CN113332707A - Data processing method for gamepad and game host - Google Patents
Data processing method for gamepad and game host Download PDFInfo
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/20—Input arrangements for video game devices
- A63F13/24—Constructional details thereof, e.g. game controllers with detachable joystick handles
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/30—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
- A63F13/32—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers using local area network [LAN] connections
- A63F13/327—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers using local area network [LAN] connections using wireless networks, e.g. Wi-Fi® or piconet
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/40—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment
- A63F13/44—Processing input control signals of video game devices, e.g. signals generated by the player or derived from the environment involving timing of operations, e.g. performing an action within a time slot
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
- A63F13/55—Controlling game characters or game objects based on the game progress
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
- A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
- A63F2300/40—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterised by details of platform network
- A63F2300/404—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterised by details of platform network characterized by a local network connection
- A63F2300/405—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterised by details of platform network characterized by a local network connection being a wireless ad hoc network, e.g. Bluetooth, Wi-Fi, Pico net
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Abstract
The application provides a data processing method for a gamepad and a game host, which comprises the following steps: the game handle captures the operation information of a player and generates a first control instruction; the game paddle obtains a plurality of prediction models, inputs a first control instruction into the plurality of prediction models and outputs a corresponding prediction instruction; the game handle determines at least one second control instruction from the plurality of predicted instructions and calculates at least one second priority information; after receiving the second control instruction and the second priority information, the game host searches second game data corresponding to the second control instruction and stores the second game data in a memory corresponding to the second priority information; and if the game host receives the control instruction which is the same as the second control instruction, reading and processing the second game data from the corresponding memory. According to the method and the device, the game host can quickly execute the instruction from the game handle under the WiFi network, and the instruction processing efficiency is improved.
Description
Technical Field
The invention relates to the field of game pads, in particular to a data processing method for a game pad and a game host.
Background
In the field of games, with the continuous development of game development technologies, the requirements on the transmission rate and the processing rate of game information become larger and larger, and the improvement of the response rate is an urgent need.
The interaction of the game console with the gamepad can be implemented using wired or wireless communication technology, for which wireless fidelity WiFi technology will be increasingly faster in the visible future as communication technology evolves and infrastructure improves, e.g., 6 th generation WiFi has been up to 1228.8 MB/sec. The use of WiFi technology for communication between the game pad and the game host becomes a possible trend, and under the condition that the data transmission rate of wireless communication is faster and faster, how to increase the data processing rate of the game host becomes a problem to be solved urgently.
Disclosure of Invention
The application provides a data processing method for a gamepad and a game host, which can realize timely response of the game host under a WiFi network with a high communication rate.
On one hand, the application provides a data processing method for a game handle and a game host, wherein the game host and the game handle perform data interaction through a WIFI network, and the method comprises the following steps:
the game handle captures the operation information of a player, generates a first control instruction based on the operation information and sends the first control instruction to the game host;
the game handle acquires a plurality of prediction models, the first control instruction is respectively input into the prediction models, and the prediction instruction corresponding to each prediction model is output;
the game handle determines at least one second control instruction from a plurality of prediction instructions and calculates at least one second priority information corresponding to the at least one second control instruction; wherein the second control instruction is an instruction of the plurality of predicted instructions that satisfies a specified number of conditions, and the second priority information indicates a probability of an accompanying occurrence of the second control instruction with the first control instruction within a first time interval;
the game handle sends the second control instruction and the second priority information to the game host after sending the first control instruction to the game host;
after receiving a second control instruction and second priority information, the game host searches second game data corresponding to the second control instruction and stores the second game data in a memory corresponding to the second priority information; the higher the probability value corresponding to the second priority information is, the higher the performance of the memory corresponding to the second priority information is;
after the game host executes the first control instruction, if a control instruction which is the same as a second control instruction and is from a game handle is received, the second game data is read from a memory corresponding to the second priority information and processed.
In a possible implementation manner, the game host includes a first storage and a second storage, the performance of the first storage is greater than that of the second storage, and the first storage or the second storage includes one of a register, a cache, a main memory RAM, and a virtual memory;
the storing the second game data in a memory corresponding to the second priority information includes: if the second priority information is larger than a second threshold and smaller than a first threshold, storing the second game data in a second memory; and if the second priority information exceeds a first threshold value, storing the second game data in a first memory.
Furthermore, in a second time interval when the first instruction is received, if a control instruction which is the same as the second control instruction and is from the gamepad is not received, game data corresponding to the second instruction is stored in a hard disk with lower performance, so that the space of a high-performance memory is saved. If the control instruction which is the same as the second control instruction and is from the gamepad is not received in the second time interval of receiving the first instruction, the second control instruction is not an instruction to be sent next by the player, and the prediction result of the current instruction is wrong. In a possible implementation manner, if the second game data is stored in the first memory, in a second time interval when the first instruction is received, if a control instruction which is the same as the second control instruction is not received from the gamepad, the second game data is transferred to the second memory, or is transferred to a nonvolatile memory such as a hard disk or Flash. If the second game data is stored in the second memory, and within a second time interval when the first command is received, if a control command which is the same as the second control command is not received from the gamepad, the second game data is transferred to a nonvolatile memory such as a hard disk or Flash.
In one possible implementation, the plurality of predictive models includes a plurality of a back propagation neural network model, a kalman filter predictive model, a time series predictive model, a random forest model, a softmax logistic regression model, and a proximity algorithm model;
alternatively, the plurality of predictive models comprises a plurality of the following models: the method comprises the following steps of a plurality of back propagation neural network models with different parameters, a plurality of Kalman filtering prediction models with different parameters, a plurality of time sequence prediction models with different parameters, a plurality of random forest models with different parameters, a plurality of softmax logistic regression models with different parameters and a plurality of proximity algorithm models with different parameters.
The game handle determines at least one second control instruction from a plurality of prediction instructions and calculates at least one second priority information corresponding to the at least one second control instruction, and the method comprises the following steps: and the game paddle takes the instruction with the largest quantity in the plurality of predicted instructions as the second control instruction, and calculates the second priority information according to the proportion of the quantity of the second control instruction in the plurality of predicted instructions.
In one possible implementation manner, the at least one second control instruction includes a third instruction and a fourth instruction, the at least one second priority information includes third priority information and fourth priority information, the second game data corresponding to the at least one second control instruction includes third game data corresponding to the third instruction and fourth game data corresponding to the fourth instruction, the gamepad determines the at least one second control instruction from a plurality of predicted instructions and calculates the at least one second priority information corresponding to the at least one second control instruction, including: the game handle takes the instruction with the largest number in the plurality of predicted instructions as the third control instruction, and calculates the third priority information according to the proportion of the number of the third control instruction in the plurality of predicted instructions; taking a second instruction in the plurality of predicted instructions as the fourth control instruction, and calculating the fourth priority information according to the proportion of the number of the fourth control instruction in the plurality of predicted instructions; the storing the second game data in a memory corresponding to the second priority information includes: storing the third game data in a first memory and the fourth game data in a second memory; if a control instruction which is the same as a second control instruction from the game handle is received, reading and processing the second game data from a memory corresponding to the second priority information, wherein the step of reading and processing the second game data comprises the following steps: if a control instruction which is the same as the third control instruction and is from the game handle is received, reading and processing the third game data from the first memory; and if a control instruction which is the same as the fourth control instruction from the game handle is received, reading and processing the fourth game data from the second memory.
Before the gamepad obtains a plurality of prediction models, the method further comprises the following steps: acquiring a historical instruction, training a plurality of prediction models through the historical instruction, and storing the trained prediction models; the gamepad obtains a plurality of predictive models, including: a stored trained plurality of predictive models is obtained.
In one possible implementation, the plurality of predictive models are associated with a first player identification, and before the gamepad obtains the plurality of predictive models, the method further includes: acquiring an identity of a player; determining whether the identification matches the first player identification; the gamepad obtains a plurality of predictive models, including: in the event that the identity matches the first player identification, obtaining a plurality of predictive models associated with the first player identification.
Furthermore, by associating the instructions of the gamepad with the player identification, the operating habits of each player can be identified, and the priority information between the control instructions corresponding to each player can be acquired in a targeted manner, so that the speed of identifying the associated instructions can be improved, and the game data can be acquired more quickly.
According to the scheme, the identity of the player can be identified, the operation habit data (such as a plurality of control instructions) of the player are matched according to the identity of the player, the control instructions which are possibly sent by the player through the game handle are predicted according to the operation habit data of the player, and the game data corresponding to the control instructions are pre-extracted to the memories with different performance levels, so that the processor can quickly acquire the game data when executing the next game handle instruction, the instructions from the game handle can be quickly executed under a WiFi network, and the game experience of the player is improved. Meanwhile, the instruction prediction is carried out by adopting various prediction models and multiple groups of parameters, so that the prediction precision can be improved, the method can be widely suitable for different scenes, and the robustness of the prediction result is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
FIG. 1 is an interactive schematic view of a gamepad and a game console as set forth in the present application;
FIG. 2 is a schematic diagram of a data processing method for a gamepad and a game host according to the present application;
fig. 3 is a schematic structural diagram of a game device according to the present application.
Detailed Description
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, or apparatus.
Fig. 1 is an interaction diagram of a game pad and a game host proposed in the present application, where the game pad 101 interacts with the game host 102 using a wireless communication WiFi network, and the game host and the game pad may be respectively coupled with a router 103. The game pad comprises a WiFi signal transceiving and processing module, the game host comprises a WiFi signal transceiving and processing module, and the communication between the game pad and the game host can adopt a plurality of WiFi protocols, such as an 802.11a protocol or an 802.11ax protocol. After receiving the signal of the game handle through the WiFi network, the game host loads and displays game data according to a game instruction so as to achieve the purpose of real-time interaction. In addition, the gamepad and the game host each include basic components such as memory, processor, and bus.
When the game host computer processes the instructions of the game handle in real time, related game data needs to be loaded and processed, and game pictures are rendered. In a WiFi network with a large transmission rate, the network capacity is generally surplus, and how to utilize the network capacity and improve the response speed of the game host is an important problem.
For a general host, the operation rate of the processor is much higher than the access rate of a memory such as a hard disk, that is, the access rate of the memory is the largest factor limiting the rate of the game host processing instructions. The data reading speed is maximized by adopting a data multistage preloading mode, and the problem of imbalance between the performance of the memory and the performance of the processor in the game host is solved by essentially consuming partial computing power and partial network capacity of the processor of the game handle, so that the response speed of the game host is improved. Such a cost is clearly acceptable today where network capacity and processor power are abundant.
The game handle controls the game host machine through instructions, and a plurality of game instructions of the player usually partially or completely present a relationship. Taking a shooting type game as an example, a player may be accustomed to controlling to open the sighting telescope after controlling the game character to crouch down, or controlling to start shooting after controlling the game character to jump. The companionship between game instructions is researched, the game process can be optimized, and the instruction processing speed is improved.
Fig. 2 is a schematic diagram of a data processing method for a game pad and a game host, which are provided by the present application, where the game host and the game pad perform data interaction through a WIFI network.
201. The game handle captures the operation information of the player, generates a first control instruction based on the operation information, and sends the first control instruction to the game host. The game pad can receive the operation information of the player through virtual or physical keys, a gyroscope, an acceleration sensor and the like, and generates a control instruction according to the operation information, wherein the control instruction is used for controlling the movement of a game character, the loading of a game picture and the setting of a game.
202. And the game paddle acquires a plurality of prediction models, inputs the first control instruction into the prediction models respectively and outputs the prediction instruction corresponding to each prediction model.
The plurality of prediction models are used for predicting instructions that the game host machine may receive in a first time interval after receiving the first control instruction (namely, the subsequent instructions of the first control instruction), namely, predicting what operation the player will perform next and what control instruction is sent. The output multiple prediction instructions are the preliminary prediction results.
In one possible implementation, the plurality of predictive models includes a plurality of a back propagation neural network model, a kalman filter predictive model, a time series predictive model, a random forest model, a softmax logistic regression model, and a proximity algorithm model.
For example, the game pad may pre-train the above 6 models, and store the trained 6 models. After the first control command is obtained, the first control command is input into the trained 6 prediction models, and the prediction commands corresponding to the 6 prediction models are output. Because various models have differences, the prediction precision and the optimal applicable scene are different, and the 6 output prediction instructions may have differences. The following table is an exemplary output result. Table 1:
| input prior instruction | Predicted post instructions | Prediction model adopted |
| Control instruction 1 | Control instruction 2 | Back propagation neural network |
| Control instruction 1 | Control instruction 2 | Kalman filtering |
| Control instruction 1 | Control instruction 2 | Time series |
| Control instruction 1 | Control instruction 4 | Random forest |
| Control instruction 1 | Control instruction 3 | softmax logistic regression |
| Control instruction 1 | Control instruction 2 | Proximity algorithm |
As can be seen from the above exemplary table, when the control command 1 is input, the prediction commands output by each model include a control command 2, a control command 3 and a control command 4, wherein the control command 2 has the greatest proportion and is the command most likely to occur along with the control command 1.
In one possible implementation, the plurality of predictive models may include a plurality of the following models: the method comprises the following steps of a plurality of back propagation neural network models with different parameters, a plurality of Kalman filtering prediction models with different parameters, a plurality of time sequence prediction models with different parameters, a plurality of random forest models with different parameters, a plurality of softmax logistic regression models with different parameters and a plurality of proximity algorithm models with different parameters. For the same model, if the model parameters are different, the prediction effect may be different. Taking a back propagation neural network model as an example, the weight matrix from the input layer to the hidden layer is [ w1, w 2; w3, w4 ]; taking the error as a constraint condition, after gradient descent training, obtaining the weight matrix value of [ w1, w2 under the condition of error convergence; w3, w4 ═ 0.15, 0.20; 0.25,0.30]. Properly adjusting the training data, and obtaining the weight matrix values [ w1, w 2; w3, w4 ═ 0.16, 0.20; 0.27,0.29]. After the two groups of weight matrixes are obtained, the two groups of weight matrixes are respectively used as parameters of the back propagation neural network model, and the neural network model with the two groups of parameters can generate a more accurate prediction result through testing. The process obtains two back propagation neural network models with different parameters. Other models may also be processed in reference to this process. The following table is an exemplary output result. Table 2:
as can be seen from the above exemplary table, when the control command 1 is input, the prediction commands output by the respective models are the control command 2, the control command 3, and the control command 4, where the proportion of the control command 2 is the largest.
The instruction prediction is carried out by adopting various prediction models and multiple groups of parameters, so that the prediction precision can be improved, different scenes can be more widely adapted, and the robustness of a prediction result is improved.
203. The game paddle determines at least one second control instruction from a plurality of prediction instructions and calculates at least one second priority information corresponding to the at least one second control instruction; wherein the second control instruction is an instruction of the plurality of predicted instructions that satisfies a specified number of conditions, and the second priority information indicates a probability of an accompanying occurrence of the second control instruction with the first control instruction within a first time interval.
The plurality of prediction models respectively generate prediction instructions, and at the moment, the larger the number of the same type of prediction instructions is, the closer the type of prediction instructions is to the actual result is, namely, the more likely the prediction instructions are sent to the game host by the player within the preset time after the first control instruction.
In a possible implementation manner, the gamepad takes the instruction with the largest number in the plurality of predicted instructions as the second control instruction, and calculates the second priority information according to the proportion of the number of the second control instruction in the plurality of predicted instructions. The most numerous predicted commands among the plurality of predicted commands have the highest probability of being accompanied by the first control command, that is, the game host receives the first control command and then has a high probability of continuing to receive the same commands as the second control command.
Taking table 1 as an example, if the number of the control commands 2 is the largest, the control command 2 is the second control command, and the corresponding second priority information is the percentage of the control command 2, that is, 66.7%.
It should be noted that the second control instruction is only a predictive instruction, and is not an instruction executed immediately, and the second control instruction is used for instructing the game host to pre-extract game data corresponding to a control instruction that may be sent by a player to a memory with a specified performance level in advance, so that when the same instruction as the second control instruction is received, the game data can be quickly read from the corresponding memory. Further, when the game paddle sends the second control instruction to the game host, the game paddle can simultaneously send prompt information indicating that the second control instruction belongs to a predictive instruction and is not an instruction executed immediately to instruct the game host to pre-extract game data corresponding to the second control instruction to a specified memory in advance and not execute the second control instruction immediately.
204. After the game handle sends the first control instruction to the game host, sending the second control instruction and the second priority information to the game host; after receiving a second control instruction and second priority information, the game host searches second game data corresponding to the second control instruction and stores the second game data in a memory corresponding to the second priority information; the higher the probability value corresponding to the second priority information is, the higher the performance of the memory corresponding to the second priority information is.
The game host comprises a first storage and a second storage, the performance of the first storage is greater than that of the second storage, and the first storage or the second storage comprises one of a register, a cache, a main memory RAM and a virtual memory. If the second priority information is larger than a second threshold and smaller than a first threshold, storing the second game data in a second memory; and if the second priority information exceeds a first threshold value, storing the second game data in a first memory.
In one possible implementation, the first memory is a cache and the second memory is a main memory RAM. Or the first memory is a register and the second memory is a cache. Or the first memory is a main memory RAM, and the second memory is a virtual memory or a nonvolatile memory. The nonvolatile memory comprises a mechanical hard disk, a solid state hard disk or a Flash memory and the like. The access rates of the register, the cache, the RAM and the nonvolatile memory are sequentially reduced, namely the register or the cache is a scarce memory with high performance, and the nonvolatile memory is a memory with large capacity and poor performance.
Taking table 1 as an example, assuming that the first threshold is 50% and the second threshold is 33%, since the control instruction 2 accounts for 50%, and the probability value corresponding to the second priority information is 50%, at this time, the first threshold is satisfied, the game data corresponding to the control instruction 2 may be extracted to the first memory (e.g., cache). Whereas if the second priority information is greater than 33% and less than 50%, the game data corresponding to the control instruction 2 may be fetched into the second memory (e.g., RAM), and if the priority information is less than 33%, no additional processing operation is performed.
In one possible implementation manner, the at least one second control instruction includes a third instruction and a fourth instruction, the at least one second priority information includes third priority information and fourth priority information, the second game data corresponding to the at least one second control instruction includes third game data corresponding to the third instruction and fourth game data corresponding to the fourth instruction, the gamepad determines the at least one second control instruction from a plurality of predicted instructions and calculates the at least one second priority information corresponding to the at least one second control instruction, including: the game handle takes the instruction with the largest number in the plurality of predicted instructions as the third control instruction, and calculates the third priority information according to the proportion of the number of the third control instruction in the plurality of predicted instructions; taking a second instruction in the plurality of predicted instructions as the fourth control instruction, and calculating the fourth priority information according to the proportion of the number of the fourth control instruction in the plurality of predicted instructions; the storing the second game data in a memory corresponding to the second priority information includes: the third game data is stored in a first memory and the fourth game data is stored in a second memory.
The operations of identifying the third control instruction, extracting the third game data to the first memory, and identifying the fourth control instruction, extracting the fourth game data to the second memory are performed simultaneously (for example, may be performed in parallel), so as to achieve the purpose of multi-level pre-storage.
Taking table 2 as an example, after the prediction of 8 prediction models, 8 prediction instructions are obtained, wherein the number of the control instructions 2 is the largest, and the control instructions serve as third control instructions, and the value of the corresponding third priority information is 50%; the second control command 3 corresponds to the fourth priority information having a value of 38% as the fourth control command. The game host extracts third game data corresponding to the third control instruction, stores the third game data in a first memory (e.g., cache), extracts fourth game data corresponding to the fourth control instruction, and stores the fourth game data in a second memory (e.g., RAM).
In addition, if the third game data is originally stored in the first memory, the game host does not need to store the third game data in the first memory; if the fourth game data is originally stored in the second memory, the game host does not need to store the fourth game data in the second memory.
In the present application, the third priority information of the third control instruction is large, which indicates that the game pad has a large probability of generating the third control instruction in a subsequent period of time after receiving the first control instruction, and this is closely associated with the operation habit of the user. Because the probability of the third control instruction is higher, the game data of the third control instruction is extracted into the first memory (such as cache) with higher access speed in advance. The fourth control command has a lower probability of being accompanied with the third control command but still reaches a certain value, and the game data of the third control command is extracted in advance into a second memory (for example, RAM) having an access speed slightly lower than that of the first memory. The most scarce and highest-performance memory is used for storing game data which appears at the maximum probability, the second scarce and second highest-performance memory is used for storing game data which appears at the next highest probability, the game data are prefetched, and multi-level management is adopted, so that the processing mode can maximally balance the relationship between the scarcity of the high-performance memory and the large storage capacity of the game data and the high speed of transmission instructions of the game handle.
205. After the game host executes the first control instruction, if a control instruction which is the same as a second control instruction and is from the game handle is received, the second game data is read from the memory corresponding to the second priority information and processed.
After the game host prestores the second game data in the corresponding memory, if a control instruction which is the same as the second control instruction is received, the second game data is directly extracted from the corresponding memory and processed, so that the extraction speed of the second game data is greatly increased, and the speed of the game host responding to the client request is increased.
Furthermore, in a second time interval when the first instruction is received, if a control instruction which is the same as the second control instruction and is from the gamepad is not received, game data corresponding to the second instruction is stored in a hard disk with lower performance, so that the space of a high-performance memory is saved. If the control instruction which is the same as the second control instruction and is from the gamepad is not received in the second time interval of receiving the first instruction, the second control instruction is not an instruction to be sent next by the player, the prediction result of the current instruction is wrong, and at the moment, the instruction is stored in a memory with lower performance, so that the storage space of a high-performance memory can be saved. For example, if the second game data is stored in the first memory, in the second time interval when the first instruction is received, if the control instruction identical to the second control instruction is not received from the game pad, the second game data is saved in the second memory, or saved in a non-volatile memory such as a hard disk or Flash.
It should be noted that the second control instruction is only a predictive instruction, and is not an instruction executed immediately, and the second control instruction is used for instructing the game host to pre-extract game data possibly sent by the player to a designated memory in advance, so that when the same instruction as the second control instruction is received, the game data can be quickly read from the corresponding memory. Furthermore, when the game paddle sends the second control instruction to the game host, it needs to send a prompt message indicating that the second control instruction belongs to the predictive instruction instead of the instruction executed immediately to the host, so as to instruct the game host to pre-extract the game data corresponding to the second control instruction to the designated memory in advance instead of executing the second control instruction immediately. And the command which is received by the game host and is the same as the second control command is an immediate execution command, and the game host immediately starts to execute the specified operation of the player after receiving the command.
The game data comprises at least one of a game image to be rendered, configuration information and a rendering instruction associated with the game image, the game image comprises a static image and a dynamic image, the configuration information comprises configuration information required by image rendering and man-machine interaction, and the rendering instruction associated with the game image comprises instructions corresponding to operations such as image compression, image filtering, image combination and image shading.
In a possible implementation manner, the at least one second control instruction includes a third instruction and a fourth instruction, the at least one second priority information includes third priority information and fourth priority information, and the second game data corresponding to the at least one second control instruction includes third game data corresponding to the third instruction and fourth game data corresponding to the fourth instruction. After game data are stored in corresponding memories in advance, if a control instruction which is the same as a third control instruction and is from a game handle is received, the third game data are read from the first memory and processed; and if a control instruction which is the same as the fourth control instruction from the game handle is received, reading and processing the fourth game data from the second memory.
Further, in order to save the high performance memory, if the same control command as the third control command is not received within the second preset time, the third game data is stored in a lower performance memory (for example, the second memory or the nonvolatile memory). If the same control instruction as the fourth control instruction is not received within the second preset time, the fourth game data is stored in a lower-performance memory (e.g., a non-volatile memory).
206. Before the game paddle obtains the plurality of prediction models, obtaining a historical instruction, training the plurality of prediction models through the historical instruction, and storing the trained plurality of prediction models; the gamepad obtains a plurality of predictive models, including: a stored trained plurality of predictive models is obtained.
The historical instructions comprise control instructions triggered by the player, and the historical instructions can be used for training a prediction model to predict instructions which are likely to occur within a preset time after the first control instruction occurs. The historical instructions for training the model may be paired, and the time interval between each pair of historical instructions is less than the first time interval. Taking table 3 as an example, table 3:
| prior history instruction | Post history instructions |
| Control instruction 1 | Control instruction 2 |
| Control instruction 1 | Control instruction 3 |
| Control instruction 2 | Control instruction 4 |
| ...... | ...... |
Table 3 is an instruction set for training a prediction model, wherein a time interval between a previous historical instruction and a subsequent historical instruction is less than a preset time interval (e.g., 2 seconds), which may enable the trained model to predict an upcoming instruction.
In one possible implementation, a plurality of predictive models are associated with a first player identification, and an identification of a player is obtained before the gamepad obtains the plurality of predictive models; determining whether the identification matches the first player identification; the gamepad obtains a plurality of predictive models, including: in the event that the identity matches the first player identification, obtaining a plurality of predictive models associated with the first player identification.
Further, a plurality of historical instructions may also be associated with the first player identification. Acquiring a historical instruction, training a plurality of prediction models through the historical instruction, and storing the trained prediction models, wherein the method comprises the following steps: acquiring a historical instruction corresponding to a first player identification, training a plurality of prediction models through the historical instruction corresponding to the first player identification, and storing the trained prediction models and the first player identification in an associated manner.
The method comprises the steps of identifying the identity of a player, associating a prediction model and a historical instruction used for training the prediction model with the identity of the player, identifying the operation habit of a specific player, predicting a control instruction possibly sent by the player through a gamepad based on the operation habit of the player, and pre-extracting game data corresponding to the control instruction to memories with different performance levels, so that a processor can quickly read the game data from the memory with higher performance when executing the next control instruction, the whole performance optimization process is more targeted, and the data processing rate of a game host can be greatly improved.
In the process of sending interactive data through a WiFi network, the interactive data comprise predicted instructions and priority information of the instructions, so that part of network capacity needs to be occupied; when the game paddle carries out instruction prediction, partial processor computing power needs to be occupied, so that the problem of imbalance between the performance of a memory in the game host and the performance of a processor is solved by essentially sacrificing partial network capacity and the processor computing power of the game paddle, and the response speed of the game host is further improved. Such a cost is clearly acceptable today where network capacity and processor power are abundant.
Fig. 3 is a schematic structural diagram of a game device according to an embodiment of the present application. Wherein, the game device can be a game host or a game handle. The apparatus comprises: at least one processor 301, such as a Central Processing Unit (CPU), at least one memory 302, and at least one bus 303.
The memory 302 may store program instructions and the processor 301 may be configured to invoke the program instructions to perform a data processing method for the gamepad and the game host. It will be understood by those of ordinary skill in the art that all or part of the steps in the methods of the above embodiments may be performed by associated hardware instructed by a program, and the program may be stored in a computer-readable storage medium, where the storage medium includes a Read Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), a one-time programmable read only memory (OTPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a read only disk (CD-ROM), a Solid State Disk (SSD), or other SSD, Disk storage, tape storage, or any other medium readable by a computer that can be used to carry or store data.
Claims (10)
1. A data processing method for a game pad and a game console, wherein the game console and the game pad perform data interaction through a WIFI network, the data processing method is characterized by comprising the following steps:
the game handle captures operation information of a player and generates a first control instruction based on the operation information;
the game handle acquires a plurality of prediction models, the first control instruction is respectively input into the prediction models, and the prediction instruction corresponding to each prediction model is output;
the game handle determines at least one second control instruction from a plurality of prediction instructions and calculates at least one second priority information corresponding to the at least one second control instruction; wherein the second control instruction is an instruction of the plurality of predicted instructions that satisfies a specified number of conditions, and the second priority information indicates a probability of an accompanying occurrence of the second control instruction with the first control instruction within a first time interval;
the game handle sends the second control instruction and the second priority information to the game host after sending the first control instruction to the game host;
after receiving a second control instruction and second priority information, the game host searches second game data corresponding to the second control instruction and stores the second game data in a memory corresponding to the second priority information; the higher the probability value corresponding to the second priority information is, the higher the performance of the memory corresponding to the second priority information is;
after the game host executes the first control instruction, if a control instruction which is the same as a second control instruction and is from a game handle is received, the second game data is read from a memory corresponding to the second priority information and processed.
2. The method of claim 1, wherein the game host comprises a first memory and a second memory, wherein the performance of the first memory is greater than the performance of the second memory, and wherein the first memory or the second memory comprises one of a register, a cache, a main memory RAM, and a virtual memory;
the storing the second game data in a memory corresponding to the second priority information includes: if the second priority information is larger than a second threshold and smaller than a first threshold, storing the second game data in a second memory; and if the second priority information exceeds a first threshold value, storing the second game data in a first memory.
3. The method of claim 2, wherein the plurality of predictive models comprises a plurality of a back propagation neural network model, a kalman filter predictive model, a time series predictive model, a random forest model, a softmax logistic regression model, and a proximity algorithm model;
alternatively, the plurality of predictive models comprises a plurality of the following models: the method comprises the following steps of a plurality of back propagation neural network models with different parameters, a plurality of Kalman filtering prediction models with different parameters, a plurality of time sequence prediction models with different parameters, a plurality of random forest models with different parameters, a plurality of softmax logistic regression models with different parameters and a plurality of proximity algorithm models with different parameters.
4. The method of claim 3, wherein the determining, by the gamepad, at least one second control command from a plurality of predicted commands and calculating at least one second priority information corresponding to the at least one second control command comprises:
and the game paddle takes the instruction with the largest quantity in the plurality of predicted instructions as the second control instruction, and calculates the second priority information according to the proportion of the quantity of the second control instruction in the plurality of predicted instructions.
5. The method of claim 2 or 3, wherein the at least one second control command comprises a third command and a fourth command, the at least one second priority information comprises a third priority information and a fourth priority information, the second game data corresponding to the at least one second control command comprises a third game data corresponding to the third command and a fourth game data corresponding to the fourth command, the game pad determines the at least one second control command from a plurality of predicted commands and calculates the at least one second priority information corresponding to the at least one second control command, comprising:
the game handle takes the instruction with the largest number in the plurality of predicted instructions as the third control instruction, and calculates the third priority information according to the proportion of the number of the third control instruction in the plurality of predicted instructions; taking a second instruction in the plurality of predicted instructions as the fourth control instruction, and calculating the fourth priority information according to the proportion of the number of the fourth control instruction in the plurality of predicted instructions;
the storing the second game data in a memory corresponding to the second priority information includes: storing the third game data in a first memory and the fourth game data in a second memory;
if a control instruction which is the same as a second control instruction from the game handle is received, reading and processing the second game data from a memory corresponding to the second priority information, wherein the step of reading and processing the second game data comprises the following steps:
if a control instruction which is the same as the third control instruction and is from the game handle is received, reading and processing the third game data from the first memory;
and if a control instruction which is the same as the fourth control instruction from the game handle is received, reading and processing the fourth game data from the second memory.
6. The method of claim 3, further comprising, prior to the gamepad obtaining a plurality of predictive models:
the game handle acquires a historical instruction, trains a plurality of prediction models through the historical instruction, and stores the trained prediction models;
the gamepad obtains a plurality of predictive models, including: a stored trained plurality of predictive models is obtained.
7. The method of claim 6, wherein the plurality of predictive models are associated with a first player identification, and further comprising, prior to the gamepad retrieving the plurality of predictive models: acquiring an identity of a player; determining whether the identification matches the first player identification;
the gamepad obtains a plurality of predictive models, including: in the event that the identity matches the first player identification, obtaining a plurality of predictive models associated with the first player identification.
8. The method of claim 7, wherein the plurality of historical instructions are associated with a first player identification, and wherein obtaining the historical instructions, training a plurality of predictive models via the historical instructions, and storing the trained plurality of predictive models comprises:
acquiring a historical instruction corresponding to a first player identification, training a plurality of prediction models through the historical instruction corresponding to the first player identification, and storing the trained prediction models and the first player identification in an associated manner.
9. A gaming system comprising a game host and a gamepad, the game host and the gamepad interacting in data over a WiFi network, the game host and the gamepad configured to perform the method of any of claims 1-8.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 8.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024138816A1 (en) * | 2022-12-29 | 2024-07-04 | 瑞声开泰声学科技(上海)有限公司 | Gamepad data processing method and apparatus, and game system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107895440A (en) * | 2017-10-26 | 2018-04-10 | 广州市雷军游乐设备有限公司 | Method, apparatus, terminal device and the storage medium that game data obtains in advance |
| US20190012876A1 (en) * | 2017-07-08 | 2019-01-10 | Gaming Analytics Inc | Machine-learning platform for operational decision making |
| US10482100B1 (en) * | 2012-01-25 | 2019-11-19 | Zynga Inc. | Method to detect and score users who are likely to make payments in online social games |
| CN110559651A (en) * | 2019-09-16 | 2019-12-13 | 网易（杭州）网络有限公司 | Control method and device of cloud game, computer storage medium and electronic equipment |
| US20200306632A1 (en) * | 2019-03-27 | 2020-10-01 | Electronic Arts, Inc. | Predictive execution of distributed game engines |
| WO2020251608A1 (en) * | 2019-06-14 | 2020-12-17 | Roblox Corporation | Predictive data preloading |
-
2021
- 2021-06-18 CN CN202110681443.1A patent/CN113332707B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10482100B1 (en) * | 2012-01-25 | 2019-11-19 | Zynga Inc. | Method to detect and score users who are likely to make payments in online social games |
| US20190012876A1 (en) * | 2017-07-08 | 2019-01-10 | Gaming Analytics Inc | Machine-learning platform for operational decision making |
| CN107895440A (en) * | 2017-10-26 | 2018-04-10 | 广州市雷军游乐设备有限公司 | Method, apparatus, terminal device and the storage medium that game data obtains in advance |
| US20200306632A1 (en) * | 2019-03-27 | 2020-10-01 | Electronic Arts, Inc. | Predictive execution of distributed game engines |
| WO2020251608A1 (en) * | 2019-06-14 | 2020-12-17 | Roblox Corporation | Predictive data preloading |
| CN110559651A (en) * | 2019-09-16 | 2019-12-13 | 网易（杭州）网络有限公司 | Control method and device of cloud game, computer storage medium and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024138816A1 (en) * | 2022-12-29 | 2024-07-04 | 瑞声开泰声学科技(上海)有限公司 | Gamepad data processing method and apparatus, and game system |
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