Student comprehensive activity behavior simulation method based on extended cellular machine and storage medium

Abstract

The invention relates to a student comprehensive activity behavior simulation method based on an extended cellular machine and a storage medium. The student comprehensive activity behavior simulation method simulates student comprehensive activity behaviors through a behavior simulation model based on an extended cellular machine, and comprises the following steps: S1, performing initial parameter configuration on the behavior simulation model; s2, calculating a cellular transition probability pi, j, z; s3, calculating a final motion probability Pijz of the student; and S4, comparing the current time step with the previous time step, judging whether the current motion state of the student is changed or not, and calculating the current intimacy of the student. Based on an extended cellular machine model, in combination with student campus activity characteristics, analysis of different time periods such as comprehensive learning, rest, examination and duplication is performed, external guide factors are added, a space-time pattern of student exercise behaviors is analyzed, key factors influencing the student exercise behaviors are explored, physical and psychological health of students is ensured, and the student exercise behavior evaluation method is suitable for popularization and application. And a basis is provided for formulating reasonable sports plans and resource allocation for schools.

Description

Technical Field

[0001] The present invention relates to the technical field of activity behavior simulation, and in particular to a student comprehensive activity behavior simulation method based on an extended cellular machine and a storage medium. Background Art

[0002] With the development of the economy and the progress of the times, more and more people are aware of the importance of good physical fitness and are more inclined to exercise. Due to the great academic pressure, students generally lack exercise, which is not conducive to the development of a good and healthy body for students, especially for boarding students. Therefore, how schools can timely and accurately judge the students' exercise status, adjust the sports plan to balance academic studies and physical exercise, ensure that students have a healthier body without affecting their studies, and promote the comprehensive development of students' morality, intelligence and physical fitness has important practical significance. However, there is currently a lack of sports models for school students and a lack of analysis and prediction of students' sports behavior, making it difficult for schools to formulate reasonable sports plans and resource allocation to promote student sports. Summary of the invention

[0003] Based on this, it is necessary to provide a student comprehensive activity behavior simulation method and storage medium based on extended cellular machines to address the current lack of sports models for school students and the lack of analysis and prediction of student sports behaviors, which makes it difficult for schools to formulate reasonable sports plans and resource allocations to promote student sports.

[0004] A method for simulating student comprehensive activity behaviors based on an extended cellular machine, which simulates student comprehensive activity behaviors through a behavior simulation model based on an extended cellular machine; the method for simulating student comprehensive activity behaviors comprises the following steps:

[0005] S1. Initial parameter configuration of the behavior simulation model;

[0006] S2. Calculate the cell transfer probability p i,j,z , and its calculation formula is:

[0007]

[0008] In the formula, i represents the index of the student's horizontal coordinate in the cellular space; j represents the index of the student's vertical coordinate in the cellular space; z represents the index of all the changeable motion features of the student; N is the adjustment parameter; S ijz represents the zth motion feature of the current student; L ij Indicates the intimacy between students; R ij Indicates the student's communication scope; k S ,k L ,k R are the sensitivity coefficients of movement characteristics, intimacy, and communication scope respectively; E represents the environmental parameter; TH Indicates holiday time, T C Indicates the time in school;

[0009] S3. Calculate the student's final movement probability P ijz , and its calculation formula is:

[0010] P ijz =KT u rp i,j,z

[0011] Where K represents the adjustment parameter; T u represents the ratio of the available exercise time to the unavailable exercise time in the time step; r represents the evolution stage of the current time step. If the evolution stage is judged to be the leisure learning period, exercise is performed with probability r=α; if the evolution stage is judged to be the regular learning period, exercise is performed with probability r=β; if the evolution stage is judged to be the key examination period, exercise is performed with probability r=γ; where α, β and γ are all reference probabilities, and 1>α>β>γ;

[0012] S4. Compare the current time step with the previous time step to determine whether the student's current motion state has changed, and calculate the student's current intimacy; if the student's current motion state has not changed, the sensitivity coefficient k of the motion feature s remains unchanged; if the student's current motion state changes, the sensitivity coefficient k of the motion feature s Reduce Δδ; if the current intimacy is greater than or equal to the set threshold, the sensitivity coefficient k of the intimacy L Increase Δσ, and the intimacy is less than the threshold, then the sensitivity coefficient k of intimacy L Reduce Δσ;

[0013] S5, the sensitivity coefficient k of the changed motion feature s and the sensitivity coefficient k of intimacy L Substitute the next time step and repeat S2 to S5 until the simulation ends.

[0014] As a preferred example, the cellular space unit cell of the behavior simulation model is 100*100, and the neighborhood type is Moore type; the space occupied by each cell is 1*1.

[0015] As a preferred example, the initial parameters include environmental parameters, student sports parameters and student status; the environmental parameters include the overall probability of students joining the club, the overall probability of students winning awards, whether the school has sports infrastructure and the time ratio of students to and from school; the student sports parameters include the sensitivity coefficient k of the sports characteristics S , the sensitivity coefficient k of intimacy L and the sensitivity coefficient k of the communication scope RThe student status includes the student's gender, the student's sport type, the student's sport intensity, the student's sport time, whether the student has joined a club, and whether the student has won an award.

[0016] As a preferred example, the motion feature S ijz The expression is:

[0017]

[0018] Where F is the adjustment parameter of motion characteristics; St ij For the current student sports type; St xy The sports types of other students in the current student's communication range; Sp ij is the current student exercise intensity; Sp xy Sd is the exercise intensity of other students in the current student’s communication range; ij is the current student exercise time frequency; Sd xy is the movement time frequency of other students in the current student's communication range; Sjc ij Whether current students join the club; Sjc xy Whether other students in the current student's social circle will join the club; ij Whether the current student has won an award; Sr xy Whether other students in the current student's social circle have won awards; t ,k p ,k d ,k jc ,k r St ij ,Sp ij ,Sd ij ,Sjc ij ,Sr ij Parameter coefficients for the five motion states.

[0019] As a preferred example, the intimacy L ij The expression is:

[0020]

[0021] Where, L ij Indicates the intimacy between the target student and other students in the communication range; L xy Indicates the intimacy of other students to the target student; G ij Indicates the gender of the target student; G xy Indicates the gender of other students at the coordinate (x, y); k ijxy Represents the weight of a special relationship; R ij Indicates the student's communication scope.

[0022] As a preferred example, the communication range R ij The expression is:

[0023]

[0024] Where Max is the maximum communication range set; L set is the average intimacy threshold set; W represents the weight of gender and bond between students, and W>1; C ij Indicates the grade of the target students; C xy Indicates the corresponding grade of other students at the coordinate (x, y).

[0025] As a preferred example, in S3, the evolution stage is divided into three stages: a leisure learning period, a regular learning period and a key examination period; wherein the leisure learning period is characterized by less class time and more rest time and time after school; the regular learning period is characterized by more class time and less free time for students; the key examination period is characterized by shorter rest time and time after school and a sharp decrease in students' free time.

[0026] As a preferred example, the three evolutionary stages are cyclically performed in a changing order, and the changing order is regular learning period--leisure learning period--regular learning period--key examination period.

[0027] As a preferred example, parameter evaluation is performed after simulating the comprehensive activity behavior of students; the parameter evaluation is used to explore the key factors that affect the learning activity behavior.

[0028] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the student comprehensive activity behavior simulation method as described above.

[0029] The beneficial effects of the present invention are as follows: the present invention is based on an extended cellular machine model, combined with the characteristics of students' campus activities, comprehensively analyzes different time periods such as study, rest, examinations, holidays, etc., adds external guiding factors, analyzes the spatiotemporal patterns of students' sports behavior, explores the key factors affecting students' sports behavior, ensures students' physical and mental health, and provides a basis for schools to formulate reasonable sports plans and resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a flow chart of the simulation method of students' comprehensive activity behavior based on the extended cellular machine;

[0031] Figure 2 A statistical graph showing the changes in different exercise intensities over time for students who choose football;

[0032] Figure 3A statistical graph showing the duration of different sports over time for students who chose football;

[0033] Figure 4 This is a statistical chart showing the changes in different exercise intensities over time for students who choose basketball;

[0034] Figure 5 A statistical graph showing the duration of different sports over time for students who chose basketball;

[0035] Figure 6 This is a statistical chart showing the changes in different exercise intensities over time for students who choose swimming;

[0036] Figure 7 A statistical graph showing the duration of different exercises over time for students who choose swimming;

[0037] Figure 8 This is a graph showing changes in the average duration of movement among students;

[0038] Fig. 9 This is a graph showing changes in the average exercise intensity of students;

[0039] Fig.10 It is a statistical diagram of the change of motion characteristics over time under different motion characteristic sensitivity coefficients;

[0040] Fig.11 It is a statistical diagram of the change of motion characteristics over time under different intimacy sensitivity coefficients;

[0041] Fig.12 It is a statistical diagram of the change of exercise intensity over time under different communication range sensitivity coefficients;

[0042] Fig.13 It is a statistical diagram of the change of exercise intensity over time under different communication range sensitivity coefficient conditions;

[0043] Fig.14 This is a statistical graph showing the change of exercise intensity over time under different communication range sensitivity coefficients. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "or / and" used herein includes any and all combinations of one or more of the related listed items.

[0046] The research based on the extended cellular machine model aims to help schools better understand and promote students' physical and mental health by simulating the comprehensive activity behavior of boarding school students, especially their sports behavior. The model in the present invention uses an extended cellular machine (extended CA) to predict students' movement patterns, and combines the characteristics of students' daily activities, such as different needs of learning, examinations and leisure, to analyze the spatiotemporal patterns of students' sports behaviors and their key influencing factors. For the physical and mental health of boarding students, it is necessary to predict students' activities, and then predict changes in students' physical and mental states, so that schools can make feedback actions in time. The present invention fixes students in a specific evolutionary space through a behavior simulation model based on an extended cellular machine, changes students' subsequent activity states through the interaction of students' own activity states and the relationship between students, and the interactive relationship between students is visualized as intimacy and communication range, reducing the amount of calculations; students' activity behaviors are replaced by motion features.

[0047] Please refer to Figure 1 This embodiment provides a method for simulating student comprehensive activity behaviors based on an extended cellular machine, which simulates student comprehensive activity behaviors through a behavior simulation model based on an extended cellular machine. The method for simulating student comprehensive activity behaviors includes the following steps:

[0048] S1. Initial parameter configuration for the behavior simulation model.

[0049] Initialize the size of the student evolution space, that is, the size of the cell space in the model is 100*100, the total number of students is 10,000, and the space occupied by each student (each cell) is 1*1. Initialize the definition of initial parameters such as school, social orientation, and student status. The initial parameters are generally divided into environmental parameters, student sports parameters, and student status. Among them, environmental parameters include the overall probability of students joining the club, the overall probability of students winning awards, whether the school has sports infrastructure, and the time ratio of students to and from get out of class. Student sports parameters include the sensitivity coefficient k of sports characteristics S , the sensitivity coefficient k of intimacy L and the sensitivity coefficient k of the communication scope R The student status includes the student's gender, grade, sport type, sport intensity, sport time, whether the student joins a club, and whether the student wins an award. The student status is defined as shown in the following table:

[0050]

[0051] In addition, without considering other special external conditions, the students' sports conditions can be divided into three evolutionary stages as the time step progresses: 1. The first evolutionary stage is the leisure learning period. In this stage, there are fewer class times and more rest time and time after school. At this time, students have more time to invest in sports, and students are easy to influence each other, expanding students' sports participation. 2. The second evolutionary stage is the regular learning period. In this stage, the class time increases, and the time that students can freely control gradually decreases, and the sports interaction between students weakens. 3. The third evolutionary stage is the key examination period. In this stage, the rest time and time after school are shortened, and the learning pressure is high. At this time, the students' free time is sharply reduced, and students are less inclined to exercise, and are less likely to be affected by the status of surrounding students. As the time step evolves forward, the above three evolutionary stages are cycled in the order of change. The order of change is regular learning period -> leisure learning period -> regular learning period -> key examination period.

[0052] S2. Calculate the transition probability of cells in the behavior simulation model.

[0053] In the behavior simulation model based on the extended cellular machine, the relevant behavior changes of students are affected by the adjacent cells. The definition of adjacent cells is shown in the following table:

[0054]

[0055] On the other hand, all the changes in the state of the motion characteristics of the student in each time step will be affected by the adjacent student states, evolutionary stages, and environmental parameters. The corresponding cell transition probability calculation formula is as follows:

[0056]

[0057] Where i represents the index of the horizontal coordinate of the student's evolution range (in the cell space). j represents the index of the vertical coordinate of the student's evolution range. z represents the index or parameter of all the student's changeable motion features. N is the adjustment parameter to ensure that p i,j,k ≤1.S ijz Indicates that the current student’s zth motion feature is affected by the same motion feature of the rest of the students and the rest of the motion features. ij Indicates intimacy. The higher the intimacy between students, the more likely they are to change their sports characteristics. ij Indicates the student's social circle. The larger the social circle, the more people can be influenced, and the lower the influence of a single student. S ,k L ,k R, are the sensitivity coefficients of motion characteristics, intimacy, and communication scope. E represents the environmental parameter. T H Indicates holiday time, T C Indicates the time spent in school. Different time periods have different effects on students' exercise status.

[0058] Among them, the above motion feature S ijz The expression is:

[0059]

[0060] In the formula, F is the adjustment parameter of the motion feature, and S is changed according to the value of the following formula ijz . St ij For the current student sports type. St xy is the movement type of other students (x, y) within the current student’s communication range. ij is the current student exercise intensity. xy Sd is the exercise intensity of other students (x, y) within the current student’s communication range. ij Sd is the current student exercise time frequency. xy is the movement time frequency of other students (x, y) in the current student’s communication range. ij For current students, please join the club. xy Whether other students (x, y) within the current student's social circle have joined the club. ij Whether the current student has won an award. xy Whether the other students (x, y) in the current student's communication range have won the award. t ,k p ,k d ,k jc ,k r St ij ,Sp ij ,Sd ij ,Sjc ij ,Sr ij Parameter coefficients of five motion states. The motion characteristics are composed of five motion parameters. For the analysis of each student's motion characteristics, it can be divided into individual motion parameters z for analysis, which is helpful for the subsequent analysis and understanding of the motion characteristics. Different motion parameters z are defined as 1, 2, 3, 4, and 5. When z = 1, it means that the motion type in the motion characteristics is selected for analysis. The formula can be expressed as:

[0061] By analogy, when z=2, S ijz =Sp ij ; When z = 3, S ijz =Sdij ; When z = 4, S ijz =Sjc ij ; When z = 5, S ijz =Sr ij .

[0062] The intimacy L in the above formula ij The expression is:

[0063]

[0064] Where, L ij Indicates the intimacy between the target student and other students in the communication range (coordinates (x, y)). xy Indicates the intimacy of other students to the target student. G ij Indicates the gender of the target student. G xy Indicates the gender of other students at the coordinate (x, y). The gender definition value can be shown as the gender parameter in the table above. k ijxy Represents the weight of the special relationship. Based on the bond between the two students, its value will also change, ranging from [0.5-2.0]. ij Represents the student's communication scope, and its expression is:

[0065]

[0066] Where Max is the maximum communication range set. set is the average intimacy threshold set; W represents the weight of parameters such as gender and bond between students, and W>1. Compared with the intimacy L ij In terms of communication scope R ij In the C ij Indicates the grade of the target students; C xy Indicates the corresponding grade of other students at the coordinate (x, y).

[0067] S3. Calculate the student's final movement probability P ijz .

[0068] Determine the current time step. If the current evolution stage is judged to be in the leisure learning period, then perform the exercise according to the probability r = α. If the current evolution stage is judged to be the regular learning period, then perform the exercise according to the probability r = β. If the current evolution stage is judged to be the key examination period, then perform the exercise according to the probability r = γ. Among them, α, β and γ are all reference probabilities, and 1>α>β>γ. At the same time, record the student's various exercise conditions and time. The final student's final exercise probability P is obtained. ijz Satisfies the following formula:

[0069] P ijz =KT u rpi,j,z

[0070] Where K represents the adjustment parameter; T u It represents the ratio of the available motion time to the unavailable motion time in the time step. ijz The larger it is, the more likely the student is to be active.

[0071] S4. Parameter update.

[0072] Students will adjust their motion characteristics within a semester and tend to imitate or follow students with whom they have a higher intimacy. Therefore, we introduce an update quantity: assume that students will modify their intimacy and change their motion characteristics at each time step. In this step, the current time step is compared with the previous time step to determine whether the student's current motion state has changed, and calculate the student's current intimacy. If the student's current motion state has not changed, the sensitivity coefficient k of the motion characteristic is s If the student's current motion state changes, the sensitivity coefficient k of the motion feature will s If the current intimacy is greater than or equal to the set threshold (generally the threshold is set to 0.5), the sensitivity coefficient k of the intimacy L Increase Δσ, and the intimacy is less than the threshold, then the sensitivity coefficient k of intimacy L Reduce Δσ.

[0073] S5, the sensitivity coefficient k of the changed motion feature s and the sensitivity coefficient k of intimacy L Substitute into the next time step and repeat S2 to S5 until the end of the simulation, completing the simulation of students' activity behaviors within the set period, and providing a basis for schools to formulate reasonable sports plans and resource allocation.

[0074] In summary, the student comprehensive activity behavior simulation method in the present embodiment, in the parameter configuration stage, not only sets the basic attributes of the students, but also sets the parameters related to the movement, including the type of movement, intensity, duration, and whether to join the community, etc. In the parameter adjustment and optimization environment, the validity of the model is ensured by adjusting and optimizing the parameters. After that, the simulation method enters the stage of motion change and motion feature judgment. If there is a motion change, the degree of influence between the students is calculated, and the motion behavior is adjusted accordingly. The model will count the status of the students in different time periods, including a comprehensive evaluation of the exercise time and intensity, and on this basis, evaluate the effect of the parameters to verify the accuracy of the model and guide the direction of future adjustments. Throughout the process, intimacy runs through as a key parameter, which affects the interaction between students: the higher the intimacy, the greater the possibility that the student changes his or her own motion characteristics. In addition, the scope of communication is also an important factor. The larger the scope of communication, the more students are affected, but the influence ratio of a single student will be reduced. Through the continuous adjustment and optimization of these parameters, the accurate simulation and analysis of the student's motion behavior is finally achieved, helping the school to better understand and promote the physical and mental health of students.

[0075] In addition, during the simulation, all states are counted, and the parameters of the simulated comprehensive student activity behavior are evaluated and verified. First, the initial school, social environment and student sports parameters are set: the overall probability of students joining the club is set to 0.2; the overall probability of students winning is set to 0.05; the school has a student sports foundation set to true; the ratio of students in class: after class is set to 8:2; the sensitivity coefficient k of the sports characteristics is set. S 3. The sensitivity coefficient k of intimacy L The sensitivity coefficient k of the communication range is 1.5. R is 1; finally observe and count the average movement status of the students. Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 and Fig. 9 .Depend on Figures 2 to 9 It can be seen that students will gradually follow each other over time. Under the pressure of regular study period, gradually reduce their exercise time and intensity ( Figures 2 to 7In the figure, power is the intensity of exercise, and duration is the duration of exercise. Red is intensity 1 (level 1) or duration <30, green is intensity 2 (level 2) or duration 30-60, and blue is intensity 3 (level 3) or duration >60). This is because students lack enthusiasm for exercise after studying hard. When T is 20-40, it is set as the holiday stage or leisure study period. At this time, students have almost no pressure to study and have enough time to relax. Students have a greater tendency to exercise, and they are more likely to choose a larger intensity and duration of exercise. After T>40, the study pressure is restored, and students will reduce the intensity and duration of exercise, but they will not stop exercising completely. When T is in the time period of 70-90, it is set as the exam week or key exam period, and the students' exercise time is sharply compressed, and the intensity and duration of students' exercise are directly reduced to the lowest level.

[0076] From this, we can see that for schools, during regular study periods, since students’ study pressure is relatively small, they can appropriately reduce intervention in student activities and let students arrange their own exercise time. During exam weeks and key exam periods, schools need to pay special attention to students’ study pressure relief and take appropriate measures to guide students to increase their enthusiasm for exercise, such as making physical education courses more interesting, holding some relaxing and enjoyable sports activities, or providing some reward mechanisms to encourage students to participate in physical exercise, so as to avoid students’ physical and mental health being affected by long-term lack of exercise.

[0077] We then evaluate the effect of each sensitivity coefficient. s The value of corresponds to the average change in the observed students’ motion characteristics. s As the horizontal axis, the motion characteristics are used as the vertical axis, and the results are as follows Fig.10 Take swimming as an example, and combine Fig.10 As shown in the figure, without considering the exam week or holiday week, the sensitivity coefficient k of the motion feature is s When k is low, the student's movement characteristics are relatively stable and will not change significantly over time, indicating that the student basically maintains his or her own exercise habits and is less affected by the exercise habits of others. Even if he or she is influenced by others, he or she will not easily change his or her exercise method. s = 8, which is converted into a code that shows a double tendency to follow others. Students will be more inclined to follow others at the beginning. Fig.10 It can be seen that most students tend to follow other sports (such as basketball and football) at the beginning, resulting in a small number of swimmers. As time goes by, the number of swimmers tends to stabilize, and students' exercise habits have been formed and tend to stabilize. sWhen k is low, students usually maintain their own exercise habits and are less likely to be influenced by the exercise habits of others. Therefore, schools can stimulate students' interest in sports by offering a variety of sports courses and facilities, and encourage students to try new sports to enrich their sports experience. s When the score is higher, students are more easily influenced by their peers. Schools can take advantage of this and organize team sports or competitions to enhance interaction among students and encourage more students to participate in sports.

[0078] Sensitivity coefficient k to intimacy L Analysis of the role of: changing the sensitivity coefficient k of intimacy L The value of corresponds to the average movement characteristic change of the observed students. L As the horizontal axis, the motion characteristics are used as the vertical axis, and the results are as follows Fig.11 As shown. Fig.11 It can be seen that the sensitivity coefficient k L and the sensitivity coefficient k s The change curve is similar to that of . High intimacy will make students more inclined to follow the movement. Once the intimacy decreases, the influence of intimacy on the overall movement characteristics of students will drop sharply. In general, the average movement performance of students will change steadily and will not be affected by intimacy. The sensitivity coefficient k for intimacy L The effect analysis shows that high intimacy leads to students being more inclined to follow others' sports behaviors. Therefore, schools should create opportunities for students to build closer relationships, such as organizing team building activities or collective sports events to enhance friendships among students and improve the collective atmosphere. When the intimacy coefficient is low, the changes in students' sports behaviors are relatively stable and not affected by external factors. At this time, schools can consider strengthening individual sports plans to help students develop independent sports habits.

[0079] The sensitivity coefficient k to the communication scope R Analysis of the role of: sensitivity coefficient k of changing the communication scope R The value of corresponds to the average movement characteristic change of the observed students. The sensitivity coefficient k of the communication range R As the horizontal axis, the motion characteristics are used as the vertical axis, and the results are as follows Fig.12 , Fig.13 and Fig.14 As shown. Figure 8 and Fig. 9 Compared with the reference, it can be concluded that when the sensitivity coefficient k is reduced R When the sensitivity coefficient k is increased, the influence of the communication range on the change of students' movement characteristics will be reduced, which will weaken the students' tendency to follow, causing the students' movement state to tend to be random, and the overall average state of students to tend to be evenly distributed and stable. R, which leads to a greater impact of the communication scope on the change of students' movement state. Students have a wider range of options to follow, and their subjectivity is poor. They will continue to follow others' movement state, resulting in a more fixed change in the movement state of a single student. The sensitivity coefficient k for the communication scope is R The analysis shows that when the communication range is small, students' sports state is more random. When the communication range is large, students tend to imitate others' sports behavior. Therefore, schools should actively organize collective sports activities on campus to increase students' communication range and improve their social skills. At the same time, they can also broaden students' horizons and promote the healthy development of students' physical and mental health by holding various sports events or interest groups.

[0080] In general, during regular study periods, schools can appropriately reduce intervention in student sports; during exam periods, measures need to be taken to relieve students' study pressure and encourage them to maintain moderate exercise. At the same time, schools should encourage a variety of sports and promote interaction between students to cultivate good exercise habits. When formulating sports plans and allocating resources, schools should fully consider the impact of these factors on students' sports behavior, and comprehensively consider students' needs and behavioral characteristics at different stages, flexibly adjust strategies, focus on the organization of collective activities, pay attention to individual differences, reasonably arrange sports courses, create a positive and healthy campus sports atmosphere, stimulate students' sports enthusiasm through various channels, and promote students' physical and mental health and all-round development.

[0081] In another embodiment, the present invention also provides a computer-readable storage medium. A computer program is stored on the computer-readable storage medium. When the above-mentioned computer program is executed by a processor, the student comprehensive activity behavior simulation method disclosed in the above-mentioned embodiment is implemented. The computer-readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc., which is used to store and install application software and an executable computer program that implement the student comprehensive activity behavior simulation method based on the extended cellular machine.

[0082] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0083] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for simulating student comprehensive activity behavior based on an extended cellular machine, characterized in that: The method simulates the comprehensive activity behavior of students through a behavior simulation model based on an extended cellular machine; the method comprises the following steps: S1. Initial parameter configuration of the behavior simulation model; S2. Calculate the cell transfer probability p i,j,z , and its calculation formula is: In the formula, i represents the index of the student's horizontal coordinate in the cellular space; j represents the index of the student's vertical coordinate in the cellular space; z represents the index of all the changeable motion features of the student; N is the adjustment parameter; S ijz represents the zth motion feature of the current student; L ij Indicates the intimacy between students; R ij Indicates the student's communication scope; k S ,k L ,k R are the sensitivity coefficients of movement characteristics, intimacy, and communication scope respectively; E represents the environmental parameter; T H Indicates holiday time, T C Indicates the time in school; S3. Calculate the student's final movement probability P ijz , and its calculation formula is: P ijz =KT u rp i,j,z Where K represents the adjustment parameter; T u represents the ratio of the available exercise time to the unavailable exercise time in the time step; r represents the evolution stage of the current time step. If the evolution stage is judged to be the leisure learning period, exercise is performed with probability r=α; if the evolution stage is judged to be the regular learning period, exercise is performed with probability r=β; if the evolution stage is judged to be the key examination period, exercise is performed with probability r=γ; where α, β and γ are all reference probabilities, and 1>α>β>γ; S4. Compare the current time step with the previous time step to determine whether the student's current motion state has changed, and calculate the student's current intimacy; if the student's current motion state has not changed, the sensitivity coefficient k of the motion feature s remains unchanged; if the student's current motion state changes, the sensitivity coefficient k of the motion feature s Reduce Δδ; if the current intimacy is greater than or equal to the set threshold, the sensitivity coefficient k of the intimacy L Increase Δσ, and the intimacy is less than the threshold, then the sensitivity coefficient k of intimacy L Reduce Δσ; S5, the sensitivity coefficient k of the changed motion feature s and the sensitivity coefficient k of intimacy L Substitute the next time step and repeat S2 to S5 until the simulation ends.

2. The method for simulating student comprehensive activity behavior based on an extended cellular machine according to claim 1, characterized in that: The cell space of the behavior simulation model is 100*100, and the neighborhood type is Moore type; the space occupied by each cell is 1*1.

3. The method for simulating comprehensive student activities based on an extended cellular machine according to claim 1, characterized in that: The initial parameters include environmental parameters, student sports parameters and student status; the environmental parameters include the overall probability of students joining the club, the overall probability of students winning awards, whether the school has sports infrastructure and the time ratio of students to and from get out of class; the student sports parameters include the sensitivity coefficient k of the sports characteristics S , the sensitivity coefficient k of intimacy L and the sensitivity coefficient k of the communication scope R The student status includes the student's gender, the student's sport type, the student's sport intensity, the student's sport time, whether the student has joined a club, and whether the student has won an award.

4. The method for simulating comprehensive student activities based on an extended cellular machine according to claim 1, characterized in that: The motion feature S ijz The expression is: Where F is the adjustment parameter of motion characteristics; St ij For the current student sports type; St xy The sports types of other students in the current student's communication range; Sp ij is the current student exercise intensity; Sp xy Sd is the exercise intensity of other students in the current student’s communication range; ij is the current student exercise time frequency; Sd xy is the movement time frequency of other students in the current student's communication range; Sjc ij Whether current students join the club; Sjc xy Whether other students in the current student's social circle will join the club; ij Whether the current student has won an award; Sr xy Whether other students in the current student's social circle have won awards; t ,k p ,k d ,k jc ,k r St ij ,Sp ij ,Sd ij ,Sjc ij ,Sr ij Parameter coefficients for the five motion states.

5. The method for simulating comprehensive student activities based on an extended cellular machine according to claim 1, characterized in that: The intimacy L ij The expression is: Where, L ij Indicates the intimacy between the target student and other students in the communication range; L xy Indicates the intimacy of other students to the target student; G ij Indicates the gender of the target student; G xy Indicates the gender of other students at the coordinate (x, y); k ijxy Represents the weight of a special relationship; R ij Indicates the student's communication scope.

6. The method for simulating student comprehensive activity behavior based on an extended cellular machine according to claim 5, characterized in that: The communication range R ij The expression is: Where Max is the maximum communication range set; L set is the average intimacy threshold set; W represents the weight of gender and bond between students, and W>1; C ij Indicates the grade of the target students; C xy Indicates the corresponding grade of other students at the coordinate (x, y).

7. The method for simulating student comprehensive activity behavior based on an extended cellular machine according to claim 1, characterized in that: In S3, the evolution stage is divided into three stages: a leisure learning period, a regular learning period and a key examination period; wherein the leisure learning period is characterized by less class time and more rest time and time after school; the regular learning period is characterized by more class time and less free time for students; the key examination period is characterized by shorter rest time and time after school and a sharp decrease in students' free time.

8. The method for simulating student comprehensive activity behavior based on an extended cellular machine according to claim 6, characterized in that: The three evolutionary stages are cyclically performed in a changing order, and the changing order is regular learning period - leisure learning period - regular learning period - key examination period.

9. The method for simulating comprehensive student activities based on an extended cellular machine according to claim 1, characterized in that: After simulating the comprehensive activity behavior of students, parameter evaluation is performed; the parameter evaluation is used to explore the key factors that affect the learning activity behavior.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for simulating student comprehensive activity behavior based on an extended cellular machine as described in any one of claims 1 to 9 is implemented.

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