Personalized event analysis broadcasting method and device based on interaction instruction and medium

Abstract

A personalized esports event analysis and broadcasting method, computer equipment, and computer-readable storage medium based on audience interaction commands are disclosed. By integrating audience interaction command parsing, natural language processing technology, multi-view game stream acquisition, target time range determination, AI model video editing, and language generation mechanism based on esports terminology database, the method converts audience comments into calculable analysis targets and perspectives, automatically aligns them with corresponding game time ranges and perspective videos, and generates virtual anchor analysis and broadcasting content with professional tactical logic. This content is then presented in real time to the audience's viewing device. This achieves the technical effects of improving the real-time performance of event analysis, enhancing the personalized viewing experience, reducing the burden on human commentators, and improving the professionalism and interactivity of event content.

Description

Technical Field

[0001] This invention relates to an esports event broadcasting technology, specifically combining game streaming acquisition technology, natural language processing technology, analytical artificial intelligence models, and a database of esports terminology. By semantically analyzing and making perspective decisions on viewer comments, it dynamically acquires corresponding video clips from the observer's or player's game perspective and generates virtual anchors to analyze and broadcast content. This invention is a personalized esports event analysis and broadcasting method based on viewer interaction commands, along with computer equipment and its computer-readable storage medium. Background Technology

[0002] With the booming development of the esports industry, esports events are mostly broadcast live via online streaming platforms. Viewers can watch the matches from an observer's perspective, with live commentary provided by human anchors. Current technology typically provides a single observer's perspective, and some platforms may also offer individual player perspectives. However, these perspectives are mostly independent streams, requiring viewers to switch manually; the system itself lacks the ability to automatically determine the optimal perspective based on tactical situations. Furthermore, the commentary relies entirely on human judgment, making it impossible to generate customized analysis in real-time based on specific tactical questions raised by viewers.

[0003] On the other hand, while streaming platforms generally have chat room mechanisms that allow viewers to send messages and interact with each other, these messages are only for text communication and are not technically linked to the match video data. When viewers ask specific tactical questions such as "Why did we fail in that last fight?" or "Who made the wrong decision in that team fight?", the existing system cannot automatically parse the meaning of the message, locate the corresponding game time point, obtain relevant video clips, or generate corresponding tactical analysis. There is a lack of an automated linkage mechanism between the message messages and the actual match footage.

[0004] Furthermore, in terms of event localization, existing technologies typically rely on fixed event markers (e.g., kill notifications) provided by the tournament system as time nodes. For more strategic tactical events such as "team fights," "ganks," "flanks," and "vision errors," which lack clear system markers, a dynamic judgment mechanism based on character distance relationships, survival status, or spatial distribution changes has not been established. Therefore, when analyzing specific tactical segments, manual review and judgment are still required, and real-time automated filtering cannot be achieved.

[0005] Furthermore, while existing virtual anchor technology can achieve character animation and voice synthesis, it mostly involves playing pre-set scripts or reading simple events. It has not yet incorporated natural language processing technology for in-depth analysis of comment meaning, nor has it integrated a database of competitive terminology to generate tactically logical analytical content. In other words, existing virtual anchor systems lack the ability to convert video event sequences into professional tactical broadcast content and cannot dynamically adjust the broadcast tone and analytical structure according to different analytical perspectives.

[0006] In summary, existing technologies have long suffered from several problems, including the inability to convert viewer comments into calculable analytical targets, the inability to automatically align comment timestamps with the game timeline, the inability to dynamically define the analytical time range based on tactical type, the inability to intelligently integrate multi-view videos, and the inability to generate professional tactical analysis broadcast content. Therefore, it is necessary to propose an integrated technical solution that combines natural language processing technology, multi-view streaming acquisition technology, event time range determination algorithms, analytical AI model editing mechanisms, and esports terminology database language generation technology to solve the aforementioned problems and improve the interactivity and professional analytical capabilities of esports streaming. Summary of the Invention

[0007] In view of the problems existing in the prior art, the present invention discloses a method for personalized e-sports event analysis and broadcasting based on audience interaction commands, a computer device and a computer-readable storage medium thereof, wherein: This invention discloses a personalized esports event analysis and broadcasting method based on audience interaction commands, which involves executing the following steps using a computer device: The process involves: acquiring the observer's game view stream and multiple player game view streams; monitoring the chat rooms of the streaming platform; when a message sent by a viewer in the chat room contains at least one keyword, obtaining the parsing target based on that keyword; using Natural Language Processing (NLP) technology to parse the message message based on the parsing target to obtain the parsing perspective; filtering the observer's game view stream for the parsing target time range that is closest to the timestamp of the message message based on the parsing target; obtaining the corresponding observer's game view parsing video or player's game view video from the observer's game view stream or each player's game view stream as the parsing target video based on the parsing perspective and the parsing target time range; using NLP technology to generate virtual anchor parsing and broadcasting content for the parsing target video based on the parsing perspective and a competitive terminology database; integrating the virtual anchor into the e-sports event stream; providing the parsing target video to the viewer's corresponding viewing device; and having the virtual anchor displayed on the viewer's corresponding viewing device broadcast the parsing target video based on the virtual anchor parsing and broadcasting content.

[0008] In one embodiment of the present invention, when the target of analysis is a team battle, the starting time point is the time point at which the distance between at least three game characters in the observer's game perspective stream is within a preset distance. The ending time point is the time point at which the distance between the remaining game characters, excluding the dead game characters, is the time point at which the distance between one of the remaining game characters is outside the preset distance. The time range from the starting time point to the ending time point is the target time range of analysis.

[0009] In one embodiment of the present invention, the method further includes filtering multiple candidate intervals based on the target of the analysis and the observer's game perspective stream. When one of the multiple candidate intervals is selected as the target time range for analysis, the observer's game perspective analysis video and multiple player's game perspective videos are obtained from the observer's game perspective stream and each player's game perspective stream, respectively, according to the selected target time range for analysis.

[0010] In one embodiment of the present invention, the method further includes using an analytical artificial intelligence model to analyze video from the observer's game perspective and editing multiple player game perspective videos to generate an analytical target video.

[0011] In one embodiment of the present invention, the parsed target video is integrated with the e-sports event stream and provided to the viewing device corresponding to the viewer, or the parsed target video is temporarily used to replace the e-sports event stream and provided to the viewing device corresponding to the viewer.

[0012] This invention discloses a computer device, which includes: The storage device stores multiple computer-readable instructions; and One or more hardware processors are electrically connected to a storage device and execute multiple computer-readable instructions to enable a computer device to implement the personalized e-sports event analysis and broadcasting method based on audience interaction instructions, as described above.

[0013] This invention discloses a computer-readable storage medium storing a computer program that, when executed by one or more hardware processors of a computer device, causes the computer device to perform a personalized e-sports event analysis and broadcasting method based on audience interaction commands.

[0014] The method, computer equipment, and computer-readable storage medium disclosed in this invention are as described above. By integrating audience interaction command parsing, natural language processing technology, multi-view game stream acquisition, target time range determination, AI model video editing, and language generation mechanism based on a competitive terminology database, the messages submitted by the audience can be converted into calculable parsing targets and parsing perspectives. The corresponding game time range and perspective video are automatically aligned, thereby generating virtual anchor parsing and broadcasting content with professional tactical logic, which is then presented to the audience's corresponding viewing device in real time.

[0015] Through the aforementioned technical means, this invention can achieve the technical effects of improving the real-time performance of esports analysis, enhancing the personalized viewing experience, reducing the burden on human commentators, and increasing the professionalism and interactivity of esports content, thereby enhancing the overall service value and audience engagement of esports streaming platforms. Attached Figure Description

[0016] Figure 1A as well as Figure 1B The diagram illustrates a flowchart of the personalized esports event analysis and broadcasting method based on audience interaction commands according to the present invention.

[0017] Figure 2 The illustration is a schematic diagram of the observer's game perspective streaming of the personalized e-sports event analysis and broadcast based on audience interaction commands according to the present invention.

[0018] Figure 3 The illustration is a schematic diagram of the player's game perspective streaming in the personalized e-sports event analysis and broadcast based on audience interaction commands according to the present invention.

[0019] Figure 4 The illustration shows a chat room based on the personalized e-sports event analysis and broadcasting of the present invention, which is based on audience interaction commands.

[0020] Figure 5 The illustration is a schematic diagram of the virtual anchor parsing and broadcasting content of the e-sports event personalized event analysis and broadcasting based on the audience interaction command of the present invention.

[0021] Figure 6 The illustration shows a viewer device that displays and analyzes target videos based on personalized e-sports event analysis and broadcasting according to the present invention, which is based on audience interaction commands.

[0022] Figure 7 The illustration shows a schematic diagram of the computer equipment system architecture for personalized e-sports event analysis and broadcasting based on audience interaction commands, as per the present invention.

[0023] The annotations in the attached figures are explained as follows: Step 101: Obtain the observer's game view stream and the game view streams of multiple players. Step 102: Monitor the chat rooms of the streaming platform Step 103: When a message sent by a viewer in the chat room contains at least one keyword, obtain the parsing target based on at least one keyword. Step 104: Use natural language processing techniques to parse the message based on the parsing target to obtain the parsing perspective. Step 105: Based on the target being parsed and the observer's game-perspective stream, filter out the target time range that is closest to the timestamp of the message. Step 106: Based on the analysis perspective and the target time range, obtain the corresponding observer's game perspective analysis video or player's game perspective video from either the observer's game perspective stream or each player's game perspective stream as the analysis target video. Step 107: Using natural language processing technology, based on the analysis perspective and in conjunction with a database of competitive terminology, generate virtual anchor analysis and broadcast content for the target video. Step 108: Integrating virtual esports anchors into esports event streaming Step 109: Provide the analyzed target video to the audience's corresponding viewing device. Step 110: The virtual anchor displayed on the viewer's corresponding viewing device broadcasts the target video based on the virtual anchor's analysis and broadcast content. 20: Observer Game Perspective Streaming 21: First-party online hero characters 22: First-party soldier role 23: Second-line top-tier hero characters 24: Second-party soldier role 25: Second Roaming Hero Character 26: Objects in the grass above 27: Grassy object below 30: Player's game perspective streaming 31: First-party online hero characters 32: First-party soldier role 33: Second-tier top-tier hero characters 34: Second-party soldier role 37: Grassy object below 40: Chat Room 50: Analysis of Virtual Anchor Broadcast Content 60: Spectator equipment 61: Virtual Reality Anchor 62: Analyze the target video 700: Computer System 701: CPU 702:ROM 703: RAM 704: Bus 705: I / O Interface 706: Input Section 707: Output Section 708: Storage Section 709: Communication part 710: Drive 711: Removable media Detailed Implementation The following will describe in detail the implementation of the present invention with reference to the accompanying drawings and embodiments, thereby enabling a full understanding of how the present invention uses technical means to solve technical problems and achieve technical effects, and allowing for its implementation.

[0024] The following section will first explain the personalized esports event analysis and broadcasting method based on audience interaction commands disclosed in this invention, and please refer to [the relevant documentation / reference]. Figure 1A as well as Figure 1B As shown, Figure 1A as well as Figure 1B The diagram illustrates a flowchart of the personalized esports event analysis and broadcasting method based on audience interaction commands according to the present invention.

[0025] This invention discloses a personalized esports event analysis and broadcasting method based on audience interaction commands, which involves executing the following steps using a computer device: Obtain the observer's game view stream and multiple players' game view streams (step 101); monitor the chat room of the streaming platform (step 102); when a message sent by a viewer in the chat room contains at least one keyword, obtain the parsing target based on at least one keyword (step 103); use natural language processing technology to parse the message based on the parsing target to obtain the parsing perspective (step 104); based on the parsing target and the observer's game view stream, select the parsing target time range that is closest to the timestamp of the message (step 105); based on the parsing perspective and the parsing target time range, analyze the observer's game view stream... Alternatively, obtain the corresponding observer game perspective analysis video or player game perspective video from each player's game perspective stream as the analysis target video (step 106); use natural language processing technology to generate virtual anchor analysis broadcast content for the analysis target video based on the analysis perspective and in conjunction with the e-sports terminology database (step 107); integrate the virtual e-sports anchor into the e-sports event stream (step 108); provide the analysis target video to the audience's corresponding viewing device (step 109); and enable the virtual e-sports anchor displayed on the audience's corresponding viewing device to broadcast the analysis target video according to the virtual anchor analysis broadcast content (step 110).

[0026] The computer device obtains the observer's game perspective stream and multiple player's game perspective stream from the competitive game server through the game application programming interface (API). The aforementioned computer device can be implemented as a single computer or a distributed processing architecture. The game application programming interface enables different systems to interoperate without revealing the internal implementation details through predefined communication specifications, data formats, and request-response mechanisms.

[0027] Please refer to the aforementioned observer game perspective streaming 20. Figure 2 As shown, Figure 2The illustration is a schematic diagram of the observer's game perspective streaming of the personalized e-sports event analysis and broadcasting based on audience interaction commands according to the present invention. Figure 2 The game features a first-team lane hero character 21, multiple first-team minion characters 22, a second-team top lane hero character 23, multiple second-team minion characters 24, and a second roaming hero character 25. The second roaming hero character 25 moves from the upper bush object 26 to the lower bush object 27. The second-team top lane hero character 23 attempts to lure the first-team lane hero character 21 to approach the lower bush object 27, thus achieving the tactical intention of the second-team top lane hero character 23 and the second roaming hero character 25 to cooperate in attacking the first-team lane hero character 21.

[0028] Please refer to the aforementioned player game perspective stream 30. Figure 3 As shown, Figure 3 The illustration is a schematic diagram of the player's game perspective streaming in the personalized e-sports event analysis and broadcasting based on audience interaction commands according to the present invention. Figure 3 The game features a first-party online hero character 31, multiple first-party minion characters 32, a second-party online hero character 33, and multiple second-party minion characters 34. The second-party online hero character 33 attempts to lure the first-party online hero character 31 to approach the bush object 37 below.

[0029] The computer device establishes a connection with the streaming platform and monitors chat room 40 on the streaming platform. Please refer to the diagram for chat room 40. Figure 4 As shown, Figure 4 The illustration shows a chat room for personalized esports event analysis and broadcasting based on audience interaction commands, as per the present invention. In chat room 40, audience A sends the message "Do you even know how to play?", audience B sends the message "Please analyze why the second-team gank on the top lane failed," and audience C sends the message "Missed my ultimate again!!!." Assuming the keywords include "GANK," "top lane," "mid lane," "bottom lane," "dragon," "baron," "red buff," "blue buff," and "river," etc., this is merely an example and does not limit the application scope of the invention. The computer device can monitor that the message sent by audience B in chat room 40 contains the keywords "GANK" and "top lane," and based on these keywords, the computer device can determine the parsing target as "GANK top lane" or "top lane GANK."

[0030] Next, the computer equipment uses Natural Language Processing (NLP) Natural Language Processing (NLP) technology analyzes message messages based on the parsing target to obtain the parsing perspective. Specifically, continuing the example above, the message (msg) is "Please analyze the reason why the second-party gank on the top lane just failed," and the parsing target (target) is "gank top lane" or "top lane gank." The computer performs NLP on the message message and uses the parsing target as the "query condition / context." First, it determines that the message message belongs to the intent of "tactical analysis / failure reason analysis." Then, it extracts fields related to the perspective decision. The aforementioned perspective decision-related fields are, for example: lane is "top lane," side is "second-party," event is "gank," analysis_need is "failure reason," and time_ref is "just now" (i.e., relative time, used for subsequent corresponding timestamps and the time range of the parsing target). It confirms that the object of "analysis" is "second-party gank on the top lane failed," and marks "reason" as the analysis aspect to be output. This forms a "structured semantic framework for perspective decision-making" as "frame={event:GANK,lane:top lane, side:second-party, ask:WHY_FAIL, The phrase "time:just now" is merely an example and is not intended to limit the scope of application of this invention.

[0031] Computer devices can generate "analyzed perspectives" based on frames. For example, they can make decisions using a rule / model hybrid approach. Continuing with the example above, if the event is "GANK" and the ask is "WHY_FAIL", the perspective of the player who is the "second roaming hero" or the "second lane top hero" is selected first to examine the entry route, skill casting, and vision occlusion. The output perspective is {primary: second roaming hero's perspective, focus: top lane bush object, dimensions: [vision, timing, path]}. This is only an example and does not limit the scope of application of this invention.

[0032] Next, the computer device filters the target time range closest to the timestamp of the message based on the target's timestamp in the observer's game view stream. Specifically, the computer device establishes an event detection condition for the target "GANK top lane" and scans multiple candidate time ranges in the observer's game view stream. For example, when it detects that the "second roaming hero" enters the top bush in the top lane area and is less than a preset distance (D_th) from the first top lane hero, accompanied by any of the following characteristics: skill casting, health decrease, or control effect, the time point is marked as "GANK". The event candidates are defined as follows: the start time (t_start) is the time when the second roaming hero first enters the bush above; the end time (t_end) is the time when the second roaming hero leaves the combat area, or when either hero dies or retreats, or when the distance between the two sides exceeds a preset distance and lasts for more than τ seconds. Thus, the computer device can generate multiple candidate intervals in the entire observer stream, such as R1=[t1_start,t1_end], R2=[t2_start,t2_end], etc. This is only an example and is not intended to limit the application scope of the present invention.

[0033] Next, the computer device calculates the representative time ti_center=(ti_start+ti_end) / 2 for each candidate interval Ri=[ti_start,ti_end], and calculates the distance value Δi=|ti_center-T_msg_game, where T_msg_game is the time that maps the timestamp of the message (T_msg) to the game timeline (e.g., using streaming timecode / in-game timer / server synchronization information for time synchronization). The Ri with the smallest Δi is taken as the "target time range for parsing". Specifically, assume T _msg_game=12:35 (i.e., game time), and the candidate intervals are R1=[11:50,12:05], R2=[12:28,12:45], and R3=[13:10,13:25]. After the above calculation, the center time of R2 is closest to 12:35. Therefore, R2 is selected as the "target time range for parsing". This is only an example and does not limit the application scope of the present invention. In addition, the computer device can also provide each candidate interval. When one of the candidate intervals is selected, the selected candidate interval is the "target time range for parsing".

[0034] In addition, when the target of analysis is a team battle, the starting time point is the time point when the distance between at least three game characters in the observer's game perspective stream is within a preset distance. The ending time point is the time point when the distance between one of the remaining game characters is beyond the preset distance, excluding the dead game characters. The time range from the starting time point to the ending time point is the target time range of analysis.

[0035] Specifically, assuming that on the game timeline of the observer stream, the computer device calculates the distance between characters dist(ri,rj) frame by frame, when the time is 18:20.0, the distance between the three hero characters (e.g., the hero character in the first team's mid lane, the hero character in the first team's bottom lane, and the hero character in the second team's mid lane) is within the preset distance, the computer device determines the start time as 18:20.0, that is, "the time point when the distance between at least three game characters is within the preset distance" is the start time of the team fight.

[0036] Next, the computer device tracks the set S of characters involved in the "core of the team fight" from the start time and excludes dead characters. For example, if a hero on the second side's middle lane dies at 18:28.3, the hero on the second side's middle lane is removed from set S. Then, it is determined whether the remaining characters have started to disperse. For example, if any pair of characters is more than the preset distance at 18:33.0, the computer device determines the end time to be 18:33.0. Therefore, the computer device outputs the target time range for the team fight analysis as Ri=[ti_start,ti_end]=[18:20.0,18:33.0].

[0037] It is worth noting that the computer device filters out multiple candidate intervals based on the target of analysis and the observer's game perspective stream. When one of the multiple candidate intervals is selected as the target time range for analysis, the computer device obtains the observer's game perspective analysis video and multiple player's game perspective videos from the observer's game perspective stream and each player's game perspective stream, respectively, according to the selected target time range for analysis. The computer device then uses the analysis artificial intelligence model to edit the observer's game perspective analysis video and multiple player's game perspective videos to produce the target video for analysis.

[0038] Specifically, the computer device has acquired the observer's game perspective video (V_obs) and multiple player game perspective videos (V_p1, V_p2...V_pn) based on the "analysis target time range". The computer device inputs V_obs and V_p1, V_p2...V_pn into the "analysis artificial intelligence model" (the aforementioned analysis artificial intelligence model is, for example, a deep learning model or a mixture of rules and models, which is only an example here and does not limit the application scope of this invention), and outputs a single "analysis target video (V_target)". The analysis artificial intelligence model uses "in-game timecode, screen HUD information, event prompts (kills, assists, tower damage, etc.)" in each perspective video as synchronization features, aligns V_obs and each V_pi to the same time axis t, avoids editing misalignment caused by different streaming delays, and finds key sub-events within the analysis target time range, such as: the second roaming hero entering the above bush object; the first party The scenarios described are: online hero characters, second-party online hero characters, and second roaming hero characters engaging in combat (skill casting, crowd control hits, rapid health depletion, etc.); first-party online hero characters, second-party top-tier online hero characters, or second roaming hero characters retreating or dying, etc. These are merely illustrative examples and do not limit the scope of application of this invention. The AI ​​analysis model selects the "most suitable perspective for analysis" from V_obs and V_p1, V_p2...V_pn for each sub-event segment. For example, in the case of "the second roaming hero character entering the upper bush object," the "second roaming hero character's perspective" will be prioritized; in the case of "the second-party online hero characters and second roaming hero characters engaging in combat," the "observer's perspective" will be prioritized; and in the case of "the first-party online hero character, second-party top-tier online hero character, or second roaming hero character retreating or dying," the "first-party online hero character's perspective" will be prioritized. These are merely illustrative examples and do not limit the scope of application of this invention.

[0039] The AI ​​analysis model strings together selected perspective segments in chronological order, and then combines this with an observer overview at the beginning of each segment (0.5–1 second), slowing down or replaying key moments (such as control hits / flashes / failed kills), etc., and finally outputs the analyzed target video.

[0040] Next, the computer device obtains the corresponding observer game perspective analysis video or player game perspective video from the observer game perspective stream or each player game perspective stream based on the analysis perspective and the analysis target time range. The computer device uses natural language processing technology to generate virtual anchor analysis and broadcast content for the analysis target video based on the analysis perspective and in conjunction with the competitive terminology database.

[0041] Specifically, the computer equipment analyzes the target video (V_target) using natural language processing technology to determine the tone and analysis framework based on the analysis perspective (e.g., explaining paths, vision, timing, etc. from the perspective of a jungler), and generates professional natural language content by combining it with a competitive terminology database (e.g., storing tactical terms, common sentence patterns, character nicknames, route nicknames, event templates, etc.).

[0042] Computer devices can obtain event sequences from V_target (which can be deduced from game data, HUD, event prompts, etc.), such as: the second roaming hero enters the top lane river bush at 12:29 (path); enters the fray at 12:33 but does not clear / sweep wards first (vision); the top lane minion wave does not advance at 12:36, and the first team can still retreat safely (timing); retreats after the fight at 12:40, and the gank fails (result), etc., and map the events to three analysis dimensions to form a "broadcast skeleton".

[0043] The terms in the aforementioned esports terminology database include, for example, flanking, vision control, ward clearing, entering bushes, minion waves, flashing, crowd control, kiting, losing rhythm, etc. Sentence patterns in the database include, for example, "The key to this {event} is {dimension}," "{side} wanted to engage {event} in {lane}, but {cause} led to {result}," and "If {fix_action} were performed first, the success rate would be higher," etc. These are merely illustrative examples and do not limit the scope of application of this invention.

[0044] The computer-generated virtual anchor's analysis and broadcast content 50 is: "This second top lane gank failed because of the path and vision. You see, when the jungler circled around to the river bush, he didn't clear wards first before entering, exposing his entry route too early. The first top laner immediately retreated. Next is timing; the minion wave wasn't pushed forward first, so the second top laner couldn't hold onto the enemy. The jungler had to use his skills to retreat, disrupting the entire rhythm. If they had cleared wards first, then circled around, and coordinated with the window before the minion wave reached the tower, the success rate would have been much higher." Please refer to the illustration of the virtual anchor's analysis and broadcast content 50. Figure 5 As shown, Figure 5 The illustration is a schematic diagram of the virtual anchor parsing and broadcasting content of the e-sports event personalized event analysis and broadcasting based on the audience interaction command of the present invention.

[0045] Please refer to Figure 6 As shown, Figure 6The illustration shows a viewing device displaying the analyzed target video based on the personalized e-sports event analysis and broadcasting based on audience interaction commands, according to the present invention. The computer device integrates a virtual e-sports anchor in the e-sports event stream and provides the analyzed target video to the viewer's corresponding viewing device 60. The virtual e-sports anchor 61 displayed on the viewer's corresponding viewing device 60 then broadcasts the analyzed target video 62 according to the virtual anchor's analyzed broadcasting content. It is worth noting that... Figure 6 The method involves integrating the parsed target video 62 with the e-sports event stream in a parent-child frame format, and then providing it to the corresponding viewing device 60. Alternatively, the parsed target video 62 can temporarily replace the e-sports event stream when providing it to the corresponding viewing device 60. This is merely an example and does not limit the scope of application of the present invention.

[0046] Embodiments of this application also provide a computer device, the computer device comprising: The storage device stores multiple computer-readable instructions; and one or more hardware processors are electrically connected to the storage device to execute the multiple computer-readable instructions, enabling the computer device to implement the virtual live streamer broadcasting method based on real-time game data analysis and context awareness as described above.

[0047] Embodiments of this application also provide a computer-readable storage medium storing a computer program that, when executed by one or more hardware processors of a computer device, causes the computer device to execute a virtual reality anchor broadcasting method based on real-time game data analysis and context awareness. This computer-readable storage medium may be included in the computer device described in the above embodiments, or it may exist independently and not be assembled into the computer device.

[0048] Please refer to Figure 7 As shown, Figure 7 The diagram illustrates the computer equipment system architecture for personalized esports event analysis and broadcasting based on audience interaction commands, as per the present invention. It should be noted that... Figure 7 The computer system 500 of the computer device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0049] like Figure 7As shown, the computer system 700 includes a Central Processing Unit (CPU) 701, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 702 or programs loaded from storage portion 708 into Random Access Memory (RAM) 703. The RAM 703 also stores various programs and data required for system operation. The CPU 701, ROM 702, and RAM 703 are interconnected via a bus 704. An Input / Output (I / O) interface 705 is also connected to the bus 704.

[0050] The following components are connected to the I / O interface 705: an input section 706 including a keyboard, mouse, etc.; an output section 707 including a cathode ray tube (CRT), liquid crystal display (LCD), and speakers, etc.; a storage section 708 including a hard disk, etc.; and a communication section 709 including a network adapter such as a LAN (Local Area Network) card, modem, etc. The communication section 709 performs communication processing via a network such as the Internet. A drive 710 is also connected to the I / O interface 705 as needed. Removable media 711, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memory, etc., are installed on the drive 710 as needed so that computer programs read from them can be installed into the storage section 708 as needed.

[0051] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 709, and / or installed from removable medium 711. When the computer program is executed by central processing unit (CPU) 701, it performs various functions defined in the system of the present invention.

[0052] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer floppy disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable signal medium may include a data signal propagated in a baseband frequency or as part of a carrier wave, wherein a computer-readable computer program is carried. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit programs for use by or in connection with an instruction execution system, apparatus, or device. Computer programs contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0053] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0054] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0055] In summary, by integrating audience interaction command parsing, natural language processing technology, multi-view game streaming acquisition, target time range determination, AI model video editing, and language generation mechanism based on a competitive terminology database, the messages submitted by the audience can be converted into calculable parsing targets and perspectives. This automatically aligns with the corresponding game time range and perspective video, thereby generating virtual anchor parsing and broadcasting content with professional tactical logic, which is then presented to the audience's corresponding viewing device in real time.

[0056] This technology can solve the problems existing in the current technology, thereby improving the real-time performance of the event analysis, enhancing the personalized viewing experience, reducing the burden on human commentators, and increasing the professionalism and interactivity of the event content.

[0057] While the embodiments disclosed in this invention are as described above, the content is not intended to directly limit the scope of patent protection of this invention. Any person skilled in the art can make modifications in form and detail without departing from the spirit and scope disclosed in this invention. The scope of patent protection of this invention shall still be determined by the scope defined in the appended claims.

Claims

1. A personalized esports event analysis and broadcasting method based on audience interaction commands, comprising the following steps performed via computer equipment: Obtain the game view stream from the observer and the game view streams from multiple players; Monitoring the chat rooms of the streaming platform; When a message sent by a viewer in the chat room is detected to contain at least one keyword, the parsing target is obtained based on the at least one keyword; Natural language processing techniques are used to parse the message based on the parsing target to obtain the parsing perspective; Based on the parsing target, the time range of the parsing target that is closest to the timestamp of the message is selected in the observer's game perspective stream; Based on the analytical perspective and the analytical target time range, the corresponding observer game perspective analysis video or player game perspective video is obtained from the observer game perspective stream or each player game perspective stream. Natural language processing technology is used to generate virtual anchor analysis and broadcast content for the target video based on the analysis perspective and in conjunction with a competitive terminology database; Integrating virtual esports anchors into esports event streaming; The target video to be analyzed is provided to the viewing device corresponding to the audience. and The virtual live streamer displayed on the viewing device corresponding to the audience broadcasts the target video based on the content analyzed and broadcast by the virtual streamer.

2. The personalized esports event analysis and broadcasting method based on audience interaction commands as described in claim 1, wherein when the analysis target is a team battle, the starting time point is the time point at which the distance between at least three game characters in the observer's game perspective stream is within a preset distance, and the ending time point is the time point at which the distance between one of the remaining game characters is beyond the preset distance after excluding the dead game characters, and the time point from the starting time point to the ending time point is the time range of the analysis target.

3. The personalized e-sports event analysis and broadcasting method based on audience interaction commands as described in claim 1, further comprising filtering multiple candidate intervals based on the analysis target in the observer's game perspective stream, and when one of the multiple candidate intervals is selected as the analysis target time range, obtaining the observer's game perspective analysis video and multiple player's game perspective videos from the observer's game perspective stream and each player's game perspective stream respectively according to the selected analysis target time range.

4. The personalized e-sports event analysis and broadcasting method based on audience interaction commands as described in claim 3, further comprising using an analytical artificial intelligence model to analyze the video from the observer's game perspective and editing multiple player game perspective videos to generate the analytical target video.

5. The personalized e-sports event analysis and broadcasting method based on audience interaction commands as described in claim 1, wherein the parsed target video is integrated with the e-sports event stream as a sub-stream and provided to the viewing device corresponding to the audience, or the parsed target video is temporarily used to replace the e-sports event stream and provided to the viewing device corresponding to the audience.

6. A computer device, the computer device comprising: Storage device, storing multiple computer-readable instructions; and One or more hardware processors, electrically connected to the storage device, execute the plurality of computer-readable instructions to enable the computer device to implement the personalized e-sports event analysis and broadcasting method based on audience interaction instructions as described in any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program stored thereon, which, when executed by one or more hardware processors of a computer device, causes the computer device to perform a personalized e-sports event analysis and broadcasting method based on audience interaction instructions as described in any one of claims 1 to 5.

Images