State intervention method and device of sailor and electronic equipment

Abstract

The invention discloses a state intervention method and device for sailors and electronic equipment, and belongs to the technical field of ship control. The method comprises the following steps: acquiring navigation information and environment information of a ship and state information of workers in the ship; according to the navigation information and the state information, determining a task allocation scheme of the staff; obtaining a state evaluation result of the worker according to the environment information and the state information; the task allocation scheme is optimized through the state evaluation result, the working state of the worker executing the task allocation scheme is obtained, state intervention is carried out on the worker according to the working state, and the working state is determined based on the state index and the working efficiency. According to the method, personalized accurate evaluation and optimization intervention are carried out on the states of the sailors, so that the safety and efficiency of ship operation and the physical and psychological health of workers are improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of ship control, and particularly relates to a crew state intervention method and device and electronic equipment. BACKGROUND

[0002] In ocean navigation, long-time high-intensity work is easy to cause fatigue and psychological problems of the crew, and these problems make the navigation of the ship exist risks, and the physical and mental health problems of the crew are gradually paid attention to. With the popularity of intelligent wearable devices, crew health monitoring and psychological state intervention are becoming a new development direction of the shipping industry.

[0003] However, the crew individuals are significantly different in physiology, psychology and work ability, and a unified standard is often used for evaluation, and individual differences are ignored, so that the evaluation is not accurate. SUMMARY

[0004] The present application aims to at least solve one of the technical problems existing in the prior art. To this end, the present application provides a crew state intervention method, device and electronic equipment, which can improve the safety, efficiency of ship operation and physical and mental health of the crew by individualized accurate evaluation and optimized intervention of the crew state.

[0005] In a first aspect, the present application provides a crew state intervention method, which comprises:

[0006] obtaining navigation information and environmental information of a ship, and state information of a worker in the ship;

[0007] determining a task allocation scheme of the worker according to the navigation information and the state information;

[0008] obtaining a state evaluation result of the worker according to the environmental information and the state information;

[0009] optimizing the task allocation scheme through the state evaluation result, and obtaining a working state of the worker executing the task allocation scheme, so as to intervene in the state of the worker according to the working state, wherein the working state is determined based on a state index and work efficiency.

[0010] According to one embodiment of the present application, obtaining a state evaluation result of the worker according to the environmental information and the state information comprises:

[0011] performing time series analysis on the state information to obtain a fatigue accumulation trend of the worker;

[0012] The environment information and the fatigue accumulation trend are bidirectional feature extraction, and a dynamic correlation feature is obtained, the dynamic correlation feature is used to represent the influence degree of the multi-dimensional parameters in the environment information on the fatigue accumulation trend of the staff;

[0013] According to the dynamic correlation feature, the fatigue sensitivity and the recovery efficiency of the staff under the environment information are determined;

[0014] According to the state information, the fatigue sensitivity and the recovery efficiency, the emotional stability, the stress level and the attention concentration degree of the staff are evaluated, and the state evaluation result is obtained.

[0015] According to an embodiment of the present application, the time series analysis of the state information is performed to obtain the fatigue accumulation trend of the staff, including:

[0016] The key physiological-work state indicators in the state information are determined;

[0017] According to the key physiological-work state indicators, the time series feature extraction of the state information is driven by dynamic feature enhancement, and a first fatigue feature sequence is obtained;

[0018] The first fatigue feature sequence is dynamically enhanced to obtain a second fatigue feature sequence;

[0019] The enhanced fatigue feature sequence is exponentially fitted to obtain the fatigue accumulation curve of the staff;

[0020] According to the attenuation law of the fatigue accumulation curve, the fatigue accumulation trend is determined.

[0021] According to an embodiment of the present application, the environment information and the fatigue accumulation trend are bidirectional feature extraction, and a dynamic correlation feature is obtained, including:

[0022] The environment information and the fatigue accumulation trend are spatio-temporal feature extraction and splicing to obtain a correlation feature vector;

[0023] The correlation feature vector is sorted by importance to obtain a key influence factor;

[0024] The time series change of the key influence factor is matched and analyzed based on dynamic time warping to obtain the explicit influence degree of each factor in the key influence factor on the fatigue accumulation trend, and the lag term in the key influence factor;

[0025] Based on the explicit influence degree, the interaction term between each factor in the key influence factor is determined;

[0026] Analyze the hysteresis term and the interaction term to obtain an implicit influence degree of each factor in the key influence factors on the fatigue accumulation trend;

[0027] According to the explicit influence degree and the implicit influence degree, weight assignment is performed on the key influence factors to obtain the dynamic correlation characteristics.

[0028] According to an embodiment of the present application, the fatigue sensitivity and recovery efficiency of the worker under the environmental information are determined according to the dynamic correlation characteristics, including:

[0029] The weights of the key influence factors in the dynamic correlation characteristics, the explicit influence degree, and the implicit influence degree are fuzzified to obtain fuzzy language variables;

[0030] Fuzzy reasoning is performed on the fuzzy language variables to obtain the fatigue sensitivity of the worker in the state space;

[0031] According to the fatigue sensitivity and the residual work capacity corresponding to the state information, the recovery efficiency of the worker is determined.

[0032] According to an embodiment of the present application, the task allocation scheme of the worker is determined according to the navigation information and the state information, including:

[0033] According to the route planning, navigation speed, weather information, and expected voyage time in the navigation information, the navigation task of the ship and the corresponding task type are determined, and the task type includes emergency task, routine task, and auxiliary task;

[0034] According to the task type, the work objectives and workloads of each type of work are matched in the flow topology graph of the navigation task;

[0035] According to the participation degree corresponding to the work objective and the work load, a work grade sequence of each type of work is generated, and the work grade sequence is used to represent the work importance and urgency of the type of work;

[0036] According to the work grade sequence and the state information, the task allocation scheme is generated.

[0037] According to an embodiment of the present application, the task allocation scheme is generated according to the work grade sequence and the state information, including:

[0038] According to the work grade sequence, the cooperation demand degree between each type of work is calculated, and the cooperation demand degree is used to represent the work dependency relationship and cooperation strength between the types of work;

[0039] group the work types according to the cooperation requirement degree, to obtain a cooperation group;

[0040] determine a work task of the cooperation group based on the work level sequence;

[0041] determine a task allocation scheme of the work staff in the work task according to the work load, the participation degree and the state information.

[0042] According to an embodiment of the present application, the state intervention on the work staff according to the task allocation scheme and the state evaluation result comprises:

[0043] determine the idle time distribution of the work staff according to the work intensity and work duration of each work item in the task allocation scheme, and the connection sequence and logical relationship between work items;

[0044] generate an energy recovery scheme of the work staff according to the state evaluation result and the idle time distribution;

[0045] perform resource scheduling according to the energy recovery scheme to perform state intervention.

[0046] In a second aspect, the present application provides a state intervention device for a crew, which comprises:

[0047] an acquisition module, configured to acquire navigation information and environmental information of a ship, and state information of work staff in the ship;

[0048] a first processing module, configured to determine a task allocation scheme of the work staff according to the navigation information and the state information;

[0049] a second processing module, configured to obtain a state evaluation result of the work staff according to the environmental information and the state information;

[0050] a third processing module, configured to optimize the task allocation scheme through the state evaluation result, and acquire a work state of the work staff in executing the task allocation scheme, so as to perform state intervention on the work staff according to the work state, the work state being determined based on a state index and work efficiency.

[0051] In a third aspect, the present application provides an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the crew state intervention method of the first aspect as described above when executing the computer program.

[0052] In a fourth aspect, the present application provides a non-transitory computer-readable storage medium having stored thereon a computer program, which, when executed by a processor, implements the crew state intervention method according to the first aspect.

[0053] In a fifth aspect, the present application provides a chip, which comprises a processor and a communication interface, the communication interface being coupled to the processor, and the processor being configured to execute a program or an instruction to implement the crew state intervention method according to the first aspect.

[0054] In a sixth aspect, the present application provides a computer program product comprising a computer program, which, when executed by a processor, implements the crew state intervention method according to the first aspect.

[0055] Additional aspects and advantages of the present application will be made apparent by the following description and the accompanying drawings.

[0056] The crew state intervention method, device and electronic equipment provided by the present application have the following beneficial effects compared with the prior art:

[0057] (1) By using the navigation information of the ship and the state information of each worker, a task allocation scheme for each worker is generated, and each worker is evaluated according to the interference factors of the environmental information and the state information, thereby improving the accuracy of the state evaluation result. The state evaluation result is combined with the task allocation scheme, and the state of the crew is evaluated and optimized individually and accurately, thereby improving the safety, efficiency of ship operation and physical and mental health of the workers.

[0058] (2) By comprehensively analyzing the fatigue accumulation trend of the crew, the environmental information and the dynamic correlation characteristics, the specific influence of different environmental factors on the fatigue sensitivity and recovery efficiency of the crew is quantified, and based on the fatigue sensitivity and recovery efficiency, the emotional stability, stress level and attention concentration degree of the crew are further analyzed, so as to obtain the state evaluation result of the crew under the current environmental information and fatigue state. The state evaluation result not only reflects the current state of the crew, but also provides a scientific basis for subsequent individualized state intervention. BRIEF DESCRIPTION OF DRAWINGS

[0059] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

[0060] Figure 1 is a flowchart of the crew state intervention method provided by the embodiments of the present application;

[0061] Figure 2is a structural schematic diagram of a crew state intervention device provided by an embodiment of the present application.

[0062] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0063] The technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art belong to the scope of protection of the present application.

[0064] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than that illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally a category and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the front and rear associated objects are in a "or" relationship.

[0065] The crew state intervention method, the crew state intervention device, the electronic device and the readable storage medium provided by the embodiments of the present application will be described in detail below with reference to the drawings and specific embodiments and their application scenarios.

[0066] The crew state intervention method can be applied to a terminal, and can be executed by hardware or software in the terminal.

[0067] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or a tablet computer having a touch-sensitive surface (e.g., a touchscreen display and / or a touchpad). It should also be understood that in some embodiments, the terminal can not be a portable communication device, but a desktop computer having a touch-sensitive surface (e.g., a touchscreen display and / or a touchpad).

[0068] In each of the following embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal can include one or more other physical user interface devices such as physical keyboards, mice, and joysticks.

[0069] The crew state intervention method provided by the embodiments of the present application can be executed by an electronic device or a functional module or functional entity in the electronic device capable of implementing the crew state intervention method. The electronic device mentioned in the embodiments of the present application includes but is not limited to a mobile phone, a tablet computer, a computer, a camera, a wearable device, and the like. The crew state intervention method provided by the embodiments of the present application is described below by taking an electronic device as an execution subject.

[0070] As shown in the figure, the crew state intervention method comprises the following steps. Figure 1

[0071] In step 110, the navigation information and the environmental information of the ship and the state information of the staff in the ship are acquired.

[0072] The navigation information is used to describe the navigation plan, the emergency, the state of the ship, the current position, the heading, the speed, and the like of the ship.

[0073] The environmental information is used to describe the weather condition, the sea condition, the channel condition, and the like around the ship. The multi-dimensional parameters in the environmental information include but are not limited to the temperature, the humidity, the noise level, the sea condition, the light intensity, and the ship motion state.

[0074] The state information includes the physiological state information and the working state information of the staff (hereinafter referred to as the crew) so as to comprehensively evaluate the working state of the crew and provide a basis for subsequent state intervention.

[0075] The physiological state information includes but is not limited to the heart rate, the blood pressure, the body temperature, and the like vital sign data. The working state information includes but is not limited to the working time, the working efficiency, the emotional state, the fatigue degree, and the like.

[0076] The navigation information, the environmental information, and the state information are acquired in real time through various sensors and monitoring devices installed on the ship so as to ensure the accuracy and timeliness of the data.

[0077] In step 110, the position, the speed, the heading, the sea condition, the weather, and the like of the ship are acquired in real time through the automatic identification system (AIS), the global positioning system (GPS), the ship sensor, and the like. Meanwhile, the route plan, the task arrangement, the estimated arrival time, and the like of the ship are collected.

[0078] ​The physiological monitoring equipment such as heart rate belt, electroencephalograph, sleep monitor and other wearable devices are equipped on the ship, which can monitor the physiological data of the crew such as heart rate, blood pressure, blood oxygen saturation and sleep quality in real time, and regularly arrange the crew to have physical examination to obtain their physical health information including vision, hearing, height, weight, blood indicators and the like. The ship management system, work log, task allocation system and the like are used to collect the data of the crew such as work task execution, work time, work intensity, operation error times and work efficiency.

[0079] Step 120, determining a task allocation scheme of the worker according to the navigation information and the state information;

[0080] The task allocation scheme is based on the state of the crew, and the ship state, current position, heading, speed, route plan and task arrangement in the navigation information.

[0081] In step 120, a reasonable and efficient task allocation scheme is formulated by comprehensively analyzing the navigation plan, current position, speed, heading of the ship and the working state, physiological data and the like of the crew. Considering the navigation requirements of the ship such as route characteristics, task urgency and the like, combining the physical condition, professional skill and work efficiency of the crew, the task is allocated to the most suitable crew to ensure the smooth completion of the navigation task. At the same time, considering the rest requirement of the crew, avoiding fatigue accumulation caused by long-time continuous work, reasonably arranging work and rest time, and protecting the work efficiency and physical health of the crew.

[0082] For example, according to the quantitative evaluation of the professional skill, work experience, physical quality and the like of the crew, the skill level is divided into primary, intermediate and advanced, and the weight value is assigned respectively, the complex task is divided into multiple subtasks according to the quantitative evaluation of the complexity, workload and the like of the task, each subtask is assigned with a corresponding workload coefficient, the task allocation model is established by taking the work ability of the crew and the navigation task requirement as input variables, the efficiency and quality of the task allocation are optimized, the optimization algorithm is used to solve the task allocation model, and the optimal task allocation scheme under the current navigation information and the state information of the worker is obtained, and the specific task content and workload of each crew are determined.

[0083] Step 130, obtaining a state evaluation result of the worker according to the environment information and the state information;

[0084] The state evaluation result is a comprehensive judgment of the physical and mental state of the crew in the current environment, including physiological health, psychological state and work efficiency and the like.

[0085] In step 130, the weather conditions, sea conditions, channel conditions, and other environmental information around the ship are combined with the physiological state information and working state information of the crew for comprehensive evaluation; using big data analysis technology, historical data and real-time data are mined and analyzed to identify factors that may affect the working efficiency and physical health of the crew at the current time, such as adverse weather conditions such as high temperature, high humidity, strong wind, and large waves in the environmental information, and fatigue accumulation caused by long-term continuous work and high-intensity work in the state information.

[0086] In addition, according to the state evaluation result, the state of the crew is divided into different levels, such as good, general, and poor, which provides a basis for subsequent state intervention.

[0087] Step 140, the task allocation scheme is optimized by the state evaluation result, and the working state of the worker executing the task allocation scheme is obtained, so as to intervene in the state of the worker according to the working state, and the working state is determined based on the state index and the working efficiency.

[0088] The working state includes state index and working efficiency, the state index includes working capacity and fatigue degree, and the working efficiency is obtained by comprehensive evaluation according to task completion rate, task completion time, operation accuracy and task completion quality. If the working efficiency is lower than the preset standard, or there is obvious fatigue or rapid decline in working capacity in the state index, the state intervention mechanism is automatically triggered.

[0089] The working capacity of the crew is evaluated based on the working efficiency of the crew, training experience, professional skills, and current physical condition and psychological state.

[0090] The state intervention measures include but are not limited to adjusting the work tasks of the crew, shortening the working time, providing rest opportunities, arranging psychological counseling or team activities to relieve the physical and mental pressure of the crew, and improving their working efficiency and safety.

[0091] In addition, the state intervention mechanism also has self-learning and optimization functions, which can continuously optimize the state evaluation model and intervention strategy according to historical data and real-time feedback, so as to adapt to different navigation environments and individual differences of the crew, and ensure that the physical and mental health of the crew is maximally guaranteed.

[0092] If the state evaluation result shows that the crew has fatigue accumulation or excessive psychological pressure, the system will automatically trigger an alarm and suggest adjusting the work tasks of the crew or arranging rest time to avoid potential safety hazards. At the same time, for the crew who has been in high-pressure state for a long time, the system will also recommend participating in psychological counseling or relaxation training to promote their mental health.

[0093] In addition, the state intervention mechanism also has self-learning and optimization capabilities, which can continuously adjust the state evaluation model and intervention strategy according to historical data and intervention effects, to adapt to different navigation conditions and individual differences of crew members, and to ensure that each intervention can achieve the best effect.

[0094] According to the state intervention method of the crew provided in the present application, the task allocation scheme of each worker is generated through the navigation information of the ship and the state information of each worker, and the state of each worker is evaluated according to the interference factors of the environmental information and the state information, which improves the accuracy of the state evaluation result. The state evaluation result is combined with the task allocation scheme, and the state of the crew is individually and accurately evaluated and optimized, which improves the safety, efficiency of ship operation and physical and mental health of the crew.

[0095] In some embodiments, according to the environmental information and the state information, the state evaluation result of the worker is obtained, comprising:

[0096] performing time series analysis on the state information to obtain the fatigue accumulation trend of the worker;

[0097] performing bidirectional feature extraction on the environmental information and the fatigue accumulation trend to obtain dynamic correlation features, the dynamic correlation features being used to represent the influence degree of the multi-dimensional parameters in the environmental information on the fatigue accumulation trend of the worker;

[0098] determining the fatigue sensitivity and recovery efficiency of the worker under the environmental information according to the dynamic correlation features;

[0099] evaluating the emotional stability, stress level and attention concentration degree of the worker according to the state information, the fatigue sensitivity and the recovery efficiency to obtain the state evaluation result.

[0100] The fatigue accumulation trend is modeled by using statistical analysis or machine learning algorithm according to the physiological data such as heart rate variability and sleep quality of the crew, and the time series change of the working state data such as working time and operation error times, and the gradual accumulation or relief trend of the fatigue degree of the crew is identified. This trend reflects the change of the fatigue state of the crew in different time periods, and provides a basis for subsequent analysis of the influence of environmental information on fatigue accumulation.

[0101] The dynamic correlation features are a set of features reflecting the dynamic relationship between the environment and the fatigue of the crew.

[0102] The fatigue sensitivity is the increase degree of the fatigue accumulation speed of the crew relative to the normal state.

[0103] The recovery efficiency is the rate of recovery of the fatigue degree of the crew after rest or environmental improvement.

[0104] Emotional stability is an evaluation of the fluctuation of emotions experienced by the crew when facing environmental changes and work pressure.

[0105] The stress level reflects the psychological burden the crew bears during the voyage. Excessive stress may cause the crew to be distracted, slow to react, and even trigger operational errors.

[0106] The degree of concentration is an important indicator of whether the crew can focus on their work. During the voyage, the crew needs to constantly monitor the dynamics of the ship and changes in the surrounding environment to ensure safe navigation. The degree of concentration is used to determine whether the crew has the necessary focus to perform their navigation tasks.

[0107] In actual implementation, time series analysis methods such as moving average and exponential smoothing are used to smooth the state information, calculate the trend of heart rate variability, EEG power spectrum changes, and eye movement parameters, and reflect the trend of fatigue accumulation to reduce the impact of short-term fluctuations and highlight the trend of fatigue accumulation.

[0108] Feature extraction is performed on the multi-dimensional parameters in the environmental information, and statistical features such as mean, standard deviation, and maximum value of parameters such as wind speed, wave height, temperature, humidity, and noise level that may affect the crew's fatigue accumulation are calculated, as well as the trend of parameter changes over time, as environmental features.

[0109] Feature extraction is performed on the trend of fatigue accumulation to obtain the trend of fatigue sensitivity over time.

[0110] Feature fusion is performed on the environmental features and trend features, and dynamic correlation features that reflect the correlation between environmental information and fatigue accumulation trend are extracted through correlation analysis, including the correlation coefficient between environmental parameters and physiological indicators and the influence coefficient of environmental changes on fatigue trend.

[0111] A fatigue sensitivity evaluation model is established by using linear regression with environmental features and trend features in dynamic correlation features as input and fatigue level of the crew as output to predict the fatigue sensitivity of the crew.

[0112] A recovery efficiency evaluation model is established by using machine learning algorithm with fatigue sensitivity and dynamic correlation features as input and physiological and environmental data of the crew during the recovery period as output to train the model and predict the recovery efficiency of the crew.

[0113] Based on the physiological state information related to emotions of the staff such as heart rate variability, brain waves, and fatigue sensitivity, an emotion stability evaluation model is established by using a machine learning algorithm to classify and evaluate the emotion stability of the staff. The evaluation result of the emotion stability output by the model can be classified into three levels of high, medium, and low.

[0114] In combination with the physiological state information of the staff such as cortisol level and blood pressure, and the working state information such as working load and task urgency, features related to stress are extracted such as the daily variation curve of cortisol level and the peak value of working load. A stress level evaluation model is established by using multiple linear regression with the extracted features, fatigue sensitivity, and recovery efficiency as independent variables, and the stress level as dependent variable, to evaluate the stress level of the staff. The quantitative value of the stress level output by the model can be compared with the standard stress level range to determine whether the stress state of the staff is abnormal, and in combination with the fatigue sensitivity and recovery efficiency, the source and duration of stress are analyzed.

[0115] An attention concentration degree evaluation model is established by using deep learning algorithm based on multi-dimensional parameters in state information such as eye movement data and brain wave data, for feature extraction and classification learning. After the eye movement data and brain wave data are preprocessed as input, the attention concentration degree is taken as label, the model is trained to identify different attention states, and the attention concentration degree of the staff is evaluated in real time. The attention concentration degree output by the model can be classified into states of high concentration, medium concentration, and dispersion.

[0116] In this embodiment, the specific influence of different environmental factors on the fatigue sensitivity and recovery efficiency of the crew is quantified by comprehensive analysis and correlation of the fatigue accumulation trend of the crew, environmental information, and dynamic features. Based on the fatigue sensitivity and recovery efficiency, the emotion stability, stress level, and attention concentration degree of the crew are further analyzed, and the state evaluation result of the crew under the current environmental information and fatigue state can be obtained, which not only reflects the current state of the crew, but also provides a scientific basis for subsequent personalized state intervention.

[0117] In some embodiments, the time series analysis of the state information to obtain the fatigue accumulation trend of the staff includes:

[0118] Determining key physiological-work state indicators in the state information;

[0119] Performing time series feature extraction driven by dynamic feature enhancement on the state information according to the key physiological-work state indicators to obtain a first fatigue feature sequence;

[0120] Performing dynamic feature enhancement on the first fatigue feature sequence to obtain a second fatigue feature sequence;

[0121] fitting the enhanced fatigue feature sequence with an exponential function, to obtain the fatigue accumulation curve of the worker;

[0122] According to the attenuation law of the fatigue accumulation curve, the fatigue accumulation trend is determined.

[0123] In actual implementation, the key physiological-work state indicators can reflect the physiological and work state data of the crew fatigue degree and work state change, such as heart rate variability, sleep quality, work duration, operation error times, etc. The change trend of these data can reveal the fatigue accumulation or relief of the crew, which is an important basis for evaluating the fatigue state of the crew.

[0124] In the dynamic feature enhancement driven time sequence feature extraction stage, the key physiological-work state indicators are modeled by using a machine learning algorithm, and the time sequence features reflecting the change of the crew fatigue state with time are extracted, forming a first fatigue feature sequence.

[0125] In order to further enhance the expression ability of the fatigue feature, additional dynamic information such as environmental changes and work tasks is introduced to refine and enhance the fatigue features in the first fatigue feature sequence, and a second fatigue feature sequence is obtained.

[0126] The second fatigue feature sequence can accurately reflect the fatigue state of the crew under different environments and tasks.

[0127] Fitting the enhanced fatigue feature sequence with an exponential function, to obtain the fatigue accumulation curve of the worker.

[0128] The fatigue accumulation curve intuitively shows the accumulation process of the crew fatigue degree, which provides a basis for subsequent determination of the fatigue accumulation trend.

[0129] The attenuation law is used to describe the characteristics of the fatigue accumulation curve changing with time, including the change of the slope of the curve, whether there is an inflection point, and the time point and fatigue degree corresponding to the change point.

[0130] For example, if the curve slope continuously increases, it indicates that the fatigue degree of the crew is accelerating, and immediate intervention measures may be needed; if the curve has an inflection point and shows a downward trend, it indicates that the fatigue state of the crew may be relieved, but continuous attention is still needed to ensure complete recovery. Accurate analysis of the attenuation law helps to develop more accurate and effective state intervention strategies, thereby maximizing the protection of the physical and mental health of the crew and the safety of navigation.

[0131] The fatigue accumulation trend is an important indicator for evaluating the change of the crew fatigue state, which is used to represent the accumulation or relief of the crew fatigue degree under different navigation stages and environments.

[0132] According to the attenuation law of the fatigue accumulation curve, the slope, inflection point and other characteristics of the curve can be analyzed to determine whether the fatigue state of the crew is gradually accumulating or being relieved, as well as the speed and degree of accumulation or relief, and to determine the fatigue accumulation trend of the crew.

[0133] In this embodiment, through in-depth analysis of the fatigue accumulation trend of the crew, the physical and mental state of the crew can be more accurately understood, providing a scientific basis for developing personalized state intervention strategies and optimizing navigation plans, which helps to improve the work efficiency and safety of the crew, and also maximizes the protection of the physical and mental health of the crew and the safety of navigation.

[0134] In some embodiments, bidirectional feature extraction is performed on the environmental information and the fatigue accumulation trend to obtain dynamic correlation features, including:

[0135] Temporal and spatial feature extraction is performed on the environmental information and the fatigue accumulation trend and spliced to obtain a correlation feature vector;

[0136] The correlation feature vector is sorted by importance to obtain key influencing factors;

[0137] The time series changes of the key influencing factors are analyzed based on dynamic time warping to obtain the explicit influence degree of each factor in the key influencing factors on the fatigue accumulation trend, and the lag term in the key influencing factors;

[0138] Based on the explicit influence degree, the interaction term between each factor in the key influencing factors is determined;

[0139] The lag term and the interaction term are analyzed to obtain the implicit influence degree of each factor in the key influencing factors on the fatigue accumulation trend;

[0140] According to the explicit influence degree and the implicit influence degree, the key influencing factors are weighted to obtain the dynamic correlation features.

[0141] The correlation feature vector is composed of features extracted from the environmental information and the fatigue accumulation trend, which can reflect the complex relationship between the environmental information and the fatigue state of the crew, so as to deeply understand the influence mechanism of the environment on the fatigue of the crew.

[0142] Importance sorting is based on the contribution of features in the correlation feature vector, and through statistical analysis and machine learning algorithms, the influence degree of each feature on the fatigue accumulation trend of the crew can be evaluated to identify key influencing factors.

[0143] The key influencing factors are environmental parameters or physiological indicators that have a significant impact on the fatigue accumulation trend of crew members, including environmental factors such as wind speed and wave height, as well as physiological factors such as heart rate variability and sleep quality.

[0144] Matching analysis based on dynamic time warping (DWT) can compare the similarities and differences between different time series, quantify the dominant influence of each key influencing factor on the fatigue accumulation trend, and identify the lag terms in the key influencing factors.

[0145] Among them, the lag term is a key influencing factor that has a certain delay in the impact on the fatigue accumulation trend in time.

[0146] The degree of dominant influence indicates the direct impact of the key influencing factors on the fatigue accumulation trend. For example, a high temperature environment directly accelerates physical energy consumption, resulting in an increased rate of fatigue accumulation.

[0147] The degree of implicit influence indicates the indirect impact of the key influencing factors on the fatigue accumulation trend through other factors, or factors that take time to manifest. For example, long-term work pressure or environmental noise may not immediately show obvious fatigue accumulation effects, but long-term exposure will slowly lead to increased fatigue.

[0148] Interaction terms are used to characterize the interactions between different key influencing factors, which may enhance or weaken the impact of a certain factor on the fatigue accumulation trend, for example, have a joint impact on the fatigue accumulation trend of crew members.

[0149] By analyzing the interaction terms, we can gain a more comprehensive understanding of the complex impact of key influencing factors on crew fatigue.

[0150] The analysis of lag terms and interaction terms helps to reveal the extent of the implicit impact of key influencing factors on fatigue accumulation trends.

[0151] The weight distribution is based on the degree of explicit influence and implicit influence, which is used to measure the importance of each key influencing factor in the dynamic correlation characteristics, so as to obtain more accurate and comprehensive dynamic correlation characteristics.

[0152] The key influencing factors are weighted according to the degree of explicit influence and implicit influence. The resulting dynamic correlation characteristics not only reflect the direct correlation between environmental information and fatigue accumulation trends, but also take into account potential delayed effects and interactions, providing a more scientific basis for formulating personalized state intervention strategies.

[0153] In actual implementation, E t is the spatiotemporal feature vector of the environmental information at time t, F tThe spatio-temporal feature vector of the fatigue accumulation trend at time t, and the environmental information and the fatigue accumulation trend are spliced into a comprehensive feature vector as the correlation feature vector X at time t t :

[0154] X t =[E t ,F t ]

[0155] The feature selection algorithm of the random forest is used to sort the features, evaluate the influence degree of each feature on the target variable (such as the fatigue accumulation trend), and select the most important m features as the key influencing factors according to the influence degree as the feature importance:

[0156] K={k1,k2,…,k m}

[0157] The time series between each key influencing factor k i ,i=1,2,…,m and the spatio-temporal feature vector F t of the fatigue accumulation trend are matched and analyzed by DTW:

[0158]

[0159] Where d(k i ,F t ) is the final value of the DTW distance, indicating the matching distance between k i and F t in the time series, the smaller d(k i ,F t ) is, the smaller the difference between the two time series in the matching process is; indicates that among all possible alignment paths π of the mapping mode, a path is selected that minimizes the cumulative distance, and the alignment path π indicates how to align the sequence k i (t) and F(t+τ i ); T is the length of the time series; ‖k i (t)-F(t+τ i )‖ 2 is the difference measure at time t; k i (t) is the value of the i-th key influencing factor k i at time t, F(t+τ i ) is the value of the fatigue accumulation trend at time t+τ i , τ i is the lag term, which represents the time lag between the key influencing factor and the fatigue accumulation trend, used to handle the time delay phenomenon in the causal relationship; the explicit influence degree α i of each key influencing factor k i,tis derived by calculating its match with the fatigue accumulation trend.

[0160] Assume the key influencing factors k i (t) and k j (t) between the interaction effect on fatigue accumulation trend, interaction term γ ij,t is expressed by the product of the explicit influence degree of k i (t) and k j (t) :

[0161] γ ij,t = α i,t · α j,t

[0162] Where, the interaction term γ ij,t represents the strength of the joint influence between different factors.

[0163] The implicit influence degree β I (t) of each key influencing factor k i,t is calculated by the lag term and the interaction term. The lag term τ j and the interaction term γ ij,t jointly affect the implicit influence of the key influencing factor k I (t) on the fatigue accumulation trend, which can be calculated by the following formula:

[0164]

[0165] This indicates the influence of the interaction effect of each factor at different time lags on the fatigue accumulation trend.

[0166] The weight ω I,t of each key influencing factor k I,t is calculated by the explicit influence degree α i and the implicit influence degree β i , and the formula is as follows:

[0167]

[0168] The dynamic correlation feature D i is obtained by weighted combination of all key influencing factors k t :

[0169]

[0170] The dynamic correlation feature D t is a feature set that can represent the dynamic changes of the fatigue accumulation trend and the comprehensive influence of the key influencing factors on fatigue accumulation.

[0171] In this embodiment, through spatiotemporal feature extraction and splicing, dynamic time-warping matching analysis, interaction term calculation, implicit impact assessment and weight allocation, the explicit and implicit roles of each key influencing factor in the fatigue accumulation trend are gradually determined, and dynamic correlation characteristics are obtained, which provides support for establishing a more accurate fatigue prediction model.

[0172] In some embodiments, determining the fatigue sensitivity and recovery efficiency of the worker under the environmental information based on the dynamic association feature includes:

[0173] Fuzzifying the weights of key influencing factors in the dynamic correlation features, the explicit influence degree, and the implicit influence degree to obtain fuzzy linguistic variables;

[0174] Performing fuzzy reasoning on the fuzzy linguistic variables to obtain the fatigue sensitivity of the worker in the state space;

[0175] The recovery efficiency of the worker is determined based on the fatigue sensitivity and the remaining work capacity corresponding to the status information.

[0176] Fuzzy linguistic variables are the conversion of the weights, explicit influence degrees and implicit influence degrees of key influencing factors into linguistic descriptions in fuzzy sets to facilitate fuzzy reasoning, such as high, medium and low levels.

[0177] The state space is a multidimensional space that describes the fatigue sensitivity and recovery efficiency of workers in different environments and physiological states. In the state space, each dimension represents a physiological or environmental parameter related to fatigue or recovery, such as heart rate variability, sleep quality, wind speed, wave height, etc.

[0178] Fuzzy reasoning processes fuzzy linguistic variables, converts the key influencing factors in dynamic correlation features into fuzzy linguistic variables, and infers the fatigue sensitivity of workers in the state space. By processing uncertain and fuzzy information, it evaluates the comprehensive impact of these factors on the fatigue sensitivity and recovery efficiency of workers, thereby improving the accuracy of fatigue sensitivity assessment.

[0179] Fatigue sensitivity reflects the worker's sensitivity to fatigue in a specific environment and physiological state, that is, the degree to which he or she is susceptible to fatigue. For example, workers with high fatigue sensitivity are more likely to feel fatigued under the same workload and environmental conditions.

[0180] Recovery efficiency is used to describe the speed and degree of a worker's ability to recover after fatigue. For example, a worker with high recovery efficiency can recover to a better working state in a shorter time, thereby maintaining higher work efficiency and safety. Low recovery efficiency may cause the worker to be in a state of fatigue for a long time, affecting his or her physical and mental health and work performance.

[0181] The residual work capacity refers to the amount or time of work that a worker can still complete in the current state after experiencing a period of fatigue accumulation. The potential work capacity in the remaining time is determined by the current fatigue level and work capacity of the worker.

[0182] In actual implementation, the weights, explicit influence degrees, and implicit influence degrees of key influencing factors in dynamic correlation features are fuzzified to improve the accuracy of fatigue sensitivity and recovery efficiency evaluation by handling uncertainty and fuzziness.

[0183] By considering the comprehensive influence of multiple factors, including environmental parameters, physiological indicators, and their interactions, the fuzzy language variables are processed, and the state space is described to infer the fatigue sensitivity of the worker in a specific environment and physiological state.

[0184] The residual work capacity is evaluated by analyzing the physiological and work state data of the worker, such as heart rate variability, brain waves, and other physiological indicators, as well as operation efficiency, task completion quality, and other work state indicators.

[0185] Based on the evaluation results of fatigue sensitivity and residual work capacity, the recovery efficiency of the worker can be determined to develop personalized state intervention strategies and optimize work arrangements.

[0186] Fatigue sensitivity and residual work capacity are used as main features, combined with key parameters in environmental information such as sea conditions, weather conditions, etc. as environmental features. The neural network model is trained with fatigue sensitivity, residual work capacity, and environmental features as inputs and historical recovery data as outputs to predict recovery efficiency.

[0187] In this embodiment, by deeply analyzing dynamic correlation features, combining fuzzy reasoning and state space description, the fatigue sensitivity of the worker in the state space is accurately evaluated. By analyzing the physiological and work state data in the state information, the residual work capacity of the worker can be evaluated, and then combined with the fatigue sensitivity, the recovery efficiency is determined to evaluate the recovery needs and capabilities of the worker in the fatigue state, providing a scientific basis for developing personalized state intervention strategies and optimizing work arrangements. It not only helps to improve the work efficiency and safety of the worker, but also maximizes the protection of physical and mental health.

[0188] In some embodiments, the task allocation scheme for the worker is determined based on the navigation information and the state information, including:

[0189] According to the route planning, sailing speed, weather information and expected voyage time in the navigation information, determine the navigation task of the ship, and the corresponding task type, the task type includes emergency task, routine task and auxiliary task;

[0190] According to the task type, in the flow topology graph of the navigation task, match the work target and work load of each work type for subtask;

[0191] According to the participation degree corresponding to the work target and the work load, generate the work level sequence of each work type, the work level sequence is used to represent the work importance and urgency of work type;

[0192] According to the work level sequence and the state information, generate the task allocation scheme.

[0193] The route planning describes the predetermined path of the ship from the starting point to the ending point; The sailing speed is determined according to the performance of the ship, the cargo capacity, the weather condition and the route characteristics, to balance the sailing time and energy consumption; The weather information includes wind speed, wind direction, sea wave, visibility and other key meteorological parameters, which directly affect the navigation strategy and response measures of the ship; The expected voyage time is the estimated time required to complete the entire navigation based on the route planning, sailing speed and possible weather changes.

[0194] The navigation task includes various specific work that the ship needs to complete in different navigation sections, such as cargo loading and unloading, equipment inspection, navigation monitoring, etc.

[0195] The task type is divided according to the urgency and importance of the task, the emergency task involves the necessary operation in the case of ship safety or emergency, which needs to be executed immediately, the routine task is the routine work in the daily operation of the ship, such as periodic equipment maintenance, navigation log recording, etc., the auxiliary task is some auxiliary work to support the smooth completion of the navigation task, such as crew living supplies, ship cleaning, etc.

[0196] The flow topology graph is a graphical representation of the decomposition and organization of the navigation task, which shows the logical relationship and execution order between the subtasks. In the flow topology graph of the navigation task, each subtask is associated with a specific work type, which clearly defines the work target and work load of the work type, which helps to reasonably allocate work tasks and ensure the efficiency and safety of navigation.

[0197] The work target is a specific and measurable target set according to the requirements of the navigation task and the responsibilities of the crew, for example, completing cargo loading and unloading on time, keeping equipment running normally, etc., which represents the specific responsibilities and expected results of the work type in the navigation task, the participation degree is used to measure the key degree of the work type to the successful completion of the task, including the required time, physical consumption, skill requirement and psychological pressure, etc.

[0198] Workload is a comprehensive evaluation of the effort and time required from the crew, taking into account the complexity and workload of the work objectives, the effort required and the stress that may be faced by the workers in performing the tasks, combined with factors such as the speed of the ship, the distance of the voyage, weather conditions and the complexity of the route.

[0199] Work grade sequence is based on the comprehensive evaluation of work objectives and participation, and is obtained by sorting the importance and urgency of the work of different types of workers, reflecting the priority and contribution of different types of workers in the navigation task. In the case of limited resources, the work requirements of key types of workers are prioritized.

[0200] In actual implementation, according to the route planning, navigation speed, weather information and expected voyage time, the navigation tasks of the ship at different stages and their corresponding task types can be determined. Task types usually include emergency tasks, routine tasks and auxiliary tasks, each task type has different requirements for workers and resource allocation. If the route planning involves complex channels and time is urgent, it may contain emergency tasks; routine cargo transportation is a routine task; maintenance of ship equipment is an auxiliary task.

[0201] Draw the flow topology diagram of the navigation task to clarify the sequence and logical relationship of each sub-task. According to the task type, match each sub-task with the work objectives and workload of each type of worker in the flow topology diagram, and clarify the specific responsibilities and task volume of each type of worker in the navigation task to ensure the rationality and effectiveness of task allocation. For example, emergency tasks involve navigation operations that ensure the safety of the ship or important time nodes, such as collision avoidance operations, emergency maintenance or passage through specific sea areas, etc. In emergency tasks, the work objective of the captain is to make quick navigation decisions, and the work objective of the crew is to quickly execute operations, with high workload.

[0202] After determining the navigation tasks and task types, the sub-tasks can be decomposed in the flow topology diagram of the navigation task, and the corresponding work objectives and workloads of each type of worker are matched. The flow topology diagram clearly shows the logical relationship and execution order of the navigation tasks, which helps to reasonably allocate and schedule tasks.

[0203] Determine the weights of the participation and workload corresponding to the work objectives, calculate the work grade of each type of worker according to the participation and workload, for example, work grade = participation x 60% + workload x 40%, sort the types of workers according to the importance and urgency of the work, and obtain the work grade sequence of each type of worker, which represents the importance and urgency of the work of the type of worker, which helps to prioritize key types of workers and important tasks in task allocation, ensuring efficient and safe execution of navigation tasks.

[0204] The state information provides the current physiological and working state data of the worker, which can reflect the current physical and mental condition and working ability of the crew. By combining the working level sequence with the state information of the worker, considering the working ability and fatigue degree of the worker, and according to the integrated information, the high working level task is preferentially assigned to the worker in good condition, and a task allocation scheme is generated which meets the navigation requirements and considers the actual condition of the worker, ensures the efficient completion of the task, optimizes the resource allocation in the navigation process, and improves the operation efficiency and navigation safety of the ship.

[0205] For example, in an emergency task, the crew with good physical condition and rich working experience are needed to be arranged to operate the key equipment and handle the emergency, so as to ensure that the ship can respond correctly and quickly when encountering an emergency; in a regular task, the crew are reasonably divided according to their working ability and expertise, so as to improve the working efficiency and task completion quality; in an auxiliary task, some crew with good physical condition but less working experience can be arranged to participate, so as to provide them with practical opportunities and exercise platform.

[0206] Through such a task allocation scheme, the advantages and potential of each crew member can be fully utilized, the execution efficiency and safety of the navigation task can be improved, and the crew fatigue and safety problems caused by improper task allocation can be effectively avoided.

[0207] The task allocation scheme also has flexibility and adjustability, which can be adjusted and optimized in time according to the actual situation of the navigation task and the state change of the crew, so as to ensure the smooth progress of the navigation task and the physical and mental health of the crew.

[0208] In this embodiment, by generating the working level sequence, the roles and positions of various types of work in the navigation task can be more clearly understood, which provides a scientific basis for task allocation.

[0209] In some embodiments, the generating the task allocation scheme according to the working level sequence and the state information comprises:

[0210] According to the working level sequence, the cooperation demand degree between each type of work is calculated, and the cooperation demand degree is used to represent the working dependence relationship and cooperation strength between the types of work;

[0211] According to the cooperation demand degree, the types of work are grouped to obtain a cooperation group;

[0212] Based on the working level sequence, the working task of the cooperation group is determined;

[0213] According to the working load, the participation degree and the state information, the task allocation scheme of the worker in the working task is determined.

[0214] The cooperation demand degree can quantify the mutual dependence and cooperation degree between different types of work in the work process.

[0215] In ship operation, there is a close working relationship and cooperation demand between different types of work, such as the cooperation between the engine officer and the driver, the coordination between the deck department and the engine department, etc. The calculation of cooperation demand degree can help identify the relevance and cooperation strength between these types of work, providing a basis for subsequent grouping and task allocation.

[0216] When grouping the types of work, considering the cooperation demand degree between the types of work, the types of work with close working relationship and high cooperation strength are grouped into the same cooperation group, which helps to improve the work efficiency and the coordination of task completion. At the same time, the grouping also needs to take into account the nature of the work and the skill requirements of the types of work, to ensure that each cooperation group has the comprehensive ability to complete the task.

[0217] When determining the work tasks of the cooperation group based on the work level sequence, the tasks with high importance and urgency are preferentially arranged to the groups with strong cooperation ability and high work efficiency, to ensure that the key tasks can be effectively executed. The complexity and diversity of the tasks also need to be considered to avoid uneven work load or too single task within the group.

[0218] Multiple factors such as work load, participation and state information are considered. By evaluating the work ability and physical condition of each worker, combined with the specific requirements of the task and the overall arrangement of the cooperation group, a task allocation scheme is developed that meets the navigation requirements and takes into account the actual condition of the workers.

[0219] In this embodiment, by considering multiple factors such as work level sequence, cooperation demand degree, work load, participation and state information, a more scientific and reasonable task allocation scheme can be developed. Such a scheme not only helps to improve the efficiency and safety of ship operation, but also effectively promotes the cooperation and coordination between workers, realizes the optimization of resource allocation and efficient execution of tasks, and at the same time guarantees the physical and mental health and job satisfaction of workers.

[0220] In some embodiments, the state intervention on the workers according to the task allocation scheme and the state evaluation result comprises:

[0221] According to the work intensity and work duration of each work item in the task allocation scheme, as well as the connection sequence and logical relationship between work items, the idle time distribution of the workers is determined;

[0222] According to the state evaluation result and the idle time distribution, an energy recovery scheme for the workers is generated;

[0223] Resource scheduling is performed according to the energy recovery scheme to intervene in the state.

[0224] Work items are specific tasks broken down from the navigation task, each carrying specific responsibilities and objectives, and are the basic units of navigation task execution.

[0225] Work intensity is used to represent the physical and mental effort required to complete the work item, and work duration is the time span required to execute the task. Work intensity and work duration together determine the workload of the worker when executing the task.

[0226] The sequence and logical relationship between work items ensure the continuity and efficiency of the navigation task. By reasonably arranging these work items, optimizing the workflow, reducing unnecessary waiting and delays, and improving overall work efficiency, the distribution of idle time for workers is also provided.

[0227] Idle time distribution includes the idle period and duration of the worker between task execution. By analyzing the time arrangement and logical relationship of each work item in the task allocation scheme, the idle period of the worker during task execution is identified. Reasonable arrangement of idle time not only helps the worker to rest and recover, but also improves work enthusiasm and satisfaction.

[0228] The state assessment result provides the current physical and mental state data of the worker, such as fatigue level, attention concentration level, etc.

[0229] According to the state assessment result and the idle time distribution, an energy recovery scheme can be tailored for the worker, including rest time, rest method, activity content, etc., to help the worker rest and recover effectively during work breaks, improve work efficiency and physical and mental health, including short rest, light physical activity, psychological adjustment, etc. According to the actual situation and needs of the worker, personalized recovery measures are provided to improve the efficiency and quality of subsequent work.

[0230] Resource scheduling is the actual arrangement and adjustment according to the energy recovery scheme, involving adjusting work tasks, providing necessary recovery facilities or environment, arranging professional personnel for guidance and support, etc. Through effective resource scheduling, the physical and mental state of the worker is actively managed and optimized.

[0231] By considering multiple factors such as task allocation scheme, state assessment result, and idle time distribution, a state intervention strategy is developed that not only meets the navigation requirements but also considers the actual situation of the worker. This not only helps to improve the operational efficiency and navigation safety of the ship, but also effectively maintains the physical and mental health and job satisfaction of the worker, providing solid human resource support for the long-term stable operation of the ship.

[0232] In some embodiments, after the state intervention is performed on the crew member according to the task allocation scheme and the state evaluation result, the method further comprises:

[0233] obtaining a work index of the crew member performing the task allocation scheme, the work index comprising work completion, work efficiency and work quality;

[0234] performing effect evaluation on the state intervention method according to the work index, to obtain an evaluation result;

[0235] optimizing and adjusting the state intervention method when the evaluation result does not reach an expected target. The optimization and adjustment can involve modification of the energy recovery scheme, rearrangement of resource scheduling or fine-tuning of the task allocation scheme, aiming to further improve the work efficiency and physical and mental health of the crew member and ensure efficient and safe execution of the navigation task.

[0236] The work index is an important basis for measuring the effect of the crew member performing the task allocation scheme and directly reflects the performance of the crew member in actual work.

[0237] The work completion evaluates whether the crew member has completed all tasks according to the task allocation scheme, the work efficiency measures the time and resource input required to complete the tasks, and the work quality focuses on the accuracy and quality level of task completion.

[0238] Through monitoring and analyzing these work indexes, problems and deficiencies in the state intervention method are obtained according to the state changes of the crew member. For example, if the work completion is low, it can be checked whether the task allocation scheme is reasonable and whether the workload is too large or the task difficulty is too high; if the work efficiency is not high, the energy recovery scheme is optimized to provide more rest and recovery time for the crew member; if the work quality is not up to standard, skill training or necessary work support can be strengthened.

[0239] When the evaluation result does not reach the expected target, based on in-depth analysis of the work index, combined with the actual needs of the navigation task and the actual situation of the crew member, the state intervention method is optimized and adjusted in a timely manner to develop a more scientific and reasonable state intervention strategy, so as to improve the work efficiency and physical and mental health of the crew member, avoid potential safety risks and efficiency loss, and provide strong support for safe operation and efficient navigation of the ship.

[0240] The crew member state intervention method provided by the embodiments of the present application can be executed by a crew member state intervention device. In the embodiments of the present application, the crew member state intervention method executed by the crew member state intervention device is taken as an example to illustrate the crew member state intervention device provided by the embodiments of the present application.

[0241] The embodiment of the present application further provides a crew state intervention device.

[0242] As shown in the figure, the crew state intervention device comprises: Figure 2

[0243] The acquisition module 210 is configured to acquire sailing information and environmental information of a ship, and state information of a worker in the ship.

[0244] The first processing module 220 is configured to determine a task allocation scheme of the worker according to the sailing information and the state information.

[0245] The second processing module 230 is configured to obtain a state evaluation result of the worker according to the environmental information and the state information.

[0246] The third processing module 240 is configured to optimize the task allocation scheme through the state evaluation result, acquire a working state of the worker in executing the task allocation scheme, and perform state intervention on the worker according to the working state, wherein the working state is determined based on a state index and a working efficiency.

[0247] According to the crew state intervention device provided by the embodiment of the present application, the task allocation scheme of each worker is generated through the sailing information of the ship and the state information of each worker, and each worker is state evaluated according to the interference factors of the environmental information and the state information, so that the accuracy of the state evaluation result is improved. The state evaluation result is combined with the task allocation scheme, the state of the crew is individually and accurately evaluated and optimized, and the safety, efficiency of the ship operation and the physical and mental health of the worker are improved.

[0248] The crew state intervention device in the embodiment of the present application can be an electronic device or a component in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices other than the terminal.

[0249] The crew state intervention device in the embodiment of the present application can be a device with an operating system. The operating system can be an Android operating system, a Linux operating system, or other possible operating systems, which are not limited in the embodiment of the present application.

[0250] The crew state intervention device provided by the embodiment of the present application can realize each process of the crew state intervention method embodiment in the above-described embodiment, and thus details are not repeated here.

[0251] In some embodiments, as Figure 3 ​As shown, the embodiment of the present application further provides an electronic device 300, which comprises a processor 301, a memory 302, and a computer program stored in the memory 302 and capable of running on the processor 301. When the processor 301 executes the program, each process of the above-mentioned crew state intervention method embodiment is implemented, and the same technical effects are achieved. To avoid repetition, details are not described herein.

[0252] It should be noted that the electronic device in the embodiment of the present application includes the above-mentioned mobile electronic device and non-mobile electronic device.

[0253] The embodiment of the present application further provides a non-transitory computer readable storage medium, which stores a computer program. When the processor executes the computer program, each process of the above-mentioned crew state intervention method embodiment is implemented, and the same technical effects are achieved. To avoid repetition, details are not described herein.

[0254] The processor is the processor in the electronic device in the above-mentioned embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0255] The embodiment of the present application further provides a computer program product, which comprises a computer program. When the processor executes the computer program, the crew state intervention method is implemented.

[0256] The processor is the processor in the electronic device in the above-mentioned embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0257] The embodiment of the present application further provides a chip, which comprises a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run a program or an instruction, so as to implement each process of the above-mentioned crew state intervention method embodiment, and the same technical effects are achieved. To avoid repetition, details are not described herein.

[0258] It should be understood that the chip mentioned in the embodiment of the present application can also be referred to as a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0259] It should be noted that, in the present document, the terms "comprises", "comprising", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises a", "comprising", or "includes a", does not, without more constraints, preclude the existence of additional identical elements in the process, method, article, or apparatus that comprises the element. Additionally, it should be noted that the scope of the methods and apparatus of the present embodiments are not limited by the order of the steps or the sequences of the steps, as some steps can occur in different orders and / or concurrently with other steps besides those depicted and / or discussed herein. Also, described features can be combined in

[0260] From the above description of the embodiments, it is apparent that the above-mentioned method can be implemented by means of software and the necessary universal hardware platform, of course, it can also be implemented by hardware, but in many cases, the former is a better implementation. Based on such understanding, the technical solutions of the present application can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a plurality of instructions for making a terminal (which can be a mobile phone, computer, server, or network device, etc.) execute the crew state intervention method of the various embodiments of the present application.

[0261] In the description of the present application, "first feature" and "second feature" can include one or more of the features.

[0262] In the description of the present application, "a plurality of" means two or more.

[0263] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-described specific embodiments, which are merely illustrative and not restrictive, and a person of ordinary skill in the art can make many forms under the inspiration of the present application without departing from the scope of the present application and the scope protected by the claims.

[0264] In the description of the application, reference has been made to descriptive terms such as "one embodiment", "some embodiments", "an embodiment", "example", "specific example" or "some examples" etc. It is emphasized that each of these terms refers to a specific feature, structure, material or characteristic described in connection with a particular embodiment or example. The descriptive terms are not necessarily referring to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0265] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since the scope of the application is defined with respect to the appended claims.

Claims

1. A crew status intervention method, characterized in that: include: Obtaining navigation information and environmental information of the vessel, as well as status information of the crew members on the vessel; Determining a task allocation plan for the staff member based on the navigation information and the status information; Obtaining a status assessment result of the worker based on the environmental information and the status information; The task allocation plan is optimized through the status evaluation result, and the working status of the staff in executing the task allocation plan is obtained, so as to perform status intervention on the staff according to the working status, which is determined based on status indicators and work efficiency.

2. The crew status intervention method according to claim 1, characterized in that: Obtaining a status assessment result of the worker based on the environmental information and the status information, including: Performing time series analysis on the status information to obtain a fatigue accumulation trend of the worker; Performing bidirectional feature extraction on the environmental information and the fatigue accumulation trend to obtain dynamic correlation features, wherein the dynamic correlation features are used to characterize the degree of influence of the multidimensional parameters in the environmental information on the fatigue accumulation trend of the worker; determining, based on the dynamic correlation characteristics, fatigue sensitivity and recovery efficiency of the worker under the environmental information; The emotional stability, stress level, and concentration level of the worker are evaluated based on the status information, the fatigue sensitivity, and the recovery efficiency to obtain the status evaluation result.

3. The crew status intervention method according to claim 2, characterized in that: The performing time series analysis on the status information to obtain the fatigue accumulation trend of the worker includes: determining key physiological-work status indicators in the status information; According to the key physiological-work state indicators, performing dynamic feature enhancement-driven time series feature extraction on the state information to obtain a first fatigue feature sequence; Performing dynamic feature enhancement on the first fatigue feature sequence to obtain a second fatigue feature sequence; Performing exponential fitting on the enhanced fatigue characteristic sequence to obtain a fatigue accumulation curve of the worker; The fatigue accumulation trend is determined according to the attenuation law of the fatigue accumulation curve.

4. The crew status intervention method according to claim 2, characterized in that: Performing bidirectional feature extraction on the environmental information and the fatigue accumulation trend to obtain dynamic correlation features, including: Extracting and concatenating spatiotemporal features of the environmental information and the fatigue accumulation trend to obtain a correlation feature vector; Sorting the importance of the associated feature vectors to obtain key influencing factors; Performing a matching analysis based on dynamic time warping on the temporal changes of the key influencing factors to obtain the degree of dominant influence of each of the key influencing factors on the fatigue accumulation trend, as well as the lag terms in the key influencing factors; Based on the degree of dominant influence, determining the interaction terms between each of the key influencing factors; Analyzing the lag term and the interaction term to obtain the implicit influence degree of each of the key influencing factors on the fatigue accumulation trend; According to the explicit influence degree and the implicit influence degree, the key influence factors are weighted to obtain the dynamic correlation feature.

5. The crew status intervention method according to claim 4, characterized in that: Determining the fatigue sensitivity and recovery efficiency of the worker under the environmental information based on the dynamic correlation characteristics includes: Fuzzifying the weights of key influencing factors in the dynamic correlation features, the explicit influence degree, and the implicit influence degree to obtain fuzzy linguistic variables; Performing fuzzy reasoning on the fuzzy linguistic variables to obtain the fatigue sensitivity of the worker in the state space; The recovery efficiency of the worker is determined based on the fatigue sensitivity and the remaining work capacity corresponding to the status information.

6. The crew status intervention method according to claim 1, characterized in that: Determining a task allocation plan for the staff member based on the navigation information and the status information includes: Determining the navigation mission of the ship and the corresponding mission type based on the route planning, navigation speed, weather information and expected voyage time in the navigation information, wherein the mission type includes emergency mission, routine mission and auxiliary mission; According to the task type, in the process topology diagram of the navigation task, matching the work objectives and workload of each type of work to the subtask; Generating a work grade sequence for each type of work based on the participation level corresponding to the work goal and the workload, wherein the work grade sequence is used to characterize the importance and urgency of the work of the type of work; The task allocation plan is generated according to the work level sequence and the status information.

7. The crew status intervention method according to claim 6, characterized in that: Generating the task allocation plan according to the work level sequence and the status information includes: Calculating the collaboration requirement between each type of work based on the work level sequence, wherein the collaboration requirement is used to characterize the work dependency and collaboration intensity between the types of work; According to the collaboration demand, the types of work are grouped to obtain collaboration groups; Determining the work tasks of the collaboration team based on the work level sequence; A task allocation scheme for the worker is determined in the work task according to the workload, the participation level and the status information.

8. The crew status intervention method according to claim 1, characterized in that: The performing status intervention on the worker according to the task allocation plan and the status assessment result includes: Determine the idle time distribution of the staff member based on the work intensity and working duration of each work item in the task allocation plan, as well as the connection sequence and logical relationship between the work items; generating an energy recovery plan for the worker according to the status assessment result and the idle time distribution; Resources are scheduled according to the energy recovery plan to perform state intervention.

9. A crew status intervention device, characterized in that: include: An acquisition module, used to acquire navigation information and environmental information of the ship, as well as status information of the crew members on the ship; A first processing module is used to determine a task allocation plan for the staff member based on the navigation information and the status information; A second processing module is used to obtain a status assessment result of the worker based on the environmental information and the status information; The third processing module is used to optimize the task allocation plan based on the status evaluation result, and obtain the working status of the staff in executing the task allocation plan, so as to perform status intervention on the staff according to the working status, and the working status is determined based on status indicators and work efficiency.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the crew status intervention method according to any one of claims 1 to 8 is implemented.

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