An AI-based early warning system for amusement rides

Abstract

This invention relates to the field of intelligent network communication, and more particularly to an early warning system for amusement park equipment based on artificial intelligence. The system includes a data acquisition module, a modeling module, a data monitoring module, a gliding state risk assessment module, and a gliding posture risk assessment module. The system acquires the user's location and the type of game area they belong to in real time, dynamically adjusts the interaction frequency based on the number of concurrent users and the density within the area, and assesses the gliding state and posture risk in real time through posture modeling and center of gravity prediction, adjusting the interaction frequency to ensure user safety. The system provides precise data monitoring and area management based on gliding area division. Furthermore, the system includes functions such as gliding trajectory tracking, speed and acceleration monitoring, and can adjust the interaction frequency based on real-time data. Through comprehensive posture modeling, gliding parameter analysis, and dynamic area management, the system achieves the application requirements for precise monitoring and risk warning of user behavior in multi-player amusement park scenarios.

Description

Technical Field

[0001] This invention relates to the field of intelligent network communication, and in particular to an early warning system for amusement equipment based on artificial intelligence. Background Technology

[0002] With continuous technological advancements and the diversification of consumer entertainment needs, the amusement equipment industry has experienced significant development. From the initial mechanical amusement rides to today's intelligent devices integrating high-tech such as virtual reality and augmented reality, the industry's form and function have undergone tremendous changes. Currently, many innovative products combining VR and AR technologies have emerged on the market, providing users with a more immersive entertainment experience. Furthermore, the development of mobile internet has freed amusement equipment from fixed locations, with smartphones and tablets becoming new gaming terminals. Simultaneously, as consumers pursue healthy lifestyles, amusement equipment and entertainment products will place greater emphasis on providing healthy entertainment options, such as games that combine exercise and fitness, promoting physical activity while providing entertainment. Therefore, the industry needs to continuously innovate and improve product quality and service levels to meet the ever-growing market demands.

[0003] Chinese Patent Publication No. CN107393207A discloses a management system and its application method for an intelligent monitoring platform for game and amusement content. The system includes amusement machines, intelligent devices, a server, an application program, and an operation management platform. All amusement machines in the same store are connected to the same intelligent device, each with a unique device ID. The amusement machines connect to the server via the intelligent devices. The application program and the operation management platform are both networked with the server and communicate with the intelligent devices through the server. The application program transmits signals to the server, which processes the signals and feeds back the information to the mobile and intelligent devices. However, this method fails to fully utilize artificial intelligence technology to analyze and predict user behavior, thus failing to improve user experience and the intelligence level of the devices. In scenarios where multiple users use the device simultaneously, it lacks effective means of monitoring the number of users and controlling density, which may lead to device overload or a decline in user experience.

[0004] Therefore, there is an urgent need for an AI-based early warning system for amusement rides that can monitor and assess the user's gliding status in real time, improve the intelligent management level of the equipment, and ensure the safety and experience of the user. Summary of the Invention

[0005] To address this, the present invention provides an early warning system for amusement equipment based on artificial intelligence, which overcomes the problems of simple user safety monitoring modes and independent risk assessment results and network transmission in existing technologies in multi-player amusement scenarios.

[0006] To achieve the above objectives, the present invention provides an early warning system for amusement park equipment based on artificial intelligence, comprising: The data acquisition module is used to collect user registration information and key point scanning data; The modeling module, which is connected to the data acquisition module, is used to construct a user's posture model based on the joint scanning data, and to construct a center of gravity position prediction model based on historical joint scanning data. The data monitoring module, which is connected to the modeling module, is used to obtain the user's real-time location and the corresponding game area type, and to determine whether to obtain gliding parameters based on the game area type. If the game area type is a third game area, the user's gliding parameters are obtained; if the game area is not a third game area, a density judgment step is performed for the corresponding area of ​​the non-third game area, and the default interaction frequency of the game area is determined based on the judgment result. The gliding state risk assessment module, which is connected to the data monitoring module, assesses the user's gliding state risk based on the gliding parameters to determine whether there is any risk in the gliding state; The gliding parameters include the gliding speed and the trend of gliding angular velocity. When the gliding speed is greater than or equal to the standard speed threshold and the change in the rate of change of angular velocity is not within the standard range, it is determined that there is a risk in the gliding state, and a gliding state risk assessment is performed on the user's gliding state. The gliding posture risk assessment module is connected to the modeling module, the data monitoring module, and the gliding state risk assessment module. When there is a risk in the gliding state, it judges the gliding posture and determines the corresponding area division method and the adjustment method of the default interaction frequency based on the gliding posture judgment result.

[0007] Furthermore, the data acquisition module includes a user registration information acquisition unit and a key point scanning data acquisition unit, wherein, The user registration information collection unit is used to collect users' historical registration information; The joint point scanning data acquisition unit is used to collect joint point coordinates and joint point connection data based on the user's historical registration information.

[0008] Furthermore, the data monitoring module includes a people monitoring unit, a people comparison unit, and a comparison analysis unit, wherein, The number of people monitoring unit is used to obtain the game area corresponding to the user's real-time location, and the number of people coexisting in the game area in real time. The number comparison unit is used to compare the real-time concurrent number of players with the corresponding standard number threshold of the game area. The comparison and analysis unit determines whether to update the default interaction frequency of the game area based on the comparison results.

[0009] Furthermore, the comparison and analysis unit includes a molecule-cutting unit, a density detection subunit, and an interaction frequency update subunit, wherein, The segmentation unit is used to obtain the block segmentation standard corresponding to the game area, and to segment the game area into several blocks according to the block segmentation standard; The density detection subunit is used to obtain the block corresponding to the user's real-time location and the density of people in the block, and compare it with the density threshold in the standard block to perform density detection. The interaction frequency update subunit updates the default interaction frequency corresponding to the game area to the real-time interaction frequency based on the density detection result and the first adjustment factor of the interaction frequency.

[0010] Furthermore, the taxiing state risk assessment module includes a taxiing speed judgment unit and a taxiing angular velocity change trend analysis unit, wherein, Obtain the user's gliding parameters, including gliding speed and gliding angular velocity; The sliding speed determination unit is used to obtain the user's sliding speed, compare the sliding speed with the standard speed threshold of the third game area, and not update the default interaction frequency of the third game area when the sliding speed is less than the standard speed threshold. The taxiing angular velocity change trend analysis unit is used to analyze the taxiing angular velocity change trend when the taxiing speed is greater than or equal to the standard speed threshold, and to determine whether there is a risk in the taxiing state based on the analysis results.

[0011] Furthermore, the gliding angular velocity change trend analysis unit includes a data acquisition subunit, a first calculation subunit, a second calculation subunit, and a comparison subunit, wherein, The acquisition subunit acquires the data acquisition frequency at the default interaction frequency, and the angular velocity data at the data acquisition frequency. The first calculation subunit calculates the difference in angular velocity between two consecutive acquisition times to obtain the rate of change of angular velocity at each acquisition time. The second calculation subunit obtains the rate of change of angular velocity corresponding to each acquisition time within a specified time window, as well as the change range of each adjacent rate of change of angular velocity within the time window; The comparison subunit compares the change range of the angular velocity rate of change with the standard range. The risk analysis subunit determines whether there is a risk in the gliding state based on the comparison results.

[0012] Furthermore, the gliding attitude risk assessment module includes a center of gravity acquisition unit, a deviation calculation unit, a center of gravity comparison unit, and a region delineation unit, wherein, The center of gravity acquisition unit is used to obtain the user's real-time center of gravity position based on real-time joint coordinates and joint connection data. The deviation calculation unit is used to determine the predicted position of the center of gravity based on the center of gravity position prediction model, and to calculate the center of gravity deviation value between the real-time center of gravity position and the predicted center of gravity position. The center of gravity comparison unit is used to obtain the comparison result between the center of gravity deviation value and the center of gravity deviation threshold, and to obtain the gliding posture judgment result based on the comparison result, including the first gliding posture judgment result and the second gliding posture judgment result. The region delineation unit is used to perform corresponding region delineation steps based on the comparison results.

[0013] Furthermore, the region delineation unit includes a first region delineation subunit, a second region delineation subunit, and an update and alert unit, wherein, The first region delineation subunit is used to execute the first region delineation step when the first gliding attitude judgment result is obtained; The second region delineation subunit is used to execute the second region delineation step after obtaining the second gliding attitude judgment result; The update and alert unit is used to send corresponding alerts to other users in each defined area based on the delineation results, and to determine the interaction frequency of updating the third game area based on the adjustment factor. The adjustment factors include a second adjustment factor and a third adjustment factor; the alarm prompts include danger prompts and warning prompts, the danger prompts include a first danger prompt and a second danger prompt, and the warning prompts include a first warning prompt and a second warning prompt.

[0014] Furthermore, the process of performing the first area delineation step is as follows: The first area is delineated to obtain a first danger zone and a second danger zone, including: Get the user's gliding direction; Select the ray that is oriented in the user's sliding direction and passes through the user's real-time position as the central axis, and the ray that passes through the user's real-time position and is perpendicular to the central axis as the intercept line; Using the first danger distance and the second danger distance as the expansion distance, the expansion is carried out in a direction perpendicular to the central axis to obtain the first initial danger zone and the second initial danger zone; Obtain the overlapping areas of the first and second initial danger zones with the first and second initial danger zones of the third game area, respectively. Then, remove the overlapping areas of the first and second initial danger zones with the third game area along the cutting line in the sliding direction, respectively, to obtain the defined first and second danger zones.

[0015] Furthermore, the process of performing the second zone delineation step is as follows: Get the user's gliding direction; Select the ray that takes the user's sliding direction as the actual direction and passes through the user's real-time position as the central axis, and the ray that passes through the user's real-time position and is perpendicular to the central axis as the intercept line; Using the first and second warning distances as the expansion distances, the system expands along a direction perpendicular to the central axis to obtain the first initial warning area and the second initial warning area. Obtain the overlapping areas of the first and second initial warning areas with the first and second initial warning areas of the third game area, respectively. Then, remove the overlapping areas of the first and second initial warning areas with the third game area along the cut-off line in the opposite direction of the sliding direction, respectively, to obtain the defined first and second warning areas.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: by using artificial intelligence-based risk assessment of sliding states, it can identify and warn of potential dangerous sliding behaviors in advance, thereby improving the safety of people in amusement parks; by dynamically adjusting the area interaction mechanism through real-time sliding parameters and posture changes, it can prevent collisions or sliding instability events; by introducing joint point scanning and center of gravity prediction models, it can improve the accuracy of individual behavior analysis, making the system's assessment of individual risks more targeted; and by dynamically optimizing the interaction frequency and personnel distribution of game areas based on real-time changes in the number of people and density within blocks, it can ensure overall flow order and sliding experience; and by dynamically delineating dangerous areas and warning areas, it can effectively control the scope of risks, reduce the impact of accidents, and improve the overall intelligence and safety level of the amusement experience.

[0017] Furthermore, by acquiring the user's real-time location coordinates and corresponding game area, and monitoring the number of concurrent users in that area, the system can effectively identify local crowding phenomena and avoid frequency adjustments under unsatisfactory conditions, thereby improving system stability and response efficiency.

[0018] In particular, by dividing the game area into several blocks according to the set block division standard and counting the number of users in each block in real time, the density of people in the block can be accurately calculated, adapting to the interaction needs in high-density usage scenarios, improving the smoothness of user operation and the continuity of experience, while reducing the risk of system response delays caused by local dense crowds. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of an early warning system for amusement equipment based on artificial intelligence, according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the connection of the comparison and analysis unit in an embodiment of the present invention; Figure 3This is a schematic diagram of the connection of the gliding angular velocity change trend analysis unit in an embodiment of the present invention; Figure 4 This is a schematic diagram of the connection of the gliding posture risk assessment module in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0023] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Please see Figure 1 As shown, this is a schematic diagram of an early warning system for amusement equipment based on artificial intelligence, according to an embodiment of the present invention. The present invention provides an early warning system for amusement equipment based on artificial intelligence, comprising: The data acquisition module is used to collect user registration information and key point scanning data; The modeling module, which is connected to the data acquisition module, is used to construct a user's posture model based on the joint scanning data, and to construct a center of gravity position prediction model based on historical joint scanning data. The data monitoring module, which is connected to the modeling module, is used to obtain the user's real-time location and the corresponding game area type, and to determine whether to obtain gliding parameters based on the game area type. If the game area type is a third game area, the user's gliding parameters are obtained; if the game area is not a third game area, a density judgment step is performed for the corresponding area of ​​the non-third game area, and the default interaction frequency of the game area is determined based on the judgment result. The gliding state risk assessment module, which is connected to the data monitoring module, assesses the user's gliding state risk based on the gliding parameters to determine whether there is any risk in the gliding state; The gliding parameters include the gliding speed and the trend of gliding angular velocity. When the gliding speed is greater than or equal to the standard speed threshold and the change in the rate of change of angular velocity is not within the standard range, it is determined that there is a risk in the gliding state, and a gliding state risk assessment is performed on the user's gliding state. The gliding posture risk assessment module is connected to the modeling module, the data monitoring module, and the gliding state risk assessment module. When there is a risk in the gliding state, the gliding posture is judged, and the corresponding area division method and the adjustment method of the default interaction frequency are determined based on the gliding posture judgment result. In this embodiment, the data monitoring module and the modeling module are linked. When the data monitoring module feeds back the user's real-time location and the corresponding game area type to the modeling module in real time, the model data built by the modeling module is called to assess the risk of gliding posture. The gliding posture risk assessment module is used to obtain game area type data from the data monitoring module. When the game area type is the third game area, the module calls the center of gravity prediction model in the modeling module to assess the gliding posture risk.

[0025] By employing AI-based risk assessment of gliding behavior, the system can identify and warn of potential dangerous gliding behaviors in advance, thereby enhancing the safety of people in amusement parks. It dynamically adjusts the area interaction mechanism based on real-time gliding parameters and posture changes to prevent collisions or gliding instability. The introduction of joint scanning and center-of-gravity prediction models improves the accuracy of individual behavior analysis, making the system's risk assessment more targeted. Furthermore, based on real-time changes in the number of people and density within each area, the system dynamically optimizes the interaction frequency and personnel distribution in the game area, ensuring overall flow order and a smooth gliding experience. The dynamic delineation of dangerous and warning zones effectively controls the scope of risk, reduces the impact of accidents, and enhances the overall intelligence and safety of the amusement experience.

[0026] Specifically, the data acquisition module includes a user registration information acquisition unit and a key point scanning data acquisition unit, wherein, The user registration information collection unit is used to collect users' historical registration information; The joint point scanning data acquisition unit is used to collect joint point coordinates and joint point connection data based on the user's historical registration information. In this embodiment, if the user's historical registration information exists, the user's historical key point scanning data and center point position prediction model are invoked. If the user's historical registration information does not exist, the joint point scanning data acquisition unit acquires the user's joint point scanning data, including joint point coordinates and joint point connection data; The modeling module constructs the user's posture model using the joint coordinates and the lines connecting the joints. Based on the historical posture data of other users and the corresponding center of gravity positions, a center of gravity position prediction model corresponding to the user posture model is constructed. Update the entered joint scan data to historical joint scan data; The system uses an RGB camera to capture motion images of the user, generates a heatmap of joint points using a convolutional neural network, and extracts the coordinates of each joint point; then it constructs a human skeleton model based on the joint point coordinates and connections. The position of the human body's center of gravity is determined by calculating the average value of all relevant node coordinates. Collect a large amount of historical posture data and corresponding center of gravity positions of other users, and use this data to train a prediction model to predict the center of gravity position of users in real-time applications. Compare the prediction results with the real-time center of gravity positions to evaluate the posture stability of users. This step constructs a user posture model by inputting user registration information and joint scanning data, and trains a center of gravity position prediction model based on historical posture data of other users, which can predict and evaluate the user's center of gravity stability in real time. By using an RGB camera and convolutional neural network to extract joint coordinates, combined with skeleton modeling and center of gravity calculation, the accuracy and real-time performance of user posture recognition are significantly improved, providing a reliable data foundation for subsequent gliding state risk assessment and area division.

[0027] Specifically, the data monitoring module includes a people monitoring unit, a people comparison unit, and a comparison analysis unit, wherein, The number of people monitoring unit is used to obtain the game area corresponding to the user's real-time location, and the number of people coexisting in the game area in real time. The number comparison unit is used to compare the real-time concurrent number of players with the corresponding standard number threshold of the game area. The comparison and analysis unit determines whether to update the default interaction frequency of the game area based on the comparison results. In this embodiment, if the number of concurrent players in real time is less than the standard number threshold for the corresponding game area, the comparison and analysis unit determines not to update the default interaction frequency of the game area. If the number of concurrent players in real time is greater than or equal to the standard player threshold of the corresponding game area, the comparison and analysis unit determines whether to update the default interaction frequency of the game area based on the player density within the block. Obtain the user's real-time location, i.e., the coordinates corresponding to the user's real-time location; The standard number of players in the first game area is 50, and the standard number of players in the second game area is 40. The game area is divided into three sections: the first section has a 15° slope, the second section has a 30° slope, and the third section has a 40° slope.

[0028] This step effectively identifies local crowding by acquiring the user's real-time location coordinates and corresponding game area, and monitoring the number of concurrent users in that area; it also avoids frequency adjustments under unfavorable conditions, thereby improving system stability and response efficiency.

[0029] See Figure 2 As shown, it is a connection diagram of the comparison and analysis unit in an embodiment of the present invention; Specifically, the comparison and analysis unit includes a block segmentation unit, a density detection subunit, and an interaction frequency update subunit. The block segmentation unit is used to obtain the block segmentation standard corresponding to the game area and to segment the game area into several blocks according to the block segmentation standard. The density detection subunit is used to obtain the block corresponding to the user's real-time location and the density of people in the block, and to compare it with the density threshold in the standard block to perform density detection. The interaction frequency update subunit updates the default interaction frequency corresponding to the game area to the real-time interaction frequency based on the density detection result and the first adjustment factor of the interaction frequency. In this embodiment, if the density of people in a block is greater than or equal to the standard density threshold in a block, the interaction frequency update subunit obtains the first adjustment factor of the interaction frequency corresponding to the density of people in the block, and updates the default interaction frequency corresponding to the game area to the real-time interaction frequency. If the density of people in a block is less than the standard block density threshold, the interaction frequency update sub-unit will not update the default interaction frequency of the game area. The block division standard is a 1-meter × 1-meter grid pattern, and the blocks are numbered. Obtain the block number to which the user's real-time location belongs, count the number of people in that block in real time, and calculate the population density within the block; The density threshold within the standard block is 3 people per square meter; The default interaction frequency of the main information channel includes the default acquisition frequency and the default upload frequency; the default acquisition frequency is 30Hz and the default upload frequency is 5Hz. The first adjustment factor for interaction frequency is used to adjust the interaction frequency when the density of people in the block is high, so as to ensure the interaction quality under high density conditions. Based on historical interaction data, the first adjustment factor for interaction frequency is determined to be 0.8. The real-time interaction frequency is the product of the first adjustment factor of the interaction frequency and the default interaction frequency. If 4 people are detected in the block in the 3rd row and 5th column, the density in the block is 4 people / square meter, which is greater than the standard density threshold in the block. The interaction frequency update subunit obtains the first adjustment factor of the interaction frequency corresponding to the density of people in the block, and updates the default interaction frequency corresponding to the game area. The updated sampling frequency is 24Hz and the updated upload frequency is 4Hz. This step divides the game area into several blocks according to a set block segmentation standard and counts the number of users in each block in real time. It can accurately calculate the density of people in each block, adapt to the interaction needs in high-density usage scenarios, improve the smoothness of user operation and the continuity of experience, and at the same time reduce the risk of system response delays caused by local dense crowds.

[0030] Specifically, the taxiing state risk assessment module includes a taxiing speed determination unit and a taxiing angular velocity change trend analysis unit, wherein, The sliding speed determination unit is used to obtain the user's sliding speed, compare the sliding speed with the standard speed threshold of the third game area, and not update the default interaction frequency of the third game area when the sliding speed is less than the standard speed threshold. The taxiing angular velocity change trend analysis unit is used to analyze the taxiing angular velocity change trend when the taxiing speed is greater than or equal to the standard speed threshold, and determine whether there is a risk in the taxiing state based on the analysis results; In this embodiment, the relative position information between the user and the artificial snow fabric is obtained through the user's wearable device, and the transmission speed of the artificial snow fabric is recorded as the reference speed. If the user remains stationary relative to the artificial snow surface during the sampling period, it is considered that the user moves synchronously with the artificial snow surface, and the user's gliding speed is equal to the reference speed. If the user's position changes relative to the artificial snow cover, determine the user's relative gliding direction and relative gliding speed relative to the artificial snow cover; If the user's relative sliding direction is along the sliding direction, the sliding speed is the sum of the track conveyor speed and the relative sliding speed; If the user's relative sliding direction is in the opposite direction to the sliding direction, the sliding speed is the difference between the track conveyor speed and the relative sliding speed; This step accurately calculates the user's actual gliding speed by determining the relative position information between the user and the artificial snow surface and the conveyor belt speed. It effectively identifies abnormal behavior or potential risks in the user's gliding state, improves the accuracy of speed determination, and enhances the system's dynamic perception of gliding behavior. It is suitable for real-time gliding monitoring and safety warning in this game environment.

[0031] See Figure 3 As shown, it is a connection diagram of the gliding angular velocity change trend analysis unit in an embodiment of the present invention; Specifically, the gliding angular velocity change trend analysis unit includes a data acquisition subunit, a first calculation subunit, a second calculation subunit, and a comparison subunit, wherein, The acquisition subunit acquires the data acquisition frequency at the default interaction frequency, and the angular velocity data at the data acquisition frequency. The first calculation subunit calculates the difference in angular velocity between two consecutive acquisition times to obtain the rate of change of angular velocity at each acquisition time. The second calculation subunit obtains the rate of change of angular velocity corresponding to each acquisition time within a specified time window, as well as the change range of each adjacent rate of change of angular velocity within the time window; The comparison subunit compares the change range of the angular velocity rate of change with the standard range. The risk analysis subunit determines whether there is a risk in the gliding state based on the comparison results. In this embodiment, if the change in the rate of change of angular velocity is within the standard range, the risk analysis subunit determines that the trend of angular velocity change is a regular decay, and that there is no risk in the gliding state. If the rate of change of angular velocity is outside the standard range, the risk analysis subunit determines that the trend of angular velocity change is irregular decay and that there is a risk in the gliding state, and further judgment on the gliding attitude is required. The gliding angular velocity is collected at a default acquisition frequency of 30Hz and a default upload frequency of 5Hz. During continuous gliding, the angular velocity data corresponding to this frequency is collected to form a continuous time series. Calculate the difference in angular velocity between any two consecutive sampling times to obtain the rate of change of angular velocity at each sampling point:

[0032] in, Let be the angular velocity of the i-th sample; Let be the angular velocity of the (i-1)th sample. Let i be the rate of change of angular velocity at the i-th sampling point; Select a time window of length T, and within this time window, calculate the magnitude of the difference in the rate of change of angular velocity between adjacent sampling points.

[0033] in, The magnitude of the change is the absolute value of the difference between adjacent rates of change of angular velocity. Obtain the sequence of change amplitudes within the time window { }; Compare each item in the above sequence of change magnitudes with the preset standard magnitude range. The comparison is performed, and when 90% or more of the difference values ​​fall within the standard range, the change in angular velocity is considered to be a regular decay; otherwise, it is judged to be an irregular decay. The standard amplitude range is set to [0.01, 0.2] rad / s²; This step achieves real-time dynamic analysis of the user's gliding posture by setting a fixed time window and standard amplitude range, combined with differential calculation of continuously high-frequency sampled angular velocity data; it statistically analyzes the fluctuation range of the angular velocity change rate, and judges whether the gliding trend is regular based on whether it stably falls within the standard range, effectively identifying irregular gliding behavior; this step improves the accuracy of risk assessment and also has the advantages of strong real-time performance and high adaptability.

[0034] See Figure 4 As shown, it is a connection diagram of the gliding posture risk assessment module in an embodiment of the present invention; Specifically, the gliding attitude risk assessment module includes a center of gravity acquisition unit, a deviation calculation unit, a center of gravity comparison unit, and a region delineation unit, wherein... The center of gravity acquisition unit is used to obtain the user's real-time center of gravity position based on real-time joint coordinates and joint connection data. The deviation calculation unit is used to determine the predicted position of the center of gravity based on the center of gravity position prediction model, and to calculate the center of gravity deviation value between the real-time center of gravity position and the predicted center of gravity position. The center of gravity comparison unit is used to obtain the comparison result between the center of gravity deviation value and the center of gravity deviation threshold, and to obtain the gliding posture judgment result based on the comparison result, including the first gliding posture judgment result and the second gliding posture judgment result. The region delineation unit is used to perform corresponding region delineation steps based on the comparison results; In this embodiment, the center of gravity comparison unit compares the center of gravity deviation value with the center of gravity deviation threshold. If the center of gravity deviation value is greater than or equal to the center of gravity deviation threshold, the center of gravity is unstable, and the first gliding posture judgment result is obtained. If the center of gravity deviation value is less than the center of gravity deviation threshold, the center of gravity is stable, and the second gliding posture judgment result is obtained. The centroid acquisition unit is used to calculate the actual centroid position of the user in the current frame based on the real-time key point coordinates and key point connection information obtained by the user's wearable device, combined with preset quality distribution weight parameters. ; The deviation calculation unit is used to construct a centroid prediction model based on the centroid trajectory information of historical F frames, and predict the predicted centroid position of the user in the current frame. The Euclidean distance between the centroid and the actual centroid is calculated as the centroid deviation value. , The calculation formula is:

[0035] The center of gravity comparison unit is used to compare the center of gravity point deviation value. Deviation threshold from the preset center of gravity The comparison is performed, and a gliding attitude judgment result is generated based on the comparison result; when When the user's current gliding posture is determined to be in an unstable state, the first gliding posture judgment result is obtained; when When the user's current gliding posture is determined to be in a stable center of gravity state, the second gliding posture determination result is obtained; This step enables dynamic assessment of the deviation between the user's actual center of gravity and the predicted center of gravity. By setting a deviation threshold and comparing the results, the user's posture stability during gliding can be quickly determined, thereby effectively identifying abnormal gliding states. This method has the advantage of high assessment accuracy and can provide precise data support for subsequent risk warnings, area delineation, or interaction strategy adjustments in the system.

[0036] Specifically, the region delineation unit includes a first region delineation subunit, a second region delineation subunit, and an update and alert unit, wherein, The first region delineation subunit is used to execute the first region delineation step when the first gliding attitude judgment result is obtained; The second region delineation subunit is used to execute the second region delineation step after obtaining the second gliding attitude judgment result; The update and alert unit is used to send corresponding alerts to other users in each defined area based on the delineation results, and to determine the interaction frequency of updating the third game area based on the adjustment factor. The adjustment factor includes a second adjustment factor and a third adjustment factor; the alarm prompt includes a danger prompt and a warning prompt, the danger prompt includes a first danger prompt and a second danger prompt, and the warning prompt includes a first warning prompt and a second warning prompt; In this embodiment, if the first gliding posture judgment result is obtained, the area is divided into a first danger area and a second danger area, and the backup information transmission channel is activated to obtain the second adjustment factor of the interaction frequency corresponding to the first gliding posture judgment result, and the default interaction frequency corresponding to the third game area is updated to the real-time interaction frequency. The main information channel is used for information transmission under normal circumstances to ensure the stable operation of the system. Its data transmission rate is 50Mbps, the default upload frequency is 5Hz, and the bandwidth range is 5MHz to 10MHz; In terms of protocols, the main communication channel uses the TCP / IP protocol; The latency must be controlled within 200ms, and AES-256 data encryption and authentication must be used; The backup information transmission channel is automatically activated when the first gliding attitude determination result is detected, and is designed to provide data transmission with high bandwidth, low latency and high upload frequency; Its data transmission rate is between 60Mbps and 100Mbps, the default upload frequency is 10Hz, and the bandwidth range is 10MHz to 20MHz; Regarding the protocol, the backup channel uses the UDP protocol; The latency requirement is controlled within 50ms. AES-256 data encryption and authentication are used, along with a wireless redundancy mechanism that supports multipath transmission and network redirection. Based on actual operating conditions, the second adjustment factor was selected as 3, the updated acquisition frequency was 90Hz, and the updated upload frequency was 15Hz. If other users in the first danger zone receive a first danger warning, "There is a risk of collision, please avoid it in time," the wearable device will promptly remind the user to leave the first danger zone. If other users are in the second danger zone outside the first danger zone and receive a second danger warning "There is a risk of collision, please leave this area", the wearable device will promptly remind the user to leave the second danger zone, so as to leave room for movement for the user in the first danger zone; If the result is the second gliding posture judgment result, obtain the third adjustment factor of the interaction frequency corresponding to the second gliding posture judgment result, and update the default interaction frequency corresponding to the third game area to the real-time interaction frequency. Based on actual operating conditions, the third adjustment factor was selected as 2, the updated acquisition frequency was 60Hz, and the updated upload frequency was 10Hz. The area was delineated to obtain a first warning area and a second warning area; If other users within the first warning area receive a first warning message stating "There is a risk of stampede, please avoid it in time," the wearable device will promptly remind the user to leave the first warning area. If other users are outside the first warning area and are in the second warning area, and receive a second warning message that "there is a risk of stampede, please leave this area", the wearable device will promptly remind the user to leave the second warning area, so as to leave room for movement for users in the first warning area; This step, by classifying the real-time judgment results of the user's gliding posture, achieves precise delineation of danger zones and warning zones. Combined with the activation mechanism of the backup communication channel and the dynamic adjustment mechanism of the interaction frequency, it ensures data transmission efficiency and safety response speed in a multi-user gliding environment. Based on the first gliding posture judgment result, the system can immediately delineate the first and second danger zones and activate the backup communication channel. With a higher data upload frequency and lower transmission latency, it quickly issues the first and second danger warnings, reminding other users to avoid or evacuate, thereby minimizing the potential collision risk. Simultaneously, by setting... The second adjustment factor increases the data collection and upload frequency in the third game area to a higher level, enhancing the system's dynamic perception and information interaction capabilities under high-risk situations. If the second gliding posture judgment result is obtained, the system delineates the first and second warning areas at a lower risk level. By appropriately increasing the data interaction frequency through the third adjustment factor, combined with the first and second warning prompts, the system achieves advance warning and flexible guidance for potential risks, improving the overall system's responsiveness to different risk levels and resource scheduling efficiency. This constructs a multi-user gliding safety control mechanism with real-time, hierarchical response, and accurate information dissemination capabilities.

[0037] Specifically, the process of performing the first region delineation step is as follows: The first area is delineated to obtain a first danger zone and a second danger zone, including: Get the user's gliding direction; Select the ray that is oriented in the user's sliding direction and passes through the user's real-time position as the central axis, and the ray that passes through the user's real-time position and is perpendicular to the central axis as the intercept line; Using the first danger distance and the second danger distance as the expansion distance, the expansion is carried out in a direction perpendicular to the central axis to obtain the first initial danger zone and the second initial danger zone; Obtain the overlapping areas of the first and second initial danger zones with the first and second initial danger zones of the third game area, respectively. Then, remove the overlapping areas of the first and second initial danger zones with the third game area along the cutting line in the sliding direction, respectively, to obtain the defined first and second danger zones. The first danger distance is the area where there is a risk of collision; in this embodiment, 1 meter is taken as the first danger distance. The first initial danger overlap area is the overlapping part of the first initial danger area and the third game area; The second initial danger overlap area is the overlapping part of the second initial danger area and the third game area; The second danger distance is the area where there is no direct collision but there is a risk of collision. In this embodiment, 1.5 meters is taken as the second danger distance.

[0038] Specifically, the process of performing the second area delineation step is as follows: Get the user's gliding direction; Select the ray that takes the user's sliding direction as the actual direction and passes through the user's real-time position as the central axis, and the ray that passes through the user's real-time position and is perpendicular to the central axis as the intercept line; Using the first and second warning distances as the expansion distances, the system expands along a direction perpendicular to the central axis to obtain the first initial warning area and the second initial warning area. Obtain the overlapping areas of the first and second initial warning areas with the first and second initial warning areas of the third game area, respectively, and remove the overlapping areas of the first and second initial warning areas with the third game area along the cut-off line in the opposite direction of the sliding direction, respectively, to obtain the defined first and second warning areas. The first danger distance is the area where there is a risk of trampling. In this embodiment, 1 meter is taken as the first danger distance. The second danger distance is the area where there is no direct trampling but there is a risk of collision and trampling. In this embodiment, 1.5 meters is taken as the second danger distance.

[0039] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0040] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An early warning system for amusement park equipment based on artificial intelligence, characterized in that, include, The data acquisition module is used to collect user registration information and key point scanning data; The modeling module, which is connected to the data acquisition module, is used to construct a user's posture model based on the joint scanning data, and to construct a center of gravity position prediction model based on historical joint scanning data. The data monitoring module, which is connected to the modeling module, is used to obtain the user's real-time location and the corresponding game area type, and to determine whether to obtain gliding parameters based on the game area type. If the game area type is a third game area, the user's gliding parameters are obtained; if the game area is not a third game area, a density judgment step is performed for the corresponding area of ​​the non-third game area, and the default interaction frequency of the game area is determined based on the judgment result. The gliding state risk assessment module, which is connected to the data monitoring module, assesses the user's gliding state risk based on the gliding parameters to determine whether there is any risk in the gliding state; The gliding parameters include the gliding speed and the trend of gliding angular velocity. When the gliding speed is greater than or equal to the standard speed threshold and the change in the rate of change of angular velocity is not within the standard range, it is determined that there is a risk in the gliding state, and a gliding state risk assessment is performed on the user's gliding state. The gliding posture risk assessment module is connected to the modeling module, the data monitoring module, and the gliding state risk assessment module. When there is a risk in the gliding state, it judges the gliding posture and determines the corresponding area division method and the adjustment method of the default interaction frequency based on the gliding posture judgment result.

2. The early warning system for amusement equipment based on artificial intelligence according to claim 1, characterized in that, The data acquisition module includes a user registration information acquisition unit and a key point scanning data acquisition unit, wherein... The user registration information collection unit is used to collect users' historical registration information; The joint point scanning data acquisition unit is used to collect joint point coordinates and joint point connection data based on the user's historical registration information.

3. The early warning system for amusement equipment based on artificial intelligence according to claim 1, characterized in that, The data monitoring module includes a people monitoring unit, a people comparison unit, and a comparison analysis unit, wherein... The number of people monitoring unit is used to obtain the game area corresponding to the user's real-time location, and the number of people coexisting in the game area in real time. The number comparison unit is used to compare the real-time concurrent number of players with the corresponding standard number threshold of the game area. The comparison and analysis unit determines whether to update the default interaction frequency of the game area based on the comparison results.

4. The early warning system for amusement equipment based on artificial intelligence according to claim 3, characterized in that, The comparison analysis unit includes a molecule-cutting unit, a density detection subunit, and an interaction frequency update subunit, wherein... The segmentation unit is used to obtain the block segmentation standard corresponding to the game area, and to segment the game area into several blocks according to the block segmentation standard; The density detection subunit is used to obtain the block corresponding to the user's real-time location and the density of people in the block, and compare it with the density threshold in the standard block to perform density detection. The interaction frequency update subunit updates the default interaction frequency corresponding to the game area to the real-time interaction frequency based on the density detection result and the first adjustment factor of the interaction frequency.

5. The early warning system for amusement equipment based on artificial intelligence according to claim 1, characterized in that, The taxiing state risk assessment module includes a taxiing speed judgment unit and a taxiing angular velocity change trend analysis unit, wherein... The sliding speed determination unit is used to obtain the user's sliding speed, compare the sliding speed with the standard speed threshold of the third game area, and not update the default interaction frequency of the third game area when the sliding speed is less than the standard speed threshold. The taxiing angular velocity change trend analysis unit is used to analyze the taxiing angular velocity change trend when the taxiing speed is greater than or equal to the standard speed threshold, and to determine whether there is a risk in the taxiing state based on the analysis results.

6. The early warning system for amusement equipment based on artificial intelligence according to claim 5, characterized in that, The gliding angular velocity change trend analysis unit includes a data acquisition subunit, a first calculation subunit, a second calculation subunit, a comparison subunit, and a risk analysis subunit, wherein... The acquisition subunit acquires the data acquisition frequency at the default interaction frequency, and the angular velocity data at the data acquisition frequency. The first calculation subunit calculates the difference in angular velocity between two consecutive acquisition times to obtain the rate of change of angular velocity at each acquisition time. The second calculation subunit obtains the rate of change of angular velocity corresponding to each acquisition time within a specified time window, as well as the change range of each adjacent rate of change of angular velocity within the time window; The comparison subunit compares the change range of the angular velocity rate of change with the standard range. The risk analysis subunit determines whether there is a risk in the gliding state based on the comparison results.

7. The early warning system for amusement equipment based on artificial intelligence according to claim 6, characterized in that, The gliding attitude risk assessment module includes a center of gravity acquisition unit, a deviation calculation unit, a center of gravity comparison unit, and a region delineation unit. The center of gravity acquisition unit is used to obtain the user's real-time center of gravity position based on real-time joint coordinates and joint connection data. The deviation calculation unit is used to determine the predicted position of the center of gravity based on the center of gravity position prediction model, and to calculate the center of gravity deviation value between the real-time center of gravity position and the predicted center of gravity position. The center of gravity comparison unit is used to obtain the comparison result between the center of gravity deviation value and the center of gravity deviation threshold, and to obtain the gliding posture judgment result based on the comparison result, including the first gliding posture judgment result and the second gliding posture judgment result. The region delineation unit is used to perform corresponding region delineation steps based on the comparison results.

8. The early warning system for amusement equipment based on artificial intelligence according to claim 7, characterized in that, The region delineation unit includes a first region delineation subunit, a second region delineation subunit, and an update and alert unit, wherein... The first region delineation subunit is used to execute the first region delineation step when the first gliding attitude judgment result is obtained; The second region delineation subunit is used to execute the second region delineation step after obtaining the second gliding attitude judgment result; The update and alert unit is used to send corresponding alerts to other users in each defined area based on the delineation results, and to determine the interaction frequency of updating the third game area based on the adjustment factor. The adjustment factors include a second adjustment factor and a third adjustment factor; the alarm prompts include danger prompts and warning prompts, the danger prompts include a first danger prompt and a second danger prompt, and the warning prompts include a first warning prompt and a second warning prompt.

9. The early warning system for amusement equipment based on artificial intelligence according to claim 8, characterized in that, The process of performing the first region delineation step is as follows: The first area is delineated to obtain a first danger zone and a second danger zone, including: Get the user's gliding direction; Select the ray that is oriented in the user's sliding direction and passes through the user's real-time position as the central axis, and the ray that passes through the user's real-time position and is perpendicular to the central axis as the intercept line; Using the first danger distance and the second danger distance as the expansion distance, the expansion is carried out in a direction perpendicular to the central axis to obtain the first initial danger zone and the second initial danger zone; Obtain the overlapping areas of the first and second initial danger zones with the first and second initial danger zones of the third game area, respectively. Then, remove the overlapping areas of the first and second initial danger zones with the third game area along the cutting line in the sliding direction, respectively, to obtain the defined first and second danger zones.

10. The early warning system for amusement equipment based on artificial intelligence according to claim 8, characterized in that, The process of performing the second area delineation step is as follows: Get the user's gliding direction; Select the ray that is oriented in the user's sliding direction and passes through the user's real-time position as the central axis, and the ray that passes through the user's real-time position and is perpendicular to the central axis as the intercept line; Using the first and second warning distances as the expansion distances, the system expands along a direction perpendicular to the central axis to obtain the first initial warning area and the second initial warning area. Obtain the overlapping areas of the first and second initial warning areas with the first and second initial warning areas of the third game area, respectively. Then, remove the overlapping areas of the first and second initial warning areas with the third game area along the cut-off line in the opposite direction of the sliding direction, respectively, to obtain the defined first and second warning areas.

Images