College student coffee entrepreneurship practical training method based on entrepreneurship guidance service

Abstract

The invention discloses a college student coffee entrepreneurship practical training method based on entrepreneurship guidance service. The method is executed through a central data processing center, and comprises the following steps: collecting student user data and generating an initial entrepreneurship ability comprehensive score; constructing a personalized virtual entrepreneurship environment based on the score, carrying out simulation operation and forming a simulation operation data set; the data set is analyzed, key decision types are identified, a real miniature entrepreneurship practical operation task list is generated, and practical operation process data are collected after execution; and comparing and analyzing the practical operation process data with the simulated operation data set, generating a personalized entrepreneurship guidance report, adjusting virtual environment model parameters, and guiding iterative practical training. According to the method, a practical training closed loop of evaluation, simulation, practical operation and iteration is constructed, and low-cost deep trial and error, data-driven accurate guidance and continuous optimization of the entrepreneurship ability of student users are realized.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a training method for college students to start a coffee business based on entrepreneurship guidance services. Background Technology

[0002] Innovation and entrepreneurship education in higher education institutions is an important component of the national innovation system, aiming to cultivate students' entrepreneurial spirit and practical abilities. Currently, entrepreneurship training methods for university students mainly include classroom teaching, case analysis, business plan writing, and short-term entrepreneurship competitions. These methods generally suffer from the following significant shortcomings:

[0003] First, existing practical training methods lack continuous simulations that are highly coupled with real business environments. Classroom teaching and case studies focus on theoretical instruction and post-event analysis, preventing students from experiencing the dynamic and continuous decision-making pressures and feedback during the entrepreneurial process. Business plan writing often stops at static documents, lacking the crucial step of implementing the plan and subjecting it to market testing. Entrepreneurial competitions are short-term and highly utilitarian, emphasizing the display of "highlights" rather than the refinement of solid, repeatable operational processes, making it difficult for students to deeply understand the intricate complexities of the entire entrepreneurial process, such as supply chain management, cost control, customer service, and cash flow management.

[0004] Secondly, existing methods provide fragmented and non-data-driven guidance services, lacking personalization and precision. Guidance often relies on the mentor's experiential judgment, lacking comprehensive and detailed data collection and analysis of students' individual entrepreneurial capabilities, decision-making processes, and operational results. Therefore, guidance feedback is often general and delayed, failing to provide students with quantifiable and actionable improvement suggestions based on their specific behavioral data and simulation results. Students struggle to accurately identify their weaknesses in specific areas such as site selection assessment, product pricing, marketing promotion, and inventory management through practical training.

[0005] Secondly, existing methods are costly in the "trial and error" stage, and students have low risk tolerance. Real coffee entrepreneurship involves substantial investments in venues, equipment, and raw materials; a wrong decision can lead to real economic losses, which severely restricts students' ability to boldly try and innovate. The lack of a safe, low-cost simulation environment that allows for repeated trial and error and observation of the consequences of different decisions is a major bottleneck in current university entrepreneurship training.

[0006] Finally, existing training methods are fragmented, failing to form a closed-loop system from competency assessment, simulation, miniature practice to iterative optimization. Students' entrepreneurial learning process is disjointed, unable to organically unify early planning, mid-term operation, and late review, making it difficult to achieve spiral-like competency improvement.

[0007] Therefore, there is an urgent need for an innovative training method that can construct a highly realistic, data-driven, low-cost trial-and-error-allowing, and complete teaching closed loop training environment for college students to start their own coffee businesses. This would solve the core problems of existing technologies, such as the disconnect between theory and practice, inaccurate guidance, high trial-and-error costs, and disjointed learning processes. Summary of the Invention

[0008] To achieve the above objectives, this invention provides a training method for college students in coffee entrepreneurship based on entrepreneurship guidance services. The method is executed through a central data processing center and includes the following steps:

[0009] Step S100: Collect initial entrepreneurial ability data from student users, process the initial entrepreneurial ability data, and generate a comprehensive score for initial entrepreneurial ability.

[0010] The initial entrepreneurial ability data includes knowledge mastery data obtained from assessment questionnaires and resource allocation decision data obtained from virtual scenario configuration tasks;

[0011] Step S200: Based on the initial entrepreneurial ability comprehensive score, configure the reinforcement learning environment and construct a personalized virtual entrepreneurial environment;

[0012] The personalized virtual entrepreneurial environment is a simulation framework built on historical consumption data, operational data, and customer behavior data. It is configured with student user agents based on machine learning models. The student user agents conduct simulated operations, record daily operational decision-making instruction sequences and corresponding operational result data sequences, and form a simulated operation dataset.

[0013] Step S300: Analyze the simulated business dataset, identify key decision types and determine the decision sequence to be verified, generate a real miniature entrepreneurship practice task list based on the decision sequence to be verified, and have student users execute the real miniature entrepreneurship practice task list and collect practice process data.

[0014] Step S400: Compare and analyze the practical process data with the corresponding data in the simulated business dataset, generate a personalized entrepreneurship guidance report, and adjust the model parameters of the personalized virtual entrepreneurship environment based on the analysis results, guiding student users back to step S200 for iterative training.

[0015] Preferably, in step S100, the process of collecting initial entrepreneurial ability data from student users and processing the initial entrepreneurial ability data to generate a comprehensive score for initial entrepreneurial ability specifically includes:

[0016] The central data processing center distributes assessment questionnaires to student users' clients through a pre-set online questionnaire system;

[0017] The assessment questionnaire included questions on coffee industry knowledge, basic financial knowledge, marketing principles, and risk management awareness.

[0018] The student user client receives student users' answer data and uploads it to the central data processing center;

[0019] The central data processing center calls the built-in first scoring algorithm to parse the answer data and generate the student user's first quantitative score in the knowledge mastery dimension;

[0020] At the same time, the central data processing center issued the initial scene configuration task for the virtual coffee shop to student user clients;

[0021] The initial scene configuration task for the virtual coffee shop includes virtual start-up capital, multiple preset virtual store location options, a virtual equipment list option, and a virtual ingredient list option.

[0022] Record the selection commands for virtual store location options, virtual equipment list options, and virtual raw material list options uploaded by student user clients to form an initial configuration scheme;

[0023] The central data processing center analyzes the initial configuration scheme based on the pre-stored virtual cost structure database, virtual device efficiency parameter database, and configuration matching rule base, and generates a second quantitative score for student users in the dimension of resource allocation decision-making ability.

[0024] The central data processing center weights the first quantitative score according to a preset first weighting coefficient, and the second quantitative score according to a preset second weighting coefficient. The weighted first quantitative score and the weighted second quantitative score are then added together to obtain the initial entrepreneurial ability comprehensive score.

[0025] Preferably, in step S200, the construction of a personalized virtual entrepreneurial environment based on the initial entrepreneurial ability comprehensive score specifically includes:

[0026] The central data processing center has a pre-stored virtual scenario library, which contains at least three virtual business scenario templates of different difficulty levels;

[0027] Each virtual business scenario template is associated with a set of scenario parameters, including virtual business district map parameters, virtual supply chain system parameters, and virtual customer group behavior model parameters.

[0028] The initial entrepreneurial ability score is compared with a preset difficulty level threshold range to determine the matching initial difficulty level.

[0029] The central data processing center retrieves the virtual business scenario template corresponding to the initial difficulty level from the virtual scenario library, and loads the virtual business district map parameters, virtual supply chain system parameters, and virtual customer group behavior model parameters.

[0030] The virtual business district map parameters include pedestrian traffic distribution data, average consumption level data, and competitor store location and type data for specific areas;

[0031] The virtual supply chain system parameters include virtual supplier network relationship data and dynamic price fluctuation cycle and amplitude rules for at least two main raw materials;

[0032] The virtual customer group behavior model parameters are generated by a machine learning model trained from historical consumption data. This model can output simulated customer purchase probability data and satisfaction evaluation data based on the input product price data, marketing activity data, and store location data.

[0033] Once the central data processing center has completed loading the parameters, the personalized virtual entrepreneurial environment is now fully constructed.

[0034] Preferably, in step S200, the student user conducts simulated business operations, recording daily business decision-making instruction sequences and corresponding operational result data sequences to form a simulated business dataset, specifically including:

[0035] After student users access the personalized virtual entrepreneurship environment, the simulated business operation proceeds on a virtual day basis.

[0036] During the operating cycle of each virtual day, the student user client receives multiple daily operational decision instructions input by student users;

[0037] The daily operational decision-making instructions include at least the raw material procurement quantity decision-making instructions, at least three types of coffee product pricing decision-making instructions, marketing activity investment amount decision-making instructions, and employee shift scheduling instructions.

[0038] The student user client uploads the daily operational decision-making instructions to the central data processing center;

[0039] After receiving the daily operational decision-making instructions, the central data processing center drives the virtual customer group behavior model, the virtual supply chain system, and the virtual business district map to perform linked simulation calculations.

[0040] The process of the linkage simulation calculation is as follows: First, based on the raw material procurement quantity decision instructions and the virtual supply chain system parameters, calculate the raw material cost data and inventory data for the day; second, combine the pricing decision instructions, the marketing activity investment amount decision instructions, the employee shift scheduling time decision instructions, and the virtual business district map parameters to generate the basic operating conditions data for the day; finally, input the basic operating conditions data for the day into the virtual customer group behavior model to obtain simulated customer purchasing behavior data, and then calculate the sales data, raw material consumption data, customer satisfaction data, and daily profit and loss data for the virtual day. These data together constitute the operational results data for the virtual day.

[0041] The central data processing center stores the received daily operational decision instructions as a decision instruction sequence and stores the calculated operational result data as an operational result data sequence. The decision instruction sequence and the operational result data sequence correspond one-to-one according to virtual day timestamps.

[0042] The central data processing center continuously drives at least 30 virtual days of simulated operation, fully recording 30 consecutive decision instruction sequences and 30 consecutive operational result data sequences, which together constitute the simulated operation dataset.

[0043] Preferably, in step S300, analyzing the simulated business dataset, identifying key decision types, and determining the decision sequence to be verified specifically includes:

[0044] The central data processing center performs variance analysis on the operational result data series in the simulated operation dataset;

[0045] The analysis of variance was performed separately for sales data, customer satisfaction data, and daily profit and loss data.

[0046] The central data processing center calculates the change in the value of each type of decision instruction relative to the previous virtual day in the daily operation decision instructions for each virtual day;

[0047] The central data processing center establishes a correlation model between the changes and the fluctuations in the operational result data sequence;

[0048] Using the correlation model, the central data processing center selects the top 3 to 5 decision instruction types that have the greatest impact on the fluctuation of the operational result data sequence, and defines them as key decision types.

[0049] The central data processing center extracts all decision instructions belonging to the key decision type that occurred within the last 7 virtual days from the simulated operation dataset, arranges them in chronological order, and forms the decision sequence to be verified.

[0050] Preferably, in step S300, generating a real-world miniature entrepreneurial practice task list based on the decision sequence to be verified specifically includes:

[0051] The central data processing center analyzes the decision sequence to be verified and identifies the decision actions, decision objects, and decision parameters contained in the sequence;

[0052] The central data processing center maps each decision action, decision object, and decision parameter to an executable task item in the real environment based on a predefined virtual-real mapping rule base.

[0053] The virtual-real mapping rule base defines the conversion ratio between virtual currency and real currency, the correspondence between virtual time units and real time units, and the equivalence between virtual material units and real material units.

[0054] The central data processing center sets uniform real execution constraints for all executable task items generated by the mapping.

[0055] The actual execution constraints include a total task execution time not exceeding 7 days, a total material cost for the task not exceeding a preset cost limit, and the physical space for task execution being limited to at least two designated alternative areas within the campus.

[0056] The central data processing center integrates all the mapped executable tasks with the actual execution constraints and outputs them in a formatted manner as the actual miniature entrepreneurial practice task list.

[0057] The real-world miniature entrepreneurship practice task list clearly outlines the content of each practice task, the required materials and quantities, the execution location, the execution time window, the data types to be collected, and the collection tools.

[0058] Preferably, in step S400, the step of comparing and analyzing the practical process data with the corresponding data in the simulated business dataset to generate a personalized entrepreneurship guidance report specifically includes:

[0059] The central data processing center receives the practical process data uploaded by student user clients;

[0060] The data from the actual operation process includes real customer traffic data, real transaction order data, real raw material consumption data, and real customer feedback rating data.

[0061] The central data processing center extracts simulated data of the same type as the actual operation data from the last 7 virtual days of the simulated operation dataset as a comparison benchmark.

[0062] The central data processing center calculates the deviation between the actual process data and the comparison benchmark data item by item;

[0063] The formula for calculating the deviation is: the deviation is equal to the absolute value of the actual operation data minus the comparison benchmark data, and then divided by the comparison benchmark data;

[0064] The central data processing center compares all calculated deviations with a preset deviation threshold, filters out data items whose deviations exceed the deviation threshold, and marks them as significant difference items;

[0065] Based on the correspondence between the data collection timestamp and the virtual day, the central data processing center traces back to the student user decision instruction corresponding to the time when the comparison benchmark data was generated in step S200.

[0066] The central data processing center analyzes the potential causes of the significant differences by combining the internal logic rules of the virtual customer group behavior model.

[0067] The potential causes include biases in estimating customer price sensitivity, biases in estimating geographic location foot traffic conversion rates, and biases in estimating actual raw material loss rates.

[0068] The personalized entrepreneurship guidance report is generated by integrating the significant differences, the corresponding student user decision instructions, the potential causes analyzed, and at least one specific optimization suggestion generated based on the potential causes.

[0069] Preferably, the personalized entrepreneurship guidance report includes at least the following parts:

[0070] The first part is an overview of the data differences, which clearly lists all significant differences, the corresponding benchmark data, the data from the actual operation process, and the calculated deviation in a table format.

[0071] The second part is the decision backtracking analysis, which details the specific decision instructions made by student users in virtual operation and real practice, corresponding to the significant difference items.

[0072] The third part is cause diagnosis, which is based on the simulation logic of the virtual customer group behavior model. It explains the reasoning process of the virtual environment generating comparative benchmark data and the real environment generating practical process data under the decision instruction, and accurately identifies at least one key hypothesis difference.

[0073] The fourth part provides optimization suggestions, offering at least two specific and actionable decision adjustment suggestions for each cause diagnosis result;

[0074] The proposed decision-making adjustments include specific percentage ranges for adjusting product pricing strategies, specific suggestions for changing marketing channels, and specific plans for optimizing raw material procurement frequency.

[0075] Preferably, in step S400, adjusting the model parameters of the personalized virtual entrepreneurial environment based on the analysis results and guiding student users back to step S200 for iterative training specifically includes:

[0076] After generating the personalized entrepreneurship guidance report, the central data processing center determines at least one target parameter that needs to be adjusted for the virtual customer group behavior model based on the cause diagnosis and optimization suggestions in the report.

[0077] The target parameters include customer price sensitivity coefficient, customer brand preference weight, and customer consumption time period distribution ratio.

[0078] The central data processing center modifies the value of the target parameter according to the preset parameter adjustment rules;

[0079] The parameter adjustment rule is as follows: when the actual transaction order data in the actual operation process data is significantly lower than the simulated sales data in the benchmark data, and the reason is the price sensitivity estimation deviation, then the customer price sensitivity coefficient is increased.

[0080] After the modifications were completed, the Central Data Processing Center used the updated virtual customer group behavior model parameters, combined with the difficulty level that student users had adapted to in the previous simulation, to generate a new, slightly more challenging personalized virtual entrepreneurial environment.

[0081] The central data processing center sends iterative training instructions and the personalized entrepreneurship guidance report to student user clients. The iterative training instructions include an entry link to enter a new round of simulated operation.

[0082] The student user client receives and displays the personalized entrepreneurship guidance report. After learning from the report, the student user triggers the central data processing center to execute step S200 again through the entry link, starting a new round of simulated operation.

[0083] Preferably, the central data processing center is also responsible for the following data management and coordination functions when performing all steps:

[0084] The central data processing center maintains an independent database of student user profiles, creating a unique profile identifier for each student user participating in the training.

[0085] The student user profile database stores the student user's initial entrepreneurial ability comprehensive score, historical simulated business datasets, historical miniature entrepreneurial practice task lists, historical practice process data, historical personalized entrepreneurial guidance reports, and historical adjustment records of virtual customer group behavior model parameters.

[0086] Between steps S300 and S400, the central data processing center coordinates the instructor's client to review and confirm the real miniature entrepreneurship practice task list, and receives the on-site supervision records uploaded by the instructor's client during the student user's execution of the practice task.

[0087] After each iteration instruction of step S400 is sent, the central data processing center automatically updates the student user profile database and summarizes the latest training progress and overall performance data of the student user to the teacher management terminal for macro-level teaching management.

[0088] The beneficial effects of this invention are:

[0089] 1. This invention creates an entrepreneurial training environment that unifies "low-cost, in-depth trial and error" with "high-fidelity practical feedback," fundamentally solving the problem of high risk and shallow experience for student users in entrepreneurial practice. This method constructs a data-driven, personalized virtual entrepreneurial environment, allowing student users to conduct zero-risk, long-term, and continuous business decision-making and trial and error in a simulated scenario that closely approximates real business logic. Subsequently, by accurately mapping key decisions identified in the simulation to a strictly constrained, miniature real-world entrepreneurial practice, student users can obtain valuable, first-hand market feedback data at extremely low economic and controllable time costs. This mechanism of "boldly exploring in the virtual world first, then verifying step by step in the real world" protects student users' entrepreneurial enthusiasm from major setbacks while avoiding the "theoretical" drawbacks of pure simulation training, achieving an organic unity of safety and practicality.

[0090] 2. This invention realizes a paradigm shift in entrepreneurship guidance services from "experience-driven, general commentary" to "data-driven, precise profiling," greatly improving the personalization and effectiveness of guidance. This method integrates data collection and analysis throughout the entire process, from the quantitative scores of initial ability assessments to the complete decision-making and outcome data sequence in simulated operations, and then to the quantitative deviation analysis between real-world practice and simulated results, constructing a dynamic, multi-dimensional "entrepreneurial ability data profile" for each student user. Based on this, the personalized entrepreneurship guidance report generated by the system can accurately pinpoint the individual student user's weaknesses in specific decision-making stages (such as pricing sensitivity judgment and marketing channel effectiveness evaluation), and trace back to potential cognitive biases leading to differences in outcomes. This makes the guidance from teachers or the system no longer general advice, but rather a clearly targeted "diagnosis" and "prescription" based on specific behavioral data, significantly improving the accuracy of teaching guidance and the learning efficiency of student users.

[0091] 3. This invention constructs a closed-loop iterative capability enhancement pathway of "evaluation-simulation-practice-analysis-optimization," making entrepreneurial learning a continuously evolving and spiraling organic process. This method is not a one-off or linear training activity, but a self-reinforcing learning cycle. After receiving data-driven guidance, the system dynamically adjusts the parameters of the subsequent virtual training environment (such as adjusting the price sensitivity coefficient of customers in the model) based on the student's actual performance, making it closer to the student's cognitive characteristics and the real market environment. It then guides the student into the next round of higher-level or more targeted simulation training. This closed loop of "practice generates data, data guides analysis, analysis optimizes the model, and the model enhances new practices" ensures that each attempt by the student provides nourishment for the next step of progress. It integrates discrete training sessions into a coherent and highly adaptable capability development system, effectively promoting the continuous and in-depth development of students' comprehensive entrepreneurial literacy. Attached Figure Description

[0092] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0093] Figure 1 This is a flowchart of the steps of the method of the present invention. Detailed Implementation

[0094] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0095] Please see Figure 1 This invention provides a method for training college students in coffee entrepreneurship based on entrepreneurship guidance services. This method is executed through a central data processing center. First, a multi-dimensional data-driven assessment of student users' entrepreneurial abilities is performed, collecting and processing data to generate an initial comprehensive entrepreneurial ability score. Second, based on this score, a reinforcement learning environment is configured to construct a personalized virtual entrepreneurial environment. This personalized virtual entrepreneurial environment is a machine learning model trained on historical data, in which student user agents are configured. These student user agents simulate business operations, and the system records the sequence of decision-making instructions and the sequence of operational results data to form a simulated business dataset.

[0096] Furthermore, the system analyzes the dataset, identifies key decision types, and determines the decision sequence to be verified. Based on this, it generates a realistic miniature entrepreneurial practice task list. Student users execute the tasks on the list and collect data from the practice process. Finally, the system compares and analyzes the practice data with the simulated data, generates a personalized entrepreneurial guidance report, and adjusts the virtual environment model parameters based on the analysis results, guiding student users back to the simulated business steps for iterative training. This complete process constitutes a closed loop of evaluation, simulation, practice, optimization, and re-simulation, achieving continuity and iterative nature in the training process.

[0097] In one possible implementation, step S100 is detailed as follows: The central data processing center sends an assessment questionnaire to student user clients through an integrated online questionnaire system. The questionnaire includes four parts: coffee industry knowledge, basic financial knowledge, marketing principles, and risk management awareness. Each part contains five standardized multiple-choice questions. After student users submit their answers, the system calls a first scoring algorithm for analysis. This algorithm pre-sets standard answers and scores for each question, calculates scores for each dimension, and synthesizes a first quantitative score according to preset weights.

[0098] Simultaneously, the system releases an initial scenario configuration task for a virtual coffee shop. The task interface displays virtual start-up capital and lists options for virtual shop location, virtual equipment list, and virtual raw material list, each accompanied by detailed cost and performance parameters. The system records all selection commands from student users, forming an initial configuration plan. The system analyzes this plan based on a pre-stored cost database, equipment efficiency parameter database, and configuration matching rule database, calculating and generating a second quantitative score.

[0099] Finally, the system weights and sums the first and second quantitative scores according to preset weighting coefficients to obtain an initial comprehensive entrepreneurial ability score. This process enables a quantitative assessment of students' theoretical knowledge and practical decision-making potential, providing a precise initial basis for subsequent personalized training.

[0100] In one possible implementation, the specific implementation of constructing a personalized virtual entrepreneurial environment in step S200 is as follows: The central data processing center maintains a virtual scenario library containing multiple difficulty levels. Each difficulty level is associated with a set of preset scenario parameters. The system compares the calculated initial entrepreneurial ability comprehensive score with the preset difficulty level threshold range, thereby matching an initial difficulty level for the student user.

[0101] Subsequently, the system invokes the corresponding level of virtual business scenario template and loads all its parameters. These parameters include virtual business district map parameters, such as pedestrian traffic distribution data, average consumption level data, and precise location and type data of competing stores in a specific area at different times. Parameters also include virtual supply chain system parameters, such as supplier network relationships and the magnitude and cycle of periodic fluctuations in the prices of major raw materials according to preset rules.

[0102] The parameters also include those for a virtual customer behavior model. This model is a machine learning model trained on a large amount of historical consumption data, capable of simulating and outputting customer purchasing decisions and satisfaction ratings based on input product prices, marketing activities, and environmental data. Once all parameters are loaded, the personalized virtual entrepreneurial environment is complete. This environment is highly realistic and matches the initial capabilities of student users.

[0103] In one possible implementation, the specific implementation of the simulated operation and data recording in step S200 is as follows: After the student user client accesses the constructed virtual entrepreneurial environment, the simulated operation proceeds on a virtual day basis. Within each virtual day's operating cycle, the student user needs to input multiple daily operating decision instructions through the client. These instructions at least cover the quantity of raw materials purchased, the sales pricing of three core products, the amount invested in marketing activities, and the employee shift schedule. The client uploads these instructions to the central data processing center. After receiving the instructions, the system drives the virtual customer group behavior model, the virtual supply chain system, and the virtual business district map to perform linked simulation calculations.

[0104] The calculation process first updates costs and inventory based on procurement instructions and supply chain rules. Then, it combines pricing, marketing, and scheduling instructions with foot traffic data from the business district to generate daily operating conditions. Finally, these conditions are input into a customer behavior model to calculate simulated sales revenue, raw material consumption, customer satisfaction, and daily profit and loss data. The system stores daily received decision instructions as a decision instruction sequence and the calculated operational data as an operational result data sequence, with both strictly corresponding to timestamps. The system continuously drives simulations for at least 30 virtual days, ultimately forming a simulated business dataset containing continuous decision and result data, comprehensively recording the virtual entrepreneurial behavior and consequences of student users.

[0105] In one possible implementation, the specific implementation of identifying key decisions and determining the sequence to be verified in step S300 is as follows: The central data processing center conducts in-depth variance analysis on the operational result data sequence in the simulated operation dataset. This analysis is performed separately for key result indicators such as sales revenue, customer satisfaction, and daily profit and loss to measure their volatility. Simultaneously, the system calculates the changes in various daily operational decision instructions relative to the previous day for each virtual day.

[0106] Subsequently, the system established a statistical correlation model to analyze the correlation strength between the changes in various decision-making instructions and the fluctuations in operational results. Based on the output of the correlation model, the system selected the 3 to 5 types of decision-making instructions with the highest impact on operational result fluctuations, defining these instruction types as key decision types. After definition, the system extracted all specific decision-making instructions belonging to these key decision types that occurred within the last 7 virtual days from the complete simulated operation dataset. These instructions were arranged in chronological order of occurrence, forming the decision sequence to be verified. This process ensures that subsequent real-world operations can focus on the core decision points that have the greatest impact on student users' business results and are most worthy of verification.

[0107] In one possible implementation, the specific implementation of generating the realistic miniature entrepreneurial practice task list in step S300 is as follows: The central data processing center parses the decision sequence to be verified, identifying the decision action, decision object, and specific decision parameters contained in each decision instruction in the sequence. The system accesses a predefined virtual-real mapping rule base, which clearly defines the conversion ratio between virtual currency and real currency, the correspondence between virtual time units and real time units, and the equivalence between virtual material units and real material units. Based on this rule base, the system accurately maps each virtual decision instruction to a specific task item that can be executed in a real environment.

[0108] Subsequently, the system sets uniform realistic execution constraints for all mapped task items. These constraints include a maximum total task execution time of 7 days, a total material cost not exceeding 500 RMB, and execution physical space limited to two designated alternative areas within the campus. Finally, the system integrates all executable task items and their constraints, formatting the output into a detailed, realistic miniature entrepreneurial practice task list. The list clearly lists the content of each task, required materials, location, time window, and data types to be collected.

[0109] In one possible implementation, the specific implementation of the comparative analysis and guidance report generation in step S400 is as follows: The central data processing center receives the practical process data uploaded by student users, including actual customer traffic, completed orders, raw material consumption, and customer feedback ratings. The system extracts the simulated operation result data of the same type corresponding to the number of practical days from the simulated operation dataset as the comparison benchmark data. The system calculates the deviation between the practical data and the benchmark data item by item, which is the ratio of the absolute value of the difference between the two to the benchmark data.

[0110] The system compares all calculated deviations with a preset deviation threshold, marking items exceeding the threshold as significant differences. Based on time correspondence, the system traces back to the student user's virtual business decision-making instructions that led to the generation of this baseline data. Then, combining the internal logic rules of the virtual customer group behavior model, the system analyzes the potential causes of significant differences, such as biases in estimating customer price sensitivity or marketing campaign conversion rates. Finally, the system integrates the significant difference items, the corresponding decision-making instructions, the analyzed causes, and the specific optimization suggestions generated accordingly, formatting a personalized entrepreneurship guidance report.

[0111] In one possible implementation, the personalized entrepreneurship guidance report comprises four main parts. The first part is a data difference overview, clearly listing all marked significant differences in a table format, along with the corresponding simulated baseline data, the actual collected practical process data, and the calculated deviation values. The second part is a decision backtracking analysis, detailing the specific decision instructions made by student users in both virtual operation and real-world practice associated with each significant difference. The third part is a root cause diagnosis, explaining, based on the operational logic of the virtual customer group behavior model, why the virtual environment generated simulated baseline data while the real environment generated practical process data under the stated decision input, and pinpointing at least one key assumption difference. The fourth part provides optimization suggestions, offering at least two specific and actionable decision adjustment suggestions for each diagnosed cause, such as suggesting a specific percentage adjustment range for product pricing or recommending a change to more effective marketing channels. This structured report enables student users to clearly understand the root causes of problems and know the improvement path.

[0112] In one possible implementation, the specific implementation of parameter adjustment and guided iteration in step S400 is as follows: After generating a personalized entrepreneurship guidance report, the central data processing center determines which parameters in the virtual customer group behavior model need to be adjusted based on the cause diagnosis conclusions in the report. For example, if the diagnosis cause is a deviation in customer price sensitivity estimation, the target parameter is determined to be the customer price sensitivity coefficient in the model. The system modifies the parameter value according to preset parameter adjustment rules; for example, when actual sales are significantly lower than simulated sales, the customer price sensitivity coefficient is increased according to the rules. After the parameter modification is completed, the system uses the updated model parameters, combined with the previous difficulty level that student users have adapted to, to generate a new personalized virtual entrepreneurship environment with a slightly increased difficulty.

[0113] Subsequently, the system simultaneously sends a personalized entrepreneurship guidance report and an iterative training instruction to the student user's client. This instruction includes a link to enter a new round of simulated operation. After reviewing the report, the student user can click the link to trigger the system to execute the simulated operation steps again, thus starting the next round of closed-loop iterative training and achieving a spiral increase in capabilities.

[0114] In one possible implementation, the central data processing center is also responsible for data management and coordination throughout the entire process. The system maintains an independent student user profile database, creating a unique profile identifier for each student user and storing their initial entrepreneurial ability comprehensive score, datasets from previous simulated business operations, lists of previous real-world miniature entrepreneurial practice tasks, data from previous practice sessions, personalized entrepreneurial guidance reports, and historical records of virtual model parameter adjustments.

[0115] After generating a realistic miniature entrepreneurship practice task list, the system sends the list to the instructor's client for review and confirmation. During the students' execution of the tasks, the system receives on-site supervision records uploaded by the instructors. After each iteration command is sent, the system automatically updates the corresponding student's profile database and summarizes the student's latest training progress and overall performance data to the teacher's management terminal, forming a data dashboard for macro-level teaching management. This function ensures complete data, controlled processes, and orderly management throughout the entire training process.

[0116] Example: A coffee entrepreneurship training project at a university's Innovation and Entrepreneurship College;

[0117] This example was conducted at the Innovation and Entrepreneurship College of a university, with 50 third-year students enrolled in the "Coffee Entrepreneurship and Practice" course participating. The college was equipped with a central server serving as the central data processing center, and student users and instructors interacted via dedicated client software on personal computers or tablets. The college also designated two areas—an "Innovation Corner" and a "Living Area Plaza"—as physical spaces for real-world, miniature entrepreneurial practice.

[0118] The specific implementation process of the practical training is as follows:

[0119] Step 1: Multi-dimensional data-driven assessment of student users' entrepreneurial abilities;

[0120] Student Zhang San logs into the training client. The Central Data Processing Center sends him an evaluation questionnaire through the client. The questionnaire contains four parts with a total of 20 multiple-choice questions: 5 questions on coffee industry knowledge (such as major coffee bean producing areas and characteristics of different roasting levels), 5 questions on basic financial knowledge (such as gross profit margin calculation and the distinction between fixed and variable costs), 5 questions on marketing principles (such as the connotation of the 4P theory and the elements of target customer profiles), and 5 questions on risk management awareness (such as inventory backlog risk and early warning signals of cash flow disruption). After Zhang San completes the questionnaire, his answer data is uploaded.

[0121] The central data processing center invokes its built-in primary scoring algorithm. This algorithm pre-sets a standard answer and score weight for each question. The system analyzes Zhang San's answer, calculates his score for each knowledge section, and then performs a weighted sum based on the pre-set weights of each section (e.g., industry knowledge accounts for 30%, financial knowledge accounts for 30%, marketing knowledge accounts for 25%, and risk knowledge accounts for 15%) to generate Zhang San's primary quantitative score, assumed to be 72 points (out of 100).

[0122] Simultaneously, the client sends Zhang San a virtual coffee shop initial scene configuration task. The interface displays a virtual starting capital of 100,000 virtual currency and provides three virtual shop location options (Area A: Main road of the teaching building, high traffic but high rent; Area B: Side wing of the library, moderate traffic but quiet environment; Area C: Near the gymnasium, fluctuating traffic but low rent), a list of virtual equipment options including 10 different brands and models of coffee machines, grinders, etc. (with varying prices, efficiency, and failure rates), and a list of virtual raw material options including coffee beans, milk, syrups, etc. from three different suppliers (with varying prices, quality, and minimum order quantities). After consideration, Zhang San chooses the Area B location, a mid-to-high-end and stable equipment combination, and the raw material supplier with the best quality but the highest minimum order quantity. This series of selection instructions is recorded as Zhang San's initial configuration plan.

[0123] The central data processing center analyzes the plan based on a pre-stored database: it retrieves the rental fee for Zone B, the daily depreciation and energy consumption of the selected equipment, and the unit price of the selected raw materials from the virtual cost structure database; it retrieves the theoretical maximum cup-making speed of the equipment combination from the virtual equipment efficiency parameter database; and it evaluates the plan according to rules in the configuration matching rule base (such as "bonus points for matching high-quality raw materials with a quiet environment"). After calculation, the system generates a second quantitative score for Zhang San in the resource allocation decision-making ability dimension, assumed to be 80 points.

[0124] Finally, the central data processing center weights and merges the two scores. The first weighting coefficient is preset to 0.4, and the second weighting coefficient to 0.6. Therefore, Zhang San's initial entrepreneurial ability composite score = 72 × 0.4 + 80 × 0.6 = 76.8 points. This score will be stored in the student user profile database created for this student.

[0125] Step 2: Dynamic construction and simulation of a personalized virtual entrepreneurial environment;

[0126] The central data processing center's pre-stored virtual scenario library contains templates at three difficulty levels: beginner, intermediate, and advanced. The system's preset difficulty level thresholds are: beginner (0-70 points), intermediate (71-85 points), and advanced (86-100 points). Zhang San's score of 76.8 points falls into the intermediate range, therefore the system calls the intermediate virtual business scenario template.

[0127] The system loads the scene parameters for this template:

[0128] Virtual business district map parameters: Simulates a 500-meter radius around Zone B. The peak pedestrian flow data is 200 people / hour from 8:00-9:00 am on weekdays and 150 people / hour from 12:00-13:00 pm. The average consumption level is 15 yuan / person. The competitive store data includes 1 chain coffee shop and 1 milk tea shop.

[0129] Virtual supply chain system parameters: The virtual supplier network includes two major coffee bean suppliers, one of which adjusts its price every virtual week (7 virtual days) according to a random fluctuation factor (range 0.9-1.1); milk needs to be purchased daily, and its price increases by 5% on weekends.

[0130] Virtual Customer Group Behavior Model Parameters: This model is a gradient boosting decision tree model trained based on historical campus consumption data. Its input features include: product price, promotional activity intensity (discount rate), store location attractiveness score, and the day's weather (virtual). The output is customer purchase probability data and expected satisfaction rating data (1-5 points). For example, the model might contain a rule: when a cup of Americano costs more than 18 yuan and there is no promotion, the purchase probability among student customers will be less than 30%.

[0131] After setting up the environment, Zhang San began a 30-day virtual simulation of business operations. On the 10th virtual day, he needed to make daily operational decisions: purchase 25 kilograms of coffee beans and 40 liters of milk; price the signature latte at 22 yuan and the Americano at 18 yuan; invest 200 yuan of virtual currency in a "buy one get one half price" promotion during lunchtime; and schedule two employees to work 8-hour shifts. These instructions were then uploaded.

[0132] The central data processing center initiated a coordinated simulation calculation: first, it calculated raw material costs and inventory. Next, it generated operating conditions by combining pricing, marketing investment, shift scheduling, and foot traffic data for the business district. Finally, these conditions were input into a virtual customer behavior model. Based on factors such as the latte price of 22 yuan, a half-price promotion, and lunchtime foot traffic, the model calculated that the probability of purchasing a latte that day increased to 40%, with estimated sales of 1200 yuan, corresponding raw material consumption, a simulated customer satisfaction score of 4.2, and a profit of 150 yuan after deducting all costs. These data were recorded as the operational results for the 10th virtual day. The sequence of decision-making instructions and operational results data over 30 consecutive days constituted Zhang San's simulated business dataset.

[0133] Step 3: Mapping key decision points based on simulated data and conducting real-world miniature entrepreneurial practice;

[0134] The Central Data Processing Center conducted an analysis of variance on Zhang San's 30-day simulated business dataset. It found that the fluctuations in his "sales data" were most strongly correlated with the changes in "marketing activity investment decision instructions" and "new product pricing decision instructions." The system identified these two as key decision types. From the last seven virtual days, all instructions regarding marketing investment and pricing were extracted to form a decision sequence to be verified. For example, the sequence included: "Day 28: Launch the new product 'Osmanthus Latte,' priced at 25 yuan, with a social media promotion investment of 300 yuan on that day."

[0135] The system maps virtual currency (300 yuan) to real-world promotional fees at a 100:1 ratio (used only for poster production); virtual new product launches are mapped to a real new product trial sale. The real-world execution constraints are: a total duration of 3 days, a total cost cap of 500 yuan, and the location is the "Residential Area Square." The final generated real-world miniature entrepreneurial practice task list clearly states: "Task: Conduct a 3-day (June 1st-3rd, 11:00-13:00 daily) trial sale of the new 'Osmanthus Latte' at the Residential Area Square. Provide trial sale ingredients: 5kg coffee beans, 2 bottles of osmanthus syrup, 10L milk. Price: 25 yuan / cup. Price can be adjusted once within three days based on the situation. Data to be collected: hourly customer traffic, sales records for each cup, ingredient usage, and customer QR code reviews."

[0136] Zhang San carried out the task under the supervision of his instructor. He set up a simple stall, and sales were poor for the first two days when the price was 25 yuan. On the third day, he adjusted the price to 22 yuan, and sales increased significantly. He used a counter to record actual customer traffic data, an order book to record actual completed orders, a measuring cup and scale to record actual raw material consumption data, and a QR code on the stall to allow customers to provide real customer feedback ratings. This practical process data was compiled and uploaded to the system.

[0137] Step 4: Comparative analysis of virtual and real data and generation and iteration of personalized guidance reports;

[0138] The central data processing center will compare the daily average data from the three days of actual operation with the daily average benchmark data from the last three virtual days in the simulated dataset (corresponding to the new product launch phase).

[0139] Real daily sales: 200 yuan; Simulated daily sales: 450 yuan.

[0140] Real customer rating: 3.8 points; Simulated customer rating: 4.5 points.

[0141] Calculate the deviation. Taking sales revenue as an example, the deviation = |200-450| / 450 ≈ 55.6%. The system's preset deviation threshold is 30%, therefore both sales revenue and customer ratings are marked as significant differences.

[0142] System backtracking revealed that the student user decision instructions corresponding to the differences were "price at 25 yuan" and "invest 300 yuan in promotion." Logical analysis based on the virtual customer behavior model revealed the following potential reasons: the model assumed a low probability of student users purchasing latte-like drinks priced over 22 yuan, but Zhang San found in practice that real customers were even less accepting of a price of 25 yuan than the model predicted; furthermore, the model overestimated the conversion effect of small-scale offline promotional activities in the real campus environment.

[0143] Based on this, the system generates a personalized entrepreneurship guidance report. The first part of the report uses tables to display the discrepancies. The second part points out the high pricing decision. The third part diagnoses the reasons: the customer price sensitivity coefficient in the model may be too low, and the weight given to the effectiveness of offline promotion is set too high. The fourth part provides specific optimization suggestions: 1. It is recommended that the initial price of new products should not exceed 22 yuan; 2. It is recommended that promotion be combined with online community pre-heating, rather than simply offline display.

[0144] After the report was generated, the Central Data Processing Center, based on the diagnosis, adjusted the customer price sensitivity coefficient in the model from 0.5 to 0.65 (a higher value indicates greater price sensitivity) and slightly reduced the weight of "small offline advertising" on the probability of purchase. Subsequently, the system notified Zhang San that his virtual entrepreneurial environment had been optimized based on his practical feedback and guided him back to step two. In the adjusted, more challenging virtual environment (such as the introduction of new competitors), he was instructed to apply the report's recommendations to begin a new round of simulation. Simultaneously, all of Zhang San's data, reports, and model adjustment records were updated in his student user profile database.

[0145] To demonstrate the effectiveness of this method, two other groups of students in the same course were selected as comparative examples for a comparative teaching experiment.

[0146] Comparative Example 1: Traditional Business Plan Teaching Method. This group of student users did not have access to the virtual simulation and miniature hands-on system of this invention, but only received theoretical instruction, and were assessed on the final task of writing a complete coffee shop business plan in teams.

[0147] Comparative Example 2: Short-Term Entrepreneurship Competition. This group of student users participated in a two-week campus coffee entrepreneurship competition, operating a pop-up store in a real venue. The final evaluation criteria were profit and scores from the on-site judges.

[0148] After the experiment period ended, the training effectiveness of the three groups of student users was evaluated from multiple dimensions, and the results are shown in the table below:

[0149] Evaluation Dimensions Embodiment Group of the Invention Comparative Example 1 (Business Plan Group) Comparative Example 2 (Entrepreneurship Competition Group) Completeness of understanding of the entrepreneurial process Extremely high. Student users experienced the entire process of evaluation, simulation, practice, review, and iteration, gaining a deep understanding of the interrelationships between site selection, procurement, pricing, marketing, and finance. Low. Cognition is focused on macro-level planning and financial forecasting, lacking awareness of the dynamics of daily operations and the interconnections between various stages. Medium level. Perception is focused on short-term promotions, costs, and on-site service, lacking experience in long-term strategic planning and risk assessment. Enhancement of key decision-making capabilities Significant improvements have been made. By comparing virtual and real data, student users can clearly see the deviations in their specific decisions regarding pricing, marketing, etc., and make targeted improvements under guidance, shifting the basis of decision-making from subjective feelings to data analysis. Limited improvement. Decision-making capabilities remain at the theoretical deduction level, lacking real data feedback for verification, and the direction for improvement is vague. There has been some improvement, but it's one-sided. Decisions are made around maximizing short-term profits, which may sacrifice long-term brand image or customer satisfaction. Furthermore, the costs of trial and error are real, and student users face significant pressure and are hesitant to try high-risk innovative strategies. The accuracy of receiving personalized guidance Extremely high. The guidance report is generated based on detailed behavioral data and outcome differences generated by individual student users, and the suggestions are specific, actionable, and personalized. Low. Guidance was based on the business plan text, and feedback was mostly vague comments such as "market analysis is not in-depth enough" and "financial forecasts are too optimistic". Medium. Guidance is based on observations during the competition and the final results. Feedback is more specific but tends to be retrospective, lacking process data support and opportunities for iterative optimization. Trial and error costs and willingness to innovate Low cost, high willingness. The virtual environment allows for zero-risk trial and error, and the miniature hands-on experience keeps the cost of real-world trial and error to a very low level, encouraging student users to try different pricing strategies and new product ideas. There was no cost, but also no real feedback; the innovation remained on paper. High costs, low willingness. Involving real financial and material investment, failure leads to actual losses, causing student users to tend to adopt conservative, imitative strategies. Objectivity and data-driven nature of competency assessment Completely data-driven and objective. From initial capabilities to each simulated operation, there are quantifiable scores or data sequences recorded, making the growth trajectory clearly visible. It is highly subjective, relying primarily on teachers' subjective evaluation of the documents. Some data is quantified (such as sales figures), but the overall evaluation still relies on the judges' subjective scoring, and process data is missing.

[0150] As can be seen from the comparison in the table above, the training method provided by this invention is significantly superior to traditional business plan teaching methods and short-term entrepreneurial competition methods in terms of entrepreneurial cognition building, refined cultivation of decision-making ability, precision of guidance, stimulation of innovative potential, and objectivity of evaluation. It successfully transforms high-risk entrepreneurial practice into a safe, data-driven, and iterative learning process.

[0151] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0152] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A training method for college students in coffee entrepreneurship based on entrepreneurship guidance services, characterized in that, The method is executed through a central data processing center and includes the following steps: Step S100: Collect initial entrepreneurial ability data from student users, process the initial entrepreneurial ability data, and generate a comprehensive score for initial entrepreneurial ability. The initial entrepreneurial ability data includes knowledge mastery data obtained from assessment questionnaires and resource allocation decision data obtained from virtual scenario configuration tasks; Step S200: Based on the initial entrepreneurial ability comprehensive score, configure the reinforcement learning environment and construct a personalized virtual entrepreneurial environment; The personalized virtual entrepreneurial environment is a simulation framework built on historical consumption data, operational data, and customer behavior data. It is configured with student user agents based on machine learning models. The student user agents conduct simulated operations, record daily operational decision-making instruction sequences and corresponding operational result data sequences, and form a simulated operation dataset. Step S300: Analyze the simulated business dataset, identify key decision types and determine the decision sequence to be verified, generate a real miniature entrepreneurship practice task list based on the decision sequence to be verified, and have student users execute the real miniature entrepreneurship practice task list and collect practice process data. Step S400: Compare and analyze the practical process data with the corresponding data in the simulated business dataset, generate a personalized entrepreneurship guidance report, and adjust the model parameters of the personalized virtual entrepreneurship environment based on the analysis results, guiding student users back to step S200 for iterative training.

2. The method for training college students in coffee entrepreneurship based on entrepreneurship guidance services according to claim 1, characterized in that, In step S100, the process of collecting initial entrepreneurial ability data from student users and processing the initial entrepreneurial ability data to generate a comprehensive score for initial entrepreneurial ability specifically includes: The central data processing center distributes assessment questionnaires to student users' clients through a pre-set online questionnaire system; The assessment questionnaire included questions on coffee industry knowledge, basic financial knowledge, marketing principles, and risk management awareness. The student user client receives student users' answer data and uploads it to the central data processing center; The central data processing center calls the built-in first scoring algorithm to parse the answer data and generate the student user's first quantitative score in the knowledge mastery dimension; At the same time, the central data processing center issued the initial scene configuration task for the virtual coffee shop to student user clients; The initial scene configuration task for the virtual coffee shop includes virtual start-up capital, multiple preset virtual store location options, a virtual equipment list option, and a virtual ingredient list option. Record the selection commands for virtual store location options, virtual equipment list options, and virtual raw material list options uploaded by student user clients to form an initial configuration scheme; The central data processing center analyzes the initial configuration scheme based on the pre-stored virtual cost structure database, virtual device efficiency parameter database, and configuration matching rule base, and generates a second quantitative score for student users in the dimension of resource allocation decision-making ability. The central data processing center weights the first quantitative score according to a preset first weighting coefficient, and the second quantitative score according to a preset second weighting coefficient. The weighted first quantitative score and the weighted second quantitative score are then added together to obtain the initial entrepreneurial ability comprehensive score.

3. The method for training college students in coffee entrepreneurship based on entrepreneurship guidance services according to claim 1, characterized in that, In step S200, the construction of a personalized virtual entrepreneurial environment based on the initial entrepreneurial ability comprehensive score specifically includes: The central data processing center has a pre-stored virtual scenario library, which contains at least three virtual business scenario templates of different difficulty levels; Each virtual business scenario template is associated with a set of scenario parameters, including virtual business district map parameters, virtual supply chain system parameters, and virtual customer group behavior model parameters. The initial entrepreneurial ability score is compared with a preset difficulty level threshold range to determine the matching initial difficulty level. The central data processing center retrieves the virtual business scenario template corresponding to the initial difficulty level from the virtual scenario library, and loads the virtual business district map parameters, virtual supply chain system parameters, and virtual customer group behavior model parameters. The virtual business district map parameters include pedestrian traffic distribution data, average consumption level data, and competitor store location and type data for specific areas; The virtual supply chain system parameters include virtual supplier network relationship data and dynamic price fluctuation cycle and amplitude rules for at least two main raw materials; The virtual customer group behavior model parameters are generated by a machine learning model trained from historical consumption data. This model can output simulated customer purchase probability data and satisfaction evaluation data based on the input product price data, marketing activity data, and store location data. Once the central data processing center has completed loading the parameters, the personalized virtual entrepreneurial environment is now fully constructed.

4. The method for training college students in coffee entrepreneurship based on entrepreneurship guidance services according to claim 3, characterized in that, In step S200, the student user conducts simulated business operations, recording daily business decision-making instruction sequences and corresponding operational result data sequences to form a simulated business dataset, specifically including: After student users access the personalized virtual entrepreneurship environment, the simulated business operation proceeds on a virtual day basis. During the operating cycle of each virtual day, the student user client receives multiple daily operational decision instructions input by student users; The daily operational decision-making instructions include at least the raw material procurement quantity decision-making instructions, at least three types of coffee product pricing decision-making instructions, marketing activity investment amount decision-making instructions, and employee shift scheduling instructions. The student user client uploads the daily operational decision-making instructions to the central data processing center; After receiving the daily operational decision-making instructions, the central data processing center drives the virtual customer group behavior model, the virtual supply chain system, and the virtual business district map to perform linked simulation calculations. The process of the linkage simulation calculation is as follows: First, based on the raw material procurement quantity decision instructions and the virtual supply chain system parameters, calculate the raw material cost data and inventory data for the day; second, combine the pricing decision instructions, the marketing activity investment amount decision instructions, the employee shift scheduling time decision instructions, and the virtual business district map parameters to generate the basic operating conditions data for the day; finally, input the basic operating conditions data for the day into the virtual customer group behavior model to obtain simulated customer purchasing behavior data, and then calculate the sales data, raw material consumption data, customer satisfaction data, and daily profit and loss data for the virtual day. These data together constitute the operational results data for the virtual day. The central data processing center stores the received daily operational decision instructions as a decision instruction sequence and stores the calculated operational result data as an operational result data sequence. The decision instruction sequence and the operational result data sequence correspond one-to-one according to virtual day timestamps. The central data processing center continuously drives at least 30 virtual days of simulated operation, fully recording 30 consecutive decision instruction sequences and 30 consecutive operational result data sequences, which together constitute the simulated operation dataset.

5. A method for training college students in coffee entrepreneurship based on entrepreneurship guidance services, as described in claim 1, is characterized in that... In step S300, analyzing the simulated business dataset, identifying key decision types, and determining the decision sequence to be verified specifically includes: The central data processing center performs variance analysis on the operational result data series in the simulated operation dataset; The analysis of variance was performed separately for sales data, customer satisfaction data, and daily profit and loss data. The central data processing center calculates the change in the value of each type of decision instruction relative to the previous virtual day in the daily operation decision instructions for each virtual day; The central data processing center establishes a correlation model between the changes and the fluctuations in the operational result data sequence; Using the correlation model, the central data processing center selects the top 3 to 5 decision instruction types that have the greatest impact on the fluctuation of the operational result data sequence, and defines them as key decision types. The central data processing center extracts all decision instructions belonging to the key decision type that occurred within the last 7 virtual days from the simulated operation dataset, arranges them in chronological order, and forms the decision sequence to be verified.

6. A method for training college students in coffee entrepreneurship based on entrepreneurship guidance services, as described in claim 5, is characterized in that... In step S300, generating a real-world miniature entrepreneurial practice task list based on the decision sequence to be verified specifically includes: The central data processing center analyzes the decision sequence to be verified and identifies the decision actions, decision objects, and decision parameters contained in the sequence; The central data processing center maps each decision action, decision object, and decision parameter to an executable task item in the real environment based on a predefined virtual-real mapping rule base. The virtual-real mapping rule base defines the conversion ratio between virtual currency and real currency, the correspondence between virtual time units and real time units, and the equivalence between virtual material units and real material units. The central data processing center sets uniform real execution constraints for all executable task items generated by the mapping. The actual execution constraints include a total task execution time not exceeding 7 days, a total material cost for the task not exceeding a preset cost limit, and the physical space for task execution being limited to at least two designated alternative areas within the campus. The central data processing center integrates all the mapped executable tasks with the actual execution constraints and outputs them in a formatted manner as the actual miniature entrepreneurial practice task list. The real-world miniature entrepreneurship practice task list clearly outlines the content of each practice task, the required materials and quantities, the execution location, the execution time window, the data types to be collected, and the collection tools.

7. A method for training college students in coffee entrepreneurship based on entrepreneurship guidance services, as described in claim 1, is characterized in that... In step S400, comparing and analyzing the practical process data with the corresponding data in the simulated business dataset to generate a personalized entrepreneurship guidance report specifically includes: The central data processing center receives the practical process data uploaded by student user clients; The data from the actual operation process includes real customer traffic data, real transaction order data, real raw material consumption data, and real customer feedback rating data. The central data processing center extracts simulated data of the same type as the actual operation data from the last 7 virtual days of the simulated operation dataset as a comparison benchmark. The central data processing center calculates the deviation between the actual process data and the comparison benchmark data item by item; The formula for calculating the deviation is: the deviation is equal to the absolute value of the actual operation data minus the comparison benchmark data, and then divided by the comparison benchmark data; The central data processing center compares all calculated deviations with a preset deviation threshold, filters out data items whose deviations exceed the deviation threshold, and marks them as significant difference items; Based on the correspondence between the data collection timestamp and the virtual day, the central data processing center traces back to the student user decision instruction corresponding to the time when the comparison benchmark data was generated in step S200. The central data processing center analyzes the potential causes of the significant differences by combining the internal logic rules of the virtual customer group behavior model. The potential causes include biases in estimating customer price sensitivity, biases in estimating geographic location foot traffic conversion rates, and biases in estimating actual raw material loss rates. The personalized entrepreneurship guidance report is generated by integrating the significant differences, the corresponding student user decision instructions, the potential causes analyzed, and at least one specific optimization suggestion generated based on the potential causes.

8. A method for training college students in coffee entrepreneurship based on entrepreneurship guidance services, as described in claim 7, is characterized in that... The personalized entrepreneurship guidance report shall include at least the following sections: The first part is an overview of the data differences, which clearly lists all significant differences, the corresponding benchmark data, the data from the actual operation process, and the calculated deviation in a table format. The second part is the decision backtracking analysis, which details the specific decision instructions made by student users in virtual operation and real practice, corresponding to the significant difference items. The third part is cause diagnosis, which is based on the simulation logic of the virtual customer group behavior model. It explains the reasoning process of the virtual environment generating comparative benchmark data and the real environment generating practical process data under the decision instruction, and accurately identifies at least one key hypothesis difference. The fourth part provides optimization suggestions, offering at least two specific and actionable decision adjustment suggestions for each cause diagnosis result; The proposed decision-making adjustments include specific percentage ranges for adjusting product pricing strategies, specific suggestions for changing marketing channels, and specific plans for optimizing raw material procurement frequency.

9. A method for training college students in coffee entrepreneurship based on entrepreneurship guidance services, as described in claim 1, is characterized in that... In step S400, adjusting the model parameters of the personalized virtual entrepreneurial environment based on the analysis results and guiding student users back to step S200 for iterative training specifically includes: After generating the personalized entrepreneurship guidance report, the central data processing center determines at least one target parameter that needs to be adjusted for the virtual customer group behavior model based on the cause diagnosis and optimization suggestions in the report. The target parameters include customer price sensitivity coefficient, customer brand preference weight, and customer consumption time period distribution ratio. The central data processing center modifies the value of the target parameter according to the preset parameter adjustment rules; The parameter adjustment rule is as follows: when the actual transaction order data in the actual operation process data is significantly lower than the simulated sales data in the benchmark data, and the reason is the price sensitivity estimation deviation, then the customer price sensitivity coefficient is increased. After the modifications were completed, the Central Data Processing Center used the updated virtual customer group behavior model parameters, combined with the difficulty level that student users had adapted to in the previous simulation, to generate a new, slightly more challenging personalized virtual entrepreneurial environment. The central data processing center sends iterative training instructions and the personalized entrepreneurship guidance report to student user clients. The iterative training instructions include an entry link to enter a new round of simulated operation. The student user client receives and displays the personalized entrepreneurship guidance report. After learning from the report, the student user triggers the central data processing center to execute step S200 again through the entry link, starting a new round of simulated operation.

10. A training method for college students' coffee entrepreneurship based on entrepreneurship guidance services according to any one of claims 1 to 9, characterized in that, The central data processing center is also responsible for the following data management and coordination functions when executing all steps: The central data processing center maintains an independent database of student user profiles, creating a unique profile identifier for each student user participating in the training. The student user profile database stores the student user's initial entrepreneurial ability comprehensive score, historical simulated business datasets, historical miniature entrepreneurial practice task lists, historical practice process data, historical personalized entrepreneurial guidance reports, and historical adjustment records of virtual customer group behavior model parameters. Between steps S300 and S400, the central data processing center coordinates the instructor's client to review and confirm the real miniature entrepreneurship practice task list, and receives the on-site supervision records uploaded by the instructor's client during the student user's execution of the practice task. After each iteration instruction of step S400 is sent, the central data processing center automatically updates the student user profile database and summarizes the latest training progress and overall performance data of the student user to the teacher management terminal for macro-level teaching management.

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