feature vectors and psychological load index labels of historical monitoring data of different ship drivers;
Step S402, dividing a plurality of sample data into a training data set and a test data set;
Step S403, optimizing a psychological load analysis model based on a support vector machine algorithm by using a training data set, and evaluating the optimized psychological load analysis model by using a test data set to obtain a model evaluation result, wherein the evaluation result comprises a mean square error and a decision coefficient;
Step S404, when the evaluation result meets the preset requirement, outputting a trained psychological load analysis model.
In this embodiment, a large number of historical monitoring data (including heart rate variability, skin temperature, activity state and skin electric activity) of different users and corresponding psychological load index labels are obtained by scoring the users in corresponding historical environments, and corresponding feature vectors are constructed according to the historical monitoring data of the users to obtain sample data. The acquired sample data is divided into training data sets and test data sets, for example, in a proportion of 70% training data sets and 30% test data sets. A psychological load analysis model (i.e., an SVM regression model) of a support vector machine algorithm (SVM) is trained using the training data set to enable the model to output a psychological load index based on the input feature vectors. The support vector machine algorithm is suitable for classification and regression tasks, and SVM regression can be used for fusing a plurality of physiological indexes according to index weights to calculate a basic psychological load index.
In the model weight parameter updating process, a kernel function (such as an RBF kernel function) can be used to process the nonlinear relationship, and the kernel function is as follows:
K(Xi,Xj)＝exp(-γ||Xi-Xj||2);
And (3) tuning the super parameters (such as penalty parameters C and kernel function parameters gamma) of the model by using a cross-validation method, and selecting an optimal parameter combination. Evaluating the performance of an SVM regression model by using a test data set, calculating indexes such as Mean Square Error (MSE), decision coefficient (R) and the like, obtaining a trained psychological load analysis model when the model performance (namely evaluation result) reaches a certain requirement, and calculating a real-time basic psychological load index CMWI real-time by using the psychological load analysis model, wherein the indexes are expressed as follows
CMWI real-time = SVM model (F real-time).
In some embodiments, step S303 includes, but is not limited to including, step S501 through step S503:
step S501, determining personalized reference values of all monitoring indexes according to user history monitoring data of a ship driver;
step S502, determining a personalized adjustment coefficient according to the currently collected driver monitoring data and a personalized reference value;
Step S503, inputting the personalized adjustment coefficient and the basic psychological load index into a psychological load correction model to obtain a comprehensive psychological load index.
In this embodiment, the personalized reference value of the monitoring index such as Heart Rate Variability (HRV), skin temperature, and movement state in the resting state of the driver may be determined according to the user history monitoring data of the ship driver. For exampleI represents time.
Inputting the personalized reference value of the user and the currently collected driver monitoring data into a relation function constructed in advance to obtain a personalized adjustment coefficient for compensating the index weight in the psychological analysis model.
And training a personalized adjustment model (namely a psychological load correction model) based on the historical monitoring data of the user, wherein the model can be combined with the basic psychological load index and the personalized adjustment coefficient to calculate the final comprehensive psychological load index. In the embodiment, personalized adjustment coefficients are introduced, and proper calculation models are established for different drivers, so that the accuracy of psychological load prediction is enhanced.
According to some embodiments of the present application, the psychological load index of the driver and the monitoring data can be combined to predict the psychological load change trend in a period of time in the future, so that measures are taken in advance to reduce the operation error rate.
In steps S103 to S104 of some embodiments, by measuring and calculating the comprehensive psychological load indexes under different difficulty sailing task scenes, a load threshold is set for each driver, and when the comprehensive psychological load indexes rise to the load threshold, early warning can be initiated at the user side terminal and the monitoring side terminal at the same time, and the warning modes include visual warning (screen prompt), sound warning (warning tone) and notification warning (short message, mail, etc.), so that abnormal psychological load conditions can be found and intervened in time, psychological states of the drivers are improved, and sailing safety is improved. According to the method and the device, different load thresholds are set according to the performances of different drivers under different sailing tasks, and the rationality of subsequent sailing task planning is improved.
In some embodiments, the voyage task may refer to a route, a specified voyage time, a voyage speed and the like given by the system, and when a psychological load index occurs to a driver in the process of executing the voyage task, the voyage task may be re-planned by acquiring a factor causing the psychological load of the driver to increase. When the central monitoring system monitors that the psychological load index of the driver exceeds the load threshold, a user interface can be displayed through a terminal (such as a bracelet or a ship central controller), a driver inputs factors which cause the psychological load of the driver to be increased, such as factors with great difficulty in current voyage operation, and the system re-plans the voyage in the voyage task according to the feedback of the driver, so that voyage safety is improved.
In some embodiments, the re-planning navigation task in step S104 may include, but is not limited to including steps S601 to S603:
Step S601, determining a plurality of first candidate voyages according to position information of a voyage driver;
Step S602, inquiring comprehensive psychological load indexes of ship drivers corresponding to each first candidate navigation section from a database;
and step S603, re-determining the sailing task according to the first candidate voyage segment of which the comprehensive psychological load index of the ship driver is smaller than the load threshold value.
In some embodiments, step S603 includes, but is not limited to including step S701 through step S703:
step S701, selecting a first candidate leg with the comprehensive psychological load index smaller than a load threshold value of a ship driver as a second candidate leg;
Step S702, screening a plurality of second candidate air segments according to the expected screening conditions to obtain a target air segment;
step S703, modifying the current leg in the voyage task to the target leg.
In this embodiment, a GPS data transmission module is added to the data acquisition device, so that the geographic position and the navigation path of the ship are recorded, and in combination with real-time psychological load index analysis, psychological load changes on each navigation path driven by the ship can be recorded in the database, and possible high-risk areas or navigation sections with high operation difficulty are identified, so that the navigation task of the driver is reasonably planned. Specifically, the psychological load data of the crew and the navigation path can be displayed in a superimposed manner through a Geographic Information System (GIS), and a visual psychological load map is provided. Meanwhile, the change of key physiological indexes and psychological load indexes is displayed through the data instrument panel and the real-time chart, so that management staff is helped to quickly identify abnormal conditions and take corresponding measures.
In this embodiment, the psychological load change of the driver is usually caused by a large difficulty in executing the leg, such as excessive obstacles of the leg and fierce water flow of the leg, when the system detects that the psychological load of the driver is too high, determining a plurality of first candidate legs which are located in a certain range of the current position and can reach the destination according to the position information of the navigation driver, searching the comprehensive psychological load index of the ship driver corresponding to each first candidate leg from the database, selecting the first candidate leg with the comprehensive psychological load index smaller than the load threshold value of the ship driver as the second candidate leg, then screening the plurality of second candidate legs according to the expected screening conditions such as low operation difficulty or low driving time to obtain the target leg, switching the current leg to the target leg, and realizing the re-planning of the navigation task so as to reduce the psychological load of the driver and simultaneously meet certain task expectations and improve the driving safety.
The embodiment of the application also provides a navigation task planning system for the ship pilot, which comprises the following steps:
The first module is used for acquiring driver monitoring data of a ship driver under the current sailing task through the data acquisition device, wherein the driver monitoring data comprise monitoring index data of heart rate variability, skin electric activity, skin temperature and activity state;
the second module is used for determining a comprehensive psychological load index according to the driver monitoring data through the trained psychological load analysis model;
The third module is used for judging whether the comprehensive psychological load index of the sailing task is larger than a load threshold value or not;
and a fourth module for re-planning the voyage task when the comprehensive psychological load index is greater than the load threshold.
It can be understood that the content in the above-mentioned embodiments of the method for planning a sailing task for a ship driver is applicable to the embodiments of the system, and the functions specifically implemented by the embodiments of the system are the same as those in the embodiments of the method for planning a sailing task for a ship driver, and the beneficial effects achieved by the embodiments of the method for planning a sailing task for a ship driver are the same as those achieved by the embodiments of the method for planning a sailing task for a ship driver.
The embodiment of the application also provides electronic equipment, which comprises a memory, a processor, a program stored on the memory and capable of running on the processor and a data bus for realizing connection communication between the processor and the memory, wherein the program is executed by the processor to realize the ship driver sailing task planning method. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
Referring to fig. 2, fig. 2 illustrates a hardware structure of an electronic device according to another embodiment, where the electronic device includes:
The processor 201 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solution provided by the embodiments of the present application;
The memory 202 may be implemented in the form of read-only memory (ReadOn lyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM), among others. The memory 202 may store an operating system and other application programs, and when the technical solutions provided in the embodiments of the present disclosure are implemented by software or firmware, relevant program codes are stored in the memory 202, and the processor 201 invokes a method for planning a sailing task of a ship driver to execute the embodiments of the present disclosure;
An input/output interface 203 for implementing information input and output;
the communication interface 204 is configured to implement communication interaction between the present device and other devices, and may implement communication in a wired manner (such as USB, network cable, etc.), or may implement communication in a wireless manner (such as mobile network, WI F I, bluetooth, etc.);
A bus 205 for transferring information between various components of the device (e.g., processor 201, memory 202, input/output interface 203, and communication interface 204);
Wherein the processor 201, the memory 202, the input/output interface 203 and the communication interface 204 are communicatively coupled to each other within the device via a bus 205.
The embodiment of the application also provides a storage medium which is a computer readable storage medium and is used for computer readable storage, the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors so as to realize the ship driver sailing task planning method.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor 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 embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by persons skilled in the art that the embodiments of the application are not limited by the illustrations, and that more or fewer steps than those shown may be included, or certain steps may be combined, or different steps may be included.
The system embodiments described above are merely illustrative, in that the units illustrated as separate components may or may not be physically separate, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the application and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "and/or" is used to describe an association relationship of an associated object, and indicates that three relationships may exist, for example, "a and/or B" may indicate that only a exists, only B exists, and three cases of a and B exist simultaneously, where a and B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b or c may represent a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided by the present application, it should be understood that the disclosed systems and methods may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the division of the above elements is merely a logical functional division, and there may be additional divisions in actual implementation, e.g., multiple elements 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 interface, system or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over 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 application 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 application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including multiple instructions to cause 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 of the various embodiments of the present application. The storage medium includes various media capable of storing programs, such as a U disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory RAM), a magnetic disk, or an optical disk.
The preferred embodiments of the present application have been described above with reference to the accompanying drawings, and are not thereby limiting the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.

Claims (10)

1.一种船舶驾驶员航行任务规划方法，其特征在于，包括以下步骤：1. A method for planning a navigation task for a ship driver, characterized in that it comprises the following steps: 通过数据采集装置获取船舶驾驶员在当前航行任务下的驾驶员监测数据，其中，所述驾驶员监测数据包括心率变异性、皮肤电活动、皮肤温度和活动状态的监测指标数据；Acquiring the driver monitoring data of the ship driver under the current navigation task through the data acquisition device, wherein the driver monitoring data includes monitoring index data of heart rate variability, skin electrode activity, skin temperature and activity status; 通过训练好的心理负荷分析模型，根据所述驾驶员监测数据确定综合心理负荷指数；Determining a comprehensive mental load index according to the driver monitoring data by using a trained mental load analysis model; 判断所述航行任务的综合心理负荷指数是否大于负荷阈值；Determining whether the comprehensive psychological load index of the navigation task is greater than a load threshold; 当所述综合心理负荷指数大于负荷阈值，则重新规划所述航行任务。When the comprehensive mental load index is greater than the load threshold, the navigation task is replanned. 2.根据权利要求1所述的船舶驾驶员航行任务规划方法，其特征在于，所述数据采集装置为便携式穿戴装置，所述便携式穿戴装置包括光电容积脉搏波传感器、皮电传感器、体温传感器和加速度传感器。2. The method for planning a navigation task for a ship driver according to claim 1 is characterized in that the data acquisition device is a portable wearable device, and the portable wearable device includes a photoelectric volumetric pulse wave sensor, a skin charge sensor, a body temperature sensor and an acceleration sensor. 3.根据权利要求2所述的船舶驾驶员航行任务规划方法，其特征在于，所述通过数据采集装置获取船舶驾驶员在当前航行任务下的驾驶员监测数据，包括以下步骤：3. The method for planning a navigation task for a ship driver according to claim 2, characterized in that the step of acquiring the driver monitoring data of the ship driver under the current navigation task by means of a data acquisition device comprises the following steps: 通过所述光电容积脉搏波传感器采集心率数据，并使用快速傅里叶变换和时域分析方法对所述心率数据进行特征分析得到所述驾驶员监测数据中的心率变异性，其中，所述心率变异性包括时域特征和频域特征；Collecting heart rate data through the photoplethysmography sensor, and performing feature analysis on the heart rate data using a fast Fourier transform and a time domain analysis method to obtain heart rate variability in the driver monitoring data, wherein the heart rate variability includes time domain features and frequency domain features; 通过所述皮电传感器采集皮肤导电率，对所述皮肤导电率进行特征分析得到所述驾驶员监测数据中的皮肤电活动，其中，所述皮肤电活动包括皮肤电导率和皮电反应；Collecting skin conductivity through the galvanic skin sensor, and performing feature analysis on the skin conductivity to obtain skin electrical activity in the driver monitoring data, wherein the skin electrical activity includes skin conductivity and galvanic skin response; 通过所述体温传感器采集皮肤表面温度，根据所述皮肤表面温度计算预设时间间隔内的皮肤温度平均值得到所述驾驶员监测数据中的皮肤温度；The skin surface temperature is collected by the body temperature sensor, and the skin temperature in the driver monitoring data is obtained by calculating the average skin temperature within a preset time interval according to the skin surface temperature; 通过所述加速度传感器采集三轴加速度数据，对所述三轴加速度数据进行整合得到总加速度，利用机器学习算法对所述总加速度进行分类得到所述驾驶员监测数据中的活动状态。The three-axis acceleration data is collected by the acceleration sensor, the three-axis acceleration data is integrated to obtain the total acceleration, and the total acceleration is classified by using a machine learning algorithm to obtain the activity state in the driver monitoring data. 4.根据权利要求1所述的船舶驾驶员航行任务规划方法，其特征在于，所述通过训练好的心理负荷分析模型，根据所述驾驶员监测数据确定综合心理负荷指数，包括以下步骤：4. The method for planning a navigation task for a ship driver according to claim 1, characterized in that the comprehensive psychological load index is determined according to the driver monitoring data by using a trained psychological load analysis model, comprising the following steps: 对所述驾驶员监测数据中的各个监测指标数据进行数据预处理后，根据各个所述监测指标数据构建特征向量；After performing data preprocessing on each monitoring indicator data in the driver monitoring data, constructing a feature vector according to each monitoring indicator data; 将所述特征向量输入训练好的心理负荷分析模型，得到基础心理负荷指数；Inputting the characteristic vector into a trained mental load analysis model to obtain a basic mental load index; 根据所述基础心理负荷指数确定综合心理负荷指数。A comprehensive mental load index is determined according to the basic mental load index. 5.根据权利要求4所述的船舶驾驶员航行任务规划方法，其特征在于，所述心理负荷分析模型通过以下步骤训练得到：5. The method for planning a navigation task for a ship driver according to claim 4, wherein the psychological load analysis model is trained by the following steps: 获取多个样本数据，其中，多个样本数据包括不同船舶驾驶员的历史监测数据的特征向量和心理负荷指数标签；Acquire a plurality of sample data, wherein the plurality of sample data include feature vectors and mental load index labels of historical monitoring data of different ship drivers; 将多个样本数据划分为训练数据集和测试数据集；Divide multiple sample data into training data sets and test data sets; 利用所述训练数据集对基于支持向量机算法的心理负荷分析模型进行优化，并采用测试数据集对优化后的心理负荷分析模型进行评估得到模型评估结果，其中，所述评估结果包括均方误差和决定系数；The training data set is used to optimize the mental workload analysis model based on the support vector machine algorithm, and the test data set is used to evaluate the optimized mental workload analysis model to obtain a model evaluation result, wherein the evaluation result includes a mean square error and a determination coefficient; 当所述评估结果满足预设要求，则输出训练好的心理负荷分析模型。When the evaluation results meet the preset requirements, the trained mental load analysis model is output. 6.根据权利要求4所述的船舶驾驶员航行任务规划方法，其特征在于，所述根据所述基础心理负荷指数确定综合心理负荷指数，包括以下步骤：6. The method for planning a navigation task for a ship driver according to claim 4, characterized in that the step of determining the comprehensive mental load index according to the basic mental load index comprises the following steps: 根据船舶驾驶员的用户历史监测数据确定各个监测指标的个性化基准值；Determine the personalized benchmark value of each monitoring indicator based on the user's historical monitoring data of the ship driver; 根据当前采集的所述驾驶员监测数据和所述个性化基准值确定个性化调整系数；Determining a personalized adjustment coefficient according to the currently collected driver monitoring data and the personalized reference value; 将所述个性化调整系数和所述基础心理负荷指数输入心理负荷修正模型，得到综合心理负荷指数。The personalized adjustment coefficient and the basic mental load index are input into a mental load correction model to obtain a comprehensive mental load index. 7.根据权利要求1所述的船舶驾驶员航行任务规划方法，其特征在于，所述重新规划所述航行任务包括以下步骤：7. The method for planning a navigation task for a ship driver according to claim 1, wherein the re-planning of the navigation task comprises the following steps: 根据航行驾驶员的位置信息确定多个第一候选航段；determining a plurality of first candidate flight segments according to the position information of the navigation pilot; 从数据库中查询各个所述第一候选航段对应的船舶驾驶员的综合心理负荷指数；Querying the comprehensive psychological load index of the ship driver corresponding to each of the first candidate segments from the database; 根据船舶驾驶员的综合心理负荷指数小于负荷阈值的第一候选航段重新确定所述航行任务。The navigation task is re-determined according to the first candidate segment in which the comprehensive psychological load index of the ship driver is less than the load threshold. 8.一种船舶驾驶员航行任务规划系统，其特征在于，包括：8. A navigation task planning system for ship drivers, characterized by comprising: 第一模块，用于通过数据采集装置获取船舶驾驶员在当前航行任务下的驾驶员监测数据，其中，所述驾驶员监测数据包括心率变异性、皮肤电活动、皮肤温度和活动状态的监测指标数据；The first module is used to obtain the driver monitoring data of the ship driver under the current navigation task through the data acquisition device, wherein the driver monitoring data includes monitoring index data of heart rate variability, skin electrode activity, skin temperature and activity state; 第二模块，用于通过训练好的心理负荷分析模型，根据所述驾驶员监测数据确定综合心理负荷指数；The second module is used to determine the comprehensive mental load index according to the driver monitoring data by using the trained mental load analysis model; 第三模块，用于判断所述航行任务的综合心理负荷指数是否大于负荷阈值；The third module is used to determine whether the comprehensive psychological load index of the navigation task is greater than the load threshold; 第四模块，用于当所述综合心理负荷指数大于负荷阈值，则重新规划所述航行任务。The fourth module is used to re-plan the navigation task when the comprehensive psychological load index is greater than the load threshold. 9.一种电子设备，其特征在于，所述电子设备包括存储器、处理器、存储在所述存储器上并可在所述处理器上运行的程序以及用于实现所述处理器和所述存储器之间的连接通信的数据总线，所述程序被所述处理器执行时实现如权利要求1至7任一项所述的方法的步骤。9. An electronic device, characterized in that the electronic device comprises a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein the program, when executed by the processor, realizes the steps of the method described in any one of claims 1 to 7. 10.一种存储介质，所述存储介质为计算机可读存储介质，用于计算机可读存储，其特征在于，所述存储介质存储有一个或者多个程序，所述一个或者多个程序可被一个或者多个处理器执行，以实现权利要求1至7中任一项所述的方法的步骤。10. A storage medium, which is a computer-readable storage medium used for computer-readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any one of the methods of claims 1 to 7.
CN202411122323.8A 2024-08-15 2024-08-15 Ship driver navigation task planning method, system, device and storage medium Pending CN119228005A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN120180771A (en) * 2025-05-21 2025-06-20 中国船舶集团有限公司第七一九研究所 Auxiliary evaluation method, device, equipment and medium for a transport device control panel

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN120180771A (en) * 2025-05-21 2025-06-20 中国船舶集团有限公司第七一九研究所 Auxiliary evaluation method, device, equipment and medium for a transport device control panel
CN120180771B (en) * 2025-05-21 2025-08-26 中国船舶集团有限公司第七一九研究所 Auxiliary evaluation method, device, equipment and medium for a transport device control panel

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