A training method and system based on big data

Abstract

The present application provides a training method and system based on big data, which belongs to the technical field of big data education; wherein, a training method based on big data includes obtaining a history lesson plan data set; determining basic recommendation data for each history lesson plan based on the history test paper score; the basic recommendation data is an average score calculated based on the history test paper score; determining the arrangement order data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyzing first recommendation influence data and second recommendation influence data based on the arrangement order data; calculating the lesson plan recommendation value of each history lesson plan based on the basic recommendation data, the first recommendation influence data and the second recommendation influence data, and generating a lesson plan recommendation result based on the lesson plan recommendation value and preset recommendation rules; through this scheme, a training lesson plan that is more suitable for students can be obtained.

Description

Technical Field

[0001] The present application relates to the technical field of big data education, and in particular to a training method and system based on big data. Background Art

[0002] Lesson plans are an important part of big data training. Teachers teach students through pre-prepared lesson plans, explaining knowledge points and related questions. Lesson plans are composed of a fixed framework, which is filled with relevant content of knowledge points and the teacher's own understanding and annotations. Therefore, the order of different knowledge points in different lesson plans is different, and there is a sequence between knowledge points. Generally speaking, knowledge points are determined by the examination syllabus, and the lesson plan covers all knowledge points covered by the examination syllabus. The lesson plan is also equipped with a corresponding test paper, and the training effect of the lesson plan can be seen based on the test paper score feedback. Different lesson plans have different corresponding training effects. In order to enhance the training effect, multiple lesson plans can be used as reference, so how to screen the lesson plans is very important. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a training method and system based on big data.

[0004] In a first aspect, the present application provides a training method based on big data. The method comprises:

[0005] Acquire a history lesson plan data set; the history lesson plan data set includes multiple history lesson plans and history usage data of each history lesson plan; the history usage data includes a history test paper score of each history lesson plan; the history test paper score is a history test paper score of each student taught based on the history lesson plan;

[0006] Determine basic recommendation data for each history lesson plan based on the history test paper scores; the basic recommendation data is an average score calculated based on the history test paper scores;

[0007] Determine arrangement order data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyze first recommendation influence data and second recommendation influence data according to the arrangement order data; the first recommendation influence data and the second recommendation influence data are both associated with the arrangement order data;

[0008] The lesson plan recommendation value of each history lesson plan is calculated according to the basic recommendation data, the first recommendation influence data and the second recommendation influence data, and the lesson plan recommendation result is generated according to the lesson plan recommendation value and preset recommendation rules.

[0009] Furthermore, determining arrangement sequence data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyzing the first recommendation influence data and the second recommendation influence data according to the arrangement sequence data includes:

[0010] Retrieving a preset knowledge point group set; the preset knowledge point group set includes a plurality of knowledge point groups; each knowledge point group includes two different knowledge points; one of the knowledge points included in the knowledge point group is a first knowledge point, and the other knowledge point is a second knowledge point;

[0011] Select any knowledge point group, and select a first target set and a second target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the first target set is a first history lesson plan; the first knowledge point in the first history lesson plan is located before the second knowledge point; the history lesson plan included in the second target set is a second history lesson plan; the second knowledge point in the second history lesson plan is located before the first knowledge point;

[0012] Calculate a first average score and a second average score; the first average score is the average score of the history test paper scores of all the first history lesson plans; the second average score is the average score of the history test paper scores of all the second history lesson plans;

[0013] Calculating the order influence data of any knowledge point group; the order influence data is the difference between the first average score and the second average score;

[0014] The first recommendation impact data is analyzed according to the sequential impact data.

[0015] Furthermore, determining arrangement sequence data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyzing the first recommendation influence data and the second recommendation influence data according to the arrangement sequence data includes:

[0016] Select any knowledge point group, and select a third target set and a fourth target set from the history lesson plan data set based on the arrangement order data; the history lesson plans included in the third target set are third history lesson plans; the first knowledge point and the second knowledge point in the third history lesson plan are adjacent; the history lesson plans included in the fourth target set are fourth history lesson plans; and there is at least one other knowledge point between the first knowledge point and the second knowledge point in the fourth history lesson plan;

[0017] Calculate a third average score and a fourth average score; the third average score is the average score of the history test scores of all the third history lesson plans; the fourth average score is the average score of the history test scores of all the fourth history lesson plans;

[0018] Calculating the association influence data of any knowledge point group; the association influence data is the difference between the third average score and the fourth average score;

[0019] The second recommended influence data is analyzed according to the associated influence data.

[0020] Furthermore, the calculation method of the teaching plan recommendation value includes:

[0021] Assume that the history lesson plan contains n knowledge point groups, and the order influence data of the i-th knowledge point group is , the associated influence data of the i-th knowledge point group is ;

[0022] ,

[0023] In the formula, Recommended value for the lesson plan, As the basic recommended data, The first recommended impact data, Influence data for the second recommendation; , , They are respectively a preset first weight, a preset second weight and a preset third weight.

[0024] Further, .

[0025] In a second aspect, the present application provides a training system based on big data, the system comprising:

[0026] An acquisition module is used to acquire a history lesson plan data set; the history lesson plan data set includes multiple history lesson plans and history usage data of each history lesson plan; the history usage data includes a history test paper score of each history lesson plan; the history test paper score is a history test paper score of each student taught based on the history lesson plan;

[0027] A determination module, configured to determine basic recommendation data for each history lesson plan based on the history test paper scores; the basic recommendation data is an average score calculated based on the history test paper scores;

[0028] An analysis module, configured to determine arrangement sequence data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyze first recommendation influence data and second recommendation influence data according to the arrangement sequence data; the first recommendation influence data and the second recommendation influence data are both associated with the arrangement sequence data;

[0029] A calculation module is used to calculate the lesson plan recommendation value of each history lesson plan based on the basic recommendation data, the first recommendation influence data and the second recommendation influence data, and generate a lesson plan recommendation result based on the lesson plan recommendation value and preset recommendation rules.

[0030] Furthermore, the analysis module is further configured to determine the arrangement order data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyze the first recommendation influence data and the second recommendation influence data according to the arrangement order data, including:

[0031] Retrieving a preset knowledge point group set; the preset knowledge point group set includes a plurality of knowledge point groups; each knowledge point group includes two different knowledge points; one of the knowledge points included in the knowledge point group is a first knowledge point, and the other knowledge point is a second knowledge point;

[0032] Select any knowledge point group, and select a first target set and a second target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the first target set is a first history lesson plan; the first knowledge point in the first history lesson plan is located before the second knowledge point; the history lesson plan included in the second target set is a second history lesson plan; the second knowledge point in the second history lesson plan is located before the first knowledge point;

[0033] Calculate a first average score and a second average score; the first average score is the average score of the history test paper scores of all the first history lesson plans; the second average score is the average score of the history test paper scores of all the second history lesson plans;

[0034] Calculating the order influence data of any knowledge point group; the order influence data is the difference between the first average score and the second average score;

[0035] The first recommendation impact data is analyzed according to the sequential impact data.

[0036] Furthermore, the analysis module is further configured to determine arrangement order data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyze the first recommendation influence data and the second recommendation influence data packet according to the arrangement order data:

[0037] Select any knowledge point group, and select a third target set and a fourth target set from the history lesson plan data set based on the arrangement order data; the history lesson plans included in the third target set are third history lesson plans; the first knowledge point and the second knowledge point in the third history lesson plan are adjacent; the history lesson plans included in the fourth target set are fourth history lesson plans; and there is at least one other knowledge point between the first knowledge point and the second knowledge point in the fourth history lesson plan;

[0038] Calculate a third average score and a fourth average score; the third average score is the average score of the history test scores of all the third history lesson plans; the fourth average score is the average score of the history test scores of all the fourth history lesson plans;

[0039] Calculating the association influence data of any knowledge point group; the association influence data is the difference between the third average score and the fourth average score;

[0040] The second recommended influence data is analyzed according to the associated influence data.

[0041] Furthermore, the calculation module is further configured such that the calculation method of the teaching plan recommendation value includes:

[0042] Assume that the history lesson plan contains n knowledge point groups, and the order influence data of the i-th knowledge point group is , the associated influence data of the i-th knowledge point group is ;

[0043] ,

[0044] In the formula, Recommended value for the lesson plan, As the basic recommended data, The first recommended impact data, Influence data for the second recommendation; , , They are respectively a preset first weight, a preset second weight and a preset third weight.

[0045] Furthermore, the calculation module is further configured to: .

[0046] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, wherein:

[0048] Figure 1 A flowchart of a training method based on big data in an embodiment of the present application is shown;

[0049] Figure 2 A block diagram of a big data-based training system in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0052] In a first aspect, the present application provides a training method based on big data. Figure 1 The specific steps included in the method are as follows.

[0053] Step S110: Acquire a history lesson plan data set; the history lesson plan data set includes multiple history lesson plans and history usage data of each history lesson plan; the history usage data includes a history test score of each history lesson plan; the history test score is a history test score of each student taught based on the history lesson plan.

[0054] It is understandable that the knowledge points mentioned in this plan are based on the knowledge points involved in the standard national examination syllabus; each knowledge point will contain multiple small knowledge points, such as trigonometric functions, and sine function, cosine function, etc. are all trigonometric functions; therefore, when analyzing the history lesson plans, if similar situations are encountered, they will be classified under the same knowledge point; and in the actual process of teachers teaching students, the explanation of trigonometric functions is also specific trigonometric functions such as sine and cosine; therefore, each history lesson plan and the knowledge point framework it contains are also understandable; the knowledge point framework refers to the arrangement order of knowledge points in the history lesson plan. Taking any two knowledge points as an example, in the history lesson plan, based on the arrangement order, you can know whether the two knowledge points are adjacent, which knowledge point is in front, and which knowledge point is in the back.

[0055] Step S120: determining basic recommendation data for each history lesson plan based on the history test paper scores; the basic recommendation data is an average score calculated based on the history test paper scores.

[0056] It can be understood that the history test score refers to the score that the teacher gets by teaching students through the history lesson plan and the students get by answering the test questions on the test paper that comes with the history lesson plan; therefore, based on the history test score, you can know each student's test score and the number of students, and the average score can be obtained by dividing the sum of the test scores by the sum of the number of students.

[0057] Step S130: determining arrangement order data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyzing first recommendation influence data and second recommendation influence data according to the arrangement order data; the first recommendation influence data and the second recommendation influence data are both associated with the arrangement order data.

[0058] In an embodiment of the present application, the arrangement order data of the knowledge points in each history lesson plan is determined based on the history lesson plan data set, and the first recommendation influence data and the second recommendation influence data are analyzed according to the arrangement order data, specifically including: retrieving a preset knowledge point group set; the preset knowledge point group set includes multiple knowledge point groups; each knowledge point group includes two different knowledge points; one of the knowledge points included in the knowledge point group is a first knowledge point, and the other knowledge point is a second knowledge point; selecting any knowledge point group, and filtering the first target set and the second target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the first target set is the first history lesson plan ; The first knowledge point in the first history lesson plan is located before the second knowledge point; The history lesson plan included in the second target set is the second history lesson plan; The second knowledge point in the second history lesson plan is located before the first knowledge point; Calculate the first average score and the second average score; The first average score is the average score of the history test scores of all the first history lesson plans; The second average score is the average score of the history test scores of all the second history lesson plans; Calculate the sequence influence data of any knowledge point group; The sequence influence data is the difference between the first average score and the second average score; Analyze the first recommendation influence data based on the sequence influence data.

[0059] Further, select any knowledge point group, and screen the third target set and the fourth target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the third target set is the third history lesson plan; the first knowledge point and the second knowledge point in the third history lesson plan are adjacent; the history lesson plan included in the fourth target set is the fourth history lesson plan; there is at least one other knowledge point between the first knowledge point and the second knowledge point in the fourth history lesson plan; calculate the third average score and the fourth average score; the third average score is the average score of the history test paper scores of all the third history lesson plans; the fourth average score is the average score of the history test paper scores of all the fourth history lesson plans; calculate the associated influence data of any knowledge point group; the associated influence data is the difference between the third average score and the fourth average score; analyze the second recommendation influence data based on the associated influence data.

[0060] It can be understood that, for a knowledge point group, it is composed of any two knowledge points. Since there is a sequence between the two knowledge points, the preset knowledge point group is equivalent to containing two complete sets of knowledge points; in an actual history lesson plan, for any two knowledge points, no matter which knowledge point comes first, they will be included in the preset knowledge point group. Therefore, the sequence influence data and association influence data corresponding to any knowledge point group calculated by the above method can be reflected in any history lesson plan; when analyzing a specific history lesson plan, it is only necessary to form multiple knowledge point groups in sequence according to the established knowledge point arrangement order in the history lesson plan, such as the first knowledge point and the second knowledge point are the first knowledge point group, and the second knowledge point and the third knowledge point are the second knowledge point group, and then match this part of the knowledge point group with the knowledge point group in the preset knowledge point group set, and find the same knowledge point group, so as to obtain the sequence influence data and association influence data corresponding to each knowledge point group in the history lesson plan.

[0061] Step S140: Calculate the lesson plan recommendation value of each history lesson plan according to the basic recommendation data, the first recommendation influence data and the second recommendation influence data, and generate a lesson plan recommendation result according to the lesson plan recommendation value and preset recommendation rules.

[0062] In the embodiment of the present application, the method for calculating the teaching plan recommendation value specifically includes:

[0063] Assume that the history lesson plan contains n knowledge point groups, and the order influence data of the i-th knowledge point group is , the associated influence data of the i-th knowledge point group is ;

[0064] ,

[0065] In the formula, Recommended value for the lesson plan, As the basic recommended data, The first recommended impact data, Influence data for the second recommendation; , are respectively a preset first weight, a preset second weight and a preset third weight; wherein, .

[0066] It is understandable that in the calculated order influence data and association influence data, each value may be positive or negative. In the actual formula calculation, the positive and negative values ​​are substituted to reflect the degree to which each history lesson plan is worth recommending. For example, in history lesson plan A, the order influence data of knowledge point group b is negative, indicating that from the perspective of historical big data, the order of the two knowledge points in knowledge point group b has a negative impact on history lesson plan A. On the contrary, if the order influence data of knowledge point group b is positive, then its impact on history lesson plan A is positive. By summing up each knowledge point group, the overall impact of each knowledge point group in the history lesson plan can be analyzed.

[0067] In the embodiment of the present application, generally speaking, the preset first weight can be set to 0.5, and the preset second weight and the preset third weight can both be set to 0.25.

[0068] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required by the present application.

[0069] In the second aspect, the present application provides a training system based on big data. Figure 2As shown, the system includes an acquisition module 210 for acquiring a history lesson plan data set; the history lesson plan data set includes multiple history lesson plans and historical usage data of each history lesson plan; the historical usage data includes a history test score of each history lesson plan; the history test score is a history test score of each student taught based on the history lesson plan; a determination module 220 is used to determine basic recommendation data for each history lesson plan based on the history test score; the basic recommendation data is an average score calculated based on the history test score; an analysis module 230 is used to determine the arrangement order data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyze the first recommendation influence data and the second recommendation influence data according to the arrangement order data; the first recommendation influence data and the second recommendation influence data are both associated with the arrangement order data; a calculation module 240 is used to calculate the lesson plan recommendation value of each history lesson plan according to the basic recommendation data, the first recommendation influence data and the second recommendation influence data, and generate a lesson plan recommendation result according to the lesson plan recommendation value and a preset recommendation rule.

[0070] The analysis module 230 is further configured to determine the arrangement order data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyze the first recommendation influence data and the second recommendation influence data according to the arrangement order data, specifically including: calling a preset knowledge point group set; the preset knowledge point group set includes a plurality of knowledge point groups; each knowledge point group includes two different knowledge points; one of the knowledge points included in the knowledge point group is a first knowledge point, and the other knowledge point is a second knowledge point;

[0071] Select any knowledge point group, and filter the first target set and the second target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the first target set is the first history lesson plan; the first knowledge point in the first history lesson plan is located before the second knowledge point; the history lesson plan included in the second target set is the second history lesson plan; the second knowledge point in the second history lesson plan is located before the first knowledge point; calculate the first average score and the second average score; the first average score is the average score of the history test paper scores of all the first history lesson plans; the second average score is the average score of the history test paper scores of all the second history lesson plans; calculate the sequence influence data of any knowledge point group; the sequence influence data is the difference between the first average score and the second average score; analyze the first recommendation influence data according to the sequence influence data; select any knowledge point group knowledge point group, based on the arrangement order data, a third target set and a fourth target set are selected from the history lesson plan data set; the history lesson plan included in the third target set is the third history lesson plan; the first knowledge point and the second knowledge point in the third history lesson plan are adjacent; the history lesson plan included in the fourth target set is the fourth history lesson plan; there is at least one other knowledge point between the first knowledge point and the second knowledge point in the fourth history lesson plan; a third average score and a fourth average score are calculated; the third average score is the average score of the history test paper scores of all the third history lesson plans; the fourth average score is the average score of the history test paper scores of all the fourth history lesson plans; the associated influence data of any knowledge point group is calculated; the associated influence data is the difference between the third average score and the fourth average score; and the second recommendation influence data is analyzed according to the associated influence data.

[0072] The calculation module 240 is further configured to calculate the teaching plan recommendation value by:

[0073] Assume that the history lesson plan contains n knowledge point groups, and the order influence data of the i-th knowledge point group is , the associated influence data of the i-th knowledge point group is ;

[0074] ,

[0075] In the formula, Recommended value for the lesson plan, As the basic recommended data, The first recommended impact data, Influence data for the second recommendation; , are respectively a preset first weight, a preset second weight and a preset third weight; wherein, .

[0076] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described device can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0077] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A training method based on big data, characterized in that: include: Acquire a history lesson plan data set; the history lesson plan data set includes multiple history lesson plans and history usage data of each history lesson plan; the history usage data includes a history test paper score of each history lesson plan; the history test paper score is a history test paper score of each student taught based on the history lesson plan; Determining basic recommended data for each history lesson plan based on the history test scores; The basic recommendation data is an average score calculated based on the historical test paper scores; Determine arrangement order data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyze first recommendation influence data and second recommendation influence data according to the arrangement order data; the first recommendation influence data and the second recommendation influence data are both associated with the arrangement order data; Calculating a lesson plan recommendation value for each history lesson plan according to the basic recommendation data, the first recommendation influence data, and the second recommendation influence data, and generating a lesson plan recommendation result according to the lesson plan recommendation value and a preset recommendation rule; Determining the arrangement sequence data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyzing the first recommendation influence data and the second recommendation influence data according to the arrangement sequence data includes: Retrieving a preset knowledge point group set; the preset knowledge point group set includes a plurality of knowledge point groups; each knowledge point group includes two different knowledge points; one of the knowledge points included in the knowledge point group is a first knowledge point, and the other knowledge point is a second knowledge point; Select any knowledge point group, and select a first target set and a second target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the first target set is a first history lesson plan; the first knowledge point in the first history lesson plan is located before the second knowledge point; the history lesson plan included in the second target set is a second history lesson plan; the second knowledge point in the second history lesson plan is located before the first knowledge point; Calculate a first average score and a second average score; the first average score is the average score of the history test paper scores of all the first history lesson plans; the second average score is the average score of the history test paper scores of all the second history lesson plans; Calculating the order influence data of any knowledge point group; the order influence data is the difference between the first average score and the second average score; analyzing the first recommendation impact data according to the sequential impact data; Determining the arrangement sequence data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyzing the first recommendation influence data and the second recommendation influence data according to the arrangement sequence data includes: Select any knowledge point group, and select a third target set and a fourth target set from the history lesson plan data set based on the arrangement order data; the history lesson plans included in the third target set are third history lesson plans; the first knowledge point and the second knowledge point in the third history lesson plan are adjacent; the history lesson plans included in the fourth target set are fourth history lesson plans; and there is at least one other knowledge point between the first knowledge point and the second knowledge point in the fourth history lesson plan; Calculate a third average score and a fourth average score; the third average score is the average score of the history test scores of all the third history lesson plans; the fourth average score is the average score of the history test scores of all the fourth history lesson plans; Calculating the association influence data of any knowledge point group; the association influence data is the difference between the third average score and the fourth average score; analyzing second recommended influence data according to the associated influence data; The calculation method of the teaching plan recommendation value includes: Assume that the history lesson plan contains n knowledge point groups, and the order influence data of the i-th knowledge point group is , the associated influence data of the i-th knowledge point group is ; ， In the formula, Recommended value for the lesson plan, As the basic recommended data, The first recommended impact data, Influence data for the second recommendation; , They are respectively a preset first weight, a preset second weight and a preset third weight.

2. The training method based on big data according to claim 1, characterized in that: 。 3. A training system based on big data, characterized in that: include: An acquisition module (210) is used to acquire a history lesson plan data set; the history lesson plan data set includes a plurality of history lesson plans and history usage data of each history lesson plan; the history usage data includes a history test paper score of each history lesson plan; the history test paper score is a history test paper score of each student taught based on the history lesson plan; A determination module (220), configured to determine basic recommended data for each history lesson plan based on the history test paper score; The basic recommendation data is an average score calculated based on the historical test paper scores; An analysis module (230) is used to determine arrangement sequence data of knowledge points in each history lesson plan based on the history lesson plan data set, and analyze first recommendation influence data and second recommendation influence data according to the arrangement sequence data; the first recommendation influence data and the second recommendation influence data are both associated with the arrangement sequence data; A calculation module (240), configured to calculate a lesson plan recommendation value for each history lesson plan based on the basic recommendation data, the first recommendation influence data, and the second recommendation influence data, and to generate a lesson plan recommendation result based on the lesson plan recommendation value and a preset recommendation rule; The analysis module (230) is further configured to determine the arrangement sequence data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyze the first recommendation influence data and the second recommendation influence data according to the arrangement sequence data, including: Retrieving a preset knowledge point group set; the preset knowledge point group set includes a plurality of knowledge point groups; each knowledge point group includes two different knowledge points; one of the knowledge points included in the knowledge point group is a first knowledge point, and the other knowledge point is a second knowledge point; Select any knowledge point group, and select a first target set and a second target set from the history lesson plan data set based on the arrangement order data; the history lesson plan included in the first target set is a first history lesson plan; the first knowledge point in the first history lesson plan is located before the second knowledge point; the history lesson plan included in the second target set is a second history lesson plan; the second knowledge point in the second history lesson plan is located before the first knowledge point; Calculate a first average score and a second average score; the first average score is the average score of the history test paper scores of all the first history lesson plans; the second average score is the average score of the history test paper scores of all the second history lesson plans; Calculating the order influence data of any knowledge point group; the order influence data is the difference between the first average score and the second average score; analyzing the first recommendation impact data according to the sequential impact data; The analysis module (230) is further configured to determine the arrangement sequence data of the knowledge points in each history lesson plan based on the history lesson plan data set, and analyze the first recommendation influence data and the second recommendation influence data according to the arrangement sequence data, including: Select any knowledge point group, and select a third target set and a fourth target set from the history lesson plan data set based on the arrangement order data; the history lesson plans included in the third target set are third history lesson plans; the first knowledge point and the second knowledge point in the third history lesson plan are adjacent; the history lesson plans included in the fourth target set are fourth history lesson plans; and there is at least one other knowledge point between the first knowledge point and the second knowledge point in the fourth history lesson plan; Calculate a third average score and a fourth average score; the third average score is the average score of the history test scores of all the third history lesson plans; the fourth average score is the average score of the history test scores of all the fourth history lesson plans; Calculating the association influence data of any knowledge point group; the association influence data is the difference between the third average score and the fourth average score; analyzing second recommended influence data according to the associated influence data; The calculation module (240) is further configured such that the calculation method of the teaching plan recommendation value includes: Assume that the history lesson plan contains n knowledge point groups, and the order influence data of the i-th knowledge point group is , the associated influence data of the i-th knowledge point group is ; ， In the formula, Recommended value for the lesson plan, As the basic recommended data, The first recommended impact data, Influence data for the second recommendation; , They are respectively a preset first weight, a preset second weight and a preset third weight.

4. The big data-based training system according to claim 3, characterized in that: The calculation module (240) is further configured to: .

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