Learning plate distribution method, system and equipment for general practitioner and medium

Abstract

The invention discloses a general practitioner learning plate distribution method, system and device and a medium, and the method comprises the steps: obtaining the treatment behavior data of a target general practitioner under preset simulated marine treatment scene parameters; acquiring a plurality of treatment capability dimensions of preset simulated marine treatment scene parameters; evaluating the ability score of the target general practitioner in each treatment ability dimension based on the sea condition environment parameters, the treatment scene label set and the treatment behavior data to obtain an evaluation result; constructing an ability portrait map of the target general practitioner based on the evaluation result; calculating the ability improvement priority and the task urgency priority of the target general practitioner in each learning section based on the preset simulated marine treatment scene parameters, the evaluation result and the ability portrait map; and distributing all the learning plates based on the capability improvement priority and the task urgency priority. The method can improve the distribution efficiency of training resources.

Description

Technical Field

[0001] This application relates to the field of medical education technology, specifically to a method, system, device, and medium for allocating learning modules for general practitioners. Background Technology

[0002] With the rapid development of the marine economy, the number of personnel working at sea is constantly increasing, and the demand for maritime medical rescue is becoming increasingly prominent. General practitioners, as a vital force in maritime medical rescue, play a crucial role in safeguarding the lives and health of those working at sea through continuous improvement of their professional capabilities.

[0003] In related technologies, training for general practitioners mainly adopts a standardized training program. Training institutions typically allocate learning modules according to a pre-established syllabus, with a fixed curriculum and schedule.

[0004] However, due to the complex and ever-changing environment of maritime medical rescue, there is often a significant discrepancy between the professional competence demonstrated by doctors in actual work and the effectiveness of training. Existing methods for allocating learning modules are insufficient to accurately identify doctors' performance in real-world maritime scenarios, resulting in inefficient allocation of training resources. Summary of the Invention

[0005] This application provides a method, system, device, and medium for allocating learning modules for general practitioners, which can improve the efficiency of training resource allocation.

[0006] The first aspect of this application provides a method for allocating learning modules for general practitioners, applied in a server. The method includes: acquiring treatment behavior data of a target general practitioner under preset simulated maritime treatment scenario parameters, the preset simulated maritime treatment scenario parameters including sea environment parameters, treatment tasks, and a treatment scenario tag set; acquiring multiple treatment capability dimensions of the preset simulated maritime treatment scenario parameters; evaluating the target general practitioner's capability score in each treatment capability dimension based on the sea environment parameters, treatment scenario tag set, and treatment behavior data, obtaining an evaluation result; constructing a capability profile map of the target general practitioner based on the evaluation result; calculating the capability improvement priority and task urgency priority of the target general practitioner in each learning module based on the preset simulated maritime treatment scenario parameters, evaluation result, and capability profile map; and allocating all learning modules based on the capability improvement priority and task urgency priority.

[0007] Optionally, the assessment results are obtained by evaluating the target general practitioner's competency score in each treatment capability dimension based on sea state environmental parameters, treatment scenario label set, and treatment behavior data. Specifically, this includes: constructing a label-capability mapping equation based on sea state environmental parameters and treatment scenario label set, which is used to characterize the importance of each treatment capability dimension under preset simulated sea treatment scenario parameters; and calculating the target general practitioner's competency score in each treatment capability dimension based on treatment behavior data and label-capability mapping equation.

[0008] Optionally, a label-capability mapping equation is constructed based on sea state environmental parameters and a treatment scenario label set. Specifically, this includes: determining the first scenario difficulty level of each treatment scenario label in the treatment scenario label set based on preset rules; determining a disturbance factor based on sea state environmental parameters, calculating the influence of sea state environmental parameters on treatment behavior data based on the disturbance factor, and obtaining a disturbance adjustment factor; adjusting the first scenario difficulty level of each treatment scenario label based on the disturbance adjustment factor to obtain a second scenario difficulty level for each treatment scenario label; inputting the second scenario difficulty level of each treatment scenario label into a preset treatment capability dimension response function family to calculate the capability requirement weight vector corresponding to each treatment scenario label, where each treatment capability dimension response function in the preset treatment capability dimension response function family contains the treatment scenario label corresponding to the treatment capability dimension response function; and constructing a label-capability mapping equation based on the treatment scenario label and the capability requirement weight vector corresponding to the treatment scenario label.

[0009] Optionally, a capability profile map of the target general practitioner can be constructed based on the assessment results. Specifically, this includes: comparing the capability score of each treatment capability dimension in the assessment results with the preset capability standards to determine the relative capability level of the target general practitioner in each treatment capability dimension and generating a capability level label set; constructing a label-capability-scenario association structure unit according to the treatment scenario label corresponding to each relative capability level label in the capability level label set; and clustering and integrating the label-capability-scenario association structure unit according to the treatment capability dimension to construct the capability profile map.

[0010] Optionally, based on preset simulated maritime treatment scenario parameters, evaluation results, and competency profile maps, the competency improvement priority and task urgency priority of the target general practitioner in each learning module are calculated. Specifically, this includes: calculating the task urgency corresponding to the preset simulated maritime treatment scenario parameters, which reflects the urgency of the target general practitioner to improve the corresponding treatment capabilities in each treatment capability dimension under different treatment tasks; calculating the competency improvement space coefficient for each learning module based on the competency score of each treatment capability dimension in the evaluation results; determining the competency improvement priority based on the competency improvement space coefficient; and analyzing the correlation between the treatment scenario tags and treatment tasks corresponding to each learning module based on the tag-capability-scenario association structure unit in the competency profile map to obtain the task relevance index for each learning module, and weighting the task relevance index according to the task urgency to obtain the task urgency priority.

[0011] Optionally, based on the ability scores of each treatment ability dimension in the assessment results, calculate the potential improvement coefficient of the target general practitioner for each learning module. Specifically, this includes: based on the ability scores of each treatment ability dimension in the assessment results, arranging all treatment ability dimensions from high to low according to their ability scores to obtain a treatment ability sequence; calculating the score difference between the first and second treatment ability dimensions in the treatment ability sequence, where the first and second treatment ability dimensions are any two adjacent treatment ability dimensions in the treatment ability sequence; determining the position between the target first and target second treatment ability dimensions in the treatment ability sequence where the score difference is greater than a preset difference threshold as a capability development breakpoint; dividing the treatment ability sequence into multiple capability level intervals based on the capability development breakpoints; and determining the potential improvement coefficient of the target general practitioner for each learning module based on the capability level intervals and the treatment ability dimensions of each learning module.

[0012] Optionally, based on the competency level intervals and the treatment competency dimensions of each learning module, the potential improvement coefficient for the target general practitioner's competency in each learning module is determined. Specifically, this includes: determining the number of times the target treatment competency dimension of the target learning module crosses the competency level intervals to obtain the learning module span coefficient, where the target learning module is any learning module; based on the learning module span coefficient, calculating the level difference between the highest and lowest competency level intervals within the target learning module, and determining the module imbalance degree as the ratio of the level difference value to the preset number of treatment competency dimensions in the target learning module; and obtaining the competency improvement potential coefficient of the learning module by multiplying the module imbalance degree by the module span coefficient.

[0013] A second aspect of this application provides a learning module allocation system for general practitioners, comprising: The first acquisition module is used to acquire treatment behavior data of the target general practitioner under preset simulated maritime treatment scenario parameters. The preset simulated maritime treatment scenario parameters include sea environment parameters, treatment tasks, and treatment scenario tag sets. The second acquisition module is used to acquire multiple dimensions of treatment capabilities for preset simulated maritime treatment scenarios. The assessment module is used to evaluate the competency scores of target general practitioners in each treatment capability dimension based on sea state environmental parameters, treatment scenario label sets, and treatment behavior data, and obtain the assessment results. The building module is used to construct a competency profile of the target general practitioners based on the assessment results; The calculation module is used to calculate the priority of general practitioners' ability improvement and the urgency of tasks in each learning module based on preset simulated maritime treatment scenario parameters, evaluation results and ability profile maps; The allocation module is used to allocate all learning modules based on the priority of ability improvement and the urgency of tasks.

[0014] A third aspect of this application provides an electronic device including a processor, a memory, a user interface, and a network interface. The memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory to cause the electronic device to perform the method described in any of the above.

[0015] In a fourth aspect, this application provides a computer-readable storage medium storing instructions that, when executed, perform the method described in any of the foregoing descriptions.

[0016] In summary, one or more technical solutions provided in this application have at least the following technical effects or advantages: 1. By acquiring treatment behavior data of target general practitioners in simulated maritime treatment scenarios and evaluating them in conjunction with multiple treatment capability dimensions, the performance of target general practitioners in specific environments can be quantified. Based on the evaluation results, a capability profile map is constructed, clearly presenting the doctor's capability structure and shortcomings. Furthermore, by combining simulation task parameters, the capability improvement priority and task urgency priority of each learning module are calculated, realizing dynamic matching between learning content and task requirements. Finally, the learning modules are allocated in a targeted manner, effectively solving the problem of lack of personalization and practical orientation in the allocation of learning content in existing technologies, and improving the efficiency of training resource allocation.

[0017] 2. By constructing a label-capability mapping equation based on sea state environmental parameters and a set of treatment scenario labels, the initial scenario difficulty level for each label is first determined. A perturbation factor is then introduced to quantify the impact of sea state on behavioral data, resulting in a perturbation adjustment factor. This factor dynamically corrects the scenario difficulty, yielding a second scenario difficulty level that more closely reflects the actual environment. The corrected difficulty level is then input into a pre-defined family of treatment capability dimension response functions to calculate the capability requirement weight vector corresponding to each label, thus establishing a mapping relationship between labels and capability dimensions. Based on this mapping equation, combined with the treatment behavior data of the target general practitioners, their capability scores on each capability dimension can be accurately calculated, forming an assessment result. This effectively integrates the complex marine environment and scenario difficulty into the capability assessment process, improving the contextual adaptability and accuracy of the assessment results, and providing a scientific basis for capability profile construction and learning module allocation.

[0018] 3. By comparing the competency scores of each treatment capability dimension in the assessment results with preset competency standards, the relative competency levels of general practitioners in each dimension are determined, and a competency level label set is generated. This quantifies the relative performance of doctors across different competency dimensions. Furthermore, based on the competency level labels and corresponding treatment scenario labels, a label-competency-scenario association structure unit is constructed. This unit is then clustered and integrated according to competency dimensions to build a competency profile map, enabling a visual representation and differentiated characterization of the doctor's competency structure. Based on this profile map, combined with preset simulation task parameters and assessment results, the competency improvement priority and task urgency priority of learning modules are calculated: the competency scores determine the potential for improvement in each learning module, clarifying the value of learning enhancement; and the urgency and scenario relevance are combined to calculate the task urgency priority, ensuring that learning allocation not only addresses competency weaknesses but also considers task requirements. This improves the personalization, task orientation, and timeliness of learning content allocation, solving the problem of inaccurate learning path planning in existing technologies. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of an embodiment of a learning module allocation method or a learning module allocation system for general practitioners, which applies the present application. Figure 2 This is a flowchart illustrating a method for allocating learning modules for general practitioners, as described in an embodiment of this application. Figure 3 This is a schematic diagram of the structure of a learning module allocation system for general practitioners in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of the electronic device in the embodiments of this application.

[0020] Explanation of reference numerals in the attached drawings: 301, First acquisition module; 302, Second acquisition module; 303, Evaluation module; 304, Construction module; 305, Calculation module; 306, Allocation module; 401, Processor; 402, Communication bus; 403, User interface; 404, Network interface; 405, Memory. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0022] In the description of the embodiments of this application, the words "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design that is described as "for example" or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design options. Rather, the use of the words "for example" or "for instance" is intended to present the relevant concepts in a specific manner.

[0023] In the description of the embodiments of this application, the term "multiple" means two or more. For example, multiple systems means two or more systems, and multiple screen terminals means two or more screen terminals. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature. The terms "comprising," "including," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0024] Figure 1 An exemplary system architecture 100 is shown, which can be applied to an embodiment of a learning module allocation method or a learning module allocation system for general practitioners.

[0025] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0026] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as model training applications, video recognition applications, web browser applications, social platform software, etc.

[0027] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, e-book readers, laptops, and desktop computers. When terminal devices 101, 102, and 103 are software, they can be installed in the aforementioned electronic devices. They can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services) or as a single software program or software module. No specific limitations are imposed here.

[0028] When terminals 101, 102, and 103 are hardware devices, video capture devices can also be installed on them. These video capture devices can be various devices capable of capturing video, such as cameras, sensors, etc. Users can use the video capture devices on terminals 101, 102, and 103 to capture video.

[0029] Server 105 can be a server that provides various services, such as a backend server for processing data displayed on terminal devices 101, 102, and 103. The backend server can analyze and process the received data and can feed back the processing results (such as recognition results) to the terminal devices.

[0030] It should be noted that a server can be either hardware or software. When the server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When the server is software, it can be implemented as multiple software programs or software modules (e.g., multiple software programs or software modules used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0031] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included. In particular, if the target data does not need to be obtained remotely, the above system architecture may exclude the network and include only terminal devices or servers.

[0032] Figure 2 This is a flowchart illustrating a method for allocating learning modules for general practitioners, as described in an embodiment of this application.

[0033] Please see Figure 2 This application provides a method for allocating learning modules for general practitioners, applied in a server. The method includes: S201. Obtain treatment behavior data of the target general practitioner under preset simulated maritime treatment scenario parameters. The preset simulated maritime treatment scenario parameters include sea environment parameters, treatment tasks, and treatment scenario label set. To assess the clinical coping abilities of target general practitioners in the complex maritime environment and thus develop personalized learning and improvement strategies, it is first necessary to obtain treatment behavior data of target general practitioners under preset simulated maritime treatment scenario parameters. During implementation, the preset simulated maritime treatment scenario parameters are first determined, including sea environment parameters, treatment tasks, and a treatment scenario label set.

[0034] The sea state environmental parameters mainly include simulated physical environmental variables such as wind speed, wave height, ship rolling frequency, and noise interference level. These parameters are used to simulate external disturbances in actual maritime operations and are dynamically presented through a multi-degree-of-freedom platform simulation system. For example, a six-degree-of-freedom motion platform combined with an audio disturbance system can simulate the real state of a doctor practicing medicine in wind and waves. The purpose of setting these sea state environmental parameters is to examine the stability, accuracy of judgment, and psychological resilience of the target general practitioner in unstable environments.

[0035] The treatment tasks are modeled based on typical maritime medical needs, including but not limited to trauma management, acute disease diagnosis, and decision-making under limited medical resources. Each treatment task is developed by medical experts based on a database of actual cases from the naval fleet and undergoes multiple rounds of simulation verification to ensure its representativeness and challenge. Through a task-driven approach, the target general practitioners must complete the diagnosis and treatment process within a limited time, thereby revealing their strengths and weaknesses in actual operation.

[0036] The treatment scenario tag set is used to perform multi-dimensional semantic annotation of each simulated treatment task, including dimensions such as task urgency level, disease complexity, resource scarcity, and patient cooperation level. The tag set is organized in the form of structured metadata, which can track the context of each treatment behavior. For example, a task may be labeled as "high urgency + multiple diseases + low resources + high interference". By combining the tag sets, typical stress scenarios of maritime medical care can be constructed.

[0037] The entire process of general practitioners' operations under preset simulated maritime treatment scenario parameters is recorded in real time by a multi-dimensional behavior acquisition terminal, including video monitoring, physiological sensors, a voice recognition module, and an operation trajectory recording module. Treatment behavior data includes, but is not limited to: operation duration, operation path, response time of key judgment nodes, fluctuations in physiological indicators (such as heart rate changes), and quality of verbal communication. All data is uniformly synchronized via timestamps and stored in a behavior analysis database, providing foundational data support for subsequent competency assessments.

[0038] S202. Obtain multiple dimensions of treatment capabilities from preset simulated maritime treatment scenario parameters; In step S202, the first step is to establish a treatment capability dimension system based on the compositional characteristics of the preset simulated maritime treatment scenario parameters and the actual needs of shipborne medical missions at sea. The construction of the treatment capability dimensions is based on a generalized medical capability model, incorporating adaptability to the special maritime environment, and employing an expert knowledge graph construction method for hierarchical decomposition. Through analysis and modeling of maritime medical expert experience data, surgical records, and emergency maritime incident handling procedures, a capability dimension system tailored to the maritime operational environment is formed.

[0039] Based on the core principle of task-driven approach, each type of treatment task is broken down into operational steps. By combining sea state environmental parameters with the semantic tags in the treatment scenario tag set, the capability elements required for doctors' operations are extracted. For example, in a simulated high-wave emergency rescue scenario, doctors need to complete bandaging, hemostasis, and airway establishment operations under severe ship rolling and noise interference. Therefore, they need to possess capabilities such as "dynamic operational stability," "clinical decision-making ability under limited resources," and "information extraction ability under high interference environment."

[0040] To ensure the scientific rigor and quantifiability of the capability dimensions, a capability index matrix construction method is employed to refine each capability dimension into several capability indicators. Each capability indicator corresponds to an observable operational behavior or physiological / psychological response. For example, "dynamic operational stability" is further refined into indicators such as "operation trajectory deviation," "instrument grip stability," and "critical operation time delay"; "clinical decision-making ability" is refined into indicators such as "rationality of diagnostic pathway" and "rationality of drug selection priority." Each capability indicator is linked to a corresponding behavioral data acquisition channel to enable subsequent data mapping and evaluation.

[0041] After constructing the capability dimension system, a mapping relationship between capability dimensions and scenario parameters is established to ensure that each treatment task corresponds to a specific set of capability dimensions for subsequent targeted evaluation. The mapping relationship between capability dimensions and scenario parameters is expressed in the form of a knowledge graph, where nodes represent capability dimensions and edges represent their dependencies on treatment tasks and environmental influencing factors, supporting capability combination modeling under complex tasks.

[0042] S203. Based on sea state environmental parameters, treatment scenario label sets, and treatment behavior data, assess the general practitioner's competency score in each treatment capability dimension to obtain the assessment results; In a simulated maritime integrated treatment scenario, accurately assessing the capabilities of the target general practitioner is a crucial step in achieving personalized learning task allocation and capability enhancement path planning. Therefore, in step S203, based on sea environment parameters, treatment scenario label sets, and treatment behavior data, the target general practitioner's capability score is evaluated in each treatment capability dimension, thus obtaining a structured evaluation result. This may include the following steps: constructing a label-capability mapping equation based on sea environment parameters and the treatment scenario label set; the label-capability mapping equation is used to characterize the importance of each treatment capability dimension under preset simulated maritime treatment scenario parameters; and calculating the target general practitioner's capability score in each treatment capability dimension based on treatment behavior data and the label-capability mapping equation, thus obtaining the evaluation result.

[0043] Because the task characteristics and ability requirements represented by different treatment scenario labels vary, and sea state environmental parameters have a significant interfering effect on behavioral performance, a scenario difficulty level and a perturbation moderating factor are introduced to combine static labels with dynamic environmental influences, constructing a responsive label-ability mapping equation. This may include steps S2031-S2035: S2031. Based on preset rules, determine the first scenario difficulty level of each treatment scenario label in the treatment scenario label set. In constructing the label-ability mapping equation, accurately expressing the complexity of the task represented by each treatment scenario label is a fundamental prerequisite. Therefore, a quantifiable difficulty assessment mechanism is first established to initially assign values ​​to the treatment scenario labels, obtaining the first scenario difficulty level for each label. The first scenario difficulty level characterizes the basic challenge intensity posed by each treatment scenario label to the doctor's ability under standard environmental conditions, and serves as the starting point for subsequent perturbation adjustment and weight allocation.

[0044] This step employs an expert knowledge modeling approach combined with a quantitative scoring mechanism. The pre-defined rules consist of a multi-level set of evaluation criteria, encompassing dimensions such as task urgency, disease complexity, resource accessibility, operational timeliness, and patient compliance. Each dimension has established corresponding scoring standards through historical data analysis and expert interviews, with weighted coefficients reflecting its contribution to the overall scenario difficulty. In implementation, the Analytic Hierarchy Process (AHP) is used as the computational model to structurally model each evaluation indicator in the pre-defined rules, resulting in a difficulty level judgment model. Each treatment scenario label is then substituted into this model, and scores are assigned based on the semantic features of the label across each evaluation dimension. The final result is a value between 0 and 1, representing the first scenario difficulty level for that label. To improve system adaptability, the scoring process supports rule expansion and dynamic label updates. When new labels or scenarios appear, they can be evaluated using the same rule system without modifying the overall model structure, thus achieving system scalability and maintainability. For example, for a treatment scenario label "enclosed space + single-person first aid + acute airway obstruction", according to the preset rules, its task urgency dimension score is 0.9, the operation time limit score is 0.85, and the resource accessibility score is 0.4. After comprehensive weight calculation, the first scenario difficulty level of this label is 0.78, indicating that the task represented by this label has a high level of challenge in standard sea state environment.

[0045] S2032. Determine the disturbance factor based on the sea state environmental parameters, calculate the degree of influence of the sea state environmental parameters on the treatment behavior data based on the disturbance factor, and obtain the disturbance regulation factor. In step S2032, to accurately reflect the interference of the marine environment on the general practitioner's treatment ability and to dynamically adjust the scenario difficulty level in the label-ability mapping equation, a disturbance factor needs to be introduced. Marine environmental parameters refer to the physical disturbance variables used to recreate the actual marine working environment in the simulated marine treatment scenario, mainly including wave height, wind speed, ship roll amplitude, and noise intensity. These parameters are collected and recorded in real time during the doctor's participation in the simulated treatment task, exhibiting highly dynamic and nonlinear characteristics.

[0046] In the implementation process, the system first calls the environmental data acquisition module to standardize various sea state environmental parameters in the simulation platform. Standardization involves normalizing the original environmental variables to a range of 0 to 1, facilitating subsequent unified modeling. Next, by establishing a disturbance factor mapping model, the standardized sea state environmental parameters are converted into a set of disturbance factor vectors. Each disturbance factor represents the potential impact of a type of environmental variable on the doctor's behavioral performance. For example, wave height is mapped to an "operational stability disturbance factor," and noise level is mapped to an "information transmission interference factor." This mapping model is fitted based on simulation training data and actual combat feedback data to ensure that the generation of disturbance factors has practical interference significance.

[0047] After generating perturbation factors, it is necessary to further evaluate the specific impact of each perturbation factor on the target general practitioner's treatment behavior data, thereby obtaining perturbation moderating factors. Treatment behavior data includes multiple dimensions such as operational path deviation, critical task response delay, verbal communication failure rate, and operational interruption frequency. The system employs multivariate regression analysis, using perturbation factors as independent variables and behavioral data volatility as the dependent variable, to establish a perturbation-behavior impact function: Where, α ij β is the baseline offset value for the behavioral indicator. ij The disturbance sensitivity coefficient, obtained by fitting historical data, reflects the intensity of the behavioral index's response to disturbances; D i f represents the actual input value of the disturbance factor (e.g., standardized wave height = 0.7). ij (D i ) represents the impact of the i-th disturbance factor on the j-th behavioral indicator, i.e., the relative deviation of the disturbance on the behavioral indicator under the current conditions.

[0048] By modeling a large number of operational samples of the target physician under different sea state parameters, the system can calculate the corresponding disturbance adjustment factor: Among them, T i w is the adjustment contribution value corresponding to the i-th disturbance factor. j f represents the weight of the j-th therapeutic behavior indicator in the scenario difficulty. ij (D i Let be the impact of the i-th perturbation factor on the j-th behavioral indicator, and let R be the normalized weighted sum of all perturbation adjustment contributions. Where n represents the total number of disturbance factors, and the disturbance adjustment factor represents the magnitude of interference caused by the sea state environment to the behavioral data.

[0049] The perturbation adjustment factor, as a dynamic coefficient, is used to adjust the difficulty level of the first scenario of the treatment scenario label in subsequent steps, thereby reflecting the level of capability challenge embodied by the same label under different sea state backgrounds. For example, in an environment with wave heights reaching 2.5 meters and wind speeds exceeding 20 knots, the operator deviation rate increases to 1.6 times the baseline value when performing intravenous puncture. Based on this, the system calculates a perturbation adjustment factor of 1.6, meaning that the environment has a significant impact on task completion, and the difficulty level of the scenario label should be appropriately increased.

[0050] S2033. Adjust the first scene difficulty level of each treatment scene label based on the perturbation adjustment factor to obtain the second scene difficulty level of each treatment scene label; The calculated disturbance adjustment factor is incorporated into the difficulty level system of the scene label, and the statically set first scene difficulty level is adjusted in real time to obtain a second scene difficulty level that more closely reflects the real interference environment. The first scene difficulty level only reflects the task complexity of the scene label under standard conditions, without considering the interference of sea state disturbances on the doctor's operational stability, information recognition ability, and decision-making efficiency. Therefore, before assessing the doctor's ability, it is necessary to dynamically adjust it using the disturbance adjustment factor to accurately reflect the actual level of challenge of the label in the current environment.

[0051] In the specific implementation process, the system first obtains the first scenario difficulty level corresponding to each treatment scenario label from the behavior modeling module. This level is a normalized value, usually between 0 and 1, representing the task complexity of that label in an ideal environment. Subsequently, the system obtains the perturbation adjustment factor within the current task cycle from the environmental disturbance analysis module. Based on the perturbation adjustment factor and the first scenario difficulty level, an adjustment model is constructed. The specific adjustment formula is as follows: in, This is the second scene difficulty level. R represents the difficulty level of the first scenario for the k-th treatment scenario label; R is the perturbation adjustment factor. The basic principle of the adjustment model is: in an environment with enhanced sea state disturbance, the ability challenge faced by doctors in the same type of task increases significantly, so the original scenario level needs to be scaled up proportionally; if the perturbation is small or the environment is close to static, the adjustment range approaches 1, keeping the original level unchanged.

[0052] S2034. Input the second scene difficulty level of each treatment scene label into the preset treatment ability dimension response function family to calculate the ability requirement weight vector corresponding to each treatment scene label. Each treatment ability dimension response function in the preset treatment ability dimension response function family contains the treatment scene label corresponding to the treatment ability dimension response function. In step S2034, the family of response functions for the healing ability dimension is a set of structured mathematical functions. Each function corresponds to a healing ability dimension and is used to characterize the response curve of the healing ability dimension under different difficulty levels of the second scenario, thereby measuring the trend of the importance of the healing ability dimension changing with the difficulty of the scenario. The design of the family of response functions for the ability dimension is based on a large amount of historical simulation data and expert training experience, and is implemented by function fitting modeling. The input of each response function is the adjusted difficulty level of the second scenario, and the output is the ability requirement weight of the target scenario label on the ability dimension, that is, the position value of the corresponding dimension in the ability requirement weight vector. The function form can be a linear function, an exponential function, a sigmoid function, or a piecewise polynomial function, and the specific choice depends on the sensitivity and nonlinear characteristics of the ability dimension to the difficulty of the scenario.

[0053] During implementation, the system first retrieves the second scenario difficulty level corresponding to each treatment scenario label and then iterates through the family of treatment capability dimension response functions. For each treatment capability dimension, the second scenario difficulty is input into the treatment capability dimension response function corresponding to the treatment scenario label, calculating the capability requirement weight of the treatment capability dimension in the current scenario. The combined output of multiple capability dimension response functions forms a capability requirement weight vector, which represents the capability structure required for the label in the current sea state environment and the importance ratio of each dimension. The construction process of the capability dimension response function family has been completed in advance, possessing reusability and scalability. It can be dynamically updated through machine learning to improve the system's adaptability.

[0054] For example, if the difficulty level of the second scene under a certain scene label is 1.20, the system will input this value into the response functions of the following three capability dimensions: Basic operational capability response function: f1(x) = 0.4 + 0.2x, output f1(1.2) = 0.64; Clinical judgment ability response function: f2(x)=0.3+0.5x, output f2(1.2)=0.9; Communication and collaboration ability response function: The output is f3(1.2)≈0.60; The corresponding competency requirement weight vector can be represented as [0.64, 0.90, 0.60]. In subsequent competency scoring calculations, the system will use the competency requirement weight vector as the weighting basis to give differentiated evaluations of the target general practitioners' performance in each competency dimension.

[0055] S2035. Construct a label-capability mapping equation based on the treatment scenario label and the corresponding capability requirement weight vector.

[0056] After calculating a large number of treatment scenario labels and their corresponding capability requirement weight vectors, the system needs to construct a label-capability mapping equation with generalization ability so that when faced with new label combinations, it can directly infer the corresponding capability requirement weight vector through the mapping equation without repeatedly calling steps S2031 to S2034.

[0057] During implementation, the system first structures the capability requirement weight vectors representing label-weight pairs obtained in step S2034 to form the training dataset {(T k W k )}, where T k W represents the label feature representation of the k-th scene label combination. k Let W represent the capability requirement weight vector corresponding to the k-th scene label combination. To achieve the mapping from the label space to the weight space, the system employs a multi-output regression model structure to construct the label-capability mapping equation F: F:T→W. This equation is used to quickly output the capability weight vector W for any given label combination T. The label combination T can be represented using sparse vectors or embedded vectors, and the weight vector W is a continuous value vector corresponding one-to-one with the healing capability dimension. Model training can utilize mathematical models such as linear regression, ridge regression, support vector regression, and fully connected neural networks, with the specific choice depending on the size of the training samples and the distribution of nonlinear features. During training, the system continuously adjusts the model parameters by minimizing the mean squared error between the predicted vector and the true weight vector as the loss function until a stable mapping relationship is fitted.

[0058] S204. Construct a competency profile of the target general practitioner based on the assessment results; In step S204, to further realize the visualized expression and contextualized management of the comprehensive ability status of the target general practitioner, after completing the scoring calculation of each treatment ability dimension, the system constructs a capability profile map of the doctor based on the evaluation results. The capability profile map is a graph-based data structure that expresses the target doctor's ability level, label correlation, and scenario adaptability in each treatment ability dimension in a structured and clustered manner. The capability profile map integrates capability level labels, scenario labels, and cross-correlation between capability dimensions, and is expressed through a label-ability-scenario ternary structure, thus possessing stronger semantic clarity and decision-making reference value. It may include the following steps: comparing the capability score of each treatment ability dimension in the evaluation results with preset capability standards to determine the relative capability level of the target general practitioner in each treatment ability dimension, and generating a capability level label set; constructing a label-ability-scenario association structure unit based on the treatment scenario label corresponding to each relative capability level label in the capability level label set; and clustering and integrating the label-ability-scenario association structure unit according to the treatment ability dimension to construct the capability profile map.

[0059] In the specific implementation process, after calculating the competency scores of the target general practitioners across various treatment competency dimensions and obtaining the evaluation results, the system needs to standardize the interpretation of these scores to construct a comparable and tiered competency level system. Therefore, the system compares the competency score for each treatment competency dimension with preset competency standards to determine the doctor's relative competency level in the target dimension. The preset competency standards are thresholds for grading based on the average performance and distribution range of a group of doctors in similar scenarios within historical task data, typically divided into a three- or five-level system (such as low, medium, high, or ABCDE). The comparison process uses an interval mapping method, mapping the interval into which the competency score falls to a specific relative competency level and generating a competency level label set. Each label identifies the doctor's level status in any treatment competency dimension. For example, if a doctor scores 0.82 in the "Operational Stability" dimension, the system determines that they belong to the "Advanced" level according to the preset standards, and generates the label "Operational Stability - High".

[0060] After obtaining the set of ability level labels, the system further back-associates each relative ability level label with the treatment scenario labels involved in the evaluation process, constructing one or more label-ability-scenario association structural units. Each label-ability-scenario association structural unit is a triplet structure, containing: an ability level label, the corresponding ability dimension, and the treatment scenario label that triggered that ability performance. The construction logic of the label-ability-scenario association structural unit lies in identifying the ability level exhibited by the target doctor in the scenario type, thereby forming a contextual association between the ability level and the task context in the graph. For example, if a doctor exhibits a "medium" level of clinical judgment ability in the scenario of "high-speed shaking + solo operation + acute trauma," then the structural unit is formed as: <medium clinical judgment ability, clinical judgment ability, high-speed shaking + solo operation + acute trauma>. The generation of this structural unit not only describes the ability state but also assigns it a scenario background, which helps in accurate scenario matching in subsequent training stages.

[0061] After generating multiple tag-ability-scenario related structural units, the system clusters and integrates these structural units based on a predetermined set of ability dimensions to construct a complete ability profile graph. The clustering operation uses the ability dimension as the main axis, aggregating multiple level tags and their corresponding scenario tags under the same ability dimension, and displaying them hierarchically according to level strength, ultimately forming an ability graph structure with the ability dimension as the core, ability level as the identifier, and scenario tags as the semantic background. This graph can be expressed in the form of a graph structure, vector matrix, or multi-layer knowledge network, providing a good foundation for visualization and data analysis. For example, under the "communication and collaboration ability" dimension, the graph shows that the doctor reaches a "high" level in the "multiple injured patients + multi-person collaboration" scenario, but only a "medium" level in the "high noise + visual occlusion" scenario. This structured representation can intuitively reveal the doctor's strengths and weaknesses in different task contexts, thus providing multi-dimensional support for personalized training path planning, job matching analysis, and ability growth suggestions.

[0062] S205. Based on preset simulated maritime treatment scenario parameters, evaluation results, and competency profile maps, calculate the competency improvement priority and task urgency priority of the target general practitioner in each learning module; To facilitate the transition from competency assessment results to personalized learning task recommendations, step S205 introduces a task- and competency-driven priority calculation mechanism to determine the competency improvement priorities and task urgency priorities for the target general practitioner across various learning modules. Step S205 uses preset simulated maritime mission parameters, the doctor's competency assessment results, and competency profile maps as inputs. By analyzing the gap between the doctor's current competency status and task requirements, as well as the urgency of the task itself and the label matching degree, a dual-channel priority assessment model is constructed. The process may include the following steps: calculating the task urgency corresponding to preset simulated maritime treatment scenario parameters, whereby the task urgency reflects the urgency of the target general practitioner to improve the corresponding treatment capabilities for each treatment capability dimension in different treatment tasks under the preset simulated maritime treatment scenario parameters; calculating the capability improvement space coefficient for each learning module of the target general practitioner based on the capability score of each treatment capability dimension in the evaluation results; determining the capability improvement priority based on the capability improvement space coefficient; and analyzing the correlation between the treatment scenario label and treatment task corresponding to each learning module based on the label-capability-scenario association structure unit in the capability profile map, obtaining the task relevance index for each learning module, and weighting the task relevance index according to the task urgency to obtain the task urgency priority.

[0063] In the specific implementation process, the system calls the task parameter management module to parse the preset simulated maritime treatment scenario parameters used for training simulation. The preset simulated maritime treatment scenario parameters include multiple structured fields such as task type (e.g., emergency rescue for sudden chronic diseases at sea or emergency response to multiple external injuries under high-intensity sea conditions), resource conditions (e.g., limited communication, single-person operation, simplified equipment), time window limits (e.g., completing initial treatment within 10 minutes), and task objectives (e.g., maintaining airway patency or achieving hemostasis and immobilization). Different combinations of task parameters represent different task complexities and scenario stress states.

[0064] To transform the parameters of the pre-set simulated maritime treatment scenario into quantifiable indicators for priority assessment, the system established a task urgency calculation model based on expert experience modeling and historical task performance data. The task urgency calculation model employs a multi-dimensional weighted scoring method, constructing evaluation factors from the perspectives of task timeliness, severity of patient condition, resource scarcity, and task continuity. These factors are then normalized to synthesize the task urgency value. Task urgency is defined as a continuous numerical value, typically between 0 and 1. A higher value indicates greater importance of the task within the current training cycle, and doctors should prioritize improving the skills involved in completing this task.

[0065] Task urgency serves not only as a priority indicator for the task itself but also needs to be further mapped to various treatment capability dimensions to measure the urgency of training different capability dimensions in the treatment task. To this end, the system utilizes the capability requirement weight vector already constructed in the label-capability mapping equation, combining the scenario labels involved in the treatment task into the mapping equation to obtain the capability weight structure corresponding to the treatment task. Subsequently, the capability weight vector is weighted dimension-by-dimensionally with the task urgency to generate a capability dimension-oriented urgency response vector, where each element represents the urgency of improving the corresponding capability dimension in the task. For example, if the urgency of the task "high-speed shaking + multiple traumas + solo operation" is 0.85, and its capability weight structure is [0.3, 0.5, 0.2], then the corresponding urgency response is [0.255, 0.425, 0.17], indicating that the capability is the one that needs the highest priority for improvement in the current task.

[0066] To achieve precise allocation of learning resources and individualized matching of training content, the system further identifies the capability gap structure of target general practitioners across various treatment capability dimensions and calculates the capability improvement space coefficient for each learning module. The capability improvement space coefficient quantifies the growth potential of a physician in a capability dimension associated with a learning module, reflecting the potential and necessity for capability improvement. This may include the following steps: based on the capability scores of each treatment capability dimension in the assessment results, all treatment capability dimensions are arranged from highest to lowest score to obtain a treatment capability sequence; the score difference between the first and second treatment capability dimensions in the treatment capability sequence is calculated, where the first and second treatment capability dimensions are any two adjacent treatment capability dimensions in the sequence; the position between the target first and second treatment capability dimensions in the treatment capability sequence where the score difference is greater than a preset difference threshold is determined as a capability development breakpoint; the treatment capability sequence is divided into multiple capability level intervals based on the capability development breakpoints; and the capability improvement space coefficient for each learning module is determined based on the capability level intervals and the treatment capability dimension of each learning module.

[0067] The system constructs a ranked sequence of treatment capabilities based on the capability scores obtained in the assessment phase for each treatment capability dimension. This sequence reflects the relative strengths and weaknesses of the target general practitioner across each dimension. In practice, the system arranges all treatment capability dimensions in descending order of their corresponding capability scores, forming an ordered sequence. This ranking not only provides an intuitive representation of the doctor's current capability structure but also provides an index for identifying capability gaps. For example, if a doctor's scores in the four dimensions of "Operational Stability," "Rapid Judgment," "Communication and Collaboration," and "Stress Coping" are 0.86, 0.81, 0.65, and 0.48, respectively, the ranked treatment capability sequence would be: [Operational Stability, Rapid Judgment, Communication and Collaboration, Stress Coping], with the corresponding score sequence being: [0.86, 0.81, 0.65, 0.48].

[0068] After obtaining the treatment capability sequence, the system further calculates the capability score difference between any two adjacent treatment capability dimensions in the sequence to identify significant gaps in the capability structure, i.e., capability development breakpoints. The score difference reflects the degree of capability gap between doctors between adjacent capability dimensions; the larger the difference, the more significant the capability difference. The system calculates the score difference for each pair of adjacent capability dimensions and compares the difference with a preset difference threshold. This threshold is set by training experts or obtained through historical data statistics and represents the system's judgment standard for the significance of capability gaps. When the score difference exceeds the threshold, the system marks the position as a capability development breakpoint. For example, if the preset difference threshold is 0.12, in the above score sequence, the difference between 0.81 and 0.65 is 0.16, and the difference between 0.65 and 0.48 is 0.17, both exceeding the threshold. Therefore, the system identifies two capability development breakpoints, located between rapid judgment and communication / collaboration, and between communication / collaboration and stress coping, respectively.

[0069] Based on the identified skill development breakpoints, the system divides the entire treatment skill sequence into multiple skill level intervals, each representing a skill development stage or skill level band. The principle behind this interval division is to group dimensions with similar skill performance into the same level, identifying which areas the target physician possesses relatively mature skills and which areas have significant weaknesses. During the division process, the system uses breakpoint locations as boundaries to group adjacent skill dimensions into the same interval. For example, in the aforementioned example, the skill sequence is divided into three skill level intervals: the first level is [operational stability, rapid judgment], the second level is [communication and collaboration], and the third level is [stress management]. This hierarchical structure provides a foundation for matching subsequent learning modules with skill levels, enabling the system to determine whether a learning module is located in a high-level interval, a developmental edge interval, or a skill weakness interval, thus providing a structural basis for calculating the skill improvement potential coefficient.

[0070] Finally, based on the competency level intervals and the treatment competency dimensions of each learning module, the potential improvement coefficient for the target general practitioner's competency in each learning module is determined. This can include the following steps: determining the number of times the target treatment competency dimension of the target learning module spans across the competency level intervals, obtaining the learning module span coefficient, where the target learning module is any learning module; based on the learning module span coefficient, calculating the level difference between the highest and lowest competency level intervals within the target learning module, and determining the module imbalance degree as the ratio of the level difference value to the preset number of treatment competency dimensions in the target learning module; and obtaining the competency improvement potential coefficient of the learning module by multiplying the module imbalance degree by the module span coefficient.

[0071] After the aforementioned ability level intervals are divided, to further characterize the distribution and improvement potential of the ability dimensions involved in different learning modules, the system calculates the ability improvement space coefficient for each learning module based on the span of the associated therapeutic ability dimension within the ability level structure. The ability improvement space coefficient measures the structural complexity and uneven development of the ability dimensions within a learning module, and is a key indicator for subsequent training task ranking and resource allocation. The calculation of the ability improvement space coefficient includes three core steps: determining the span coefficient of the learning module, calculating its module imbalance, and finally obtaining the ability improvement space coefficient through the product of the two. The implementation methods for each step are explained below.

[0072] After acquiring the set of target therapeutic ability dimensions corresponding to each learning module, the system needs to determine the number of levels these ability dimensions span within the defined ability level intervals to determine the span coefficient of that learning module. The span coefficient reflects the breadth of the hierarchical distribution of ability dimensions within the learning module, i.e., whether the ability distribution is concentrated or dispersed. In practice, the system first retrieves the ability level interval number where each therapeutic ability dimension is located, extracts the maximum and minimum level numbers from all dimensions, and calculates the difference between the two plus one to obtain the number of level spans, which is the span coefficient. For example, if the learning module involves three ability dimensions—"operational stability," "communication and collaboration," and "stress coping"—and they are at levels 1, 2, and 3 respectively, then the span coefficient is 3 (i.e., 3-1+1). The larger the span coefficient, the greater the difference in the developmental stages of the abilities involved in the module, requiring both primary and advanced ability goals to be considered during training, thus increasing the difficulty of task design.

[0073] Based on the determined span coefficient, the system further calculates the segment imbalance degree of the learning segment to quantify the asymmetry of the ability distribution within the segment. The calculation principle of segment imbalance degree is as follows: within the same learning segment, if the gap between the hierarchical levels of ability dimensions is large and the number of dimensions is small, the ability structure of the segment is more unbalanced, and the distribution of training resources is more difficult to match accurately. In specific implementation, the system first extracts the difference between the interval number of the highest ability level and the interval number of the lowest ability level within the learning segment to obtain the level gap value; then, it divides the level gap value by the number of healing ability dimensions covered by the learning segment to obtain the imbalance degree of the learning segment. For example, if the learning segment contains two ability dimensions, located between the 1st and 3rd levels, the level gap is 2, and the imbalance degree is 2 / 2 = 1. If the number of dimensions increases to 4, but the gap remains 2, the imbalance degree drops to 0.5, demonstrating that increasing the number of dimensions helps to buffer the training conflict caused by structural imbalance.

[0074] After obtaining the span coefficient and the segment imbalance degree, the system multiplies them to obtain the final capability improvement space coefficient for the learning segment. The capability improvement space coefficient comprehensively reflects the horizontal span and vertical imbalance degree of capability development within the segment, and is an important basis for evaluating the training priority of that segment. The calculation formula is: Capability Improvement Space Coefficient = Span Coefficient × Segment Imbalance Degree. A larger capability improvement space coefficient indicates greater differences in capability development and a more unbalanced structure within the learning segment, indicating greater training potential for doctors in that segment, and the system should prioritize recommending training tasks related to that segment. For example, if the learning segment's span coefficient is 3 and its imbalance degree is 0.8, then its capability improvement space coefficient is 2.4; while another segment has a span coefficient of 2 but an imbalance degree of 1.1, its coefficient is 2.2, with the former having a slightly higher priority.

[0075] After calculating the capability enhancement space coefficient for each learning module, the system further determines the capability enhancement priority for each module based on this coefficient. This clarifies the areas where doctors should prioritize training to improve their skills within the current cycle. The essence of capability enhancement priority is to rank modules where doctors have significant room for development within their overall capability structure and where the training structure is highly complex. This ensures that training resources are prioritized for capability dimension combinations where there is both potential for improvement and structural contradictions. The system normalizes the capability enhancement space coefficients corresponding to all learning modules to construct a comparable ranking vector, and then assigns priority labels in descending order of value. The normalization operation uses a min-max standardization formula, placing all capability enhancement space coefficients between 0 and 1 to avoid ranking distortion due to differences in the original numerical scale. The final ranking result is the capability enhancement priority sequence for each learning module, providing structured input for subsequent training task recommendations. For example, if the capability enhancement space coefficient for the "Stress Management + Communication & Collaboration" module is 2.4, ranking first among all learning modules, the system assigns it the highest capability enhancement priority, labeled "P1".

[0076] Building upon the priority of generating capabilities, the system also needs to assess the task urgency priority of each learning module within the context of the current simulated task, ensuring that the allocation of learning modules not only matches the doctor's capability structure but also is highly relevant to the current task's execution requirements. The system first calls upon the pre-constructed tag-capability-scenario association structure unit in the capability profile map to extract the set of treatment scenario tags involved in each learning module and performs a semantic comparison with the scenario parameters of the currently preset simulated maritime task. The semantic comparison uses a tag matching degree calculation mechanism, which calculates the ratio of the number of intersections to the number of unions between the learning module tag set and the task tag set, resulting in a task relevance index reflecting tag relevance. A higher task relevance index indicates a higher degree of adaptation between the training content of the learning module and the capabilities required for the current task. Subsequently, the system performs a weighted multiplication of this task relevance index with the previously calculated task urgency level, obtaining the task urgency priority of each learning module under the current task conditions. This priority not only reflects the semantic fit of the training content but also incorporates the urgency of the task, forming a comprehensive ranking result driven by the scenario and supported by the capability structure. For example, if the relevance index of the learning section is 0.75 and the urgency of the current task is 0.88, then the urgency priority value of the task in this section is 0.66.

[0077] S206. All learning modules are allocated based on the priority of ability improvement and the urgency of tasks.

[0078] After calculating the priority of ability improvement and the priority of task urgency for each learning module, it is necessary to further rank and allocate resources for all learning modules based on these two dimensions. In practice, the system first standardizes the ability improvement priority and task urgency priority of each learning module into a unified-dimensional score value, and constructs a binary ranking vector for each module, forming a two-dimensional priority matrix. Based on this, the system introduces a fusion priority scoring function to combine the two dimensions into a single comprehensive ranking index. The fusion priority scoring function can adopt a weighted addition model, in the form: Comprehensive Priority = α × Ability Improvement Priority Score + β × Task Urgency Priority Score, where α and β are weight coefficients, reflecting the system's emphasis on long-term ability growth and short-term task response in the current training phase, respectively. The weight coefficients can be set by administrators or automatically adjusted according to the doctor's current training cycle; for example, prioritizing task urgency in the initial stage and emphasizing ability improvement priority in the ability assessment stage.

[0079] After the integrated score is calculated, the system sorts all learning modules from highest to lowest overall priority, resulting in a final learning module allocation sequence. Subsequently, the system combines training time resources, doctors' learning pace preferences, and the complexity of the module content to schedule the pace and allocate resources, generating a structured training schedule and task list. The system can also display the ranking results in a graph format, comparing them with a competency profile graph, enabling doctors to intuitively understand the matching logic and growth path between the current learning task and their own competency structure.

[0080] Please see Figure 3 This is a schematic diagram of the structure of a learning module allocation system for general practitioners provided in an embodiment of this application. The learning module allocation system 300 for general practitioners specifically includes: The first acquisition module 301 is used to acquire treatment behavior data of the target general practitioner under preset simulated maritime treatment scenario parameters. The preset simulated maritime treatment scenario parameters include sea environment parameters, treatment tasks, and treatment scenario tag set. The second acquisition module 302 is used to acquire multiple dimensions of treatment capabilities for preset simulated maritime treatment scenario parameters; The assessment module 303 is used to assess the competency scores of the target general practitioners in each treatment capability dimension based on sea state environmental parameters, treatment scenario label sets, and treatment behavior data, and to obtain the assessment results. Module 304 is used to construct a competency profile map of the target general practitioners based on the assessment results; Calculation module 305 is used to calculate the priority of ability improvement and task urgency of target general practitioners in each learning module based on preset simulated maritime treatment scenario parameters, evaluation results and ability profile map; The allocation module 306 is used to allocate all learning modules based on the priority of ability improvement and the urgency of tasks.

[0081] Optionally, the evaluation module 303 is specifically used to: construct a label-capability mapping equation based on sea state environmental parameters and treatment scenario label set, the label-capability mapping equation being used to characterize the importance of each treatment capability dimension under preset simulated sea treatment scenario parameters; and calculate the capability score of the target general practitioner in each treatment capability dimension based on treatment behavior data and the label-capability mapping equation to obtain the evaluation result.

[0082] Optionally, the evaluation module 303 is further specifically used for: determining the first scenario difficulty level of each treatment scenario label in the treatment scenario label set based on preset rules; determining a disturbance factor based on sea state environmental parameters, calculating the degree of influence of sea state environmental parameters on treatment behavior data based on the disturbance factor, and obtaining a disturbance adjustment factor; adjusting the first scenario difficulty level of each treatment scenario label based on the disturbance adjustment factor to obtain a second scenario difficulty level of each treatment scenario label; inputting the second scenario difficulty level of each treatment scenario label into a preset treatment capability dimension response function family to calculate the capability requirement weight vector corresponding to each treatment scenario label, wherein each treatment capability dimension response function in the preset treatment capability dimension response function family contains the treatment scenario label corresponding to the treatment capability dimension response function; and constructing a label-capability mapping equation based on the treatment scenario label and the capability requirement weight vector corresponding to the treatment scenario label.

[0083] Optionally, module 304 is specifically used to: compare the ability score of each treatment ability dimension in the assessment results with the preset ability standard, determine the relative ability level of the target general practitioner in each treatment ability dimension, and generate an ability level label set; construct a label-ability-scenario association structure unit according to the treatment scenario label corresponding to each relative ability level label in the ability level label set; and cluster and integrate the label-ability-scenario association structure unit according to the treatment ability dimension to construct an ability profile map.

[0084] Optionally, the calculation module 305 is specifically used for: calculating the task urgency corresponding to the preset simulated maritime treatment scenario parameters. The task urgency reflects the urgency of the target general practitioner for improving the corresponding treatment capabilities in each treatment capability dimension under the preset simulated maritime treatment scenario parameters; calculating the capability improvement space coefficient for each learning module of the target general practitioner based on the capability score of each treatment capability dimension in the evaluation results; determining the capability improvement priority based on the capability improvement space coefficient; and analyzing the correlation between the treatment scenario label and treatment task corresponding to each learning module based on the label-capability-scenario association structure unit in the capability profile map, obtaining the task relevance index of each learning module, and weighting the task relevance index according to the task urgency to obtain the task urgency priority.

[0085] Optionally, the calculation module 305 is further specifically used for: based on the ability score of each treatment ability dimension in the evaluation results, arranging all treatment ability dimensions in descending order of ability score to obtain a treatment ability sequence; calculating the score difference between the first treatment ability dimension and the second treatment ability dimension in the treatment ability sequence, where the first treatment ability dimension and the second treatment ability dimension are any two adjacent treatment ability dimensions in the treatment ability sequence; determining the position between the target first treatment ability dimension and the target second treatment ability dimension in the treatment ability sequence where the score difference is greater than a preset difference threshold as an ability development breakpoint; dividing the treatment ability sequence into multiple ability level intervals based on the ability development breakpoints; and determining the target general practitioner's ability improvement space coefficient for each learning module based on the ability level intervals and the treatment ability dimension of each learning module.

[0086] Optionally, the calculation module 305 is further specifically used for: determining the number of times the target therapeutic ability dimension of the target learning module spans across the ability level interval, obtaining the learning module span coefficient, wherein the target learning module is any learning module; based on the learning module span coefficient, calculating the level difference value between the highest and lowest ability level intervals within the target learning module, and determining the ratio of the level difference value to the number of preset therapeutic ability dimensions of the target learning module as the module imbalance degree; and obtaining the ability improvement space coefficient of the learning module based on the product of the module imbalance degree and the module span coefficient.

[0087] It should be noted that the above embodiments of the apparatus are only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments, which will not be repeated here.

[0088] This embodiment also discloses an electronic device, as shown in the reference. Figure 4 The electronic device may include: at least one processor 401, at least one communication bus 402, a user interface 403, a network interface 404, and at least one memory 405. The communication bus 402 is used to enable communication between these components. The user interface 403 may include a display screen or a camera; optionally, the user interface 403 may also include a standard wired interface or a wireless interface. The network interface 404 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0089] The processor 401 may include one or more processing cores. The processor 401 connects to various parts of the server using various interfaces and lines, and performs various server functions and processes data by running or executing instructions, programs, code sets, or instruction sets stored in memory 405, and by calling data stored in memory 405. Optionally, the processor 401 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). The processor 401 may integrate one or a combination of several of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor 401.

[0090] The memory 405 may include random access memory (RAM) or read-only memory. Optionally, the memory 405 may include a non-transitory computer-readable storage medium. The memory 405 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory 405 may also be at least one storage device located remotely from the aforementioned processor 401. Figure 4 As shown, the memory 405, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for allocating learning modules for general practitioners.

[0091] exist Figure 4 In the electronic device shown, the user interface 403 is mainly used to provide an input interface for the user and to obtain the user input data; while the processor 401 can be used to call an application stored in the memory 405 for a method of allocating learning modules for general practitioners. When executed by one or more processors 401, the electronic device performs one or more methods as described in the above embodiments.

[0092] It should be noted that, for the foregoing method embodiments, for the sake of simplicity, they are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, because according to this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for this application. In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces. The indirect coupling or communication connection of devices or units can be electrical or other forms.

[0093] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0094] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory 405 and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory 405 includes various media capable of storing program code, such as a USB flash drive, external hard drive, magnetic disk, or optical disk.

[0095] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Other embodiments of this disclosure will be readily apparent to those skilled in the art upon consideration of the disclosure in this specification. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described in this disclosure. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

Claims

1. A method for allocating learning modules for general practitioners, characterized in that, When applied to a server, the method includes: Acquire treatment behavior data of target general practitioners under preset simulated maritime treatment scenario parameters, wherein the preset simulated maritime treatment scenario parameters include sea environment parameters, treatment tasks, and treatment scenario tag sets; Obtain multiple dimensions of treatment capabilities from the preset simulated maritime treatment scenario parameters; Based on the sea state environmental parameters, the treatment scenario label set, and the treatment behavior data, the target general practitioner's ability score in each treatment ability dimension is evaluated to obtain the evaluation result; Based on the assessment results, a competency profile of the target general practitioner is constructed. Based on the preset simulated maritime treatment scenario parameters, the evaluation results, and the competency profile map, the competency improvement priority and task urgency priority of the target general practitioner in each learning module are calculated. All learning modules are allocated based on the priority of capability enhancement and the priority of task urgency.

2. The method according to claim 1, characterized in that, The evaluation results, obtained by assessing the target general practitioner's competency score in each of the treatment competency dimensions based on the sea state environmental parameters, the treatment scenario label set, and the treatment behavior data, specifically include: Based on the sea state environmental parameters and the treatment scenario label set, a label-capability mapping equation is constructed. The label-capability mapping equation is used to characterize the importance of each treatment capability dimension under the preset simulated marine treatment scenario parameters. Based on the treatment behavior data and the label-ability mapping equation, the target general practitioner's ability score for each treatment ability dimension is calculated to obtain the evaluation result.

3. The method according to claim 2, characterized in that, The construction of the label-capability mapping equation based on the sea state environmental parameters and the treatment scenario label set specifically includes: The first scene difficulty level of each treatment scene tag in the treatment scene tag set is determined based on preset rules; Based on the sea state environmental parameters, a disturbance factor is determined, and based on the disturbance factor, the degree of influence of the sea state environmental parameters on the treatment behavior data is calculated to obtain a disturbance regulation factor; The first scene difficulty level of each treatment scene label is adjusted based on the perturbation adjustment factor to obtain the second scene difficulty level of each treatment scene label; The second scene difficulty level of each treatment scene label is input into a preset treatment ability dimension response function family to calculate the ability requirement weight vector corresponding to each treatment scene label. Each treatment ability dimension response function in the preset treatment ability dimension response function family contains the treatment scene label corresponding to the treatment ability dimension response function. A label-capability mapping equation is constructed based on the treatment scenario label and the capability requirement weight vector corresponding to the treatment scenario label.

4. The method according to claim 3, characterized in that, The construction of the competency profile map of the target general practitioner based on the assessment results specifically includes: The ability score of each treatment ability dimension in the assessment results is compared with the preset ability standard to determine the relative ability level of the target general practitioner in each treatment ability dimension, and an ability level label set is generated. Construct a tag-ability-scenario association structure unit based on the treatment scenario tag corresponding to each relative ability level tag in the ability level tag set; The tag-ability-scenario associated structural units are clustered and integrated according to the treatment ability dimension to construct the ability profile map.

5. The method according to claim 4, characterized in that, The calculation of the target general practitioner's competency improvement priority and task urgency priority in each learning module, based on the preset simulated maritime treatment scenario parameters, the evaluation results, and the competency profile map, specifically includes: Calculate the task urgency corresponding to the preset simulated maritime treatment scenario parameters. The task urgency is used to reflect the urgency of the target general practitioner to improve the treatment capabilities corresponding to each of the treatment capability dimensions in different treatment tasks under the preset simulated maritime treatment scenario parameters. Based on the ability score of each of the treatment ability dimensions in the assessment results, calculate the potential improvement coefficient of the target general practitioner for each of the learning modules; The priority of capability enhancement is determined based on the capability enhancement space coefficient. Based on the tag-ability-scenario association structure unit in the capability profile map, the degree of association between the treatment scenario tag corresponding to each learning module and the treatment task is analyzed to obtain the task relevance index for each learning module. The task relevance index is then weighted according to the task urgency to obtain the task priority.

6. The method according to claim 5, characterized in that, The step of calculating the potential improvement coefficient of the target general practitioner for each learning module based on the ability score of each treatment ability dimension in the evaluation results specifically includes: Based on the ability score of each of the treatment ability dimensions in the evaluation results, all the treatment ability dimensions are arranged from high to low according to the ability score to obtain a treatment ability sequence. The score difference between the first treatment ability dimension and the second treatment ability dimension in the treatment ability sequence is calculated. The first treatment ability dimension and the second treatment ability dimension are any two adjacent treatment ability dimensions in the treatment ability sequence. The position between the target first treatment ability dimension and the target second treatment ability dimension in the treatment ability sequence, where the score difference is greater than a preset difference threshold, is determined as the ability development breakpoint. Based on the aforementioned capability development breakpoints, the therapeutic capability sequence is divided into multiple capability level intervals; Based on the capability level range and the treatment capability dimension of each learning module, the target general practitioner's capability improvement potential coefficient for each learning module is determined.

7. The method according to claim 6, characterized in that, The determination of the target general practitioner's potential improvement coefficient for each learning module, based on the capability level interval and the treatment capability dimension of each learning module, specifically includes: The number of times the target therapeutic ability dimension of the target learning module spans the ability level interval is determined to obtain the learning module span coefficient, wherein the target learning module is any of the learning modules. Based on the learning segment span coefficient, the level difference value between the highest and lowest ability level intervals within the target learning segment is calculated, and the ratio of the level difference value to the preset number of treatment ability dimensions of the target learning segment is determined as the segment imbalance degree. The capability enhancement potential coefficient of the learning segment is obtained by multiplying the segment imbalance degree by the segment span coefficient.

8. A learning module allocation system for general practitioners, characterized in that, include: The first acquisition module is used to acquire treatment behavior data of the target general practitioner under preset simulated maritime treatment scenario parameters, wherein the preset simulated maritime treatment scenario parameters include sea environment parameters, treatment tasks and treatment scenario tag set; The second acquisition module is used to acquire multiple treatment capability dimensions of the preset simulated maritime treatment scenario parameters; The assessment module is used to evaluate the target general practitioner's competency score in each of the treatment competency dimensions based on the sea state environmental parameters, the treatment scenario label set, and the treatment behavior data, and to obtain the assessment result; The construction module is used to construct a competency profile of the target general practitioner based on the assessment results; The calculation module is used to calculate the priority of the target general practitioner's ability improvement and the priority of the task urgency in each learning module based on the preset simulated maritime treatment scenario parameters, the evaluation results and the ability profile map; The allocation module is used to allocate all the learning modules based on the capability enhancement priority and the task urgency priority.

9. An electronic device, characterized in that, include: One or more processors and memory; The memory is coupled to the one or more processors, the memory being used to store computer program code, the computer program code including computer instructions, the one or more processors invoking the computer instructions to cause the electronic device to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that, When the instructions are executed on an electronic device, the electronic device causes the electronic device to perform the method as described in any one of claims 1-7.

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