An online-offline mixed competition management method and system

Abstract

The present disclosure relates to an online-offline mixed competition management method and system. The method comprises: converting the sports results in all competition modes into effective speeds; the competition mode is online competition or offline competition; after the competition ends, the effective speed of the participating user is calculated according to the completed sports data of the participating user; if the competition mode is online competition, the effective speed is used as the ranking speed of the participating user; if the competition mode is offline competition, a calibration coefficient is determined based on the historical match win rate, the effective speed is compensated by using the calibration coefficient, and the compensated effective speed is used as the ranking speed of the participating user; in the same sports type, all the ranking speeds are obtained to generate a unified ranking. The present disclosure dynamically determines the calibration coefficient based on the historical match win rate, individually compensates the offline competition results, makes the results in different competition environments truly reflect the level of the user, and realizes the fair and unified ranking across the competition modes.

Description

Technical Field

[0001] This disclosure relates to the field of sports management technology, and in particular to an online-offline hybrid competition management method and system. Background Technology

[0002] Sports competition platforms typically employ two modes: real-time online matchmaking or appointment-based events. Real-time online matchmaking requires both participants to be online simultaneously, providing a real-time interactive competitive experience, but it limits the flexibility of users' participation time. Appointment-based events, while supporting asynchronous participation, lack the immersive feeling of real-time competition and are unlikely to provide effective competitive motivation for participants.

[0003] To balance flexibility and competitive experience, some platforms are experimenting with integrating online and offline competition modes. Taking an existing competition system as an example, its typical technical architecture includes a two-layer structure: rooms and activities. The room layer manages competition access and matching, while the activity layer manages the specific competition process and results. The system creates competition rooms, adds bots or virtual participants, adjusts motion parameters, and obtains room status through an external game server. Simultaneously, it maintains key data in the cache, such as the set of room members, member readiness status, the set of matchable rooms, and the set of virtual participant rooms, to support the flow of room status. For offline competitions, existing solutions allow users to challenge offline opponents. The system marks the offline competition type, and the querying and sorting of competition results use the same interfaces and logic as online competitions.

[0004] However, existing technical solutions have the following shortcomings: First, offline competitions lack a virtual opponent generation mechanism. When the challenged opponent is offline, the system cannot generate a simulated movement progress based on the opponent's historical movement data, resulting in users being unable to perceive changes in the opponent's performance. In asynchronous competition scenarios, there is a lack of immediate immersion and competitive engagement.

[0005] Second, the comparison of results between online and offline competitions fails to consider environmental differences. In online competitions, users are psychologically motivated by seeing their opponents' progress in real time, and this real-time feedback significantly impacts their performance. In offline competitions, users complete the competition relatively independently, lacking the same motivating factors. The competitive psychological environments of the two competition methods are fundamentally different, but the existing scheme uses the exact same result ranking logic, failing to calibrate the fairness of offline competition results, leading to an unreasonable uniform ranking.

[0006] In summary, how to generate meaningful simulated opponents and reasonably compensate for performance under different competition environments while supporting users' flexible choice of online or offline competition methods, so as to achieve fair and unified ranking across competition methods, is an urgent problem to be solved in this field.

[0007] Therefore, it is necessary to improve one or more of the problems existing in the above-mentioned related technical solutions.

[0008] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0009] The purpose of this disclosure is to provide an online-offline hybrid competition management method and system, thereby at least solving one or more technical problems existing in the prior art.

[0010] The objective of this invention is achieved through the following technical solution: In a first aspect, the present invention provides a method for managing online and offline hybrid competitions, the method comprising: Multiple sports types are acquired, and multiple competition modes are configured for each sports type. The sports results under all competition modes are converted into effective speeds. In response to a competition request initiated by a participating user, the type of sport, competition mode, and competition method indicated by the competition request are determined; wherein, the competition method is an online competition or an offline competition; When the competition mode is an online competition, real-time communication is established between the participating user and the online opponent. During the competition, the real-time movement data of the online opponent is obtained, and the real-time movement progress of the online opponent is displayed to the participating user based on the real-time movement data. When the competition mode is an offline competition, the historical movement data of the offline opponent is obtained, and a simulated movement progress is generated and displayed to the participating user based on the historical movement data. After the competition, the effective speed of the participating users is calculated based on their completed exercise data. If the competition is an online competition, the effective speed is used as the ranking speed for the participating user; if the competition is an offline competition, the historical win rate of the participating user is obtained, a calibration coefficient is determined based on the historical win rate, the effective speed is compensated using the calibration coefficient to obtain the compensated effective speed, and the compensated effective speed is used as the ranking speed for the participating user. Under the same sport type, the ranking speed of each participating user is obtained, and the obtained ranking speeds are sorted to generate a unified ranking; wherein, the unified ranking covers all participating users under the same sport type who use different competition modes and different competition methods.

[0011] Optionally, the step of acquiring multiple sports types and configuring multiple competition modes for each sports type, wherein the sports results under all competition modes are converted into effective speeds, includes: The sports type includes at least running; the competition mode includes at least timed race mode and distance race mode.

[0012] Optionally, when the competition mode is an offline competition, the step of obtaining the historical movement data of offline opponents and generating a simulated movement progress based on the historical movement data to display to the participating users includes: Based on the historical motion data of the offline opponent, a segmented speed curve is generated to simulate the motion progress and display it to the participating user; wherein, the segmented speed curve includes: the starting segment, the constant speed segment, and the sprint segment.

[0013] Optionally, when the competition mode is an offline competition, the step of obtaining the historical movement data of offline opponents and generating a simulated movement progress based on the historical movement data to display to the participating users includes: The speed of the sprint phase is increased by a preset percentage based on the constant speed phase; the preset percentage increase is determined based on the historical win rate of the offline opponent.

[0014] Optionally, the step of determining the sport type, competition mode, and competition method indicated by the competition request in response to a participating user's competition request includes: In response to a competition request initiated by a participating user, the competition level is determined based on the participating user's historical competition ranking, and an opponent is matched with the participating user based on the competition level.

[0015] Optionally, if the competition method is an offline competition, the steps of obtaining the historical win rate of the participating user, determining a calibration coefficient based on the historical win rate, compensating the effective speed using the calibration coefficient to obtain the compensated effective speed, and using the compensated effective speed as the ranking speed of the participating user include: The user type of the participating user is determined based on the user's historical win rate, and the calibration coefficient is determined based on the user type; the user type includes stable, balanced and competitive, wherein the calibration coefficient corresponding to the stable type is less than or equal to the calibration coefficient corresponding to the balanced type, and the calibration coefficient corresponding to the balanced type is less than or equal to the calibration coefficient corresponding to the competitive type.

[0016] Optionally, if the competition method is an offline competition, the steps of obtaining the historical win rate of the participating user, determining a calibration coefficient based on the historical win rate, compensating the effective speed using the calibration coefficient to obtain the compensated effective speed, and using the compensated effective speed as the ranking speed of the participating user include: The user type with a historical win rate of less than 30% is described as the stable type. The user type with a historical win rate greater than or equal to 30% and less than or equal to 60% is the balanced type. The user type with a historical win rate greater than 60% is the competitive type. The calibration coefficient corresponding to the stable type is a speed improvement ratio of 0 to 1%; The calibration coefficient corresponding to the balanced type is a speed improvement ratio of 1% to 3%; The calibration coefficient corresponding to the competitive type is a speed improvement ratio of 3% to 5%.

[0017] Optionally, it also includes: Cheating detection is performed on the participating users in the unified ranking. Participating users detected as cheating are marked as cheating users, and their ranking in the unified ranking is set to the bottom, and their eligibility for prize money is cancelled.

[0018] Optionally, it also includes: Obtain the deposit paid by each participating user at the time of participation; Users who are not marked as cheating are marked as normal users. The total amount of deposits paid by all normal users is calculated and the total amount of deposits is determined as the distributable prize money. The distributable bonus is allocated to each of the normal users according to the preset allocation ratio corresponding to their ranking in the unified ranking.

[0019] Secondly, the present invention also provides an online-offline hybrid competition management system, comprising: The room layer is responsible for competition access and matching management; The activity layer is responsible for managing the competition process and results. The room layer and the activity layer are managed using any of the online-offline hybrid competition management methods described above.

[0020] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects: In the embodiments of this disclosure, by generating simulated movement progress based on the opponent's historical movement data and displaying it to the user during offline competitions, users can still perceive changes in the opponent's movement performance in asynchronous competition scenarios, significantly enhancing the immersion and competitive engagement of offline competitions. At the same time, to address the ranking inaccuracies caused by differences in the competitive psychological environment between online and offline competitions, a calibration coefficient is dynamically determined based on the user's historical win rate, a behavioral indicator reflecting individual psychological characteristics, to provide personalized compensation for offline competition results. This ensures that results in different competition environments can truly reflect the level that users should possess in actual competitive confrontations, thereby achieving fair and unified rankings across competition modes.

[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 flowchart of an online / offline hybrid competition management method in an exemplary embodiment of this disclosure. Detailed Implementation

[0024] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0025] 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.

[0026] This example implementation first provides a method for managing online and offline hybrid competitions, referencing... Figure 1As shown, the method may include the following steps: Step S101: Obtain multiple sports types and configure multiple competition modes for each sports type. The sports results under all competition modes are converted into effective speeds.

[0027] Step S102: In response to a competition request initiated by a participating user, determine the type of sport, competition mode, and competition method indicated by the competition request; wherein the competition method is an online competition or an offline competition.

[0028] Step S103: When the competition mode is an online competition, establish real-time communication between the participating user and the online opponent, obtain the real-time movement data of the online opponent during the competition, and display the real-time movement progress of the online opponent to the participating user based on the real-time movement data.

[0029] Step S104: When the competition mode is offline competition, obtain the historical movement data of the offline opponent, generate a simulated movement progress based on the historical movement data and display it to the participating user.

[0030] Step S105: After the competition ends, calculate the effective speed of the participating user based on the completed exercise data of the participating user.

[0031] Step S106: If the competition mode is an online competition, the effective speed is used as the ranking speed of the participating user; if the competition mode is an offline competition, the historical win rate of the participating user is obtained, a calibration coefficient is determined based on the historical win rate, the effective speed is compensated using the calibration coefficient to obtain the compensated effective speed, and the compensated effective speed is used as the ranking speed of the participating user.

[0032] Step S107: Under the same sport type, obtain the ranking speed of each participating user, sort the obtained ranking speeds, and generate a unified ranking.

[0033] The unified ranking covers all participating users in the sports category who use different competition modes and methods.

[0034] It's important to understand that all motion results in all competition modes are converted into effective speed (v_eff), and the formula for calculating effective speed is: v_eff=runMileage / runTime Among them, runMileage is the distance the participant completes the exercise (unit: meters), runTime is the time the participant completes the exercise (unit: seconds), and the unit of effective speed is meters per second (m / s).

[0035] By uniformly converting the results of different competition modes into effective speed, the impact of differences in target distance and target time on performance comparison is eliminated, allowing for a fair comparison of participant performance across different competition modes on the same scale. For example, in the timed race mode, participant A completes 6500 meters in 30 minutes, with an effective speed of 6500 / 1800 ≈ 3.61 m / s; in the distance race mode, participant B completes 5000 meters in 1400 seconds, with an effective speed of 5000 / 1400 ≈ 3.57 m / s. Although the two differ in competition mode, the normalization of effective speed allows for a fair comparison under the same standard.

[0036] It is also important to understand that participating users can initiate a competition request through a client application (App). The competition request must include at least the following information: type of sport (e.g., running), competition mode (e.g., timed race or distance race), and competition method (online or offline race).

[0037] Competition requests can also include other information, such as target distance or target duration, deposit amount, opponent selection (specified friend or random match), room type (public or private), etc.

[0038] It's also important to understand that when a participating user selects online competition, the system first confirms that the online opponent is ready—that is, the opponent is currently online and has accepted the competition invitation. Then, it establishes a real-time communication connection between the participating user and their online opponent using real-time communication protocols such as WebSocket. WebSocket is a protocol for full-duplex communication over a single Transmission Control Protocol (TCP) connection.

[0039] During the competition, the system acquires real-time movement data of online opponents, including but not limited to: current distance traveled, current speed, time elapsed, and remaining distance. This real-time movement data is pushed to the participating users' clients via WebSocket. The participating users' clients then render the online opponents' movement progress in real-time within a 3D scene or 2D interface, allowing the participating users to intuitively perceive changes in the opponents' positions and speeds.

[0040] The real-time feedback mechanism in online competitions creates a competitive arousal effect on participating users. When users see their opponents leading, they feel a sense of urgency and motivation to catch up; when they see themselves leading, they feel pressure to maintain their advantage. This real-time interaction significantly improves the athletic performance and competitive experience of participating users.

[0041] It's also important to understand that when a participating user selects offline competition as the competition mode, the system retrieves the historical movement data of the offline opponent, i.e., the challenged party. The offline opponent's historical movement data may include one or more of the following: average pace in recent races, win rate in recent head-to-head matches, exercise frequency and volume in recent days, best historical performance, speed distribution in different stages of the race, etc.

[0042] The system generates a simulated running progress (i.e., a virtual opponent, or GhostRunner) based on the acquired historical running data and displays this simulated running progress to participating users in real time. Specifically, the system determines the baseline speed based on the average pace of offline opponents, determines the adjustment parameters of the speed curve based on the offline opponents' PK win rate, and generates a segmented speed curve to simulate the opponent's running progress.

[0043] It's also important to understand that after the competition ends (i.e., when the participating user reaches the target distance or time), the system obtains the user's completed exercise data, including the completed distance (runMileage) and the completed time (runTime). Then, the system calculates the user's effective speed using the following formula: v_eff = runMileage / runTime. The unit of effective speed is meters per second (m / s). In abnormal cases, such as when the exercise time is 0 (the participating user did not generate effective exercise), the effective speed is considered 0.

[0044] It's also important to understand that for users participating in online competitions, their effective speed is directly used for ranking without additional calibration. This is because users in online competitions are already influenced by real-time competitive incentives, and their scores already reflect their true performance in the online competition environment.

[0045] For users participating in offline competitions, their performance may be lower than their actual performance in online competitions due to the lack of real-time competitive incentives (i.e., the absence of competitive arousal, catch-up effect, and social facilitation effect). Therefore, it is necessary to calibrate and compensate for their effective speed.

[0046] It's also important to understand that, within the same sport type (e.g., all running), the system obtains the ranking speed for each participating user. For users in online competitions, the ranking speed is the effective speed v_eff; for users in offline competitions, the ranking speed is the compensated effective speed v_eff_calibrated. The system then sorts all participating users in descending order of their ranking speed to generate a unified ranking. Users with higher ranking speeds rank higher.

[0047] When ranking speeds are the same, further sorting can be done according to the following secondary rules: Sorted by distance in descending order: Participants with greater distances are ranked first (applicable in timed race mode, where those who run further win).

[0048] Sorted in ascending order by exercise time: participants with shorter times are ranked first (this applies to distance races, where those with shorter times win).

[0049] The unified ranking covers all participants in this sport who use different competition modes (timed races, distance races, etc.) and different competition methods (online races, offline races). Through effective speed normalization and offline performance calibration compensation, a fair and unified ranking is achieved across competition methods and modes.

[0050] By employing a hybrid online and offline competition management approach, simulated progress is generated based on opponents' historical movement data and displayed to users during offline matches. This allows users to perceive changes in their opponents' performance even in asynchronous competition scenarios, significantly enhancing the immersion and competitive engagement of offline competitions. Furthermore, addressing the ranking inaccuracies caused by differences in the psychological environment between online and offline competitions, a calibration coefficient is dynamically determined based on the user's historical win rate—a behavioral indicator reflecting individual psychological characteristics—to provide personalized compensation for offline competition results. This ensures that results in different competition environments accurately reflect the user's actual competitive level, thereby achieving fair and unified rankings across different competition formats.

[0051] The following will describe in more detail each step of the online-offline hybrid competition management method described in this example embodiment.

[0052] In one embodiment, step S101 may include: The sports type includes at least running; the competition mode includes at least timed race mode and distance race mode.

[0053] It is important to understand that exercise types are not limited to running; they can also include other aerobic exercise types such as cycling, swimming, rowing machines, and elliptical trainers, as well as any combination of these types of exercise.

[0054] The timed race mode involves participants completing the longest possible distance within a preset time, with the distance traveled serving as the basis for evaluation. The distance-based race mode involves participants completing the shortest possible distance within a preset time, with the time taken as the basis for evaluation. It should be understood that the competition modes are not limited to timed and distance-based races; other modes may also be included, such as unlimited-time sprint races, ladder races, and elimination races.

[0055] In one embodiment, step S102 may include: In response to a competition request initiated by a participating user, the competition level is determined based on the participating user's historical competition ranking, and an opponent is matched with the participating user based on the competition level.

[0056] It's important to understand that the system can query a participant's historical competition data, including the number of times they've participated, their historical ranking, and their historical average score, to comprehensively determine the participant's competition level (e.g., beginner, intermediate, elite, master, etc.). Then, based on the determined competition level, it matches opponents from the corresponding user pool or NPC room pool to ensure that the participant and their opponents have similar athletic abilities, thereby guaranteeing the fairness and competitiveness of the competition.

[0057] Specifically, when a participating user initiates a random matching request, the system executes an intelligent NPC room matching process, replacing the traditional random selection strategy.

[0058] First, the system constructs a motion profile for each participating user. This profile is built based on existing statistical methods in the user motion data statistics module, and includes the following dimensions: Average pace (seconds / km): The average pace based on the user's recent exercise data.

[0059] PK win rate (0~1): Based on the user's win rate statistics in the most recent PK matches.

[0060] Exercise frequency: Based on the user's exercise frequency over the past few days.

[0061] Recent Device Type: The type of equipment used by the user in their most recent exercise session (treadmill / stationary bike / pedal / rowing machine, etc.).

[0062] Then, the system retrieves a list of available NPC rooms (with expiration times) from the cache service and queries the room configuration of each NPC room, including information such as target mileage, target duration, and device type.

[0063] Next, the system calculates a three-dimensional matching score for each NPC room R_j: Score(R_j)=w1·S1(R_j)+w2·S2(R_j)+w3·S3(R_j) In this configuration, w1, w2, and w3 are dimensional weights, satisfying w1 + w2 + w3 = 1. For example, typical values ​​are w1 = 0.5, w2 = 0.3, and w3 = 0.2. This configuration is based on the following: speed matching directly determines the intensity and suspense of the competition, making it a core indicator of competitive matching, hence it receives the highest weight; equipment matching determines the consistency of the physical benchmark for sports data collection, significantly impacting the reliability of normalized comparisons, hence it receives the second highest weight; and the win rate dimension, as a higher-order feature at the psychological and statistical levels, is mainly used to exclude extreme mismatches and has a relatively low impact on the immediate experience, hence it receives the lowest weight. Through this tiered weight allocation, a progressive, multi-layered matching optimization is achieved, progressing from physical ability to environmental compatibility and then to psychological experience.

[0064] The three dimensions are defined as follows: Speed ​​matching degree S1(R_j): measures the degree of matching between the user's average speed and the target speed of the NPC room.

[0065] The calculation formula is: S1(R_j)=1-| _u-v_target(R_j)| / v_target(R_j) in, _u represents the user's average speed (m / s, converted from the average pace of the most recent matches), and v_target(R_j) = D_target / T_target represents the target speed (m / s) of the NPC room. S1 takes values ​​[0,1], and is clamped to 0 if the calculated result is less than 0. The higher the speed matching degree, the closer the user's speed level is to the target speed of the room, and the better the fairness of the competition.

[0066] Equipment Matching S2(R_j): This measures the degree of match between the user's most recently used equipment type and the NPC room's equipment type. S2=1 when the user's most recently used equipment type matches the NPC room's equipment type; otherwise, S2=0. Equipment matching ensures that users compete on familiar equipment, avoiding performance issues caused by unfamiliarity with the equipment.

[0067] Difficulty Matching S3(R_j): Determines the appropriate difficulty level based on the user's win rate in PvP. The calculation formula is: When |w_u-w_target(R_j)|≤0.5, S3=1-|w_u-w_target(R_j)|; When |w_u-w_target(R_j)|>0.5, S3=0.

[0068] Where w_u represents the user's PK win rate, and w_target(R_j) is the benchmark win rate value corresponding to the difficulty level of the NPC room. Difficulty matching ensures that users are matched to a difficulty level that matches their competitive skill level, avoiding negative impacts on the competitive experience due to excessively high or low difficulty.

[0069] Finally, the system selects the NPC room with the highest rating as the optimal matchmaking room: R*=argmax_{R_j}Score(R_j) The room will then be returned to the user for them to join.

[0070] Through the aforementioned intelligent matching mechanism, users are matched with a competition environment that best matches their athletic ability, significantly improving the fairness and fun of the competition experience.

[0071] In one embodiment, step S104 may include: Based on the historical motion data of the offline opponent, a segmented speed curve is generated to simulate the motion progress and display it to the participating user; wherein, the segmented speed curve includes: the starting segment, the constant speed segment, and the sprint segment.

[0072] It is necessary to understand that, assuming _opp represents the opponent's average pace (seconds per kilometer) over their most recent matches, indicating the opponent's average speed. _opp=1000 / _opp (unit: meters per second). Let w_opp be the opponent's PK win rate (value 0~1), D_target be the PK target distance (meters), and d be the distance the virtual opponent has run so far (meters, range [0, D_target]).

[0073] Piecewise velocity curve v_ghost(d) = _opp×γ(d / D_target), where γ(d / D_target) is the piecewise correction coefficient.

[0074] Starting phase: γ(x) = γ_startup, when x∈[0,0.1). γ_startup is the starting coefficient, typically 0.8, used to simulate the acceleration of a real runner from a standstill. In the starting phase, the virtual opponent's speed is approximately 80% of the average speed, reflecting the acceleration characteristics of the starting phase.

[0075] Constant speed segment: γ(x) = 1.0, when x ∈ [0.1, 0.8). In the constant speed segment, the virtual opponent maintains an average speed, simulating the movement state of a real runner maintaining a stable pace in the middle of the race.

[0076] Sprint phase: γ(x) = γ_sprint, when x∈[0.8,1.0]. In the sprint phase, the speed of the virtual opponent increases by a preset ratio based on the constant speed phase, simulating the motion state of a real runner sprinting at the end of the race.

[0077] In one embodiment, step S104 may include: The speed of the sprint phase is increased by a preset percentage based on the constant speed phase; the preset percentage increase is determined based on the historical win rate of the offline opponent.

[0078] It's important to understand that the sprint coefficient is dynamically adjusted based on the opponent's win rate in PvP: γ_sprint=γ_base+k_sprint×w_opp Where γ_base is the sprint base coefficient (typically 1.0), k_sprint is the win rate impact coefficient on sprint (typically 0.1), and w_opp is the opponent's PK win rate (value 0~1).

[0079] The higher the win rate in the PK (player kill) phase, the more pronounced the acceleration during the sprint, simulating the powerful sprint ability of high-level athletes at the end of a race. For example, when w_opp=0.7, γ_sprint=1.0+0.1×0.7=1.07, meaning the sprint speed is 107% of the constant speed speed; when w_opp=0.2, γ_sprint=1.0+0.1×0.2=1.02, meaning the sprint speed is 102% of the constant speed speed.

[0080] It should be noted that the upper limit of γ_sprint can be clamped to 1.2, meaning that the speed of the sprint segment can be up to 120% of that of the constant speed segment, in order to prevent the generation of unreasonable sprint speeds.

[0081] Boundary condition: If the opponent has no historical motion data ( If _opp is empty, then the target speed configured in the room, v_target=D_target / T_target, is used as the base speed, where T_target is the target duration.

[0082] After generating the segmented velocity curves, the system adds virtual opponents to the 3D game room through the communication interface of the 3D game server. Specifically, the system calls the 3D game server's add-robot interface ( / add-robot), passing parameters such as the virtual opponent's user ID, room ID, route ID, display name, gender, and avatar URL, so that the virtual opponent is presented as an independent character in the 3D scene.

[0083] During the competition, the system calculates the current position of the virtual opponent in real time. Specifically, whenever the system receives a real-time motion data push from a participating user (typically once per second), it performs the following steps: Step 1: Calculate the user's progress P_progress = d_user / D_target, where d_user is the user's current cumulative mileage (meters), D_target is the target mileage (meters), and P_progress takes the value [0,1].

[0084] Step 2: Calculate the current segment speed of the virtual opponent, v_segment=v_ghost(d_user), that is, based on the user's current distance, query the segment speed curve to determine the speed of the virtual opponent at the current progress.

[0085] Step 3: Safety clamp v_adjusted=max(0,min(v_max,v_segment)), where v_max is the maximum safe speed set by the system to prevent abnormal speed values.

[0086] Step 4: Call the 3D game server's speed adjustment interface (set-robot-speed) to pass the adjusted speed, current distance, and remaining distance parameters to the 3D game server to achieve real-time updates of the virtual opponent's position.

[0087] Meanwhile, the system pushes the real-time location data of the virtual opponent to the participating users' clients via WebSocket, and the participating users' clients render the movement progress of the virtual opponent in real time in the 3D scene.

[0088] Through the aforementioned virtual opponent generation and control mechanism, participating users in offline PK scenarios can obtain a real-time competitive experience similar to online PK, significantly enhancing the immersion and competitive engagement of offline competitions.

[0089] In one embodiment, step S106 may include: The user type of the participating user is determined based on the user's historical win rate, and the calibration coefficient is determined based on the user type; the user type includes stable, balanced and competitive, wherein the calibration coefficient corresponding to the stable type is less than or equal to the calibration coefficient corresponding to the balanced type, and the calibration coefficient corresponding to the balanced type is less than or equal to the calibration coefficient corresponding to the competitive type.

[0090] It's important to understand that the system obtains the participating user's historical win rate (i.e., the user's win rate in historical PvP, with a value between 0 and 1), determines the calibration coefficient α_calib based on the historical win rate, and then calculates the compensated effective speed according to the following formula: v_eff_calibrated=v_eff×(1+α_calib) Where α_calib is the calibration coefficient, which is a positive value and represents the speed improvement compensation for offline scores.

[0091] In one embodiment, step S106 may include: The user type with a historical win rate of less than 30% is described as the stable type.

[0092] The user type with a historical win rate greater than or equal to 30% and less than or equal to 60% is the balanced type.

[0093] The user type with a historical win rate greater than 60% is described as the competitive type.

[0094] It is important to understand that the criteria for classifying user types are as follows: Users with a historical win rate of less than 30% are classified as stable. These users are less affected by the competitive environment; their athletic performance shows little difference whether online or offline, and therefore they are given a smaller calibration factor.

[0095] Users with a historical win rate of 30% or higher and 60% or lower are classified as balanced. These users are moderately affected by the competitive environment during matches, experiencing some degree of competitive arousal during online games, but the magnitude is not significant; therefore, they are given a moderate calibration coefficient.

[0096] Users with a historical win rate greater than 60% are classified as competitive. These users are significantly influenced by the competitive environment in online matches. Seeing their opponents in real-time generates a strong drive to catch up and a competitive arousal effect, resulting in significantly better athletic performance than in offline matches. Therefore, they are given a larger calibration coefficient.

[0097] The calibration coefficient corresponding to the stable type is a speed improvement ratio of 0 to 1%. That is, α_calib∈[0,0.01].

[0098] The calibration coefficient corresponding to the equalization type is a speed improvement ratio of 1% to 3%. That is, α_calib∈[0.01,0.03].

[0099] The calibration coefficient corresponding to the competitive type is a speed improvement ratio of 3% to 5%. That is, α_calib∈[0.03,0.05].

[0100] It is important to understand that the range of values ​​for the above calibration coefficients is determined based on sports psychology research and extensive experimental data, and can reasonably reflect the performance differences between online and offline competitions for users with different psychological characteristics. It should be understood that the above ranges are merely examples and can be appropriately adjusted in practical applications based on factors such as the specific type of sport and the characteristics of the user group.

[0101] It should also be noted that when both participants in an offline competition are real users (i.e., both complete the competition independently offline), the effective speeds of both participants are directly compared without introducing a calibration coefficient. This is because both participants are in the same offline competition environment and lack real-time competitive incentives. The environmental differences are negated by the fact that "both participants complete the competition offline," and both participants' scores are affected by the environment to the same extent, thus eliminating the need for calibration compensation.

[0102] In one embodiment, the following steps may be included after step S107: Step S108: Detect cheating among the participating users in the unified ranking, mark the participating users who are detected as cheating users as cheating users, set the ranking of the cheating users to the bottom in the unified ranking, and cancel their eligibility to receive the prize money.

[0103] It is important to understand that the online-offline hybrid competition management method also includes a step of detecting cheating among participating users in the unified ranking.

[0104] Specifically, the system performs cheating detection on each participating user in the unified ranking. Cheating detection can employ various methods, such as: Sports data analysis: Detecting abnormal patterns in the sports data of participating users, such as sudden speed changes, mismatch between heart rate and speed, and abnormal GPS tracks.

[0105] Device fingerprint detection: Detects whether participating users are using cheating tools such as emulators or plug-ins.

[0106] Behavioral pattern detection: Detects whether the operating behavior of participating users conforms to the laws of human movement, such as whether there is non-human precise control, etc.

[0107] Multi-dimensional cross-validation: Cross-validation is performed on data from multiple dimensions (speed, cadence, heart rate, altitude change, etc.) to determine whether there is any suspicion of cheating.

[0108] The system will mark participants detected as cheating as cheaters and take the following actions against them: The ranking of cheating users will be set to the bottom in the unified ranking (i.e., the ranking will be set to the total number of participants N_total).

[0109] The cheating user will be disqualified from receiving bonuses (bonuses will be reset to zero).

[0110] The deposit paid by cheating users will not be refunded (if a deposit exists).

[0111] The aforementioned cheating detection and handling mechanisms ensure the fairness of the competition and the security of prize money distribution.

[0112] In one embodiment, step S108 may include: Obtain the deposit paid by each participating user at the time of participation.

[0113] Users who are not flagged as cheating are marked as normal users. The total amount of deposits paid by all normal users is calculated, and the total amount of deposits is determined as the distributable prize money.

[0114] The distributable bonus is allocated to each of the normal users according to the preset allocation ratio corresponding to their ranking in the unified ranking.

[0115] It is important to understand that the online-offline hybrid competition management method also includes the prize money settlement step.

[0116] Specifically, the system obtains the ticket price paid by each participating user when entering the competition. The ticket price can be monetary funds, platform points, or other equivalents.

[0117] The system marks participating users who are not marked as cheating as normal users, calculates the total amount of deposits paid by all normal users, and determines the total amount of deposits as the distributable prize money (i.e., the total prize pool).

[0118] Then, the system distributes the distributable bonus to each normal user according to the preset allocation ratio (ratio(rank)) corresponding to the ranking of each normal user in the unified ranking.

[0119] Specifically, the bonus distribution is calculated according to the following formula: NetPool=TotalPool×(1-commissionRate) Here, commissionRate is the platform's commission rate (derived from the reward configuration, with a value of 0~1), and NetPool is the net amount of distributable bonuses.

[0120] For each legitimate user i who was not identified as cheating: award_i=NetPool×ratio(rank_i) Where award_i is the prize money earned by user i, rank_i is the ranking of user i in the unified ranking, and ratio(rank_i) is the reward percentage corresponding to the ranking (from the ranking reward configuration, such as 70% for the first place and 30% for the second place, etc.).

[0121] Meanwhile, the deposit of normal users will be fully refunded: refund_i = ticketPrice_i For users who are judged to have cheated: The ranking is set to the last place: rank_i = N_total (N_total is the total number of participants); Award amount reset to zero: award_i = 0; The deposit is non-refundable: refund_i=0 (when a deposit exists, the cheater's deposit will not be included in the prize pool distribution and will belong directly to the platform).

[0122] Boundary condition handling: If all participating users are found to have cheated, the entire prize pool will belong to the platform and will not be distributed to any user. If the net allocatable prize pool NetPool ≤ 0 (e.g., free events or a guaranteed prize pool of 0), then the prize award_i for each normal user is 0.

[0123] The aforementioned bonus-anti-cheating collaborative settlement mechanism establishes an explicit collaborative link between cheating detection results and bonus distribution, ensuring that cheaters cannot obtain any bonus benefits while protecting the bonus rights of legitimate users, thereby enhancing the fairness and security of the competition.

[0124] This application also provides a system architecture supporting the above-described online-offline hybrid competition management method. The online-offline hybrid competition management system of this disclosure adopts a two-tier architecture of a room layer and an activity layer. The room layer and the activity layer are managed using any of the above-described online-offline hybrid competition management methods.

[0125] The room layer is responsible for competition access and matching management, including the following functional modules: Room Management Module: Responsible for room creation, joining, starting matches, and disbanding. Room records include the following key information: room number, room member type (single / multiplayer), room type (public / private), time type (instant start / scheduled start), room status (scheduled not started / not started / started / disbanded), number of activities (records the number of PK rounds conducted in a room), creation type (player-created / bot-created / NPC-created / partner-created), room mode (normal mode / item mode), room owner identifier, etc.

[0126] The room configuration includes the following key information: target type (distance target / duration target), target mileage, target duration, deposit type (free / amount deposit / points deposit), deposit amount, associated route markers, equipment type (treadmill / stationary bike / pedal / rowing machine), etc.

[0127] Intelligent Matching Engine: Based on user movement profiles, it intelligently matches NPC rooms, replacing the traditional random allocation strategy.

[0128] The activity layer is responsible for managing the competition process and results, and includes the following functional modules: The sports activity management module is responsible for activities such as creating activities, adding participants, associating 3D game rooms, and activity settlement. Activity records include the following key information: activity type (challenge run / new PK activity / offline PK cheat detection, etc.), activity subtype (random PK / friend PK / offline PK / newcomer guidance PK / user 1v1 PK / user multiplayer PK, etc.), completion rules (by mileage / by time), reward rules (free / deposit / points), activity configuration (comprehensive configuration in JSON format, including commission rate, reward rate, ranking reward configuration, etc.), whether it includes bots, and whether it includes pacers, etc.

[0129] The activity user record includes the following key information: user status (no reply / accepted / rejected / running / finished / exited), sub-status (participated / achieved goal / failed to achieve goal and exceeded 1 minute), ranking, running time, running distance, whether completed, whether it was a bot, user reward amount, etc.

[0130] Ghost Runner Generator: Generates motion parameters for virtual opponents based on their historical motion data, and connects the virtual opponents to the game room via the 3D game server communication interface.

[0131] The system provides interface services to the outside world through the REST API service layer, realizes real-time data synchronization during the game through the WebSocket real-time push service, handles background tasks such as settlement and anti-cheating through the asynchronous message queue service, and realizes 3D scene rendering and virtual opponent control through the 3D game server.

[0132] 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 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. Components shown as modules or units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without any inventive effort.

[0133] In addition to the above-mentioned online-offline hybrid competition management method, this application also provides a more specific example of the generation and control of virtual opponents (Ghost Runner).

[0134] When a participating user initiates an offline PK, the system executes the virtual opponent generation and control process.

[0135] (1) Virtual opponent data collection The system utilizes existing methods in the user motion data service module to obtain the opponent's motion ability data: Check the average pace and duration of your opponent's most recent matches; Check your opponent's win rate in their most recent PvP matches; Query the opponent's exercise data (exercise frequency, exercise volume, etc.) for the past few days.

[0136] (2) Virtual opponent configuration generation Based on the collected motion capability data, the system generates motion parameters of the virtual opponent according to the segmented speed curve generation method described in step S104, including starting speed, constant speed, sprint speed, and transition nodes for each stage.

[0137] (3) Virtual opponents access 3D game rooms The system adds virtual opponents to the 3D game room through the communication interface of the 3D game server. The specific process is as follows: First, the system creates a game room on the 3D game server (if it hasn't been created yet). It then calls the 3D game server's room creation interface (add-room), passing parameters such as the room ID and game mode.

[0138] Then, the system calls the 3D game server's add-robot interface, passing the following parameters: userid: The virtual opponent's user ID (a unique identifier assigned to the virtual opponent by the system). roomid: The room number you want to join; roadid: 3D runway ID; firstname: The display name of the virtual opponent (displayed above their head in the 3D scene); gender: gender (influences the selection of 3D character models); avatar: Avatar URL (used for displaying the avatar in the scene); activityid: The ID of the associated activity; country: Nationality code.

[0139] Meanwhile, the system pushes a notification of the virtual opponent's online arrival to all online users in the room via WebSocket (websocket synRobot), enabling all clients to instantiate the virtual opponent's character model in the 3D scene.

[0140] (4) Real-time control of virtual opponents During the game, the system controls the speed and position of the virtual opponent in real time through the 3D game server's speed adjustment interface (set-robot-speed). The parameters of the speed adjustment interface include: userid: The user ID of the virtual opponent; speed: Current speed (meters per second); distance: Current distance run (meters); remain-distance: remaining distance (meters).

[0141] The system calculates the current speed of the virtual opponent based on the real-time progress of the participating user according to the method described in step S104, and calls the speed adjustment interface at a typical frequency of once per second to realize the real-time update of the virtual opponent's position.

[0142] Meanwhile, the system pushes the real-time motion data of the virtual opponent to the participating users' clients via WebSocket, and the participating users' clients render the virtual opponent's motion progress in real time in the 3D scene.

[0143] (5) End and removal of virtual opponents Once the virtual opponent completes the running plan (reaches the target mileage or target time), the system calls the 3D game server's delete-robot interface to remove the virtual opponent from the 3D game room. Simultaneously, the system sends a notification via WebSocket to all online users in the room that the virtual opponent has logged off.

[0144] Through the aforementioned virtual opponent generation and control mechanism, participating users in offline PK scenarios can obtain a real-time competitive experience similar to online PK, significantly enhancing the immersion and competitive engagement of offline competitions.

[0145] In addition to the above-mentioned online-offline hybrid competition management method, this application also provides a more specific example of environmental difference calibration and ranking determination.

[0146] To address the issue of inaccurate rankings caused by differences in the competitive psychological environment between online and offline competitions, the system has introduced an environmental difference calibration mechanism.

[0147] (1) The nature of environmental differences The core difference between online and offline competition lies not in the distance or time standards, as they use the same target configuration (the same target mileage or target duration), but in the different psychological environments of the competition.

[0148] In online competitions, participants can see their opponents' progress in real time, and this real-time feedback triggers the following psychological effects: Competitive arousal effect: Seeing the opponent's position triggers adrenaline secretion, improving athletic performance; The catch-up effect: When one falls behind, a sense of urgency and motivation to catch up arises, leading to an inclination to accelerate the process. Social facilitation effect: The feeling of being watched increases effort and performance level; Rhythm disruption effect: Chasing opponents may disrupt one's own rhythm, resulting in significant pace fluctuations.

[0149] In offline competitions, participants complete the exercise independently, lacking the aforementioned real-time competitive incentives. This is mainly manifested in self-motivation, lack of real-time reference, solo running pace, and lack of a sense of urgency.

[0150] Sports psychology research shows that the above-mentioned psychological factors have a significant impact on athletic performance. The pace of participants in online mode is usually 2% to 5% faster than that in offline mode.

[0151] (2) Determination of calibration coefficients To eliminate the impact of the aforementioned environmental differences on the fairness of the rankings, the system introduces an environmental calibration coefficient α_calib for offline competition results.

[0152] The calibration coefficients are determined based on the participants' historical win rates, a behavioral indicator reflecting individual psychological characteristics. As mentioned earlier, users are categorized into three types—stable, balanced, and competitive—based on their historical win rates, each corresponding to a different range of calibration coefficients.

[0153] (3) Complete ranking determination process The complete ranking process is as follows: Step 1: Obtain the performance data (runMileage, runTime) of both participants.

[0154] Step 2: Calculate the effective speed v_eff = runMileage / runTime.

[0155] Step 3: Check if the exercise duration is 0. If the exercise duration is 0, then v_eff = 0 (no effective exercise was generated).

[0156] Step 4: Check if the participants include a virtual opponent (Ghost Runner). If a virtual opponent is included, their performance will be directly included in the ranking (their mileage and time are preset values, obtained by integrating the segmented speed curve formula).

[0157] Step 5: Check if both parties are offline during the PK. If both parties are offline (both are real users), then directly compare v_eff without introducing a calibration coefficient (the environmental differences have been offset by the fact that "both parties are offline").

[0158] Step Six: Check if a cross-mode comparison exists (online vs. offline). If a cross-mode comparison exists, apply calibration to the offline side: v_eff_calibrated = v_eff × (1 + α_calib).

[0159] Step 7: If both sides are online in a PK, directly compare v_eff.

[0160] Step 8: Sort: Sort by v_eff (or v_eff_calibrated) in descending order; if the speeds are the same, sort by mileage in descending order; if the mileages are the same, sort by time in ascending order.

[0161] Step 9: Determine the final ranking.

[0162] Through the aforementioned environmental difference calibration mechanism, the results under different competition environments can truly reflect the level that users should possess in actual competitive combat, achieving fair and unified ranking across competition modes.

[0163] In addition to the above-mentioned online-offline hybrid competition management method, this application also provides a more specific example of collaborative settlement of prize money and anti-cheating measures.

[0164] The system has established an explicit coordination mechanism between cheating detection results and reward distribution.

[0165] (1) Cheating detection After the competition ends, the system will perform cheating detection on all participating users in the unified ranking and mark the participating users who are detected as cheating users.

[0166] (2) Handling of cheating For each participant i identified as cheating, the following actions will be taken: rank_i = N_total (ranked last, N_total is the total number of participants); award_i=0 (prize money is reset to zero); refund_i=0 (the deposit is non-refundable when a deposit exists).

[0167] (3) Calculation of the prize pool Only the deposit of legitimate users (those not identified as cheaters) will be calculated: TotalPool=Σ_{isCheat_i=false}ticketPrice_i NetPool=TotalPool×(1-commissionRate) In this table, TotalPool represents the total prize pool amount, commissionRate is the platform's commission rate, and NetPool is the net amount of distributable prize money. The deposit of cheaters is not included in the prize pool allocation and belongs directly to the platform.

[0168] (4) Bonus distribution for normal users For each participating user i who was not judged to have cheated: award_i=NetPool×ratio(rank_i) refund_i = ticketPrice_i (Full refund of deposit) Where ratio(rank_i) is the reward ratio corresponding to the rank (from the ranking reward configuration).

[0169] (5) Boundary condition handling If all participating users are found to have cheated, the entire prize pool will belong to the platform and will not be distributed to any user. If NetPool≤0 (e.g., free events or a deposit of 0), award_i=0.

[0170] (6) Settlement records The system updates the activity user records, recording the reward amount and distribution time for each user.

[0171] The aforementioned bonus-anti-cheating collaborative settlement mechanism establishes an explicit collaborative link between cheating detection results and bonus distribution, ensuring that cheaters cannot obtain any bonus benefits while protecting the bonus rights of legitimate users, thereby enhancing the fairness and security of the competition.

[0172] In conjunction with the above-mentioned online-offline hybrid competition management method, this application also provides a more specific example of the communication process for virtual opponents to access 3D game rooms.

[0173] The complete communication process from matchmaking to entering the 3D game room and completing the match with a virtual opponent is as follows: Phase 1: Matching and Activity Creation Users initiate PK matching requests by calling the matching API (api act match) through the client app.

[0174] The system creates a MindUserMatch record (a matching record, with a status of waiting for matching).

[0175] The system executes the matching logic through an asynchronous thread, selecting a suitable virtual opponent from the bot pool based on the matching type (random PK / offline PK / newbie PK, etc.) or matching with a real user.

[0176] The system creates a RunActivity record (activity record) and a RunActivityUser record (activity user record, one for each).

[0177] Phase Two: Virtual Opponents Access the 3D Room The system calls the 3D game server's room creation interface (add-room), passing parameters such as room ID and game mode to create a game room on the 3D game server.

[0178] The system pushes a notification that a virtual opponent has come online to all online users in the room via WebSocket (websocketsynRobot, isOnline=1), and the client instantiates the virtual opponent's character model in the 3D scene.

[0179] The system calls the add-robot interface of the 3D game server, passing parameters such as the virtual opponent's user ID, room ID, route ID, display name, gender, avatar URL, and nationality to register the virtual opponent in the 3D game server.

[0180] The system records the 3D room status of the virtual opponent in the robot_3d_room table.

[0181] Phase 3: Real-time Running The user client collects device motion data (speed, distance, time) in real time and submits it to the backend via HTTP / WebSocket.

[0182] The backend broadcasts the user's real-time motion data to all online clients in the room via WebSocket.

[0183] The backend uses RobotRunPlanManager to calculate the robot's (virtual opponent's) current progress every second, calls the 3D game server's speed adjustment interface (set-robot-speed), and transmits parameters such as speed, current distance, and remaining distance to update the virtual opponent's position in the 3D game server.

[0184] The backend pushes real-time location data of all players (including virtual opponents) to all clients via WebSocket, and the clients update the positions of all characters in the scene in the local Unity engine.

[0185] Phase Four: Closure and Settlement When a user finishes a match, the client calls the end API.

[0186] The backend calls the 3D game server's delete-robot interface to remove the virtual opponent from the 3D game room.

[0187] The backend updates the status of the virtual opponent in the robot_3d_room table to "deleted".

[0188] The backend calculates the ranking (based on rules such as descending order of effective speed) and executes the bonus settlement (in collaboration with anti-cheating detection).

[0189] The backend pushes the competition results (rankings, prize money, etc.) to the client via WebSocket.

[0190] Through the complete communication process described above, virtual opponents can seamlessly access the 3D game room and compete in real time with real users in the same 3D scene, achieving an immersive competitive experience in offline PK scenarios.

[0191] 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 application 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 examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A hybrid online and offline competition management method, characterized in that, The method includes: Multiple sports types are acquired, and multiple competition modes are configured for each sports type. The sports results under all competition modes are converted into effective speeds. In response to a competition request initiated by a participating user, the type of sport, competition mode, and competition method indicated by the competition request are determined; wherein, the competition method is an online competition or an offline competition; When the competition mode is an online competition, real-time communication is established between the participating user and the online opponent. During the competition, the real-time movement data of the online opponent is obtained, and the real-time movement progress of the online opponent is displayed to the participating user based on the real-time movement data. When the competition mode is an offline competition, the historical movement data of the offline opponent is obtained, and a simulated movement progress is generated and displayed to the participating user based on the historical movement data. After the competition, the effective speed of the participating users is calculated based on their completed exercise data. If the competition is an online competition, the effective speed is used as the ranking speed for the participating user; if the competition is an offline competition, the historical win rate of the participating user is obtained, a calibration coefficient is determined based on the historical win rate, the effective speed is compensated using the calibration coefficient to obtain the compensated effective speed, and the compensated effective speed is used as the ranking speed for the participating user. Under the same sport type, the ranking speed of each participating user is obtained, and the obtained ranking speeds are sorted to generate a unified ranking; wherein, the unified ranking covers all participating users under the same sport type who use different competition modes and different competition methods.

2. The online / offline hybrid competition management method according to claim 1, characterized in that, The steps of acquiring multiple sports types, configuring multiple competition modes for each sports type, and converting the sports results under all competition modes into effective speed include: The sports type includes at least running; the competition mode includes at least timed race mode and distance race mode.

3. The online / offline hybrid competition management method according to claim 1, characterized in that, When the competition mode is an offline competition, the step of obtaining the historical movement data of offline opponents and generating a simulated movement progress based on the historical movement data to display to the participating users includes: Based on the historical motion data of the offline opponent, a segmented speed curve is generated to simulate the motion progress and display it to the participating user; wherein, the segmented speed curve includes: the starting segment, the constant speed segment, and the sprint segment.

4. The online / offline hybrid competition management method according to claim 3, characterized in that, When the competition mode is an offline competition, the step of obtaining the historical movement data of offline opponents and generating a simulated movement progress based on the historical movement data to display to the participating users includes: The speed of the sprint phase is increased by a preset percentage based on the constant speed phase; the preset percentage increase is determined based on the historical win rate of the offline opponent.

5. The online / offline hybrid competition management method according to claim 1, characterized in that, The steps of responding to a competition request initiated by a participating user and determining the type of sport, competition mode, and competition method indicated in the competition request include: In response to a competition request initiated by a participating user, the competition level is determined based on the participating user's historical competition ranking, and an opponent is matched with the participating user based on the competition level.

6. The online / offline hybrid competition management method according to claim 1, characterized in that, If the competition mode is an offline competition, the steps of obtaining the historical win rate of the participating user, determining a calibration coefficient based on the historical win rate, compensating the effective speed using the calibration coefficient to obtain the compensated effective speed, and using the compensated effective speed as the ranking speed of the participating user include: The user type of the participating user is determined based on the user's historical win rate, and the calibration coefficient is determined based on the user type; the user type includes stable, balanced and competitive, wherein the calibration coefficient corresponding to the stable type is less than or equal to the calibration coefficient corresponding to the balanced type, and the calibration coefficient corresponding to the balanced type is less than or equal to the calibration coefficient corresponding to the competitive type.

7. The online / offline hybrid competition management method according to claim 6, characterized in that, If the competition mode is an offline competition, the steps of obtaining the historical win rate of the participating user, determining a calibration coefficient based on the historical win rate, compensating the effective speed using the calibration coefficient to obtain the compensated effective speed, and using the compensated effective speed as the ranking speed of the participating user include: The user type with a historical win rate of less than 30% is described as the stable type. The user type with a historical win rate greater than or equal to 30% and less than or equal to 60% is the balanced type. The user type with a historical win rate greater than 60% is the competitive type. The calibration coefficient corresponding to the stable type is a speed improvement ratio of 0 to 1%; The calibration coefficient corresponding to the balanced type is a speed improvement ratio of 1% to 3%; The calibration coefficient corresponding to the competitive type is a speed improvement ratio of 3% to 5%.

8. The online / offline hybrid competition management method according to any one of claims 1-7, characterized in that, Also includes: Cheating detection is performed on the participating users in the unified ranking. Participating users detected as cheating are marked as cheating users, and their ranking in the unified ranking is set to the bottom, and their eligibility for prize money is cancelled.

9. The online / offline hybrid competition management method according to claim 8, characterized in that, Also includes: Obtain the deposit paid by each participating user at the time of participation; Users who are not marked as cheating are marked as normal users. The total amount of deposits paid by all normal users is calculated and the total amount of deposits is determined as the distributable prize money. The distributable bonus is allocated to each of the normal users according to the preset allocation ratio corresponding to their ranking in the unified ranking.

10. An online-offline hybrid competition management system, characterized in that, include: The room layer is responsible for competition access and matching management; The activity layer is responsible for managing the competition process and results. The room layer and the activity layer are managed using the online-offline hybrid competition management method described in any one of claims 1-9.

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