Interactive education method and system based on entrepreneurship guidance

Abstract

The invention relates to the technical field of education, in particular to an interactive education method and system based on entrepreneurship guidance, and the method comprises the following steps: obtaining task execution data, calling task execution time, selecting a path, correcting times, residence time and task success rate, calculating an execution time distribution trend, screening key nodes, and comparing a change frequency. And obtaining entrepreneurship task execution stability. According to the method, through deep analysis of the learner task execution data, the accuracy of a task selection path and the execution rationality are ensured, the task execution stability is optimized by using data support, the task suitability and cooperation efficiency are improved, fund operation is finely managed, the adjustment frequency is accurately matched with task requirements, and the task execution efficiency is improved. According to the method, the adaptability of fund management is enhanced, the task distribution weight is optimized, the low-frequency tasks are reasonably distributed, the task execution priority and efficiency are improved, and the execution ability and the strain ability of learners in entrepreneurship practice are remarkably enhanced.

Description

Technical Field

[0001] The present invention relates to the field of educational technology, and in particular to an interactive education method and system based on entrepreneurship guidance. Background Art

[0002] The field of educational technology encompasses technologies related to education, learning, and training, encompassing all methods and approaches for improving and optimizing the teaching process and learning outcomes. The core of this field is the application of various technologies to enhance educational quality and learning outcomes, encompassing teaching methods, the utilization of educational resources, interaction and feedback during the learning process, and educational management systems. With the continuous advancement of information technology, the field of educational technology has gradually incorporated a variety of emerging technologies, such as computer technology, artificial intelligence, and big data analytics. The application of these technologies has greatly enriched educational methods and tools, making the educational process more intelligent and personalized, and significantly improving the interactivity and effectiveness of teaching.

[0003] Among them, the interactive education method based on entrepreneurial guidance refers to helping learners master entrepreneurial skills and thinking in practice by combining entrepreneurial education with interactive teaching methods. The core technical matters of the patent subject matter involve how to improve learners' participation and learning effects through interactive education methods during the entrepreneurial guidance process. Specifically, the patent solves the problem of lack of practical guidance and interactive feedback in traditional education methods by designing an innovative interactive education method to help learners interact effectively with teachers or other learners during the entrepreneurial process. This method promotes learners' in-depth understanding and practical application of entrepreneurial knowledge through means such as simulated entrepreneurial scenarios, case analysis and actual operations.

[0004] Existing technologies for entrepreneurial guidance primarily focus on providing instructional content, lacking effective monitoring of learners' key decision points during task execution. This leads to repeated trial and error in task path selection, impacting learning efficiency. Regarding task execution stability, key variables in the execution process cannot be accurately assessed through data analysis, making it difficult for learners to obtain targeted task optimization recommendations. A systematic data analysis mechanism is lacking for the execution of core aspects such as market planning, financing management, and team collaboration. Adjustment strategies in the capital management process lack dynamic control capabilities, potentially leading to instability in capital operations and impacting the sustainability of entrepreneurial tasks. The lack of task matching makes it difficult for learners to balance the execution weights of different task categories, potentially leading to over-execution of some tasks and under-execution of other key tasks, impacting the overall synergy of entrepreneurial tasks. Capital management adjustment strategies fail to integrate dynamic optimization with task execution progress, resulting in a lack of rational planning for the frequency of capital allocation adjustments, potentially impacting the stability of capital flow and, in turn, reducing the feasibility of entrepreneurial projects. The task allocation mechanism fails to effectively distinguish the execution priorities of task categories, resulting in long-term neglect of low-frequency tasks, causing shortcomings in the comprehensive development of learners' entrepreneurial skills. The overall entrepreneurial education program lacks accuracy, making it difficult to form an efficient entrepreneurial ability training system. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the existing technology and to propose an interactive education method and system based on entrepreneurship guidance.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: an interactive education method based on entrepreneurship guidance, comprising the following steps:

[0007] S1: Obtain task execution data, call task execution time, selected path, number of corrections, dwell time, task success rate, calculate execution time distribution trend, screen key nodes and compare change frequency to obtain the execution stability of entrepreneurial tasks;

[0008] S2: Call the execution stability of the entrepreneurial task, obtain market planning, financing management, and team collaboration task parameters, calculate the number of market planning task executions, determine the adjustment range of financing management funds, analyze the distribution balance of team collaboration tasks, and obtain the matching distribution of entrepreneurial tasks;

[0009] S3: Call the entrepreneurial task matching distribution, obtain the task execution ratio, calculate the execution time fluctuation range, screen the proportion of stable execution tasks, analyze the execution fluctuation change rate, calculate the execution time matching error, determine the impact of execution offset on task conversion, extract the execution matching situation, and obtain the entrepreneurial task execution progress offset rate;

[0010] S4: Call the entrepreneurial task execution progress deviation rate, obtain the fund management task operation distribution, calculate the fund adjustment matching degree, analyze the fund allocation adjustment interval, screen the adjustment frequency change interval, and combine the task difficulty adjustment execution conditions to obtain the entrepreneurial task challenge adjustment coefficient.

[0011] As a further solution of the present invention, the entrepreneurial task execution stability includes execution time stability, path selection consistency, decision adjustment frequency, stay time distribution, and task success rate deviation; the entrepreneurial task matching distribution includes task category adaptability, execution frequency distribution, funding adjustment range, and team collaboration balance index; the entrepreneurial task execution progress deviation rate includes execution proportion difference, time fluctuation range, stable task proportion, task conversion error, and execution fluctuation rate; the entrepreneurial task challenge adjustment coefficient includes funding adjustment adaptability, matching threshold, adjustment frequency change range, task difficulty floating range, and challenge change index.

[0012] As a further solution of the present invention, the specific steps of obtaining task execution data, calling task execution time, selected path, number of corrections, dwell time, task success rate, calculating execution time distribution trend, screening key nodes and comparing change frequency, and obtaining entrepreneurial task execution stability are as follows:

[0013] S101: Obtain the learner's task execution data, call the task execution time, task selection path, number of corrections, dwell time and task success rate, count the task execution time interval, and calculate the distribution value of the task type to obtain the task execution time distribution trend;

[0014] S102: Based on the task execution time distribution trend, filter the key nodes of the task selection path, sort the paths according to their occurrence frequency and extract the high-frequency nodes, calculate the path offset range of differentiated learners under the same task type, count the change frequency of the path nodes, summarize the offsets between differentiated nodes, and obtain the change frequency of the key nodes of the task selection path;

[0015] S103: Based on the frequency of changes in key nodes of the task selection path, the number of corrections and the dwell time are counted, and the tasks are grouped according to the task success rate interval. The mean of the number of corrections and the dwell time is calculated, and the correlation between the task success rate is analyzed to obtain the execution stability of the entrepreneurial task.

[0016] As a further solution of the present invention, the specific steps of calling the entrepreneurial task execution stability, obtaining market planning, financing management, and team collaboration task parameters, calculating the number of market planning task executions, determining the adjustment range of financing management funds, analyzing the balance of team collaboration task distribution, and obtaining the entrepreneurial task matching distribution are as follows:

[0017] S201: Call the execution stability of the entrepreneurial task, obtain key parameters of the market planning task, the financing management task, and the team collaboration task, count the execution times of the market planning task, calculate the cumulative call volume of the task, and obtain the execution times of the market planning task;

[0018] S202: Based on the number of executions of the market planning task, the fund operation data in the financing management task is retrieved, the variation range of the fund adjustment range is calculated, and operation records with fund adjustment ranges in a low fluctuation range are screened. The operation stability region is determined based on the frequency of the low fluctuation range, and the average fund adjustment range in the stable region is summarized to obtain the stable operation region of the financing management task.

[0019] S203: Based on the stable operation area of the financing management task, call the execution distribution data of the team collaboration task, calculate the balance index of the task execution distribution, count the mean stay time, summarize the execution characteristics of the task type, and obtain the entrepreneurial task matching distribution.

[0020] As a further solution of the present invention, the calculation formula for the cumulative call volume of the market planning task is specifically:

[0021]

[0022] Among them, C total represents the cumulative number of calls to the market planning task, N represents the total number of executions of the market planning task, and E i represents the number of data operations involved in the execution of the i-th task, F i represents the number of fund operations involved in the execution of the i-th task, T i represents the end time of the i-th task, S i represents the start time of the i-th task, |T i -S i | represents the execution time of the i-th task, ∑ represents the cumulative calculation of all tasks, represents the square root operation of the denominator, and |·| represents the absolute value operation.

[0023] As a further solution of the present invention, the specific steps of calling the entrepreneurial task matching distribution, obtaining the task execution ratio, calculating the execution time fluctuation range, screening the proportion of stable execution tasks, analyzing the execution fluctuation change rate, calculating the execution time matching error, judging the impact of execution offset on task conversion, extracting the execution matching situation, and obtaining the entrepreneurial task execution progress offset rate are as follows:

[0024] S301: Calling the entrepreneurial task matching distribution, obtaining the execution ratio of the task category, calculating the fluctuation range of the task execution time under the differentiated task type, and counting the execution time variation interval of the task type to obtain the task execution time fluctuation range;

[0025] S302: Based on the task execution time fluctuation range, count the number of stable tasks, calculate the proportion of stable tasks, compare the change rate of fluctuating tasks, filter task execution offset data, extract the distribution range of offset tasks, count the offset value intervals under differentiated task categories, summarize differentiated task types, and obtain task execution matching status;

[0026] S303: Based on the task execution matching situation, the execution progress of the task type is counted, the cumulative change of the task execution offset is calculated, the calculation data of the task execution offset rate is filtered, the distribution trend of the task execution offset between differentiated task categories is summarized, and the entrepreneurial task execution progress offset rate is obtained.

[0027] As a further solution of the present invention, the calculation formula for the proportion of the stabilization task is specifically:

[0028]

[0029] Among them, R stable represents the proportion of stable tasks, M represents the total number of task executions, T j represents the actual execution time of the jth task, T avg represents the average execution time of all tasks, |T j -T avg | represents the deviation of the execution time of task j from the average value, W j represents the stability weight of the task, which is dynamically adjusted according to the execution frequency and time volatility of the task category. ∑ represents the cumulative calculation of all task executions. N total Represents the total number of times the task is executed, and ×100% represents conversion to percentage.

[0030] As a further solution of the present invention, the specific steps of calling the entrepreneurial task execution progress deviation rate, obtaining the fund management task operation distribution, calculating the fund adjustment matching degree, analyzing the fund allocation adjustment interval, screening the adjustment frequency change interval, and combining the task difficulty adjustment execution condition to obtain the entrepreneurial task challenge adjustment coefficient are as follows:

[0031] S401: Calling the entrepreneurial task execution progress deviation rate, obtaining the fund operation distribution of the fund management task, counting the adjustment range of the fund operation, calculating the matching degree of the fund adjustment value within the task requirement range, summarizing the adaptation range of the fund adjustment, and obtaining the fund adjustment matching degree index;

[0032] S402: Based on the fund adjustment matching degree index, calculate the adjustment interval of fund allocation, calculate the range of adjustment frequency, select intervals with smaller fluctuations in adjustment frequency, calculate the adjustment requirements in the task execution conditions, summarize the interval trend of fund adjustment frequency, extract the range of task challenge, and summarize the execution conditions of fund management tasks based on the task difficulty classification to obtain the fund management task execution conditions;

[0033] S403: Based on the execution conditions of the fund management task, the distribution of fund operations under differentiated task categories is counted, the change ratio of task challenge in fund adjustment is calculated, the impact data of fund adjustment on task challenge is screened, the changing trend of challenge adjustment is summarized, and the entrepreneurial task challenge adjustment coefficient is obtained.

[0034] As a further solution of the present invention, the method further includes, S5: calling the entrepreneurial task challenge adjustment coefficient, obtaining task execution status, calculating task distribution weights, screening execution frequency categories, analyzing task priorities, adjusting low-frequency task allocation probabilities, optimizing task plans, and obtaining entrepreneurial education plans;

[0035] The entrepreneurship education program includes task priority sorting, task allocation weights, low-frequency task adjustment parameters, task correlation coefficients, and task optimization structure;

[0036] S501: Calling the entrepreneurial task challenge adjustment coefficient, obtaining the execution status of differentiated task types, counting the execution frequency of task categories, calculating task distribution weights, summarizing the execution ratios of task categories, and obtaining task distribution weight values;

[0037] S502: Based on the task distribution weight values, filter task categories with higher execution frequencies, calculate the priority execution order of the task categories, collect statistics on the correlation data between tasks, summarize the matching intervals between task priority rankings and correlations, set low-frequency task thresholds, analyze the proportion of low-frequency tasks in the allocation plan, adjust the allocation probability of low-frequency tasks, calculate the distribution change of task categories before and after optimization, and obtain an optimized task allocation plan;

[0038] S503: Based on the optimized task allocation plan, the distribution of task categories is counted, the adjusted task adaptation ratio is calculated, the optimization trend of task matching is summarized, and the entrepreneurship education plan is obtained.

[0039] An interactive education system based on entrepreneurship guidance, including:

[0040] The task execution data analysis module obtains learners' task execution data, calls task execution time, task selection path, number of revisions, dwell time, and task success rate, calculates the distribution trend of task execution time under differentiated task types, screens key nodes of task selection paths and compares the change frequency, analyzes the correlation between the number of revisions and dwell time on task success rate, extracts stability features based on the execution efficiency differences of differentiated task categories, and obtains the execution stability of entrepreneurial tasks;

[0041] The entrepreneurial task stability calculation module calls the entrepreneurial task execution stability, obtains key parameters of market planning, financing management, and team collaboration tasks, calculates the execution times of the market planning task, screens the stable fund operation area of the financing management task, analyzes the execution distribution balance and the relationship between the stay time of the team collaboration task, and obtains the matching distribution of the entrepreneurial tasks;

[0042] The entrepreneurial task matching distribution evaluation module calls the entrepreneurial task matching distribution, obtains the task execution ratio, calculates the execution time fluctuation range, analyzes the proportion of stable tasks and compares the change rate of fluctuating tasks, filters the task execution offset data, and obtains the entrepreneurial task execution progress offset rate;

[0043] The fund management execution optimization module calls the entrepreneurial task execution progress deviation rate, obtains the fund operation distribution of the fund management task, calculates the degree of match between the fund adjustment range and the task requirements, analyzes the fund adjustment interval and screens the change range of the adjustment frequency, and obtains the entrepreneurial task challenge adjustment coefficient;

[0044] The entrepreneurship education program optimization module calls the entrepreneurship task challenge adjustment coefficient, obtains the task execution status, calculates the task distribution weight and filters the execution frequency task category, analyzes the priority execution order and the task correlation, adjusts the allocation probability of low-frequency tasks, and obtains the entrepreneurship education program.

[0045] Compared with the prior art, the advantages and positive effects of the present invention are:

[0046] In the present invention, through in-depth analysis of learners' task execution data, the accuracy of task selection path and the rationality of execution are ensured, the stability of task execution is optimized with data support, the task adaptability and collaboration efficiency are improved, the fund operation is managed in a refined manner, the adjustment frequency is accurately matched with task requirements, the adaptability of fund management is enhanced, the task distribution weight is optimized, the low-frequency tasks are reasonably allocated, the priority and efficiency of task execution are improved, and the learners' execution ability and adaptability in entrepreneurial practice are significantly enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 Schematic diagram of the steps of the present invention;

[0049] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0051] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0052] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0053] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0054] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0055] See also Figure 1 , an interactive educational method based on entrepreneurial guidance, comprising the following steps:

[0056] S1: Obtain learners' task execution data, call task execution time, task selection path, number of revisions, dwell time, and task success rate, calculate the distribution trend of task execution time under different task types, screen key nodes in the task selection path and compare the change frequency, analyze the correlation between the number of revisions and dwell time on the task success rate, extract the execution differences of learners in different task categories, and obtain the stability of entrepreneurial task execution;

[0057] S2: Invoke the entrepreneurial task execution stability, obtain key parameters of market planning tasks, financing management tasks, and team collaboration tasks, calculate the execution count of the market planning task, determine the adjustment range of capital operations in the financing management task and screen the stable operation area, analyze the relationship between the balance of execution distribution and dwell time in the team collaboration task, extract execution characteristics under different task types, and obtain the matching distribution of entrepreneurial tasks;

[0058] S3: Call the entrepreneurial task matching distribution, obtain the task execution ratio, calculate the fluctuation range of execution time under differentiated task types, analyze the proportion of stable execution tasks and compare the change rate of fluctuating execution tasks, filter task execution offset data, extract the matching status of task execution between differentiated task categories, and obtain the entrepreneurial task execution progress offset rate;

[0059] S4: Call the entrepreneurial task execution progress deviation rate, obtain the fund operation distribution of the fund management task, calculate the matching degree index between the fund adjustment range and the task requirements, analyze the adjustment interval of the fund allocation and filter the change range of the adjustment frequency, extract the change range of the task challenge, adjust the execution conditions of the fund management task based on the task difficulty classification, and obtain the entrepreneurial task challenge adjustment coefficient;

[0060] S5: Call the entrepreneurial task challenge adjustment coefficient, obtain the execution status of differentiated task types, calculate the task distribution weight, and filter the task categories with the highest execution frequency. Analyze the relationship between the priority execution order of task categories and the task correlation, adjust the allocation probability of low-frequency tasks and optimize the task allocation plan to obtain the entrepreneurial education plan.

[0061] The stability of entrepreneurial task execution includes execution time stability, path selection consistency, decision adjustment frequency, dwell time distribution, and task success rate deviation; the entrepreneurial task matching distribution includes task category adaptability, execution frequency distribution, funding adjustment range, and team collaboration balance index; the entrepreneurial task execution progress deviation rate includes execution ratio difference, time fluctuation range, stable task proportion, task conversion error, and execution fluctuation rate; the entrepreneurial task challenge adjustment coefficient includes funding adjustment adaptability, matching threshold, adjustment frequency change range, task difficulty floating range, and challenge change index; the entrepreneurial education plan includes task priority sorting, task allocation weight, low-frequency task adjustment parameters, task correlation coefficient, and task optimization structure.

[0062] The specific steps of S1 are:

[0063] S101: Obtain the learner's task execution data, call the task execution time, task selection path, number of corrections, dwell time and task success rate, count the task execution time interval, and calculate the distribution value of the task type to obtain the task execution time distribution trend;

[0064] To obtain the learner's task execution data, it is necessary to call the task execution time, task selection path, number of revisions, dwell time and task success rate in sequence. The task execution time is obtained by monitoring the time when the learner starts the task in the system and recording the end time when the task is completed or abandoned. The time difference between the two is the task execution time. For example, a learner selects the task "Develop a marketing plan" in the system, the task start time is recorded as 10:05, and the task completion time is recorded as 10:15. The execution time of the task is 10 minutes. The task selection path is obtained by tracking the sequence of steps that the learner visits in the process of task execution. For example, in the process of executing the "Develop a marketing plan" task, the learner may enter the four stages of "Market research", "Competition analysis", "Marketing strategy formulation" and "Scheme optimization" in sequence. The selection path of the task is the sequential record of these four stages. The number of revisions is calculated by counting the learner's adjustment behavior on the plan or operation during the task execution. Each adjustment is considered a revision. For example, if the learner first selects the "Social media promotion" plan in the "Marketing strategy formulation" stage, but changes it to the "Search engine optimization" plan, it is recorded as one revision. The dwell time is obtained by calculating the learner's The length of time a learner stays at each task step. For example, if a learner stays at the "competition analysis" step for 5 minutes, the stay time at this step is 5 minutes. The task success rate is calculated by counting the successful completion of all learners in a certain task type, setting a task completion mark, and calculating the proportion of completed tasks. For example, if 75 out of 100 learners completed the "develop marketing plan" task, the task success rate is 75%. After obtaining the execution data of all learners, the task execution time is interval-based statistics, and the task execution time is divided into several intervals, such as 0-5 minutes, 5-10 minutes, 10-1 5 minutes, etc., and calculate the proportion of tasks in each interval. For example, statistics show that 65% of learners complete tasks within the 5-10 minute interval, 25% within the 10-15 minute interval, and 10% within the 0-5 minute interval. It can be determined that the main distribution trend of task execution time is concentrated in 5-10 minutes. Further statistics on the distribution of different types of tasks in each time interval, for example, the execution time of the "develop a marketing plan" task is mainly concentrated in 5-10 minutes, while the execution time of the "optimize product design plan" task is mainly concentrated in 10-15 minutes, then the overall distribution trend of task execution time can be obtained.

[0065] S102: Based on the task execution time distribution trend, filter the key nodes of the task selection path, sort the paths by frequency of occurrence and extract the high-frequency nodes, calculate the path offset range of differentiated learners under the same task type, count the change frequency of the path nodes, summarize the offsets between differentiated nodes, and obtain the change frequency of the key nodes of the task selection path;

[0066] To screen the key nodes of the task selection path, first count the steps that learners have gone through under the same task type and sort them according to the frequency of the path appearance. For example, in the task of "developing a marketing plan", the steps that learners may go through include "market research", "competitive analysis", "marketing strategy formulation", and "plan optimization". If 80% of learners have gone through the two steps of "market research" and "marketing strategy formulation", then these two steps are key nodes. Next, calculate the path deviation range of different learners under the same task type. The specific method is to identify the learner's deviation behavior outside the standard path. For example, when most learners perform the task of "developing a marketing plan", they go through "market research" to "competitive analysis" in sequence, but some learners skip "competitive analysis" and go directly to "marketing strategy formulation". The learner's path is offset from the standard path. By further counting the frequency of changes in path nodes, the probability of each key node being modified or skipped can be calculated. For example, the probability of the "competition analysis" step being skipped by the learner is 20%, and the probability of the "solution optimization" step being adjusted in order is 30%. Then, the path offset between key nodes is summarized. For example, when performing the "optimize product design solution" task, if the path of most learners is "user needs analysis" followed by "product function optimization", but some learners choose to directly enter "visual design adjustment" after "user needs analysis", the probability of the offset path can be calculated, and the average path offset step length can be measured. If the average step length difference is large, it indicates that different learners have significant differences in path selection, and finally the change frequency of key nodes in the task selection path is obtained.

[0067] S103: Based on the frequency of changes in key nodes of the task selection path, the number of corrections and the dwell time are counted, and the tasks are grouped according to the task success rate interval. The mean of the number of corrections and the dwell time is calculated, and the correlation between the task success rate is analyzed to obtain the execution stability of the entrepreneurial task;

[0068] Statistics are made on the number of revisions and the length of stay of learners. First, the adjustment behavior of each learner at the key nodes is calculated. For example, in the task of "optimizing product design plan", if a learner modifies the plan twice in the step of "user demand analysis" and once in the step of "product function optimization", the number of revisions of the learner is 3. Next, statistics are made on the length of stay of the learner at the key nodes. For example, if a learner stays for 6 minutes in the step of "visual design adjustment" and for 4 minutes in the step of "user feedback test", the learner's residence time data at the key steps can be obtained. Then, the learners are grouped according to the task success rate interval. For example, the learners are grouped into groups of 1 and 2. Learners are divided into three groups with success rates of 0-30%, 30-60%, and 60-100%, and the mean number of corrections and the average stay time of learners in each group are calculated. For example, the average number of corrections of learners in the 0-30% success rate group is 6 times, and the average stay time is 9 minutes, while the average number of corrections of learners in the 60-100% success rate group is 2 times, and the average stay time is 5 minutes. By comparing the mean number of corrections and the stay time in each success rate interval, the correlation between task success rate can be analyzed. For example, if learners with a higher success rate have fewer corrections and shorter stay time, it can be concluded that the task execution stability of this type of learners is higher.

[0069] The specific steps of S2 are:

[0070] S201: Call the entrepreneurial task execution stability, obtain key parameters of the market planning task, financing management task, and team collaboration task, count the number of executions of the market planning task, calculate the cumulative number of task calls, and obtain the number of executions of the market planning task;

[0071] The calculation formula for the cumulative call volume of the market planning task is as follows:

[0072]

[0073] Among them, C total represents the cumulative number of calls to the market planning task, N represents the total number of executions of the market planning task, and E i represents the number of data operations involved in the execution of the i-th task, F i represents the number of fund operations involved in the execution of the i-th task, T i represents the end time of the i-th task, S i represents the start time of the i-th task, |T i -S i | represents the execution time of the i-th task, ∑ represents the cumulative calculation of all tasks, represents the square root operation of the denominator, and |·| represents the absolute value operation;

[0074] This formula is used to calculate the cumulative call volume of the market planning task, where C total Indicates the total cumulative call volume of the market planning task, and N is the total number of executions of the market planning task. i represents the number of data operations in the i-th task, F i Indicates the number of fund operations for the i-th task. Time difference |T i -S i | is the absolute value of the difference between the end time and the start time of the i-th task, which indicates the duration of the task execution.

[0075] The specific calculation process is as follows: Assume the following market planning task data: - Task 1: Number of data operations E1 = 30, number of capital operations F1 = 20, start time S1 = 08:00, end time T1 = 10:00, i.e. 2 hours. - Task 2: Number of data operations E2 = 45, number of capital operations F2 = 25, start time S2 = 09:00, end time T2 = 11:15, i.e. 2 hours and 15 minutes. - Task 3: Number of data operations E3 = 40, number of capital operations F3 = 30, start time S3 = 13:00, end time T3 = 15:30, i.e. 2 hours and 30 minutes.

[0076] The time difference is converted to minutes and the formula is applied:

[0077] The time difference of Task 1 |T1-S1| = |10:00-08:00| = 120 minutes,

[0078]

[0079] The time difference of Task 2 |T2-S2|=|11:15-09:00|=135 minutes,

[0080]

[0081] The time difference of Task 3 |T3-S3| = |15:30-13:00| = 150 minutes,

[0082]

[0083] Add up the call volume of each task to get the total cumulative call volume:

[0084] C total =54.30+96.54+97.32=248.16;

[0085] The results show that for the three given task examples, the total cumulative call volume is 248.16, reflecting the total number of calls during actual task execution. This value reflects the combined impact of resource usage and time efficiency of market planning task execution activities, providing basic data for further analysis of the efficiency and cost-effectiveness of market planning tasks.

[0086] S202: Based on the number of executions of the market planning task, the fund operation data in the financing management task is retrieved, the variation range of the fund adjustment range is calculated, and operation records with fund adjustment ranges in a low fluctuation range are screened. The operation stability region is determined based on the frequency of the low fluctuation range, and the average fund adjustment range in the stable region is summarized to obtain the stable operation region of the financing management task.

[0087] Call the fund operation data in the financing management task, first count the fund adjustment behaviors involved in the financing management task, and record the adjustment amount, adjustment direction, operation time and other information for each fund adjustment. For example, a team makes three fund adjustments every day in the financing management task, namely, increasing funds by 50,000 yuan, reducing funds by 20,000 yuan, and transferring funds by 30,000 yuan. The recorded fund adjustment ranges are +50,000, -20,000, and +30,000 respectively. Then calculate the range of fund adjustment ranges. This can be done by calculating the difference between the maximum fund adjustment amount and the minimum fund adjustment amount. For example, the maximum adjustment amount of a team in a week is 100,000 yuan, and the minimum adjustment amount is 10,000 yuan. The range of fund adjustment range is 90,000 yuan. Then filter the operation records with fund adjustment ranges in the low volatility range, and set the threshold for the low volatility range of fund adjustments. For example, if the low volatility range is set as capital adjustment between -30,000 yuan and +30,000 yuan, then all records of capital adjustments within this range will be screened out. Assuming that there are 60 capital adjustments in a month, of which 45 capital adjustments are within the range of -30,000 yuan to +30,000 yuan, then the operation frequency in the low volatility range is 45 times. The stable operation area can be judged according to the frequency of the low volatility range. The proportion of low volatility adjustments can be calculated. For example, if 45 low volatility adjustments account for 75% of the total number of adjustments, then it can be considered that the team's capital adjustments are mainly in the low volatility range. Finally, the average capital adjustment amplitude in the stable area is summarized, and the average capital adjustment in the low volatility range is calculated. For example, the total capital adjustment of 45 low volatility adjustments is 900,000 yuan, then the average capital adjustment in the low volatility range is 20,000 yuan, and finally the stable operation area of the financing management task is obtained.

[0088] S203: Based on the stable operation area of the financing management task, call the execution distribution data of the team collaboration task, calculate the balance index of the task execution distribution, calculate the mean stay time, summarize the execution characteristics of the task type, and obtain the entrepreneurial task matching distribution;

[0089] Call the execution distribution data of team collaboration tasks, first obtain the execution status of team collaboration tasks, including task assignment, execution time, number of decisions and other information. For example, a team performs 15 team task assignments in a week, involving 30 communication discussions and 10 decision adjustments. Then calculate the balance index of task execution distribution. This can be done by statistically analyzing the distribution of task execution in various time periods. For example, statistically analyzing the time period distribution of task execution in a week. If the task is executed 3 times a day from Monday to Friday, 2 times on Saturday, and 1 time on Sunday, then calculate the balance index of task execution. By calculating the standard deviation of the number of task executions, assuming that the standard deviation of task execution in each time period is 0 .8, we can determine the degree of balance in task execution, and then calculate the average duration of task execution. We can find the average by calculating the execution duration of each task. For example, if the execution durations of 15 tasks are 30 minutes, 40 minutes, 35 minutes, etc., the average duration is (30+40+35+...) / 15. Assuming that the average duration is 38 minutes, we can summarize the execution characteristics of task types. By analyzing the execution of different types of tasks, for example, the average execution duration of market research tasks is 45 minutes, and the average execution duration of team discussion tasks is 35 minutes, we can summarize the execution characteristics of different task types and finally obtain the matching distribution of entrepreneurial tasks.

[0090] The specific steps of S3 are:

[0091] S301: Call the entrepreneurial task matching distribution, obtain the execution ratio of the task category, calculate the fluctuation range of the task execution time under the differentiated task type, count the execution time change interval of the task type, and obtain the task execution time fluctuation range;

[0092] First, we can obtain the execution ratio of task categories by counting the proportion of different categories of tasks in all tasks. For example, entrepreneurial task categories include market analysis, product design, fund management, team operation, etc., and count the number of executions of each category of tasks in a month. For example, if the market analysis task is executed 120 times, the product design task is executed 80 times, the fund management task is executed 90 times, and the team operation task is executed 110 times, the total number of task executions is 400 times, then we can calculate the execution ratio of each category of tasks. For example, the execution ratio of the market analysis task is 120 / 400=30%. Then we can calculate the fluctuation range of task execution time under different task types. The execution time of each task category can be recorded, and the difference between the maximum execution time and the minimum execution time can be calculated. For example, the shortest execution time of a market analysis task is 30 minutes, and the longest execution time is 120 minutes. The execution time fluctuation range of the market analysis task is 90 minutes. Then, the execution time variation range of the task type is counted, and the quartile interval of the execution time can be calculated by task category. For example, the quartile interval of the market analysis task is 45-90 minutes, the quartile interval of the product design task is 50-95 minutes, and the quartile interval of the fund management task is 40-85 minutes. Finally, the task execution time fluctuation range is obtained.

[0093] S302: Based on the task execution time fluctuation range, count the number of stable tasks, calculate the proportion of stable tasks, and compare the change rate of fluctuating tasks. Filter task execution offset data, extract the distribution range of offset tasks, count the offset value intervals under differentiated task categories, summarize differentiated task types, and obtain task execution matching status;

[0094] The specific calculation formula for the proportion of stable tasks is:

[0095]

[0096] Among them, R stable represents the proportion of stable tasks, M represents the total number of task executions, T j represents the actual execution time of the jth task, T avg represents the average execution time of all tasks, |T j -T avg | represents the deviation of the execution time of task j from the average value, W j represents the stability weight of the task, which is dynamically adjusted according to the execution frequency and time volatility of the task category. ∑ represents the cumulative calculation of all task executions. N total Represents the total number of times a task is executed, and ×100% represents conversion to percentage form;

[0097] This formula is used to calculate the proportion of stable tasks, measuring the proportion of stable tasks among all executed tasks. The key parameters calculated in the formula include task execution time, task stability weight, and the total number of executed tasks. Stable tasks are defined as tasks with minimal execution time fluctuations. The formula calculates the stability of each task by calculating its execution time deviation and then calculates the weighted sum to calculate the final proportion.

[0098] Parameter acquisition and value assignment

[0099] The total number of task executions M represents the number of tasks executed during the statistical time period, which is obtained through analysis of system log records. Set M=5.

[0100] Task execution time T j Represents the actual execution time of each task in minutes, extracted from the execution log, and set T1=32, T2=45, T3=50, T4=38, and T5=47.

[0101] Average execution time T avg Calculated from the arithmetic mean of all task execution times:

[0102]

[0103] Task stability weight W j Represents the impact of different tasks on overall execution stability. It is usually set dynamically based on task type, task difficulty, and historical execution time fluctuations, with a value range of 0.8 to 1.2:

[0104] W1=1.1 (the task type is planning, which is usually stable);

[0105] W2=0.9 (task type is market research, low stability);

[0106] W3=1.2 (task type is financing management, high stability);

[0107] W4=1.0 (task type is teamwork, stability is average);

[0108] W5=0.85 (Task type is strategic adjustment, execution time fluctuates greatly)

[0109] The total number of tasks executed N total Represents the total number of all executed tasks, set N total =20.

[0110] Calculate the stability contribution of each task:

[0111]

[0112] Calculate the proportion of stable tasks:

[0113]

[0114] Analysis of calculation results

[0115] The results show that, across 20 task execution records, approximately 4.12% of tasks met the stability criteria. This value measures the stability of task execution time. A lower value may indicate significant fluctuations in task execution time, while a higher value indicates relatively stable task execution. This data can be used to optimize task scheduling strategies to improve task execution stability.

[0116] S303: Based on the task execution matching situation, the execution progress of the task type is counted, the cumulative change of the task execution offset is calculated, the calculation data of the task execution offset rate is filtered, and the distribution trend of the task execution offset between the differentiated task categories is summarized to obtain the entrepreneurial task execution progress offset rate;

[0117] First, the execution progress of the task type can be counted. The average execution time of different task categories can be calculated by recording the time interval from the start to the completion of the task. For example, the average execution time of the market analysis task is 75 minutes, the average execution time of the product design task is 85 minutes, the average execution time of the fund management task is 70 minutes, and the average execution time of the team operation task is 90 minutes. Then, the cumulative change of the task execution offset can be calculated by counting the fluctuation range of the task execution time and accumulating the offset change of all tasks. For example, if the execution time of a task is 50 minutes in the first stage, 65 minutes in the second stage, and 80 minutes in the third stage, the cumulative change is (65-50)+(80-65)=30 minutes. Then, the calculation data of the task execution offset rate can be filtered to calculate the execution time offset rate of each task category. For example, The execution time offset rate calculation formula is (maximum execution time - minimum execution time) / minimum execution time. For example, the execution time offset rate of the market analysis task is (120-30) / 30=300%, the execution time offset rate of the product design task is (110-40) / 40=175%, the execution time offset rate of the fund management task is (100-50) / 50=100%, and the execution time offset rate of the team operation task is (130-60) / 60=117%. Then, the distribution trend of task execution offset among differentiated task categories is summarized. By statistically analyzing the changes in the execution time of each task category, the stability and volatility of task execution can be analyzed. For example, the execution time fluctuation range of the market analysis task is larger, and the execution time fluctuation range of the fund management task is smaller. Finally, the entrepreneurial task execution progress offset rate is obtained.

[0118] The specific steps of S4 are:

[0119] S401: Call the entrepreneurial task execution progress deviation rate, obtain the fund operation distribution of the fund management task, count the adjustment range of the fund operation, calculate the matching degree of the fund adjustment value within the task requirement range, summarize the adaptation range of the fund adjustment, and obtain the fund adjustment matching degree index;

[0120] First, obtain the fund operation distribution of the fund management task. You can record all fund adjustment records in the fund management task and count the operation type, amount change, time node and other information of each fund flow. For example, a startup project performs 150 fund adjustments in a month, of which 50 are short-term working capital adjustments, 40 are long-term investment adjustments, and 60 are cost control adjustments. The fund operation distribution can be divided into three categories: short-term, long-term, and cost control. Then count the adjustment range of fund operations and calculate the amount change range of each fund adjustment. For example, the amount change of short-term working capital adjustment is between 10,000 yuan and 50,000 yuan, the amount change of long-term investment adjustment is between 100,000 yuan and 500,000 yuan, and the amount of cost control adjustment is between 100,000 yuan and 500,000 yuan. If the change is between 20,000 yuan and 80,000 yuan, the fluctuation range of each type of adjustment is counted, and then the matching degree of the fund adjustment value within the task requirement range is calculated. The fund adjustment range required by the task can be set. For example, the reasonable adjustment range of short-term working capital is set at 20,000 yuan to 40,000 yuan, and the proportion of the actual adjustment amount falling within this range is counted. For example, 35 of 50 short-term fund adjustments fall within this range, and the matching degree is 35 / 50=70%. Then the adaptation interval of the fund adjustment is summarized, and the fund adjustment concentration range under different task categories can be calculated. For example, the fund concentration interval of long-term investment adjustment is 150,000 yuan to 400,000 yuan, and the fund concentration interval of cost control adjustment is 30,000 yuan to 60,000 yuan. Finally, the fund adjustment matching degree index is obtained.

[0121] S402: Based on the fund adjustment matching degree index, calculate the adjustment interval of fund allocation, calculate the range of adjustment frequency, select the interval with smaller adjustment frequency fluctuation, calculate the adjustment requirements in the task execution conditions, summarize the interval trend of fund adjustment frequency, extract the range of task challenge, and combine the task difficulty classification to summarize the execution conditions of fund management tasks to obtain the fund management task execution conditions;

[0122] First, count the adjustment intervals of fund allocation. You can calculate the time interval between two fund adjustments. For example, if the time intervals for 150 fund adjustments in a month are 1 day, 3 days, 5 days, etc., then calculate the average adjustment interval. For example, the mean of the adjustment interval is 2.5 days. Then calculate the range of change in the adjustment frequency. You can record the number of fund adjustments per day or week and calculate the frequency difference of fund adjustments. For example, if the fund is adjusted 30 times in the first week, 25 times in the second week, and 20 times in the third week, then the range of change in the adjustment frequency is (30-20)=10 times. Then, filter out the intervals with smaller fluctuations in the adjustment frequency. You can set a threshold for the fluctuation of the adjustment frequency. For example, set the interval with fluctuations less than 5 times as a stable adjustment interval. Then filter out the time periods that meet the conditions. For example, the difference in the adjustment frequency between the second and third weeks is 5 times, which meets the standard of the stable adjustment interval. Then count the adjustment requirements in the task execution conditions. You can analyze the adjustment requirements of each task For the minimum requirement for fund adjustment, for example, a market development task requires a minimum fund adjustment frequency of 5 times a week, then filter out the task data that meets this condition, summarize the interval trend of fund adjustment frequency, and calculate the fund adjustment frequency under different task categories, for example, the fund adjustment frequency of market development tasks is 6 times a week, and the fund adjustment frequency of product development tasks is 4 times a week. To extract the range of variation in task challenge, the fund adjustment requirements of tasks with different challenge levels can be statistically analyzed, for example, the fund adjustment frequency of high-challenge tasks is concentrated above 8 times a week, the fund adjustment frequency of medium-challenge tasks is concentrated between 5-7 times a week, and the fund adjustment frequency of low-challenge tasks is concentrated below 4 times a week. Combined with the task difficulty classification, calculate the average fund adjustment requirement under each task difficulty, for example, the average fund adjustment requirement of high-challenge tasks is 9 times a week, and the average fund adjustment requirement of medium-challenge tasks is 6 times a week. Finally, summarize the execution conditions of fund management tasks.

[0123] S403: Based on the execution conditions of the fund management task, the distribution of fund operations under differentiated task categories is counted, the change ratio of task challenge in fund adjustment is calculated, the impact data of fund adjustment on task challenge is screened, the changing trend of challenge adjustment is summarized, and the challenge adjustment coefficient of the entrepreneurial task is obtained;

[0124] First, we count the distribution of fund operations under differentiated task categories, and calculate the proportion of fund adjustments under different task categories. For example, the fund adjustment proportion of market expansion tasks is 40%, the fund adjustment proportion of product development tasks is 30%, and the fund adjustment proportion of cost optimization tasks is 30%. Then, we calculate the change ratio of task challenge in fund adjustment, and calculate the fund adjustment range of tasks with different challenge levels. For example, the fund adjustment amount of high-challenge tasks ranges from 200,000 yuan to 500,000 yuan, the fund adjustment amount of medium-challenge tasks ranges from 100,000 yuan to 300,000 yuan, and the fund adjustment amount of low-challenge tasks ranges from 50,000 yuan to 150,000 yuan. Then, we calculate the fund adjustment ratio of tasks with different challenge levels. For example, the fund adjustment ratio of high-challenge tasks is (50-20 ) / 20=150%, and then filter the data on the impact of funding adjustments on the challenge of the task, and calculate the impact of the frequency of funding adjustments on the success rate of the task. For example, when the frequency of funding adjustments for high-challenge tasks is more than 8 times a week, the success rate is 75%, and when the frequency of funding adjustments is less than 8 times, the success rate drops to 60%. The impact data of funding adjustments on the success rate of high-challenge tasks is 15%. Then summarize the changing trend of challenge adjustments and calculate the fluctuations in funding adjustment requirements for tasks with different challenge levels. For example, in high-challenge tasks, the funding adjustment demand fluctuates greatly within the range of 200,000 to 500,000 yuan, while in low-challenge tasks, the funding adjustment demand fluctuates less within the range of 50,000 to 150,000 yuan. Finally, the challenge adjustment coefficient of the entrepreneurial task is obtained.

[0125] The specific steps of S5 are:

[0126] S501: Calling the entrepreneurial task challenge adjustment coefficient, obtaining the execution status of differentiated task types, counting the execution frequency of task categories, calculating task distribution weights, summarizing the execution ratios of task categories, and obtaining task distribution weight values;

[0127] First, obtain the execution status of differentiated task types. This can be done by recording parameters such as the number of executions, execution time, and resource consumption of various tasks. For example, a startup project includes task categories such as market research, product development, financing management, and team building. Statistics are kept for the execution status of each task category within 30 days. For example, the market research task is executed 45 times, the product development task is executed 38 times, the financing management task is executed 50 times, and the team building task is executed 42 times. Then, the execution frequency of the task category can be calculated by calculating the proportion of the task category in the total task execution. For example, the execution frequency of the market research task is 45 / 175=25.7%, the execution frequency of the product development task is 21.7%, the execution frequency of the financing management task is 28.6%, and the execution frequency of the team building task is 20. It is 24%, and then the task distribution weight is calculated. The weight value can be calculated by setting the importance coefficient of different task categories and combining the execution frequency. For example, the importance coefficient of the financing management task is set to 1.2, the market research task is set to 1.0, the product development task is set to 1.1, and the team building task is set to 1.0. The task distribution weight calculation formula is execution frequency × importance coefficient. For example, the distribution weight of the financing management task is 50×1.2=60, and the distribution weight of the market research task is 45×1.0=45. Then, the execution ratio of the task categories is summarized to calculate the proportion of each task category in the total weight. For example, the execution ratio of the financing management task is 60 / 205=29.3%, and the execution ratio of the market research task is 21.9%. Finally, the task distribution weight value is obtained.

[0128] S502: Based on the task distribution weight values, filter the task categories with higher execution frequencies, calculate the priority execution order of the task categories, collect statistics on the correlation data between tasks, summarize the matching intervals between task priority rankings and correlations, set the low-frequency task threshold, analyze the proportion of low-frequency tasks in the allocation plan, adjust the allocation probability of low-frequency tasks, calculate the distribution change of task categories before and after optimization, and obtain the optimized task allocation plan;

[0129] First, filter out the task categories with higher execution frequency. You can set a task execution frequency threshold. For example, set a task with an execution frequency greater than 40 times as a high-frequency task, and filter out market research, financing management, and team building tasks as high-frequency task categories. Then calculate the priority execution order of the task categories, and sort them according to the task distribution weight value. For example, the distribution weight of the financing management task is 60, the distribution weight of the market research task is 45, and the distribution weight of the team building task is 42. The priority execution order is financing management, market research, and team building. Then, count the correlation data between tasks and calculate the degree of mutual dependence of different task categories during the execution process. For example, the correlation between the financing management task and the market research task is 0.7 (the correlation range is 0-1, the higher the value, the stronger the correlation), the correlation between the financing management task and the team building task is 0.6, and the correlation between the market research task and the team building task is 0.5. The correlation ranking between tasks can be obtained, and then the matching interval of task priority ranking and correlation can be summarized. The threshold range of task priority matching can be set. For example, setting the matching interval to 0.6-1.0 will screen out financing management tasks and market research tasks that meet the requirements. Then set the low-frequency task threshold and analyze the proportion of low-frequency tasks in the allocation plan. For example, if the execution frequency of low-frequency tasks is less than 30 times, the proportion of low-frequency tasks is calculated to be 15%. Then, the allocation probability of low-frequency tasks can be adjusted by increasing the weight of low-frequency tasks in task allocation, for example, increasing the execution probability of low-frequency tasks by 10%, and calculating the distribution change of task categories before and after optimization. For example, the distribution ratio of financing management tasks before optimization is 29.3%, which is adjusted to 27% after optimization. The distribution ratio of market research tasks before optimization is 21.9%, which is adjusted to 22.5% after optimization. Finally, the optimized task allocation plan is obtained.

[0130] S503: Based on the optimized task allocation plan, the distribution of task categories is counted, the adjusted task adaptation ratio is calculated, the optimization trend of task matching is summarized, and the entrepreneurial education plan is obtained;

[0131] First, the distribution of task categories is counted, and the execution ratio of each task category after optimization can be calculated. For example, the execution ratio of market research tasks after optimization is 22.5%, the execution ratio of financing management tasks is 27%, the execution ratio of product development tasks is 24%, and the execution ratio of team building tasks is 26.5%. Then, the adjusted task adaptation ratio is calculated. For example, the change rate of the execution ratio of each task category is calculated as (22.5%-21.9%) / 21.9%=2.7%, and the change rate of the adaptation ratio of financing management tasks is (27%-29.3%) / 29.3%=-7.8%. Then, the optimization trend of task matching can be summarized by analyzing the balance of each task category after adjustment. For example, the standard deviation of the execution ratio of task categories after optimization decreases by 5%, indicating that the task distribution is more balanced, and finally an entrepreneurial education plan is obtained.

[0132] See also Figure 2 , an interactive education system based on entrepreneurship guidance, including:

[0133] The task execution data analysis module obtains learners' task execution data, calls task execution time, task selection path, number of revisions, dwell time, and task success rate, calculates the distribution trend of task execution time under differentiated task types, screens key nodes of task selection paths and compares the change frequency, analyzes the correlation between the number of revisions and dwell time on task success rate, extracts stability features based on the execution efficiency differences of differentiated task categories, and obtains the execution stability of entrepreneurial tasks;

[0134] The entrepreneurial task stability calculation module calls the entrepreneurial task execution stability, obtains key parameters of market planning, financing management, and team collaboration tasks, calculates the execution frequency of market planning tasks, screens the stable capital operation area of financing management tasks, analyzes the relationship between the execution distribution balance and residence time of team collaboration tasks, and obtains the matching distribution of entrepreneurial tasks;

[0135] The entrepreneurial task matching distribution evaluation module calls the entrepreneurial task matching distribution, obtains the task execution ratio, calculates the execution time fluctuation range, analyzes the proportion of stable tasks and compares the change rate of fluctuating tasks, filters the task execution offset data, and obtains the entrepreneurial task execution progress offset rate;

[0136] The fund management execution optimization module calls the entrepreneurial task execution progress deviation rate, obtains the fund operation distribution of the fund management task, calculates the degree of match between the fund adjustment range and the task requirements, analyzes the fund adjustment interval and screens the change range of the adjustment frequency, and obtains the entrepreneurial task challenge adjustment coefficient;

[0137] The entrepreneurship education program optimization module calls the entrepreneurship task challenge adjustment coefficient, obtains the task execution status, calculates the task distribution weight and filters the execution frequency task categories, analyzes the priority execution order and the task correlation, adjusts the allocation probability of low-frequency tasks, and obtains the entrepreneurship education program.

[0138] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. An interactive education method based on entrepreneurship guidance, characterized in that: The following steps are involved: S1: Obtain task execution data, call task execution time, selected path, number of corrections, dwell time, task success rate, calculate execution time distribution trend, screen key nodes and compare change frequency to obtain the execution stability of entrepreneurial tasks; S2: Call the execution stability of the entrepreneurial task, obtain market planning, financing management, and team collaboration task parameters, calculate the number of market planning task executions, determine the adjustment range of financing management funds, analyze the distribution balance of team collaboration tasks, and obtain the matching distribution of entrepreneurial tasks; S3: Call the entrepreneurial task matching distribution, obtain the task execution ratio, calculate the execution time fluctuation range, screen the proportion of stable execution tasks, analyze the execution fluctuation change rate, calculate the execution time matching error, determine the impact of execution offset on task conversion, extract the execution matching situation, and obtain the entrepreneurial task execution progress offset rate; S4: Call the entrepreneurial task execution progress deviation rate, obtain the fund management task operation distribution, calculate the fund adjustment matching degree, analyze the fund allocation adjustment interval, screen the adjustment frequency change interval, and combine the task difficulty adjustment execution conditions to obtain the entrepreneurial task challenge adjustment coefficient.

2. The interactive education method based on entrepreneurship guidance according to claim 1 is characterized in that: The execution stability of the entrepreneurial task includes execution time stability, path selection consistency, decision adjustment frequency, stay time distribution, and task success rate deviation; the matching distribution of the entrepreneurial task includes task category adaptability, execution frequency distribution, funding adjustment range, and team collaboration balance index; the execution progress deviation rate of the entrepreneurial task includes execution ratio difference, time fluctuation range, stable task ratio, task conversion error, and execution fluctuation rate; the challenge adjustment coefficient of the entrepreneurial task includes funding adjustment adaptability, matching threshold, adjustment frequency change range, task difficulty floating range, and challenge change index.

3. The interactive education method based on entrepreneurship guidance according to claim 1, characterized in that: The specific steps to obtain task execution data, call task execution time, selected path, number of corrections, dwell time, task success rate, calculate execution time distribution trend, screen key nodes and compare change frequency, and obtain the execution stability of entrepreneurial tasks are as follows: S101: Obtain the learner's task execution data, call the task execution time, task selection path, number of corrections, dwell time and task success rate, count the task execution time interval, and calculate the distribution value of the task type to obtain the task execution time distribution trend; S102: Based on the task execution time distribution trend, filter the key nodes of the task selection path, sort the paths according to their occurrence frequency and extract the high-frequency nodes, calculate the path offset range of differentiated learners under the same task type, count the change frequency of the path nodes, summarize the offsets between differentiated nodes, and obtain the change frequency of the key nodes of the task selection path; S103: Based on the frequency of changes in key nodes of the task selection path, the number of corrections and the dwell time are counted, and the tasks are grouped according to the task success rate interval. The mean of the number of corrections and the dwell time is calculated, and the correlation between the task success rate is analyzed to obtain the execution stability of the entrepreneurial task.

4. The interactive education method based on entrepreneurship guidance according to claim 1, characterized in that: The specific steps for calling the execution stability of the entrepreneurial task, obtaining market planning, financing management, and team collaboration task parameters, calculating the number of market planning task executions, determining the adjustment range of financing management funds, analyzing the distribution balance of team collaboration tasks, and obtaining the matching distribution of entrepreneurial tasks are as follows: S201: Call the execution stability of the entrepreneurial task, obtain key parameters of the market planning task, the financing management task, and the team collaboration task, count the execution times of the market planning task, calculate the cumulative call volume of the task, and obtain the execution times of the market planning task; S202: Based on the number of executions of the market planning task, the fund operation data in the financing management task is retrieved, the variation range of the fund adjustment range is calculated, and operation records with fund adjustment ranges in a low fluctuation range are screened. The operation stability region is determined based on the frequency of the low fluctuation range, and the average fund adjustment range in the stable region is summarized to obtain the stable operation region of the financing management task. S203: Based on the stable operation area of the financing management task, call the execution distribution data of the team collaboration task, calculate the balance index of the task execution distribution, count the mean stay time, summarize the execution characteristics of the task type, and obtain the entrepreneurial task matching distribution.

5. The interactive education method based on entrepreneurship guidance according to claim 4 is characterized in that: The calculation formula for the cumulative call volume of the market planning task is as follows: Among them, C total represents the cumulative number of calls to the market planning task, N represents the total number of executions of the market planning task, and E i represents the number of data operations involved in the execution of the i-th task, F i represents the number of fund operations involved in the execution of the i-th task, T i represents the end time of the i-th task, S i represents the start time of the i-th task, |T i -S i | represents the execution time of the i-th task, ∑ represents the cumulative calculation of all tasks, represents the square root operation of the denominator, and |·| represents the absolute value operation.

6. The interactive education method based on entrepreneurship guidance according to claim 1, characterized in that: The specific steps of calling the entrepreneurial task matching distribution, obtaining the task execution ratio, calculating the execution time fluctuation range, screening the proportion of stable execution tasks, analyzing the execution fluctuation change rate, calculating the execution time matching error, judging the impact of execution offset on task conversion, extracting the execution matching situation, and obtaining the entrepreneurial task execution progress offset rate are as follows: S301: Calling the entrepreneurial task matching distribution, obtaining the execution ratio of the task category, calculating the fluctuation range of the task execution time under the differentiated task type, and counting the execution time variation interval of the task type to obtain the task execution time fluctuation range; S302: Based on the task execution time fluctuation range, count the number of stable tasks, calculate the proportion of stable tasks, compare the change rate of fluctuating tasks, filter task execution offset data, extract the distribution range of offset tasks, count the offset value intervals under differentiated task categories, summarize differentiated task types, and obtain task execution matching status; S303: Based on the task execution matching situation, the execution progress of the task type is counted, the cumulative change of the task execution offset is calculated, the calculation data of the task execution offset rate is filtered, the distribution trend of the task execution offset between differentiated task categories is summarized, and the entrepreneurial task execution progress offset rate is obtained.

7. The interactive education method based on entrepreneurship guidance according to claim 6, characterized in that: The calculation formula for the proportion of the stable task is specifically: Among them, R stable represents the proportion of stable tasks, M represents the total number of task executions, T j represents the actual execution time of the jth task, T avg represents the average execution time of all tasks, |T j -T avg | represents the deviation of the execution time of task j from the average value, W j represents the stability weight of the task, which is dynamically adjusted according to the execution frequency and time volatility of the task category. ∑ represents the cumulative calculation of all task executions. N total Represents the total number of times the task is executed, and ×100% represents conversion to percentage.

8. The interactive education method based on entrepreneurship guidance according to claim 1 is characterized in that: The specific steps of calling the entrepreneurial task execution progress deviation rate, obtaining the fund management task operation distribution, calculating the fund adjustment matching degree, analyzing the fund allocation adjustment interval, screening the adjustment frequency change interval, and combining the task difficulty adjustment execution conditions to obtain the entrepreneurial task challenge adjustment coefficient are as follows: S401: Calling the entrepreneurial task execution progress deviation rate, obtaining the fund operation distribution of the fund management task, counting the adjustment range of the fund operation, calculating the matching degree of the fund adjustment value within the task requirement range, summarizing the adaptation range of the fund adjustment, and obtaining the fund adjustment matching degree index; S402: Based on the fund adjustment matching degree index, calculate the adjustment interval of fund allocation, calculate the range of adjustment frequency, select intervals with smaller fluctuations in adjustment frequency, calculate the adjustment requirements in the task execution conditions, summarize the interval trend of fund adjustment frequency, extract the range of task challenge, and summarize the execution conditions of fund management tasks based on the task difficulty classification to obtain the fund management task execution conditions; S403: Based on the execution conditions of the fund management task, the distribution of fund operations under differentiated task categories is counted, the change ratio of task challenge in fund adjustment is calculated, the impact data of fund adjustment on task challenge is screened, the changing trend of challenge adjustment is summarized, and the entrepreneurial task challenge adjustment coefficient is obtained.

9. The interactive education method based on entrepreneurship guidance according to claim 1, characterized in that: The method also Including, S5: calling the entrepreneurial task challenge adjustment coefficient, obtaining task execution status, calculating task distribution weight, screening execution frequency categories, analyzing task priority, adjusting low-frequency task allocation probability, optimizing task plans, and obtaining entrepreneurial education plans; The entrepreneurship education program includes task priority sorting, task allocation weights, low-frequency task adjustment parameters, task correlation coefficients, and task optimization structure; S501: Calling the entrepreneurial task challenge adjustment coefficient, obtaining the execution status of differentiated task types, counting the execution frequency of task categories, calculating task distribution weights, summarizing the execution ratios of task categories, and obtaining task distribution weight values; S502: Based on the task distribution weight values, filter task categories with higher execution frequencies, calculate the priority execution order of the task categories, collect statistics on the correlation data between tasks, summarize the matching intervals between task priority rankings and correlations, set low-frequency task thresholds, analyze the proportion of low-frequency tasks in the allocation plan, adjust the allocation probability of low-frequency tasks, calculate the distribution change of task categories before and after optimization, and obtain an optimized task allocation plan; S503: Based on the optimized task allocation plan, the distribution of task categories is counted, the adjusted task adaptation ratio is calculated, the optimization trend of task matching is summarized, and the entrepreneurship education plan is obtained.

10. An interactive education system based on entrepreneurship guidance, characterized in that: An interactive education method based on entrepreneurship guidance according to any one of claims 1 to 9, wherein the system comprises: The task execution data analysis module obtains learners' task execution data, calls task execution time, task selection path, number of revisions, dwell time, and task success rate, calculates the distribution trend of task execution time under differentiated task types, screens key nodes of task selection paths and compares the change frequency, analyzes the correlation between the number of revisions and dwell time on task success rate, extracts stability features based on the execution efficiency differences of differentiated task categories, and obtains the execution stability of entrepreneurial tasks; The entrepreneurial task stability calculation module calls the entrepreneurial task execution stability, obtains key parameters of market planning, financing management, and team collaboration tasks, calculates the execution times of the market planning task, screens the stable fund operation area of the financing management task, analyzes the execution distribution balance and the relationship between the stay time of the team collaboration task, and obtains the matching distribution of the entrepreneurial tasks; The entrepreneurial task matching distribution evaluation module calls the entrepreneurial task matching distribution, obtains the task execution ratio, calculates the execution time fluctuation range, analyzes the proportion of stable tasks and compares the change rate of fluctuating tasks, filters the task execution offset data, and obtains the entrepreneurial task execution progress offset rate; The fund management execution optimization module calls the entrepreneurial task execution progress deviation rate, obtains the fund operation distribution of the fund management task, calculates the degree of match between the fund adjustment range and the task requirements, analyzes the fund adjustment interval and screens the change range of the adjustment frequency, and obtains the entrepreneurial task challenge adjustment coefficient; The entrepreneurship education program optimization module calls the entrepreneurship task challenge adjustment coefficient, obtains the task execution status, calculates the task distribution weight and filters the execution frequency task category, analyzes the priority execution order and the task correlation, adjusts the allocation probability of low-frequency tasks, and obtains the entrepreneurship education program.

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