Knowledge learning management method and device based on data analysis

Abstract

The invention discloses a knowledge learning management method and device based on data analysis, and the method comprises the steps: obtaining a scene behavior data set, the scene behavior data set comprises the behavior data of each pharmacy employee in different business scenes, and the business scenes at least comprise a drug recommendation scene, a drug taboo reminding scene and a special medication consultation scene; constructing a behavior model according to the scenarized behavior data set, and extracting behavior feature information of the drugstore employees in different business scenes through the behavior model; determining a knowledge point set and a knowledge blind area which need to be mastered by the drugstore employee in each business scene based on the behavior feature information; constructing a knowledge graph according to the scenarized behavior data set, the knowledge blind area, the knowledge point set and the association relationship between the knowledge point set and the business scene; and based on the knowledge graph, determining professional learning contents of the drugstore employees and priorities corresponding to the professional learning contents. The professional knowledge level of drugstore employees can be comprehensively improved.

Description

Technical Field

[0001] The present application relates to the field of data analysis, and specifically to a knowledge learning management method and device based on data analysis. Background Art

[0002] With the rapid development of information technology, big data and artificial intelligence technologies have gradually penetrated into various industries, especially in the retail industry. In the field of retail pharmacies, improving the professional quality and service level of pharmacy employees is not only related to the competitiveness of enterprises, but also directly affects public health and drug safety.

[0003] In the prior art, there are already some solutions for knowledge management and learning of pharmacy employees. For example, standardized training courses are provided for pharmacy employees through online learning systems, which improves the convenience of employee learning to a certain extent.

[0004] However, although the online learning system provides rich course resources, it only allocates fixed learning content according to positions, resulting in uneven professional knowledge levels of pharmacy employees, making it difficult to comprehensively improve the professional knowledge levels of pharmacy employees. Summary of the invention

[0005] The present application provides a knowledge learning management method and device based on data analysis, which is used to improve the professional knowledge level of pharmacy employees.

[0006] In a first aspect of the present application, a knowledge learning management method based on data analysis is provided, which is applied to a server, and the method comprises: Obtain a scenario-based behavior data set, which includes the behavior data of each pharmacy employee in different business scenarios, and the business scenarios include at least drug recommendation scenarios, drug contraindication reminder scenarios, and special medication consultation scenarios; build a behavior model based on the scenario-based behavior data set, and extract the behavior characteristic information of pharmacy employees in different business scenarios through the behavior model; determine the set of knowledge points and knowledge blind spots that pharmacy employees need to master in each business scenario based on the behavior characteristic information; build a knowledge graph based on the scenario-based behavior data set, knowledge blind spots, knowledge point sets, and the relationship between knowledge point sets and business scenarios; based on the knowledge graph, determine the professional learning content of pharmacy employees and the corresponding priority of professional learning content.

[0007] Optionally, based on the behavioral characteristic information, determine the set of knowledge points that the pharmacy employees need to master in the business scenario and the knowledge blind spots of the pharmacy employees, including: Based on the behavioral characteristic information, the set of knowledge points that pharmacy employees need to master in business scenarios is determined through a preset knowledge demand prediction model. The scenario-based behavioral data set is correlated with the knowledge point set to determine the knowledge blind spots of pharmacy employees. The knowledge blind spots are the set of knowledge points that the pharmacists' mastery level does not meet the preset requirements.

[0008] Optionally, the scenario-based behavior dataset is associated with the knowledge point set to determine the knowledge blind spots of pharmacy employees, including: The behavioral feature information is converted into a behavioral feature vector, and the knowledge point set is converted into a knowledge feature vector; the association strength matrix between the behavioral feature vector and the knowledge feature vector is obtained through a preset bidirectional attention model based on the behavioral feature vector and the knowledge feature vector; confidence scores are performed on the association values ​​in the association strength matrix that exceed a preset association threshold; and the knowledge points corresponding to the association values ​​whose confidence scores are less than the preset scoring threshold are determined as knowledge blind spots.

[0009] Optionally, a knowledge graph is constructed based on scenario-based behavior data sets, knowledge blind spots, knowledge point sets, and the relationship between knowledge point sets and business scenarios, including: Mark the mastery scores of all knowledge points in the knowledge blind spots in the knowledge point set to obtain the target knowledge point set; based on the scenario-based behavior data set and the target knowledge point set, analyze the usage characteristics of the target knowledge point set in the business scenario and determine the association between the target knowledge point set and the business scenario; construct a knowledge graph based on the target knowledge point set and the association.

[0010] Optionally, after constructing a knowledge graph according to the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the association between the knowledge point set and the business scenario, the method further includes: Obtain the problem-solving solutions and experience data of target pharmacy employees in business scenarios, where the target pharmacy employees are pharmacy employees whose problem-solving abilities meet preset requirements; annotate the problem-solving solutions and experience data according to knowledge points to obtain annotated results, associate the annotated results with business scenarios and knowledge point sets to obtain an experience set; and supplement the knowledge point set in the knowledge graph based on the experience set.

[0011] Optionally, after determining the professional learning content for pharmacy employees and the priority corresponding to the professional learning content based on the knowledge graph, the method further includes: In response to the learning requests of pharmacy employees, learning tasks are created. The learning requests carry business scenarios, knowledge point sets, and knowledge blind spots. The knowledge conversion effects produced after the pharmacy employees complete the learning tasks are obtained. The knowledge conversion effects include the application of knowledge points and task completion by the pharmacy employees. The priority of the learning content is adjusted according to the knowledge conversion effects.

[0012] Optionally, a knowledge learning management method based on data analysis is applied to a server, the method also including: obtaining emotional interaction data of pharmacy employees, and calculating the emotional values ​​of pharmacy employees through a preset emotional prediction model based on the emotional interaction data, the emotional interaction data including sound data and expression information; based on the emotional value, judging the learning status of the pharmacy employees, the learning status including a negative state, an anxious state and a positive state; when the learning status is a negative state or an anxious state, generating a target learning task based on the professional learning content and the priority corresponding to the professional learning content, the target learning task being the learning content whose learning difficulty and learning form meet the preset requirements.

[0013] In a second aspect of the present application, a knowledge learning management device based on data analysis is provided, comprising: The acquisition module is used to obtain the scenario-based behavior data set, which includes the behavior data of each pharmacy employee in different business scenarios. The business scenarios include at least drug recommendation scenarios, drug contraindication reminder scenarios, and special medication consultation scenarios. The extraction module is used to build a behavior model based on the scenario-based behavior data set, and extract the behavior feature information of pharmacy employees in different business scenarios through the behavior model; The first determination module is used to determine the knowledge points and knowledge blind spots that pharmacy employees need to master in various business scenarios based on the behavioral feature data; A construction module is used to construct a knowledge graph based on scenario-based behavior data sets, knowledge blind spots, knowledge point sets, and the association between knowledge point set business scenarios; The second determination module is used to determine the professional learning content of pharmacy employees and the corresponding priority of the professional learning content based on the knowledge graph.

[0014] In the third aspect of the present application, an electronic device is provided, 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 so that the electronic device executes any one of the methods described above.

[0015] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, any of the methods described above is executed.

[0016] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Through in-depth mining of scenario-based behavioral data and accurate extraction of behavioral characteristics, the knowledge gaps of pharmacy employees in actual business scenarios are dynamically captured, and the sets of knowledge points and knowledge blind spots that need to be mastered are accurately identified. Further combining the relationship between scenario-based behavioral data and business scenarios, a knowledge graph is constructed to optimize the organization and presentation of the knowledge structure, and dynamically display the relationship between knowledge points and their applicability in the scenario. Based on the knowledge graph, the learning content of different pharmacy employees is determined and the learning content is prioritized, which enables accurate recommendations of learning content based on the professional knowledge level and business needs of pharmacy employees, improves the pertinence of professional knowledge learning of pharmacy employees, and thus comprehensively improves the professional knowledge level of pharmacy employees.

[0017] 2. The bidirectional attention model is used to mine the complex relationship between behavioral characteristics and knowledge characteristics, and dynamically capture the degree of knowledge mastery of pharmacy employees; low-quality association values ​​are filtered out in combination with confidence scores to ensure the scientificity and accuracy of knowledge blind spot identification. It can comprehensively locate the weak links in the knowledge mastery of pharmacy employees and provide a reliable basis for the recommendation of personalized learning content.

[0018] 3. By quantifying the degree of mastery of knowledge blind spots, we can accurately extract key knowledge points that pharmacy employees need to master, analyze the usage characteristics of target knowledge points in the context of business scenarios, and dynamically model their association paths with business scenarios to ensure that the construction of the knowledge graph is targeted and has practical value. The constructed knowledge graph not only intuitively presents the relationship between knowledge points and their role in business scenarios, but also provides a basis for the recommendation and priority sorting of subsequent learning content.

[0019] 4. Through the introduction of empirical data, the association of knowledge points in the knowledge graph has been further expanded and supplemented, overcoming the limitations of relying solely on static knowledge point analysis and realizing the dynamic update and self-optimization of the knowledge graph. By integrating empirical data with the knowledge graph, the content dimension of the knowledge points is enriched, and the applicability of the knowledge graph in actual business scenarios is improved. It can more accurately guide pharmacy employees to solve practical problems, significantly improve learning efficiency and business capabilities, and further help improve the overall operation efficiency and service quality of pharmacies.

[0020] 5. Through practical evaluation of the understanding and application capabilities of pharmacy employees, the quantitative measurement of learning effects and the closed feedback loop are realized. Furthermore, the priority of learning content is dynamically adjusted according to the knowledge conversion effect, so that learning resources are efficiently allocated to key points, thereby accelerating the process of converting knowledge into actual business capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a knowledge learning management method based on data analysis in an embodiment of the present application; Figure 2 It is a structural diagram of a knowledge learning management system based on data analysis in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of an electronic device in an embodiment of the present application.

[0022] Explanation of the accompanying drawings: 201, acquisition module; 202, extraction module; 203, first determination module; 204, construction module; 205, second determination module; 206, labeling module; 207, learning module; 208, generation module; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0023] In order to enable technicians in this field 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 in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0024] In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

[0025] In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0026] Figure 1 It is a flow chart of a knowledge learning management method based on data analysis in an embodiment of the present application.

[0027] See also Figure 1 In an embodiment of the present application, a knowledge learning management method based on data analysis is applied to a server, and the method includes: S101. Obtain a scenario-based behavior data set, where the scenario-based behavior data set includes behavior data of each pharmacy employee in different business scenarios, where the business scenarios include at least a drug recommendation scenario, a drug contraindication reminder scenario, and a special medication consultation scenario; In step S101, the scenario-based behavior data set includes the behavior data of each pharmacy employee in different business scenarios, wherein the business scenarios include at least drug recommendation scenarios, drug contraindication reminder scenarios, and special drug consultation scenarios, and may also include extended scenarios, such as health guidance scenarios, drug promotion scenarios, and complaint handling scenarios. Behavioral data can be collected in a variety of ways, and these methods can be used alone or in combination to ensure that the behavioral characteristics of pharmacy employees are fully covered. For example, through POS system records, the behavior data of pharmacy employees in drug sales and recommendation scenarios, such as the types of drug recommendations, the implementation of promotional activities, etc., can be obtained; through voice interaction data collection (such as recording equipment or intelligent question-and-answer terminals), the communication content between pharmacy employees and customers can be captured, and their language expression ability and application of professional knowledge can be analyzed; through video surveillance analysis, the behavior of pharmacy employees in service scenarios can be recorded, such as whether they actively receive customers or whether they actively recommend drugs. Customer feedback data reflects customers' subjective evaluation of pharmacy employees' services through questionnaires or online evaluations, including scores on professionalism and service attitude. You can also use IoT device data (such as health monitors, etc.) to collect operational behavior data of pharmacy employees using technology-assisted means.

[0028] The collected behavioral data needs to be converted into a scenario-based behavioral data set through steps such as cleaning, classification, and labeling. Data cleaning can be used to remove invalid or noisy data, such as removing background noise or invalid video clips in recordings. Data classification divides behavioral data by scenario, such as drug recommendations, drug contraindication reminders, and special medication consultations. Finally, data labeling is used to add labels to the data (such as whether the behavior is standardized and whether the contraindication reminders are accurate), and the data is integrated into a unified data set to obtain a scenario-based behavioral data set.

[0029] S102. Build a behavior model based on the scenario-based behavior data set, and extract behavior feature information of pharmacy employees in different business scenarios through the behavior model; Based on the scenario-based behavior data set, a behavior model is built for each business scenario to analyze the specific behavior patterns of pharmacy employees in the scenario. According to the data characteristics and scenario requirements of each scenario behavior data in the scenario-based behavior data set, an appropriate modeling method is selected. For example, in the drug recommendation scenario, a behavior model can be built through rule modeling, classification algorithm or association analysis technology behavior model, and the recommendation pattern of pharmacy employees can be identified by analyzing the types of drugs recommended by pharmacy employees, the basis for recommendation (such as customer symptoms, age, gender, etc.) and customer feedback data (such as purchase rate, evaluation, etc.). For example, the model can determine whether pharmacy employees give priority to recommending high-priced drugs, whether they provide alternative solutions, or whether they can adjust the recommended content according to customer needs. In the contraindication reminder scenario, for voice interaction data, natural language processing technology can be used to extract the reminder content; for structured data (such as operation logs), the regularity of reminder behavior can be identified through statistical analysis. Analyze the accuracy, coverage and customer response (such as whether further consultation) of pharmacy employees' reminders of drug contraindications. For example, the model can identify whether pharmacy staff have omitted important contraindication reminders, or whether personalized guidance is provided based on the customer's health information (such as chronic disease history, allergy history). In the special medication consultation scenario, the modeling of this scenario can be achieved through deep learning technology by combining voice data (used to analyze the consultation content) and customer feedback data. The behavioral model analyzes whether the pharmacy staff's medication recommendations for special groups (such as pregnant women, children, and the elderly) or special diseases are standardized, whether specific guidance is provided in combination with the individual needs of customers, and customer feedback on the recommendations. For example, the behavioral model can identify whether the pharmacy staff accurately answered the customer's questions or provided sufficiently detailed medication instructions.

[0030] The behavioral model is used to extract behavioral feature information of pharmacy employees in different scenarios. These features reflect the operating habits, ability levels, and behavioral patterns of pharmacy employees in specific business scenarios. For example, in the drug recommendation scenario, the extracted features may include the accuracy of the recommendation, the rationality of the recommendation basis, the matching degree of customer needs, and the customer's acceptance of the recommendation (such as the purchase rate). In the contraindication reminder scenario, the extracted features may include the comprehensiveness and accuracy of the reminder content, and the customer response. In the special medication consultation scenario, the extracted features may include the standardization of the recommendations, the degree of personalization, and customer satisfaction.

[0031] S103. Determine, based on the behavior characteristic information, a set of knowledge points that the pharmacy employees need to master in the business scenario through a preset knowledge demand prediction model; The construction of the preset knowledge demand prediction model is based on the past business data of the pharmacy, the correlation analysis between the behavioral characteristics of the pharmacy employees and the knowledge mastery, for example, by analyzing the impact of missing knowledge points on the behavioral performance of pharmacy employees, and establishing a mapping relationship between behavioral characteristics and knowledge points. Specifically, the establishment of the knowledge demand prediction model includes the following aspects: defining the knowledge point library that pharmacy employees need to master (such as drug classification, drug contraindications, medication guidance for special populations, etc.), collecting historical data (such as training records, behavioral performance, customer feedback), summarizing the association rules between behavioral characteristics and knowledge needs (such as low recommendation accuracy may be related to "lack of knowledge of drug indications", etc.), and forming preliminary prediction capabilities through machine learning or rule engines.

[0032] In step S102, the behavioral feature information of pharmacy employees in different business scenarios has been extracted through the behavioral model, and the behavioral feature information becomes the direct input of the knowledge demand prediction model. The knowledge demand prediction model obtains the mastery and deficiency of knowledge points of pharmacy employees in specific scenarios by analyzing the correlation between behavioral feature information and the mastery of knowledge points. For example, the model determines whether pharmacy employees lack drug classification knowledge or recommendation logic knowledge by analyzing the accuracy and rationality of the recommendation in the drug recommendation scenario; by analyzing the coverage and accuracy of contraindication reminders, it determines whether they need to supplement drug contraindication classification knowledge or personalized guidance methods; by analyzing the standardization and customer feedback of special medication consultation, it determines whether they need to master the medication knowledge of special populations (such as children, pregnant women, etc.) or disease-related medication plans. The knowledge demand prediction model maps behavioral feature information with knowledge point requirements, and finally outputs a set of knowledge points that pharmacy employees need to master in different business scenarios. For example, for pharmacy employees with average performance in drug recommendation scenarios, the model may output "knowledge of drug indications" and "knowledge of drug substitution options" as priority learning content; for pharmacy employees with low coverage of contraindication reminders, the model may output "knowledge of drug contraindication classification" and "knowledge of drug interactions" as key learning directions; for pharmacy employees who lack special medication consultation capabilities, the model may output knowledge points such as "knowledge of risk assessment of medication for children" and "guidance on medication for chronic diseases in the elderly".

[0033] S104, converting the behavior feature information into a behavior feature vector, and converting the knowledge point set into a knowledge feature vector; in step S104, firstly, the behavior feature information is vectorized, and the behavior features of the pharmacy employees in different business scenarios are converted into numerical behavior feature vectors; secondly, the knowledge point set predicted in step S103 is vectorized, and the knowledge points are converted into numerical knowledge feature vectors. Converting the behavior features and knowledge point features into a unified numerical representation lays the foundation for subsequent two-way matching and analysis.

[0034] In step S102, the scenario-based behavior data set has extracted the behavioral feature information of pharmacy employees in different business scenarios through the behavioral model. The behavioral feature information of pharmacy employees in each business scenario is mapped to a high-dimensional vector space through a numerical method. Among them, each behavioral feature corresponds to one dimension of the vector, and the feature value is normalized or standardized for unified quantification. For example, the recommendation accuracy is normalized to the range of [0,1]. For non-numerical features (such as recommendation method classification, behavior labels, etc.), one-hot encoding (One-Hot Encoding) or embedding vector (Embedding) technology can be used for processing.

[0035] The knowledge point set predicted in step S103 is numerically processed and converted into a knowledge feature vector. The knowledge point set contains knowledge points that pharmacy employees need to master in specific business scenarios, such as "drug classification knowledge", "indication knowledge", "drug alternatives knowledge", etc. During vectorization, each knowledge point is first mapped to a dimension in the vector, and all knowledge points in the scene together constitute a high-dimensional vector space. For a set of knowledge points without priority settings, binary representation (such as a knowledge point that needs to be mastered is set to 1, and if not, it is set to 0) or mean initialization (such as the default value of all knowledge points is set to 1.0 or 0.5) can be used to equally represent the status of each knowledge point. In addition, if there is a hierarchical structure of knowledge points (such as basic knowledge and extended knowledge), the hierarchical relationship between knowledge points can be further represented by nested vectors.

[0036] S105, obtaining a correlation strength matrix between the behavior feature vector and the knowledge feature vector through a preset bidirectional attention model according to the behavior feature vector and the knowledge feature vector; The bidirectional attention model is a deep learning-based mechanism used to establish importance weights between two information sources. For example, for a behavior feature vector, the model can calculate the degree of dependence of each behavior feature (such as recommendation accuracy, comprehensiveness of contraindication reminders) on different knowledge points (such as drug indication knowledge, drug contraindication classification knowledge); at the same time, for a knowledge feature vector, the model will also reversely calculate the importance of each knowledge point to different behavior features. This bidirectional calculation can fully reflect the correlation between behavior and knowledge.

[0037] The output of the bidirectional attention model is an association strength matrix, in which each element is an association value, indicating the strength of the association between a certain behavior feature and a certain knowledge feature. The rows of the matrix represent the different dimensions of the behavior feature vector (such as recommendation accuracy, reminder accuracy, etc.), and the columns represent the different dimensions of the knowledge feature vector (such as drug classification knowledge, personalized reminder knowledge, etc.). The association strength value is usually a normalized value (such as between 0 and 1), indicating the strength of the association relationship.

[0038] S106, performing confidence scoring on the correlation values ​​in the correlation strength matrix that exceed a preset correlation threshold; The correlation value in the correlation strength matrix indicates the strength of the correlation between the behavior feature and the knowledge feature, but not all correlation values ​​have practical significance. Therefore, a preset correlation threshold (such as 0.5 or 0.7) is needed to filter out low correlation values ​​and only retain behavior-knowledge pairs with strong correlation. For example, only when the correlation value between a behavior feature (such as recommendation conversion rate) and a knowledge feature (such as drug indication knowledge) exceeds the threshold, its confidence will be further evaluated.

[0039] When performing confidence scoring on association values ​​that exceed the preset association threshold, the reliability of the association relationship is quantitatively evaluated by comprehensively considering multiple factors. The calculation of the confidence score mainly depends on the following three aspects: the absolute size of the association strength value (the closer the association value is to 1, the higher the score); the historical association frequency of the behavior characteristics and knowledge characteristics (such as whether the association between the behavior and the knowledge point appears multiple times in the historical data, the higher the frequency, the higher the score); and the importance weight of the knowledge point (if there is no preset weight, equal weight or dynamically generated weight can be used). The confidence score can be calculated by weighted summation or product formula, combining the contribution value of each factor, and finally generating a score result in the range of 0-1. Finally, a set of association values ​​with confidence scores is obtained, which provides a basis for identifying knowledge blind spots.

[0040] S107, determining the knowledge points corresponding to the associated values ​​whose confidence scores are less than the preset score threshold as knowledge blind spots; In step S107, for the set of association values ​​with confidence scores, the association values ​​with confidence scores lower than the preset score threshold are further screened out, and the knowledge points corresponding to these association values ​​are determined as the knowledge blind spots of the pharmacy staff. The knowledge blind spots refer to the knowledge points that the pharmacy staff have not been able to effectively master or correctly apply in a specific business scenario.

[0041] The confidence score threshold is a further screening criterion for identifying behavior-knowledge associations with low reliability. For example, if the confidence score of an association value is lower than the threshold (such as 0.6), it means that the reliability of the association relationship is insufficient, which may reflect that the pharmacy staff has insufficient or blind spots in their mastery of the knowledge point. For association values ​​with confidence scores lower than the threshold, the knowledge points corresponding to the association values ​​are extracted and identified as knowledge blind spots. For example, if a pharmacy employee performs poorly on the comprehensiveness feature of contraindication reminders, and the confidence score of the association with "drug contraindication classification knowledge" is lower than the threshold, it can be determined that the pharmacy employee's knowledge blind spots include "drug contraindication classification knowledge."

[0042] S108. Construct a knowledge graph based on scenario-based behavior data sets, knowledge blind spots, knowledge point sets, and the relationship between knowledge point sets and business scenarios; Specifically, the mastery scores of all knowledge points in the knowledge blind spots are marked in the knowledge point set to obtain the target knowledge point set; based on the scenario-based behavior data set and the target knowledge point set, the usage characteristics of the target knowledge point set in the business scenario are analyzed to determine the association between the target knowledge point set and the business scenario; and a knowledge graph is constructed based on the target knowledge point set and the association.

[0043] First, the knowledge points in the knowledge blind spots are scored for their mastery. Specifically, based on the data performance in the scenario-based behavior data set (such as task completion rate, operation accuracy, etc.), the knowledge points in each knowledge blind spot are scored, and the scoring range is usually [0,1], where 0 indicates complete lack of mastery and 1 indicates complete mastery. For example, for the knowledge blind spots of "drug classification knowledge" and "indication knowledge" of a pharmacy employee in a drug recommendation scenario, the scores can be calculated as 0.2 and 0.5 through features such as their recommendation accuracy, indicating a low degree of mastery and general mastery, respectively. Combine the set of knowledge points in the knowledge blind spots that have been scored with the set of knowledge points that need to be mastered in the business scenario to obtain the target knowledge point set.

[0044] Using the behavioral data in the scenario-based behavioral data set, the usage frequency and performance of each target knowledge point in different business scenarios are counted, such as the usage frequency of "drug classification knowledge" in the drug recommendation scenario and its impact on the recommendation accuracy, and the usage characteristics of "drug alternatives knowledge" in the special medication consultation scenario and its contribution to the consultation success rate. Based on these statistical results, the association between the target knowledge point and the business scenario is determined, and an association matrix is ​​generated, in which each association value represents the association between the target knowledge point and a business scenario (such as a number in the range of [0,1], the closer to 1, the stronger the association). A knowledge graph containing knowledge points and business scenarios is constructed based on the target knowledge point set and the strength of the association between the target knowledge point and a business scenario. The nodes of the knowledge graph include two categories: one is the knowledge point nodes in the target knowledge point set, and the other is the business scenario nodes (such as drug recommendations, contraindication reminders, special medication consultation, etc.). The edges in the knowledge graph represent the relationship between nodes, including the hierarchical or logical relationship between knowledge points (such as "drug classification knowledge" is the basis of "drug recommendation logic knowledge") and the association relationship between knowledge points and business scenarios. The weight of the edge represents the association relationship through the association value. For example, the association weight between "drug classification knowledge" and drug recommendation scenarios is 0.9.

[0045] S109. Based on the knowledge graph, determine the professional learning content for pharmacy employees and the corresponding priority of the professional learning content; combine the association weights of knowledge points and business scenarios, select knowledge points with higher association weights in business scenarios, and ensure that professional learning content is closely related to business value. For example, if the mastery score of "drug contraindications knowledge" is 0.3, and the association weight in the contraindication reminder scenario is 0.8, then this knowledge point should be included in the learning content. Finally, a list of knowledge points that pharmacy employees need to learn is extracted directly from the knowledge graph. These knowledge points are both the knowledge shortcomings of pharmacy employees and important knowledge points with high demand in business scenarios. After determining the professional learning content, set a priority for each learning content based on the existing information in the knowledge graph. The priority setting is based on two key indicators: one is the mastery score of the knowledge point (the lower the score, the higher the priority), and the other is the association weight between the knowledge point and the business scenario (the higher the weight, the higher the priority). These two indicators are weighted to calculate the comprehensive priority score of each knowledge point: priority score = α·(1-mastery score) + β·association value of knowledge point and business scenario, where α and β are weight parameters used to balance the importance of mastery and business needs. Finally, the learning content is sorted according to the priority score. For example, if the mastery score of "drug alternatives knowledge" is 0.4 and the business scenario association weight is 0.7, while the mastery score of "drug contraindications knowledge" is 0.2 and the association weight is 0.8, then "drug contraindications knowledge" has a higher priority. In this way, a list of learning content sorted by priority is output to provide clear guidance for the learning planning of pharmacy employees.

[0046] Optional, in Figure 1 After step S108 of the illustrated embodiment, the following steps may be performed: Obtain the problem-solving solutions and experience data of target pharmacy employees in business scenarios, where the target pharmacy employees are pharmacy employees whose problem-solving abilities meet preset requirements; annotate the problem-solving solutions and experience data according to knowledge points to obtain annotated results, associate the annotated results with business scenarios and knowledge point sets to obtain an experience set; and supplement the knowledge point set in the knowledge graph based on the experience set.

[0047] Among them, the target pharmacy employees refer to the pharmacy employees whose problem-solving ability in the business scenario meets the preset requirements. Specific standards may include: the operation records of the pharmacy employees in the business scenario prove that they can solve problems efficiently, or their behavioral performance (such as customer satisfaction, problem-solving rate, etc.) reaches a certain level. The preset requirements can be set according to the specific scenario, for example, the successful recommendation rate of the pharmacy employees in the drug recommendation scenario reaches a certain proportion, or they can accurately identify the contraindicated drug combination in the contraindication reminder scenario. After determining the target pharmacy employees, obtain the problem-solving solutions and experience data formed by the target pharmacy employees in the business scenario. These data mainly come from the actual operation records, case sharing and decision-making logic formed in the work process of the pharmacy employees. For example, in the drug recommendation scenario, the pharmacy employees may record the basis for recommending a certain drug, the strategy for communicating with customers and the final recommendation results; in the contraindication reminder scenario, the pharmacy employees may provide their experience data for identifying drug contraindications. These problem-solving solutions and experience data can be obtained through operation log extraction, case analysis, interview questionnaires and other methods to ensure that various problem-solving methods and experience summaries in actual business are covered.

[0048] Compare the problem-solving solutions and experience data formed by the target pharmacy employees in the business scenario with the knowledge point set, and mark the specific knowledge points involved one by one. The marking process can be completed through the knowledge point extraction algorithm based on natural language processing, for example, a piece of experience data is marked as involving specific knowledge points such as "drug classification knowledge" and "drug contraindication knowledge". The marking results can clarify the correspondence between each solution and experience data and knowledge points, thereby combining abstract knowledge points with specific solutions in actual operation scenarios.

[0049] After the annotation is completed, the annotation results are further associated with the business scenarios and knowledge point sets to form experience sets. Experience sets are a structured data form that contains problem solutions, relevant knowledge points and their application information in specific business scenarios. For example, an experience record can be described as: "In the drug recommendation scenario, 'drug classification knowledge' and 'indication knowledge' were applied to solve a certain drug selection problem, ultimately improving the recommendation accuracy."

[0050] Finally, based on the generated experience set, the knowledge point set in the knowledge graph is supplemented and improved. By analyzing the experience set, new knowledge points not included in the knowledge graph can be discovered, or additional definitions of existing knowledge points can be found. For example, an experience record may reflect a new knowledge point of "special drug substitution knowledge" or provide more application scenarios for the existing "drug contraindication knowledge". These newly added knowledge points and their associations are added to the knowledge graph, and the association weights between knowledge points and business scenarios are updated to ensure the structural integrity and real-time performance of the knowledge graph.

[0051] Optional, in Figure 1 After step S109 of the illustrated embodiment, the following steps may be performed: In response to the learning requests of pharmacy employees, learning tasks are created. The learning requests carry business scenarios, knowledge point sets, and knowledge blind spots. The knowledge conversion effects produced after the pharmacy employees complete the learning tasks are obtained. The knowledge conversion effects include the application of knowledge points and task completion by the pharmacy employees. The priority of the learning content is adjusted according to the knowledge conversion effects.

[0052] When a pharmacy employee initiates a learning request, the system first generates a learning task related to the actual work based on its business scenario, knowledge point set, and knowledge blind spots. These learning tasks simulate the problems that may be encountered in real business scenarios and are designed in combination with the knowledge points (i.e., knowledge blind spots) that pharmacy employees have not yet fully mastered. For example, for a learning request in a drug recommendation scenario, the learning task may require pharmacy employees to recommend appropriate drugs in specific situations and explain their indications and contraindications based on customer needs. When creating a learning task, the system automatically selects knowledge points related to the pharmacy employee's knowledge blind spots based on the association between knowledge points and business scenarios in the knowledge graph.

[0053] After the pharmacy employees complete the learning tasks, the system will evaluate their knowledge conversion effect. The knowledge conversion effect includes two parts: one is the application of knowledge points, that is, whether the pharmacy employees have correctly used the relevant knowledge points in the task; the other is the task completion, that is, the overall performance of the pharmacy employees in solving problems in the learning tasks, such as whether the task is completed, the completion efficiency, the quality of the solution, etc. For example, in the drug recommendation task, the system will evaluate whether the pharmacy employees have correctly applied relevant knowledge points such as "drug classification knowledge", "indication knowledge", and "drug contraindication knowledge", and score whether the final recommended drugs meet the scenario requirements. These results can be quantified into specific indicators, such as knowledge point mastery scores, task success rates, etc., to fully reflect the learning effect of pharmacy employees.

[0054] According to the knowledge conversion effect of pharmacy employees, the system dynamically adjusts the priority of learning content to ensure that the next stage of learning is more efficient and accurate. If the knowledge conversion effect shows that some knowledge points are still not applied correctly, or there are obvious knowledge blind spots in the task, the system will increase the learning priority of these knowledge points and strengthen the training of related content in subsequent learning tasks. For example, if the pharmacy staff fails to correctly identify the interaction between drugs in the contraindication reminder task, the system will increase the priority of "drug interaction knowledge" and design more relevant scenarios for pharmacy employees to practice. On the contrary, for the knowledge points that have been mastered, the priority can be lowered, so that more learning resources can be invested in the content that has not yet been mastered.

[0055] Optional, in Figure 1In the illustrated embodiment, the method further comprises: Obtain the emotional interaction data of pharmacy employees, and calculate the emotional value of pharmacy employees through a preset emotion prediction model based on the emotional interaction data. The emotional interaction data includes sound data and expression information. Based on the emotional value, judge the learning status of the pharmacy employees. The learning status includes negative state, anxious state and positive state. When the learning status is negative or anxious, generate target learning tasks based on the professional learning content and the priority corresponding to the professional learning content. The target learning tasks are learning content whose learning difficulty and learning form meet the preset requirements.

[0056] During the learning process, the system collects emotional interaction data of pharmacy employees in real time, including sound data and facial expression information. Sound data can come from voice input when pharmacy employees interact with the system, including characteristics such as tone, speaking speed, and volume; facial expression information is captured by cameras or sensors to capture changes in facial expressions of pharmacy employees, such as raised eyebrows, drooping corners of the mouth, and other emotional characteristics.

[0057] After obtaining the emotional interaction data, the system uses the preset emotion prediction model to process the data and calculate the emotional value of the pharmacy employees. The emotion prediction model is based on machine learning or deep learning algorithms, combining sound features (such as the smoothness of the tone and the speed of speech) and expression features (such as facial muscle movement trajectory and expression classification results) to predict the emotional value of pharmacy employees. The emotional value is usually a quantitative score that can reflect the current emotional state of the pharmacy employee. For example, a low tone, a slow speaking speed, and no obvious changes in facial expressions may correspond to a lower emotional value, while a brisk tone, a moderate speaking speed, and a smile may correspond to a higher emotional value.

[0058] According to the calculated emotion value, the system classifies the learning status of pharmacy employees, which includes negative state, anxious state and positive state. Specifically, when the emotion value is low (such as lower than the preset threshold), the system judges that the pharmacy employee is in a negative state, which is manifested as lack of motivation and distraction; when the emotion value is high but fluctuates violently, the system judges that the pharmacy employee is in an anxious state, which may be manifested as tension, uneasiness and other emotions; when the emotion value is high and stable, the system judges that the pharmacy employee is in a positive state, that is, the learning motivation is sufficient and the concentration is high.

[0059] When the system detects that pharmacy employees are in a negative or anxious state, in order to prevent negative emotions from further affecting the learning effect, the system generates target learning tasks based on the professional learning content of pharmacy employees and their corresponding priorities. At this time, the learning difficulty must meet the preset requirements, such as reducing the difficulty of learning tasks, giving priority to easy-to-understand content, and helping pharmacy employees find a sense of achievement in learning. For example, focus on selecting relevant content for which pharmacy employees have already mastered some basic knowledge, and avoid setting overly complex learning tasks. And the learning format must meet the preset requirements, such as adjusting the learning format to more interesting, interactive or lightweight content, such as using video explanations, gamified Q&A or case analysis, etc., to reduce learning pressure and stimulate the learning interest of pharmacy employees.

[0060] See also Figure 2 , is a schematic diagram of a knowledge learning management system based on data analysis provided in an embodiment of the present application. A knowledge learning management system based on data analysis 200 specifically includes: The acquisition module 201 is used to acquire a scenario-based behavior data set, which includes the behavior data of each pharmacy employee in different business scenarios, and the business scenarios include at least a drug recommendation scenario, a drug contraindication reminder scenario, and a special medication consultation scenario; An extraction module 202 is used to construct a behavior model based on the scenario-based behavior data set, and extract behavior feature information of pharmacy employees in different business scenarios through the behavior model; The first determination module 203 is used to determine the knowledge point set and knowledge blind spots that the pharmacy staff need to master in each business scenario based on the behavior feature data; A construction module 204 is used to construct a knowledge graph based on the association relationship between the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the business scenario of the knowledge point set; The second determination module 205 is used to determine the professional learning content of the pharmacy employees and the priority corresponding to the professional learning content based on the knowledge graph.

[0061] Optionally, the first determining module 203 is specifically configured to: Based on the behavioral characteristic information, the set of knowledge points that pharmacy employees need to master in business scenarios is determined through a preset knowledge demand prediction model. The scenario-based behavioral data set is correlated with the knowledge point set to determine the knowledge blind spots of pharmacy employees. The knowledge blind spots are the set of knowledge points that the pharmacists' mastery level does not meet the preset requirements.

[0062] Optionally, the first determining module 203 is further specifically configured to: The behavioral feature information is converted into a behavioral feature vector, and the knowledge point set is converted into a knowledge feature vector; the association strength matrix between the behavioral feature vector and the knowledge feature vector is obtained through a preset bidirectional attention model based on the behavioral feature vector and the knowledge feature vector; confidence scores are performed on the association values ​​in the association strength matrix that exceed a preset association threshold; and the knowledge points corresponding to the association values ​​whose confidence scores are less than the preset scoring threshold are determined as knowledge blind spots.

[0063] Optionally, the construction module 204 is specifically used for: Mark the mastery scores of all knowledge points in the knowledge blind spots in the knowledge point set to obtain the target knowledge point set; based on the scenario-based behavior data set and the target knowledge point set, analyze the usage characteristics of the target knowledge point set in the business scenario and determine the association between the target knowledge point set and the business scenario; construct a knowledge graph based on the target knowledge point set and the association.

[0064] Optionally, the system further includes a labeling module 206, which is specifically used to: Obtain the problem-solving solutions and experience data of target pharmacy employees in business scenarios, where the target pharmacy employees are pharmacy employees whose problem-solving abilities meet preset requirements; annotate the problem-solving solutions and experience data according to knowledge points to obtain annotated results, associate the annotated results with business scenarios and knowledge point sets to obtain an experience set; and supplement the knowledge point set in the knowledge graph based on the experience set.

[0065] Optionally, the system further includes a learning module 207, which is specifically used for: In response to the learning requests of pharmacy employees, learning tasks are created. The learning requests carry business scenarios, knowledge point sets, and knowledge blind spots. The knowledge conversion effects produced after the pharmacy employees complete the learning tasks are obtained. The knowledge conversion effects include the application of knowledge points and task completion by the pharmacy employees. The priority of the learning content is adjusted according to the knowledge conversion effects.

[0066] Optionally, the system further includes a generating module 208, which is specifically configured to: Obtain the emotional interaction data of pharmacy employees, and calculate the emotional value of pharmacy employees through a preset emotion prediction model based on the emotional interaction data. The emotional interaction data includes sound data and expression information. Based on the emotional value, judge the learning status of the pharmacy employees. The learning status includes negative state, anxious state and positive state. When the learning status is negative or anxious, generate target learning tasks based on the professional learning content and the priority corresponding to the professional learning content. The target learning tasks are learning content whose learning difficulty and learning form meet the preset requirements.

[0067] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0068] This embodiment also discloses an electronic device, referring to Figure 3 The electronic device may include: at least one processor 301 , at least one communication bus 302 , a user interface 303 , a network interface 304 , and at least one memory 305 .

[0069] The communication bus 302 is used to realize the connection and communication between these components.

[0070] The user interface 303 may include a display screen (Display) and a camera (Camera). The optional user interface 303 may also include a standard wired interface and a wireless interface.

[0071] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0072] Among them, the processor 301 may include one or more processing cores. The processor 301 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 305, and calling data stored in the memory 305. Optionally, the processor 301 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 301 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 301, and it can be implemented separately through a chip.

[0073] Among them, the memory 305 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 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 a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may optionally be at least one storage device located away from the aforementioned processor 301. As Figure 3 As shown, the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a knowledge learning management method based on data analysis.

[0074] exist Figure 3 In the electronic device shown, the user interface 303 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 301 can be used to call an application program stored in the memory 305 for a knowledge learning management method based on data analysis. When executed by one or more processors 301, the electronic device executes one or more methods in the above-mentioned embodiments.

[0075] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0076] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0077] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as 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, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0078] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0080] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory 305. Based on this understanding, the technical solution of the present application, 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, which is stored in a memory 305 and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the various embodiments of the present application. The aforementioned memory 305 includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk.

[0081] The above are only exemplary embodiments of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptive change of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A knowledge learning management method based on data analysis, characterized in that: Applied in a server, the method comprises: Obtaining a scenario-based behavior data set, wherein the scenario-based behavior data set includes behavior data of each pharmacy employee in different business scenarios, wherein the business scenarios include at least a drug recommendation scenario, a drug contraindication reminder scenario, and a special medication consultation scenario; According to the scenario-based behavior data set, a behavior model is constructed, and behavior feature information of the pharmacy employees in different business scenarios is extracted through the behavior model; Determine, based on the behavior characteristic information, a set of knowledge points and knowledge blind spots that the pharmacy employee needs to master in each of the business scenarios; Constructing a knowledge graph according to the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the association between the knowledge point set and the business scenario; Based on the knowledge graph, the professional learning content of the pharmacy employees and the priority corresponding to the professional learning content are determined.

2. The method according to claim 1, characterized in that The determining, based on the behavior characteristic information, a set of knowledge points that the pharmacy employee needs to master in the business scenario and the knowledge blind spots of the pharmacy employee specifically includes: According to the behavior characteristic information, a set of knowledge points that the pharmacy employee needs to master in the business scenario is determined by a preset knowledge demand prediction model; The scenario-based behavior data set is associated with the knowledge point set for analysis to determine the knowledge blind spots of the pharmacy employees, where the knowledge blind spots are knowledge point sets whose mastery of the pharmacy employees does not meet preset requirements.

3. The method according to claim 2, characterized in that The correlating analysis of the scenario-based behavior data set with the knowledge point set to determine the knowledge blind spots of the pharmacy employees specifically includes: Converting the behavior feature information into a behavior feature vector, and converting the knowledge point set into a knowledge feature vector; Obtaining a correlation strength matrix between the behavior feature vector and the knowledge feature vector through a preset bidirectional attention model according to the behavior feature vector and the knowledge feature vector; Performing confidence scoring on correlation values ​​in the correlation strength matrix that exceed a preset correlation threshold; The knowledge points corresponding to the associated values ​​whose confidence scores are less than a preset score threshold are determined as the knowledge blind spots.

4. The method according to claim 1, characterized in that: The constructing of a knowledge graph according to the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the association between the knowledge point set and the business scenario specifically includes: Marking the mastery scores of all knowledge points in the knowledge blind spot in the knowledge point set to obtain a target knowledge point set; Based on the scenario-based behavior data set and the target knowledge point set, analyzing the usage characteristics of the target knowledge point set in the business scenario, and determining the association relationship between the target knowledge point set and the business scenario; Construct a knowledge graph based on the target knowledge point set and the association relationship.

5. The method according to claim 1, characterized in that After constructing the knowledge graph according to the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the association between the knowledge point set and the business scenario, the method further includes: Obtaining problem-solving solutions and experience data of target pharmacy employees in the business scenario, wherein the target pharmacy employees are pharmacy employees whose problem-solving abilities meet preset requirements; The problem solution and the experience data are annotated according to the knowledge points to obtain an annotation result, and the annotation result is associated with the business scenario and the knowledge point set to obtain an experience set; Based on the experience set, the knowledge point set in the knowledge graph is supplemented.

6. The method according to claim 1, characterized in that After determining the professional learning content of the pharmacy staff and the priority corresponding to the professional learning content based on the knowledge graph, the method further includes: In response to the learning request of the pharmacy employee, creating a learning task, wherein the learning request carries the business scenario, the knowledge point set and the knowledge blind spot; Obtaining the knowledge conversion effect generated by the pharmacy employee after completing the learning task, wherein the knowledge conversion effect includes the application of knowledge points and task completion of the pharmacy employee; The priority of the learning content is adjusted according to the knowledge conversion effect.

7. The method according to claim 1, characterized in that The method further comprises: Acquire the emotional interaction data of the drugstore employee, and calculate the emotional value of the drugstore employee through a preset emotional prediction model according to the emotional interaction data, wherein the emotional interaction data includes sound data and expression information; Based on the emotion value, determining the learning state of the pharmacy employee, wherein the learning state includes a negative state, an anxious state, and a positive state; When the learning state is the negative state or the anxious state, a target learning task is generated based on the professional learning content and the priority corresponding to the professional learning content. The target learning task is a learning content whose learning difficulty and learning form meet preset requirements.

8. A knowledge learning management system based on data analysis, characterized in that: include: An acquisition module is used to acquire a scenario-based behavior data set, wherein the scenario-based behavior data set includes the behavior data of each pharmacy employee in different business scenarios, wherein the business scenarios include at least a drug recommendation scenario, a drug contraindication reminder scenario, and a special medication consultation scenario; An extraction module, used to construct a behavior model according to the scenario-based behavior data set, and extract the behavior feature information of the pharmacy employees in different business scenarios through the behavior model; A first determination module is used to determine the set of knowledge points and knowledge blind spots that the pharmacy employees need to master in each of the business scenarios based on the behavior characteristic data; A construction module, used to construct a knowledge graph according to the scenario-based behavior data set, the knowledge blind spot, the knowledge point set, and the association relationship between the business scenario of the knowledge point set; The second determination module is used to determine the professional learning content of the pharmacy employees and the priority corresponding to the professional learning content based on the knowledge graph.

9. A knowledge learning management device based on data analysis, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, wherein the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the data analysis-based knowledge learning management device to execute the method as described in any one of claims 1-7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a knowledge learning management device based on data analysis, the knowledge learning management device based on data analysis executes the method as described in any one of claims 1 to 7.

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