CN116645251A - Panoramic knowledge training method, device, equipment and medium based on artificial intelligence - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence and financial science and technology, and discloses a panoramic knowledge training method, device, equipment and medium based on artificial intelligence, which comprises the following steps: acquiring panoramic knowledge points of a project to be trained, wherein the panoramic knowledge points comprise a plurality of knowledge points; establishing a plurality of virtual reality scenes according to different knowledge points; training a learner in a virtual reality scene; analyzing learning data of a student, and dividing the student into different grades and capability portraits; and outputting a learning path plan according to the level and the capability representation of the learner. Panoramic knowledge points of a project to be trained can be obtained, multiple virtual reality scenes are established for training according to different knowledge points, comprehensiveness of training content can be guaranteed, students are trained through artificial intelligence digital virtual persons, the students are trained automatically by adopting an artificial intelligence technology, learning paths are planned, training efficiency is improved, and the training effect is given priority.
Description
Technical Field
The invention relates to the technical field of artificial intelligence and financial science and technology, in particular to a panoramic knowledge training method, device, equipment and medium based on artificial intelligence.
Background
With the continuous development of financial services, the types and demands of the financial services are continuously increased, the consultation quantity of clients for the services is also increased, financial service providers face tremendous service consultation pressure, and the answer to the clients' consultation by adopting a manual mode is still a mainstream mode. When facing business consultation, staff is limited by the influence of subjective factors such as knowledge level, working time and personal emotion of staff, and the processing effect of business consultation inevitably has differences. Therefore, it is a necessary link for staff to perform post training.
At present, the staff is mainly trained in the post mode of watching training videos on line and listening to training lectures off line, and the inventor realizes that the current training mode is difficult to lift training enthusiasm of staff, the training content is not comprehensive enough, the training efficiency is low and the training effect is poor.
Disclosure of Invention
The invention provides a panoramic knowledge training method, device, equipment and medium based on artificial intelligence, which are used for solving the technical problems that the training enthusiasm of staff is difficult to lift, the training content is not comprehensive enough, the training efficiency is low and the training effect is poor in the conventional training mode.
In a first aspect, there is provided an artificial intelligence based panoramic knowledge training method, including:
acquiring panoramic knowledge points of a project to be trained, wherein the panoramic knowledge points comprise a plurality of knowledge points;
establishing a plurality of virtual reality scenes according to different knowledge points;
training a learner in the virtual reality scene;
analyzing the learning data of the students, and dividing the students into different grades and capability portraits;
and outputting a learning path plan according to the level and the capability representation of the learner.
In a second aspect, a panoramic knowledge training device based on an artificial intelligent digital virtual person is provided, including:
the system comprises an acquisition module, a training module and a training module, wherein the acquisition module is used for acquiring panoramic knowledge points of a project to be trained, and the panoramic knowledge points comprise a plurality of knowledge points;
the building module is used for building a plurality of virtual reality scenes according to different knowledge points;
the training module is used for training students in the virtual reality scene;
the analysis module is used for analyzing the learning data of the students and dividing the students into different levels and capability portraits;
and the output module is used for outputting a learning path plan according to the level and the capability portraits of the students.
In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the artificial intelligence based panoramic knowledge training method described above when the computer program is executed by the processor.
In a fourth aspect, a computer readable storage medium is provided, the computer readable storage medium storing a computer program, which when executed by a processor, implements the steps of the artificial intelligence based panoramic knowledge training method described above.
According to the panoramic knowledge training method, device, computer equipment and storage medium based on artificial intelligence, panoramic knowledge points of a project to be trained can be obtained, multiple virtual reality scenes are established for training according to different knowledge points, and comprehensiveness of training content can be guaranteed. The training method can train the trainees in the virtual reality scene, and can enable the trainees to be put in the virtual reality scene, simulate the real customer business consultation scene and mobilize the training enthusiasm of the trainees. The learning data of the students can be analyzed, the students are divided into different grades and capability portraits, then learning path planning is output according to the grades and the capability portraits of the students, the students are automatically trained by adopting an artificial intelligence technology, learning paths are planned, training efficiency is improved, and the training effect is prioritized.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of an application environment of an artificial intelligence based panoramic knowledge training method in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of an artificial intelligence based panoramic knowledge training method in accordance with an embodiment of the present application;
FIG. 3 is a schematic diagram of a panoramic knowledge training apparatus based on artificial intelligence digital virtual persons in accordance with an embodiment of the present application;
FIG. 4 is a schematic diagram of a computer device according to an embodiment of the present application;
fig. 5 is a schematic diagram of another configuration of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The panoramic knowledge training method based on artificial intelligence provided by the embodiment of the invention can be applied to an application environment as shown in fig. 1, wherein a client communicates with a server through a network. The server side can acquire panoramic knowledge points of the items to be trained; establishing a plurality of virtual reality scenes according to different knowledge points; training a student in the virtual reality scene through an artificial intelligent digital virtual person; and analyzing the learning data of the students, and outputting learning results and learning path planning according to the learning data. And the learning result and the learning path plan are sent to the client, in the invention, panoramic knowledge points of the item to be trained can be obtained, and various virtual reality scenes are established for training according to different knowledge points, so that the comprehensiveness of training content can be ensured. The training method can train the trainee through the artificial intelligent digital virtual person in the virtual reality scene, and can enable the trainee to put in the virtual reality scene, simulate the real customer business consultation scene and mobilize the training enthusiasm of the trainee. The learning data of the students can be analyzed, learning results and learning path planning can be automatically output according to the learning data, the students are automatically trained by adopting an artificial intelligence technology, the learning paths are planned, the training efficiency is improved, and the training effect is prioritized. The clients may be, but are not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be implemented by a stand-alone server or a server cluster formed by a plurality of servers. The present invention will be described in detail with reference to specific examples.
Referring to fig. 2, fig. 2 is a schematic flow chart of an artificial intelligence-based panoramic knowledge training method according to an embodiment of the invention.
The panoramic knowledge training method based on artificial intelligence provided by the invention can be applied to business training in various application scenes. For example, in the field of insurance application, insurance business can be trained by the artificial intelligence-based panoramic knowledge training method. In the field of banking application, banking business can be trained by the panoramic knowledge training method based on artificial intelligence.
The panoramic knowledge training method based on artificial intelligence comprises the following steps:
s101: and acquiring panoramic knowledge points of the items to be trained.
The panoramic knowledge point comprises a plurality of knowledge points, and each knowledge point can correspond to one content to be trained.
The items to be trained can be insurance business items, banking business items and the like.
The panoramic knowledge point refers to comprehensive, deep and systematic understanding and mastering of a certain field or theme. It goes beyond the scattered knowledge of fragmentation and integrates various related concepts, facts, principles and skills to form a complete knowledge system.
The panoramic knowledge points of the items to be trained are carded, so that the integrity and the comprehensiveness of the content of the business training can be ensured, guidance is provided for the design and implementation of the training, and the effect of the business training is improved.
In one possible embodiment, step S101 specifically includes substeps S1011 to S1015:
s1011: and collecting client data, transaction data, market data, demand investigation data and sales data of the items to be trained.
Wherein the customer data, transaction data, market data, demand research data, and sales data for the item to be trained may be collected from internal systems, third party data suppliers, social media, and the like.
Where the customer data typically includes customer base information, contact details, purchase records, service requests, customer satisfaction surveys, customer complaints, and the like. The transaction data includes financial transaction records such as transaction amount, transaction time, etc. Market data includes financial market research data, competitor information, financial industry reports, and the like. The demand investigation data includes market investigation, user investigation, demand analysis and the like. Sales data includes sales, sales channels, sales areas, sales cycles, and the like.
It should be noted that, by analyzing the client data, the characteristics, behaviors and preferences of the client can be known in depth. The analysis client data includes analysis of basic information of clients, histories of purchasing financial products, habits of consuming the financial products, and the like. Through customer data analysis, the needs and interests of customers to purchase financial products, understand their purchase patterns and preferences, and their manner of interaction with the products or services can be insight. By analyzing the transaction data, the sales trend of the financial product, the hot sales condition of the financial product, the sales channel effect of the financial product and the like can be revealed. This helps to understand sales of financial products or financial services, identify popular financial products and unpopular financial products, and evaluate the effectiveness of sales strategies. By analyzing the market data, potential opportunities and threats of the financial market can be known, market share and strategies of competitors can be evaluated, and development trends of the financial industry can be predicted. This helps to guide market positioning, product development, and marketing strategies. By analyzing the demand investigation data, the opinion, preference and demand of the customer can be known. This facilitates improvements and innovations in financial products to meet customer expectations and provide a solution that better meets market demands. By analyzing sales data, it is possible to understand the periodic variation of sales, regional differences, product combination effects, and the like. This helps optimize sales strategies, adjust inventory and supply chain management, and predict sales trends and risks.
For example, in banking, customer group characteristics, transaction data, market data, demand investigation data, and sales data of financial products are analyzed to learn about investment demands of customers, current money, funds, trends and risk situations of stock markets.
S1012: and analyzing the behavior mode of the client according to the client data, the transaction data, the market data, the demand investigation data and the sales data.
The behavior mode of the client may be specifically a consumption habit, a consumption mode, a consumption trend, and the like of the client.
It should be noted that, by data mining and analyzing the customer data, the transaction data, the market data, the demand investigation data, and the sales data, a behavior pattern of the customer may be obtained. Such as by transaction data and sales data, analyzing customer buying patterns and trends, etc. According to the behavior module of the client, the content to be trained can be more accurately summarized, so that the client can be better provided with service, and the experience of the client is improved.
Optionally, the substep S1012 specifically includes:
preprocessing the customer data, the transaction data, the market data, the demand investigation data and the sales data.
The preprocessing comprises data cleaning, data deduplication, missing value filling, outlier processing, characteristic engineering and the like.
Wherein, the data deduplication can detect and delete duplicate data records, ensuring the uniqueness of each instance in the dataset.
Wherein, the missing value padding can fill in blank items in the data set. Interpolation, mean substitution, regression models, etc. may be used to fill in missing values to preserve data integrity.
Wherein, statistical methods, data distribution analysis or expert domain knowledge can be used to process outliers, avoiding distortion of analysis results due to data outliers.
At the same time, the data is converted into an analyzable format through preprocessing, and the data is subjected to characteristic engineering, so that useful characteristics are extracted to build a model.
Feature engineering refers to feature extraction and conversion of raw data according to domain knowledge and business objectives to create more meaningful and useful features. This includes normalization of numeric features, encoding of class features, creation of new features, etc.
And exploratory analysis is carried out on the preprocessed client data, the transaction data, the market data, the demand investigation data and the sales data, so that a behavior mode of the client is analyzed.
Among them, data exploratory analysis (Exploratory Data Analysis, EDA) is a method of initially exploring and understanding data. The goal of EDA is to reveal the structure, pattern, outliers and potential associations of the dataset, and to discover trends and rules in the data, through visualization and statistical methods. By visualizing and statistically analyzing the data, the rules, trends and anomalies in the data are found, the behavior patterns of the clients are analyzed, and guidance is provided for subsequent data processing and analysis.
The data exploratory analysis may include, among other things, data visualization analysis, customer segment analysis, association analysis, and the like.
Specifically, the pre-processed customer data, the transaction data, the market data, the demand investigation data and the sales data can be subjected to data visualization analysis to display the distribution, trend and association relationship of the data by using visualization tools such as charts, graphs and the like. For example, histograms, scatter plots, line plots, pie charts, etc. may be drawn to show customer purchases, sales changes, market share, etc. Based on the visual analysis, the consumption habit, consumption mode, consumption trend and the like of the client are obtained.
Specifically, the client data, the transaction data, the market data, the demand investigation data and the sales data after preprocessing can be subjected to client subdivision analysis, and clients are divided into different groups or classifications so as to identify the behavior patterns and characteristics of different client groups. Common customer segmentation methods include cluster analysis, classification algorithms, and the like.
Specifically, the pre-processed customer data, the transaction data, the market data, the demand investigation data and the sales data may be subjected to association analysis, and association relationships between data and frequently occurring patterns may be found. Association rules mining methods, such as Apriori algorithm (a priori algorithm), can be used to find the association between different data items, so as to learn about the purchasing preferences of customers, cross-selling opportunities, etc.
It should be noted that EDA has the advantage that it can help us to make preliminary knowledge and insight into the data before performing deeper data analysis. Through visualization and statistical analysis, EDA can reveal implicit information, anomalies, and interesting associations in the data, guiding subsequent data processing, feature engineering, and modeling processes.
S1013: and analyzing the transaction mode and the transaction trend of the client according to the behavior mode of the client.
Specifically, analyzing the transaction pattern and transaction trend of the customer refers to knowing the purchase preference, consumption habit, transaction frequency, transaction amount, etc. of the customer by analyzing the purchase behavior, transaction record, and transaction data of the customer according to the behavior pattern of the customer. Statistical analysis and data mining methods, such as frequency analysis, purchase pattern recognition, etc., may be used to reveal the customer's transaction patterns and transaction trends.
S1014: and according to the transaction mode and the transaction trend of the client, summarizing to obtain the content required to be trained.
Specifically, by sorting and classifying the purchasing behavior, transaction data, etc. of customers, different types of customer groups can be found, knowing their purchasing preferences, consumption characteristics, transaction habits, etc. Therefore, clients can be classified and grouped, and corresponding training plans and contents are formulated according to training requirements of different groups.
It should be noted that, the transaction mode and the transaction trend of the client fully reflect what the content of the client is most concerned with, what kind of service is needed to be provided most, and the content to be trained can be obtained according to the transaction mode and the transaction trend of the client.
For example, in the financial field, the transaction mode and the transaction trend of the customers buying and selling the funds fully reflect what the contents of the funds are most concerned by the customers, whether the contents are related to risk assessment of the funds, real-time price of the funds and the like, and finally, what services the users need to provide for the fund products can be summarized, and the contents needing training can be summarized according to the services provided by the users.
For example, in banking, the customer is concerned about the annual interest rate, closing period, etc. of the financial product, and therefore, for the banking training program, the financial product introduction service should be taken as one of the panoramic knowledge points. When training the trainee later, the financial product should be introduced as one of the main training contents.
S1015: summarizing according to the summarized content to be trained, and obtaining the panoramic knowledge points of the items to be trained.
Specifically, keyword extraction can be performed on the summarized content requiring training or a text summarization algorithm can be used to extract core information of the text. And then, automatically grouping related text data through a clustering algorithm to obtain panoramic knowledge points of the items to be trained.
It is to be noted that, summarizing is performed according to the summarized content to be trained to obtain the panoramic knowledge points of the item to be trained, so that the panoramic knowledge points can cover all business training content, the integrity and the comprehensiveness of the content of the business training are ensured, guidance is provided for the design and implementation of the training, and the effect of the business training is improved.
S102: and establishing a plurality of virtual reality scenes according to different knowledge points.
Specifically, the situation of the virtual reality scene can be designed according to the characteristics of specific knowledge points and training targets, and elements such as virtual environment, scene arrangement, role setting, display content and the like are considered to be used, so that a learning scene with reality sense is created. Virtual reality development tools and techniques are utilized to fabricate virtual environments and interactive elements related to knowledge points. And then adding interactive elements into the virtual reality scene to enable students to interact and operate with objects in the scene.
It should be noted that, multiple virtual reality scenes are established for different knowledge points, so that comprehensiveness of training contents can be guaranteed, and meanwhile, a real customer service consultation scene is simulated through the virtual reality scene, so that training enthusiasm of students is mobilized.
S103: training a trainee in the virtual reality scenario.
On the one hand, the student is put in the virtual reality scene to simulate a real customer business consultation scene, and practice and simulation scenes are provided, so that the student actively participates in the learning process, and the training enthusiasm of the student is mobilized. Artificial intelligence, on the other hand, can provide a personalized learning experience depending on the needs and level of the learner. The training content and the difficulty can be adjusted according to feedback and performance of the students, and a customized learning path is provided, so that the learning requirements of the students are met to the greatest extent. Further, feedback and assessment can be immediately given to the trainee to help them correct errors and improve performance. The learner can learn own learning progress and weakness through the guidance and evaluation of the virtual person, and timely adjust the learning strategy and improve the learning effect. Meanwhile, compared with the traditional training mode, the cost and resources can be saved by training the trainees in the virtual reality scene. The actual classrooms and physical equipment are not needed, and students can learn only through virtual reality equipment or smart phones. Meanwhile, the virtual reality technology can provide training for a plurality of students at the same time, and the expandability and the efficiency of the training are greatly improved.
For example, in banking, a loan process is simulated by building a virtual reality scene, so that a learner can learn interactively in the virtual scene. The virtual reality scene can simulate different types of loan applications, so that students can know the details and requirements of each flow. In addition, by training the trainees in the virtual reality scenario, the trainees are enabled to experience various risk management situations, including identifying, evaluating, controlling risks and the like. For example, by simulating fraud cases, the learner is informed of how to identify fraud and take corresponding measures. Further, the customer service scene can be simulated through artificial intelligence, so that a learner can learn how to communicate with and exchange with a customer. The virtual person may act as a different type of customer, allowing the learner to learn how to deal with different customer needs and problems. In addition, through virtual reality scene, let the student know different grade type investment product and investment tactics to simulate real investment environment, let the student exercise investment decision and risk management. Specifically, a bank learner can conduct analog transactions and conversations with an artificial intelligent digital virtual person, thereby learning how to handle various customer demands and cope with market changes.
For example, in the insurance business, theoretical knowledge in terms of insurance product knowledge, legal requirements, sales skills and the like can be taught to students through virtual lectures, teaching videos, simulated classes and the like in a virtual reality scene. Personalized learning content and scheduling may be provided according to the needs and level of the learner. Virtual sales scenes and customer conversations can be provided through virtual reality technology, so that students can demonstrate products and simulate sales processes. The learner may conduct conversations with the dummy, answer customer questions, explain insurance clauses, etc., exercise sales skills and customer communication capabilities. Further, the case and flow of claims can be simulated, allowing the learner to simulate and practice the claims process. In addition, the risk assessment and prediction scene can be simulated, so that a student can learn how to identify and assess different risk factors, and a corresponding insurance solution is provided. The virtual reality technology can provide learning content such as case analysis, data analysis, risk model application and the like, and helps students understand risk management and decision making processes in insurance business.
S104: and analyzing the learning data of the students, and dividing the students into different grades and capability portraits. In a possible implementation manner, the step S104 specifically includes substeps S1041 to S1043:
S1041: and collecting learning results and performance data of the students as the learning data.
Wherein, the study result includes: course completion, score of test, etc. The performance data includes: learning time, job quality, problem solving ability.
S1042: and establishing a student performance evaluation model, and inputting the learning result and the performance data of the student into the student performance evaluation model.
Specifically, the performance evaluation model may be a decision tree, a random forest, a support vector machine, a neural network, or the like. The model is trained and optimized by using the collected learning results and performance data as a training set and the learner's performance data as a label.
S1043: and outputting the level and capability representation of the student through the student performance evaluation model.
It will be appreciated that the performance assessment model may take as input the learner's learning outcome and performance data, automatically outputting a level and competence representation of the learner.
Wherein, the capability portrait refers to an image or a summary which comprehensively describes and presents the capability and skills of an individual. The competence image reflects the individual's competence level, skill level, and related features and characteristics in a particular field or task.
It should be noted that, by establishing the student performance evaluation model, the learning performance and the ability level of the student can be objectively evaluated. Through the level and capability portraits of the trainees, the learning progress and potential of the trainees can be better known, and basis is provided for subsequent training and guidance. The method is favorable for personalized learning path planning and accurate learning support, and improves the learning effect and satisfaction of students.
S105: and outputting a learning path plan according to the level and the capability representation of the learner.
Specifically, according to the capability images and learning targets of the students, the current capability level and the target level required to be achieved of the students are determined, and further knowledge points and skills required to be mastered are determined. And planning a corresponding learning path according to the knowledge points and skills to be mastered. According to the learning path planning, it is ensured that the learner can grasp the required knowledge and skills step by step. In addition, in the learning process, the learning progress and learning effect of the learner are monitored in time, corresponding feedback and advice are given according to the performance of the learner, and the learning path planning is automatically adjusted.
It should be noted that, according to the level and the capability portrait output learning path plan of the trainees, personalized training can be performed according to the requirements of different trainees so as to meet different learning targets of the trainees.
For example, in the banking field, artificial intelligence digital virtual persons may provide general banking knowledge and transaction skills to primary trainees; while for advanced students, artificial intelligence digital dummy can provide deeper market analysis and investment strategies.
It should be noted that, by analyzing learning data of students, learning progress, weaknesses and advantages thereof can be known. According to the information, personalized learning support and guidance can be provided for each student, and better learning results can be obtained in the required field. Learning data analysis can help a learner utilize learning time and resources more efficiently. By knowing the learning progress and learning preference of the learner, the learning plan and resource allocation can be optimized, and the learner is ensured to obtain the maximum learning benefit in the key field. Through learning data analysis, learning progress and performance of a learner can be obtained in time. This may prompt the educator or trainer to provide real-time feedback and adjust the learning path to meet the learning needs of the learner and to solve the problems in learning.
In one possible implementation manner, the step S103 is specifically:
in the virtual reality scene, an artificial intelligent digital virtual person is constructed by adopting a preset construction scheme, and students are trained through the artificial intelligent digital virtual person.
Wherein, the artificial intelligence digital virtual person refers to a virtual entity which is created by utilizing artificial intelligence technology and computer graphics and has human appearance and intelligence. They can simulate the appearance, expression, speech and behavior of humans, with the ability to interact and communicate with humans. In the invention, the artificial intelligent digital virtual person can play the role of a training person to train each student.
In particular, the specific construction scheme of the artificial intelligent digital virtual human is as follows: first, it is necessary to define the purpose and function of the artificial intelligence digital dummy for trainee training, determine the tasks, interaction modes, appearance, etc. of the dummy. Second, data related to the dummy, including text, images, audio, etc., needs to be collected and collated. Again, it is desirable to select an appropriate artificial intelligence model, such as a natural language processing model, a computer vision model, etc., that is trained using the collected data to enable the virtual person to understand and respond to the user's input. Later, a dialog system for virtual persons needs to be designed and developed, including modules for speech recognition, semantic understanding, dialog generation, etc., which is responsible for parsing the user's input, understanding the user's intent, and generating a corresponding reply or operation. Finally, the appearance and animation of the virtual person need to be designed according to the purpose and function of the virtual person for training the trainee, so as to enhance the communication and interaction experience of the user.
It should be noted that, the core technologies of the artificial intelligent digital virtual person include computer vision, natural language processing, speech synthesis, man-machine interaction, and the like. Through computer vision technology, a virtual person can perceive and understand the surrounding environment, and recognize facial expressions, gestures and actions of the human. Natural language processing techniques enable virtual persons to understand and generate human language, to perform conversations and answer questions. Thus, an artificial intelligence digital dummy can use natural language processing techniques to converse with a learner so that the learner can more easily communicate with it. Speech synthesis technology enables a virtual person to generate realistic speech and communicate with humans through speech. Man-machine interaction techniques provide a variety of ways, such as touch screens, gesture recognition, voice commands, etc., to enable a user to interact with a virtual person.
After step S105, the panoramic knowledge training method further includes:
s106: and acquiring an evaluation index for the artificial intelligent digital virtual person.
The evaluation index may be accuracy, response time, customer satisfaction, etc.
S107: and collecting evaluation data corresponding to the evaluation index.
The evaluation data may include, among other things, simulation test data, laboratory test data, user feedback data, and behavioral data. The user feedback data may include user satisfaction, use experience, functional requirements, and the like. User feedback data may be obtained through user surveys, feedback forms, user reviews and suggestions, and the like. The behavior data refers to the interactive behavior data of the recorded user and the artificial intelligent digital virtual person, and comprises clicking, browsing, inputting, operating and the like of the user. The behavior data can reflect the use behavior and habit of the user on the virtual person, and evaluate the functions and performances of the virtual person. The test data of the simulation test and the laboratory test can reflect the processing efficiency and the processing accuracy of the artificial intelligent digital virtual person.
S108: and analyzing the evaluation data to obtain an evaluation result.
The evaluation result may include customer satisfaction, financial product sales, training effect, accuracy of answering consultation, efficiency of answering consultation, response time of answering consultation, interface friendliness, user participation and the like.
Specifically, statistical analysis is applied to the evaluation data, for example, an average value, a median, a frequency distribution, and further, for example, hypothesis testing analysis, correlation analysis, regression analysis, and the like. And then, according to the characteristics and the targets of the evaluation data, establishing a machine learning or deep learning model to predict or infer an evaluation result. Thereafter, the analysis results are interpreted and an evaluation report is output, which should contain the results, conclusions, and suggestions of the data analysis in order to comprehensively evaluate the performance and effect of the evaluation object.
Wherein, the evaluation result can reflect the ability of the artificial intelligence digital dummy to process the transaction to be trained.
Alternatively, the evaluation result may be presented in the form of a score value. It can also be presented with parameters such as task completion rate and error rate.
S109: the evaluation result is compared with an expected target.
Among other things, the intended targets may specifically include: business objectives, performance metrics, and user experience metrics. In the financial field, business objectives may include customer satisfaction, financial product sales, training effects, etc., performance metrics may include accuracy, efficiency, response time, etc. of answering consultations, and user experience metrics may include interface friendliness, user engagement, etc.
It should be noted that, by comparing, it can be determined whether the performance and effect of the artificial intelligent digital virtual person reach the expected targets.
S110: and adjusting the construction scheme of the artificial intelligent digital virtual man in case that the evaluation result fails to reach the expected target. The specific construction scheme of the artificial intelligent digital virtual man is as follows: first, it is necessary to define the purpose and function of the artificial intelligence digital dummy for trainee training, determine the tasks, interaction modes, appearance, etc. of the dummy. Second, data related to the dummy, including text, images, audio, etc., needs to be collected and collated. Again, it is desirable to select an appropriate artificial intelligence model, such as a natural language processing model, a computer vision model, etc., that is trained using the collected data to enable the virtual person to understand and respond to the user's input. Later, a dialog system for virtual persons needs to be designed and developed, including modules for speech recognition, semantic understanding, dialog generation, etc., which is responsible for parsing the user's input, understanding the user's intent, and generating a corresponding reply or operation. Finally, the appearance and animation of the virtual person need to be designed according to the purpose and function of the virtual person for training the trainee, so as to enhance the communication and interaction experience of the user.
In particular, adjusting the build scheme of the intelligent digital virtual person may be adjusting the selection of the artificial intelligence model, re-collecting training data, and so forth.
It should be noted that, by adjusting the design and training scheme of the artificial intelligence digital virtual person, the ability of the artificial intelligence digital virtual person to process the transaction to be trained can be changed.
Further, as the artificial intelligence digital dummy is put into use, the artificial intelligence digital dummy can be periodically and continuously evaluated to maintain consistency of the artificial intelligence digital dummy with customer needs and market changes. The assessment process may provide important information and feedback that improves the artificial intelligence digital virtual person and bank knowledge panorama training patterns, thereby enabling continued improvement.
In one possible implementation, adjusting the construction scheme of the artificial intelligence digital virtual person includes:
and comparing the models of different constructed artificial intelligent digital virtual persons to determine the model with the optimal evaluation result.
It should be noted that, the artificial intelligent digital virtual person may be constructed by the natural language processing model, the computer vision model and other models, and the effects of the virtual person constructed by different models may be compared, for example, the accuracy of answering consultation may be compared, so as to determine the model with the optimal evaluation result.
And constructing the artificial intelligent digital virtual man by adopting a model with an optimal evaluation result.
It should be noted that, the performance and accuracy of the artificial intelligent digital virtual person can be improved by constructing the artificial intelligent digital virtual person by using the model with the optimal evaluation result. The artificial intelligent digital virtual person with the optimal evaluation result can more accurately understand the problem of the user, provide useful answers and solutions and perform natural and smooth interaction with the user. And the model with the optimal evaluation result is selected as a basis, so that a good starting point can be provided for subsequent improvement and optimization. By analyzing and adjusting the optimal model, the performance and the function of the artificial intelligent digital virtual man can be further improved, so that the artificial intelligent digital virtual man can adapt to the continuously changing requirements and environments.
In one possible implementation, after step S104, the panoramic knowledge training method further includes:
s110: an adjustment input is received for the learner.
Wherein the adjustment input is an active intervention for the artificial intelligence digital dummy. The adjustment input can be a modification input to the background of the artificial intelligent digital virtual person, can be an adjustment input to a learning plan provided by the artificial intelligent digital virtual person, and can also be an active skip input to a certain learning content by a learner.
S111: in response to the adjustment input, an adjustment is made to the learned path plan.
Specifically, after the learner performs adjustment such as new addition, skip, deletion, etc. on the original learning plan, it is necessary to regenerate a new learning path plan, and re-plan the corresponding learning sequence and learning time.
In addition to automatically generating the learned path plan, the learned path plan may be actively adjusted. On the one hand, special cases can be dealt with, for example, because special cases require compression training cycles, etc. On the other hand, if the learner exhibits a higher degree of understanding and mastery on a certain topic, the relevant content may be skipped, saving time and providing a deeper learning opportunity. Conversely, if a learner has difficulty on a certain topic, additional coaching and practice may be provided to help him overcome the obstacle. And the learning power and participation of the students can be enhanced by actively adjusting the learning path. As the learner feels the individuality and pertinence of the learning path, they are more likely to maintain an active learning attitude and continue to participate in the learning process. The learner feels that their learning needs are paid attention to and can autonomously grasp the learning progress, thereby enhancing learning power and input. The learner can learn new knowledge and skills more attentively without wasting time on what is already familiar. Meanwhile, through actively adjusting the learning path, the learning characteristics and demands of students are adjusted, more accurate and effective learning resources and learning activities can be provided, the learning time is saved, and the learning progress is quickened.
Therefore, in the scheme, panoramic knowledge points of the item to be trained can be obtained, and various virtual reality scenes are established for training according to different knowledge points, so that the comprehensiveness of training content can be ensured. The training method can train the trainee through the artificial intelligent digital virtual person in the virtual reality scene, and can enable the trainee to put in the virtual reality scene, simulate the real customer business consultation scene and mobilize the training enthusiasm of the trainee. The learning data of the students can be analyzed, learning results and learning path planning can be automatically output according to the learning data, the students are automatically trained by adopting an artificial intelligence technology, the learning paths are planned, the training efficiency is improved, and the training effect is prioritized.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In one possible embodiment, an artificial intelligence-based panoramic knowledge training device is provided, and the panoramic knowledge training device corresponds to the panoramic knowledge training method in the embodiment one by one. As shown in fig. 3, the artificial intelligence based panoramic knowledge training device 30 includes an acquisition module 301, a setup module 302, a training module 303, an analysis module 304, and an output module 305. The functional modules are described in detail as follows:
The acquiring module 301 is configured to acquire a panoramic knowledge point of a project to be trained, where the panoramic knowledge point includes a plurality of knowledge points;
the establishing module 302 is configured to establish a plurality of virtual reality scenarios according to different knowledge points;
a training module 303, configured to train a learner in the virtual reality scenario;
the analysis module 304 is used for analyzing the learning data of the students and dividing the students into different levels and capability portraits;
the output module 305 outputs a learning path plan based on the level and ability portraits of the learner.
In one possible embodiment, the obtaining module 301 is specifically configured to:
collecting client data, transaction data, market data, demand investigation data and sales data of the items to be trained;
analyzing a behavior mode of a client according to the client data, the transaction data, the market data, the demand investigation data and the sales data;
according to the behavior mode of the client, analyzing the transaction mode and the transaction trend of the client;
summarizing according to the transaction mode and the transaction trend of the client to obtain the content required to be trained;
summarizing according to the summarized content to be trained, and obtaining the panoramic knowledge points of the items to be trained.
In a possible embodiment, the obtaining module 301 is further configured to:
preprocessing the client data, the transaction data, the market data, the demand investigation data and the sales data;
and exploratory analysis is carried out on the preprocessed client data, the transaction data, the market data, the demand investigation data and the sales data, so that a behavior mode of the client is analyzed.
In one possible embodiment, the analysis module 304 is specifically configured to:
collecting learning results and performance data of a learner as the learning data;
establishing a student performance evaluation model, and inputting the learning result and performance data of a student into the student performance evaluation model;
and outputting the level and capability representation of the student through the student performance evaluation model.
In a possible embodiment, the training module 303 is specifically configured to construct an artificial intelligent digital virtual person in the virtual reality scenario by adopting a preset construction scheme, and train a learner through the artificial intelligent digital virtual person;
the panorama knowledge training device 30 based on the artificial intelligence digital dummy further comprises:
the definition module is used for acquiring an evaluation index of the artificial intelligent digital virtual person;
The collection module is used for collecting the evaluation data corresponding to the evaluation index;
the analysis module is used for analyzing the evaluation data to obtain an evaluation result;
a comparison module for comparing the evaluation result with an expected target;
and the adjusting module is used for adjusting the construction scheme of the artificial intelligent digital virtual man under the condition that the evaluation result fails to reach the expected target.
In a possible embodiment, the adjustment module is specifically configured to:
comparing different models of the constructed artificial intelligent digital virtual person to determine a model with an optimal evaluation result;
and constructing the artificial intelligent digital virtual man by adopting a model with an optimal evaluation result.
In one possible embodiment, the artificial intelligence digital virtual person based panoramic knowledge training apparatus 30 further comprises:
and the receiving module is used for receiving the adjustment input of the student.
And the response module is used for responding to the adjustment input and adjusting the learning path planning.
The invention provides a panoramic knowledge training device based on an artificial intelligent digital virtual person, which can acquire panoramic knowledge points of a project to be trained, and establishes various virtual reality scenes for training according to different knowledge points, so that the comprehensiveness of training contents can be ensured. The training method can train the trainee through the artificial intelligent digital virtual person in the virtual reality scene, and can enable the trainee to put in the virtual reality scene, simulate the real customer business consultation scene and mobilize the training enthusiasm of the trainee. The learning data of the students can be analyzed, learning results and learning path planning can be automatically output according to the learning data, the students are automatically trained by adopting an artificial intelligence technology, the learning paths are planned, the training efficiency is improved, and the training effect is prioritized.
For specific limitations on the artificial intelligent digital virtual person-based panoramic knowledge training apparatus, reference is made to the above limitation on the intelligent question-answering method, and no further description is given here. The modules in the panoramic knowledge training device based on the artificial intelligent digital virtual person can be fully or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one possible embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes non-volatile and/or volatile storage media and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is for communicating with an external client via a network connection. The computer program, when executed by a processor, performs functions or steps on a server side of a panoramic knowledge training method.
In one possible embodiment, a computer device is provided, which may be a client, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is for communicating with an external server via a network connection. The computer program, when executed by a processor, performs functions or steps on a client side of a panoramic knowledge training method.
In one possible embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring panoramic knowledge points of a project to be trained, wherein the panoramic knowledge points comprise a plurality of knowledge points;
Establishing a plurality of virtual reality scenes according to different knowledge points;
training a learner in the virtual reality scene;
analyzing the learning data of the students, and dividing the students into different grades and capability portraits;
and outputting a learning path plan according to the level and the capability representation of the learner.
In one possible embodiment, a computer readable storage medium is provided, having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring panoramic knowledge points of a project to be trained, wherein the panoramic knowledge points comprise a plurality of knowledge points;
establishing a plurality of virtual reality scenes according to different knowledge points;
training a learner in the virtual reality scene;
analyzing the learning data of the students, and dividing the students into different grades and capability portraits;
and outputting a learning path plan according to the level and the capability representation of the learner.
It should be noted that, the functions or steps implemented by the computer readable storage medium or the computer device may correspond to the relevant descriptions of the server side and the client side in the foregoing method embodiments, and are not described herein for avoiding repetition.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (10)
1. The panoramic knowledge training method based on artificial intelligence is characterized by comprising the following steps of:
acquiring panoramic knowledge points of a project to be trained, wherein the panoramic knowledge points comprise a plurality of knowledge points;
Establishing a plurality of virtual reality scenes according to different knowledge points;
training a learner in the virtual reality scene;
analyzing the learning data of the students, and dividing the students into different grades and capability portraits;
and outputting a learning path plan according to the level and the capability representation of the learner.
2. The artificial intelligence based panoramic knowledge training method as recited in claim 1, wherein said obtaining panoramic knowledge points of items to be trained comprises:
collecting client data, transaction data, market data, demand investigation data and sales data of the items to be trained;
analyzing a behavior mode of a client according to the client data, the transaction data, the market data, the demand investigation data and the sales data;
according to the behavior mode of the client, analyzing the transaction mode and the transaction trend of the client;
summarizing according to the transaction mode and the transaction trend of the client to obtain the content required to be trained;
summarizing according to the summarized content to be trained, and obtaining the panoramic knowledge points of the items to be trained.
3. The artificial intelligence based panoramic knowledge training method of claim 2, wherein said analyzing a behavior pattern of a customer based on said customer data, said transaction data, said market data, said demand research data, and said sales data comprises:
Preprocessing the client data, the transaction data, the market data, the demand investigation data and the sales data;
and exploratory analysis is carried out on the preprocessed client data, the transaction data, the market data, the demand investigation data and the sales data, so that a behavior mode of the client is analyzed.
4. The artificial intelligence based panoramic knowledge training method of claim 1, wherein said analyzing learning data of said learner to divide the learner into different levels and capability portraits comprises:
collecting learning results and performance data of a learner as the learning data;
establishing a student performance evaluation model, and inputting the learning result and performance data of a student into the student performance evaluation model;
and outputting the level and capability representation of the student through the student performance evaluation model.
5. The artificial intelligence based panoramic knowledge training method of claim 1,
training a student in the virtual reality scene specifically comprises the following steps:
in the virtual reality scene, an artificial intelligent digital virtual person is constructed by adopting a preset construction scheme, and students are trained through the artificial intelligent digital virtual person;
After the learning path planning is output according to the level and the capability portrait of the learner, the method further comprises the following steps:
acquiring an evaluation index for the artificial intelligent digital virtual person;
collecting evaluation data corresponding to the evaluation index;
analyzing the evaluation data to obtain an evaluation result;
comparing the evaluation result with an expected target;
and adjusting the construction scheme of the artificial intelligent digital virtual man in case that the evaluation result fails to reach the expected target.
6. The artificial intelligence based panoramic knowledge training method of claim 5, wherein adjusting the artificial intelligence digital virtual man's construction scheme comprises:
comparing different models of the constructed artificial intelligent digital virtual person to determine a model with an optimal evaluation result;
and constructing the artificial intelligent digital virtual man by adopting a model with an optimal evaluation result.
7. The artificial intelligence based panoramic knowledge training method of claim 1, further comprising, after said analyzing learning data of said learner, outputting a learning result and a learning path plan based on said learning data:
receiving an adjustment input of the learner;
In response to the adjustment input, an adjustment is made to the learned path plan.
8. Panoramic knowledge training device based on artificial intelligence, characterized by comprising:
the system comprises an acquisition module, a training module and a training module, wherein the acquisition module is used for acquiring panoramic knowledge points of a project to be trained, and the panoramic knowledge points comprise a plurality of knowledge points;
the building module is used for building a plurality of virtual reality scenes according to different knowledge points;
the training module is used for training students in the virtual reality scene;
the analysis module is used for analyzing the learning data of the students and dividing the students into different levels and capability portraits;
and the output module is used for outputting a learning path plan according to the level and the capability portraits of the students.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the artificial intelligence based panoramic knowledge training method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the artificial intelligence based panoramic knowledge training method of any one of claims 1 to 7.
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| CN117350695B (en) * | 2023-12-04 | 2024-05-07 | 四川省农业农村人才发展服务中心 | Agricultural technology training method and system based on cloud platform |
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