A track map system for automatic map generation and intelligent path division

Abstract

The present invention provides a track map system for map self-generation and intelligent path division, and the system includes: a map generation module, configured to obtain target route data and transmit the target route data to a preset system for deployment to generate an online track map; a synchronization module, configured to obtain map features of the online track map and deploy them in a target offline area based on the map features to determine an offline race path; a path division module, configured to automatically divide a target track in the online track map according to the participation level of a rider, and at the same time, select a target mode for the race based on the target track. By generating an online track map and extracting map features, it is beneficial to accurately synchronously generate an offline race path, realize the synchronization and same frequency of online and offline in a cycling race, and further improve the versatility and experience of the game.

Description

Technical Field

[0001] The present invention relates to the technical field of cycling track communication, and particularly to a track map system for map self-generation and intelligent path division. Background Art

[0002] At present, mountain bike riding has different degrees of attraction to people. Users can achieve the purpose of fitness through riding, and can make riding more interesting through riding competitions, changing the previous boring exercise mode;

[0003] However, nowadays, mountain bike riding competitions often only have the offline competition mode, and do not achieve synchronous and same-frequency online and offline games, that is, what maps, tracks and upgrade modes are available in the online game can be actually experienced offline. In order to achieve online and offline matching, the present invention provides a track map system for map self-generation and intelligent path division. Summary of the Invention

[0004] The present invention provides a track map system for map self-generation and intelligent path division, which is used to generate an online track map and extract map features, so as to facilitate the accurate synchronous generation of the offline track path, realize the synchronous and same-frequency online and offline in the riding competition, and further improve the versatility and experience of the game.

[0005] The present invention provides a track map system for map self-generation and intelligent path division, including:

[0006] A map generation module, configured to obtain target route data and transmit the target route data to a preset system for deployment to generate an online track map;

[0007] A synchronization module, configured to obtain the map features of the online track map and deploy them in the offline target area based on the map features to determine the offline track path;

[0008] A path division module, configured to automatically divide a target track in the online track map according to the participation level of the rider, and at the same time, select a target mode for the competition based on the target track.

[0009] Preferably, a track map system for map self-generation and intelligent path division includes:

[0010] The target mode includes: an online mobile game mode, a VR virtual scene mode, and an offline competition mode;

[0011] Among them, the VR virtual scene mode only includes straight tracks;

[0012] Among them, when the target mode selected by the rider is the online mobile game mode or the VR virtual scene mode, an online track map is selected;

[0013] When the target mode selected by the rider is the offline competition mode, the offline competition path is selected for the competition.

[0014] Preferably, a track map system with map self-generation and intelligent path division, the map generation module includes:

[0015] A route reading unit for reading the target route and obtaining the route identification points of the target route;

[0016] The route reading unit is further configured to determine the curvature of the target route and the direction represented by the target route based on the route identification points;

[0017] A data generation unit for determining the characteristics of the target route based on the curvature of the target route and the direction represented by the target route, and generating corresponding target route data based on the characteristics of the target route.

[0018] Preferably, a track map system with map self-generation and intelligent path division, the path division module includes:

[0019] A rider confirmation unit for obtaining the game account of the rider and determining the identity information of the rider based on the game account;

[0020] A level confirmation unit for reading the identity information of the rider and determining the riding level of the rider based on the reading result;

[0021] A matching unit for matching the riding level with the difficulty level of the tracks in the online track map, and automatically dividing the target track according to the matching result.

[0022] Preferably, a track map system with map self-generation and intelligent path division, the synchronization module includes:

[0023] A detection unit for real-time detecting the operation data of the online mobile game mode;

[0024] A data comparison unit for comparing the operation data with the original data to determine whether the online mobile game mode has been updated;

[0025] Among them, when the operation data is consistent with the original data, it is determined that the online mobile game mode has not been updated;

[0026] Otherwise, it is determined that the online mobile game mode has been updated;

[0027] A data analysis unit, configured to determine mode update features according to the operation data when the online mobile game mode is updated;

[0028] An offline synchronization unit, configured to perform synchronous update offline according to the mode update features.

[0029] Preferably, a track map system for map self-generation and intelligent path division, the synchronization module includes:

[0030] A map reading unit, configured to determine a map reading instruction for reading the online track map, and read the online track map based on the map reading instruction to determine the map data of the online track map;

[0031] A data analysis unit, configured to identify the map data, determine the data identifier of the map data, and analyze the data identifier to determine the map feature points of the online track map;

[0032] The map reading unit is further configured to read the map feature points, determine the distribution positions of each map feature point on the online track map, and at the same time, determine the attributes of each map feature point;

[0033] A labeling unit, configured to determine the weight value of each map feature point according to the distribution position of each map feature point on the online track map and the attributes of each map feature point, and at the same time, label the map feature points in descending order of the weight value;

[0034] A map contour confirmation unit, configured to create a map sheet of the online track map according to the labeling result, and determine the contour of the online track map based on the map sheet of the online track;

[0035] An offline map data acquisition unit, configured to determine the range of the online track based on the contour of the online track map, and at the same time, determine the scale between the online track map and the offline actual map, and generate path data of the corresponding offline track path according to the scale;

[0036] A data reading unit, configured to read the path data of the offline track path, extract the data distribution characteristics of the path data, and generate a path deployment plan according to the data distribution characteristics;

[0037] An offline track path generation unit, configured to generate an offline track path based on the path deployment plan.

[0038] Preferably, a track map system for map self-generation and intelligent path division, the offline track path generation unit further includes:

[0039] A path testing unit for performing path safety testing on the offline race path and obtaining safety test data;

[0040] A data processing unit for comparing the safety test data with a preset safety range, determining data in the safety test data that exceeds the preset safety range, and taking the data that exceeds the preset safety range as potential safety data;

[0041] A position confirmation unit for reading the potential safety data, determining the position of the potential safety data in the offline race path based on the reading result, and taking the position as the sensitive position;

[0042] A safety monitoring setting unit for setting safety detection points and an automatic alarm system at the sensitive position, and automatically alarming according to the automatic alarm system when a safety accident occurs to a rider at the sensitive position.

[0043] Preferably, a track map system for map self-generation and intelligent path division, the map generation module further includes:

[0044] A data receiving unit for receiving the obtained target route data and determining the characteristic information of the target route data, where the target route is at least two;

[0045] A data cleaning unit for selecting a target data cleaning rule from a preset data cleaning rule library based on the characteristic information, and analyzing the target route data based on the target data cleaning rule to obtain a group of data to be cleaned corresponding to the target route data, where the group of data to be cleaned is used to represent abnormal data with missing values in the target route data, and there is at least one target route data with a missing value in the group of data to be cleaned;

[0046] The data cleaning unit is further configured to construct a neural network model, divide the group of data to be cleaned into a test data set and a training data set, train the neural network model based on the training data set, and perform testing based on the test data set after the training is completed to obtain the processing accuracy rate of the neural network model;

[0047] Compare the processing accuracy rate with a preset processing accuracy rate;

[0048] If the processing accuracy rate is less than the preset processing accuracy rate, it is determined that the training of the neural network model is unqualified, and the neural network model is retrained;

[0049] Otherwise, it is determined that the training of the neural network model is qualified, and the abnormal data with missing values in the target route data is filled based on the trained neural network model to obtain standard target route data;

[0050] A data classification unit for clustering the standard target route data based on a preset number of clusters and classifying the standard target route data based on the clustering result, where the classification result includes slope data, straight-line data, low-lying data, curvature data, and flatness data;

[0051] A map generation unit for obtaining the classification result of the standard target route data and storing the standard target route data corresponding to each category into the corresponding target layer based on the classification result;

[0052] The map generation unit is used to determine the geographical information of the standard target route data in each target layer and determine the proportional conversion coefficient between the geographical information of the to-be-generated online track map and the standard target route data;

[0053] The map generation unit is further used to determine the feature points in the geographical information of the target route data and determine the actual coordinates of the feature points on the to-be-generated online track map based on the proportional conversion coefficient, where there are multiple feature points;

[0054] The map generation unit is further used to generate a sub-online track map corresponding to each target layer based on the actual coordinates of the feature points on the to-be-generated online track map and merge the sub-online track maps corresponding to each target layer to obtain an online track map.

[0055] Preferably, a track map system for map self-generation and path intelligent division, the map generation unit includes:

[0056] A map acquisition unit for acquiring the generated online track map and performing simulation tests on the generated online track map based on the online mobile game mode and the VR virtual scene mode to obtain corresponding test data;

[0057] A data analysis unit for analyzing and processing the test data to determine the completion degree of the user for the online track map based on the online mobile game mode and the VR virtual scene mode and the accident rate during the game process;

[0058] A data comparison unit for comparing the completion degree and the accident rate during the game process with a first preset threshold and a second preset threshold respectively;

[0059] If the completion degree is less than the first preset threshold or the accident rate occurring during the game is greater than the second preset threshold, it is determined that there are defects in the generated online track map, and the online track map is redeployed based on the target route data until the completion degree is greater than or equal to the first preset threshold and the accident rate occurring during the game is less than or equal to the second preset threshold;

[0060] Otherwise, it is determined that the generated online track map is qualified, and the test of the online track map is completed.

[0061] Preferably, a track map system for map self-generation and path intelligent division, the map generation unit further includes:

[0062] A map receiving unit, configured to receive the generated online track map and decompose the online track map into M map blocks according to a preset ratio;

[0063] An identification marking unit, configured to set a unique identifier for each of the M map blocks, where the identifier is used to mark the position of each map block in the online track map;

[0064] A storage unit, configured to compress and store the M map blocks and the corresponding identifiers into a map file, and at the same time, create a read index based on the storage path to complete the storage of the online track map.

[0065] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written specification, claims, and drawings.

[0066] The technical solutions of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0067] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0068] Figure 1 It is a structural diagram of a track map system for map self-generation and path intelligent division in an embodiment of the present invention;

[0069] Figure 2 It is a structural diagram of a map generation module in a track map system for map self-generation and path intelligent division in an embodiment of the present invention;

[0070] Figure 3This is the structural diagram of the path division module in a track map system for map self-generation and intelligent path division in an embodiment of the present invention. Detailed implementation manners

[0071] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for illustrating and explaining the present invention, and are not used to limit the present invention.

[0072] Embodiment 1:

[0073] This embodiment provides a track map system for map self-generation and intelligent path division. As Figure 1 shown, it includes:

[0074] A map generation module, configured to obtain target route data and transmit the target route data to a preset system for deployment to generate an online track map;

[0075] A synchronization module, configured to obtain the map features of the online track map and deploy them in the offline target area based on the map features to determine the offline track path;

[0076] A path division module, configured to automatically divide a target track in the online track map according to the participation level of the rider. At the same time, a target mode is selected based on the target track for the competition.

[0077] In this embodiment, the target modes include: an online mobile game mode, a VR virtual scene mode, and an offline competition mode; among them, the VR virtual scene mode only includes straight tracks; among them, when the target mode selected by the rider is the online mobile game mode or the VR virtual scene mode, the online track map is selected; when the target mode selected by the rider is the offline competition mode, the offline track path is selected for the competition.

[0078] In this embodiment, the target route data may be the route data required to construct the track used in the competition. For example, it may be the slope data, bend data, etc. on the route, and there may be multiple target routes.

[0079] In this embodiment, the preset system is pre-set and is used to generate a corresponding route map according to the route data.

[0080] In this embodiment, the VR mode can increase the difficulty of the obstacle track on the basis of a straight line, such as adding obstacle facilities, such as increasing the slope elevation angle, increasing the number of continuous slopes, a single-plank bridge, a wading track, etc.

[0081] In this embodiment, the map features may be the slope existing in the online track map, the number of turns, the ratio of the map to the actual site, and the outline of the map, etc.

[0082] In this embodiment, the target area can be a venue for building a competition track offline.

[0083] In this embodiment, the target track can be a track suitable for the current rider's competition level. For example, if the rider's competition level is advanced, the selected track is a high-difficulty track.

[0084] The beneficial effects of the above technical solution are: by generating an online track map and extracting map features, it is beneficial to accurately synchronously generate the offline track path, realize the synchronization and same frequency of online and offline in the cycling competition, and further improve the versatility and experience of the game.

[0085] Embodiment 2:

[0086] Based on the above Embodiment 1, this embodiment provides a track map system for map sub-generation and intelligent path division, as Figure 2 shown, the map generation module includes:

[0087] A route reading unit, configured to read the target route and obtain the route identification points of the target route;

[0088] The route reading unit is further configured to determine the curvature of the target route and the direction indicated by the target route based on the route identification points;

[0089] A data generation unit, configured to determine the features of the target route based on the curvature of the target route and the direction indicated by the target route, and generate corresponding target route data based on the features of the target route.

[0090] In this embodiment, the route identification point can be a label for marking route features. For example, the number of turns can determine a track.

[0091] In this embodiment, the direction indicated by the target route can be the extending direction of the target route.

[0092] In this embodiment, the features of the target route can be the width, length of the target route, and the number of turns of the target route, etc.

[0093] The beneficial effects of the above technical solution are: by obtaining the route features and route identification points of the target route to be constructed, it is beneficial to accurately obtain the route data of the target route, thereby realizing the accurate generation of the online track map and providing convenience for accurately synchronously generating the offline track path.

[0094] Embodiment 3:

[0095] Based on the above Embodiment 1, this embodiment provides a track map system for map self-generation and intelligent path division, asFigure 3 As shown in Figure 3 , the path division module includes:

[0096] A rider confirmation unit, configured to obtain the game account of the rider and determine the identity information of the rider based on the game account;

[0097] A level confirmation unit, configured to read the identity information of the rider and determine the riding level of the rider based on the reading result;

[0098] A matching unit, configured to match the riding level with the difficulty level of the track in the online track map and automatically divide the target track according to the matching result.

[0099] In this embodiment, the identity information may be the name, age, gender, etc. of the rider.

[0100] The beneficial effect of the above technical solution is that by obtaining the identity information of the rider and determining the seven-star level of the rider according to the identity information, it is beneficial to accurately match the track with a difficulty level suitable for the rider level, improving the versatility and experience of the game.

[0101] Embodiment 4:

[0102] Based on the above Embodiment 1, this embodiment provides a track map system for automatic map generation and intelligent path division. The synchronization module includes:

[0103] A detection unit, configured to detect the operation data of the online mobile game mode in real time;

[0104] A data comparison unit, configured to compare the operation data with the original data to determine whether the online mobile game mode has been updated;

[0105] Among them, when the operation data is consistent with the original data, it is determined that the online mobile game mode has not been updated;

[0106] Otherwise, it is determined that the online mobile game mode has been updated;

[0107] A data analysis unit, configured to determine the mode update characteristics according to the operation data when the online mobile game mode is updated;

[0108] An offline synchronization unit, configured to perform offline synchronization update according to the mode update characteristics.

[0109] In this embodiment, the operation data may be data generated when the user plays the game through the network mode online.

[0110] In this embodiment, the original data may be the data corresponding to the online track map when the initial map construction is completed.

[0111] In this embodiment, the updated feature may be the obvious feature information in the updated mode. For example, it may be an increase in the number of turns.

[0112] The beneficial effects of the above technical solution are as follows: By detecting the operation data of the online mobile game mode and analyzing the operation data, an accurate judgment can be made on whether the mobile game mode is updated, and synchronous updates are performed offline during the update, improving the user experience.

[0113] Embodiment 5:

[0114] Based on the above Embodiment 1, this embodiment provides a track map system for automatic map generation and intelligent path division. The synchronization module includes:

[0115] A map reading unit for determining a map reading instruction for reading the online track map, and reading the online track map based on the map reading instruction to determine the map data of the online track map;

[0116] A data analysis unit for identifying the map data, determining the data identifier of the map data, and analyzing the data identifier to determine the map feature points of the online track map;

[0117] The map reading unit is further configured to read the map feature points, determine the distribution positions of each map feature point in the online track map, and at the same time, determine the attributes of each map feature point;

[0118] A labeling unit for determining the weight value of each map feature point according to the distribution position of each map feature point in the online track map and the attribute of each map feature point, and at the same time, labeling the map feature points in descending order of the weight value;

[0119] A map contour confirmation unit for creating a map sheet of the online track map according to the labeling result and determining the contour of the online track map based on the map sheet of the online track;

[0120] An offline map data acquisition unit for determining the range of the online track based on the contour of the online track map, determining the scale between the online track map and the offline actual map, and generating the path data of the corresponding offline track path according to the scale;

[0121] A data reading unit for reading the path data of the offline track path, extracting the data distribution characteristics of the path data, and generating a path deployment plan according to the data distribution characteristics;

[0122] An offline race track path generation unit for generating an offline race track path based on the path deployment plan.

[0123] In this embodiment, the map data may be in a data form corresponding to the online race track map.

[0124] In this embodiment, the data identifier may be a kind of label used to mark data. For example, the first type of data may be marked with Arabic numerals, etc.

[0125] In this embodiment, the map feature points may be points in the map that can significantly represent the path features. For example, it may be the highest point of the terrain.

[0126] In this embodiment, the attribute of the map feature point may be the specific value corresponding to the feature point. For example, the altitude of the highest point is 2000 meters.

[0127] In this embodiment, the map sheet may be the paper size or dimension used to display the race track map.

[0128] In this embodiment, the path data of the offline race track may be obtained by converting the online race track map into corresponding path data through data conversion, facilitating the synchronization of the offline race track according to the online race track map.

[0129] In this embodiment, the data distribution feature refers to the distribution of various data in the offline path data on the race track path.

[0130] In this embodiment, the path deployment plan may be a plan or means for deploying the offline race track path according to the path data.

[0131] The beneficial effects of the above technical solution are as follows: By obtaining the feature points of the online race track map and the attributes corresponding to the feature points, the distribution feature of the path data is determined through the attributes, and then the corresponding path deployment plan is determined according to the distribution feature, realizing the synchronous update of the online and offline race track paths, ensuring the accuracy and timeliness of the synchronous update of the online and offline race tracks, facilitating users' personal experience offline, and improving the gaming experience.

[0132] Embodiment 6:

[0133] Based on the above Embodiment 5, this embodiment provides a race track map system for automatic map generation and intelligent path division. The offline race track path generation unit further includes:

[0134] A path testing unit for performing path safety testing on the offline race track path and obtaining safety test data;

[0135] A data processing unit, configured to compare the safety test data with a preset safety range, determine data in the safety test data that exceeds the preset safety range, and use the data that exceeds the preset safety range as potential hazard safety data;

[0136] A position confirmation unit, configured to read the potential hazard safety data, determine the position of the potential hazard safety data in the offline race track path based on the reading result, and set the position as the sensitive position;

[0137] A safety monitoring setting unit, configured to set a safety detection point and an automatic alarm system at the sensitive position, and automatically give an alarm according to the automatic alarm system when a safety accident occurs to a cyclist at the sensitive position.

[0138] In this embodiment, the path safety test can be a method or means for detecting potential safety hazards or accident-causing factors existing in the deployed offline race track.

[0139] In this embodiment, the preset safety range is set in advance and is used to verify the detection data and determine whether there are potential safety hazards in the generated offline race track path. It is a measurement standard for measuring the safety of the race track.

[0140] In this embodiment, the sensitive position can be a frequently accident-prone area or a dangerous position in the offline race track.

[0141] The beneficial effects of the above technical solution are: By performing safety detection on the offline race track path, it is convenient to timely discover potential hazard points in the offline race track path, and it is also convenient for the staff to take first aid measures in a timely manner, improving the safety of the offline experience and also enhancing the user experience of the offline game.

[0142] Embodiment 7:

[0143] Based on the above Embodiment 1, this embodiment provides a race track map system for automatic map generation and intelligent path division. Its map generation module further includes:

[0144] A data receiving unit, configured to receive the obtained target route data and determine the characteristic information of the target route data, where the target route is at least two;

[0145] A data cleaning unit, configured to select a target data cleaning rule from a preset data cleaning rule library based on the characteristic information, and analyze the target route data based on the target data cleaning rule to obtain a group of to-be-cleaned data corresponding to the target route data, where the group of to-be-cleaned data is used to represent abnormal data with missing values in the target route data, and there is at least one target route data with a missing value in the group of to-be-cleaned data;

[0146] The data cleaning unit is further configured to construct a neural network model, divide the data group to be cleaned into a test data set and a training data set, train the neural network model based on the training data set, and perform testing based on the test data set after the training is completed to obtain the processing accuracy rate of the neural network model;

[0147] Compare the processing accuracy rate with a preset processing accuracy rate;

[0148] If the processing accuracy rate is less than the preset processing accuracy rate, it is determined that the training of the neural network model is unqualified, and the neural network model is retrained;

[0149] Otherwise, it is determined that the training of the neural network model is qualified, and the abnormal data with missing values in the target route data is filled based on the trained neural network model to obtain standard target route data;

[0150] The data classification unit is configured to perform clustering processing on the standard target route data based on a preset number of clusters, and classify the standard target route data based on the clustering result, where the classification result includes slope data, straight line data, low-lying data, curvature data, and flatness data;

[0151] The map generation unit is configured to obtain the classification result of the standard target route data, and store the standard target route data corresponding to each category into the corresponding target layer based on the classification result;

[0152] The map generation unit is configured to determine the geographical information of the standard target route data in each target layer, and determine the proportional conversion coefficient between the geographical information of the to-be-generated online track map and the standard target route data;

[0153] The map generation unit is further configured to determine the feature points in the geographical information of the target route data, and determine the actual coordinates of the feature points in the to-be-generated online track map based on the proportional conversion coefficient, where there are multiple feature points;

[0154] The map generation unit is further configured to generate a sub-online track map corresponding to each target layer based on the actual coordinates of the feature points in the to-be-generated online track map, and merge the sub-online track maps corresponding to each target layer to obtain an online track map.

[0155] In this embodiment, the feature information may be used to represent key data segments or words in the target route data.

[0156] In this embodiment, the preset data cleaning rule library is set in advance and stores multiple data cleaning rules internally.

[0157] In this embodiment, the target data cleaning rule can be a data cleaning rule applicable to cleaning the target route data, and belongs to one or more in the preset data rule library.

[0158] In this embodiment, the data group to be cleaned can be some route data in the target route data that needs to be cleared or filled with data values.

[0159] In this embodiment, the preset processing accuracy rate is set in advance, used to measure whether the processing accuracy rate of the constructed neural network model for data is qualified, and can be set artificially.

[0160] In this embodiment, the abnormal data with missing values can be the data with missing data values in the target route data, that is, the data has no specific value.

[0161] In this embodiment, the standard target route data can be the data obtained after processing the abnormal data in the target route data, and there is no abnormal data in this data.

[0162] In this embodiment, the target layer is used to store different types of route data, facilitating the generation of maps with different features according to different types of route data.

[0163] In this embodiment, the geographical information can be used to represent the geographical features of the race track, such as the environment where the race track is located, etc.

[0164] In this embodiment, the proportional conversion coefficient can be the conversion ratio between the actual position coordinates and the map coordinates.

[0165] In this embodiment, the feature points in the geographical information can be the reference points that can represent the obvious features of the race track in the race track, such as the point with the highest altitude in the race track.

[0166] In this embodiment, the sub-online track map can be a part of the online track map to be generated, for example, it can be the map of the low-lying part in the online track map.

[0167] The beneficial effects of the above technical solutions are as follows: By obtaining the target route data and cleaning and classifying the target route data, it is convenient to obtain accurate route data, thereby improving the accurate deployment of the online race track path. At the same time, determining the coordinate value conversion ratio between the map to be displayed and the actual path realizes the accurate generation of the online track map, and at the same time improves the accuracy of deploying the offline race track path according to the online track map, improving the user's online and offline game experience.

[0168] Embodiment 8:

[0169] Based on the above-mentioned Embodiment 7, this embodiment provides a track map system for automatic map generation and intelligent path division. The map generation unit includes:

[0170] A map acquisition unit, configured to acquire the generated online track map, and perform simulation tests on the generated online track map based on the online mobile game mode and the VR virtual scene mode to obtain corresponding test data;

[0171] A data analysis unit, configured to analyze and process the test data to determine the completion degree of the user based on the online mobile game mode and the VR virtual scene mode for completing the online track map and the accident rate occurring during the game process;

[0172] A data comparison unit, configured to compare the completion degree and the accident rate occurring during the game process with a first preset threshold and a second preset threshold respectively;

[0173] If the completion degree is less than the first preset threshold or the accident rate occurring during the game process is greater than the second preset threshold, it is determined that there are defects in the generated online track map, and the online track map is redeployed based on the target route data until the completion degree is greater than or equal to the first preset threshold and the accident rate occurring during the game process is less than or equal to the second preset threshold;

[0174] Otherwise, it is determined that the generated online track map is qualified, and the test of the online track map is completed.

[0175] In this embodiment, the first preset threshold can be a measurement standard for measuring the completion degree of the user completing the track when testing the generated online track, and it can be set artificially.

[0176] In this embodiment, the first preset threshold can be a measurement standard for measuring the accident rate when the user rides on the track during the test of the generated online track, and it can be set artificially.

[0177] In this embodiment, the accident rate occurring during the game process can be the probability of a cyclist colliding when turning, etc. based on the VR virtual scene mode obtained in the online mobile game mode.

[0178] In this embodiment, the specific working process of determining the completion degree of the user based on the online mobile game mode and the VR virtual scene mode for completing the online track map includes:

[0179] Acquire the total length of the online track map and the difficulty coefficient of the online track map;

[0180] Based on the total length of the online track map and the difficulty coefficient of the online track map, calculate the completion degree of the user;

[0181]

[0182] Among them, W represents the completion degree of the user; μ represents the error factor, and its value range is (0.08, 0.09); δ represents the difficulty coefficient of the game difficulty level selected by the user on the online track map. When the user selects the difficulty level as easy, the difficulty coefficient value is 1%; when the user selects the difficulty level as normal, the difficulty coefficient value is 2%; when the user selects the difficulty level as difficult, the difficulty coefficient value is 3%; L represents the total length value of the online track map; l represents the length value that the user has completed;

[0183] Based on the calculation result, determine the completion degree of the user for completing the online track map based on the online mobile game mode and the VR virtual mode.

[0184] Above, for the formula When μ = 0.09, δ = 1%, L = 100, l = 50, the completion degree W of the user is 45%.

[0185] In this embodiment, the specific working process for determining the accident rate that occurs to the user during the game based on the online mobile game mode and the VR virtual scene mode includes:

[0186] Obtain the number of monitoring points in the online track map. At the same time, determine the safety performance coefficient of the racing car used by the user;

[0187] Calculate the accident rate that occurs to the user during the game based on the number of monitoring points and the safety performance coefficient of the racing car used by the user;

[0188]

[0189] Among them, ξ represents the accident rate that occurs to the user during the game; τ represents the danger coefficient in the track map, and its value range is (0.01, 0.05); ζ represents the safety performance coefficient of the racing car used by the user; ν represents the speed factor, and its value range is (0.96, 0.98); n represents the number of accidents that the user has at the monitoring points; N represents all the monitoring points in the online track map;

[0190] According to the calculation result, complete the determination of the accident rate that occurs to the user during the game based on the online mobile game mode and the VR virtual scene mode.

[0191] As mentioned above, the safety performance coefficient represents the performance value of the driving anti-collision system of the racing car. Generally, the safety performance of the racing car is divided into three levels: low, medium, and high. Among them, the safety performance coefficient corresponding to the low level is 0.8, the safety performance coefficient value corresponding to the medium level is 0.9, and the safety performance coefficient corresponding to the high level is 1. Among them, the higher the safety performance coefficient value, the lower the probability of the user having an accident in the game accident.

[0192] As mentioned above, the speed factor can be the influence value of the driving speed of the user during the race on the accident rate of the user during the game, that is, when the driving speed of the user is faster during the race and the speed factor is larger, the accident rate of the user during the game is larger.

[0193] As mentioned above, for When τ = 0.02, ζ = 1, ν = 0.97, n = 12, N = 50, the accident rate ξ of the user during the game is 23.74%, that is, the accident rate of the user during the game is 23.74%.

[0194] The beneficial effects of the above technical solution are: by simulating and testing the generated online track map, it is convenient to timely discover the dangerous places in the online track map, convenient to timely adjust the dangerous places, improve the feasibility of offline track synchronization update, ensure the safety of user games, and at the same time improve the user experience during the game.

[0195] Example 9:

[0196] Based on the above Example 7, this example provides a track map system for automatic map generation and intelligent path division. The map generation unit further includes:

[0197] A map receiving unit for receiving the generated online track map and decomposing the online track map into M map blocks according to a preset ratio;

[0198] An identification marking unit for respectively setting a unique identifier for the M map blocks, where the identifier is used to mark the position of each map block in the online track map;

[0199] A storage unit for compressing and storing the M map blocks and the corresponding identifiers into a map file, and at the same time creating a read index based on the storage path to complete the storage of the online track map.

[0200] In this example, the preset ratio is set in advance and is used to divide the generated online track map. For example, it can be divided into a 3*3 grid.

[0201] In this example, the identifier is used to mark the position of each map block in the online track map.

[0202] In this embodiment, the reading index can be a general map outline for the user to read during the game, that is, through this index, the user can know the difficulty level of the selected track, etc.

[0203] The beneficial effects of the above technical solution are as follows: By dividing the generated online track map into multiple map blocks and marking the multiple map blocks, the generated online track map is compressed and stored, saving a large amount of storage space. At the same time, based on the storage location, a reading index is created to facilitate the user to quickly find the corresponding map information during the game, thereby enhancing the gaming experience.

[0204] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these changes and modifications.

Claims

1. A track map system for map self-generation and intelligent path division, characterized in that, it includes: A map generation module, which is used to obtain target route data and transmit the target route data to a preset system for deployment to generate an online track map; A synchronization module, which is used to obtain the map features of the online track map and deploy them in the offline target area based on the map features to determine the offline track path; A path division module, which is used to automatically divide the target track in the online track map according to the rider's participation level. At the same time, a target mode is selected based on the target track for the competition; The synchronization module includes: A map reading unit, which is used to determine a map reading instruction for reading the online track map, and read the online track map based on the map reading instruction to determine the map data of the online track map; A data analysis unit, which is used to identify the map data, determine the data identifier of the map data, and analyze the data identifier to determine the map feature points of the online track map; The map reading unit is also used to read the map feature points, determine the distribution position of each map feature point in the online track map, and at the same time, determine the attribute of each map feature point; A labeling unit, which is used to determine the weight value of each map feature point according to the distribution position of each map feature point in the online track map and the attribute of each map feature point. At the same time, the map feature points are labeled according to the order from high to low of the weight value; A map contour confirmation unit, which is used to create a map sheet of the online track map according to the labeling result and determine the contour of the online track map based on the map sheet of the online track; An offline map data acquisition unit, which is used to determine the range of the online track based on the contour of the online track map, and at the same time, determine the scale ratio between the online track map and the offline actual map, and generate the path data of the corresponding offline track path according to the scale ratio; A data reading unit, which is used to read the path data of the offline track path, extract the data distribution characteristics of the path data, and generate a path deployment plan according to the data distribution characteristics; An offline track path generation unit, which is used to generate an offline track path based on the path deployment plan.

2. The track map system for map self-generation and intelligent path division according to claim 1, characterized in that, it includes: The target mode includes: an online mobile game mode, a VR virtual scene mode, and an offline competition mode; Among them, the VR virtual scene mode only includes straight tracks; Among them, when the target mode selected by the rider is the online mobile game mode or the VR virtual scene mode, the online track map is selected; When the target mode selected by the rider is the offline competition mode, the offline track path is selected for the competition.

3. The track map system for map self-generation and intelligent path division according to claim 1, characterized in that, The map generation module includes: A route reading unit for reading the target route and obtaining the route identification points of the target route; The route reading unit is further configured to determine the curvature of the target route and the direction represented by the target route based on the route identification points; A data generation unit for determining the characteristics of the target route based on the curvature of the target route and the direction represented by the target route, and generating corresponding target route data based on the characteristics of the target route.

4. A track map system for map self-generation and path intelligent division according to claim 1, characterized in that, The path division module includes: A rider confirmation unit for obtaining the game account of the rider and determining the identity information of the rider based on the game account; A level confirmation unit for reading the identity information of the rider and determining the riding level of the rider based on the reading result; A matching unit for matching the riding level with the difficulty level of the tracks in the online track map and automatically dividing the target track according to the matching result.

5. A track map system for map self-generation and path intelligent division according to claim 1, characterized in that, The synchronization module includes: A detection unit for real-time detecting the operation data of the online mobile game mode; A data comparison unit for comparing the operation data with the original data to determine whether the online mobile game mode has been updated; Wherein, when the operation data is consistent with the original data, it is determined that the online mobile game mode has not been updated; Otherwise, it is determined that the online mobile game mode has been updated; A data analysis unit for determining the mode update characteristics according to the operation data when the online mobile game mode is updated; An offline synchronization unit for performing offline synchronization update according to the mode update characteristics.

6. A track map system for map self-generation and path intelligent division according to claim 1, characterized in that, The offline track path generation unit further includes: A path test unit for performing path safety test on the offline track path and obtaining safety test data; A data processing unit for comparing the safety test data with a preset safety range, determining that there is data in the safety test data that exceeds the preset safety range, and taking the data that exceeds the preset safety range as potential safety data; A position confirmation unit for reading the potential safety data and determining the position of the potential safety data in the offline track path based on the reading result, and taking the position as a sensitive position; A safety monitoring setting unit for setting a safety detection point and an automatic alarm system at the sensitive position, and automatically alarming according to the automatic alarm system when a safety accident occurs to the rider at the sensitive position.

7. A track map system for map self-generation and path intelligent division according to claim 1, characterized in that, The map generation module further includes: A data receiving unit, configured to receive the obtained target route data and determine the characteristic information of the target route data, wherein there are at least two target routes; A data cleaning unit, configured to select a target data cleaning rule from a preset data cleaning rule library based on the characteristic information, and analyze the target route data based on the target data cleaning rule to obtain a data group to be cleaned corresponding to the target route data, wherein the data group to be cleaned is used to represent abnormal data with missing values in the target route data, and there is at least one target route data with a missing value in the data group to be cleaned; The data cleaning unit is further configured to construct a neural network model, divide the data group to be cleaned into a test data set and a training data set, train the neural network model based on the training data set, and perform a test based on the test data set after the training is completed to obtain the processing accuracy rate of the neural network model; Compare the processing accuracy rate with a preset processing accuracy rate; If the processing accuracy rate is less than the preset processing accuracy rate, determine that the training of the neural network model is unqualified, and retrain the neural network model; Otherwise, determine that the training of the neural network model is qualified, and fill in the missing values of the abnormal data in the target route data based on the trained neural network model to obtain standard target route data; A data classification unit, configured to perform clustering processing on the standard target route data based on a preset number of clusters, and classify the standard target route data based on the clustering result, wherein the classification result includes slope data, straight line data, low-lying data, curvature data, and flatness data; A map generation unit, configured to obtain the classification result of the standard target route data, and store the standard target route data corresponding to each category into the corresponding target layer based on the classification result; The map generation unit is configured to determine the geographical information of the standard target route data in each target layer, and determine the proportional conversion coefficient between the geographical information of the to-be-generated online track map and the standard target route data; The map generation unit is further configured to determine the feature points in the geographical information of the target route data, and determine the actual coordinates of the feature points on the to-be-generated online track map based on the proportional conversion coefficient, wherein there are multiple feature points; The map generation unit is further configured to generate a sub-online track map corresponding to each target layer based on the actual coordinates of the feature points on the to-be-generated online track map, and merge the sub-online track maps corresponding to each target layer to obtain an online track map.

8. A track map system for map self-generation and path intelligent division according to claim 7, wherein, The map generation unit includes: A map acquisition unit, configured to acquire the generated online track map, and perform simulation tests on the generated online track map based on the online mobile game mode and the VR virtual scene mode to obtain corresponding test data; A data analysis unit for analyzing and processing the test data to determine the completion rate of the online track map completed by the user based on the online mobile game mode and the VR virtual scene mode and the accident rate occurring during the game process; A data comparison unit for comparing the completion rate and the accident rate occurring during the game process with a first preset threshold and a second preset threshold respectively; If the completion rate is less than the first preset threshold or the accident rate occurring during the game process is greater than the second preset threshold, it is determined that there are defects in the generated online track map, and the online track map is redeployed based on the target route data until the completion rate is greater than or equal to the first preset threshold and the accident rate occurring during the game process is less than or equal to the second preset threshold; Otherwise, it is determined that the generated online track map is qualified, and the test of the online track map is completed.

9. A track map system for map self-generation and path intelligent division according to claim 7, characterized in that, The map generation unit further includes: A map receiving unit for receiving the generated online track map and decomposing the online track map into M map blocks according to a preset ratio; An identification marking unit for respectively setting unique identifiers for the M map blocks, wherein the identifiers are used to mark the positions of each map block in the online track map; A storage unit for compressing and storing the M map blocks and the corresponding identifiers into a map file, and at the same time, creating a read index based on the storage path to complete the storage of the online track map.

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