A golden investment intelligent practical teaching system and teaching method fusing mindfulness emotional intervention

Abstract

The application discloses a kind of golden investment intelligent practical teaching system and teaching method of fusion mindfulness emotional intervention, it is related to artificial intelligence teaching technical field, system includes student terminal, cloud algorithm middle station, teaching research background, resource database, middle station carries four big algorithm engine of self-research emotional quantification, mindfulness matching, market classification, lesson plan generation;Through multidimensional acquisition of student questionnaire and simulated transaction data by psychological test, calculate four special gold trading emotion indexes of chasing impulse, position anxiety, loss fear, greed heavy position, automatically generate psychological portrait;When index exceeds, the system intelligently pushes corresponding mindfulness guidance course, with five gold market classification practical training, 433 position practical training, loss order is automatically traced to distinguish technology / psychological defects and directional remedial education, AI automatically generates learning lesson plan.The application solves the industry pain points of traditional gold financial literacy teaching, such as heavy technology, no emotional quantification, no mindfulness correction and unified teaching, and realizes the intelligent closed loop of whole-process teaching.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence education technology, specifically to an intelligent training and teaching system and method for gold investment that integrates mindfulness-based emotion intervention. Background Technology

[0002] Currently, the domestic gold financial literacy education and training industry generally suffers from the following technical shortcomings: First, the existing gold investment teaching system focuses heavily on candlestick analysis, technical indicator analysis, and fundamental analysis, lacking a quantitative assessment system for trading emotions and a mindfulness-based psychological correction system. During practical training, students are severely hampered by irrational emotional factors such as greedy over-leveraging, fear of stop-loss orders, impulsive opening of positions, and holding onto losing trades. This leads to a disconnect between theoretical learning and actual trading, resulting in low course monetization and renewal rates.

[0003] Secondly, commercially available financial simulation trading software only has basic functions such as historical candlestick chart review and virtual fund trading. It lacks a dedicated psychological assessment model and graded mindfulness course library adapted to the gold trading scenario, and cannot accurately push corrective content based on the student's psychological deficiencies.

[0004] Third, traditional training institutions use a standardized teaching model with uniform courseware and uniform practical training tasks, which cannot generate personalized learning plans based on students' psychological characteristics, risk preferences, learning weaknesses, and profit and loss data.

[0005] Fourth, the industry lacks a complete closed-loop teaching chain, encompassing psychological profile assessment, basic knowledge learning, scenario-based simulation training, loss cause analysis, mindfulness-based targeted correction, and iterative optimization. Teaching data is scattered, making it difficult for the teaching and research department to quantify and manage student growth data, and course iterations also lack sufficient data support. Therefore, we propose a smart training system and teaching method for gold investment that integrates mindfulness-based emotional intervention. Summary of the Invention

[0006] To address the shortcomings of existing gold investment literacy education, which emphasizes technical skills over psychological aspects, suffers from homogenized instruction, and lacks a closed-loop practical training system, this invention provides an intelligent practical training system and method for gold investment that integrates mindfulness-based emotional intervention. This invention utilizes a self-developed four-dimensional emotion quantification algorithm specific to gold, a mindfulness-based course intelligent matching engine, a five-market-specific automatic classification engine, and an AI-generated personalized lesson plan generation engine to achieve digital assessment of students' trading psychology, targeted mindfulness intervention for emotional issues, personalized teaching tailored to each individual, and a full-chain closed-loop practical training, thereby filling a technological gap in the industry.

[0007] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: This invention provides a smart training and teaching system for gold investment that integrates mindfulness-based emotion intervention, including a student terminal module, a cloud-based algorithm platform module, a teaching and research management backend module, and a cloud-based resource database; The cloud-based resource database contains a full-cycle international gold historical market data database, a multi-level mindfulness guidance course resource library, a gold investment knowledge question bank, a self-developed 433 capital allocation teaching rule library, and a tiered stop-loss risk control case library. The cloud-based algorithm platform has a built-in gold trading sentiment quantitative calculation engine, a mindfulness course intelligent matching engine, a multi-scenario market automatic classification engine, and a personalized lesson plan generation engine. The system includes a psychological record-keeping and emotion assessment subsystem, a mindfulness intelligent intervention subsystem, a golden-level scenario training subsystem, and an AI intelligent lesson plan management subsystem that operate in a coupled manner. The psychological profile and emotion assessment subsystem is equipped with an assessment question bank unit, a multi-source data acquisition unit, an emotion index calculation unit, and a psychological profile generation unit. The multi-source data acquisition unit is used to collect standardized psychological questionnaire answers and simulated trading behavior data from trainees. The emotion index calculation unit calculates the chasing impulse index A1, holding anxiety index A2, loss fear index A3, and greedy heavy position index A4 based on the collected data. The psychological profile generation unit generates psychological classification tags for trainees based on the calculation results of the four emotion indices.

[0008] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotional intervention as described in this invention, the simulated trading behavior data collected by the multi-source data acquisition unit includes the frequency of opening positions, average holding period, proportion of funds occupied by a single position, stop-loss records, records of holding positions, and the profit and loss range of a single transaction.

[0009] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotional intervention as described in this invention, the psychological profile generation unit classifies student label types based on the thresholds of the four emotional indices. The label types include aggressive and impulsive, conservative and fearful, volatile and anxious, and balanced and stable.

[0010] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotion intervention as described in this invention, the mindfulness-based intelligent intervention subsystem includes a tiered mindfulness course library, an intelligent task push unit, a training check-in recording unit, and a mindset improvement data backtracking unit. The intelligent task push unit receives four emotion index data output by the emotion index calculation unit. When any one of the emotion indices exceeds the system's preset critical threshold, it automatically retrieves the corresponding mindfulness guidance course and position guidance training task from the tiered mindfulness course library and pushes them to the student's terminal.

[0011] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotional intervention as described in this invention, when the greed index A4 exceeds a preset critical threshold, the intelligent task push unit automatically pushes mindfulness training courses on position management and courses on managing anxiety from large holdings to the student's terminal.

[0012] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotional intervention as described in this invention, the gold tiered scenario training subsystem includes a historical market data intelligent splitting unit, a tiered virtual capital training unit, a 433 sub-account teaching unit, and a training loss tracing unit. The historical market data intelligent segmentation unit automatically captures historical data of gold daily and hourly charts and intelligently divides them into five training scenarios: bull market trend, bear market decline, wide-range fluctuation, non-farm payroll data release, and bullish / bearish shakeout. The tiered virtual fund training unit is set up with three levels of fund permissions: novice small simulated fund, intermediate simulated fund and advanced large simulated fund. The three levels of fund permissions are unlocked step by step as the learner progresses. The training loss tracing unit captures all the trainees' loss orders and distinguishes between two types of loss causes: lack of technical knowledge and irrational emotional operations.

[0013] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotional intervention as described in this invention, the training loss tracing unit associates loss orders marked as lacking technical knowledge points with corresponding knowledge point remedial courses, and associates loss orders marked as irrational emotional operations with corresponding mindfulness correction courses.

[0014] As a preferred embodiment of the intelligent training and teaching system for gold investment that integrates mindfulness-based emotion intervention as described in this invention, the AI ​​intelligent lesson plan management subsystem includes a multi-dimensional data capture unit, an automatic lesson plan generation unit, and a teaching and research visualization backend unit. The multidimensional data capture unit captures student psychological tags, monthly profit and loss data of practical training, data on collected incorrect questions, and data on course completion progress; The lesson plan automatic generation unit, based on the data captured by the multi-dimensional data capture unit, automatically generates a personalized learning plan, practical training assignments, and key review content for each student every day. The teaching and research visualization backend unit is used to display class psychological distribution reports, practical training profit and loss statistics, and the ranking data of weak knowledge points of the whole class to the instructors in batches.

[0015] A smart training method for gold investment that integrates mindfulness-based emotion intervention, using the aforementioned system, includes the following steps: S1: Students complete registration and standardized psychological assessment through the student terminal. The system collects questionnaire answer data and simulated trading behavior data, calculates four emotion indices: chasing the rise impulse index A1, holding anxiety index A2, loss fear index A3, and greedy heavy position index A4, and generates a student's exclusive psychological profile classification label based on the calculation results. S2: The system matches the corresponding basic teaching courses and introductory mindfulness training courses with the psychological profile classification tags and initial emotion index, and pushes them to the student's terminal to complete the pre-learning. S3: The system grants simulated training access to corresponding market scenarios based on the student's learning level. Students use virtual funds of the corresponding level for practical training. The system records the operation data of each opening position, closing position, position setting, and stop-loss setting in real time. S4: After the training, the loss tracing unit automatically locates the cause of loss for each loss order and marks the cause of loss as a technical shortcoming or an emotional shortcoming. S5: For losing orders marked as emotional weaknesses, the system automatically pushes targeted mindfulness correction courses; for losing orders marked as technical weaknesses, the system automatically pushes corresponding knowledge point remedial courses. S6: After completing the error correction training, trainees will enter the same market scenario for a second retest training to verify the correction effect. S7: The teaching and research team summarizes multi-dimensional teaching data of all students within the cycle and iteratively optimizes the teaching plans for the offline financial literacy training camp.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention pioneers a four-dimensional emotion quantification model for gold trading and an intelligent matching algorithm for mindfulness courses, achieving deep integration of psychology, financial investment, and artificial intelligence, unlike existing single-function simulation software. This invention breaks the traditional uniform teaching model, realizing personalized intelligent teaching for each individual, effectively improving students' pain points of irrational trading, and significantly increasing course completion and repurchase rates. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below.

[0018] This system is built collaboratively with four hardware and software components: a student terminal module, a cloud-based algorithm platform module, a teaching and research management backend module, and a cloud-based resource database. All components are deployed on a cloud server cluster leased independently by the enterprise, with distributed data storage. The specific deployment configuration is as follows: The student terminal module is divided into two types of implementation platforms: ① Online mobile terminal: adapted for Android and iOS mobile apps, with built-in audio and video playback, simulated trading operations, psychological questionnaire interaction, and mindfulness check-in functions; ② Offline training terminal: desktop computers and tablet training terminals in the financial literacy training camp computer lab, equipped with a large-screen market data display window. The terminal has real-time data upload, offline mindfulness course caching, and training task pop-up reminder functions. All student answers and trading behavior data are synchronized to the cloud in milliseconds.

[0019] The cloud-based algorithm platform module independently deploys four self-developed algorithm engines, each running independently with interconnected data: a gold trading sentiment quantitative calculation engine, a mindfulness course intelligent matching engine, a multi-scenario market automatic classification engine, and a personalized lesson plan generation engine; the platform acts as the core data transfer hub, connecting in real time with student terminals, resource databases, and teaching and research backends.

[0020] The teaching and research management backend module adopts a PC web-based management system with three levels of account permissions: enterprise administrator, training camp instructor, and teaching assistant. The backend has built-in functions such as visual data dashboard, batch course push, student data export, and offline lesson plan editing, and supports viewing teaching data from all dimensions for classes and individuals.

[0021] The cloud-based resource database adopts a dual-storage architecture of time-series database and MySQL distributed storage, divided into four independent data storage partitions, pre-stored with standardized teaching resources: (1) International Gold Historical Price Zone: Stores complete daily, 4-hour and hourly K-line data of London gold and Shanghai gold from 2000 to the present, and synchronizes sudden price events such as non-farm payrolls, interest rate decisions and geopolitical events; (2) Graded Mindfulness Course Resources: Mindfulness materials are divided into three levels: introductory, corrective, and advanced, including breathing meditation audio, emotional guidance videos, and psychological review and check-in questionnaires; (3) Gold Investment Knowledge Question Bank Section: Includes teaching videos and multiple-choice questions on K-line patterns, technical indicators, support and resistance, position risk control, and fundamental analysis; (4) Self-developed 433 fund allocation teaching rule library: solidify standardized teaching risk control model, stipulate that the total funds should be allocated as 40% of the base position, 30% of the swing position, and 30% of the short-term position, with supporting warning logic for illegal heavy positions.

[0022] Detailed Implementation Process of Four Coupled Subsystems (I) Implementation of the Psychological Record-keeping and Emotional Assessment Subsystem This module is used to collect basic student data, quantify trading sentiment, and generate a permanent psychological profile. Complete implementation steps: Assessment Question Bank Unit: Includes 28 questions on a psychological scale specifically for gold trading. The questions correspond to four types of irrational trading behaviors: impulsive buying, anxiety about holding positions, fear of losses, and greedy over-leveraging. Students are required to complete the scale after registration. No simulation training function can be unlocked without completing the assessment.

[0023] Multi-source data acquisition unit: Simultaneously acquires two types of core data sources: ①Static questionnaire data: The scores for each item on the questionnaire are uploaded to the database in real time; ②Dynamic behavioral data of simulated trading: Real-time capture of the opening frequency, capital ratio of a single position, average holding time, record of holding positions, stop-loss execution status, and profit / loss margin of each simulated trade, collected continuously around the clock.

[0024] Sentiment Index Calculation Unit (Deployment of Self-Developed Quantitative Algorithm) The system calculates four core sentiment indices in real time based on collected data, and sets fixed threshold values ​​as the judgment criteria: A1 Chasing Rally Impulse Index: Calculated based on weighted average of short-term continuous opening positions and chasing orders after market breakouts. The threshold is 60. Exceeding this threshold indicates an excessive impulse. A2 Position Anxiety Index: Calculated based on the weighted average of long-term inactive positions and frequent market refreshes, with a threshold of 65. Exceeding this threshold indicates excessive anxiety. A3 Loss Fear Index: Calculated by weighting the behavior of quickly closing positions with small losses and being afraid to place orders when stop loss is reached. The threshold is 60. If it is exceeded, it is considered that the fear is too high. A4 Greedy Over-Position Index: Calculated by weighting the amount of a single position exceeding 50% of the total capital and the behavior of adding to or expanding positions after making a profit. The threshold is 55. Exceeding this threshold is considered greedy and exceeds the limit.

[0025] The student psychological profile generation unit automatically categorizes students into four types based on four index thresholds, permanently archives them in the student's electronic file, and dynamically updates the profile after each training session. ① Aggressive and impulsive type: S1 index exceeds the standard; ② Conservative and fearful type: S3 index exceeds the standard; ③ Volatile and anxious type: S2 index exceeds the standard; ④ Balanced and stable type: all four indices are below the critical threshold.

[0026] Implementation of the Mindfulness-Based Intelligent Intervention Subsystem This module provides targeted psychological correction training for students experiencing emotional distress, achieving a closed-loop intervention in trading mindset: Hierarchical Mindfulness Course Library with Layered Storage Beginner Level: 5-minute basic breathing and meditation training, designed for complete beginners; Corrective Level: Four types of targeted guidance courses, corresponding to restraint in chasing high prices, guidance on stop-loss fear, stress reduction in holding positions, and correction of greed due to heavy positions; Advanced: 30-minute meditation for stabilizing a long-term trading mindset, designed for advanced trainees.

[0027] Intelligent task push unit linkage logic Every 30 minutes, the system automatically reviews the student's four emotional indices. If any index exceeds the preset threshold, a corresponding mindfulness course pop-up will be automatically pushed to the student's terminal. A completion lock mechanism is set up: a new round of simulated training cannot be started if the mindfulness training is not completed.

[0028] For example: If a student's greed index A4 = 62 > 55, the system will automatically push the video course "Mindfulness Guidance for Greedy Large Positions" and the position management check-in task.

[0029] The training check-in record unit fully records the completion time of the mindfulness course, the score of the post-course psychological questionnaire, and the completion time. Unfinished tasks are marked as pending tasks, and the data is synchronized to the teaching and research management backend. Instructors can view the mindfulness training completion rate of the whole class.

[0030] The mindset improvement data tracking unit automatically generates monthly reports on the changes in four emotional indices of trainees, visually displaying the changes in emotional fluctuations before and after mindfulness training, serving as a basis for evaluating the effectiveness of mindset correction.

[0031] Implementation of the Gold-Level Scenarios Training Subsystem This module provides simulated trading training with varying difficulty levels and market conditions, and automatically identifies the root causes of losses: Historical market data is intelligently divided into units. The algorithm automatically identifies the historical K-line trend of gold and automatically divides the massive market data into five independent training scenarios. Instructors can manually specify the scenarios, and the system can randomly assign them: bull market trend, bear market decline, wide-range fluctuation, non-farm payroll data release, and shakeout market.

[0032] The tiered virtual capital training unit (tier unlocking mechanism) has three levels of training access. Students can unlock higher tiers only after completing the corresponding training tasks. Beginner level: 10,000 yuan in virtual funds, only open to basic bull market and sideways market conditions; Advanced Tier: 100,000 yuan in virtual funds, unlocking all five major market scenarios and the 433 sub-account tutorial function; Advanced level: 500,000 yuan in virtual funds, with open training on challenging market conditions such as non-farm payrolls and market shakeouts.

[0033] The self-developed 433 capital allocation teaching unit has a built-in allocation calculator in the training interface, which forcibly guides students to allocate funds according to 40% base position, 30% swing trading, and 30% short-term trading. If a student operates with full or heavy positions, the system will pop up a risk control reminder in real time and record the violation into the student's trading behavior database.

[0034] The logic for determining the source of losses in the training unit is as follows: After each round of training, all loss orders are automatically traversed, and the cause of the loss is determined in two steps: Step 1: Compare with the gold knowledge question bank. If the loss is caused by incorrect judgment of candlestick charts, indicators, or trends, mark it as a technical weakness. Step 2: If the loss is caused by psychological behaviors such as holding onto a large position, fear of stop loss, or impulsive chasing of orders, mark it as an emotional weakness. The two types of weaknesses are respectively linked to corresponding knowledge point remedial courses and mindfulness correction courses.

[0035] Implementation of AI-powered intelligent lesson plan management subsystem This module is designed for teaching and research instructors, automatically generating personalized teaching plans and enabling visualized management and control of class data. The multi-dimensional data capture unit automatically summarizes the periodic data of all students every day at midnight. The data collection dimensions include: student psychological tags, total monthly training profit and loss, error collection data, mindfulness course completion progress, and risk assessment scores.

[0036] The lesson plan is automatically generated in units, which, based on the captured multi-dimensional data, generate a personalized daily learning plan for each student. The plan includes: the day's practical market scenario, virtual capital levels, essential gold knowledge points, matching mindfulness training tasks, and practical review questions. The plan is automatically pushed to the student's terminal every day.

[0037] The teaching and research visualization backend unit is configured with class data dashboards, which display the percentage of students in the four types of psychology in the whole class, the profit and loss statistics of practical training, and the ranking of frequently and weak knowledge points in real time; it supports the export of Excel reports, targeted batch push of courses, and query of the loss traceability records of all students. Instructors can optimize the teaching content of offline training camps based on statistical data.

[0038] Complete Implementation Steps of a Smart Practical Training Method for Gold Investment Integrating Mindfulness-Based Emotional Intervention S1: Student registration and file creation on the terminal, completion of standardized psychological assessment. Students register by logging into the APP / website and filling in information. The system generates a unique electronic student ID. A 28-question gold trading psychological questionnaire is forcibly popped up. After completing the questionnaire, the system collects the questionnaire data. New students without historical trading records are given a simplified basic simulation training to generate trading behavior data. The emotion index calculation unit calculates four emotion indices from S1 to S4 and generates a unique psychological profile label, which is then stored in the student's file.

[0039] S2: System-based basic instruction + introductory mindfulness preparatory course Based on the trainee's psychological tags and training level, the system automatically combines and pushes two types of pre-training content: basic knowledge courses such as golden candlestick charts and indicators, and a 5-minute introductory breathing mindfulness training; the system has a progress lock, and trainees cannot enter the simulated training stage until all courses are completed.

[0040] S3: Practical Training on Virtual Funds in Tiered Scenarios The system allocates a corresponding amount of virtual funds based on the student's unlock level. Five market scenarios are assigned manually by the instructor or randomly by the system. Students complete all operations such as opening positions, stopping losses, and adding or reducing positions on the demo account. The system retains the data of each transaction in real time and uploads it to the cloud database simultaneously.

[0041] S4: Automatic source tracing of training losses, distinguishing defect types. After a single training session, the loss tracing unit goes through all loss orders, determines the cause of each loss, marks it as a technical or emotional shortcoming, and generates a training defect report that is displayed on the student's terminal homepage.

[0042] S5: Targeted delivery of corrective learning content If marked as a technical weakness: automatically retrieve the corresponding key knowledge point teaching videos and accompanying exercises; If marked as an emotional weakness: retrieve the matching graded mindfulness correction course, set a fixed completion deadline, and the next round of training cannot be started if the deadline is not met.

[0043] S6: Second round of retesting and training in peer interaction scenarios to verify the corrective effect. After completing all the corrective courses, the system assigns students to retest and train in the same market scenario. The system compares the profit and loss data and the frequency of irrational operations between the two training sessions. If the losses and impulsive trading behavior are significantly reduced, the correction is deemed effective, and the next stage of training is unlocked. If the emotional problems do not improve, a higher-level mindfulness guidance course is recommended.

[0044] S7: The teaching and research team summarizes periodic data and iterates offline financial literacy lesson plans. Every Monday, the teaching and research backend automatically generates a full-cycle data report for the class, including student psychological distribution, statistics on loss triggers, and high-frequency weak knowledge points for the whole class. Based on the report, the instructors adjust the teaching content of the offline "Enlightened Investor" training camp, add special teaching sessions for high-frequency emotional issues and high-frequency technical shortcomings, and iterate and optimize the offline courseware.

[0045] Two practical business implementation examples Example 1: Implementation of an offline, closed-door gold financial literacy training camp Fusman Education's Golden Life Offline Training Camp, with 52 students per session, uses PC terminals centrally deployed in the training lab, all connected to the cloud platform of this system, with a complete implementation process: On the first day of the camp, all students logged into the computer lab to register and create files, and completed psychological assessments in a unified manner; the teaching and research backend calculated four emotion indices in batches and generated class psychological distribution reports, which were then viewed by instructors on-site to identify student psychological categories. We will push exclusive pre-courses to students based on their psychological tags: the aggressive and impulsive group will receive a course on mindfulness of chasing high prices and practical training on small position limits; the conservative and fearful group will receive a course on mindfulness of stopping losses and anxiety relief and special teaching on phased position building; the volatile and anxious group will receive meditation training on stress reduction while holding positions; and the balanced and stable group will receive an advanced gold trading technique course. The daily training sessions are randomly assigned to high-volatility market conditions such as non-farm payrolls and wide-range fluctuations, and students use 100,000 virtual funds for practice. If a student suffers losses due to holding onto a losing position or heavy leverage, the system will push the corresponding mindfulness course in real time, and the student is required to complete the daily check-in. Every day at dawn, the AI ​​lesson plan engine automatically generates a personalized learning plan for each student. Based on the class's big data reports, the instructors adjust the focus of the next day's offline teaching and add special explanations for common problems in the class. Upon completion of the training camp, the system exports a complete mindset growth curve and a full-cycle profit and loss report for each participant, which serves as the basis for a comprehensive evaluation of the participants' graduation.

[0046] Example 2: Implementation of an Online SaaS Paid Membership System Fusman's online financial literacy SaaS paid members access the system via a mobile app, completing the entire online business implementation process: New paid members will automatically complete a psychological assessment upon registration, generating a permanent personal psychological profile, and will unlock higher tiers of virtual training funds monthly as their learning progress progresses. The system captures members’ simulated trading behavior daily and dynamically updates four emotion indices. When the indices exceed the limits, a mindfulness meditation course is pushed to the mobile phone via a pop-up window. It supports offline audio caching, making it convenient for students to complete mindfulness training at any time. After completing the daily simulation training, members automatically receive a loss tracing report and receive targeted push notifications of knowledge point tutoring or mindfulness correction courses. The system pushes monthly big data reports of all paid members to the teaching and research team, and the R&D team iterates and optimizes the online "Enlightened Investor" series of paid teaching courses.

[0047] Although the present invention has been described above with reference to embodiments, various modifications can be made thereto without departing from the scope of the invention. In particular, the exhaustive description of these combinations is not provided in this specification merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A smart training system for gold investment that integrates mindfulness-based emotion intervention, characterized in that: It includes a student terminal module, a cloud-based algorithm platform module, a teaching and research management backend module, and a cloud-based resource database; The cloud-based resource database contains a full-cycle international gold historical market data database, a multi-level mindfulness guidance course resource library, a gold investment knowledge question bank, a self-developed 433 capital allocation teaching rule library, and a tiered stop-loss risk control case library. The cloud-based algorithm platform has a built-in gold trading sentiment quantitative calculation engine, a mindfulness course intelligent matching engine, a multi-scenario market automatic classification engine, and a personalized lesson plan generation engine. The system includes a psychological record-keeping and emotion assessment subsystem, a mindfulness intelligent intervention subsystem, a golden-level scenario training subsystem, and an AI intelligent lesson plan management subsystem that operate in a coupled manner. The psychological profile and emotion assessment subsystem is equipped with an assessment question bank unit, a multi-source data acquisition unit, an emotion index calculation unit, and a psychological profile generation unit. The multi-source data acquisition unit is used to collect standardized psychological questionnaire answers and simulated trading behavior data from trainees. The emotion index calculation unit calculates the chasing impulse index A1, holding anxiety index A2, loss fear index A3, and greedy heavy position index A4 based on the collected data. The psychological profile generation unit generates psychological classification tags for trainees based on the calculation results of the four emotion indices.

2. The system according to claim 1, characterized in that, The simulated trading behavior data collected by the multi-source data acquisition unit includes the frequency of opening positions, average holding period, proportion of funds occupied by a single position, stop-loss records, records of holding positions, and the profit or loss margin of a single transaction.

3. The system according to claim 1, characterized in that, The psychological profile generation unit classifies trainees into label types based on the thresholds of the four emotion indices. The label types include radical and impulsive, conservative and fearful, volatile and anxious, and balanced and stable.

4. The system according to claim 1, characterized in that, The mindfulness intelligent intervention subsystem includes a tiered mindfulness course library, an intelligent task push unit, a training check-in record unit, and a mindset improvement data backtracking unit. The intelligent task push unit receives four emotion index data output by the emotion index calculation unit. When any of the emotion indices exceeds the system's preset critical threshold, it automatically retrieves the corresponding mindfulness guidance course and stock management training task from the tiered mindfulness course library and pushes them to the student's terminal.

5. The system according to claim 4, characterized in that, When the Greedy Heavy Position Index A4 exceeds the preset critical threshold, the intelligent task push unit automatically pushes the position management mindfulness training course and the large position anxiety relief course to the student's terminal.

6. The system according to claim 1, characterized in that, The gold tiered scenario training subsystem includes a historical market data intelligent splitting unit, a tiered virtual capital training unit, a 433 sub-account teaching unit, and a training loss tracing unit. The historical market data intelligent segmentation unit automatically captures historical data of gold daily and hourly charts and intelligently divides them into five training scenarios: bull market trend, bear market decline, wide-range fluctuation, non-farm payroll data release, and bullish / bearish shakeout. The tiered virtual fund training unit is set up with three levels of fund permissions: novice small simulated fund, intermediate simulated fund and advanced large simulated fund. The three levels of fund permissions are unlocked step by step as the learner progresses. The training loss tracing unit captures all the trainees' loss orders and distinguishes between two types of loss causes: lack of technical knowledge and irrational emotional operations.

7. The system according to claim 6, characterized in that, The training loss tracing unit will associate loss orders marked as lacking technical knowledge points with corresponding knowledge point remedial courses, and loss orders marked as irrational emotional operations with corresponding mindfulness correction courses.

8. The system according to claim 1, characterized in that, The AI-powered intelligent lesson plan management subsystem includes a multi-dimensional data capture unit, an automatic lesson plan generation unit, and a teaching and research visualization backend unit. The multidimensional data capture unit captures student psychological tags, monthly profit and loss data of practical training, data on collected incorrect questions, and data on course completion progress; The lesson plan automatic generation unit, based on the data captured by the multi-dimensional data capture unit, automatically generates a personalized learning plan, practical training assignments, and key review content for each student every day. The teaching and research visualization backend unit is used to display class psychological distribution reports, practical training profit and loss statistics, and the ranking data of weak knowledge points of the whole class to the instructors in batches.

9. A smart practical training method for gold investment that integrates mindfulness-based emotion intervention, characterized in that: The system described in any one of claims 1 to 8 comprises the following steps: S1: Students complete registration and standardized psychological assessment through the student terminal. The system collects questionnaire answer data and simulated trading behavior data, calculates four emotion indices: chasing the rise impulse index A1, holding anxiety index A2, loss fear index A3, and greedy heavy position index A4, and generates a student's exclusive psychological profile classification label based on the calculation results. S2: The system matches the corresponding basic teaching courses and introductory mindfulness training courses with the psychological profile classification tags and initial emotion index, and pushes them to the student's terminal to complete the pre-learning. S3: The system grants simulated training access to corresponding market scenarios based on the student's learning level. Students use virtual funds of the corresponding level for practical training. The system records the operation data of each opening position, closing position, position setting, and stop-loss setting in real time. S4: After the training, the loss tracing unit automatically locates the cause of loss for each loss order and marks the cause of loss as a technical shortcoming or an emotional shortcoming. S5: For losing orders marked as emotional weaknesses, the system automatically pushes targeted mindfulness correction courses; for losing orders marked as technical weaknesses, the system automatically pushes corresponding knowledge point remedial courses. S6: After completing the error correction training, trainees will enter the same market scenario for a second retest training to verify the correction effect. S7: The teaching and research team summarizes multi-dimensional teaching data of all students within the cycle and iteratively optimizes the teaching plans for the offline financial literacy training camp.

10. The method according to claim 9, characterized in that, In step S1, the student-specific psychological profile classification labels include four types: radical and impulsive, conservative and fearful, volatile and anxious, and balanced and stable. In step S3, the market scenarios include five categories: bull market trend, bear market decline, wide-range fluctuation, non-farm payroll data release, and shakeout market with false breakouts and false breakouts. In step S5, the mindfulness correction course includes at least one of meditation courses, position management training tasks, and position control training courses.