A VR large-space collaborative management platform and its dynamic path planning method

Abstract

The present invention provides a VR large-space collaborative management platform and its dynamic path planning method, belonging to the technical field of VR game management. The platform includes: a ticket-checking terminal for ticket management, player account management, and team formation settings; a on-site control terminal for automatically allocating VR devices and managing the game processes of players and teams; a VR management background for managing VR devices, setting game parameters, planning play paths according to game parameters and player play data, performing player interaction recognition, handling abnormalities during the play process, and updating VR projects. The dynamic path planning method dynamically plans play paths through an improved A* algorithm, social force model, and Q-Learning algorithm and performs multi-dimensional optimization.

Description

Technical Field

[0001] The present invention relates to a VR large - space collaborative management platform and its dynamic path planning method, belonging to the technical field of VR game management. Background Art

[0002] With the rise of VR technology, in recent years, there have been more and more VR game projects offline, and they are gradually developing towards large - space and multi - person interaction. However, most of the current VR devices and their matching platforms have relatively single functions. They basically only have the basic functions of VR terminal device management and do not have functions such as ticketing, project management, and venue control, making it difficult to meet the actual operation needs of VR game projects and increasing the operation cost of VR game projects. Moreover, most of the existing VR management platforms are limited to the operation solutions of handle interaction in small spaces. They either only support single - person in - place experience or only support the joint play experience of a small number of players, with low utilization rate of large spaces. When there are many players, they need to queue up to enter the venue, resulting in low venue operation efficiency and poor play experience.

[0003] In addition, the play routes of most VR game venues on the market are fixed, with low utilization rate of conventional spaces and poor play flexibility, unable to fully exert the immersive experience of VR games. Therefore, VR game projects lack reasonable dynamic path planning methods, which affect the development and operation of VR play projects. Summary of the Invention

[0004] The purpose of the present invention is to provide a VR large - space collaborative management platform and its dynamic path planning method. Through the collaborative management platform, functions such as VR project management, VR device management, ticketing management, and venue control are simultaneously realized, improving the adaptability of the VR management platform. At the same time, the platform of the present invention supports multiple groups of players to play simultaneously in the same venue space through dynamic planning of play routes, improving the venue operation efficiency and the player play experience.

[0005] To achieve the above object / To solve the above - mentioned technical problems, the present invention is implemented by adopting the following technical solutions:

[0006] On the one hand, the present invention provides a VR large - space collaborative management platform, including:

[0007] A ticket - checking terminal, used for ticketing management, player account management, and team - forming settings;

[0008] A on - site control terminal, used for automatically allocating VR devices and managing the game processes of players and teams;

[0009] The VR management background is used to manage VR devices, set game parameters, plan play paths according to game parameters and player play data, perform interactive recognition during the player's play process, handle exceptions during the play process, and is also used to update VR projects.

[0010] In combination with the first aspect, further, the VR management background includes a VR device management subsystem, a game setting subsystem, a play path planning subsystem, an interactive recognition subsystem, an exception handling subsystem, and a project update subsystem; the VR device management subsystem is used to enter information of all VR devices in the venue and manage VR device data; the game setting subsystem is used to import the venue map and VR game content, and set game boundaries, game effects, and game rules; the play path planning subsystem is used to dynamically plan play paths according to player play data using improved A*, social force model, and Q-Learning algorithms; the interactive recognition subsystem is used to identify the position and posture of VR devices in space using ArUco marker technology, detect changes in the field of view angle caused by the movement of the player's head and body using 6DoF tracking technology, and identify changes in the player's hand bones using gesture recognition technology to trigger preset gesture events; the project update subsystem is used to update VR game projects.

[0011] In combination with the first aspect, further, the project update subsystem updates VR game projects, including:

[0012] Obtain a new VR game project package through the project update subsystem and automatically review the data in the project package;

[0013] If the review passes, generate a version list through the project update subsystem and push the project package to some VR devices in the venue;

[0014] In response to the project package installation status of VR devices, push the project package to all VR devices in batches through the project update subsystem to complete project hot update;

[0015] If the review fails, return an error report to the developer through the project update subsystem.

[0016] In the second aspect, the present invention provides a dynamic path planning method for a VR large space collaborative management platform, including the following steps:

[0017] Obtain VR large space venue information and player real-time play data through the VR large space collaborative management platform;

[0018] Discretize the venue into a grid map according to the VR large space venue information;

[0019] In the grid map, generate an initial play path using the improved A* algorithm according to the player real-time play data;

[0020] Execute the play path and periodically detect path conflict situations;

[0021] When a path conflict is detected, fine-tune the play path based on the social force model;

[0022] If there is still a path conflict after fine-tuning, use the improved A* algorithm to perform incremental optimization on the play path.

[0023] Combined with the second aspect, further, the heuristic function of the improved A* algorithm is:

[0024] (3)

[0025] where h(n) represents the heuristic function of the improved A* algorithm, represents the heuristic function of the traditional A* algorithm, represents the influence factor of the player density around node n, represents the load penalty term of the VR device associated with node n, is the crowd density adjustment factor, is the device load penalty weight;

[0026] The calculation formula of is:

[0027] (4)

[0028] (5)

[0029] where, represents the player density of the grid where node n is located, is the density threshold, q is the smoothing coefficient, represents the number of players in the grid where node n is located, represents the area of a single grid in the grid map;

[0030] The calculation formula of is:

[0031] (6)

[0032] where Load(n) represents the computational load cost of the VR device associated with the node, represents the GPU utilization rate of node n.

[0033] Combined with the second aspect, further, use the cost function to train and optimize the improved A* algorithm, and the expression of the cost function is:

[0034] C(n) = α•D(n) + β•Crowd(n) + γ•Load(n) (8)

[0035] Among them, C(n) represents the cost function of the nth node, α is the path distance weight, D(n) represents the geometric distance cost from the current node n to the target point, β is the crowd density weight, Crowd(n) represents the player density cost around node n, and γ is the device load weight;

[0036] The calculation formula of D(n) is as follows:

[0037] (9)

[0038] Among them, represents the Euclidean distance from the current node n to node G, and D max represents the maximum diagonal distance in the VR large space venue;

[0039] The calculation formula of Crowd(n) is as follows:

[0040] (10)

[0041] Among them, N is the total number of players in the current venue, represents the Euclidean distance from the current node n to the ith player and represents the real-time coordinates of the ith player, is the density influence radius;

[0042] The calculation formula of Load(n) is as follows:

[0043] (11)

[0044] Among them, M represents the number of VR devices associated with node n, GPUUtil m represents the GPU utilization rate of the mth VR device, and NetLatency m represents the network latency of the mth VR device, and L max represents the maximum allowable latency.

[0045] Combined with the second aspect, further, when a new obstacle appears within 5m around the current path node or the crowd density change exceeds the preset change threshold, it is regarded as a path conflict, and the play path is fine-tuned based on the social force model. The adjusted play path is:

[0046] (12)

[0047] Among them, ([[]] , ) represents the adjusted path, represents the obstacle avoidance force in the x direction, represents the obstacle avoidance force in the y direction, is the adjustment coefficient;

[0048] The calculation formula of the obstacle avoidance force vector is:

[0049] (13)

[0050] where, represents the obstacle avoidance force vector, A is the force intensity coefficient, represents the expected safety distance between player i and player j, represents the actual distance between player i and player j, B is the attenuation speed parameter, is the unit direction vector.

[0051] Combined with the second aspect, further, if there are still path conflicts after fine-tuning, the improved A* algorithm is used to incrementally optimize the playing path, including:

[0052] Retain 90% of the playing path, and use the improved A* algorithm to re-plan 10% of the playing path related to the conflict position.

[0053] Combined with the second aspect, further, during the path execution process, cubic spline interpolation is used for path smoothing.

[0054] Combined with the second aspect, further, after each playing path is executed, the data related to the playing path is saved, and the Q-Learning algorithm is used for data optimization to learn data features.

[0055] Compared with the prior art, the beneficial effects achieved by the present invention:

[0056] The present invention proposes a VR large-space collaborative management platform and its dynamic path planning method. The platform can simultaneously realize functions such as VR project management, VR device management, ticket management, and venue control, improve the adaptability of the VR management platform, and at the same time manage the player's playing process through technologies such as automatic path planning and gesture recognition, improving the player's playing experience. The dynamic path planning method proposed by the present invention can realize playing path planning and multi-dimensional optimization, reduce the path planning calculation amount, shorten the path planning time-consuming, improve the rationality and intelligence of path planning, avoid path conflict problems when multiple groups of players play simultaneously, effectively improve the large-space utilization rate, and improve the venue operation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Shown is a schematic structural diagram of a VR large-space collaborative management platform provided by the present invention;

[0058] Figure 2 The following is a schematic diagram of the play path planning of the VR management background in the embodiment of the present invention;

[0059] Figure 3 The following is a schematic diagram of the process of hot update by the project update subsystem in the embodiment of the present invention;

[0060] Figure 4 The following is a schematic diagram of the global overview page of the VR management background in the embodiment of the present invention;

[0061] Figure 5 The following is a schematic diagram of the steps of a dynamic path planning method for a VR large space collaborative management platform provided by the present invention. Detailed implementation manners

[0062] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. Without conflict, the technical features in the embodiments of the present invention and the embodiments can be combined with each other.

[0063] Embodiment 1

[0064] This embodiment introduces a VR large space collaborative management platform, as Figure 1 shown, mainly including a ticket checking terminal, a on-site control terminal and a VR management background.

[0065] The ticket checking terminal is mainly used to connect to a third-party ticketing system for ticket management. At the same time, the ticket checking terminal is also used for player account management and team setting.

[0066] After the ticket checking terminal completes ticket checking, it automatically generates a player account corresponding to the ticket information. Players can modify the information in the player account through the ticket checking terminal, customize the player ID, virtual image, etc., support customizing the face, clothing, and generating a virtual form using photos, etc., so that players have a unique virtual image in the game.

[0067] In the present invention, players can play alone or in a team. A team composed of multiple player accounts is established through the ticket checking terminal, and information such as the team name, team color, and team leader can be customized. During the subsequent play process, both the on-site control terminal and the VR management background can view the player account information and team information.

[0068] The on-site control terminal is used for on-site staff to manage the game processes of players and teams. After receiving the player account information and team information from the ticket-checking terminal, the on-site control terminal automatically assigns VR devices to players. After the players wear the VR devices and are ready, the game officially starts through the operation of the on-site control terminal. When a player's VR device fails or other abnormalities occur, the on-site control terminal displays the specific faulty device and warning information, and on-site staff assist the players to quickly replace the normal device or handle the abnormality and then continue the game.

[0069] The VR management background is used for global management and control, including but not limited to the VR device management subsystem, game setting subsystem, play path planning subsystem, interaction recognition subsystem, exception handling subsystem, project update subsystem, operation analysis subsystem, and global overview page.

[0070] The VR device management subsystem is used to uniformly input the information of all VR devices in the venue into the VR management background, manage the VR device data, and at the same time support the quick access of new devices.

[0071] The game setting subsystem is used to import the venue map and VR game content, and set game-related parameters such as game boundaries, game effects, and game rules.

[0072] The play path planning subsystem is used to dynamically plan the play path according to the player play data. The play path planning subsystem reads the data from the ticket-checking terminal and the on-site control terminal in real time, combines the pre-set game parameters, automatically plans the play path for the players, and dynamically predicts and generates the next level area in real time to avoid overlap of the play paths of different players within the routine, resulting in blockage or conflict, as Figure 2 shown. By dynamically managing the path, the utilization rate of the venue space is improved, and the high-efficiency operation of the venue is realized. In addition, after each group of players enters the venue, the next group of players can enter immediately. By dynamically planning the path, the distance between different players is adjusted, so that the experience content of each group of players is independent of each other.

[0073] In the embodiment of the present invention, the play path planning subsystem uses technologies such as improved A* algorithm, social force model, Q-Learning algorithm, etc. for multi-dimensional path planning and path optimization.

[0074] The exception handling subsystem is used to alarm and handle exceptions during the player's play process, including VR device failure alarm, play path conflict alarm, player exception alarm, etc. When a player leaves the safe area, the exception handling subsystem generates a warning message and feeds it back to the on-site control terminal and the VR device terminal.

[0075] The interactive recognition subsystem is used to monitor the player's gameplay in real time. Using the ArUco marker technology as a spatial anchor, it determines the position and orientation of the VR device in space. It uses 6DoF tracking technology to detect the change in the field of view angle caused by the player's head rotation, and can also detect the change in the displacement in the up-down, front-back, left-right directions caused by the player's body movement. It interacts with the player through gesture recognition and somatosensory interaction to provide the player with a better immersive experience and interactive experience.

[0076] In the embodiment of the present invention, the development environment is PICO 4 all-in-one machine development (Android), and the relevant PICO UE5-SDK is adopted. According to the Open XR standard, the hand bones are divided to obtain the Transform of each bone body of both hands. When the hand gesture approaches a certain specific gesture, a gesture event is triggered. In a VR project, when zooming in or out on an object, the pinch gesture is recognized. After both hands are pinched, the distance between the two hands is judged. If the distance becomes longer, it is defined as zooming in, and if the distance becomes shorter, it is defined as zooming out.

[0077] The project update subsystem supports the hot update of VR projects. When the existing game projects in the VR game venue need to be updated or new game projects need to be connected to the system, the hot update is carried out through the project update subsystem. As Figure 3 shown, the developer uploads the new project package to the project update subsystem. The project update subsystem automatically audits the data in the project package. If the audit is passed, a version list is generated, and a pre-release package is pushed to some VR devices in the venue. In response to the project package installation status of the VR device, the project update subsystem then pushes the full package in batches to complete the project hot update. If the project update subsystem fails to audit the project package, an error report is returned to the developer.

[0078] The global overview page sorts out and displays the player's gameplay process data, and provides an overview of the gameplay of each player and team in the venue on one screen. As Figure 4 shown, the global overview page includes a team dashboard and a venue dashboard. The team dashboard can display dynamic data such as team progress, gameplay time, equipment usage, and abnormal alarms. The venue dashboard visually presents dynamic data such as the venue map, team gameplay paths, player positions, and abnormal alarms.

[0079] The operation analysis subsystem is used to analyze the ticket inspection data, player gameplay data, etc., to obtain the operation analysis report of the current venue. The operation analysis report includes multi-dimensional revenue statistics, revenue trend analysis, traffic density analysis, traffic structure analysis, etc.

[0080] The platform architecture of the present invention adopts an independent teaming server. The VR device of each player is regarded as a terminal. The terminals of a team are uniformly connected to an independent teaming server, and multiple independent teaming servers are connected to a server cluster. The server cluster conducts data interaction with the ticket checking end, the on-site control end, and the VR management background.

[0081] The collaborative management platform of the present invention adopts Lic authorization, supports online and offline control, and supports custom function permissions and authorization validity periods.

[0082] Embodiment 2

[0083] Based on the management platform introduced in Embodiment 1, this embodiment introduces a dynamic path planning method for a VR large-space collaborative management platform, as Figure 5 shown, including the following steps:

[0084] Step A: Obtain VR large-space venue information and real-time player gameplay data through the VR large-space collaborative management platform. The real-time player gameplay data includes the real-time coordinates of the player, the player's game progress, etc.

[0085] Step B: Discretize the venue into a grid map according to the VR large-space venue information.

[0086] The formula for grid discretization is as follows:

[0087] (1)

[0088] Formula (1) can convert the continuous physical space into a discrete grid map, which is beneficial to reducing the computational complexity and can also retain sufficient accuracy.

[0089] The grid resolution can be changed according to project requirements. In the embodiment of the present invention, the grid resolution is 0.5 m.

[0090] In the grid map, the levels or key coordinate points on the gameplay path are used as nodes on the grid map. The node is defined as n = (x, y, t) ∈ Z3, where x and y represent the coordinates of node n in the discretized grid map (unit: meter), and t is the time stamp (unit: second), which is used for time dimension modeling of the dynamic scene.

[0091] Step C: Generate an initial gameplay path according to the real-time player gameplay data by using an improved A* algorithm.

[0092] In the traditional A* algorithm, the estimated minimum cost from the current node n to the target node G, is calculated by the following formula:

[0093] (2)

[0094] where, wd represents the distance weight coefficient, with a value range of 0.8 - 1.2, and generally defaulting to 1.0; represents the Euclidean distance from the current node n to the target node G; is the abscissa of node n, is the ordinate of node n, is the abscissa of the target point G, is the ordinate of the target point G.

[0095] When there are few obstacles in the site, the traditional A* algorithm uses the Euclidean distance for calculation, with a small amount of calculation. When the site terrain is complex, the traditional A* algorithm uses the Manhattan distance for calculation, and the path feasibility is higher.

[0096] The present invention improves the traditional A* algorithm to obtain a new incremental PE - A* algorithm, and the calculation formula of its heuristic function is:

[0097] (3)

[0098] where h(n) represents the heuristic function of the improved A* algorithm, represents the heuristic function of the traditional A* algorithm, also known as the basic heuristic function, represents the influence factor of the player density around node n, represents the load penalty term of the VR device associated with node n, is the crowd density adjustment factor, is the device load penalty weight. Among them, the area around node n is defined as the grid area where node n is located or the area centered on node n with a preset distance as the radius. In the embodiment of the present invention, the grid area where node n is located is adopted.

[0099] The normalized influence coefficient reflecting the crowd density around node n has the following calculation formula:

[0100] (4)

[0101] (5)

[0102] Among them, represents the player density of the grid where node n is located; is the density threshold, that is, the critical value for triggering avoidance; q is the smoothing coefficient, used to control the steepness of the function; represents the number of players in the grid where node n is located, represents the area of a single grid in the grid map.

[0103] Generally = 1.2 people / ㎡ When sudden gatherings are detected, it is automatically lowered to 0.8

[0104] The calculation formula is as follows:

[0105] (6)

[0106] Among them, Load(n) represents the cost of the computing load (such as GPU utilization rate, network latency) of the node associated with the VR device, represents the GPU utilization rate of node n.

[0107] When the device load exceeds 70%, the cost increases non-linearly to force balanced resource allocation.

[0108] In the improved A* algorithm of the present invention, the parameter σ is adjusted according to the hourly cycle. During the daytime peak period, the weight of the crowd influence is increased to improve the time sensitivity of the model, so as to plan a more reasonable play path. At the same time, when the GPU utilization rate of the node > 70%, the load penalty term is activated, so as to better balance the node resources when planning the path.

[0109] Under the same test scenario, the effects of path planning by the traditional A* algorithm and the improved A* algorithm of the present invention are compared as shown in Table 1:

[0110] Table 1 Comparison of the effects of the traditional A* algorithm and the improved A* algorithm

[0111] ;

[0112] The initial play path planned by the improved A* algorithm is a discrete grid path. In order to improve the naturalness of the VR movement experience, the present invention smooths the discrete grid path through a Bezier curve, so as to convert the discrete path points into a continuous smooth curve. The formula of the Bezier curve is:

[0113] (7)

[0114] Among them, B(t) represents the smooth play path, represents the k-th intermediate quantity, The value range of is [0,1],[[ID=4s]] represents the coordinate of the k-th node in the initial play path planned by the improved A* algorithm.

[0115] The present invention expands the path planning problem from single-distance optimization to multi-objective collaborative optimization, uses the three key factors of balanced path, crowd density, and device load as optimization objectives, and constructs the cost function of the dynamic path planning model. The formula is as follows:

[0116] C(n) = α•D(n) + β•Crowd(n) + γ•Load(n) (8)

[0117] Among them, C(n) represents the cost function of the nth node, α is the path distance weight, D(n) represents the geometric distance cost from the current node n to the next node, β is the crowd density weight, Crowd(n) represents the player density cost around the node calculated based on the real-time heat map, γ is the device load weight, and Load(n) represents the calculation load (such as GPU utilization, network latency) cost of the VR device associated with the node.

[0118] The present invention trains and optimizes the improved A* algorithm through formula (8), comprehensively considering the path length, path congestion degree, and device resource allocation, and making the path length of the play path predicted by the model as short as possible, avoiding path congestion, and balancing resource allocation.

[0119] The calculation formula of D(n) is as follows:

[0120] (9)

[0121] Among them, G represents the target point, represents the Euclidean distance from the current node n to the node G, D max represents the maximum diagonal distance (normalization factor) in the VR large space venue.

[0122] The calculation formula of Crowd(n) is as follows:

[0123] (10)

[0124] Among them, N is the total number of players in the current venue, represents the Euclidean distance from the current node n to the ith player of, represents the real-time coordinates of the ith player, is the density influence radius. In the embodiment of the present invention, = 3 meters.

[0125] The calculation formula of Load(n) is as follows:

[0126] (11)

[0127] Among them, M represents the number of VR devices associated with the node n, GPUUtil m represents the GPU utilization rate of the mth device, NetLatency m represents the network latency of the mth device, L max represents the maximum allowable latency (normalization factor).

[0128] In the present invention, the weight coefficients α + β + γ = 1 (α, β, γ ∈ [0, 1]). The weight system is configured differently in different usage scenarios. For high-density offline entertainment scenarios: α + β + γ -> 0.4 + 0.5 + 0.1. For low-density industrial simulation scenarios: α + β + γ -> 0.5 + 0.3 + 0.2.

[0129] Step D: Execute the play path. When a path conflict is detected, the play path is dynamically adjusted in real time based on the social force model.

[0130] When a new obstacle or a sudden change in crowd density (>15% change) is detected within 5 m around the current path node, the present invention uses the social force model to calculate the obstacle avoidance force, and then adjusts the local path according to the obstacle avoidance force. Its core function is to mathematically model the crowd interaction behavior, guiding the path planning algorithm to actively avoid potential conflicts and prevent collisions between the play paths of different players.

[0131] The social force model dynamically adjusts the play path in real time. The adjusted path is:

[0132] (12)

[0133] (17) , ) represents the adjusted path, represents the obstacle avoidance force in the x direction, represents the obstacle avoidance force in the y direction, is the adjustment coefficient, = 0.1.

[0134] The formula for calculating the obstacle avoidance force of the social force model is as follows:

[0135] (13)

[0136] Among them, represents the obstacle avoidance force vector, the avoidance force generated by an individual due to surrounding pedestrians or obstacles, and its direction is the direction away from the threat; A is the force intensity coefficient, and the larger the value, the more sensitive it is to threats at close range; represents the expected safe distance between player i and player j; represents the actual distance between player i and player j; B is the attenuation speed parameter, and the smaller the value, the more rapidly the force increases at close range; is the unit direction vector.

[0137] When calculating , in formula (11) 、 calculates the distance in the x direction. When calculating formula (11) , The calculated distance is in the y - direction.

[0138] According to formula (7), when < happens, the exponential term exp(⋅) generates a non - linear growth avoidance force, which can simulate human emergency avoidance behavior.

[0139] Through experimental calibration, when parameters A = 2.0 and B = 0.3, the sensitivity and stability can be well balanced.

[0140] Step D fine - tunes the path points along the obstacle avoidance force direction with a small step size to achieve smooth avoidance and avoid VR motion sickness caused by path mutations. Compared with modifying the play path using the improved A* algorithm after an anomaly is detected, fine - tuning the path using the social force model can greatly reduce the computational load. According to experiments, it only requires 3% - 5% of the computational load of the improved A* algorithm.

[0141] Step E: Determine whether there are still conflicts in the play path. If there are still path conflicts, use the improved A* algorithm to perform incremental optimization on the play path.

[0142] Retain 90% of the play path, and re - plan the remaining 10% of the play path related to the conflict, which can solve the path conflict problem while reducing the computational load.

[0143] In the embodiment of the present invention, the crowd density around the play path (within a preset distance) can be calculated through a heat map, and combined with the VR device load to determine whether the player will conflict with obstacles or other players in the venue during the play process, so as to determine whether to adjust the path.

[0144] In the embodiment of the present invention, when the cumulative number of fine - tuning (step D) exceeds 3 times and the conflict is still not eliminated, incremental optimization can be performed through step E.

[0145] Step F: During the path execution process, the present invention uses cubic spline interpolation for path smoothing to generate a smooth movement trajectory, ensuring that the VR device motion is continuously differentiable and avoiding sudden acceleration changes.

[0146] In addition, the present invention can also perform hierarchical warnings on the situation where the player deviates from the play path. According to the player's real - time coordinates and the planned play path, calculate the distance that the player deviates from the play path. When it exceeds the preset deviation threshold, warnings are given on the VR device side, the on - site control side, and the VR management background. There can be multiple preset deviation thresholds, and different warning contents are set according to different deviation thresholds. For example, when the deviation exceeds 0.5m, the player is warned of deviating from the path, and when the deviation exceeds 1m, the player is warned to pause the game, etc.

[0147] Step G: After each execution of the play path, save the data related to the play path, and adopt the Q-Learning algorithm to optimize the data, fully learn the data features, so as to further optimize the path planning effect.

[0148] The update rule of the Q-Learning algorithm is:

[0149] (14)

[0150] Wherein, represents the Q value corresponding to state s and action a, s represents the state, that is, the information set of the current environment of the system, and a represents the action, that is, the operation that the agent can perform in the current state during the deep learning process. is the learning factor, R is the reward function, which is a feedback signal used to quantify the quality of actions. is the discount factor. represents the Q value corresponding to the new state s’ and the new action a’.

[0151] In the embodiment of the present invention, the reward function is used to guide the algorithm to balance conflict avoidance (+10), load balancing (+5) and path efficiency (-0.3 × length).

[0152] According to the priority, replay the experience samples with high prediction errors first to accelerate the learning of key scenarios (such as sudden gatherings and equipment failures). The expression of the priority is:

[0153] (15)

[0154] In summary, the collaborative management platform of the present invention adapts the same game content to different areas, shapes, and obstacle conditions without additional development; provides the ability to enter at any time, maximizes the operation efficiency, and enables users not to wait for sessions and peak devices not to be idle; realizes the dynamic path planning of the player's play path, ensures that different contents achieve high floor efficiency operation in the current venue; timely alarms for abnormal situations during the play process, which is beneficial to improving the operation stability; improves the game interaction experience through designs such as gesture recognition and somatosensory interaction.

[0155] The dynamic path planning method proposed by the present invention can achieve multi-dimensional optimization, reduce the calculation amount of path planning, shorten the time-consuming of path planning, improve the rationality and intelligence of path planning, avoid path conflict problems when multiple groups of players play at the same time, effectively improve the utilization rate of large spaces, and improve the operation efficiency of the venue.

[0156] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative rather than restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the spirit of the present invention and the scope protected by the claims. All of these fall within the protection scope of the present invention.

Claims

1. A VR large-space collaborative management platform, characterized in that Including: The ticket checking terminal is used for ticket management, player account management, and team formation settings. The on-site control terminal is used for automatically allocating VR devices and managing the game processes of players and teams. The VR management background is used for managing VR devices, setting game parameters, planning play paths using improved A*, social force model, and Q-Learning algorithms based on game parameters and player play data, performing interaction recognition during the player's play process, handling exceptions during the play process, and also used for updating VR projects. The heuristic function of the improved A* algorithm is: ； Among them, h(n) represents the heuristic function of the improved A* algorithm, represents the heuristic function of the traditional A* algorithm, represents the influence factor of the player density around node n, represents the load penalty term of the VR device associated with node n, is the crowd density adjustment factor, is the device load penalty weight; The calculation formula is as follows: ； ； Among them, represents the player density of the grid where node n is located, is the density threshold, q is the smoothing coefficient, represents the number of players in the grid where node n is located, represents the area of a single grid in the grid map; The calculation formula is as follows: ； Among them, Load(n) represents the computational load cost of the node associated with the VR device, represents the GPU utilization rate of node n; Based on the social force model, fine-tune the play path, and the adjusted play path is: ； Among them, ( , ) represents the adjusted path, represents the obstacle avoidance force in the x direction, represents the obstacle avoidance force in the y direction, is the adjustment coefficient; The calculation formula for the obstacle avoidance force vector is: ； Among them, represents the obstacle avoidance force vector, A is the force intensity coefficient, represents the expected safe distance between player i and player j, represents the actual distance between player i and player j, B is the attenuation speed parameter, is the unit direction vector.

2. The VR large-space collaborative management platform according to claim 1, wherein, The VR management background includes a VR device management subsystem, a game setting subsystem, a play path planning subsystem, an interaction recognition subsystem, an exception handling subsystem, and a project update subsystem; the VR device management subsystem is used for entering the information of all VR devices in the venue and managing VR device data; the game setting subsystem is used for importing the venue map and VR game content, setting game boundaries, game effects, and game rules; the play path planning subsystem is used for dynamically planning play paths according to player play data using improved A*, social force model, and Q-Learning algorithms. The interaction recognition subsystem is used for identifying the position and posture of VR devices in space using ArUco marker technology, detecting the change in the field of view angle caused by the movement of the player's head and body using 6DoF tracking technology, and identifying the change in the player's hand bones using gesture recognition technology to trigger preset gesture events. The project update subsystem is used for updating VR game projects.

3. The VR large-space collaborative management platform according to claim 2, wherein The project update subsystem updates VR game projects, including: Obtain a new VR game project package through the project update subsystem and automatically review the data in the project package. If the review passes, generate a version list through the project update subsystem and push the project package to some VR devices in the venue. In response to the project package installation status of VR devices, push the project package to all VR devices in batches through the project update subsystem to complete project hot update. If the review fails, return an error report to the developer through the project update subsystem.

4. A dynamic path planning method for the VR large-space collaborative management platform according to claim 2, characterized in that Including the following steps: Obtain VR large space venue information and player real-time play data through the VR large space collaborative management platform. Discretize the venue into a grid map according to the VR large space venue information. In the grid map, generate an initial play path using the improved A* algorithm according to the player real-time play data. Execute the play path and periodically detect path conflict situations. When a path conflict is detected, fine-tune the play path based on the social force model. If there is still a path conflict after fine-tuning, perform incremental optimization on the play path using the improved A* algorithm.

5. The dynamic path planning method of the VR large-space collaborative management platform according to claim 4, characterized in that The heuristic function of the improved A* algorithm is: ； Among them, h(n) represents the heuristic function of the improved A* algorithm, represents the heuristic function of the traditional A* algorithm, represents the influence factor of the player density around node n, represents the load penalty term of the VR device associated with node n, is the crowd density adjustment factor, is the device load penalty weight; The calculation formula is as follows: ； ； Among them, represents the player density of the grid where node n is located, is the density threshold, q is the smoothing coefficient, represents the number of players in the grid where node n is located, represents the area of a single grid in the grid map; The calculation formula is as follows: ； Among them, Load(n) represents the computational load cost of the node associated with the VR device, represents the GPU utilization rate of node n.

6. The dynamic path planning method of the VR large-space collaborative management platform according to claim 4, wherein Train and optimize the improved A* algorithm using the cost function, and the expression of the cost function is: C(n) = α•D(n) + β•Crowd(n) + γ•Load(n); Among them, C(n) represents the cost function of the nth node, α is the path distance weight, D(n) represents the geometric distance cost from the current node n to the target point, β is the crowd density weight, Crowd(n) represents the cost of the player density around node n, and γ is the device load weight; The calculation formula of D(n) is as follows: ； Among them, represents the Euclidean distance from the current node n to node G, D max represents the maximum diagonal distance in the VR large space venue; The calculation formula of Crowd(n) is as follows: ； where N is the total number of players in the current venue, represents the Euclidean distance from the current node n to the i-th player ; represents the real-time coordinate of the i-th player, and is the density influence radius; The calculation formula of Load(n) is as follows: ； Among them, M represents the number of VR devices associated with node n, and GPUUtil m represents the GPU utilization rate of the m-th VR device, and NetLatency m represents the network latency of the m-th VR device, and L max represents the maximum allowable latency.

7. The dynamic path planning method of the VR large-space collaborative management platform according to claim 4, characterized in that When a new obstacle appears within 5m around the current path node or the crowd density change exceeds the preset change threshold, it is regarded as a path conflict. Based on the social force model, the playing path is fine-tuned. The adjusted playing path is: ； Among them, ( , ) represents the adjusted path, represents the obstacle avoidance force in the x direction, represents the obstacle avoidance force in the y direction, is the adjustment coefficient; The calculation formula of the obstacle avoidance force vector is: ； Among them, represents the obstacle avoidance force vector, A is the force intensity coefficient, represents the expected safe distance between player i and player j, represents the actual distance between player i and player j, B is the attenuation speed parameter, is the unit direction vector.

8. The dynamic path planning method of the VR large-space collaborative management platform according to claim 4, characterized in that If there is still a path conflict after fine-tuning, the improved A* algorithm is used to incrementally optimize the playing path, including: 90% of the playing path is retained, and the improved A* algorithm is used to re-plan 10% of the playing path related to the conflict location.

9. The dynamic path planning method of the VR large-space collaborative management platform according to claim 4, wherein, During the path execution process, cubic spline interpolation is used for path smoothing.

10. The dynamic path planning method of the VR large-space collaborative management platform according to claim 4, wherein After each execution of the playing path is completed, the data related to the playing path is saved, and the Q-Learning algorithm is used for data optimization.

Images