Multi-age child recreation behavior preference analysis system

Abstract

The invention relates to the technical field of child behavior analysis, and discloses a multi-age child recreation behavior preference analysis system, which comprises a multi-dimensional acquisition module and an intelligent analysis module. According to the system, management data of urban green space and recreation behavior management data of participated children are acquired through a multi-dimensional acquisition module and are classified to form a data set, and an intelligent analysis module evaluates the popularity degree of each green space and generates an attraction index, so that data support is provided for facility configuration optimization and people flow guidance; the practical value of the green space is improved, the correlation coefficients of the age, the height and the duration of two recreation behaviors are accurately calculated, the adaptation rule of the child growth stage and the recreation behaviors is revealed, the preference index is generated, the matching level of green space configuration and child preference is accurately reflected, the comprehensive analysis precision is high, optimization measures are specifically triggered, and the method is suitable for popularization and application. Through the modes of adjusting the facility layout, dividing exclusive areas, adding facilities and the like, the greenbelt space is ensured to be adaptive to the multi-age children, and personalized management and recreation experience are good.

Description

Technical Field

[0001] This invention relates to the field of children's behavior analysis technology, specifically a multi-age children's recreational behavior preference analysis system. Background Technology

[0002] Children's recreational behavior refers to a series of activities and behaviors that children engage in during their leisure time based on their own will, for the purpose of entertainment, relaxation, and physical and mental development. It is an indispensable part of children's growth. Activity settings encompass diverse spaces such as urban green spaces, community parks, playgrounds, and school open spaces. Activity forms can be divided into two main categories: physical recreation and cognitive recreation. Physical recreation includes running, climbing, cycling, and ball games. These activities can improve children's physical coordination and fitness, while also cultivating a sense of rules and social skills through interaction with peers. Cognitive recreation includes observational science learning, educational games, and handicrafts, which help stimulate children's curiosity, imagination, and creativity, and promote the development of language expression and logical thinking abilities. From a developmental psychology perspective, there are significant differences in recreational behavior among children of different ages. Younger children (3-6 years old) prefer parent-child play, with activities mainly involving simple sensory experiences. School-aged children (7-12 years old) tend to prefer peer-cooperative play, with activities that are more complex and challenging. Children's play and recreation are not only a way for them to relax, but also an important way to develop socialization, cognitive abilities and personality, and are directly related to their overall healthy growth.

[0003] Currently, traditional children's recreational behavior preference analysis systems tend to overlook the correlation between the configuration of green space facilities, visitor flow structure and the characteristics of children's individual age, height and behavior type. They are unable to accurately reflect the core influencing factors of recreational behavior and cannot cover the personalized needs of children of different ages. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a multi-age children's recreational behavior preference analysis system, which has the advantages of comprehensive analysis with high accuracy and personalized management of recreational experience. It solves the problem that traditional children's recreational behavior preference analysis systems ignore the correlation between the configuration of green space facilities and children's individual characteristics, and cannot cover the personalized needs of children of different ages.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a multi-age children's recreational behavior preference analysis system, comprising a multi-dimensional monitoring module and an intelligent analysis module; The multi-dimensional acquisition module connects to a big data platform and database to acquire management data of urban green spaces and management data of recreational behavior of all participating children, and classifies them into green space datasets and children datasets. The intelligent analysis module includes a popularity assessment unit, a behavior analysis unit, and an optimization management unit. The popularity assessment unit is set with a monitoring period of fixed duration. Then, by combining the green space dataset, the popularity of each green space is assessed, and a corresponding attraction index is generated. The behavior analysis unit evaluates the correlation coefficients between children's developmental stages and play behavior across different dimensions based on the children's dataset. And generate the corresponding preference index. The optimization management unit is set with a fixed attraction threshold value. and preference threshold Combined with the attraction index Correlation coefficient and preference threshold It outputs corresponding suggestions for optimizing urban green space.

[0006] Preferably, the green space dataset includes the total number of visitors to each green space, the number of children playing, the total time children stay, the total queuing time for children, the number of children who played the same place twice or more, the total number of children's play facilities, and the number of facilities actually used by children. Among these, the children's play facilities include slides, swings, sandpits, climbing frames, seesaws, trampolines, science education facilities, and educational game facilities.

[0007] Preferably, the test dataset includes the age, height, type of recreational behavior, and duration of recreational behavior for each participating child. The type of recreational behavior includes physical and cognitive types. Physical recreational behavior includes using slides, swings, sandpits, climbing frames, seesaws, and trampolines. Cognitive recreational behavior includes using science-related facilities and educational play facilities.

[0008] Preferably, the attraction index The calculation process is as follows: Based on the green space dataset, the monitoring period is extracted. within, no. Management data for each green space, and the monitoring cycle. within, no. The total number of visitors to each green space is recorded as follows: The monitoring cycle within, no. The number of children playing in each green space is recorded as follows: The monitoring cycle within, no. The total time spent by all children in each green space is recorded as follows: The monitoring cycle within, no. The total queuing time for children in each green space is recorded as follows: The monitoring cycle within, no. The number of children who visited a green space twice or more is counted as [number]. The monitoring cycle within, no. The total number of children's play facilities in each green space is denoted as The monitoring cycle within, no. The actual number of facilities used by children in each green space is recorded as follows: ; In the formula, This indicates the percentage of children in the customer flow. This indicates the weighting of the proportion of children in the customer flow. This indicates the average length of time children stay. This indicates the weighting based on the average length of time children spend in the room. This indicates the average queuing time for children. The weight representing the average queuing time for children. This indicates the percentage of children who repeatedly visit the same place. The weighting indicates the percentage of children who repeatedly play the game. Indicates the utilization rate of children's play facilities. The weighting of the utilization rate of children's play facilities , , , and All are constants, and , Indicates the monitoring period within, no. The attractiveness index of each green space.

[0009] Preferably, the correlation coefficient between the age of the participating children and the duration of play behavior is... The calculation process is as follows: Based on the children's dataset, the age of each participating child was recorded as follows: , This represents the total number of participating children, and the duration of each participating child's physical recreational behavior at the same time point is recorded as follows: The duration of cognitive play behavior for each participating child at the same time point was recorded as... ; In the formula, Indicates the first The age of the participating children, , This indicates the average age of the participating children; In the formula, Indicates the first The duration of physical play behavior of each participating child This represents the average duration of physical recreational behavior among the participating children; In the formula, Indicates the first The duration of cognitive play behavior of each participating child This represents the average duration of cognitive play behavior among the participating children; In the formula, This represents the covariance between the age of the participating children and the duration of physical play activities. This represents the sum of squares of the deviations from the mean of the ages of the participating children. This represents the sum of squared deviations from the mean of the duration of physical recreational activities. This represents the correlation coefficient between the age of the participating children and the duration of physical recreational behavior; In the formula, This represents the covariance between the age of the participating children and the duration of cognitive play behavior. This represents the sum of squared deviations from the mean of the duration of cognitive recreational behavior. The correlation coefficient represents the relationship between the age of the participating children and the duration of cognitive play behavior.

[0010] Preferably, the correlation coefficient between the height of the participating children and the duration of play behavior is... The calculation process is as follows: Based on the children's dataset, the height of each participating child was recorded as follows: ; In the formula, Indicates the first The height of each participating child, , This indicates the average height of the children participating in the experiment; In the formula, This represents the covariance between the height of the participating children and the duration of physical play activities. This represents the sum of squares of the deviations from the mean in the heights of the participating children. This represents the correlation coefficient between the height of the participating children and the duration of physical recreational behavior; In the formula, This represents the covariance between the height of the participating children and the duration of cognitive play behavior. The coefficient represents the correlation between the height of the participating children and the duration of cognitive play behavior.

[0011] Preferably, the preference index The calculation process is as follows: In the formula, The weights representing the correlation coefficient between the age of the participating children and the duration of physical recreational behavior are: The weights representing the correlation coefficient between the age of the participating children and the duration of cognitive play behavior are: The weights representing the correlation coefficient between the height of the participating children and the duration of physical play behavior are indicated. The weights representing the correlation coefficient between the height of the participating children and the duration of cognitive play behavior are indicated. , , and All are constants, and .

[0012] Preferably, the attraction index ≤attraction threshold When the corresponding green space is not popular enough, the trigger measures include adding children's play facilities and shaded rest facilities, and planning and organizing festival-themed activities.

[0013] Preferably, when the correlation coefficient between the age of the participating child and the duration of recreational behavior is >0, it indicates that the age of the participating child and the duration of the corresponding type of recreational behavior are positively correlated; when the correlation coefficient between the age of the participating child and the duration of recreational behavior is <0, it indicates that the age of the participating child and the duration of recreational behavior are negatively correlated; when the correlation coefficient between the height of the participating child and the duration of recreational behavior is >0, it indicates that the height of the participating child and the duration of the corresponding type of recreational behavior are positively correlated; when the correlation coefficient between the height of the participating child and the duration of recreational behavior is <0, it indicates that the height of the participating child and the duration of the corresponding type of recreational behavior are negatively correlated.

[0014] Preferably, the preference index ≤Preference threshold When this occurs, it indicates that the urban green space is not well-suited to the recreational behavior preferences of children of different ages. Triggering measures include adjusting the layout and planning of children's play facilities, dividing dedicated activity areas according to children's height, and configuring children's play facilities of corresponding specifications for each area.

[0015] Compared with the prior art, the present invention provides a multi-age children's recreational behavior preference analysis system, which has the following beneficial effects: 1. This invention connects to a big data platform and database through a multi-dimensional acquisition module to obtain management data of urban green spaces and recreational behavior management data of all participating children. These data are then categorized into green space datasets and children's datasets, avoiding the limitations of a single data dimension. The intelligent analysis module is set with a fixed monitoring period. Then, by combining the green space dataset, the popularity of each green space is assessed, and a corresponding attraction index is generated. This provides data support for optimizing facility configuration and managing pedestrian flow, enhancing the practical value of green spaces. The intelligent analysis module accurately calculates the correlation coefficients between age, height, and the duration of two types of recreational activities. This study reveals the adaptation patterns between children's developmental stages and play behaviors, and generates corresponding preference indices. It accurately reflects the matching level between green space configuration and children's preferences, helps identify the differences in needs among different groups, and provides a comprehensive analysis with high accuracy.

[0016] 2. This invention uses an intelligent analysis module to set a fixed attraction threshold value. and preference threshold Combined with the attraction index Correlation coefficient and preference threshold Targeted optimization measures were implemented to address both the lack of popularity and the shortcomings in suitability. By adjusting the layout of facilities, dividing dedicated areas, and adding facilities, the green spaces were made suitable for children of all ages while providing a personalized and enjoyable recreational experience. Attached Figure Description

[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example Please see Figure 1 Based on the experimental data of attraction index in Table 1 and preference index in Table 2, this invention provides a multi-age children's recreational behavior preference analysis system, including a multi-dimensional monitoring module and an intelligent analysis module. The multi-dimensional data acquisition module connects to big data platforms and databases to obtain management data of urban green spaces and management data of recreational behavior of all participating children, and classifies them into green space datasets and children datasets. The green space dataset includes the total number of visitors, the number of children, the total time children spend in each green space, the total queuing time for children, the number of children who visited the same green space twice or more, the total number of children's play facilities, and the number of facilities actually used by children. Among these, children's play facilities include slides, swings, sandpits, climbing frames, seesaws, trampolines, science education facilities, and educational game facilities. The dataset includes each participating child's age, height, type of play behavior, and duration of play behavior. The types of play behavior include physical and cognitive types. Physical play behavior includes using slides, swings, sandpits, climbing frames, seesaws, and trampolines. Cognitive play behavior includes using science facilities and educational play facilities. Specifically, age reflects the level of cognitive and physical development, while height affects the suitability of facilities. For example, young children cannot use high climbing frames. This is the core basis for subsequent analysis of the relationship between growth stage and play behavior. The type of play behavior can clarify the core needs of children's play. For example, young children prefer physical play, while older children may prefer cognitive play. The duration of play behavior directly reflects the intensity of needs. Combining the two can accurately uncover the linear relationship between characteristics and behavior. The intelligent analysis module includes a popularity assessment unit, a behavior analysis unit, and an optimization management unit. The popularity assessment unit is set with a fixed monitoring period. Then, by combining the green space dataset, the popularity of each green space is assessed, and a corresponding attraction index is generated. The calculation process is as follows: Based on the green space dataset, the monitoring period is extracted. within, no. Management data for each green space, and the monitoring cycle. within, no. The total number of visitors to each green space is recorded as follows: The monitoring cycle within, no. The number of children playing in each green space is recorded as follows: The monitoring cycle within, no. The total time spent by all children in each green space is recorded as follows: The monitoring cycle within, no. The total queuing time for children in each green space is recorded as follows: The monitoring cycle within, no. The number of children who visited a green space twice or more is counted as [number]. The monitoring cycle within, no. The total number of children's play facilities in each green space is denoted as The monitoring cycle within, no. The actual number of facilities used by children in each green space is recorded as follows: ; In the formula, This indicates the percentage of children in the customer flow. This indicates the weighting of the proportion of children in the customer flow. This indicates the average length of time children stay. This indicates the weighting based on the average length of time children spend in the room. This indicates the average queuing time for children. The weight representing the average queuing time for children. This indicates the percentage of children who repeatedly visit the same place. The weighting indicates the percentage of children who repeatedly play the game. Indicates the utilization rate of children's play facilities. The weighting of the utilization rate of children's play facilities , , , and All are constants, and , Indicates the monitoring period within, no. The attractiveness index of green spaces; Specifically, by covering core dimensions such as visitor flow, children's experience engagement, demand for play facilities, and resource matching for each green space, the popularity of each green space can be objectively quantified, providing accurate data support for subsequent operation and management. This will help to optimize facility configuration and pedestrian flow in a targeted manner, thereby enhancing the practical value of green spaces. The following are the experimental data for the attraction index, as shown in Table 1: Table 1: Experimental Data for the Attraction Index Table 1 shows the experimental data on the attraction index, in which the green space in the western suburbs of the city was selected as the experimental target, and the monitoring period was... Set to 30 days, weight , , , , ; The optimized management unit is set with a fixed attraction threshold value. This is used to quickly determine whether the popularity of green spaces meets the standards. The value directly affects the accuracy and suitability of the judgment on the popularity status of green spaces. An excessively high attraction threshold... This could lead to the system being overly sensitive to normal fluctuations in the popularity of green spaces, frequently triggering facility additions and event planning mechanisms, increasing the operating costs and manpower required for green space management. Conversely, it could reduce the system's sensitivity to identifying unpopular green spaces, making it impossible to promptly improve the attractiveness of spaces through effective measures, resulting in the idle and wasted resources of green spaces. Therefore, the optimal value of this parameter needs to be determined through the following system calibration experiments: attraction threshold. The calibration method is as follows: Using research tools, we simulated different usage scenarios for various types of green spaces, setting different population densities, dwell times, and activity types to conduct multiple experiments. In each experiment, we recorded the attractiveness index of the green space. Based on the change curves and system judgment results, we simulated green space usage scenarios under different seasons and time periods (such as weekdays and weekends, daytime and nighttime), adjusted pedestrian flow distribution characteristics and demand preferences, conducted multiple sets of experiments, and recorded the attraction index. The fluctuations and whether the system triggers measures will be considered. For each candidate threshold, the collected attraction index will be... Using the change curve as input, the system counts the number of times green spaces are actually unpopular but no measures are triggered due to improper threshold settings (counted as missed detections) and the number of times green spaces are actually popular but measures are mistakenly triggered (counted as false detections). The final value that minimizes both the missed detection and false detection rates is selected as the attraction threshold. The preferred value; In Table 1, the attraction index experimental data includes the attraction threshold. The preferred value was set to 2.2. Based on this, the attraction index of the green space in the western suburbs of the city was determined to be... <Attraction threshold This indicates that the popularity of parks and green spaces in the western suburbs of the city has not met the standards, and the triggering measures include adding children's play facilities and sunshade and rest facilities, and planning and organizing festival-themed activities; The behavioral analysis unit assesses the correlation coefficients between children's developmental stages and play behavior across different dimensions using a children's dataset. And generate the corresponding preference index. ; Correlation coefficient between the age of the participating children and the duration of play behavior The calculation process is as follows: Based on the children's dataset, the age of each participating child was recorded as follows: , This represents the total number of participating children, and the duration of each participating child's physical recreational behavior at the same time point is recorded as follows: The duration of cognitive play behavior for each participating child at the same time point was recorded as... ; In the formula, Indicates the first The age of the participating children, , This indicates the average age of the participating children; In the formula, Indicates the first The duration of physical play behavior of each participating child This represents the average duration of physical recreational behavior among the participating children; In the formula, Indicates the first The duration of cognitive play behavior of each participating child This represents the average duration of cognitive play behavior among the participating children; In the formula, This represents the covariance between the age of the participating children and the duration of physical play activities. This represents the sum of squares of the deviations from the mean of the ages of the participating children. This represents the sum of squared deviations from the mean of the duration of physical recreational activities. This represents the correlation coefficient between the age of the participating children and the duration of physical recreational behavior; In the formula, This represents the covariance between the age of the participating children and the duration of cognitive play behavior. This represents the sum of squared deviations from the mean of the duration of cognitive recreational behavior. This represents the correlation coefficient between the age of the participating children and the duration of cognitive play behavior; Correlation coefficient between height and duration of play behavior in participating children The calculation process is as follows: Based on the children's dataset, the height of each participating child was recorded as follows: ; In the formula, Indicates the first The height of each participating child, , This indicates the average height of the children participating in the experiment; In the formula, This represents the covariance between the height of the participating children and the duration of physical play activities. This represents the sum of squares of the deviations from the mean in the heights of the participating children. This represents the correlation coefficient between the height of the participating children and the duration of physical recreational behavior; In the formula, This represents the covariance between the height of the participating children and the duration of cognitive play behavior. This represents the correlation coefficient between the height of the participating children and the duration of cognitive play behavior; Preference Index The calculation process is as follows: In the formula, The weights representing the correlation coefficient between the age of the participating children and the duration of physical recreational behavior are: The weights representing the correlation coefficient between the age of the participating children and the duration of cognitive play behavior are: The weights representing the correlation coefficient between the height of the participating children and the duration of physical play behavior are indicated. The weights representing the correlation coefficient between the height of the participating children and the duration of cognitive play behavior are indicated. , , and All are constants, and ; Specifically, by integrating the degree of fit between children's developmental stages and different types of play behaviors across multiple dimensions, the preference index... It can accurately reflect the matching level between the recreational configuration of urban green space and the recreational preferences of children of different ages. It can not only provide clear data support for the subsequent optimization of urban green space and help accurately identify the differences in recreational needs of children of different ages and heights, but also adjust the facility configuration, layout planning and management strategies accordingly. While ensuring the adaptability of green space to children of different ages, it can effectively improve children's recreational experience. The following is the experimental data for the preference index, as shown in Table 2: Table 2: Experimental Data for the Preference Index Table 2 shows the experimental data for the preference index. Children 1, 2, 3, 4, and 5 were selected as experimental subjects. Recreational behavior management data were collected within the same hour and weighted accordingly. , , , ; When the correlation coefficient between the age of the participating children and the duration of play behavior is >0, it indicates a positive correlation between the age of the participating children and the duration of the corresponding type of play behavior. The older the child, the longer the time spent on the corresponding type of play behavior. For example, a positive correlation between age and the duration of cognitive play behavior indicates that older children are more willing to spend time on science facilities and intellectual games. When the correlation coefficient between the age of the participating children and the duration of play behavior is <0, it indicates a negative correlation between the age of the participating children and the duration of play behavior. The older the child, the shorter the time spent on the corresponding type of play behavior. For example, a negative correlation between age and the duration of physical play behavior indicates that younger children prefer to participate in physical activities such as slides and swings for longer periods of time. When the correlation coefficient between height and duration of play behavior is >0, it indicates that the height of the participating children is positively correlated with the duration of the corresponding type of play behavior. The taller the child, the longer the duration of the corresponding type of play behavior. For example, height is positively correlated with the duration of use of physical facilities such as climbing frames and trampolines, indicating that the higher the height fit, the stronger the child's willingness to participate. When the correlation coefficient between height and duration of play behavior is <0, it indicates that the height of the participating children is negatively correlated with the duration of the corresponding type of play behavior. The taller the child, the shorter the duration of use of the corresponding type of play behavior. For example, height is negatively correlated with the duration of use of low-fitness facilities such as sandpits and seesaws, indicating that as height increases, children's interest in facilities designed for younger children decreases. The authentication management unit has a fixed preference threshold. This is used to quickly determine whether the fit between urban green spaces and the recreational behavior preferences of children of different ages is insufficient. The numerical value directly affects the accuracy and rationality of the fit assessment. An excessively high preference threshold... This can lead to the system becoming overly sensitive to slight mismatches between green space and children's play preferences, frequently triggering adjustments to facility layout and zoning mechanisms, increasing construction costs and resource consumption for green space renovations. Conversely, it can reduce the system's ability to identify insufficient adaptation, making it unable to promptly meet the play needs of children of different ages through scientific adjustments, thus affecting children's experience and safety when using green spaces. Therefore, the optimal value of this parameter needs to be determined through the following system calibration experiments: preference threshold. The calibration method is as follows: Using simulation tools, the spatial layout of urban green spaces of different sizes was recreated. Recreational behavior characteristics (including activity range, frequency of facility use, and interaction needs) of children of different age groups (e.g., preschool children aged 3-6 and school-aged children aged 7-12) were studied. Multiple sets of experiments were conducted, and the green space preference index was recorded in each set of experiments. Based on the change curves and system judgment results, we simulated children's play scenarios under different facility configuration schemes (such as existing facility specifications, layout density, and functional types), adjusted the facility layout logic and area division methods, conducted multiple sets of experiments, and recorded the preference index. The fluctuations and whether the system triggers adjustment measures will be considered. For each candidate threshold, the collected preference index will be used. Using the change curve as input, the system counts the number of times that the actual adaptability of green space is insufficient due to improper threshold setting but adjustment measures are not triggered, which are recorded as missed judgments. It also counts the number of times that the actual adaptability of green space meets the standard but adjustment measures are mistakenly triggered, which are recorded as false judgments. Finally, the value that minimizes both the missed judgment rate and the false judgment rate is selected as the preferred threshold. The preferred value; In the experimental data of the preference index in Table 2, the preference threshold... The preferred value was set to 0.5. Based on the assessment, the preference indices for children 1, 2, 3, 4, and 5 were... <Preference threshold This indicates that urban green spaces are not adequately adapted to the recreational behavior preferences of children of different ages. Triggering measures include adjusting the layout and planning of children's play facilities, dividing dedicated activity areas according to children's height, and configuring children's play facilities of corresponding specifications for each area.

[0020] In this embodiment, the multi-dimensional acquisition module comprehensively integrates urban green space management data and the recreational behavior data of participating children, classifying and constructing green space datasets and children's datasets. This provides a rich and accurate data source for subsequent analysis, avoiding the limitations of a single data dimension and ensuring the reliability of the analysis results. The intelligent analysis module is based on a fixed monitoring period. The attraction index is calculated by weighting multiple indicators. This objectively quantifies the popularity of green spaces, providing data support for optimizing facility configuration and managing pedestrian flow, thereby enhancing the practical value of green spaces. The intelligent analysis module accurately calculates the correlation coefficients between age, height, and the duration of two types of recreational activities. This study reveals the adaptation patterns between children's developmental stages and play behaviors, and generates corresponding preference indices. It accurately reflects the matching level between green space configuration and children's preferences, helps identify the differences in needs of different groups, and triggers corresponding optimization measures in a targeted manner. It not only solves the problem of insufficient popularity, but also makes up for the shortcomings of insufficient suitability. By adjusting the layout of facilities, dividing exclusive areas, and adding facilities, it effectively improves children's recreational experience while ensuring that green space is suitable for children of multiple ages.

[0021] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0022] The above formulas are all derived from software simulation using a large amount of data and are selected to be close to the actual values. The coefficients in the formulas are set by those skilled in the art according to the actual situation. The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the protection scope of the present invention.

Claims

1. A multi-age children's recreational behavior preference analysis system, characterized in that: Includes a multi-dimensional monitoring module and an intelligent analysis module; The multi-dimensional acquisition module connects to a big data platform and database to acquire management data of urban green spaces and management data of recreational behavior of all participating children, and classifies them into green space datasets and children datasets. The intelligent analysis module includes a popularity assessment unit, a behavior analysis unit, and an optimization management unit. The popularity assessment unit is set with a monitoring period of fixed duration. Then, by combining the green space dataset, the popularity of each green space is assessed, and a corresponding attraction index is generated. The behavior analysis unit evaluates the correlation coefficients between children's developmental stages and play behavior across different dimensions based on the children's dataset. And generate the corresponding preference index. The optimization management unit is set with a fixed attraction threshold value. and preference threshold Combined with the attraction index Correlation coefficient and preference threshold It outputs corresponding suggestions for optimizing urban green space.

2. The multi-age children's recreational behavior preference analysis system according to claim 1, characterized in that: The green space dataset includes the total number of visitors, the number of children, the total time children spend in each green space, the total queuing time for children, the number of children who visited the same green space twice or more, the total number of children's play facilities, and the number of facilities actually used by children. Among these, children's play facilities include slides, swings, sandpits, climbing frames, seesaws, trampolines, science education facilities, and educational game facilities.

3. The multi-age children's recreational behavior preference analysis system according to claim 2, characterized in that: The dataset includes each participating child's age, height, type of recreational behavior, and duration of recreational behavior. The types of recreational behavior include physical and cognitive behaviors. Physical recreational behaviors include using slides, swings, sandpits, climbing frames, seesaws, and trampolines. Cognitive recreational behaviors include using science-related facilities and educational play facilities.

4. The multi-age children's recreational behavior preference analysis system according to claim 3, characterized in that: The attraction index The calculation process is as follows: Based on the green space dataset, the monitoring period is extracted. within, no. Management data for each green space, and the monitoring cycle. within, no. The total number of visitors to each green space is recorded as follows: The monitoring cycle within, no. The number of children playing in each green space is recorded as follows: The monitoring cycle within, no. The total time spent by all children in each green space is recorded as follows: The monitoring cycle within, no. The total queuing time for children in each green space is recorded as follows: The monitoring cycle within, no. The number of children who visited a green space twice or more is counted as [number]. The monitoring cycle within, no. The total number of children's play facilities in each green space is denoted as The monitoring cycle within, no. The actual number of facilities used by children in each green space is recorded as follows: ; In the formula, This indicates the percentage of children in the customer flow. This indicates the weighting of the proportion of children in the customer flow. This indicates the average length of time children stay. This indicates the weighting based on the average length of time children spend in the room. This indicates the average queuing time for children. The weight representing the average queuing time for children. This indicates the percentage of children who repeatedly visit the same place. The weighting indicates the percentage of children who repeatedly play the game. Indicates the utilization rate of children's play facilities. The weighting of the utilization rate of children's play facilities , , , and All are constants, and , Indicates the monitoring period within, no. The attractiveness index of each green space.

5. The multi-age children's recreational behavior preference analysis system according to claim 4, characterized in that: The correlation coefficient between the age of the participating children and the duration of play behavior The calculation process is as follows: Based on the children's dataset, the age of each participating child was recorded as follows: , This represents the total number of participating children, and the duration of each participating child's physical recreational behavior at the same time point is recorded as follows: The duration of cognitive play behavior for each participating child at the same time point was recorded as... ; In the formula, Indicates the first The age of the participating children, , This indicates the average age of the participating children; In the formula, Indicates the first The duration of physical play behavior of each participating child This represents the average duration of physical recreational behavior among the participating children; In the formula, Indicates the first The duration of cognitive play behavior of each participating child This represents the average duration of cognitive play behavior among the participating children; In the formula, This represents the covariance between the age of the participating children and the duration of physical play activities. This represents the sum of squares of the deviations from the mean of the ages of the participating children. This represents the sum of squared deviations from the mean of the duration of physical recreational activities. This represents the correlation coefficient between the age of the participating children and the duration of physical recreational behavior; In the formula, This represents the covariance between the age of the participating children and the duration of cognitive play behavior. This represents the sum of squared deviations from the mean of the duration of cognitive recreational behavior. The correlation coefficient represents the relationship between the age of the participating children and the duration of cognitive play behavior.

6. The multi-age children's recreational behavior preference analysis system according to claim 5, characterized in that: The correlation coefficient between the height of the participating children and the duration of play behavior The calculation process is as follows: Based on the children's dataset, the height of each participating child was recorded as follows: ; In the formula, Indicates the first The height of each participating child, , This indicates the average height of the children participating in the experiment; In the formula, This represents the covariance between the height of the participating children and the duration of physical play activities. This represents the sum of squares of the deviations from the mean in the heights of the participating children. This represents the correlation coefficient between the height of the participating children and the duration of physical recreational behavior; In the formula, This represents the covariance between the height of the participating children and the duration of cognitive play behavior. The coefficient represents the correlation between the height of the participating children and the duration of cognitive play behavior.

7. The multi-age children's recreational behavior preference analysis system according to claim 6, characterized in that: The preference index The calculation process is as follows: In the formula, The weights representing the correlation coefficient between the age of the participating children and the duration of physical recreational behavior are: The weights representing the correlation coefficient between the age of the participating children and the duration of cognitive play behavior are: The weights representing the correlation coefficient between the height of the participating children and the duration of physical play behavior are indicated. The weights representing the correlation coefficient between the height of the participating children and the duration of cognitive play behavior are indicated. , , and All are constants, and .

8. The multi-age children's recreational behavior preference analysis system according to claim 7, characterized in that: The attraction index ≤Attraction threshold When the green space is not popular enough, the trigger measures include adding children's play facilities and shaded rest facilities, and planning and organizing festival-themed activities.

9. The multi-age children's recreational behavior preference analysis system according to claim 8, characterized in that: When the correlation coefficient between the age of the participating children and the duration of recreational behavior is >0, it indicates a positive correlation between the age of the participating children and the duration of the corresponding type of recreational behavior; when the correlation coefficient between the age of the participating children and the duration of recreational behavior is <0, it indicates a negative correlation between the age of the participating children and the duration of recreational behavior. When the correlation coefficient between the height of the participating children and the duration of recreational behavior is >0, it indicates a positive correlation between the height of the participating children and the duration of the corresponding type of recreational behavior; when the correlation coefficient between the height of the participating children and the duration of recreational behavior is <0, it indicates a negative correlation between the height of the participating children and the duration of the corresponding type of recreational behavior.

10. A multi-age children's recreational behavior preference analysis system according to claim 9, characterized in that: The preference index ≤Preference threshold When this occurs, it indicates that the urban green space is not well-suited to the recreational behavior preferences of children of different ages. Triggering measures include adjusting the layout and planning of children's play facilities, dividing dedicated activity areas according to children's height, and configuring children's play facilities of corresponding specifications for each area.

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