An improved method of emulating data push

Abstract

The application discloses an improved simulation data pushing method, which is an improved method combining one-dimensional linear fitting and a rotating door, and is used for supporting an immersive simulation confrontation game, in particular, for supporting a confrontation game more realistically close to a real society, it is necessary to introduce various entity and behavior model simulations. Introducing the model simulation relates to how to selectively push various complicated and huge simulation model output data after simulation to the confrontation game system. The improved simulation data pushing method disclosed by the application determines a pushing time interval of simulation data. In the simulation data pushing process, the pushing time interval is adaptively modified according to data quantity and data conditions, so that the effect of smooth and real visual sense of players can be achieved, and complicated data can be removed, and the effectiveness of data and minimization of data quantity are maintained.

Description

Technical Field

[0001] This invention relates to a data push method, and more particularly to an improved simulation data push method. Background Technology

[0002] When sending simulation data to competitive game systems, a data push algorithm with equal time intervals is generally used, periodically pushing simulation data according to a pre-set time interval. The determination of the interval is generally based on experience without a specific standard. If the interval is set too long, some important simulation information may be lost; if the interval is too short, it will result in huge network energy consumption. Although the equal time interval data push algorithm is simple, it completely ignores the changing characteristics of simulation data. The massive amounts of data contain little key information, and a large amount of redundant and useless data. Pushing simulation data to numerous competitive game system clients for a long time not only consumes network resources but also makes players lose focus on the large amount of data. These shortcomings have become the core problems restricting the smooth operation of competitive games. Summary of the Invention

[0003] Purpose of the invention: The technical problem to be solved by the present invention is to provide an improved simulation data push method to address the shortcomings of the existing technology.

[0004] To address the aforementioned technical problems, this invention discloses an improved simulation data push method, comprising the following steps:

[0005] Step 1, simulation data modeling, transforms the raw simulation data into a quantized functional representation, specifically including:

[0006] Step 1-1: Convert the target's location information in the simulation data from the geodetic coordinate system to the rectangular coordinate system, as follows:

[0007] The following formula is used to convert longitude B, latitude L, and altitude H in the same geodetic coordinate system to coordinates (X, Y, Z) in the rectangular coordinate system:

[0008]

[0009] Where N is the radius of the zonal circle, a is the semi-major axis of the Earth ellipsoid, b is the semi-minor axis of the Earth ellipsoid, and e is the natural constant.

[0010] Steps 1-2 involve establishing a function with time as the variable to convert the simulation data into a quantized functional representation. Specific methods include:

[0011] Using time t as the variable, and the position (X) at time t as the variable. t Y t Z tThe function is established based on the distance between the initial position (X0, Y0, Z0) and the initial position (X0, Y0, Z0).

[0012]

[0013] Where f(t) is the distance between the position at time t and the initial position, and f(t) is the quantized function representation.

[0014] Step 2, assuming the time of the i-th simulation data push is t i The time of the (i-1)th simulation data push is t. i-1 The time interval t between the i-th simulation data push time and the (i-1)-th simulation data push time i -t i-1 Let it be Δt i According to t i and Δt i The initial simulation data push time t′ is calculated. i+1 The time interval Δt′ between the (i+1)th simulation data push time and the ith simulation data push time i+1 , at t′ i+1 Simulation data is recorded continuously, and the push interval is determined by Δt. i Transformed into Δt′ i+1 Then, calculate Δt′. i+1 The count of the number of push notifications discarded;

[0015] The initial simulation data push time t′ is calculated. i+1 The time interval Δt′ between the (i+1)th simulation data push time and the ith simulation data push time. i+1 Specific methods include:

[0016] By applying the revolving door algorithm, the initial simulation data push time interval Δt′ is obtained. i+1 Preliminary simulation data push time t′ i+1 At this moment, the simulation output data is recorded as (t′). i+1 y′ i+1 ), where y′ i+1 Indicates t′ i+1 The simulation data values ​​output at any given time.

[0017] The calculation of Δt′ i+1 The count of discarded push notifications is determined as follows:

[0018] Rounding

[0019] Where, Δt iLet Δt′ represent the time interval for the i-th determined push. i+1 This represents the initial simulation data push time interval obtained by applying the rotating door algorithm for the (i+1)th time.

[0020] Step 3, record time t i At time t′ i+1 Given n process values, a univariate linear fitting algorithm is used to determine the linear fitting function for the time variable t, and the absolute value of the slope of the fitting function is determined. The calculation method is as follows:

[0021] Recorded time t i At time t′ i+1 The simulation data values ​​for n processes in the process are {(t i1 y i1 ), (t i2 y i2 ), …, (t in y in )}, where, (t in y in () represents the nth simulation data;

[0022] Assume the fitted function is y i =a+β i t, where a represents the linear intercept of the fitted function, β i The slope of the fitted function is calculated as follows:

[0023]

[0024] Where - indicates taking the average value.

[0025] Determine the absolute value of the slope of the fitted function, that is, take the absolute value of the slope of the fitted function: |β i |

[0026] Step 4: Based on the results obtained in Step 3, an improved adaptive push time interval strategy is adopted to recalculate the interval time and obtain the actual simulation data push time t for the (i+1)th time. i+1 and push time interval Δt i+1 ;

[0027] The improved adaptive push time interval strategy is adopted to recalculate the interval time to obtain the actual simulation data push time t for the (i+1)th time. i+1 and push time interval Δt i+1 The methods include:

[0028]

[0029]

[0030] t i+1 =t i +Δt i+1

[0031] Where, Δt i The time interval t represents the time between the i-th simulation data push and the (i-1)-th simulation data push. i -t i-1 , Δt i+1 The time interval t represents the time between the (i+1)th simulation data push and the ith simulation data push. i+1 -t i ,|β i | represents the absolute value of the slope of the fitted function determined in step 3, ρ represents the fixed parameter factor; max_interval represents the maximum data push interval, and min_interval represents the minimum data push interval.

[0032] Step 5: Trigger a simulation data push, pushing the original simulation data before modeling in Step 1, and return to Step 2 to calculate the next round of simulation data.

[0033] The triggering of a simulation data push is based on the (i+1)th simulation data push time t calculated in step 4. i+1 and push time interval Δt i+1 , in t i+1 Simulation data is pushed out at any time.

[0034] Beneficial effects:

[0035] This invention presents an adaptive data push interval determination strategy based on the rotating door algorithm and a univariate fitting algorithm. This strategy determines the data push interval by combining the performance of past data with predictions of future data. The strategy primarily applies the rotating door algorithm and calculates the slope of the last time interval using the univariate fitting algorithm when the rotating door algorithm switches its initial point. Although the univariate fitting algorithm requires more data and is slightly more computationally complex, incorporating the rotating door algorithm makes it much simpler to calculate and also improves upon the rotating door algorithm. This achieves a reduction in the amount of collected simulation data while ensuring data integrity, reducing network energy consumption, and improving data quality. Attached Figure Description

[0036] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0037] Figure 1This is a flowchart of an improved algorithm combining univariate linear fitting and the rotating door algorithm in this invention.

[0038] Figure 2 This is a schematic diagram illustrating the principle of the revolving door algorithm.

[0039] Figure 3 This is a schematic diagram illustrating the location information of the target.

[0040] Figure 4 This is an improved algorithm that combines univariate linear fitting and the rotating door algorithm. The diagram shows the push point information selected by the improved algorithm.

[0041] Figure 5 This is a schematic diagram of push notification information selected at fixed time intervals.

[0042] Figure 6 This is a schematic diagram showing the push point information selected for the revolving door time interval. Detailed Implementation

[0043] The present invention will be further described below with reference to embodiments.

[0044] like Figure 1 As shown, an improved simulation data push method of the present invention includes the following steps:

[0045] Step 1: Model the simulation data by converting the simulation data into a quantized function representation;

[0046] Step 2, assuming the time of the i-th simulation data push is t i The time of the (i-1)th simulation data push is t. i-1 The time interval t between the i-th simulation data push time and the (i-1)-th simulation data push time i -t i-1 Let it be Δt i ; then according to t i and Δt i The rotating door algorithm is used to calculate the initial simulation data push time t′ for the (i+1)th time. i+1 and Δt′ i+1 , at t′ i+1 Simulation data is recorded continuously (the simulation data push event is not triggered here; steps 3 and 4 will be used later to correct the simulation push time) and the data is calculated based on the push time interval Δt. i Transformed into Δt′ i+1 After that, Δt′ i+1 The number of times push data was discarded.

[0047] Step 3, record time t i At time t′ i+1Given n process values, use a univariate linear fitting algorithm to determine the linear fitting function for the time variable t, and determine the absolute value of the slope of the fitting function.

[0048] Step 4: Based on the results obtained in Step 3, an improved adaptive push time interval strategy is adopted to recalculate the interval time and obtain the actual simulation data push time t for the (i+1)th time. i+1 and push time interval Δt i+1 ;

[0049] Step 5: Based on the simulation data push time t calculated in Step 4, the (i+1)th simulation data push time is obtained. i+1 and push time interval Δt i+1 and in t i+1 Simulation data is pushed out at any time, and the process returns to step 2 to calculate the next round of simulation data.

[0050] Taking a single-player and team-based competitive game system as an example, during the computer simulation of the opponent, various entities and action process data of the simulated opponent are simulated through a simulation model and fed back to the competitive game system in real time. In this example, the simulation output data uses the push method described in this paper to process the data push time interval, achieving the removal of some data while retaining the necessary and effective real-time action data of the opponent to be transmitted to the game, thus ensuring a realistic and smooth experience for the player. The following example illustrates the push time interval and data information of the entire process of a simulated opponent receiving a task, being transported to a fixed location by helicopter, walking to a designated location to engage in competitive game actions, and returning after completing the action. This example details the key steps of the improved algorithm combining univariate linear fitting and the revolving door algorithm in this invention, providing a detailed software flow description and verifying the effectiveness of the algorithm.

[0051] Simulation data primarily includes simulation data of the adversarial geographical environment, meteorological and water ripple simulation data, electromagnetic environment simulation data, simulated adversarial entities and their locations, simulated weapon and equipment locations, simulated combat operations, damage and results data, casualties, and simulated material and ammunition consumption data, etc. Among these data, the simulated adversarial entity and their locations, and the simulated weapon and equipment locations are the data sources with the largest volume and the most frequent data generation, and the data volume will increase as the game scale expands. Meanwhile, the location information on the map is continuous information that changes in real time based on a time series, which is the basis for our adaptive time interval data push algorithm. This article uses simulated adversarial entity location data as an example to illustrate the algorithm application.

[0052] (1) Step 1: Simulation data modeling, which converts the simulation data into a quantized function representation. The following explanation uses the simulation of the location data of the adversarial entity as an example.

[0053] 1) First, convert the geodetic coordinate system of the adversary's location information into a rectangular coordinate system, as follows:

[0054] Formula (1) is used to convert the longitude B, latitude L, and altitude H of the geodetic coordinate system under the same datum into (X, Y, Z) in the rectangular coordinate system:

[0055]

[0056] Where N is the radius of the zonal circle, a is the semi-major axis of the Earth ellipsoid, and b is the semi-minor axis of the Earth ellipsoid. The three satisfy the calculation relationship of formula (2).

[0057]

[0058]

[0059] 2) Using time t as the variable, and the position (X) at time t as the variable. t Y t Z t The function is established based on the distance between the initial position (X0, Y0, Z0) and the initial position (X0, Y0, Z0).

[0060]

[0061] Where f(t) is the distance between the position at time t and the initial position, and f(t) is the modeling function we are looking for. The final time-value curve of f(t) is shown below. Figure 4 As shown.

[0062] (2) Step 2, assuming the time of the i-th simulation data push is t i The time of the (i-1)th simulation data push is t. i-1 The time interval t between the i-th simulation data push time and the (i-1)-th simulation data push time i -t i-1 Let it be Δt i ; then according to t i and Δt i The rotating door algorithm is used to calculate the initial simulation data push time t′ for the (i+1)th time. i+1 and Δt′ i+1 , at t′ i+1 Simulation data is recorded continuously (the simulation data push event is not triggered here; steps 3 and 4 will be used later to correct the simulation push time) and the data is calculated based on the push time interval Δt. i Transformed into Δt′ i+1 After that, Δt′ i+1 The number of times push data was discarded.

[0063] The principle of the revolving door algorithm: The revolving door algorithm is a lossy compression algorithm. The algorithm principle is as follows: Figure 2 As shown in the figure. The figure contains eight data points: A, B, C, D, E, F, G, and H. The horizontal axis x represents time, and the vertical axis y represents the data value of each point. Assuming point A is the starting point, the value of A is saved. Two virtual doors are created by taking the intercept of Δx upwards or downwards from point A as the starting points. At point B, the two doors close, and data compression begins. At the next point C, the starting points are still the upper and lower fulcrums of the original point A. One door is opened and connected to point C. The two doors will rotate and open sequentially (if the slope of the upper and lower doors changes, the rotating door will be retained; otherwise, it will not). Subsequent points are processed by rotating sequentially. Once the rotating door is open, it will not close again. When the interior angle between the two doors is less than 180 degrees, it means the data point is "enclosed" and will be discarded; when the sum of the interior angles between the two doors is greater than or equal to 180 degrees, the current data node is saved (e.g., ...). Figure 2 (Point D in the diagram). Then, using the current data point as the new starting point, a new round of processing is performed using the method described above.

[0064] According to the revolving door algorithm, the values ​​are Δx = 0.1 km, initial time interval Interval = 0.1 minutes, maximum time interval max_interval = 0.5 minutes, minimum time interval min_interval = 0.05 minutes, and ρ = 0.09.

[0065] (3) Step 3, record time t i At time t′ i+1 Given n process values, use a univariate linear fitting algorithm to determine the linear fitting function for the time variable t, and determine the absolute value of the slope of the fitting function.

[0066] (4) Step 4: Based on the results obtained in Step 3, adopt the improved adaptive push time interval strategy and recalculate the interval time to obtain the actual simulation data push time t for the (i+1)th time. i+1 and push time interval Δt i+1 ;

[0067] (5) Step 5: Based on the simulation data push time t calculated in step 4, the simulation data push time t is obtained for the (i+1)th time. i+1 and push time interval Δt i+1 and in t i+1 Simulation data is pushed out at any time, and the process returns to step 2 to calculate the next round of simulation data.

[0068] Repeat steps (1)-(4) to determine the next interval time, until the end. This completes the entire process, yielding the desired result. Figure 4This is an information map of the push data points selected for the improved algorithm combining univariate linear fitting and the rotating door algorithm. Δx = 0.1, Interval = 0.1, max_interval = 0.5, min_interval = 0.05, ρ = 0.09

[0069] from Figure 3 (Modeling function graph of original output data in simulation) and Figure 4 The comparison of the push data graphs (selected using the push algorithm described in this article) shows that the improved algorithm selects data points that can encompass key data information and reflect the patterns of data changes.

[0070] Comparison of the advantages and disadvantages of the improved hybrid algorithm and the fixed push interval: Data selection and push are performed using a fixed time interval, such as... Figure 5 As shown, comparison Figure 4 and Figure 5 The results showed that the improved algorithm in this paper only pushed data 22 times in 12 minutes, while the algorithm with a fixed time interval pushed 120 data points in the same period. This demonstrates that the improved algorithm saves 75% of bandwidth compared to the algorithm with a fixed time interval.

[0071] Comparison of the advantages and disadvantages of the improved hybrid algorithm and the simple rotating door algorithm: Data selection is performed by applying the rotating door algorithm with a push time interval. Figure 6 As shown, comparison Figure 4 and Figure 6 The improved algorithm is better able to reflect the changing patterns of the data.

[0072] A comparison of the advantages and disadvantages of the improved and hybrid algorithms with the univariate linear fitting algorithm: The application of the univariate linear fitting algorithm for data selection by pushing data time intervals is not shown here. However, determining the data push time interval using the univariate linear fitting algorithm requires a large amount of data, and each point requires a new linear fitting, resulting in a significant waste of computer resources. The algorithm presented in this paper, on the other hand, performs univariate linear fitting based on the rotating door algorithm, thus greatly reducing the number of univariate linear fitting iterations and saving computational resources.

[0073] This invention provides an improved approach and method for pushing simulation data. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment of the invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

Claims

1. An improved simulation data push method, characterized in that, Includes the following steps: Step 1: Simulation data modeling, converting the raw simulation data into a quantized function representation; Step 2, assuming the first The simulation data push time is , No. The simulation data push time is , No. The timing of the second simulation data push and the first The time interval between the simulation data pushes Recorded as ;in accordance with and Calculate the preliminary first Simulation data push time and the The timing of the second simulation data push and the first The time interval between the simulation data pushes ,exist Simulation data is recorded continuously, and the data is pushed at intervals. Become Then, calculate Number of push notifications discarded ; Step 3, record the time. At the time During the process The process values ​​are used to determine the time variable using a univariate linear fitting algorithm. Find the linear fitting function and determine the absolute value of the slope of the fitting function; Step 4: Based on the results obtained in Step 3, an improved adaptive push interval strategy is adopted to recalculate the interval time to obtain the true first... Simulation data push time and push interval The improved adaptive push time interval strategy is used to recalculate the interval time to obtain the true first... Simulation data push time and push interval The methods include: ； ； ； in, Indicates the first The timing of the second simulation data push and the first The time interval between the simulation data pushes , Indicates the first The timing of the second simulation data push and the first The time interval between the simulation data pushes , This represents the absolute value of the slope of the fitted function determined in step 3. Indicates a fixed parameter factor; Indicates the maximum data push interval. Indicates the minimum data push interval; Step 5: Trigger a simulation data push, pushing the original simulation data before modeling in Step 1, and return to Step 2 to calculate the next round of simulation data.

2. The improved simulation data push method according to claim 1, characterized in that, The method for modeling simulation data described in step 1 specifically includes: Step 1-1: Convert the target's location information in the simulation data from the geodetic coordinate system to the rectangular coordinate system; Steps 1-2: Using time as a variable, establish a function to convert the simulation data into a quantized functional representation.

3. The improved simulation data push method according to claim 2, characterized in that, The method for converting the target's position information in the simulation data from the geodetic coordinate system to the rectangular coordinate system, as described in step 1-1, is as follows: The following formula is used to convert longitude B, latitude L, and altitude H in the same geodetic coordinate system to coordinates in a rectangular coordinate system. : ； where, is the radius of the prime vertical circle, is the semi-major axis of the Earth ellipsoid, is the semi-minor axis of the Earth ellipsoid, e is a natural constant, and the following relationship is satisfied: 。 4. The improved simulation data push method according to claim 3, characterized in that, The methods for establishing functions described in steps 1-2 include: In time As variables, with Location at any moment The initial position at the initial time is the same as the initial position. The distance between them is used to establish a function: ； in, Let be the distance between the position at time t and the initial position. This is the quantized function representation.

5. An improved simulation data push method according to claim 4, characterized in that, The preliminary calculation of the first step in step 2 Simulation data push time and the The timing of the second simulation data push and the first The time interval between the next simulation data push. Specific methods include: Applying the revolving door algorithm, we obtain the initial first... The time interval between simulation data pushes Preliminary simulation data push time Record the simulation output data at this moment. ,in express The simulation data values ​​output at any given time.

6. An improved simulation data push method according to claim 5, characterized in that, The calculation described in step 2 Number of push notifications discarded The specific method is as follows: ； in, express , This represents the initial result obtained by applying the rotating door algorithm. The time interval for pushing simulation data.

7. An improved simulation data push method according to claim 6, characterized in that, The slope of the fitting function mentioned in step 3 is calculated as follows: Recorded moments At the time During the process The simulation data value of the process is ,in, This represents the nth simulation data; Assume the fitting function is ,in, This represents the linear intercept of the fitted function. The slope of the fitted function is calculated as follows: ； in, This indicates taking the average value.

8. An improved simulation data push method according to claim 7, characterized in that, The step 3 involves determining the absolute value of the slope of the fitted function, which means taking the absolute value of the slope of the fitted function: .

9. An improved simulation data push method according to claim 8, characterized in that, Step 5 refers to triggering a simulation data push, which is based on the data calculated in step 4. Simulation data push time and push interval ,exist Simulation data is pushed out at any time.

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