A virtual battlefield scene simulation system and method

Abstract

The present invention discloses a virtual battlefield scene simulation system and method thereof, and specifically relates to the field of scene simulation technology. By acquiring various information in the scene tree update process, including data loading information, event triggering information and hardware platform performance information, abnormal situations in the update process are timely perceived, and an update hidden danger analysis model is established according to the acquired information. By comprehensively analyzing factors such as data loading speed, event triggering response and hardware performance, the update hidden danger of the scene tree is evaluated, and it is more accurately determined whether the scene tree has update hidden dangers, so as to avoid abnormal situations in the update process causing abnormal virtual battlefield scene images, and to achieve real-time monitoring and judgment of the scene tree update process. According to different update status signals, the update status of the scene tree is classified and warned, and the update strategy is adjusted in time to ensure the stability and fluency of the virtual battlefield scene.

Description

Technical Field

[0001] The present invention relates to the technical field of scene simulation, and more specifically, to a virtual battlefield scene simulation system and method thereof. Background Art

[0002] The virtual battlefield scene simulation system is designed to provide a virtual environment for military training, tactical exercises and weapon testing. The system uses advanced graphics processing technology and physical simulation algorithms to present various battlefield scenes, equipment and combat units to users in a realistic way. Through the virtual battlefield scene simulation system, military personnel can conduct actual combat simulations in a safe and economical environment, thereby enhancing their tactical awareness, decision-making ability and ability to respond to emergencies. The system can also record and analyze the behavior and decision-making of participants in real time, providing data support and feedback for subsequent training.

[0003] Among them, the construction of the virtual battlefield scene is particularly important. The current mainstream virtual battlefield scene simulation technology mainly uses a three-dimensional rendering engine to model and render objects in the virtual battlefield scene, including creating a scene browser, creating and organizing a scene tree, setting the rendering state of nodes in the scene tree, creating a camera, creating an event handler, loading the scene browser with the event handler to create an operator, loading the scene browser with the operator and other steps. After completing the above-mentioned multiple preset steps, the scene browser begins to render the virtual battlefield scene, and each time a specific number of frames (one or more frames) is rendered, the scene tree or the rendering tree will be updated to achieve the construction of the virtual battlefield scene. However, when an abnormal hidden danger occurs in the update process of the scene tree or the rendering tree, the existing virtual battlefield scene simulation system cannot perceive and adjust the update strategy in time. When the virtual battlefield scene picture after the scene tree or the rendering tree is updated has obvious abnormalities, adjusting the update strategy may cause the scene tree or the rendering tree to be updated incorrectly, thereby causing the picture of the virtual battlefield scene to be abnormal, such as model dislocation, texture error, flickering and other problems.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a virtual battlefield scene simulation system and method thereof to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A virtual battlefield scene simulation method comprises the following steps:

[0008] Step S1, obtaining data loading information, event triggering information and hardware platform performance information of the scene tree update process, and calculating the data loading speed abnormality coefficient according to the data loading information, calculating the event triggering response abnormality coefficient according to the event triggering information, and calculating the hardware performance degradation coefficient according to the hardware platform performance information;

[0009] Step S2, establishing an update risk analysis model based on the data loading speed abnormality coefficient, the event trigger response abnormality coefficient, and the hardware performance degradation coefficient, comprehensively analyzing whether there is an update risk in the scene tree, and generating an update risk fluctuation index;

[0010] Step S3, comparing the update hidden danger fluctuation index with a preset update hidden danger fluctuation index threshold, classifying whether there is an update hidden danger in the update process of the scene tree, and generating different update status signals;

[0011] Step S4: classify and warn the update status of the scene tree according to the update status signal, and adjust the update strategy according to the classification warning result.

[0012] In a preferred embodiment, data loading information of the scene tree update process is obtained, the abnormal degree of the data loading speed is analyzed, and the data loading speed abnormality coefficient is obtained to measure the data loading situation of the scene tree update process. The logic for obtaining the data loading speed abnormality coefficient is as follows:

[0013] Acquire data loading information of the scene tree update process within a time period of T, convert the data loading information into a feature vector, and use each item of data loading information as a feature corresponding to the feature vector. The data loading information includes but is not limited to loading time and loading frame number.

[0014] Organize the data set, each scene object represents a loading event, and the feature vector represents the data loading information of the loading event;

[0015] An isolation forest model is constructed based on the data set. The isolation forest constructs a binary tree with scene objects as leaf nodes by randomly selecting features and randomly selecting partition values. The binary tree construction process is repeated to obtain multiple binary trees with scene objects as leaf nodes. The constructed multiple binary trees are combined into an isolation forest.

[0016] The steps to build an isolation forest model using the dataset are as follows:

[0017] Step A1, randomly select features and partition values: for each binary tree, randomly select a feature and a partition value. The feature can be randomly selected from all features in the data set, and the partition value can be randomly selected within the value range of the feature.

[0018] Step A2, tree construction: the scene objects in the data set are divided into two subsets according to the selected features and the partition value, the left subtree contains scene objects less than or equal to the partition value, and the right subtree contains scene objects greater than the partition value;

[0019] Recursively divide the left and right subsets until the stopping condition is met, and build a binary tree;

[0020] Step A3, repeat the above steps to construct multiple binary trees;

[0021] Step A4, combining the constructed multiple binary trees into an isolation forest;

[0022] For each loading event, the path length in each binary tree is calculated using the isolation forest model, which represents the path length from the root node to the leaf node where the scene object is located;

[0023] For the i-th binary tree, the path length h of loading event j ij The calculation expression is as follows: ij =path(x j , S i ), where x j The feature vector representing the loading event j, S i represents the i-th binary tree, path(x j , S i ) represents a function that calculates the length of the path from the root node to the leaf node;

[0024] Calculate the average path length, the expression is as follows Where N represents the number of binary trees in the constructed isolation forest;

[0025] Calculate the data loading speed abnormal coefficient, the expression is as follows Where szj represents the data loading speed anomaly coefficient, M represents the total number of loading events, min(h) represents the minimum value of the average path length of all loading events, and max(h) represents the maximum value of the average path length of all loading events.

[0026] In a preferred embodiment, the event triggering information of the scene tree update process is obtained, the abnormal degree of the response time of the event triggering is analyzed, and the event triggering response abnormality coefficient is obtained to measure the response of the event triggering between scene objects during the scene tree update process. The logic for obtaining the event triggering response abnormality coefficient is as follows:

[0027] Get the response time sequence of events triggered between scene objects during the scene tree update process, take the T time period as a time window, and calculate the moving average of the triggering events according to the time window. The expression is as follows Where KN represents the moving average of the triggering event, cf c paf represents the response time of the cth trigger event, T represents the time window size, and q represents the total number of trigger events. The response time moving deviation value is calculated based on the response time of each trigger event and the moving average value. The expression is as follows: c =|cf c -KN|, where paf c Indicates the response time moving deviation value. Compare the response time moving deviation value with the preset abnormal deviation threshold, count the number of trigger events cs when the response time moving deviation value is greater than the preset abnormal deviation threshold, and calculate the event trigger response abnormal coefficient. The expression is as follows Where ahk represents the event trigger response abnormality coefficient.

[0028] In a preferred embodiment, the hardware platform performance information of the scene tree update process is obtained, and during the scene tree update process, whether the hardware platform performance of the virtual battlefield simulation system has a potential downward trend is analyzed, and the hardware performance degradation coefficient is obtained to measure whether the hardware platform performance has a potential downward trend during the scene tree update process. The logic for obtaining the hardware performance degradation coefficient is as follows:

[0029] The time series data of the hardware performance data is obtained within the T time period of the scene tree update process, and is marked as XN = (xn1, xn2, xn3, ..., xn v );

[0030] Define the sign indicator function sgn(xn w -xn r ), specific r=1,2,3,...,v-1, w=r+1;

[0031] Calculate the Z value, the expression is as follows

[0032] Calculate the variance Var, the expression is as follows In the formula, g represents the grouping of repeated data in the time series data of hardware performance data, t p Indicates the number of identical data in the pth group;

[0033] Calculate the hardware performance degradation coefficient, the expression is as follows Where yjx represents the hardware performance degradation coefficient.

[0034] In a preferred embodiment, the acquired data loading speed abnormal coefficient, event trigger response abnormal coefficient, and hardware performance degradation coefficient are normalized, and an update hidden danger analysis model is established based on the normalized data loading speed abnormal coefficient, event trigger response abnormal coefficient, and hardware performance degradation coefficient. The expression based on the model is as follows: Where ghx represents the update risk fluctuation index, a1, a2, and a3 represent the preset proportional coefficients of the data loading speed abnormality coefficient, the event trigger response abnormality coefficient, and the hardware performance degradation coefficient, respectively, and a1, a2, and a3 are all greater than 0.

[0035] In a preferred embodiment, the update hidden danger fluctuation index is compared with a preset update hidden danger fluctuation index threshold, and whether there is an update hidden danger in the update process of the scene tree is classified, and different update status signals are generated, as follows:

[0036] If the update hidden danger fluctuation index is greater than the preset update hidden danger fluctuation index threshold, an update suspicious signal is generated;

[0037] If the update hidden danger fluctuation index is less than or equal to the preset update hidden danger fluctuation index threshold, an update stability signal is generated.

[0038] In a preferred embodiment, when an update suspicious signal is generated, the update hidden danger fluctuation index generated in the subsequent multiple T time periods is obtained, and an update status data set is established, and the update status data set is marked as gx={ghx e}, where e = {1, 2, 3…y}, y is a positive integer;

[0039] Calculate the standard deviation of the update hidden danger fluctuation index in the update status data set. The expression is as follows In the formula, bzc represents the standard deviation of the updated hidden danger fluctuation index, It represents the average value of the update hidden danger fluctuation index in the update status data set. The expression is as follows

[0040] The standard deviation of the update hidden danger fluctuation index is compared with the preset warning threshold, and the update status of the scene tree is classified and warned. The specific classification is as follows:

[0041] If the standard deviation of the updated hidden danger fluctuation index is greater than the preset warning threshold, a warning signal is generated;

[0042] If the standard deviation of the updated hidden danger fluctuation index is less than or equal to the preset warning threshold, there is no need to generate a warning signal.

[0043] In a preferred embodiment, a virtual battlefield scene simulation system includes:

[0044] A data acquisition module is used to obtain data loading information, event triggering information and hardware platform performance information of the scene tree update process, and calculate the data loading speed abnormality coefficient according to the data loading information, calculate the event triggering response abnormality coefficient according to the event triggering information, and calculate the hardware performance degradation coefficient according to the hardware platform performance information;

[0045] The model analysis module establishes an update risk analysis model based on the data loading speed abnormality coefficient, event trigger response abnormality coefficient, and hardware performance degradation coefficient, conducts a comprehensive analysis on whether there are update risks in the scene tree, and generates an update risk fluctuation index;

[0046] The update determination module compares the update hidden danger fluctuation index with the preset update hidden danger fluctuation index threshold, classifies whether there is an update hidden danger in the update process of the scene tree, and generates different update status signals;

[0047] The classification warning module performs classification warning on the update status of the scene tree according to the update status signal, and adjusts the update strategy according to the classification warning result.

[0048] Technical effects and advantages of the present invention:

[0049] 1. The present invention obtains various information during the scene tree update process, including data loading information, event triggering information, and hardware platform performance information, timely perceives abnormal situations during the update process, establishes an update hidden danger analysis model based on the obtained information, and evaluates the update hidden dangers of the scene tree by comprehensively analyzing factors such as data loading speed, event triggering response, and hardware performance, so as to more accurately determine whether the scene tree has update hidden dangers, avoid abnormal virtual battlefield scene images caused by abnormal situations during the update process, and realize real-time monitoring and judgment of the scene tree update process. The update status of the scene tree is classified and warned according to different update status signals, and the update strategy is adjusted in time to ensure the stability and fluency of the virtual battlefield scene. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0051] Figure 1 This is a schematic diagram of the structure of the method of Example 1 of the present invention;

[0052] Figure 2 Schematic diagram of the structure of the system of Example 2 of the present invention. DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] Example 1

[0055] like Figure 1 The present invention provides a virtual battlefield scene simulation method, comprising the following steps:

[0056] Step S1, obtaining data loading information, event triggering information and hardware platform performance information of the scene tree update process, and calculating the data loading speed abnormality coefficient according to the data loading information, calculating the event triggering response abnormality coefficient according to the event triggering information, and calculating the hardware performance degradation coefficient according to the hardware platform performance information;

[0057] Data loading speed refers to the speed required to load the battlefield scene model during the scene tree update process. This is critical for the update of the virtual battlefield scene screen. The data loading speed directly affects the update frequency and smoothness of the virtual battlefield scene screen. When the data loading speed is abnormal, it may cause the following impacts on the update status of the scene tree:

[0058] Update delay: When data loading speed is abnormally slow, the update of the scene tree may be delayed, resulting in the failure to load new scene information into the screen in time, which reduces the screen update frequency and increases the screen delay perceived by users;

[0059] Screen freeze: If the data loading speed is extremely slow or interrupted, it may cause the screen to freeze, that is, the screen suddenly pauses or becomes incoherent during the update process, giving users a bad viewing experience;

[0060] Picture confusion: When the data loading speed is abnormal, some models or textures may not be loaded correctly, or the loading order may be disordered, which may cause errors in the update status of the scene tree, resulting in model dislocation and texture errors in the picture, reducing the realism and credibility of the scene;

[0061] Update failure: In extreme cases, if the data loading speed continues to be abnormal and cannot be restored, the scene tree update may fail completely, making it impossible to update the screen, and the user's trust in the virtual battlefield scene simulation system will be reduced;

[0062] Therefore, by obtaining the data loading information of the scene tree update process, analyzing the abnormal degree of the data loading speed, and obtaining the data loading speed abnormality coefficient, the data loading situation of the scene tree update process is measured. The logic for obtaining the data loading speed abnormality coefficient is as follows:

[0063] Acquire data loading information of the scene tree update process within a time period of T, convert the data loading information into a feature vector, and use each item of data loading information as a feature corresponding to the feature vector. The data loading information includes but is not limited to loading time and loading frame number.

[0064] Organize the data set, each scene object represents a loading event, and the feature vector represents the data loading information of the loading event;

[0065] An isolation forest model is constructed based on the data set. The isolation forest constructs a binary tree with scene objects as leaf nodes by randomly selecting features and randomly selecting partition values. The binary tree construction process is repeated to obtain multiple binary trees with scene objects as leaf nodes. The constructed multiple binary trees are combined into an isolation forest.

[0066] It should be noted that the steps to build an isolation forest model using the dataset are as follows:

[0067] Step A1, randomly select features and partition values: for each binary tree, randomly select a feature and a partition value. The feature can be randomly selected from all features in the data set, and the partition value can be randomly selected within the value range of the feature.

[0068] Step A2, tree construction: the scene objects in the data set are divided into two subsets according to the selected features and the partition value, the left subtree contains scene objects less than or equal to the partition value, and the right subtree contains scene objects greater than the partition value.

[0069] Recursively divide the left and right subsets until the stopping condition is met (for example, the maximum depth is reached, the number of samples is less than a certain threshold), and build a binary tree;

[0070] Step A3, repeat the above steps to construct multiple binary trees. The number of binary trees can be set by a person skilled in the art according to the scale of the data set, which will not be described in detail here;

[0071] Step A4, combining the constructed multiple binary trees into an isolation forest;

[0072] For each loading event, the path length in each binary tree is calculated using the isolation forest model, which represents the path length from the root node to the leaf node where the scene object is located;

[0073] For the i-th binary tree, the path length h of loading event j ijThe calculation expression is as follows: ij =path(x j , S i ), where x j The feature vector representing the loading event j, S i represents the i-th binary tree, path(x j , S i ) represents a function that calculates the length of the path from the root node to the leaf node;

[0074] It should be noted that the specific path length calculation function depends on the specific implementation of the binary tree, which is usually implemented recursively;

[0075] Calculate the average path length, the expression is as follows Where N represents the number of binary trees in the constructed isolation forest;

[0076] Calculate the data loading speed abnormal coefficient, the expression is as follows Where szj represents the data loading speed anomaly coefficient, M represents the total number of loading events, min(h) represents the minimum value of the average path length of all loading events, and max(h) represents the maximum value of the average path length of all loading events;

[0077] From the above calculation, it can be seen that the larger the data loading speed abnormality coefficient is, the greater the abnormality of the data loading speed is, which reflects that the abnormal hidden dangers in the scene tree data loading process are greater, and the possibility of adversely affecting the update status of the scene tree is greater;

[0078] Event triggering refers to whether a specific event is triggered during the update process of the scene tree, such as object collision, trigger activation, etc. The event triggering situation can reflect the interactivity and logic of the scene tree update process, and the event triggering response time is used to measure whether the duration of event triggering between scene objects during the scene tree update process is within the expected triggering time. When the event triggering response time is abnormal, it may cause the following impacts on the update status of the scene tree:

[0079] Decreased update frequency: If the response time of an event trigger is too long, the efficiency of the virtual battlefield scene simulation system in processing events may be reduced, resulting in a decrease in the update frequency, which slows down the update of the virtual scene and affects the user's real-time perception and interactive experience;

[0080] Reduced real-time performance: Some events require timely responses, such as object collisions, trigger activation, etc. If the response time of an event trigger is too long, the event may not be processed in time, reducing the real-time performance of the virtual battlefield scene simulation system and the interactivity and realism of the virtual scene;

[0081] Degraded user experience: If the response time of an event trigger is too long, users will feel slow feedback when performing interactive operations, which will reduce the user experience. Users may feel that the operation is not smooth and even lose patience, which reduces the attractiveness and playability of the virtual scene.

[0082] Therefore, by obtaining the event triggering information of the scene tree update process, analyzing the abnormal degree of the event triggering response time, and obtaining the event triggering response abnormality coefficient, the response of the event triggering between scene objects in the scene tree update process is measured. The logic for obtaining the event triggering response abnormality coefficient is as follows:

[0083] Get the response time sequence of events triggered between scene objects during the scene tree update process, take the T time period as a time window, and calculate the moving average of the triggering events according to the time window. The expression is as follows Where KN represents the moving average of the triggering event, cf c paf represents the response time of the cth trigger event, T represents the time window size, and q represents the total number of trigger events. The response time moving deviation value is calculated based on the response time of each trigger event and the moving average value. The expression is as follows: c =|cf c -KN|, where paf c Indicates the response time moving deviation value. Compare the response time moving deviation value with the preset abnormal deviation threshold, count the number of trigger events cs when the response time moving deviation value is greater than the preset abnormal deviation threshold, and calculate the event trigger response abnormal coefficient. The expression is as follows Where ahk represents the event trigger response anomaly coefficient;

[0084] From the above calculation, it can be seen that the larger the event trigger response abnormality coefficient is, the more serious the delay abnormality of the event trigger response time is, which reflects that the event trigger response time between scene objects may be delayed during the scene tree update process, and the greater the possibility of adversely affecting the scene tree update state.

[0085] Due to the different hardware platforms used in the virtual battlefield scene simulation system, the update status of the scene tree is different on different hardware platforms. There is a lack of a universal judgment standard to judge the impact of different hardware platform performance on the scene tree update process.

[0086] In the existing virtual battlefield simulation system, the update of the scene tree is one of the key factors for the stability and performance of the virtual battlefield simulation system. However, due to the heterogeneity of different hardware platforms, including processor performance, graphics card performance, memory bandwidth, etc., the performance of the same virtual battlefield simulation system on different hardware platforms may vary greatly. How to balance this difference and establish a universal judgment standard to accurately evaluate the relationship between hardware platform performance and the scene tree update process becomes crucial.

[0087] When the performance of the hardware platform has an abnormal downward trend, the following impacts may be caused on the update status of the scene tree:

[0088] Update delay: Hardware performance degradation may cause increased update delays in the scene tree;

[0089] Frame rate drop: Hardware performance degradation may cause frame rate drop during scene tree update, thus affecting the smoothness and user experience of the virtual battlefield simulation system;

[0090] Screen freeze: Hardware performance degradation may also cause screen freeze or freeze during scene tree update, thus affecting the visual effect and immersion of the virtual battlefield simulation system;

[0091] Update error: When hardware performance degrades, update errors may occur during the scene tree update process, such as model misalignment, texture errors, etc., which affects the realism and stability of the virtual battlefield simulation system;

[0092] Therefore, by obtaining the hardware platform performance information during the scene tree update process, the hardware platform performance of the virtual battlefield simulation system is analyzed to see if there is a potential risk of a downward trend during the scene tree update process, and the hardware performance degradation coefficient is obtained to measure whether there is a potential risk of a downward trend during the scene tree update process. The logic for obtaining the hardware performance degradation coefficient is as follows:

[0093] The time series data of the hardware performance data is obtained within the T time period of the scene tree update process, and is marked as XN = (xn1, xn2, xn3, ..., xn v );

[0094] It should be noted that hardware performance data includes but is not limited to processor clock frequency, memory usage, and hard disk read and write speed;

[0095] Define the sign indicator function sgn(xn w -xn r ), specific r=1,2,3,...,v-1, w=r+1;

[0096] It should be noted that the symbol indicator function sgn(xn w -xn r ) is used to calculate the difference between adjacent data points in the time series data of hardware performance data and determine its sign in order to analyze the performance trend of different hardware platforms;

[0097] Calculate the Z value, the expression is as follows

[0098] It should be noted that the Z value is used to measure the trend direction of data in the time series data of hardware performance data. It means the sum of the size relationship between the data points arranged in front and the data points arranged in the back in a given time series compared with the subsequent time points.

[0099] Calculate the variance Var, the expression is as follows In the formula, g represents the grouping of repeated data in the time series data of hardware performance data, t p Indicates the number of identical data in the pth group;

[0100] For example, in the time series data {23, 24, 29, 6, 29, 24, 24, 29, 23} of the given hardware performance data, g=3, for the node value 23 t1=2, for the node value 24 t1=3, for the node value 29 t1=3;

[0101] Calculate the hardware performance degradation coefficient, the expression is as follows Where yjx represents the hardware performance degradation coefficient;

[0102] From the above calculation, it can be seen that the larger the hardware performance degradation coefficient is, the greater the probability that the hardware platform's performance has a downward trend, and thus the greater the possibility of adversely affecting the scene tree update status;

[0103] Step S2, establishing an update risk analysis model based on the data loading speed abnormality coefficient, the event trigger response abnormality coefficient, and the hardware performance degradation coefficient, comprehensively analyzing whether there is an update risk in the scene tree, and generating an update risk fluctuation index;

[0104] The acquired data loading speed abnormal coefficient, event trigger response abnormal coefficient, and hardware performance degradation coefficient are normalized, and an update hidden danger analysis model is established based on the normalized data loading speed abnormal coefficient, event trigger response abnormal coefficient, and hardware performance degradation coefficient. The expression based on it is as follows In the formula, ghx represents the update risk fluctuation index, a1, a2, and a3 represent the preset proportional coefficients of the data loading speed abnormal coefficient, the event trigger response abnormal coefficient, and the hardware performance degradation coefficient, respectively, and a1, a2, and a3 are all greater than 0;

[0105] It can be seen from the above calculation expression that the larger the data loading speed abnormality coefficient, the larger the event trigger response abnormality coefficient, and the larger the hardware performance degradation coefficient, the larger the update hidden danger fluctuation index, indicating that the abnormal hidden dangers in the scene tree data loading speed, the event trigger response time, and the performance of the hardware platform are greater, and the possibility of affecting the update status of the scene tree is greater. Conversely, the smaller the data loading speed abnormality coefficient, the smaller the event trigger response abnormality coefficient, and the smaller the hardware performance degradation coefficient, the smaller the update hidden danger fluctuation index, indicating that the abnormal hidden dangers in the scene tree data loading speed, the event trigger response time, and the performance of the hardware platform are smaller, and the possibility of adversely affecting the update status of the scene tree is greater;

[0106] Step S3, comparing the update hidden danger fluctuation index with a preset update hidden danger fluctuation index threshold, classifying whether there is an update hidden danger in the update process of the scene tree, and generating different update status signals;

[0107] The update hidden danger fluctuation index is compared with the preset update hidden danger fluctuation index threshold, and whether there is an update hidden danger in the update process of the scene tree is classified, and different update status signals are generated, as follows:

[0108] If the update hidden danger fluctuation index is greater than the preset update hidden danger fluctuation index threshold, it indicates that the abnormal hidden dangers in the scene tree data loading speed, the event triggering response time and the hardware platform performance have a greater adverse impact on the scene tree update process, and the scene tree update process may have update hidden dangers, and an update suspicious signal is generated;

[0109] If the update hidden danger fluctuation index is less than or equal to the preset update hidden danger fluctuation index threshold, it indicates that the abnormal hidden dangers in the scene tree data loading speed, the event triggering response time and the hardware platform performance have little adverse impact on the scene tree update process, and there is no update hidden danger in the scene tree update process, and an update stability signal is generated;

[0110] Step S4, classifying and warning the update status of the scene tree according to the update status signal, and adjusting the update strategy according to the classification warning result;

[0111] When an update suspicious signal is generated, the update hidden danger fluctuation index generated in the subsequent multiple T time periods is obtained, and an update status data set is established, and the update status data set is marked as gx={ghx e}, where e = {1, 2, 3…y}, y is a positive integer;

[0112] Calculate the standard deviation of the update hidden danger fluctuation index in the update status data set. The expression is as follows Where bzc represents the standard deviation of the update hidden danger fluctuation index, and ghx represents the average value of the update hidden danger fluctuation index in the update status data set. The expression is as follows:

[0113] The standard deviation of the update hidden danger fluctuation index is compared with the preset warning threshold, and the update status of the scene tree is classified and warned. The specific classification is as follows:

[0114] If the standard deviation of the update risk fluctuation index is greater than the preset warning threshold, it means that the update status of the scene tree is continuously decreasing, and a warning signal is generated to prompt relevant managers to adjust the update strategy of the scene tree, for example, adjust the update frequency, optimize the update algorithm, etc.;

[0115] If the standard deviation of the update hidden danger fluctuation index is less than or equal to the preset warning threshold, it means that the update status of the scene tree has not shown an obvious downward trend, and there is no need to generate a warning signal;

[0116] The present invention obtains various information during the scene tree update process, including data loading information, event triggering information, and hardware platform performance information, timely perceives abnormal situations during the update process, establishes an update hidden danger analysis model based on the obtained information, and evaluates the update hidden dangers of the scene tree by comprehensively analyzing factors such as data loading speed, event triggering response, and hardware performance, so as to more accurately determine whether the scene tree has update hidden dangers, avoid abnormal virtual battlefield scene images caused by abnormal situations during the update process, and realize real-time monitoring and judgment of the scene tree update process. The update status of the scene tree is classified and warned according to different update status signals, and the update strategy is adjusted in time to ensure the stability and fluency of the virtual battlefield scene.

[0117] Example 2

[0118] This embodiment is an introduction to a virtual battlefield scene simulation system. Figure 2 As shown, it includes

[0119] A data acquisition module is used to obtain data loading information, event triggering information and hardware platform performance information of the scene tree update process, and calculate the data loading speed abnormality coefficient according to the data loading information, calculate the event triggering response abnormality coefficient according to the event triggering information, and calculate the hardware performance degradation coefficient according to the hardware platform performance information;

[0120] The model analysis module establishes an update risk analysis model based on the data loading speed abnormality coefficient, event trigger response abnormality coefficient, and hardware performance degradation coefficient, conducts a comprehensive analysis on whether there are update risks in the scene tree, and generates an update risk fluctuation index;

[0121] The update determination module compares the update hidden danger fluctuation index with the preset update hidden danger fluctuation index threshold, classifies whether there is an update hidden danger in the update process of the scene tree, and generates different update status signals;

[0122] The classification warning module performs classification warning on the update status of the scene tree according to the update status signal, and adjusts the update strategy according to the classification warning result;

[0123] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0124] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center containing one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0125] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0126] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and method described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0127] In several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways.

[0128] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A virtual battlefield scene simulation method, Features: The steps include: Step S1, obtaining data loading information, event triggering information and hardware platform performance information of the scene tree update process, and calculating the data loading speed abnormality coefficient according to the data loading information, calculating the event triggering response abnormality coefficient according to the event triggering information, and calculating the hardware performance degradation coefficient according to the hardware platform performance information; Get the data loading information of the scene tree update process, analyze the abnormality of the data loading speed, and get the data loading speed abnormality coefficient to measure the data loading situation of the scene tree update process. The logic of obtaining the data loading speed abnormality coefficient is as follows: Acquire data loading information of the scene tree update process within a time period of T, convert the data loading information into a feature vector, and use each item of data loading information as a feature corresponding to the feature vector. The data loading information includes but is not limited to loading time and loading frame number. Organize the data set, each scene object represents a loading event, and the feature vector represents the data loading information of the loading event; An isolation forest model is constructed based on the data set. The isolation forest constructs a binary tree with scene objects as leaf nodes by randomly selecting features and randomly selecting partition values. The binary tree construction process is repeated to obtain multiple binary trees with scene objects as leaf nodes. The constructed multiple binary trees are combined into an isolation forest. The steps to build an isolation forest model using the dataset are as follows: Step A1, randomly select features and partition values: for each binary tree, randomly select a feature and a partition value. The feature can be randomly selected from all features in the data set, and the partition value can be randomly selected within the value range of the feature. Step A2, tree construction: the scene objects in the data set are divided into two subsets according to the selected features and the partition value, the left subtree contains scene objects less than or equal to the partition value, and the right subtree contains scene objects greater than the partition value; Recursively divide the left and right subsets until the stopping condition is met, and build a binary tree; Step A3, repeat the above steps to construct multiple binary trees; Step A4, combining the constructed multiple binary trees into an isolation forest; For each loading event, the path length in each binary tree is calculated using the isolation forest model, which represents the path length from the root node to the leaf node where the scene object is located; For the i-th binary tree, the path length h of loading event j ij The calculation expression is as follows: ij =path(x j , S i ), where x j The feature vector representing the loading event j, S i represents the i-th binary tree, path(x j , S i ) represents a function that calculates the length of the path from the root node to the leaf node; Calculate the average path length, the expression is as follows Where N represents the number of binary trees in the constructed isolation forest; Calculate the data loading speed abnormal coefficient, the expression is as follows Where szj represents the data loading speed anomaly coefficient, M represents the total number of loading events, min(h) represents the minimum value of the average path length of all loading events, and max(h) represents the maximum value of the average path length of all loading events; Get the event triggering information of the scene tree update process, analyze the abnormal degree of the event triggering response time, and get the event triggering response abnormality coefficient to measure the response of the event triggering between scene objects during the scene tree update process. The logic for obtaining the event triggering response abnormality coefficient is as follows: Get the response time sequence of events triggered between scene objects during the scene tree update process, take the T time period as a time window, and calculate the moving average of the triggering events according to the time window. The expression is as follows Where KN represents the moving average of the triggering event, cf c paf represents the response time of the cth trigger event, T represents the time window size, and q represents the total number of trigger events. The response time moving deviation value is calculated based on the response time of each trigger event and the moving average value. The expression is as follows: c =|cf c -KN|, where paf c Indicates the response time moving deviation value. Compare the response time moving deviation value with the preset abnormal deviation threshold, count the number of trigger events cs when the response time moving deviation value is greater than the preset abnormal deviation threshold, and calculate the event trigger response abnormal coefficient. The expression is as follows Where ahk represents the event trigger response anomaly coefficient; The hardware platform performance information of the scene tree update process is obtained. During the scene tree update process, the hardware platform performance of the virtual battlefield simulation system is analyzed to see whether there is a potential risk of a downward trend, and the hardware performance degradation coefficient is obtained to measure whether there is a potential risk of a downward trend in the hardware platform performance during the scene tree update process. The logic for obtaining the hardware performance degradation coefficient is as follows: The time series data of the hardware performance data is obtained within the T time period of the scene tree update process, and is marked as XN = (xn 1 ,xn 2 ,xn 3 ,...,xn v ); Define the sign indicator function sgn(xn w -xn r ), specific Calculate the Z value, the expression is as follows Calculate the variance Var, the expression is as follows In the formula, g represents the grouping of repeated data in the time series data of hardware performance data, t p Indicates the number of identical data in the pth group; Calculate the hardware performance degradation coefficient, the expression is as follows Where yjx represents the hardware performance degradation coefficient; Step S2, establishing an update risk analysis model based on the data loading speed abnormality coefficient, the event trigger response abnormality coefficient, and the hardware performance degradation coefficient, comprehensively analyzing whether there is an update risk in the scene tree, and generating an update risk fluctuation index; The acquired data loading speed abnormal coefficient, event trigger response abnormal coefficient, and hardware performance degradation coefficient are normalized, and an update hidden danger analysis model is established based on the normalized data loading speed abnormal coefficient, event trigger response abnormal coefficient, and hardware performance degradation coefficient. The expression based on it is as follows In the formula, ghx represents the update risk fluctuation index, a 1 、a 2 、a 3 They represent the preset proportional coefficients of the data loading speed abnormality coefficient, the event trigger response abnormality coefficient, and the hardware performance degradation coefficient, respectively, and a 1 、a 2 、a 3 All are greater than 0; Step S3, comparing the update hidden danger fluctuation index with a preset update hidden danger fluctuation index threshold, classifying whether there is an update hidden danger in the update process of the scene tree, and generating different update status signals; Step S4: classify and warn the update status of the scene tree according to the update status signal, and adjust the update strategy according to the classification warning result.

2. A virtual battlefield scene simulation method according to claim 1, Features: The update hidden danger fluctuation index is compared with the preset update hidden danger fluctuation index threshold, and whether there is an update hidden danger in the update process of the scene tree is classified, and different update status signals are generated, as follows: If the update hidden danger fluctuation index is greater than the preset update hidden danger fluctuation index threshold, an update suspicious signal is generated; If the update hidden danger fluctuation index is less than or equal to the preset update hidden danger fluctuation index threshold, an update stability signal is generated.

3. A virtual battlefield scene simulation method according to claim 2, Features: When an update suspicious signal is generated, the update hidden danger fluctuation index generated in the subsequent multiple T time periods is obtained, and an update status data set is established, and the update status data set is marked as gx={ghx e }, where e = 1, 2, 3…y}, y is a positive integer; Calculate the standard deviation of the update hidden danger fluctuation index in the update status data set. The expression is as follows In the formula, bzc represents the standard deviation of the updated hidden danger fluctuation index, It represents the average value of the update hidden danger fluctuation index in the update status data set. The expression is as follows The standard deviation of the update hidden danger fluctuation index is compared with the preset warning threshold, and the update status of the scene tree is classified and warned. The specific classification is as follows: If the standard deviation of the updated hidden danger fluctuation index is greater than the preset warning threshold, a warning signal is generated; If the standard deviation of the updated hidden danger fluctuation index is less than or equal to the preset warning threshold, there is no need to generate a warning signal.

4. A virtual battlefield scene simulation system, used to implement a virtual battlefield scene simulation method as described in any one of claims 1 to 3, Features: include A data acquisition module is used to obtain data loading information, event triggering information and hardware platform performance information of the scene tree update process, and calculate the data loading speed abnormality coefficient according to the data loading information, calculate the event triggering response abnormality coefficient according to the event triggering information, and calculate the hardware performance degradation coefficient according to the hardware platform performance information; The model analysis module establishes an update risk analysis model based on the data loading speed abnormality coefficient, event trigger response abnormality coefficient, and hardware performance degradation coefficient, conducts a comprehensive analysis of whether the scene tree has update risks, and generates an update risk fluctuation index; The update determination module compares the update hidden danger fluctuation index with the preset update hidden danger fluctuation index threshold, classifies whether there is an update hidden danger in the update process of the scene tree, and generates different update status signals; The classification warning module performs classification warning on the update status of the scene tree according to the update status signal, and adjusts the update strategy according to the classification warning result.

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