Game skill consumption determination method, optimization method, device, medium, and apparatus
By calculating the correlation coefficient between game skills and processor load, high-consumption skills are accurately identified, solving the problem of poor positioning of high-consumption skills in existing technologies and achieving efficient skill optimization.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- NETEASE (HANGZHOU) NETWORK CO LTD
- Filing Date
- 2023-04-07
- Publication Date
- 2026-07-10
AI Technical Summary
In existing multiplayer online games, high-cost skills are poorly positioned, resulting in high CPU resource consumption and high optimization costs.
By acquiring game skill release data and processor load consumption data within a specified time period, the correlation coefficient between game skills and processor load is calculated to accurately identify high-consumption game skills.
It improves the accuracy and efficiency of identifying high-consumption skills, while reducing human and material costs.
Smart Images
Figure CN116421980B_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of game technology, and in particular to a method for determining game skill consumption, a method for optimizing game skills, an apparatus, a computer-readable storage medium, and an electronic device. Background Technology
[0002] Multiplayer online battles (MOBAs) are the core gameplay of most online games, where players unleash various skills in the same arena to compete. However, this process incurs very high computational costs, consuming significant CPU (Central Processing Unit) resources. If certain skills are poorly designed, they can consume a large amount of CPU resources. To reduce CPU consumption, some high-cost skills are typically optimized, thereby reducing the pressure on the server.
[0003] Currently, there are two main optimization methods. The first method targets the online environment, periodically sampling or modeling the call stack of the online server, organizing the results into a flame graph, and then optimizing the call processes with high CPU consumption. Although this method can obtain the real online environment, since the flame graph counts the call stack of all skills at the same time, it is impossible to know which skill consumes the most CPU resources. The second method involves building a stress testing environment offline, stress testing each skill, obtaining CPU consumption data and flame graphs, and then locating and optimizing high-consuming skills. However, there may be hundreds or thousands of skills in the game, and stress testing them one by one is extremely wasteful of manpower and resources.
[0004] Therefore, the current positioning of high-consumption skills is not very effective. Summary of the Invention
[0005] This disclosure provides a method for determining game skill consumption, a method for optimizing game skills, a device, a computer-readable storage medium, and an electronic device, thereby improving the positioning effect of high-consumption skills.
[0006] In a first aspect, one embodiment of this disclosure provides a method for determining game skill consumption, including:
[0007] Obtain skill release data for each game skill within a specified time period, as well as processor load consumption data for the corresponding game within the specified time period; wherein, the skill release data includes at least: skill identifier, and the number of skills released within each statistical time period;
[0008] For each game skill, the correlation coefficient between the game skill and the processor load is determined based on skill release data and load consumption data;
[0009] High-consumption game skills are identified from the game skills based on the correlation coefficient corresponding to each game skill.
[0010] Secondly, one embodiment of this disclosure provides a game skill optimization method, including:
[0011] High-cost game skills are determined according to any of the above methods for determining game skill costs.
[0012] Optimize the skills of each high-consumption game skill.
[0013] Thirdly, one embodiment of this disclosure provides a device for determining game skill consumption, comprising:
[0014] The acquisition module is used to acquire skill release data for each game skill within a specified time period, as well as processor load consumption data for the game within the specified time period; wherein, the skill release data includes at least: skill identifier, and the number of skills released within each statistical time period;
[0015] The first determining module is used to determine the correlation coefficient between the game skill and the processor load for each game skill based on skill release data and load consumption data;
[0016] The second determination module is used to identify high-consumption game skills from the game skills based on the correlation coefficient corresponding to each game skill.
[0017] Fourthly, one embodiment of this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the method described above.
[0018] Fifthly, one embodiment of this disclosure provides an electronic device, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the above-described method by executing the executable instructions.
[0019] The technical solution disclosed herein has the following beneficial effects:
[0020] The game skill consumption determination method provided in this embodiment first obtains the skill release data of each game skill within a specified time period, and the load consumption data of the corresponding processor within the specified time period. Then, for each game skill, the correlation coefficient between the game skill and the processor load is determined based on the skill release data and the load consumption data. Finally, high-consumption game skills are determined from the game skills according to the correlation coefficient corresponding to each game skill. This method can accurately determine high-consumption skills without requiring excessive staff involvement, thereby solving the current technical problem of poor positioning effect for high-consumption skills. It achieves the technical effects of improving the accuracy and efficiency of high-consumption skill determination and reducing determination costs.
[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.
[0023] Figure 1 This diagram illustrates a method for determining game skill consumption in this exemplary embodiment.
[0024] Figure 2 This diagram illustrates a method for determining game skill consumption in this exemplary embodiment.
[0025] Figure 3 This diagram illustrates a method for determining game skill consumption in this exemplary embodiment.
[0026] Figure 4 This exemplary embodiment shows a graph of processor load consumption versus the number of game skills released.
[0027] Figure 5 This example illustrates a processor load consumption curve and a curve of the number of game skills released after time-dimensional calibration in this exemplary embodiment.
[0028] Figure 6 This diagram illustrates a flowchart of a game skill optimization method in this exemplary embodiment;
[0029] Figure 7 This diagram illustrates the structure of a game skill consumption determination device according to this exemplary embodiment.
[0030] Figure 8 This diagram illustrates the structure of a game skill optimization device according to this exemplary embodiment.
[0031] Figure 9 A schematic diagram of the structure of an electronic device in this exemplary embodiment is shown. Detailed Implementation
[0032] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this disclosure more comprehensive and complete, and to fully convey the concept of exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of embodiments of this disclosure. However, those skilled in the art will recognize that the technical solutions of this disclosure can be practiced with one or more specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this disclosure.
[0033] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.
[0034] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.
[0035] In related technologies, multiplayer online competition is the core gameplay of most online games, where players unleash various skills in the same scene to compete in esports. However, this process incurs very high computational costs, with CPU (Central Processing Unit / Processor) resources being heavily consumed. If certain skills are poorly designed, they can consume a lot of CPU resources. To reduce CPU consumption, some high-consumption skills are generally optimized to reduce the pressure on the online server. Current optimization methods mainly include two approaches. The first approach targets the online environment, periodically sampling or modeling the call stack of the online server, organizing the results into a flame graph, and then optimizing the high-consumption call processes. While this method can capture the real online environment, since the flame graph statistically analyzes the call stack of all skills at the same time, it's impossible to know which skill consumes the most CPU resources. The second approach involves building a stress testing environment offline, stress testing each skill, obtaining CPU consumption data and flame graphs, and then identifying and optimizing high-consumption skills. However, games may have hundreds or thousands of skills, making individual stress testing extremely wasteful of manpower and resources. Therefore, the current positioning of high-consumption skills is not very effective.
[0036] In view of the above problems, this disclosure provides a method for determining the cost of game skills to identify high-cost skills from a variety of skills. The following is a brief introduction to the application environment of the game skill cost determination method provided by this disclosure:
[0037] The application environment of the game skill consumption determination method provided in this disclosure includes at least a game processor. This game processor is used to calculate data during game execution. A game generally includes a user terminal and a server terminal. The server terminal is the game processor terminal. The user terminal can be configured on the game processor or independently on another terminal device, allowing different players to interact with the game through different game accounts on different terminal devices. This terminal device can be a server, computer, laptop, or other wearable devices, etc., and is not exhaustively listed here. Game skills in this disclosure refer to game actions and other game mechanics or animation effects produced when virtual characters interact in the game. These generally include: attack, defense, control, and recovery. Specific game skill types and effects are set according to actual conditions and are not listed here.
[0038] The following example, using the aforementioned terminal device as the execution subject, illustrates how this method for determining game skill consumption is applied to the terminal device to identify high-consumption skills from multiple game skills. Please refer to [link / reference]. Figure 1 The method for determining game skill consumption provided in this embodiment includes the following steps 101-103:
[0039] Step 101: Obtain skill release data for each game skill within a specified time period, as well as processor load consumption data for the game within a specified time period.
[0040] The specified duration can be open-sourced and configured by players or staff, such as 1 hour, 30 minutes, etc. Skill release data refers to the data generated when a player initiates a skill. This data includes at least the skill identifier and the number of skill releases within each statistical duration. For example, skill 1 might be released 5 times within the statistical duration, skill 2 6 times, and so on. It should be noted that the statistical duration can be equal to or less than the specified duration; no specific limitation is made here. Load consumption data refers to the processor resource consumption during skill release. This can be represented by specific memory size or by the processor's memory percentage; no specific limitation is made in this embodiment.
[0041] Step 102: For each game skill, determine the correlation coefficient between the game skill and the processor load based on the skill release data and load consumption data.
[0042] The more skills are released, the greater the processor load. Therefore, when only one game skill is released at a time, the more times that skill is released, the greater the processor load, and the fewer skills are released, the less the processor load. The two are positively correlated.
[0043] The higher the load consumption of a game skill, the stronger the correlation between the number of skill releases and the processor load. For example, releasing game skill 1 twice results in a processor load of n, while releasing it six times results in a processor load of 3n. Therefore, the processor load increases with the number of times game skill 1 is released. Conversely, if releasing game skill 1 twice results in a processor load of n, while releasing it six times results in a processor load of 1.1n, the correlation between the processor load and the number of times game skill 1 is released is not significant. Therefore, this embodiment of the disclosure, for a specific game skill, determines the correlation coefficient between the game skill and the processor load by using skill release data and load consumption data. This correlation coefficient characterizes the correlation between the number of skill releases and the processor load. This correlation coefficient can be directly obtained as the ratio between the number of skill releases and the processor load, or it can be obtained through other methods such as normalization. No limitation is imposed here.
[0044] Step 103: Determine the high-consumption game skills from the game skills based on the correlation coefficient corresponding to each game skill.
[0045] The high-consumption game skill can be identified in at least two ways: First, set a correlation coefficient threshold, and compare the correlation coefficient of each game skill with the threshold. Game skills with a correlation coefficient greater than or equal to the threshold are identified as high-consumption game skills. Second, identify a certain percentage of game skills with a large correlation coefficient as high-consumption game skills. For example, identify 30% of all game skills with a large correlation coefficient as high-consumption game skills.
[0046] The game skill consumption determination method provided in this embodiment first obtains the skill release data of each game skill within a specified time period, and the load consumption data of the corresponding processor within the specified time period. Then, for each game skill, the correlation coefficient between the game skill and the processor load is determined based on the skill release data and the load consumption data. Finally, high-consumption game skills are determined from the game skills according to the correlation coefficient corresponding to each game skill. This method can accurately determine high-consumption skills without requiring excessive staff involvement, thereby solving the current technical problem of poor positioning effect for high-consumption skills. It achieves the technical effects of improving the accuracy and efficiency of high-consumption skill determination and reducing determination costs.
[0047] Please see Figure 2 In an optional embodiment of this disclosure, step 101, obtaining skill release data of each game skill within a specified time period and processor load consumption data corresponding to the game within a specified time period, includes the following steps 201-203:
[0048] Step 201: Collect the processor's load consumption data according to the preset cycle, and store the load consumption data in the log database.
[0049] The log library refers to the storage module configured in the game processor during game runtime, which stores all event information generated during game operation. The preset period can be set according to the actual situation, such as 1 second, 0.5 seconds, etc., which means that load consumption data is continuously collected from the processor at a fixed period and stored in the log library.
[0050] Step 202: In response to the release of game skills, store the skill release data in the log database.
[0051] When a player uses a game skill, corresponding skill release data will be generated. This data will be stored in the log database configured above. At this point, a player can release one game skill at a time, or multiple skills simultaneously; there are no specific limitations, as long as the system can record and store the corresponding skill release data.
[0052] Step 203: For each game skill, retrieve the skill release data and corresponding load consumption data within the same time interval from the log database.
[0053] This embodiment first collects processor load consumption data according to a preset cycle and stores the load consumption data in a log database. In response to the release of game skills, the skill release data of the game skills is stored in the log database. Finally, for each game skill, the skill release data and corresponding load consumption data within the same time interval are sequentially retrieved from the log database. This yields the skill release data and the corresponding processor load consumption data within the same time interval, which is convenient and fast. No subsequent time dimension calibration is required, and it can be used directly, which can greatly improve the efficiency of determining game skill consumption in this embodiment.
[0054] In one optional embodiment of this disclosure, the aforementioned load consumption data includes data obtained based on the parsing of processor performance metric parameters.
[0055] After obtaining the processor's performance metrics, which include all game skill data, virtual character status data, and game interaction data, this embodiment parses these metrics to obtain the load consumption data, facilitating the subsequent determination of correlation coefficients. It should be noted that this parsing process can be performed using data identifiers or other methods, without specific limitations here.
[0056] Please see Figure 3 In an optional embodiment of this disclosure, step 102, determining the correlation coefficient between the game skill and the processor load for each game skill based on skill release data and load consumption data, includes the following steps 301-303:
[0057] Step 301: Construct a first curve based on the load consumption data and determine the first curvature of the first curve.
[0058] like Figure 4 The CPU curve constructed in the figure is the first curve of load consumption data at different times. This first curvature can be obtained by numerical calculation or directly by geometric method through the ratio of the change in the vertical axis to the change in the horizontal axis. No limitation is made here.
[0059] Step 302: For each game skill, construct the second curve of the game skill based on the skill release data, and determine the second curvature of the second curve.
[0060] like Figure 4In this context, mem (storage), avatar (virtual image), magicField (magic area), and mainThreadCpuList (main thread CPU list) are other relevant computer parameters. This second curvature can be obtained through numerical calculation or directly through geometric methods using the ratio of the change in the vertical coordinate to the change in the horizontal coordinate; no specific limitation is made here.
[0061] Step 303: For each game skill, determine the correlation coefficient between the game skill and the processor load based on the ratio of the second curvature to the first curvature.
[0062] This embodiment first constructs a first curve based on load consumption data and a second curve based on skill release data for each game skill. Then, for each game skill, the correlation coefficient between the game skill and the processor load is determined based on the ratio of the second curvature to the first curvature. The method for determining the correlation coefficient is simple and quick, which can greatly improve the efficiency of determining game skill consumption.
[0063] In an optional embodiment of this disclosure, before step 303 above determines the correlation coefficient between the game skill and the processor load based on the ratio of the second curvature to the first curvature for each game skill, the game skill consumption determination method further includes the following steps:
[0064] Align the first curvature of the first curve with the second curvature of the second curve in the time dimension.
[0065] To prevent time discrepancies between skill release data and processor load consumption data, embodiments of this disclosure align the first and second curves, or the first and second curvatures, along the time dimension. For example... Figure 5 This is a graph where the first and second curves are directly aligned over time. This method ensures that skill release data and processor load consumption data are perfectly aligned over time, further improving the accuracy and reliability of the correlation coefficient.
[0066] In one optional embodiment of this disclosure, the correlation coefficient is characterized by at least one of the Pearson correlation coefficient or the rank correlation coefficient.
[0067] The Pearson correlation coefficient, also known as product-moment correlation (or product-moment correlation), can accurately measure the linear relationship of continuous data, normal distribution, etc., and is more precise. Rank correlation coefficients, such as Kendall's rank correlation coefficient and Spearman's correlation coefficient, can also calculate the correlation between two curves and are applicable to different types of curves, making them more widely applicable and flexible.
[0068] Please see Figure 6This disclosure provides a method for optimizing game skills, including the following steps 601-602:
[0069] Step 601: Determine the high-cost game skills according to the game skill consumption determination method mentioned above.
[0070] The beneficial effects of this method for determining skill consumption in the game have been described in detail in the above embodiments, and will not be repeated here.
[0071] Step 602: Optimize the high-consumption game skills.
[0072] Skill optimization includes, but is not limited to, optimizing the structure, mode, and mechanism of game skills. In this embodiment, it mainly focuses on optimizing skill consumption. The content and form of optimization do not constitute a specific limitation on the game skill optimization method of this embodiment.
[0073] By first identifying high-consumption game skills using the aforementioned method for determining game skill consumption, high-consumption game skills can be located quickly, efficiently, and accurately. Then, skill optimization for these high-consumption game skills is more efficient, further improving the optimization efficiency of the game skill game method in this embodiment.
[0074] To implement the above-mentioned method for determining game skill consumption, one embodiment of this disclosure provides a game skill consumption determination device 700. Figure 7 A schematic diagram of a game skill consumption determination device 700 is shown, including: an acquisition module 710, a first determination module 720, and a second determination module 730, wherein:
[0075] The acquisition module 710 is used to acquire skill release data of each game skill within a specified time period, as well as the processor load consumption data of the game within the specified time period; wherein, the skill release data includes at least: skill identifier, and the number of skills released within each statistical time period;
[0076] The first determining module 720 is used to determine the correlation coefficient between the game skill and the processor load based on the skill release data and load consumption data for each game skill.
[0077] The second determining module 730 is used to determine high-consumption game skills from the game skills based on the correlation coefficient corresponding to each game skill.
[0078] In one optional embodiment of this disclosure, the acquisition module 710 is specifically used to: collect processor load consumption data according to a preset period and store the load consumption data in a log database; in response to the release of game skills, store the skill release data of game skills in a log database; and for each game skill, sequentially acquire skill release data and corresponding load consumption data in the same time interval from the log database.
[0079] In one optional embodiment of this disclosure, the load consumption data includes data obtained based on the parsing of processor performance metric parameters.
[0080] In one optional embodiment of this disclosure, the first determining module 720 is specifically configured to: construct a first curve based on load consumption data and determine a first curvature of the first curve; construct a second curve for each game skill based on skill release data and determine a second curvature of the second curve; and determine a correlation coefficient between the game skill and the processor load for each game skill based on the ratio of the second curvature to the first curvature.
[0081] In an optional embodiment of this disclosure, the first determining module 720 is further configured to align the first curvature of the first curve with the second curvature of the second curve in the time dimension.
[0082] In one optional embodiment of this disclosure, the correlation coefficient is characterized by at least one of the Pearson correlation coefficient or the rank correlation coefficient.
[0083] To implement the above-mentioned game skill optimization method, one embodiment of this disclosure provides a game skill optimization device 800. Figure 8 A schematic diagram of a game skill optimization device 800 is shown, including: a third determining module 810 and an optimization module 820, wherein:
[0084] The third determining module 810 is used to determine high-consumption game skills according to any of the above game skill consumption determining methods;
[0085] This optimization module 820 is used to optimize various high-consumption game skills.
[0086] Exemplary embodiments of this disclosure also provide a computer-readable storage medium that can be implemented as a program product including program code, which, when run on an electronic device, causes the electronic device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure. In one embodiment, the program product can be implemented as a portable compact disc read-only memory (CD-ROM) and include program code, and can run on an electronic device, such as a personal computer. However, the program product of this disclosure is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0087] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0088] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.
[0089] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0090] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider). In embodiments of this disclosure, when the program code stored in the computer-readable storage medium is executed, it can implement any step of the above-described game skill consumption determination method and game skill optimization method.
[0091] Please see Figure 9 Exemplary embodiments of this disclosure also provide an electronic device 900, which can be a backend server of an information platform. References are provided below. Figure 9 This electronic device 900 is described below. It should be understood that... Figure 9 The electronic device 900 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.
[0092] like Figure 9 As shown, the electronic device 900 is presented in the form of a general-purpose computing device. The components of the electronic device 900 may include, but are not limited to: at least one processing unit 910, at least one storage unit 920, and a bus 930 connecting different system components (including the storage unit 920 and the processing unit 910).
[0093] The storage unit stores program code, which can be executed by the processing unit 910 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 910 can perform actions such as... Figure 1 The methods and steps shown are as follows.
[0094] Storage unit 920 may include volatile storage units, such as random access memory (RAM) 921 and / or cache memory 922, and may further include read-only memory (ROM) 923.
[0095] Storage unit 920 may also include a program / utility 924 having a set (at least one) program module 925, such program module 925 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.
[0096] Bus 930 may include a data bus, an address bus, and a control bus.
[0097] Electronic device 900 can also communicate with one or more external devices 2000 (e.g., keyboards, pointing devices, Bluetooth devices, etc.) via input / output (I / O) interface 940. Electronic device 900 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via network adapter 950. As shown, network adapter 950 communicates with other modules of electronic device 900 via bus 930. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 900, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0098] In this embodiment of the disclosure, when the program code stored in the electronic device is executed, it can implement any step of the above-mentioned method for determining game skill consumption and method for optimizing game skills.
[0099] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to exemplary embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.
[0100] Those skilled in the art will understand that various aspects of this disclosure can be implemented as systems, methods, or program products. Therefore, various aspects of this disclosure can be embodied in entirely hardware implementations, entirely software implementations (including firmware, microcode, etc.), or implementations combining hardware and software aspects, collectively referred to herein as “circuit,” “module,” or “system.” Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the claims.
[0101] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is defined only by the appended claims.
Claims
1. A method for determining the consumption of game skills, characterized in that, include: Acquire skill release data for each game skill within a specified time period, and processor load consumption data for the game within the specified time period; wherein, the skill release data includes at least: skill identifier, and the number of skills released within each statistical time period; For each of the aforementioned game skills, a correlation coefficient between the game skill and the processor load is determined based on the skill release data and the load consumption data; High-consumption game skills are determined from the game skills based on the correlation coefficient corresponding to each game skill; Specifically, for each of the game skills, determining the correlation coefficient between the game skill and the processor load based on the skill release data and the load consumption data includes: A first curve is constructed based on the load consumption data, and a first curvature of the first curve is determined; For each of the game skills, a second curve for the game skill is constructed based on the skill release data, and a second curvature of the second curve is determined; For each of the game skills, the correlation coefficient between the game skill and the processor load is determined based on the ratio of the second curvature to the first curvature.
2. The method according to claim 1, characterized in that, The acquisition of skill release data for each game skill within a specified time period, and the processor load consumption data corresponding to the game within the specified time period, includes: The load consumption data of the processor is collected according to a preset period, and the load consumption data is stored in the log database; In response to the release of the game skill, the skill release data of the game skill is stored in the log database; For each game skill, the skill release data and corresponding load consumption data within the same time interval are sequentially retrieved from the log database.
3. The method according to claim 1, characterized in that, The load consumption data includes data obtained by parsing the performance metric parameters of the processor.
4. The method according to claim 2, characterized in that, The step of responding to the release of the game skill by storing the skill release data in the log database includes: In response to each time a player releases a game skill or releases multiple game skills simultaneously, the system records and stores the corresponding skill release data.
5. The method according to claim 1, characterized in that, Before determining the correlation coefficient between the game skill and the processor load based on the ratio of the second curvature to the first curvature for each of the game skills, the method further includes: Align the first curvature of the first curve with the second curvature of the second curve in the time dimension.
6. The method according to claim 1, characterized in that, The correlation coefficient is characterized by at least one of the Pearson correlation coefficient or the rank correlation coefficient.
7. A method for optimizing game skills, characterized in that, include: The method for determining game skill consumption according to any one of claims 1-6 determines high-consumption game skills; Optimize the high-consumption game skills mentioned above.
8. A device for determining the consumption of game skills, characterized in that, include: The acquisition module is used to acquire skill release data of each game skill within a specified time period, as well as the processor load consumption data of the game within the specified time period; wherein, the skill release data includes at least: skill identifier, and the number of skills released within each statistical time period; The first determining module is used to determine, for each of the game skills, the correlation coefficient between the game skill and the processor load based on the skill release data and the load consumption data; The second determining module is used to determine high-consumption game skills from the game skills based on the correlation coefficient corresponding to each game skill; Specifically, for each of the game skills, determining the correlation coefficient between the game skill and the processor load based on the skill release data and the load consumption data includes: A first curve is constructed based on the load consumption data, and a first curvature of the first curve is determined; For each of the game skills, a second curve for the game skill is constructed based on the skill release data, and a second curvature of the second curve is determined; For each of the game skills, the correlation coefficient between the game skill and the processor load is determined based on the ratio of the second curvature to the first curvature.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7.
10. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1 to 7 by executing the executable instructions.