Financial training system and method based on VR interaction

Abstract

The invention relates to the technical field of financial training, and discloses a financial training system and method based on VR interaction, and the method comprises the steps: obtaining the post data of a student, judging the knowledge data of the student, generating an initial test exercise for the student, judging the level of the student before training, determining the learning path of the student, constructing a VR interaction scene, and distributing tasks. According to the financial training system and the financial training method based on VR interaction, the working ability of the student before entry and the mastering ability of professional knowledge involved in work are judged, and according to the post condition and the ability condition of the student, the learning path of the student is adjusted according to the learning feedback of the student in the VR interaction scene. According to the invention, adaptive training is carried out on the trainees, and targeted training adjustment is carried out on the trainees according to the training conditions of the trainees in the training process, so that the trainees are prevented from losing confidence due to incapability of following up the training progress, the cohesion of enterprises is improved, and the talent loss of the enterprises is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of financial training, and specifically provides a financial training system and method based on VR interaction. Background Art

[0002] Traditional financial training methods generally adopt forms such as classroom teaching, online learning, and case analysis. Although these methods are feasible, traditional training often focuses on theoretical knowledge and lacks practical experience in real scenarios, resulting in difficulties for trainees to apply theoretical knowledge to actual work. Many traditional training methods lack sufficient interactivity, with low trainee participation and difficulty in effectively stimulating learning interest. Face-to-face training requires trainees to be at specific times and locations, which cannot be flexibly arranged, restricting the popularity of training.

[0003] The financial training system based on VR interaction combines modern technology with the needs of financial education, can provide a more immersive and efficient learning experience, overcome the deficiencies of traditional training models, adapt to the needs of talent cultivation in the new era, and has broad application prospects in future financial training. Enterprises can not only improve the training effect, but also stimulate the learning interest of financial personnel, cultivate more comprehensive financial management capabilities, greatly enhance the learning experience, and make financial training more immersive and interactive.

[0004] Existing financial training systems and methods based on VR interaction cannot judge the working ability of trainees before employment and their mastery of professional knowledge involved in the work, cannot conduct adaptive training for trainees according to their job situations and ability situations, and cannot make targeted training adjustments for trainees according to their training situations during the training process, making it easy for trainees to lose confidence due to being unable to keep up with the training progress, thus easily causing enterprises to lose certain talents, and there are certain limitations in their practicality. Summary of the Invention

[0005] The present invention provides a financial training system and method based on VR interaction, which can judge the working ability of trainees before employment and their mastery of professional knowledge involved in the work. According to the trainee's position and ability, adaptive training is carried out for the trainee. According to the training situation of the trainee during the training process, targeted training adjustment is carried out for the trainee, avoiding the situation that trainees are likely to lose confidence due to being unable to keep up with the training progress, enhancing the cohesion of the enterprise, and reducing the loss of talents in the enterprise. It solves the problems mentioned in the above background technology, that is, it is impossible to judge the working ability of trainees before employment and their mastery of professional knowledge involved in the work, it is impossible to carry out adaptive training for trainees according to their position and ability, and it is impossible to carry out targeted training adjustment for trainees according to their training situation during the training process, which makes trainees easily lose confidence due to being unable to keep up with the training progress, thus easily causing the enterprise to lose some talents, and there are certain limitations in its practicability.

[0006] The present invention provides the following technical solutions: A financial training method based on VR interaction includes:

[0007] S1. Obtain all positions of the enterprise to form a position set, denoted as {P1, P2, P3, P4};

[0008] Among them, P1, P2, P3, and P4 are financial-related positions in the enterprise;

[0009] Obtain the position data of the trainee, denoted as PO, and PO ∈ {P1, P2, P3, P4};

[0010] S2. According to the position data PO, judge the knowledge data of the trainee, and the knowledge data is the professional knowledge required for the trainee's position and the courses including professional knowledge;

[0011] S21. Through the knowledge function, extract the professional knowledge required for the position to form a position knowledge set:

[0012]

[0013] Among them, K P (·) is the knowledge function, K P (PO) is the position knowledge set, and K P11 -K P43 is the professional knowledge required for positions P1 - P4;

[0014] S22. Define the mapping relationship between professional knowledge and courses:

[0015]

[0016] Among them, K is the professional knowledge required for the trainee's position, and course is the course containing the professional knowledge K;

[0017] S3. Generate initial test questions for the trainee according to the trainee's knowledge data, and judge the trainee's level before training.

[0018] As a financial training method based on VR interaction according to the present invention, among them: the generating initial test questions for the trainee according to the trainee's knowledge data includes determining the proportion of professional knowledge in relevant courses, specifically:

[0019] Obtain the trainee's position data, denoted as PO;

[0020] Extract the set of position knowledge K corresponding to the position data P (PO);

[0021] Obtain the mapping relationship M(K) between each professional knowledge K in the set of position knowledge K P (PO) and the course;

[0022] Set an extraction function to extract the proportion of professional knowledge in each course, denoted as W course，K :

[0023] W course，K = CourseOutline[course][K];

[0024] Among them, CourseOutline[·][·] is the extraction function, and CourseOutline is the course data structure of each course;

[0025] Set a data integration function to form a set of proportions of professional knowledge in all relevant courses, denoted as W K :

[0026] W K = {(course, W course，K )|course ∈ M(K)}.

[0027] As a financial training method based on VR interaction according to the present invention, among them: obtaining the course data structure of each course corresponding to each professional knowledge, specifically:

[0028] Obtain the name of the course, denoted as course name;

[0029] Obtain the professional knowledge included in the course, denoted as professional knowledge;

[0030] Obtain the proportion of each professional knowledge in the course, denoted as proportion;

[0031] Generate the course data structure corresponding to the course:

[0032] CourseOutline = {course name: {professional knowledge, proportion}};

[0033] Among them, each course corresponds to an entry, including the course name "course name" and a dictionary. The key of the dictionary is the professional knowledge "professional knowledge", and the value is the proportion "proportion" of this knowledge in the course.

[0034] As a financial training method based on VR interaction according to the present invention, wherein: generating initial test questions for the trainee according to the knowledge data of the trainee includes obtaining the mastery situation of the trainee in professional knowledge before the training, specifically:

[0035] Obtain the position data of the trainee, denoted as PO;

[0036] Extract the set of position knowledge K corresponding to the position data P (PO);

[0037] Obtain the mapping relationship M(K) between each professional knowledge K in the set of position knowledge K P (PO) and the course;

[0038] Obtain the proportion set of each professional knowledge in the set of position knowledge K P (PO), denoted as W K ;

[0039] Obtain the in-school grades of the trainee for each course, denoted as C(course);

[0040] Calculate the comprehensive mastery score of the trainee for each professional knowledge K before the training, denoted as C K :

[0041]

[0042] As a financial training method based on VR interaction according to the present invention, wherein: generating initial test questions for the trainee according to the knowledge data of the trainee includes generating initial test questions, specifically:

[0043] Obtain the comprehensive mastery score of the trainee for each professional knowledge K, denoted as C K ;

[0044] Set a proportion adjustment function to calculate the test proportion of each professional knowledge, denoted as W K :

[0045]

[0046] Among them, W K (C K ) is the proportion adjustment function;

[0047] Obtain the question bank database of the enterprise, denoted as Q;

[0048] Q = {q|q = {K q , D q , qc, qa}};

[0049] Among them, q is the question in the question bank database Q, K q is the professional knowledge to which the question q belongs, D q is the difficulty level of the question q, qc is the content of the question q, and qa is the answer to the question q;

[0050] Calculate the number of test questions for each professional knowledge K, denoted as N K :

[0051] N K = W K ·N;

[0052] Among them, N is the total number of questions in the initial test exercises;

[0053] Calculate the number of questions for each difficulty level under each professional knowledge K, denoted as N K，D :

[0054]

[0055] For each professional knowledge K, according to its test proportion W K , randomly select N K number of questions from the question bank database Q, and the number of questions for each difficulty level is N K，D ;

[0056] Shuffle and merge all the selected questions to generate the initial test exercises.

[0057] As a financial training method based on VR interaction according to the present invention, wherein: the judging of the level of the trainee before training is specifically:

[0058] Obtain the initial test score of the initial test exercises of the trainee, denoted as score;

[0059] Classify the trainees based on the allocation function and design the learning path:

[0060]

[0061] Among them, L personalized(·, ·) is a distribution function, L personalized (PO, score) is the learning path of the trainee, PO is the trainee's position data, K P (PO) is the set of position knowledge, Knowledge-LOW is the primary learning path, Knowledge-MIDDLE is the intermediate learning path, and Knowledge-HIGH is the advanced learning path.

[0062] As a financial training method based on VR interaction according to the present invention, wherein: according to the trainee's initial test score and learning path, a VR interaction scenario is constructed and tasks are assigned, specifically:

[0063] Obtain the trainee's learning path L personalized (PO, score);

[0064] Set a mapping function to determine the mapping relationship between the VR scenario and the task:

[0065]

[0066] Among them, V(·) is the mapping function, V(M) is the mapping relationship between the VR scenario and the task, M is the professional knowledge module in the VR scenario, M1-M4 are all professional knowledge modules in the VR scenario, and {M1, M2, M3, M4} = K P (PO), task11-task42 are the VR tasks corresponding to each professional knowledge module M;

[0067] Merge the tasks of all modules to generate the final VR task set, denoted as T VR (L personalized );

[0068]

[0069] Among them, T VR (·) is the function for merging modules, L personalized (PO, score) is the trainee's learning path;

[0070] According to the task set T VR (L personalized ), construct a corresponding VR interaction scenario for each task.

[0071] As a financial training method based on VR interaction according to the present invention, wherein: according to the trainee's learning feedback, adjust their learning path, specifically:

[0072] Obtain the trainee's current learning path L personalized (PO, score);

[0073] Define the execution result of the task in the VR interaction scenario, denoted as F(t):

[0074] F(t) = {success, feedback};

[0075] Where success is a boolean value indicating whether the task of the trainee is successfully completed, and feedback is the feedback information of the trainee;

[0076] Obtain the execution results of all tasks of the trainee in the VR interaction scenario, denoted as F:

[0077] F = {F(t) | t ∈ T VR (L personalized )};

[0078] Generate an adjusted learning path based on the path adjustment function, denoted as L adjusted (L personalized , F):

[0079]

[0080] Where L adjusted (·, ·) is the path adjustment function, Ex tu : · is the extra tutoring module, and F(t).success is the execution result of the task of the trainee in the VR interaction scenario.

[0081] As the present invention also discloses a system for implementing a financial training method based on VR interaction, wherein: it includes:

[0082] Initial determination module: used to judge the level of the trainee before training based on the post data PO of the trainee, and determine the learning path L personalized (PO, score);

[0083] VR interaction module: used to construct a VR interaction scenario and assign tasks according to the initial test score score of the trainee and the learning path L personalized (PO, score), and conduct corresponding training on the trainee;

[0084] Feedback adjustment module: used to adjust the learning path of the trainee according to the learning feedback of the trainee in the VR interaction scenario, and form an adjusted learning path L adjusted (L personalized , F).

[0085] The present invention has the following beneficial effects:

[0086] 1. The financial training system and method based on VR interaction obtain the positions of trainees, judge the professional knowledge required for these positions, determine which professional courses each piece of professional knowledge is included in, judge the comprehensive mastery of professional knowledge by trainees based on the grades of their professional courses in school and the proportion of professional knowledge in each professional course, and automatically generate initial test questions suitable for trainees according to the comprehensive mastery of each piece of professional knowledge by trainees, so as to judge the working ability of trainees before employment and their mastery of professional knowledge involved in work, enabling enterprises, training teachers, and training systems to quickly understand the situation of trainees.

[0087] 2. The financial training system and method based on VR interaction obtain the scores of trainees' initial test questions, judge the ability situation of trainees, determine the learning paths of trainees, and conduct adaptive training for trainees, avoiding trainees losing confidence due to being unable to keep up with the training progress, enhancing the cohesion of enterprises, and reducing the situation of brain drain in enterprises.

[0088] 3. The financial training system and method based on VR interaction obtain the training situation of trainees during the training process, including the completion of tasks and the feedback of trainees during the task completion process, and conduct targeted training adjustments for trainees. If a trainee fails to execute a certain task in the VR interaction scenario, an additional tutoring module is added to the learning path to conduct intensive training on the knowledge points involved in this task, avoiding trainees losing confidence due to being unable to keep up with the training progress, enhancing the cohesion of enterprises, and reducing the situation of brain drain in enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 It is a flowchart of the financial training method based on VR interaction of the present invention;

[0090] Figure 2 It is a system block diagram for implementing the financial training method based on VR interaction of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0091] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0092] Embodiment 1. A financial training method based on VR interaction, referring to Figure 1 , includes:

[0093] S1. Obtain all the positions in the enterprise to form a position set, denoted as {P1, P2, P3, P4};

[0094] Among them, P1, P2, P3, and P4 are positions related to finance in the enterprise. For example, P1, P2, P3, and P4 are the cashier, accountant, financial analyst, and finance manager respectively;

[0095] Obtain the position data of the trainee, denoted as PO, where PO ∈ {P1, P2, P3, P4};

[0096] S2. According to the position data PO, judge the knowledge data of the trainee;

[0097] S21. Through the knowledge function, extract the professional knowledge required for the position to form a position knowledge set:

[0098]

[0099] Among them, K P (·) is the knowledge function, K P (PO) is the position knowledge set, K P11 -K P43 is the professional knowledge required for positions P1 - P4. For example, K P11 -K P43 are cash management, bank settlement, financial accounting, tax treatment, financial statement preparation, financial analysis, budget management, cost control, financial management, strategic planning, and risk control respectively. If the position data PO = P1, then K P (PO) = K P11 , K P12 ;

[0100] S22. Define the mapping relationship between professional knowledge and courses:

[0101]

[0102] Among them, K is the professional knowledge required for the trainee's position, and course is the course containing the professional knowledge K. For example, if the position data PO = P1, K P (PO) = K P11 , K P12 , then K = K P11 and K P12 , then M(K) = course 111 , course 112 , course 121 , course 122 ;

[0103] S3. According to the knowledge data of the trainee, generate initial test questions for the trainee to judge the trainee's level before training.

[0104] Among them, generating initial test questions for the trainee according to the trainee's knowledge data includes determining the proportion of professional knowledge in relevant courses, specifically:

[0105] Obtain the trainee's position data, denoted as PO;

[0106] Extract the position knowledge set K P (PO);

[0107] Obtain the mapping relationship M(K) between each professional knowledge K in the position knowledge set K P (PO) and the course, that is, find all courses containing the professional knowledge K. For example, if the position data PO = P1, K P (PO) = K P11 , K P12 , then K = K P11 and K P12 , then M(K) = course 111 , course 112 , course 121 , course 122 ;

[0108] Set an extraction function to extract the proportion of professional knowledge in each course, denoted as W course，K :

[0109] W course，K = CourseOutline[course][K];

[0110] Among them, CourseOutline[·][·] is the extraction function, and CourseOutline is the course data structure of each course, that is, extract the proportion of the professional knowledge K in the course course from the course data structure;

[0111] Set a data integration function to form a set of the proportions of professional knowledge in all relevant courses, denoted as W K :

[0112] W K = {(course, W course，K )|course ∈ M(K)}.

[0113] Among them, obtaining the course data structure of each course corresponding to each professional knowledge, specifically:

[0114] Obtain the name of the course, denoted as course name;

[0115] Obtain the professional knowledge included in the course, denoted as professional knowledge;

[0116] Obtain the proportion of each professional knowledge in the course, denoted as proportion;

[0117] Generate the course data structure corresponding to the course:

[0118] CourseOutline = {course name: {professional knowledge, proportion}};

[0119] Among them, each course corresponds to an entry, including the course name course name and a dictionary. The key of the dictionary is the professional knowledge professional knowledge, and the value is the proportion proportion of this knowledge in the course.

[0120] This embodiment also provides that generating initial test questions for the trainee according to the knowledge data of the trainee includes obtaining the trainee's mastery of professional knowledge before training, specifically:

[0121] Obtain the trainee's position data, denoted as PO;

[0122] Extract the set of position knowledge K corresponding to the position data P (PO);

[0123] Obtain each professional knowledge K in the set of position knowledge K P (PO)'s mapping relationship M(K) with the course;

[0124] Obtain each professional knowledge in the set of position knowledge K P (PO)'s set of proportions, denoted as W K ;

[0125] Obtain the trainee's in-school grades for each course, denoted as C(course);

[0126] Calculate the comprehensive mastery score of each professional knowledge K by the trainee before training, denoted as C K :

[0127]

[0128] This embodiment also provides that generating initial test questions for the trainee according to the knowledge data of the trainee includes generating initial test questions, specifically:

[0129] Obtain the comprehensive mastery score of each professional knowledge K by the trainee, denoted as C K ;

[0130] Set a proportion adjustment function to calculate the test proportion of each professional knowledge, denoted as W K :

[0131]

[0132] Among them, W K (C K ) is the proportion adjustment function. If C K < 80, it means that the trainee has a poor grasp of this knowledge and needs to increase the proportion of relevant questions in the test. Therefore, the weight W K is higher. When C K is lower, W K is higher. If 80 ≤ C K < 90, it means that the trainee has a medium grasp of this knowledge, and the weight is set to a fixed value of 0.5. If C K ≥ 90, it means that the trainee has a good grasp of this knowledge, reduces the proportion of relevant questions, and the weight is set to 0.2;

[0133] Obtain the question bank database of the enterprise, denoted as Q. The question bank database is a database containing all questions, and each question has a corresponding professional knowledge K to which it belongs and a difficulty level;

[0134] Q = {q|q = {K q , D q , qc, qa}};

[0135] Among them, q is the question in the question bank database Q, K q is the professional knowledge to which the question q belongs, D q is the difficulty level of the question q. For example, the difficulty level D is D1 - D5, D1 is the easiest, D5 is the most difficult, qc is the content of the question q, and qa is the answer to the question q;

[0136] Calculate the number of test questions for each professional knowledge K, denoted as N K :

[0137] N K = W K ·N;

[0138] Among them, N is the total number of questions in the initial test exercises. For example, the total number of questions in the initial test exercises is 50 questions;

[0139] Calculate the number of questions for each difficulty level under each professional knowledge K, denoted as N K，D :

[0140]

[0141] For each professional knowledge K, according to its test proportion W K, randomly select N K questions from the question bank database Q, and the number of questions for each difficulty level is N K，D ;

[0142] Shuffle and merge all the selected questions to generate the initial test exercises.

[0143] This embodiment also provides that the method for judging the level of the trainee before training is specifically:

[0144] Obtain the initial test score of the trainee's initial test exercises, denoted as score;

[0145] Based on the allocation function, classify the trainees and design the learning path:

[0146]

[0147] Among them, L personalized (·, ·) is the allocation function, L personalized (PO, score) is the trainee's learning path, PO is the trainee's job data, K P (PO) is the job knowledge set, Knowledge-LOW is the primary learning path, such as basic knowledge reinforcement, Knowledge-MIDDLE is the intermediate learning path, such as intermediate skill improvement, and Knowledge-HIGH is the advanced learning path, such as advanced management knowledge.

[0148] Through the above method, judge the trainee's work ability before employment and the mastery ability of the professional knowledge involved in the work. According to the trainee's job situation and ability situation, conduct adaptive training for the trainee. According to the training situation of the trainee during the training process, conduct targeted training adjustments for the trainee, avoid the trainee from easily losing confidence due to being unable to keep up with the training progress, enhance the cohesion of the enterprise, and reduce the situation of talent loss in the enterprise.

[0149] Embodiment 2, this embodiment is an improvement made on the basis of Embodiment 2. The VR interaction-based financial training method constructs a VR interaction scenario and assigns tasks according to the trainee's initial test score and learning path, specifically:

[0150] Obtain the trainee's learning path L personalized (PO, score);

[0151] Set a mapping function to determine the mapping relationship between the VR scenario and the task:

[0152]

[0153] Among them, V(·) is a mapping function, V(M) is the mapping relationship between the VR scenario and the task, M is the professional knowledge module in the VR scenario, M1 - M4 are all professional knowledge modules in the VR scenario, and {M1, M2, M3, M4} = K P (PO), for example, M1 - M4 are basic accounting, tax processing, financial analysis, and cash management respectively, and task11 - task42 are the VR tasks corresponding to each professional knowledge module M, such as task11 - task42 are accounting processing, voucher filling, tax declaration, tax audit, financial statement analysis, investment decision-making, cash receipts and payments, and bank reconciliation respectively;

[0154] Merge the tasks of all modules to generate the final VR task set, denoted as T VR (L personalized ) :

[0155]

[0156] Among them, T VR (·) is the module merging function, L personalized (PO, score) is the learning path of the trainee, M ∈ L personalized(PO，score) represents each professional knowledge module M in the learning path L personalized (PO, score), where the professional knowledge module M is obtained from the job knowledge set K P (PO);

[0157] According to the task set T VR (L personalized ), construct corresponding VR interaction scenarios for each task.

[0158] Embodiment 3, this embodiment is an improvement made on the basis of Embodiment 3. In this embodiment, according to the learning feedback of the trainee, adjust its learning path, specifically:

[0159] Obtain the current learning path L personalized (PO, score) of the trainee;

[0160] Define the execution result of the task in the VR interaction scenario, denoted as F(t):

[0161] F(t) = {success, feedback};

[0162] Among them, success is a boolean value, including true (True) and false (False), indicating whether the trainee's task is successfully completed. If it is successfully completed, it is True, and if it fails, it is False. feedback is the feedback information of the trainee, used to describe the problems or suggestions in the task execution;

[0163] Obtain the execution results of all tasks of the trainee in the VR interaction scenario, denoted as F:

[0164] F = {F(t)|t ∈ T VR (L personalized )};

[0165] Generate an adjusted learning path based on the path adjustment function, denoted as L adjusted (L personalized , F):

[0166]

[0167] where L adjusted (·, ·) is the path adjustment function, Ex tu : · is the extra tutoring module, F(t).success is the execution result of the trainee's task in the VR interaction scenario, that is, if the trainee fails to execute a certain task in the VR interaction scenario, an extra tutoring module is added to the learning path to conduct intensive training on the knowledge points involved in the task. If the task is successfully completed, the learning path remains unchanged.

[0168] Example 4, this example also discloses a system for implementing a financial training method based on VR interaction. Refer to Figure 2 , including:

[0169] Initial determination module: used to judge the level of the trainee before training based on the trainee's position data PO and determine the trainee's learning path L personalized (PO, score);

[0170] VR interaction module: used to construct a VR interaction scenario and assign tasks according to the trainee's initial test score score and learning path L personalized (PO, score) to conduct corresponding training on the trainee;

[0171] Feedback adjustment module: used to adjust the trainee's learning path according to the trainee's learning feedback in the VR interaction scenario to form an adjusted learning path L adjusted (L personalized , F).

[0172] In this example, judge the working ability of the trainee before employment and the mastery of professional knowledge involved in the work. According to the trainee's position situation and ability situation, conduct adaptive training on the trainee. According to the training situation of the trainee during the training process, conduct targeted training adjustment on the trainee, avoid the trainee losing confidence due to being unable to keep up with the training progress easily, improve the cohesion of the enterprise, and reduce the situation of brain drain in the enterprise.

[0173] The embodiments of the present disclosure also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the financial training system and method based on VR interaction in the foregoing method embodiments.

[0174] It should be noted that more specific examples of the computer-readable storage medium may include a portable computer disk, a hard disk, an erasable programmable read-only memory (E2PROM or flash memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The foregoing computer-readable medium may be included in the foregoing electronic device; or may exist separately without being assembled into the electronic device.

[0175] The foregoing computer-readable medium carries one or more programs that, when executed by the electronic device, enable the electronic device to implement the solution provided in the foregoing method embodiments.

[0176] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages or combinations thereof. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0177] It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0178] The foregoing are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A financial training method based on VR interaction, characterized in that: Including: S1. Obtain all the positions of the enterprise to form a position set, denoted as {P1, P2, P3, P4}; Among them, P1, P2, P3, and P4 are positions related to finance in the enterprise; Obtain the position data of the trainee, denoted as PO, and PO ∈ {P1, P2, P3, P4}; S2. According to the position data PO, judge the knowledge data of the trainee; S21. Through the knowledge function, extract the professional knowledge required for the position to form a position knowledge set: Among them, K P (·) is a knowledge function, K P (PO) is a job knowledge set, K P11 -K P43 is the professional knowledge required for the corresponding positions P1 - P4; S22. Define the mapping relationship between professional knowledge and courses: Among them, K is the professional knowledge required corresponding to the trainee's position, and course is the course containing the professional knowledge K; S3. According to the knowledge data of the trainee, generate initial test questions for the trainee to judge the level of the trainee before training.

2. The financial training method based on VR interaction according to claim 1 is characterized in that: The generation of initial test questions for the trainee according to the knowledge data of the trainee includes determining the proportion of professional knowledge in relevant courses, specifically: Obtain the position data of the trainee, denoted as PO; Extract the job knowledge set K corresponding to the job data P (PO); Obtain the job knowledge set K P The mapping relationship M(K) between each professional knowledge K in (PO) and the course; Set an extraction function to extract the proportion of professional knowledge in each course, denoted as W course，K : W course，K = CourseOutline[course][K]; Among them, CourseOutline[·][·] is the extraction function, and CourseOutline is the course data structure of each course; Set up a data integration function to form a set of the proportions of professional knowledge in all relevant courses, denoted as W K : W K = {(course, W course，K ) | course ∈ M(K)}.

3. The financial training method based on VR interaction according to claim 2, wherein: Obtain the course data structure of each course corresponding to each professional knowledge, specifically: Obtain the name of the course, denoted as course name; Obtain the professional knowledge included in the course, denoted as professional knowledge; Obtain the proportion of each professional knowledge in the course, denoted as proportion; Generate the course data structure corresponding to the course: CourseOutline = {course name: {professional knowledge, proportion}}; Among them, each course corresponds to an entry, including the course name course name and a dictionary. The key of the dictionary is the professional knowledge professional knowledge, and the value is the proportion of this knowledge in the course proportion.

4. The financial training method based on VR interaction according to claim 2, characterized in that: The generation of initial test questions for the trainee according to the knowledge data of the trainee includes obtaining the mastery of professional knowledge by the trainee before training, specifically: Obtain the position data of the trainee, denoted as PO; Extract the job knowledge set K corresponding to the job data P (PO); Obtain the job knowledge set K P The mapping relationship M(K) between each professional knowledge K in (PO) and the courses Obtain the job knowledge set K P The weight set of each professional knowledge in (PO) is denoted as W K ; Obtain the in-school grades of the trainee in each course, denoted as C(course); Calculate the comprehensive mastery score of each professional knowledge K for the trainee before the training, denoted as C K :

5. The financial training method based on VR interaction according to claim 4, characterized in that: The generation of initial test questions for the trainee according to the knowledge data of the trainee includes generating initial test questions, specifically: Obtain the comprehensive mastery score of each professional knowledge K of the student, denoted as C K ; Set a proportion adjustment function to calculate the test proportion of each professional knowledge, denoted as W K : Among them, W K (C K ) is the proportion adjustment function; Obtain the question bank database of the enterprise, denoted as Q; Q = {q | q = {K q , D q , qc, qa}}; Among them, q is a question in the question bank database Q, K q is the professional knowledge to which the question q belongs, D q is the difficulty level of the question q, qc is the content of the question q, and qa is the answer to the question q; Calculate the number of test questions for each piece of expertise K, denoted as N K : N K = W K · N; Among them, N is the total number of questions in the initial test questions; Calculate the number of questions for each difficulty level under each professional knowledge K, denoted as N K，D : For each piece of expertise K, according to its test proportion W K , randomly select N K questions from the question bank database Q, and the number of questions for each difficulty level is N K，D ; Shuffle and merge all the selected questions to generate initial test questions.

6. The financial training method based on VR interaction according to claim 1, wherein: The judgment of the level of the trainee before training is specifically: Obtain the initial test score of the initial test questions of the trainee, denoted as score; Based on the allocation function, classify the trainee and design a learning path: Among them, L personalized (·, ·) is the allocation function, L personalized (PO, score) is the learning path of the trainee, PO is the post data of the trainee, K P (PO) is the post knowledge set, Knowledge-LOW is the junior learning path, Knowledge-MIDDLE is the intermediate learning path, and Knowledge-HIGH is the advanced learning path.

7. A financial training method based on VR interaction according to claim 1 or 6, characterized in that: According to the initial test score and learning path of the trainee, construct a VR interaction scenario and assign tasks, specifically: Obtain the learning path L of the trainee personalized (PO, score); Set a mapping function to determine the mapping relationship between the VR scenario and the task: Among them, V(·) is a mapping function, V(M) is the mapping relationship between the VR scenario and the task, M is the professional knowledge module in the VR scenario, M1 - M4 are all the professional knowledge modules in the VR scenario, and {M1, M2, M3, M4} = K P (PO), task11 - task42 are the VR tasks corresponding to each professional knowledge module M; Merge the tasks of all modules to generate the final VR task set, denoted as T VR (L personalized ): Among them, T VR (·) is the merging module function, L personalized (PO, score) is the learning path of the student; According to the task set T VR (L personalized ), construct a corresponding VR interaction scenario for each task.

8. A financial training method based on VR interaction according to claim 7, characterized in that: Adjust the learning path of the trainee according to the learning feedback of the trainee, specifically: Obtain the current learning path L of the student personalized (PO, score); Define the execution result of the task in the VR interaction scenario, denoted as F(t): F(t) = {success, feedback}; where success is a boolean value indicating whether the trainee's task is successfully completed and is the feedback information of the trainee; Obtain the execution results of all tasks of the trainee in the VR interaction scenario, denoted as F: F = {F(t) | t ∈ T VR (L personalized )}; Based on the path adjustment function, generate an adjusted learning path, denoted as L adjusted (L personalized , F): Among them, L adjusted (·, ·) is a path adjustment function, Ex tu : · is an extra tutoring module, and F(t).success is the execution result of the tasks of the trainees in the VR interaction scenario.

9. A system for implementing the financial training method based on VR interaction according to claim 1, characterized in that: including: Initial determination module: used to determine the pre-training level of the trainee based on the trainee's position data PO and determine the learning path L of the trainee personalized (PO, score); VR Interaction Module: Used to construct a VR interaction scenario and assign tasks based on the initial test score score and learning path L of the trainee personalized (PO, score), and conduct corresponding training for the trainee; Feedback adjustment module: used to adjust the learning path of the trainee according to the learning feedback in the VR interaction scenario, and form an adjusted learning path L adjusted (L personalized , F).

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