A game processing method, device and storage medium based on smart bicycle

Abstract

This application discloses a game processing method, apparatus, and storage medium based on a smart bicycle, comprising: determining the change of the first gear position of the smart bicycle corresponding to the target user within a first historical time period, and collecting multiple first gear position samples corresponding to each first time slice within the first historical time period; determining the first transition matrix corresponding to each gear position based on the multiple first gear position samples corresponding to the target user, and constructing a Markov chain; predicting the probability value corresponding to each gear position in the next time slice based on the gear position corresponding to the current time slice and the first transition matrix corresponding to each gear position; determining the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear position in the next time slice; and displaying the game screen according to the determined game parameter value in the next time slice.

Description

Technical Field

[0001] The present application relates to the technical field of game screen processing, and in particular to a game processing method, device and storage medium based on a smart bicycle. Background Art

[0002] With the continuous development of society, people pay more and more attention to their physical health, so they usually choose to improve their physical fitness through fitness. Furthermore, since most young people now usually choose to relax at home instead of going out for cycling, fitness bikes that occupy a small area and make little noise have become one of the most popular fitness equipment for young people.

[0003] However, since riding in a fixed area makes it impossible for people to experience the feeling of riding outside, in order to increase the fun of users, a smart bicycle that combines cycling fitness and virtual games has been developed. For example, during the user's riding, the display device associated with the smart bicycle can display the game screen corresponding to the virtual game, and the audio associated with the smart bicycle can play the sound effects corresponding to the game screen of the virtual game. In addition, the user can also control the wheel speed of the smart bicycle and the riding direction of the smart bicycle, thereby controlling the action, speed and direction of the game character associated with the user in the display device. Thus, the user can experience the virtual game in an immersive way while using the smart bicycle for cycling fitness.

[0004] Referring to the above, it can be seen that if the user's gaming experience is to be better, the display device needs to be able to simultaneously display the game screen of the virtual game corresponding to the wheel speed while the rider controls the wheel speed of the smart bike. That is, the game screen displayed by the display device associated with the smart bike should not be stuck or delayed, and the consistency of the game screen and the wheel speed should be ensured as much as possible.

[0005] Existing methods for solving the above problems include adjusting the game screen in real time according to the wheel speed, or adjusting the game screen with a delay according to the wheel speed. However, if the game screen is adjusted in real time according to the wheel speed, the generation state of the game screen may be unstable; if the game screen is adjusted with a delay according to the wheel speed, the game screen may be inconsistent with the wheel speed of the smart bicycle. Therefore, whether the game screen is adjusted in real time according to the wheel speed or the game screen is adjusted with a delay according to the wheel speed, the above problems cannot be solved.

[0006] With regard to the technical problem in the above-mentioned prior art that the game screen displayed by the display device associated with the smart bicycle often has freezes or delays, and the consistency between the game screen and the wheel speed cannot be guaranteed, thus affecting the user's riding experience, no effective solution has been proposed yet. Summary of the invention

[0007] The embodiments of the present disclosure provide a game processing method, device and storage medium based on a smart bicycle, so as to at least solve the technical problem in the prior art that the game screen displayed by the display device associated with the smart bicycle often has freezes or delays, and the consistency between the game screen and the wheel speed cannot be guaranteed, thereby affecting the user's riding experience.

[0008] According to one aspect of an embodiment of the present disclosure, a game processing method based on a smart bicycle is provided, including: determining a change in the first gear of a smart bicycle corresponding to a target user within a first historical period, and collecting a plurality of first gear samples corresponding to each first time slice within the first historical period, wherein the first gear samples correspond to the change in the first gear; based on the plurality of first gear samples corresponding to the target user, determining a first transfer matrix corresponding to each gear, and constructing a Markov chain, wherein the gear corresponds to a wheel speed of the smart bicycle pedaled by the target user; predicting a probability value corresponding to each gear in a next time slice according to the gear corresponding to the current time slice and the first transfer matrix corresponding to each gear; determining a game parameter value of a game character corresponding to the target user based on the probability value corresponding to each gear in the next time slice; and displaying a game screen in the next time slice according to the determined game parameter values.

[0009] According to another aspect of an embodiment of the present disclosure, a storage medium is further provided, the storage medium including a stored program, wherein when the program is running, a processor executes any one of the methods described above.

[0010] According to another aspect of the embodiment of the present disclosure, a game processing device based on a smart bicycle is also provided, including: a data acquisition module, used to determine the change of the first gear of the smart bicycle corresponding to the target user within a first historical period, and collect multiple first gear samples corresponding to each first time slice within the first historical period, wherein the first gear samples correspond to the change of the first gear; a first transfer matrix determination module, used to determine the first transfer matrix corresponding to each gear based on the multiple first gear samples corresponding to the target user, and construct a Markov chain, wherein the gear corresponds to the wheel speed of the smart bicycle pedaled by the target user; a first prediction module, used to predict the probability value corresponding to each gear in the next time slice according to the gear corresponding to the current time slice and the first transfer matrix corresponding to each gear; a game parameter value determination module, used to determine the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear in the next time slice; and a game screen display module, used to display the game screen in the next time slice according to the determined game parameter value.

[0011] According to another aspect of an embodiment of the present disclosure, a game processing device based on a smart bicycle is also provided, including: a processor; and a memory, connected to the processor, for providing the processor with instructions for processing the following processing steps: determining the change of the first gear of the smart bicycle corresponding to the target user within a first historical period, and collecting multiple first gear samples corresponding to each first time slice within the first historical period, wherein the first gear samples correspond to the change of the first gear; based on the multiple first gear samples corresponding to the target user, determining the first transfer matrix corresponding to each gear, and constructing a Markov chain, wherein the gear corresponds to the wheel speed of the smart bicycle pedaled by the target user; predicting the probability value corresponding to each gear in the next time slice according to the gear corresponding to the current time slice and the first transfer matrix corresponding to each gear; determining the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear in the next time slice; and displaying the game screen in the next time slice according to the determined game parameter value.

[0012] The present application provides a game processing method based on a smart bike. First, the processor determines the change of the first gear of the smart bike corresponding to the target user within the first historical period, and collects multiple first gear samples corresponding to each first time slice within the first historical period. Then, the processor determines the first transfer matrix corresponding to each gear based on the multiple first gear samples corresponding to the target user, and constructs a Markov chain. After that, the processor predicts the probability value corresponding to each gear in the next time slice based on the gear corresponding to the current time slice and the first transfer matrix corresponding to each gear. Further, the processor determines the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear in the next time slice. Finally, the processor displays the game screen in the next time slice according to the determined game parameter value.

[0013] With reference to the above contents, it can be known that the present application predicts the wheel speed gear of the smart bicycle corresponding to the target user in the next time slice according to the wheel speed gear of the smart bicycle corresponding to the target user in the current time slice. And according to the wheel speed gear of the smart bicycle corresponding to the target user in the next time slice, the game parameter value of the game character corresponding to the target user is determined. Then, the game screen is displayed on the display device through the determined game parameter value corresponding to the game character.

[0014] Therefore, compared with the prior art in which the game screen of the virtual game is adjusted in real time according to the wheel speed of the smart bicycle, since the present application determines the game parameter value corresponding to the game character based on the predicted wheel speed gear of the smart bicycle in the next time slice, and displays the game screen on the display device according to the predetermined game parameter value, the processor has sufficient time to display the game screen, thereby avoiding the problem of unstable generation state of the virtual scene.

[0015] Furthermore, compared to the prior art in which the game screen of the virtual game is adjusted according to the wheel speed of the smart bicycle, since the present application predicts the wheel speed gear of the smart bicycle corresponding to the target user in the next time slice in advance based on the wheel speed gear of the smart bicycle corresponding to the target user in the current time slice, the game screen finally generated based on the prediction result will not be delayed or stuck.

[0016] Thus, the consistency between the game screen of the virtual game and the wheel speed can be achieved, and the riding experience of the target user can be improved. This solves the technical problem in the prior art that the game screen displayed by the display device associated with the smart bicycle often has a freeze or delay, and the consistency between the game screen and the wheel speed cannot be guaranteed, thereby affecting the riding experience of the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present application. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0018] Figure 1 It is a hardware structure block diagram of a computing device for implementing a game processing method based on a smart bicycle according to Embodiment 1 of the present application;

[0019] Figure 2 is a schematic diagram of a game processing system based on a smart bicycle according to Embodiment 1 of the present application;

[0020] Figure 3 is a schematic diagram of a game screen displayed by the display device according to Embodiment 1 of the present application;

[0021] Figure 4 is a flowchart of the game processing method based on a smart bicycle according to Example 1 of the present application;

[0022] Figure 5 is a modular schematic diagram of a game processing device based on a smart bicycle according to Embodiment 2 of the present application;

[0023] Figure 6This is a modular schematic diagram of a smart bicycle-based game processing device according to Example 3 of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0026] Example 1

[0027] According to this embodiment, a method embodiment of game processing based on a smart bicycle is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0028] The method embodiment provided in this embodiment can be executed in a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 FIG. 1 shows a hardware structure block diagram of a computing device for implementing a game processing method based on a smart bicycle. Figure 1 As shown, the computing device may include one or more processors (the processor may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, a transmission device for communication functions, and an input / output interface. The memory, the transmission device, and the input / output interface are connected to the processor via a bus. In addition, it may also include: a display, a keyboard, and a cursor control device connected to the input / output interface. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0029] It should be noted that the one or more processors and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computing device. As involved in the embodiments of the present disclosure, the data processing circuitry acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0030] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the game processing method based on the smart bike in the embodiment of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the game processing method based on the smart bike of the above-mentioned application program is realized. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories can be connected to the computing device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0031] The transmission device is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computing device. In one example, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0032] The display may be, for example, a touch screen liquid crystal display (LCD) that may enable a user to interact with a user interface of the computing device.

[0033] It should be noted that, in some optional embodiments, the above Figure 1The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the computing devices described above.

[0034] Figure 2 is a schematic diagram of a game processing system based on a smart bicycle according to this embodiment. Figure 2 As shown, the system includes: a smart bike 110, a processor device 120, an audio system 130, and a display device 140. The smart bike 110 is connected to the processor device 120 in communication, and the processor device 120 is used to predict the wheel speed of the smart bike 110 in the next time slice according to the wheel speed of the smart bike 110 in the current time slice. The processor device 120 is also connected to the display device 140 and the audio system 130 in communication, respectively. The processor device 120 is used to determine the game parameter value of the game character corresponding to the target user based on the predicted wheel speed of the smart bike 110 in the next time slice, and control the display device 140 to display the game screen according to the determined game parameter value. And when the display device 140 displays the game screen according to the determined game parameter value, the processor device 120 controls the audio system 130 to play the corresponding sound effect.

[0035] In addition, the smart bicycle 110 is also provided with a speed measuring device for measuring the wheel speed of the smart bicycle 110 when the target user pedals. The speed measuring device can be coupled to the wheel shaft of the smart bicycle 110, so as to measure the wheel speed of the smart bicycle 110.

[0036] Furthermore, the handle of the smart bicycle 110 is also provided with direction keys and special effect keys, etc., for controlling the movement direction and special effect actions of the game character.

[0037] In addition, the smart bike 110 is also equipped with an RFID identifier, so that the target user needs to swipe the card to identify the identity when using the smart bike 110. In addition, since the processor device 120 of each smart bike is connected to the server for communication, data information corresponding to the target user can be obtained from the server, such as historical game parameter values ​​and transfer matrices.

[0038] Figure 3 is a schematic diagram of a game screen displayed by the display device 130 according to an embodiment of the present application. Figure 3As shown, the game screen may include, for example, a game character corresponding to the target user and other game characters, wherein the other game characters include, but are not limited to, game characters corresponding to other users or game characters generated by the processor device 120 in a networked environment.

[0039] In addition, the game screen also includes a "skill cooling status bar". The "skill cooling status bar" may include, for example, a "cooling indication control 1", a "cooling indication control 2" and a "cooling indication control 3".

[0040] In the above operating environment, according to the first aspect of this embodiment, a game processing method based on a smart bicycle is provided. The method comprises: Figure 2 The processor device 120 shown in FIG. Figure 4 A schematic diagram showing the process of the method is shown in FIG. Figure 4 As shown, the method includes:

[0041] S402: determining a change in the first gear position of the smart bicycle corresponding to the target user within a first historical period, and collecting a plurality of first gear position samples corresponding to each first time slice within the first historical period, wherein the first gear position samples correspond to the change in the first gear position;

[0042] S404: determining a first transfer matrix corresponding to each gear based on a plurality of first gear samples corresponding to the target user, and constructing a Markov chain, wherein the gear corresponds to a wheel speed of the smart bicycle pedaled by the target user;

[0043] S406: predicting probability values ​​corresponding to each gear position in the next time slice according to the gear position corresponding to the current time slice and the first transfer matrix corresponding to each gear position;

[0044] S408: Determine a game parameter value of a game character corresponding to the target user based on the probability values ​​corresponding to each gear position in the next time slice; and

[0045] S410: In the next time slice, the game screen is displayed according to the determined game parameter values.

[0046] Specifically, in this embodiment, the wheel speed of the smart bicycle 110 can be divided into 1 to m gears, and as the gears continue to increase, the wheel speed of the smart bicycle 110 also continues to increase. For example, Table 1 shows the gears of the wheel speed of the smart bicycle and the game parameters corresponding to the gears.

[0047] Table 1

[0048]

[0049]

[0050] As shown in Table 1, the wheel speed of the smart bicycle 110 includes m gears, and as the gears continue to increase, the wheel speed of the smart bicycle 110 also continues to increase. For example, the wheel speed of the smart bicycle 110 corresponding to the second gear is greater than the wheel speed of the smart bicycle 110 corresponding to the first gear.

[0051] In addition, the gear of the wheel speed of the smart bicycle 110 corresponds to the game parameter, and different gears correspond to different game parameter values. The game parameters corresponding to the gear of the wheel speed may include, for example, the moving speed of the game character corresponding to the target user in the game screen, the special effect brightness of the game character corresponding to the target user in the game screen, and the skill cooling time of the game character corresponding to the target user in the game screen. The above content will be described in detail later, so it will not be repeated here.

[0052] Thus, the processor device 120 can predetermine the first historical period T x For example, in the first historical period T x In the first historical period T, the processor device 120 of the smart bicycle 110 determines that the target user has ridden 10 km, and during the target user's riding of 10 km, a total of n gear changes occurred. x The n gear changes within the range are determined as the first gear change corresponding to the target user.

[0053] After the processor device 120 determines the change of the first gear of the smart bicycle 110 corresponding to the target user, the first historical period is further divided into a plurality of first time slices according to the preset time slices. And at each first time slice In the first historical period T, the gear change occurs. The size of the preset time slice may be, for example, 5s. Thus, the processor device 120 can collect x Multiple first time slices within Corresponding multiple first gear samples (S402).

[0054] For example, the processor device 120 acquires the first time slice Corresponding first gear sample The processor device 120 acquires the first time slice Corresponding first gear sample ...; The processor device 120 acquires the first time slice Corresponding first gear sample

[0055] It is worth noting that in this embodiment, each first time slice Includes a first-stage sample For example, it should be clear to those skilled in the art that when the sizes of the preset time slices are different, the first time slice The number of first-gear samples included in the first time slice is also different. Any first time slice in may include multiple first-gear samples, or may not include any first-gear samples.

[0056] Further, the processor device 120 determines a plurality of first gear samples corresponding to the target user. In the case of Determine the first transfer matrix A corresponding to each gear 1 , and construct a Markov chain (S404). The gear position corresponds to the wheel speed of the smart bicycle pedaled by the target user.

[0057] Specifically, when the processor device 120 determines a plurality of first gear samples corresponding to the target user, a first transfer matrix A corresponding to each gear is defined: 1 :

[0058]

[0059] Among them, parameter a 11 represents the probability of changing from the first gear to the second gear; parameter a 12 represents the probability of changing from the first gear to the second gear; parameter a 1m represents the probability of changing from the first gear to the mth gear; parameter a 21 represents the probability of changing from the second gear to the first gear; parameter a 22 represents the probability of changing from the second gear to the second gear; parameter a 2m represents the probability of changing from the second gear to the mth gear; parameter a m1 represents the probability of changing from the mth gear to the first gear; parameter a m2 represents the probability of changing from the mth gear to the second gear; and parameter a m3 Represents the probability of changing from the mth gear to the third gear.

[0060] Furthermore, the processor device 120 defines a first transfer matrix A corresponding to each gear position. 1 In the case of, the first transfer matrix A is further calculated based on the following formula 1 The values ​​of each parameter.

[0061]

[0062] Among them, N 1 represents the number of first gear samples corresponding to the first gear; N 1,1 It indicates that among the first gear samples corresponding to the first gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the first gear.

[0063]

[0064] Among them, N 1 represents the number of first gear samples corresponding to the first gear; N 1,2 It indicates that among the first gear samples corresponding to the first gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the second gear.

[0065]

[0066] Among them, N 1 represents the number of first gear samples corresponding to the first gear; N 1,m It indicates that among the first gear samples corresponding to the first gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the mth gear.

[0067]

[0068] Among them, N 2 represents the number of first gear samples corresponding to the second gear; N 2,1 It indicates that among the first gear samples corresponding to the second gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the first gear.

[0069]

[0070] Among them, N 2 represents the number of first gear samples corresponding to the second gear; N 2,2 It indicates that among the first gear samples corresponding to the second gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the second gear.

[0071]

[0072] Among them, N 2 represents the number of first gear samples corresponding to the second gear; N 2,mIt indicates that among the first gear samples corresponding to the second gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the mth gear.

[0073]

[0074] Among them, N m represents the number of first gear samples corresponding to the mth gear; N m,1 It indicates that among the first gear samples corresponding to the mth gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the first gear.

[0075]

[0076] Among them, N m represents the number of first gear samples corresponding to the mth gear; N m,2 It indicates that among the first gear samples corresponding to the mth gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the second gear.

[0077]

[0078] Among them, N m represents the number of first gear samples corresponding to the mth gear; N m,m It indicates that among the first gear samples corresponding to the m-th gear, the number of first gear samples of the next first time slice corresponding to the current first time slice is the m-th gear.

[0079] Therefore, it can be seen from the above that the processor device 120 determines a plurality of first gear samples corresponding to the target user. In the case of , the first transfer matrix A corresponding to each gear can be determined 1 .

[0080] Further, after the processor device 120 determines the first transfer matrix corresponding to each gear, a Markov chain is constructed. The state space of the Markov chain can be defined as k 1 :

[0081]

[0082] in, represents the probability value corresponding to the first gear in the next time slice, represents the probability value corresponding to the second gear in the next time slice, Represents the probability value corresponding to the mth gear in the next time slice.

[0083] Further, the processor device 120 predicts the probability values ​​corresponding to the respective gears in the next time slice according to the gear corresponding to the current time slice and the first transfer matrix corresponding to the respective gears (S406). Specifically, since the processor device 120 has determined the first transfer matrix corresponding to the respective gears and constructed the Markov chain, when the processor device 120 determines the gear corresponding to the current time slice, the probability values ​​corresponding to the respective gears in the next time slice can be predicted.

[0084] For example, if the gear corresponding to the current time slice is the first gear, the corresponding identity matrix is: Then the processor device 120 determines the value corresponding to the Markov chain k according to the following formula 1 Corresponding parameter values:

[0085]

[0086] Thus, the processor device 120 can predict the probability value corresponding to each gear in the next time slice based on the above formula:

[0087] And the processor device 120 predicts the probability value corresponding to each gear in the next time slice In the case of , the probability values ​​corresponding to each gear in the next time slice can be Determine the game parameter value of the game character corresponding to the target user (S408). For example, the processor device 120 predicts the probability value corresponding to each gear in the next time slice As the first weight value corresponding to each gear And the processor device 120 determines the first weight value corresponding to each gear In this case, the game parameter value of the game character corresponding to the target user is further determined.

[0088] Referring to Table 1 above, it can be seen that the game parameters in the game screen may include, for example, the moving speed of the game character in the game screen, the special effect brightness of the game character in the game screen, and the skill cooling time of the game character in the game screen, and each of the above game parameters has a game parameter value corresponding to each gear. Thus, the processor device 120 can determine the game parameter value of the game character corresponding to the target user in the next time slice based on the first weight value corresponding to each gear and the game parameter value corresponding to each gear. The above content will be described in detail later, so it will not be repeated here.

[0089] Finally, at the next time slice, the processor device 120 displays the game screen according to the determined game parameter value corresponding to the game character of the target user (S410). For example, the processor device 120 determines the moving speed of the game character corresponding to the target user in the game screen, and controls the moving speed of the game character according to the determined moving speed value. The processor device 120 determines the special effect brightness value used by the game character corresponding to the target user in the game screen, and controls the special effect brightness according to the determined special effect brightness value. The processor device 120 determines the skill cooling time value of the skill released by the game character corresponding to the target user, and controls the skill cooling time according to the determined skill cooling time value.

[0090] As described in the background technology, if you want to provide users with a better gaming experience, it is necessary that when the rider controls the wheel speed of the smart bike, the display device can simultaneously display the game screen of the virtual game corresponding to the wheel speed. That is, the game screen displayed by the display device associated with the smart bike should not be stuck or delayed, and the consistency of the game screen and the wheel speed should be ensured as much as possible.

[0091] Existing methods for solving the above problems include adjusting the game screen in real time according to the wheel speed, or adjusting the game screen with a delay according to the wheel speed. However, if the game screen is adjusted in real time according to the wheel speed, the generation state of the game screen may be unstable; if the game screen is adjusted with a delay according to the wheel speed, the game screen may be inconsistent with the wheel speed of the smart bicycle. Therefore, whether the game screen is adjusted in real time according to the wheel speed or the game screen is adjusted with a delay according to the wheel speed, the above problems cannot be solved.

[0092] In view of this, the present application provides a game processing method based on a smart bicycle. And because the present application predicts the wheel speed gear of the smart bicycle corresponding to the target user in the next time slice according to the wheel speed gear of the smart bicycle corresponding to the target user in the current time slice. And according to the wheel speed gear of the smart bicycle corresponding to the target user in the next time slice, the game parameter value of the game character corresponding to the target user is determined. Then, the game screen is displayed on the display device through the determined game parameter value corresponding to the game character.

[0093] Therefore, compared with the prior art in which the game screen of the virtual game is adjusted in real time according to the wheel speed of the smart bicycle, since the present application determines the game parameter value corresponding to the game character based on the predicted wheel speed gear of the smart bicycle in the next time slice, and displays the game screen on the display device according to the predetermined game parameter value, the processor has sufficient time to display the game screen, thereby avoiding the problem of unstable generation state of the virtual scene.

[0094] Furthermore, compared to the prior art in which the game screen of the virtual game is adjusted according to the wheel speed of the smart bicycle, since the present application predicts the wheel speed gear of the smart bicycle corresponding to the target user in the next time slice in advance based on the wheel speed gear of the smart bicycle corresponding to the target user in the current time slice, the game screen finally generated based on the prediction result will not be delayed or stuck.

[0095] Thus, the consistency between the game screen of the virtual game and the wheel speed can be achieved, and the riding experience of the target user can be improved. This solves the technical problem in the prior art that the game screen displayed by the display device associated with the smart bicycle often has a freeze or delay, and the consistency between the game screen and the wheel speed cannot be guaranteed, thereby affecting the riding experience of the user.

[0096] Furthermore, referring to the above content, it can be known that the historical data corresponding to different users are different. And because the historical data of different users are different, the Markov chains constructed according to different users are also different, and their transfer matrices are also different. Therefore, under the same wheel speed gear, the game screen displayed by different users in the next time slice may be different. Through the above method, the technical effect of being able to customize the personalized game display for the historical data of different users is achieved.

[0097] In addition, the processor device 120 may also calculate the fourth historical period T e The fourth gear change of the smart bicycle 110 corresponding to the sample user and the resistance change corresponding to the corresponding fourth gear change are used to predict the resistance gear of the smart bicycle 110 of the target user in the next time slice. It is worth noting that before the resistance gear of the smart bicycle 110 is predicted, the sample user needs to manually adjust the resistance gear of the smart bicycle 110.

[0098] For example, after the processor device 120 determines the change of the fourth gear of the smart bicycle 110 corresponding to the sample user 1, the fourth historical period T e Divided into multiple fourth time slices And in every fourth time slice The gear change (i.e., the gear change of the wheel speed) has occurred. Thus, the processor device 120 can collect the data related to the fourth historical period T e Multiple fourth time slices within Corresponding multiple fourth gear samples Among them, the fourth historical period T e The duration of the first historical period T xThe same or different, the duration of the fourth time slice may also be the same or different from that of the first time slice, which is not limited here.

[0099] And the processor device 120 determines the fourth historical period T e And the fourth historical period T e The fourth time slices included Afterwards, we can further obtain the sample user 1 at the fourth time slice The fourth gear samples Corresponding multiple resistance samples Among them, with each fourth gear sample Corresponding multiple resistance samples For example, the sample user 1 manually adjusts the resistance gear based on the change in wheel speed, and as the wheel speed gear changes, the sample user 1 manually adjusts the resistance gear.

[0100] Thus, the processor device 120 can collect a plurality of fourth gear samples and a plurality of resistance samples corresponding to a plurality of sample users 1 to G in the same manner as described above. And multiple resistance samples Multiple fourth-level samples corresponding to sample user 2 And multiple resistance samples ...; multiple fourth-level samples corresponding to sample user G And multiple resistance samples

[0101] Afterwards, the processor device 120 inputs the plurality of fourth gear samples and the plurality of resistance samples corresponding to the plurality of sample users 1 to G into the pre-built neural network model to train the neural network model. Thus, the trained neural network model can predict the corresponding resistance gear based on the wheel speed gear. Finally, the above-predicted speed gear in the next time slice corresponding to the target user is input into the pre-trained neural network model, and finally the resistance gear in the next time slice is predicted.

[0102] Therefore, by utilizing the pre-trained neural network model and the corresponding resistance control gear when the wheel speed of the smart bicycle 110 of multiple sample users changes, it is possible to further predict the resistance gear in the next time slice based on the predicted wheel speed gear of the target user in the next time slice and the neural network model.

[0103] In addition, it is worth noting that after the neural network model is trained, the processor device 120 can automatically adjust the resistance level, and the target user no longer needs to manually adjust the resistance level, thereby achieving the technical effect of facilitating the target user to use the smart bicycle and further increasing the target user's interest in using the smart bicycle.

[0104] Furthermore, the processor device 120 may also display the predicted turn of the smart bicycle 110 within the next time slice on the display device 140 in the same manner as described above, which will not be described in detail here.

[0105] Optionally, the operation of determining the game parameter values ​​of the game character corresponding to the target user based on the probability values ​​corresponding to each gear position in the next time slice includes: using the probability values ​​corresponding to each gear position in the next time slice as the first weight values ​​corresponding to each gear position; and determining the movement speed, special effect brightness and / or skill cooling time of the game character corresponding to the target user in the next time slice based on the first weight values ​​corresponding to each gear position.

[0106] Specifically, the processor device 120 predicts the probability values ​​corresponding to each gear in the next time slice. As the first weight value corresponding to each gear

[0107] Then, the processor device 120 calculates the weight of the gears based on the first weight values ​​corresponding to the gears. Determine the moving speed value v corresponding to the target user's game character in the next time slice. The specific calculation formula is as follows:

[0108]

[0109] Where v represents the moving speed corresponding to the target user's game character, v 1 Indicates the moving speed value corresponding to the first gear, represents the first weight value corresponding to the first gear, v 2 Indicates the moving speed value corresponding to the second gear, represents the first weight value corresponding to the second gear, v m Indicates the moving speed value corresponding to the mth gear position, Represents the first weight value corresponding to the mth gear.

[0110] Similarly, the processor device 120 determines the special effect brightness value l of the special effect used by the game character of the target user in the next time slice based on the first weight value corresponding to each gear. The specific calculation formula is as follows:

[0111]

[0112] Where l represents the brightness value of the special effects used by the target user's game character, v 1 Indicates the special effect brightness value corresponding to the first gear. represents the first weight value corresponding to the first gear, v 2 Indicates the special effect brightness value corresponding to the second gear. represents the first weight value corresponding to the second gear, v m Indicates the special effect brightness value corresponding to the mth gear. Represents the first weight value corresponding to the mth gear.

[0113] Similarly, the processor device 120 determines the skill cooldown time c of the skill released by the game character of the target user in the next time slice based on the first weight value corresponding to each gear position. The specific calculation formula is as follows:

[0114]

[0115] Among them, c represents the skill cooldown time of the skill released by the game character of the target user, c 1 Indicates the skill cooldown time value corresponding to the first gear. represents the first weight value corresponding to the first gear, c 2 Indicates the skill cooldown time value corresponding to the second gear. represents the first weight value corresponding to the second gear, c m Indicates the skill cooldown time value corresponding to the mth gear. Represents the first weight value corresponding to the mth gear.

[0116] Therefore, when the processor device 120 determines the movement speed value, special effect brightness value and skill cooling time value of the game character corresponding to the target user in the next time slice, the stability of the game screen can be guaranteed to the greatest extent, thereby achieving the technical effect of enhancing the experience of the target user when using the smart bicycle.

[0117] Optionally, it also includes: determining the second gear change of the smart bicycle corresponding to the target user in the second historical period, and collecting multiple second gear samples corresponding to each second time slice in the second historical period, wherein the second gear sample corresponds to the second gear change; based on the multiple second gear samples, determining the second transfer matrix corresponding to each gear; determining the third gear change of the smart bicycle corresponding to the target user in the third historical period, and collecting multiple third gear change corresponding to each third time slice in the third historical period, wherein the third gear sample corresponds to the third gear change; based on the multiple third gear samples, determining the third transfer matrix corresponding to each gear; and fusing the second transfer matrix and the third transfer matrix to determine the first transfer matrix for predicting the probability value corresponding to each gear in the next time slice. Further optionally, the operation of fusing the second transfer matrix and the third transfer matrix to determine the first transfer matrix for predicting the probability value corresponding to each gear in the next time slice includes: determining the second weight value corresponding to the second transfer matrix and the third weight value corresponding to the second transfer matrix; and determining the first transfer matrix according to the following formula: A 1 =A 2 × 2 +A 3 × 3 Among them, A 1 represents the first transfer matrix, A 2 represents the second transfer matrix, w 2 represents the second weight value corresponding to the second transfer matrix, A 3 represents the third transfer matrix, w 3represents a third weight value corresponding to the third transfer matrix. Further optionally, the operation of determining the second weight value corresponding to the second transfer matrix and the third weight value corresponding to the third transfer matrix includes: determining a plurality of first prediction results corresponding to each second time slice in the second historical period according to the second transfer matrix corresponding to each gear, wherein the first prediction result is used to indicate the probability value corresponding to each gear in the next second time slice of each current second time slice; determining a first actual result corresponding to each second time slice, and calculating a first similarity between the first prediction result and the first actual result, wherein the first actual result is used to indicate the real gear corresponding to the next second time slice of each current second time slice; determining a plurality of second prediction results corresponding to each third time slice in the third historical period according to the third transfer matrix corresponding to each gear, wherein the second prediction result is used to indicate the probability value corresponding to each gear in the next third time slice of each current third time slice; determining a second actual result corresponding to each third time slice, and calculating a second similarity between the second prediction result and the second actual result, wherein the second actual result is used to indicate the real gear corresponding to the next time slice of each current third time slice; and determining a second weight value and a third weight value based on the first similarity and the second similarity, respectively.

[0118] Specifically, in an embodiment of the present application, in addition to determining the first transfer matrix corresponding to each gear based on multiple first gear samples corresponding to the target user, the first transfer matrix corresponding to each gear can also be determined by fusing the second transfer matrix and the third transfer matrix. The second transfer matrix corresponds to the second historical period, the third transfer matrix corresponds to the third historical period, and the duration of the second historical period is greater than the duration of the third historical period. That is, the second transfer matrix is ​​a long-term transfer matrix, and the third transfer matrix is ​​a short-term transfer matrix.

[0119] For example, the processor device 120 may determine the second historical period T y The second gear position change of the smart bicycle 110 corresponding to the target user in the second historical period T y In the example, the processor device 120 of the smart bicycle 110 shows that the target user has ridden 50 km, and during the target user's riding of 50 km, a total of j gear changes occurred. Therefore, the processor device 120 can determine the j gear changes in the second historical period as the second gear change corresponding to the target user.

[0120] After the processor device 120 determines the change of the second gear of the smart bicycle 110 corresponding to the target user, the second historical period T is further divided according to the preset time slice. y Divide into multiple second time slices And in each second time slice In the second historical period T, the gear change occurs. The size of the preset time slice may be, for example, 5s. Thus, the processor device 120 can collect y Multiple second time slices within Corresponding multiple second gear samples

[0121] Further, the processor device 120 determines a plurality of second gear samples corresponding to the target user. In the case of Determine the second transfer matrix A corresponding to each gear 2 Among them, based on multiple second gear samples Determine the second transfer matrix A corresponding to each gear 2 The method steps are similar to those described above based on multiple first gear samples Determine the first transfer matrix A corresponding to each gear 1 The method steps are the same, so they will not be repeated here.

[0122] The processor device 120 may then determine a third historical period T z The third gear position change of the smart bicycle 110 corresponding to the target user in the third historical period T z In the example, the processor device 120 of the smart bicycle 110 shows that the target user has ridden 5 km, and during the target user's riding of 5 km, a total of h gear changes occurred. Therefore, the processor device 120 can determine the h gear changes in the third historical period as the third gear change corresponding to the target user.

[0123] After the processor device 120 determines the change of the third gear of the smart bicycle 110 corresponding to the target user, the third historical period T is further divided according to the preset time slice. z Divided into multiple third time slices And in every third time slice In the third historical period T, the gear change occurs. The size of the preset time slice may be, for example, 5s. Thus, the processor device 120 can collect z Multiple third time slices within Corresponding multiple third gear samples

[0124] Further, the processor device 120 determines a plurality of third gear samples corresponding to the target user. In the case of Determine the third transfer matrix A corresponding to each gear 3 Among them, based on multiple third gear samples Determine the third transfer matrix A corresponding to each gear 3 The method steps are similar to those described above based on multiple first gear samples Determine the first transfer matrix A corresponding to each gear 1 The method steps are the same, so they will not be repeated here.

[0125] The processor device 120 determines a second transfer matrix A 2 and the third transfer matrix A 3 In the case of 2 , determine the second historical period T y Each second time slice within The corresponding multiple first prediction results, and according to the third transfer matrix A 3 , determine the third historical period T z Each third time slice within Corresponding multiple second prediction results.

[0126] Specifically, since the processor device 120 has determined the second transfer matrix A corresponding to each gear 2 Therefore, when the processor device 120 determines the gear corresponding to the current second time slice, the probability values ​​corresponding to each gear in the next second time slice can be predicted. Among them, the probability values ​​corresponding to each gear in the next second time slice are predicted Method steps and prediction of the probability values ​​corresponding to each gear in the next time slice The method steps are the same, so they will not be repeated here.

[0127] Similarly, since the processor device 120 has determined the third transfer matrix A corresponding to each gear position 3 Therefore, when the processor device 120 determines the gear corresponding to the current third time slice, the probability values ​​corresponding to each gear in the next third time slice can be predicted. Among them, the probability values ​​corresponding to each gear in the next third time slice are predicted Method steps and prediction of the probability values ​​corresponding to each gear in the next time slice The method steps are the same, so they will not be repeated here.

[0128] Further, the processor device 120 determines the second time slices The first actual result is used to indicate the actual gear position corresponding to the next second time slice of each current second time slice.

[0129] And the processor device 120 determines the second time slices In the case of a corresponding first actual result, the processor device 120 calculates a first similarity between the first actual result and the first predicted result.

[0130] For example, if the processor device 120 predicts that the first prediction result corresponding to the next second time slice of the current second time slice includes: the probability of the second gear is 60%, and the probability of the second gear is greater than the probability of other gears. Therefore, the processor device 120 only needs to determine whether the real gear corresponding to the next second time slice of each current second time slice (i.e., the first real result) is the second gear, and if the real gear corresponding to the next second time slice of each current second time slice is the second gear, it means that the first prediction result is correct; if the real gear corresponding to the next second time slice of each current second time slice is not the second gear, it means that the first prediction result is wrong.

[0131] Based on the above manner, the processor device 120 can determine the prediction accuracy corresponding to each second time slice, and use the prediction accuracy as the first similarity between the first prediction result and the first actual result.

[0132] Similarly, the processor device 120 determines the time slices corresponding to each third time slice. The corresponding second actual result is used to indicate the actual gear position corresponding to the next third time slice of each current third time slice.

[0133] And the processor device 120 determines the third time slices In the case of a corresponding second actual result, the processor device 120 calculates a second similarity between the second actual result and the second predicted result.

[0134] For example, if the processor device 120 predicts that the second prediction result corresponding to the next third time slice of the current third time slice includes: the probability of the fourth gear is 70%, and the probability of the fourth gear is greater than the probability of other gears. Therefore, the processor device 120 only needs to determine whether the real gear corresponding to the next third time slice of each current third time slice (i.e., the second real result) is the fourth gear, and if the real gear corresponding to the next third time slice of each current third time slice is the fourth gear, it means that the second prediction result is correct; if the real gear corresponding to the next third time slice of each current third time slice is not the fourth gear, it means that the second prediction result is wrong.

[0135] Based on the above manner, the processor device 120 can determine the prediction accuracy corresponding to each third time slice, and use the prediction accuracy as the second similarity between the second prediction result and the second actual result.

[0136] The processor device 120 then uses the determined first similarity as the second transfer matrix A 2 The corresponding second weight value w 2 , and the determined second similarity is used as the third transfer matrix A 3 The corresponding third weight value w 3 .

[0137] Finally, the processor device 120 determines a first transfer matrix for predicting probability values ​​corresponding to each gear in the next time slice according to the following formula:

[0138] A 1 =A 2 × 2 +A 3 × 3

[0139] Among them, A 1 represents the first transfer matrix, A 2 represents the second transfer matrix, w 2 represents the second weight value corresponding to the second transfer matrix, A 3 represents the third transfer matrix, w 3 represents a third weight value corresponding to the third transfer matrix.

[0140] Thus, by fusing the long-time transfer matrix (i.e., the second transfer matrix) and the short-time transfer matrix (i.e., the third transfer matrix) to determine the first transfer matrix for predicting the probability values ​​corresponding to each gear in the next time slice, the accuracy of the predicted first transfer matrix is ​​improved, thereby ensuring the consistency of the game screen and the wheel speed, and improving the riding experience of the target user. Technical effect.

[0141] Therefore, according to the first aspect of this embodiment, the technical effect of ensuring the consistency between the virtual scene and the wheel speed and improving the riding experience of the target user is achieved.

[0142] In addition, reference Figure 1 As shown, according to the second aspect of this embodiment, a storage medium is provided, wherein the storage medium includes a stored program, wherein when the program is run, a processor executes any one of the above methods.

[0143] Therefore, according to this embodiment, the technical effect of ensuring the consistency between the virtual scene and the wheel speed and improving the riding experience of the target user is achieved.

[0144] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0145] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0146] Example 2

[0147] Figure 5 FIG. 5 shows a game processing device 500 based on a smart bicycle according to this embodiment, and the device 500 corresponds to the method according to Embodiment 1. Figure 5As shown, the device 500 includes: a data acquisition module 510, which is used to determine the change of the first gear of the smart bicycle corresponding to the target user in the first historical period, and collect multiple first gear samples corresponding to each first time slice in the first historical period, wherein the first gear samples correspond to the change of the first gear; a first transfer matrix determination module 520, which is used to determine the first transfer matrix corresponding to each gear based on the multiple first gear samples corresponding to the target user, and construct a Markov chain, wherein the gear corresponds to the wheel speed of the smart bicycle pedaled by the target user; a first prediction module 530, which is used to predict the probability value corresponding to each gear in the next time slice according to the gear corresponding to the current time slice and the first transfer matrix corresponding to each gear; a game parameter value determination module 540, which is used to determine the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear in the next time slice; and a game screen display module 550, which is used to display the game screen according to the determined game parameter value in the next time slice.

[0148] Optionally, the game parameter value determination module 540 includes: a first weight value determination module, which is used to use the probability value corresponding to each gear in the next time slice as the first weight value corresponding to each gear; and a game parameter value determination module, which is used to determine the movement speed, special effect brightness and / or skill cooling time of the game character corresponding to the target user in the next time slice based on the first weight value corresponding to each gear.

[0149] Optionally, the first transfer matrix determination module 520 includes: a first transfer matrix definition module, configured to define a first transfer matrix A corresponding to each gear position. 1 :

[0150]

[0151] Among them, parameter a 11 represents the probability of changing from the first gear to the second gear; parameter a 12 represents the probability of changing from the first gear to the second gear; parameter a 1m represents the probability of changing from the first gear to the mth gear; parameter a 21 represents the probability of changing from the second gear to the first gear; parameter a 22 represents the probability of changing from the second gear to the second gear; parameter a 2m represents the probability of changing from the second gear to the mth gear; parameter a m1 represents the probability of changing from the mth gear to the first gear; parameter a m2 represents the probability of changing from the mth gear to the second gear; parameter a m3represents the probability of changing from the mth gear to the third gear; and a parameter value determination module, used to calculate the parameter values ​​corresponding to each parameter in the first transfer matrix based on multiple first gear samples corresponding to the target user.

[0152] Optionally, the first transfer matrix determination module 520 includes: a Markov chain definition module for defining a state space k based on the Markov chain 1 :

[0153]

[0154] in, represents the probability value corresponding to the first gear in the next time slice, represents the probability value corresponding to the second gear in the next time slice, Represents the probability value corresponding to the mth gear in the next time slice.

[0155] Optionally, the device 500 also includes: a second gear sample collection module, which is used to determine the second gear change of the smart bicycle corresponding to the target user within the second historical period, and collect multiple second gear samples corresponding to each second time slice within the second historical period, wherein the second gear sample corresponds to the second gear change; a second transfer matrix determination module, which is used to determine the second transfer matrix corresponding to each gear based on the multiple second gear samples; a third gear sample collection module, which is used to determine the third gear change of the smart bicycle corresponding to the target user within the third historical period, and collect multiple third gear change situations corresponding to each third time slice within the third historical period, wherein the third gear sample corresponds to the third gear change; a third transfer matrix determination module, which is used to determine the third transfer matrix corresponding to each gear based on the multiple third gear samples; and a fusion module, which is used to fuse the second transfer matrix and the third transfer matrix, so as to determine the first transfer matrix for predicting the probability value corresponding to each gear in the next time slice.

[0156] Optionally, the fusion module includes: a second weight value determination module, used to determine a second weight value corresponding to the second transfer matrix and a third weight value corresponding to the second transfer matrix; and a first transfer matrix calculation module, used to determine the first transfer matrix according to the following formula:

[0157] A 1 =A 2 × 2 +A 3 × 3

[0158] Among them, A 1 represents the first transfer matrix, A 2 represents the second transfer matrix, w2 represents the second weight value corresponding to the second transfer matrix, A 3 represents the third transfer matrix, w 3 represents a third weight value corresponding to the third transfer matrix.

[0159] Optionally, the second weight value determination module includes: a first prediction result determination module, which is used to determine multiple first prediction results corresponding to each second time slice in the second historical period according to the second transfer matrix corresponding to each gear, wherein the first prediction result is used to indicate the probability value corresponding to each gear in the next second time slice of each current second time slice; a first similarity determination module, which is used to determine the first actual result corresponding to each second time slice, and calculate the first similarity between the first prediction result and the first actual result, wherein the first actual result is used to indicate the real gear corresponding to the next second time slice of each current second time slice; a second prediction result determination module, which is used to According to the third transfer matrix corresponding to each gear, multiple second prediction results corresponding to each third time slice within the third historical period are determined, wherein the second prediction results are used to indicate the probability values ​​corresponding to each gear in the next third time slice of each current third time slice; a second similarity determination module is used to determine the second actual result corresponding to each third time slice, and calculate the second similarity between the second prediction result and the second actual result, wherein the second actual result is used to indicate the real gear corresponding to the next time slice of each current third time slice; and a second weight value determination submodule is used to determine the second weight value and the third weight value based on the first similarity and the second similarity, respectively.

[0160] Therefore, according to this embodiment, the technical effect of ensuring the consistency between the virtual scene and the wheel speed and improving the riding experience of the target user is achieved.

[0161] Example 3

[0162] Figure 6 FIG. 6 shows a game processing device 600 based on a smart bicycle according to this embodiment, and the device 600 corresponds to the method according to Embodiment 1. Figure 6As shown, the device 600 includes: a processor 610; and a memory 620, which is connected to the processor 610 and is used to provide the processor 610 with instructions for processing the following processing steps: determining the change of the first gear of the smart bicycle corresponding to the target user within the first historical period, and collecting multiple first gear samples corresponding to each first time slice within the first historical period, wherein the first gear samples correspond to the change of the first gear; based on the multiple first gear samples corresponding to the target user, determining the first transfer matrix corresponding to each gear, and constructing a Markov chain, wherein the gear corresponds to the wheel speed of the smart bicycle pedaled by the target user; predicting the probability value corresponding to each gear in the next time slice according to the gear corresponding to the current time slice and the first transfer matrix corresponding to each gear; determining the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear in the next time slice; and displaying the game screen in the next time slice according to the determined game parameter value.

[0163] Therefore, according to this embodiment, the technical effect of ensuring the consistency between the virtual scene and the wheel speed and improving the riding experience of the target user is achieved.

[0164] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0165] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0167] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0168] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0169] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), mobile hard disk, magnetic disk or optical disk and other media that can store program codes.

[0170] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A game processing method based on a smart bicycle, characterized in that: include: Determine a change in the first gear position of the smart bicycle corresponding to the target user within a first historical period, and collect a plurality of first gear position samples corresponding to each first time slice within the first historical period, wherein the first gear position samples correspond to the first gear position change; Based on a plurality of first gear samples corresponding to the target user, determining a first transfer matrix corresponding to each gear, and constructing a Markov chain, wherein the gear corresponds to a wheel speed of the smart bicycle pedaled by the target user; Predicting probability values ​​corresponding to each gear position in a next time slice according to the gear position corresponding to the current time slice and the first transfer matrix corresponding to each gear position; Determining a game parameter value of a game character corresponding to the target user based on the probability values ​​corresponding to the respective gears in the next time slice; as well as At the next time slice, the game screen is displayed according to the determined game parameter values.

2. The method according to claim 1, characterized in that The operation of determining the game parameter value of the game character corresponding to the target user based on the probability values ​​corresponding to the respective gears in the next time slice includes: Using the probability value corresponding to each gear position in the next time slice as the first weight value corresponding to each gear position; and Based on the first weight values ​​corresponding to the respective gears, a moving speed, special effect brightness and / or skill cooling time of the game character corresponding to the target user in the next time slice is determined.

3. The method according to claim 1, characterized in that The operation of determining a first transfer matrix corresponding to each gear position based on a plurality of first gear position samples corresponding to the target user includes: Define a first transfer matrix A1 corresponding to each gear position: Among them, parameter a 11 represents the probability of changing from the first gear to the second gear; Parameter a 12 represents the probability of changing from the first gear to the second gear; Parameter a 1m represents the probability of changing from the first gear to the mth gear; Parameter a 21 represents the probability of changing from the second gear to the first gear; Parameter a 22 represents the probability of changing from the second gear to the second gear; Parameter a 2m represents the probability of changing from the second gear to the mth gear; Parameter a m1 represents the probability of changing from the mth gear to the first gear; Parameter a m2 represents the probability of changing from the mth gear to the second gear; Parameter a m3 represents the probability of shifting from the mth gear to the third gear; and Based on a plurality of first gear samples corresponding to the target user, parameter values ​​corresponding to the respective parameters in the first transfer matrix are calculated.

4. The method according to claim 3, characterized in that The operations to construct a Markov chain include: Define the state space k1 based on the Markov chain: in, represents the probability value corresponding to the first gear in the next time slice, represents the probability value corresponding to the second gear in the next time slice, Represents the probability value corresponding to the mth gear in the next time slice.

5. The method according to claim 1, characterized in that Also includes: Determine a change in the second gear position of the smart bicycle corresponding to the target user within a second historical period, and collect a plurality of second gear position samples corresponding to each second time slice within the second historical period, wherein the second gear position samples correspond to the change in the second gear position; Based on the plurality of second gear samples, determining a second transfer matrix corresponding to each gear; Determine a change in the third gear of the smart bicycle corresponding to the target user within a third historical period, and collect a plurality of third gear change conditions corresponding to each third time slice within the third historical period, wherein the third gear samples correspond to the third gear change conditions; Based on a plurality of third gear samples, determining a third transfer matrix corresponding to each gear; and The second transfer matrix and the third transfer matrix are merged to determine a first transfer matrix for predicting probability values ​​corresponding to the respective gears in the next time slice.

6. The method according to claim 5, characterized in that The operation of fusing the second transfer matrix and the third transfer matrix to determine a first transfer matrix for predicting probability values ​​corresponding to the respective gears in the next time slice includes: determining a second weight value corresponding to the second transfer matrix and a third weight value corresponding to the second transfer matrix; and The first transfer matrix is ​​determined according to the following formula: A1=A2×w2+A3×w3 Among them, A1 represents the first transfer matrix, A2 represents the second transfer matrix, w2 represents the second weight value corresponding to the second transfer matrix, A3 represents the third transfer matrix, and w3 represents the third weight value corresponding to the third transfer matrix.

7. The method according to claim 6, characterized in that The operation of determining a second weight value corresponding to the second transfer matrix and a third weight value corresponding to the third transfer matrix includes: Determine, according to the second transfer matrix corresponding to the respective gears, a plurality of first prediction results corresponding to the respective second time slices within the second historical period, wherein the first prediction results are used to indicate a probability value corresponding to the respective gears in a next second time slice of the respective current second time slice; Determine a first actual result corresponding to each of the second time slices, and calculate a first similarity between the first predicted result and the first actual result, wherein the first actual result is used to indicate a real gear position corresponding to a second time slice next to each current second time slice; Determine, according to a third transfer matrix corresponding to each gear position, a plurality of second prediction results corresponding to each third time slice within the third historical period, wherein the second prediction results are used to indicate a probability value corresponding to each gear position in a next third time slice of each current third time slice; Determining a second actual result corresponding to each of the third time slices, and calculating a second similarity between the second predicted result and the second actual result, wherein the second actual result is used to indicate a real gear position corresponding to a next time slice of each current third time slice; and Based on the first similarity and the second similarity, the second weight value and the third weight value are determined respectively.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the processor executes the method according to any one of claims 1 to 7.

9. A game processing device based on a smart bicycle, characterized in that: include: a data collection module, configured to determine a change in the first gear position of the smart bicycle corresponding to the target user within a first historical period, and collect a plurality of first gear position samples corresponding to each first time slice within the first historical period, wherein the first gear position samples correspond to the first gear position change; a first transfer matrix determination module, configured to determine a first transfer matrix corresponding to each gear based on a plurality of first gear samples corresponding to the target user, and to construct a Markov chain, wherein the gear corresponds to a wheel speed of the smart bicycle pedaled by the target user; A first prediction module, configured to predict probability values ​​corresponding to each gear position in a next time slice according to the gear position corresponding to the current time slice and a first transfer matrix corresponding to each gear position; A game parameter value determination module, used to determine the game parameter value of the game character corresponding to the target user based on the probability value corresponding to each gear position in the next time slice; as well as The game screen display module is used to display the game screen in the next time slice according to the determined game parameter values.

10. A game processing device based on a smart bicycle, characterized in that: include: processor; as well as A memory, connected to the processor, configured to provide the processor with instructions for processing the following processing steps: Determine a change in the first gear position of the smart bicycle corresponding to the target user within a first historical period, and collect a plurality of first gear position samples corresponding to each first time slice within the first historical period, wherein the first gear position samples correspond to the first gear position change; Based on a plurality of first gear samples corresponding to the target user, determining a first transfer matrix corresponding to each gear, and constructing a Markov chain, wherein the gear corresponds to a wheel speed of the smart bicycle pedaled by the target user; Predicting probability values ​​corresponding to each gear position in a next time slice according to the gear position corresponding to the current time slice and the first transfer matrix corresponding to each gear position; Determining a game parameter value of a game character corresponding to the target user based on the probability values ​​corresponding to the respective gears in the next time slice; as well as At the next time slice, the game screen is displayed according to the determined game parameter values.

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