Game interaction method, system and equipment based on real scenic spots and medium

Abstract

The invention is applicable to the technical field of games, and provides a game interaction method based on a real scenic spot, which comprises the following steps: acquiring real-time scene data of a target real scenic spot, dynamically generating a game layer in a virtual game map associated with the target real scenic spot based on the real-time scene data, and determining at least one game challenge task; in response to an exploration behavior of the player in the virtual game map, triggering and guiding the player to learn cultural knowledge points associated with the current exploration position or the game challenge task; after the player completes learning verification operation on the culture knowledge points, the culture knowledge points are converted into culture energy values, and a game challenge task is completed; and according to the accumulatively obtained and / or effectively applied cultural energy value of the player in the game process, the game reward and the reality incentive associated with the target reality scenic spot are settled and issued, and dynamic synchronization of the game content and the reality scene, deep fusion of the cultural knowledge and the game ability and strong association of the incentive and the participation quality are achieved.

Description

Technical Field

[0001] This application belongs to the field of game technology, and in particular relates to a game interaction method, system, device and medium based on real-world attractions. Background Technology

[0002] In recent years, with the integration of digital entertainment and cultural tourism industries, incorporating real-world tourist attractions into virtual game scenarios has become an important trend for enhancing the cultural connotation of games and expanding the promotional and educational functions of scenic spots. Through gamified interactive methods, a wider range of users, especially the younger generation, can be attracted, stimulating their interest in real-world attractions through virtual exploration. This achieves the dual goals of edutainment and the subtle dissemination of cultural knowledge, thereby extending the tourism industry chain and enhancing cultural influence.

[0003] However, existing technologies struggle to deeply integrate real-world attractions with games. Common approaches typically involve replicating static 3D models of these attractions as background scenes within the game, or displaying text boxes with relevant historical and geographical information when players reach specific coordinates. These solutions merely achieve a visual resemblance and a simple overlay of information. The game content is fixed, failing to reflect real-time changes at the attractions, resulting in a weak sense of immersion. Furthermore, the knowledge delivery is disconnected from the game's progress, leading to passive player reception and poor learning outcomes. Therefore, there is an urgent need for a game-based interactive method that can promote both real-time data of scenic spots and their cultural connotations, transforming knowledge into game skills. Summary of the Invention

[0004] This application provides a game interaction method, system, device, and medium based on real-world attractions, which can solve one of the problems of the prior art mentioned above.

[0005] In a first aspect, embodiments of this application provide a game interaction method based on real-world tourist attractions, including: Acquire real-time scene data of the target real-world attraction, including environmental data and operational data; Based on the real-time scene data, a game layer is dynamically generated in the virtual game map associated with the target real-world attraction, and at least one game challenge task is determined. In response to the player's exploration behavior in the virtual game map, the system triggers and guides the player to learn cultural knowledge points associated with the current exploration location or the game challenge task. After the player completes the learning and verification of the cultural knowledge points, the cultural knowledge points are converted into cultural energy points, and the game challenge tasks are completed based on the cultural energy points. Based on the cultural energy points accumulated and / or effectively utilized by players during the game, game rewards and real-world incentives associated with the target real-world attractions will be calculated and distributed.

[0006] Furthermore, based on the real-time scene data, dynamically generating a game layer in a virtual game map associated with the target real-world attraction, and determining at least one game challenge task, includes: Construct a multi-layered game structure associated with the virtual game map, wherein the game layer structure includes at least an environment state layer and an event task layer; The environmental data is converted into dynamic attribute values ​​of each map unit in the environmental state layer by the state mapping engine according to the predefined mapping function. The task generator integrates real-time scene data, player status, and related cultural knowledge points to generate and configure at least one game challenge task in the event task layer in real time.

[0007] Furthermore, the response to the player's exploration behavior in the virtual game map, triggering and guiding the player to learn cultural knowledge points associated with the current exploration location or the game challenge task, includes: By triggering the judgment engine, based on the player's current spatial location, real-time scene data, and the relevant cultural knowledge points of the current game challenge, it determines whether to trigger learning guidance on cultural knowledge points. In response to a judgment trigger, the core content of the cultural knowledge points is presented to the player in an interactive form that integrates with the environment or story experience; After the content is presented, players are required to complete a learning verification process, and a comprehension assessment score is generated based on the players' performance.

[0008] Furthermore, the step of converting the cultural knowledge points into cultural energy values ​​after the player completes the learning and verification operation includes: Based on the static knowledge graph, determine the cultural energy category or skill type corresponding to the cultural knowledge point, as well as the difficulty level; Based on the comprehension assessment score and the difficulty level, the cultural energy value of the corresponding cultural knowledge point is dynamically calculated.

[0009] Furthermore, completing the game challenge task based on the cultural energy value includes: In response to the game challenges faced by players, a contextualized resource call interface is provided. Based on the game challenges faced by players, the contextualized resource call interface suggests at least one recommended cultural energy category or skill type to call. In response to player commands, the application performance data is dynamically calculated based on resource parameters, context parameters, and real-time scene data. Based on the application performance data, players will receive instant rewards related to cultural knowledge points.

[0010] Furthermore, the calculation and distribution of game rewards and real-world incentives associated with the target real-world attraction based on the cultural energy points accumulated and / or effectively utilized by the player during the game includes: Based on the total accumulated value of cultural energy and application effectiveness data, the comprehensive contribution coefficient of players is calculated through a multi-dimensional contribution evaluation model. The corresponding levels of virtual game rewards are determined and distributed based on the total value of cultural knowledge reserves, application efficiency data, and the number of scenic spots unlocked by players. After a player achieves the preset exploration goals, the probability of obtaining real-world incentives is dynamically calculated based on the comprehensive contribution coefficient, and the real-world incentives are extracted and distributed accordingly.

[0011] Furthermore, the aforementioned game interaction method based on real-world tourist attractions also includes: Acquire basic action points by completing pre-set, generic game tasks that are unrelated to the cultural background of the target real-world attraction; Players' exploration activities in the virtual game map require the consumption of the basic action points.

[0012] Secondly, embodiments of this application provide a game interaction system based on real-world tourist attractions, including: First processing module: used to acquire real-time scene data of the target real-world scenic spot, the real-time scene data including environmental data and operational data; The second processing module is used to dynamically generate a game layer in a virtual game map associated with the target real-world attractions based on the real-time scene data, and to determine at least one game challenge task. The third processing module is used to respond to the player's exploration behavior in the virtual game map, triggering and guiding the player to learn cultural knowledge points related to the current exploration location or the game challenge task; The fourth processing module is used to convert the cultural knowledge points into cultural energy values ​​after the player completes the learning and verification operation of the cultural knowledge points, and to complete the game challenge task based on the cultural energy values. The fifth processing module is used to calculate and distribute game rewards and real-world incentives associated with the target real-world attraction based on the cultural energy value accumulated and / or effectively used by the player during the game.

[0013] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described game interaction method based on real-world attractions.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium, including a computer program stored in the computer-readable storage medium, which, when executed by a processor, implements the aforementioned game interaction method based on real-world attractions.

[0015] The beneficial effects of the embodiments in this application compared with the prior art are: This application discloses a game interaction method based on real-world attractions. It transforms discrete scene data of real-world attractions into structured information with rich contextual semantics that can be directly understood and utilized by the game engine, laying a solid data foundation for dynamic content generation. At the same time, it integrates real-time scene data with a static knowledge graph to generate a scene state description. This scene state description connects the original data with the gameplay, thereby enabling real-time scene data to be automatically and instantly transformed into perceptible and interactive dynamic game content in a virtual game map, thus forming a game world that connects the real world and has a virtual experience. Furthermore, the learning of cultural knowledge is integrated into the core gameplay, guiding players to explore and acquire relevant cultural knowledge points during gameplay. Verified cultural knowledge points are converted into cultural energy points, quantifying the learned knowledge into player abilities during gameplay, thus addressing the disconnect between knowledge and application. The cultural energy points accumulated by players in the virtual game map are then converted into interactive gameplay behaviors in game challenges, achieving a learning effect of applying knowledge to practice. A reward system linked to player interactions in game challenges is established, precisely connecting virtual achievements with real-world incentives, strengthening players' understanding of cultural knowledge points, and enabling them to acquire knowledge in the game and then gain recognition for that knowledge. Therefore, this application achieves dynamic synchronization between game content and real-world scenarios, deep integration of cultural knowledge and gameplay abilities, and a strong correlation between incentives and participation quality, increasing the game's interactivity and fun, and stimulating players' desire for exploration and sense of accomplishment. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a game interaction method based on real-world tourist attractions provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a game interaction system based on real-world tourist attractions provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0019] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0020] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0022] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0024] Please see Figure 1 As shown, the present invention is a game interaction method based on real-world tourist attractions, comprising the following steps: S100. Obtain real-time scene data of the target real-world scenic spot, wherein the real-time scene data includes environmental data and operational data; In this embodiment, the scene data of discrete real-world attractions are transformed into structured information with rich contextual semantics that can be directly understood and utilized by the game engine, laying a solid data foundation for the dynamic content generation in subsequent steps.

[0025] Specifically, the real-world scenario data includes environmental data and operational data. The environmental data is obtained by accessing the meteorological department's public API to obtain weather data for the area where the target attraction is located, including but not limited to weather phenomena such as sunny, rainy, snowy, and foggy weather, as well as temperature, humidity, wind speed, wind direction, visibility, and sunrise / sunset times. The operational data establishes a data interface with the scenic area's smart management platform to obtain real-time visitor flow statistics, regional congestion index, operating status of facilities such as cable cars and ropeways, and announcements and schedules for special events.

[0026] In addition, environmental data may come from multiple meteorological service providers, and the accuracy can be improved through weighted averaging or voting mechanisms. As for operational data, if the data interface of the scenic area is unavailable, it can be downgraded to use operator base station signal density data or heat map data from public platforms for estimation to obtain real-time visitor flow statistics and regional congestion index.

[0027] In some embodiments, the real-world scenario data also includes humanistic data. A scenic spot knowledge graph is maintained synchronously through the scenic spot culture database. This scenic spot knowledge graph uses geographical coordinates as nodes and associates multi-dimensional information such as historical events, cultural allusions, geological features, biological resources, poems and couplets, and architectural styles. The credibility level and correlation strength of the information are marked so that players can trigger cultural knowledge points of relevant locations in the game.

[0028] In some embodiments, a unified data adapter is designed for real-world data from different sources to convert the raw data into an internally unified standard data object, which includes fields such as timestamp, data value, confidence level, and data source identifier.

[0029] In some embodiments, the obtained real-time scene data is associated and fused with a static knowledge graph to generate a scene state description. This scene state description connects the original data with the gameplay, so that in subsequent steps, the real-time scene data can be converted into corresponding game content.

[0030] Specifically, a static knowledge graph is an enhanced knowledge graph that drives game interaction. Based on the cultural data of scenic spots, it connects real-world cultural information with virtual game mechanics by adding game logic-related metadata and structured relationships. Specifically, the static knowledge graph contains a multi-layered semantic network. Its core entity nodes include at least: geographical entity nodes, cultural knowledge point nodes, and game logic concept nodes. Geographical entity nodes correspond one-to-one with specific locations in real-world scenic spots, thus enhancing in-game coordinate mapping. Cultural knowledge point nodes each represent an independent unit of cultural knowledge, such as "Huangshan Quaternary glacial relics" or "Li Bai's 'Sending Wen Chushi Back to His Old Residence at Huangshan White Goose Peak'". Game logic concept nodes are used to label abstract concepts within the game, such as "movement obstacles", "environmental puzzles", "cooperative challenges", and "historical plot fragments".

[0031] More specifically, each entity node has a globally unique Node_ID and a set of attributes, and geographical entity nodes are connected to cultural knowledge point nodes through relationships such as "located in" and "related to".

[0032] More specifically, each cultural knowledge point node has attributes including basic cultural content and game-driven attributes. Basic cultural content specifically includes the textual content and media links related to the culture. Game-driven attributes, used to drive the associated game logic, specifically include knowledge point type, associated energy category tag, trigger scenario preference, gameplay tag, difficulty level, and prerequisite dependencies. The knowledge point type uses enumerated values, such as [historical events, geological science, literature and art, ecological protection, architectural techniques]. This attribute is associated with determining the cultural energy category in subsequent steps. The associated energy category tag explicitly specifies the cultural energy category that can be converted into for use in the game after mastering the cultural knowledge point. The categories include: skill type; triggering context preference, which describes the most suitable real-world context for triggering this cultural knowledge point, such as {"best weather": ["rain", "fog"], "best time": ["early morning"], "related event": ["Double Ninth Festival"]}; gameplay tags, which describe the types of game challenges that can be set for this cultural knowledge point, such as [puzzle solving, path planning, resource repair, NPC dialogue, geological surveying]; and difficulty level and prerequisites, which define the difficulty of understanding the cultural knowledge point and provide a list of Node_IDs of other cultural knowledge points that may need to be mastered before learning this cultural knowledge point, used to plan the player's learning path in the game system.

[0033] Furthermore, the static knowledge graph defines relationships that serve the game's progress, such as empowering relationships, combinatorial relationships, and temporal / narrative relationships. Empowering relationships are additional challenge relationships between cultural knowledge points and game challenges; for example, pointing from "cultural knowledge point A" to "game challenge task B" indicates that mastering cultural knowledge point A provides an advantage or necessary condition for solving game challenge task B. Combination relationships connect multiple cultural knowledge point nodes, indicating that different cultural knowledge points are closely related in reality or logically, and when players master them simultaneously, they can trigger combined effects or synergistic skills. Temporal / narrative relationships connect multiple cultural knowledge point nodes or geographical entity nodes, forming an implicit narrative line or exploration sequence, which can be used to generate the player's exploration behavior chain in the virtual game map. Thus, the cultural knowledge of target real-world locations can be accurately located, transmitted, transformed, and applied, serving the transformation of game content.

[0034] Furthermore, it receives real-time scene data from the target real-world location, specifically environmental and operational data from the aforementioned real-world scene data. Combining this with context such as system time and the virtual area where the player is located, it constructs a current context object. Using this current context object as input, it performs a correlation query on the aforementioned static knowledge graph and matches it with a pre-set association rule library. It then triggers all rules that meet the conditions, generates game instruction elements, and aggregates them into a dynamic scene state description file according to a preset template. This file not only lists the real-time scene data but also contains instructional information on what game mechanics should be triggered under what conditions.

[0035] Specifically, the association rule base uses a declarative rule language for definition and storage. Each rule includes a condition part and an action part. The condition part defines the combination of data conditions required to trigger the rule, supporting logical and comparison operators. The action part defines two types of actions to be executed after the rule is triggered: knowledge association actions and semantic generation actions. Knowledge association actions are used to associate one or more knowledge graph node IDs, and semantic generation actions are used to generate one or more game semantic instructions. For example: Rule 1: IF Weather phenomenon == "Rain" AND Current season == "Spring" THEN Associate knowledge graph nodes ["Causes of Huangshan Cloud Sea", "Vegetation characteristics in the rainy season"]; Generate game semantic instructions: ["Road slipperiness coefficient +0.3", "Cloud and fog visibility obstruction rate +0.5"]. Rule 2: IF Real-time time is between [Sunset time -30 minutes, Sunset time +30 minutes] THEN Associate knowledge graph nodes ["Huangshan sunset viewing point", "Related ancient poems"]; Generate game semantic instructions: ["Probability of triggering 'Glorious Sunset' environmental effect +70%"]. Rule 3: IF Area A Crowd Level == "High" AND Area B Crowd Level == "Low" THEN Generate game semantic instructions: ["Generate 'Crowd Control' task in Area A of the virtual map", "Generate 'Quiet Exploration' buff in Area B"].

[0036] In addition, the association rule base also supports reasoning based on simple facts. For example, a rule can be defined as IF Weather == “Snow” THEN infer temperature < 0°C, and another rule can be triggered based on temperature < 0°C, such as a rule about the “freezing” effect.

[0037] S200. Based on the real-time scene data, dynamically generate a game layer in the virtual game map associated with the target real-world attraction, and determine at least one game challenge task; In some embodiments, step S200 above includes: Construct a multi-layered game structure associated with the virtual game map, wherein the game layer structure includes at least an environment state layer and an event task layer; The environmental data is converted into dynamic attribute values ​​of each map unit in the environmental state layer by the state mapping engine according to the predefined mapping function. The task generator integrates real-time scene data, player status, and related cultural knowledge points to generate and configure at least one game challenge task in the event task layer in real time.

[0038] In this embodiment, the real-time scene data from step S100 is automatically and in real time transformed into perceptible and interactive dynamic game content in the virtual game map, including environmental conditions and game challenge tasks, thereby forming a game world that connects to the real world and has a virtual experience.

[0039] Specifically, the virtual game map is logically modularized and layered, comprising a basic geographic layer, an environmental state layer, and an event / task layer. The basic geographic layer describes the static skeleton of the virtual game map and is associated with geographic entity nodes in the static knowledge graph, specifically including fixed terrain, path networks, and landmark coordinates. The environmental state layer is a real-time updated data layer that divides the virtual game map into several map units, such as grids or path segments. Each map unit is bound to a set of dynamic attribute values, such as road surface friction coefficient, visibility, and background sound effects. The event / task layer is a logical layer used to store and describe game challenges, temporary NPCs, and interactive objects generated in real time.

[0040] More specifically, the state mapping engine loads dynamic scene state description files and, based on a pre-configured mapping function library and combined with game semantic instructions, converts the scene in the virtual game map into specific game parameters, mapping them to the map units corresponding to the environment state layer. It's worth noting that the mapping function library contains multiple built-in mapping functions based on real-time scene data. Specifically, it maps weather phenomena and intensity parameters in the environmental data to the movement resistance coefficient and visibility parameters of the corresponding map units; and / or maps regional passenger flow data in the operational data to the non-player character density base or environmental sound effect parameters of the corresponding virtual area. For example: road friction coefficient = base value - (rainfall intensity * road slippage coefficient α); visibility = base value * (1 - haze concentration) * time factor; virtual area NPC density base = function (real-time passenger flow, area area, time factor), etc. This state mapping engine ensures that the in-game environment state remains synchronized with the real-time scene data.

[0041] Furthermore, through a task responder, game challenge tasks are generated by integrating real-time scene data, player status, and related cultural knowledge points. The task responder includes a scenario-triggered generation mode and a knowledge-guided generation mode. The scenario-triggered generation mode generates a game challenge task when a specific combination of real-time scene data reaches a preset threshold. The specific combination of real-time scene data includes at least two of weather data, time data, and passenger flow data. The knowledge-guided generation mode generates a game challenge task when a player approaches geographical coordinates associated with a cultural knowledge point, and the current real-time scene data matches the trigger scenario preference of that cultural knowledge point.

[0042] Specifically, for the scenario-triggered generation mode, a complex event pattern library is maintained. Each event pattern includes an event pattern ID, an event pattern description, an event trigger condition, a trigger priority, and an associated task template ID. The event pattern ID is a unique identifier for the event pattern. The event pattern description is a semantic description of the event under a specific combination of real-time scenario data, such as a quiet environment with low passenger flow during a snowstorm. The event trigger condition is a threshold combination of real-time scenario data corresponding to the event pattern, such as weather phenomenon == "snow" AND snowfall intensity == "strong" AND passenger flow == "low". The associated task template ID is the task template associated with the event pattern, and corresponding game challenge tasks can be generated based on this task template.

[0043] In practical applications, the data in the dynamic scene state description file is matched in real time with all event triggering conditions in the complex event pattern library. When one or more event triggering conditions are met, one or more event instances are generated. Each instance contains an event pattern ID, real-time scene data that meets the event triggering conditions, a trigger timestamp, and the affected map units. If multiple event instances are triggered simultaneously, arbitration is performed based on the triggering priority of the event instance and the number of currently triggered tasks to select the most suitable event for response. For each selected event instance, the corresponding task template is loaded according to the associated task template ID in the event instance. This task template defines the basic framework of the task, specifically the task objective, task type, and basic reward. The task objective is associated with the knowledge graph nodes associated in the dynamic scene description file. Thus, the task instance generated by the corresponding task template is associated with the corresponding cultural knowledge points, and the specific context data in the event instance is injected into the task template to generate a unique task instance as a game challenge task.

[0044] More specifically, for the knowledge-guided generation mode, tasks are generated based on the player's state, and real-time matching is performed through a task rule matching library. The task rule matching library includes at least the following matching rules: Rule A: The player's current location ∈ the geographical influence range of cultural knowledge point G; Rule B: The state of cultural knowledge point G is either unmastered or can be deepened, and the matching degree between real-time scene data and the triggering context preference of cultural knowledge point G exceeds a threshold; Rule C: Among the cultural knowledge points that the player has mastered, there are prerequisites required to learn cultural knowledge point G.

[0045] When a player's status meets the requirements of the aforementioned task rule matching library, a game challenge task is generated. This task can be generated by integrating one or more cultural knowledge points that the player has previously mastered. Specifically, the type of game challenge task is determined based on the knowledge point type. For example, if the knowledge point type corresponds to geology, the corresponding gameplay tag is "Geological Survey." Actions are then selected from the task atom action library based on this gameplay tag to form a task logic chain. Taking "Flying Stone Light and Shadow Measurement" as an example, the following actions are selected from the task atom action library: Action 1, used to guide observation, projects special light and shadow scale marks onto the "Flying Stone" virtual model at a specific game time. These marks are only visible when the player has the prior cultural knowledge point "Introduction to Geology." Action 2, used for interactive operation, requires the player to operate a virtual measurement tool to record data on shadow length and angle. Action 3, used for data analysis, involves filling the data into a pop-up simplified simulation calculation interface. Finally, a task instance corresponding to the cultural knowledge point is generated as the game challenge task.

[0046] It is worth noting that the task instances generated by the above-mentioned scenario-triggered generation mode and knowledge-guided generation mode are all equipped with corresponding task difficulty coefficients and recommended solution sets. The task difficulty coefficient is related to real-time scene data and player level. For example, in one embodiment, the task difficulty coefficient = a × (1 - player proficiency) + b × basic difficulty constant, where the player proficiency is based on the player's historical completion evaluation of the cultural field involved in the task, and the basic difficulty constant is determined based on the player level. The recommended solution set is based on the empowerment relationship in the static knowledge graph, automatically associating and recommending one or more available cultural energy categories or skill types.

[0047] S300, in response to the player's exploration behavior in the virtual game map, triggers and guides the player to learn cultural knowledge points related to the current exploration location or the game challenge task; In some embodiments, step S300 above includes: By triggering the judgment engine, based on the player's current spatial location, real-time scene data, and the relevant cultural knowledge points of the current game challenge, it determines whether to trigger learning guidance on cultural knowledge points. In response to a judgment trigger, the core content of the cultural knowledge points is presented to the player in an interactive form that integrates with the environment or story experience; After the content is presented, players are required to complete a learning verification process, and a comprehension assessment score is generated based on the players' performance.

[0048] In this embodiment, the learning of cultural knowledge is introduced into the core game process, guiding players to explore and receive relevant cultural knowledge points during the game, and providing a foundation for the subsequent conversion of cultural knowledge points into practical cultural energy values.

[0049] Specifically, a trigger determination engine is set up to trigger relevant cultural knowledge points in the virtual game map. This trigger determination engine is associated with the scenario-triggered generation mode and knowledge-guided generation mode of the task generator in step S200 above. It can determine the cultural knowledge points required by the player in the corresponding game challenge task and trigger generation in response to the player's exploration status in the virtual game map.

[0050] More specifically, the triggering conditions of the triggering judgment engine are a set of multi-factor conditions. The corresponding learning guidance is generated only when the preset logical combination is met. The triggering conditions include spatial location signals, real-time scene signals, game task signals, and player knowledge status. The spatial location signal is used to determine whether the player's virtual character is located in the geographical location corresponding to the cultural knowledge point, which can be determined by querying the static knowledge graph. The real-time scene signal is used to determine whether the environmental data and operational data in the real-time scene data meet the triggering scene preference of the corresponding cultural knowledge point. The game task signal is used to determine the cultural knowledge points that the player relies on in the game task challenge and the prerequisites of the cultural knowledge points. The player knowledge status is used to determine the player's mastery status of each cultural knowledge point, such as not mastered, can be advanced, mastered, etc.

[0051] When multiple triggering conditions are met simultaneously, the triggering engine arbitrates according to preset priorities to determine the knowledge point that should be triggered most, thus avoiding information overload. In a preferred embodiment, the preset priority is game task signal > real-time scene signal > spatial location signal. The player's knowledge status is a prerequisite for each triggering condition. The player must be in a state of not yet mastering or being able to advance their knowledge of the corresponding cultural knowledge point before the corresponding learning guidance is generated. It is worth noting that the state of being able to advance means that the knowledge point mastered by the player has reached the threshold for in-depth learning, that is, the player has not fully mastered the corresponding cultural knowledge point.

[0052] When the learning of corresponding cultural knowledge points is triggered, corresponding interactive methods are generated based on the type of knowledge point, such as environmental integration or plot experience, to show players the core knowledge of the corresponding cultural knowledge points. This abandons the single text box display method and improves the player's experience and knowledge reception. Specifically, the environmental integration method is for geological and architectural knowledge. Virtual models such as historical structure restoration and geological profile maps are superimposed on the real-world model of the virtual game map in the player's current game screen to present the corresponding knowledge in real time. As for the plot experience method, for historical events and legends, a short immersive role-playing micro-plot is generated, in which the player participates in key decision points from a first-person perspective.

[0053] In this embodiment, after the knowledge content of the cultural knowledge points is displayed, an interactive verification step is used to assess the player's instantaneous comprehension level. The form is a game-like learning verification operation, including but not limited to: chronological ordering of historical events, matching the association between celebrities and their deeds, restoring the patterns of cultural relics, and quick knowledge Q&A. Finally, a comprehension assessment score is output, which is calculated based on the accuracy of the answer and the time taken.

[0054] Specifically, for cultural knowledge points whose comprehension assessment scores reach the threshold for in-depth learning, they are marked as being ready for advancement. The knowledge will be relearned the next time it is triggered. In addition, the comprehension assessment score also affects the cultural energy value of cultural knowledge point transformation in subsequent steps.

[0055] S400. After the player completes the learning and verification operation of the cultural knowledge points, the cultural knowledge points are converted into cultural energy values, and the game challenge task is completed based on the cultural energy values. In some embodiments, the step of converting the cultural knowledge points into cultural energy values ​​after the player completes the learning and verification operation of the cultural knowledge points includes: Based on the static knowledge graph, determine the cultural energy category or skill type corresponding to the cultural knowledge point, as well as the difficulty level; Based on the comprehension assessment score and the difficulty level, the cultural energy value of the corresponding cultural knowledge point is dynamically calculated.

[0056] In this embodiment, the cultural knowledge points verified by the player are converted into cultural energy points, thereby quantifying the learned cultural knowledge points into the player's ability in the game process, solving the problem of the disconnect between knowledge and application.

[0057] Specifically, the associated energy category tags corresponding to cultural knowledge points are queried from the static knowledge graph of step S100 above to determine the corresponding cultural energy category or skill type, and a corresponding basic energy coefficient is assigned. The basic energy coefficient is predetermined and is used to characterize the difficulty of cultural knowledge points. It can be quantified by the difficulty level corresponding to the cultural knowledge points. Secondly, the associated energy category tags are associated with the knowledge point type, and each cultural knowledge point is divided according to the cultural field to generate a cultural energy category, which represents the player's understanding depth and knowledge reserves in that cultural field, such as historical energy category, geological culture energy category, literary and artistic energy category, ecological culture energy category, architectural craft energy category, etc. Skills are bound to specific scenarios or challenge types. They are active or passive abilities with specific effect descriptions and may include cooldown time or trigger conditions, such as the plank road stabilization technique, which can reduce the probability of falling on dangerous sections; and the cloud and fog identification technique, which can temporarily improve visibility in fog, etc.

[0058] For each cultural energy category, a corresponding cultural energy value is generated. The cultural energy value is calculated as follows: base energy coefficient × comprehension assessment score × (1 + specialization bonus coefficient). The specialization bonus coefficient is dynamically calculated based on the cultural energy value that the player has accumulated in that cultural energy category.

[0059] In some embodiments, completing the game challenge task based on the cultural energy value includes: In response to the game challenges faced by players, a contextualized resource call interface is provided. Based on the game challenges faced by players, the contextualized resource call interface suggests at least one recommended cultural energy category or skill type to call. In response to player commands, the application performance data is dynamically calculated based on resource parameters, context parameters, and real-time scene data. Based on the application performance data, players will receive instant rewards related to cultural knowledge points.

[0060] In this embodiment, the cultural energy value accumulated by players in the virtual game map is converted into actual game behaviors in game challenge tasks for interaction, so as to achieve the learning effect of applying what has been learned.

[0061] The game challenge tasks generated by the knowledge-guided generation mode are marked with the geographical locations of the corresponding cultural knowledge points on the virtual game map. When players explore to the corresponding geographical unit, the challenge task is triggered. During the challenge, the corresponding cultural energy category and skill type are generated by integrating the cultural knowledge points that the player has learned to help the player complete the challenge.

[0062] For game challenge tasks generated by the scenario-triggered generation mode, the corresponding game challenge task is triggered when the dynamic scene state description file generated in the real-time scene environment meets the event triggering conditions in the complex event pattern library. The player is then reminded to complete the corresponding game challenge task. During the challenge, the cultural knowledge points corresponding to the knowledge graph nodes associated with the dynamic scene state description file are integrated to generate the corresponding cultural energy category and skill type to assist the player in completing the challenge.

[0063] Specifically, through a contextualized resource call interface, the system receives metadata of the game challenge tasks corresponding to each task instance in the task generator. This includes the game challenge task type, implicit knowledge domain, task difficulty coefficient, and recommended solution set. The implicit knowledge domain refers to the cultural knowledge points associated with the player completing the game challenge task, while the recommended solution set determines the cultural energy category and skill type of the player to complete the corresponding game challenge task.

[0064] Furthermore, after a player completes a game challenge by invoking the corresponding cultural energy category and / or skill type, the specific impact of this application on the game challenge is calculated, i.e., application energy efficiency data. Specifically, application energy efficiency data = f(resource parameters, context parameters, real-time adjustment factor), where f represents a calculation function calculated based on the game rules and design logic of the specific game challenge, combined with resource parameters, context parameters, and real-time adjustment factor. For example, in one embodiment, if the application effect of the invoked cultural energy category and / or skill type in the game challenge is a percentage increase in task completion, then application energy efficiency data = resource parameters × context parameters × real-time adjustment factor × base effect coefficient. The base effect coefficient is a constant set according to the game rules and design logic of the game challenge, used to adjust the magnitude of the overall effect. Thus, in practical applications, the calculation function f is a complexly designed function based on the specific rules and design logic of the game, which may include conditional judgments, loop calculations, and the interaction of multiple parameters. In some embodiments, game developers may use more complex algorithms or models, such as machine learning models, to dynamically calculate application energy efficiency data to provide a richer and more realistic gaming experience.

[0065] Furthermore, in the application energy efficiency data, resource parameters represent the specific values ​​of the cultural energy categories and / or skill types invoked. Specifically, this includes the total accumulated cultural energy value of each cultural energy category, calculated by summing the cultural energy values ​​of all cultural knowledge points within that category, and the basic skill effectiveness of each skill type, i.e., the task effect that the skill can achieve in the game challenge. For example, if the skill "Cloud and Fog Identification" is invoked in a game challenge and can temporarily increase visibility by 10% in fog, then the basic skill effectiveness is 10%. For multiple skills invoked in the game challenge, a weighted summation method is used to calculate the basic skill effectiveness of each skill. The weight is determined based on the importance of each skill in the game challenge. Scenario parameters correspond to the task difficulty coefficient of the game challenge. The real-time adjustment factor is determined by the degree of matching between the real-time scene data and the knowledge background of the cultural energy categories and / or skill types invoked by the player. For example, in one embodiment, if the real-time scene environment is raining and the player's invoked cultural energy category has the knowledge background of "drainage knowledge," then a gain ratio is added, such as S = 1 + gain ratio. The gain ratio is determined based on the degree of matching between the real-time scene data and the knowledge background.

[0066] Furthermore, to reinforce correct player actions, multi-tiered, real-time rewards are provided after the application's effects are realized. Specifically, rewards related to cultural knowledge points are distributed based on application efficiency data. For example, successfully using "calligraphy knowledge" to solve a puzzle rewards a virtual collectible of "rubbings of famous calligraphic works"; successfully using "traditional painting techniques" to repair a mural rewards a "vintage paint set," and so on. In addition, animations show the effects of resources after players utilize them to complete game challenges, such as energy beams and skill effects corresponding to the cultural energy category, as well as real-time updates to the challenge progress bar and environmental status indicators, allowing players to clearly perceive the power of knowledge.

[0067] S500. Based on the cultural energy value accumulated and / or effectively used by the player during the game, calculate and distribute game rewards and real-world incentives associated with the target real-world attraction.

[0068] In some embodiments, step S500 above includes: Based on the total accumulated value of cultural energy and application effectiveness data, the comprehensive contribution coefficient of players is calculated through a multi-dimensional contribution evaluation model. The corresponding levels of virtual game rewards are determined and distributed based on the total value of cultural knowledge reserves, application efficiency data, and the number of scenic spots unlocked by players. After a player achieves the preset exploration goals, the probability of obtaining real-world incentives is dynamically calculated based on the comprehensive contribution coefficient, and the real-world incentives are extracted and distributed accordingly.

[0069] In this embodiment, a reward system is established that is linked to the player's game interaction in game challenge tasks, thereby achieving a precise connection between virtual achievements and real-world incentives, strengthening the player's understanding of cultural knowledge points, and enabling them to gain knowledge in the game and then obtain value recognition from that knowledge.

[0070] Specifically, a multi-dimensional contribution evaluation model is constructed to calculate the overall knowledge contribution of players during gameplay. Specifically, the total cultural energy accumulation value obtained in step S400 and the application efficiency data are weighted and integrated to generate a comprehensive contribution coefficient, which characterizes the degree of knowledge contribution by players throughout the game interaction. By adjusting the corresponding weights of the total cultural energy accumulation value and the application efficiency data, the influence of both on the comprehensive contribution coefficient can be relatively balanced, thereby promoting the comprehensiveness and diversity of the game.

[0071] Furthermore, based on three dimensions—total value of cultural knowledge reserves, application efficiency data, and the number of scenic spots unlocked by players—corresponding levels of virtual game rewards are determined and distributed. This provides players with honor and resources commensurate with their efforts. Specifically, virtual game rewards are divided into different categories and levels according to value and cultural field, such as resource, honor, function, and content categories. Resource categories provide players with corresponding levels of virtual currency and materials; honor categories include honorary titles and avatar frames; function categories include special equipment and convenient skills; and content categories include exclusive storylines and hidden areas.

[0072] More specifically, the total cultural knowledge reserve value refers to the cultural energy value accumulated by players in the corresponding cultural energy category. When the total cultural knowledge reserve value reaches a certain threshold, a series of honorary titles and avatar frames for that cultural category are unlocked. For example, players in the history energy category may have matching honorary titles such as "Rising Star of Historiography" and "Erudite," along with corresponding appearances. When their total cultural knowledge reserve value reaches the corresponding threshold, they will receive the corresponding honorary title and avatar frame as a form of honor-based virtual game reward. Application efficiency data is used to distribute functional rewards to players. When application efficiency data reaches a certain threshold, players will receive corresponding functional items to enhance the efficiency of game challenge tasks, such as "Spirit Crystals," which shorten skill cooldowns. Furthermore, when players unlock a corresponding scenic area, they will receive corresponding resource-based and content-based virtual game rewards.

[0073] In addition, it includes the distribution of real-world incentives, such as attraction tickets and souvenirs. After players complete the preset exploration requirements of the virtual game map, the probability of them receiving real-world incentives is calculated based on the average K of the comprehensive contribution coefficients of each game challenge task during the exploration process. Specifically, P = min(P0 × K, P_max), where P0 is the preset base probability used to reflect the scarcity of incentives, and P_max is the protective upper limit probability to ensure the fairness of the system. Thus, players can increase the K value by learning and applying cultural knowledge points, thereby increasing the probability of receiving real-world incentives. This makes the ultimate incentive strongly correlated with the interactive behavior of the game, greatly enhancing the motivation and sense of fairness for long-term participation.

[0074] In some embodiments, the above-described game interaction method based on real-world attractions further includes: Acquire basic action points by completing pre-set, generic game tasks that are unrelated to the cultural background of the target real-world attraction; Players' exploration activities in the virtual game map require the consumption of the basic action points.

[0075] The game interaction method based on real-world attractions in this application also maintains a general task pool, where task types include, but are not limited to, combat, puzzle-solving, gathering, and building. Each task is bound to a basic action point reward value. Based on the player's initial behavior data, such as login frequency, average time spent completing historical tasks, and failure rate, a personalized initial task guidance sequence is dynamically generated for each player, guiding the player to complete the corresponding general game tasks and obtain the corresponding action point reward value, which is used for exploration in the virtual game map.

[0076] Please see Figure 2 As shown, the present invention also provides a game interaction system based on real-world tourist attractions, the system comprising: First processing module 201: used to acquire real-time scene data of the target real-world scenic spot, the real-time scene data including environmental data and operational data; The second processing module 202 is used to dynamically generate a game layer in a virtual game map associated with the target real-world attraction based on the real-time scene data, and to determine at least one game challenge task. The third processing module 203 is used to respond to the player's exploration behavior in the virtual game map, trigger and guide the player to learn cultural knowledge points related to the current exploration location or the game challenge task; The fourth processing module 204 is used to convert the cultural knowledge points into cultural energy values ​​after the player completes the learning and verification operation of the cultural knowledge points, and to complete the game challenge task based on the cultural energy values. The fifth processing module 205 is used to calculate and distribute game rewards and real-world incentives associated with the target real-world attraction based on the cultural energy value accumulated and / or effectively used by the player during the game.

[0077] It is understandable that, such as Figure 1 The content of the game interaction method embodiment based on real-world attractions shown is applicable to this game interaction system embodiment based on real-world attractions. The specific functions implemented by this game interaction system embodiment based on real-world attractions are the same as those shown in the example. Figure 1 The illustrated example of a game interaction method based on real-world tourist attractions is the same, and the beneficial effects achieved are the same as those described above. Figure 1 The beneficial effects achieved by the illustrated embodiment of the game interaction method based on real-world attractions are also the same.

[0078] It should be noted that the information interaction and execution process between the above systems are based on the same concept as the method embodiments of the present invention. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0080] Please see Figure 3 As shown, this embodiment of the invention also provides a computer device 3, including: a memory 302 and a processor 301, and a computer program 303 stored on the memory 302. When the computer program 303 is executed on the processor 301, it implements the game interaction method based on real-world attractions as described in any of the above methods.

[0081] The computer device 3 may be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device 3 may include, but is not limited to, a processor 301 and a memory 302. Those skilled in the art will understand that... Figure 3 The computer device 3 is merely an example and does not constitute a limitation on the computer device 3. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0082] The processor 301 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0083] In some embodiments, the memory 302 may be an internal storage unit of the computer device 3, such as a hard disk or memory of the computer device 3. In other embodiments, the memory 302 may be an external storage device of the computer device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 3. Furthermore, the memory 302 may include both internal and external storage units of the computer device 3. The memory 302 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 302 can also be used to temporarily store data that has been output or will be output.

[0084] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the game interaction method based on real-world attractions as described in any of the above methods.

[0085] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / computer device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0086] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A game interaction method based on real-world tourist attractions, characterized in that, include: Acquire real-time scene data of the target real-world attraction, including environmental data and operational data; Based on the real-time scene data, a game layer is dynamically generated in the virtual game map associated with the target real-world attraction, and at least one game challenge task is determined. In response to the player's exploration behavior in the virtual game map, the system triggers and guides the player to learn cultural knowledge points associated with the current exploration location or the game challenge task. After the player completes the learning and verification of the cultural knowledge points, the cultural knowledge points are converted into cultural energy points, and the game challenge tasks are completed based on the cultural energy points. Based on the cultural energy points accumulated and / or effectively utilized by players during the game, game rewards and real-world incentives associated with the target real-world attractions will be calculated and distributed.

2. The method as described in claim 1, characterized in that, The process of dynamically generating a game layer in a virtual game map associated with the target real-world attraction based on the real-time scene data, and determining at least one game challenge task, includes: Construct a multi-layered game structure associated with the virtual game map, wherein the game layer structure includes at least an environment state layer and an event task layer; The environmental data is converted into dynamic attribute values ​​of each map unit in the environmental state layer by the state mapping engine according to the predefined mapping function. The task generator integrates real-time scene data, player status, and related cultural knowledge points to generate and configure at least one game challenge task in the event task layer in real time.

3. The method as described in claim 1, characterized in that, The response to a player's exploration behavior in the virtual game map, triggering and guiding the player to learn cultural knowledge points associated with the current exploration location or the game challenge task, includes: By triggering the judgment engine, based on the player's current spatial location, real-time scene data, and the relevant cultural knowledge points of the current game challenge, it determines whether to trigger learning guidance on cultural knowledge points. In response to a judgment trigger, the core content of the cultural knowledge points is presented to the player in an interactive form that integrates with the environment or story experience; After the content is presented, players are required to complete a learning verification process, and a comprehension assessment score is generated based on the players' performance.

4. The method as described in claim 3, characterized in that, After the player completes the learning and verification operation of the cultural knowledge points, the cultural knowledge points are converted into cultural energy values, including: Based on the static knowledge graph, determine the cultural energy category or skill type corresponding to the cultural knowledge point, as well as the difficulty level; Based on the comprehension assessment score and the difficulty level, the cultural energy value of the corresponding cultural knowledge point is dynamically calculated.

5. The method as described in claim 1, characterized in that, The process of completing the game challenge task based on the cultural energy value includes: In response to the game challenges faced by players, a contextualized resource call interface is provided. Based on the game challenges faced by players, the contextualized resource call interface suggests at least one recommended cultural energy category or skill type to call. In response to player commands, the application performance data is dynamically calculated based on resource parameters, context parameters, and real-time scene data. Based on the application performance data, players will receive instant rewards related to cultural knowledge points.

6. The method as described in claim 1, characterized in that, The process of calculating and distributing game rewards and real-world incentives associated with the target real-world attraction based on the cultural energy points accumulated and / or effectively utilized by the player during the game includes: Based on the total accumulated value of cultural energy and application effectiveness data, the comprehensive contribution coefficient of players is calculated through a multi-dimensional contribution evaluation model. The corresponding levels of virtual game rewards are determined and distributed based on the total value of cultural knowledge reserves, application efficiency data, and the number of scenic spots unlocked by players. After a player achieves the preset exploration goals, the probability of obtaining real-world incentives is dynamically calculated based on the comprehensive contribution coefficient, and the real-world incentives are extracted and distributed accordingly.

7. The method as described in claim 1, characterized in that, Also includes: Acquire basic action points by completing pre-set, generic game tasks that are unrelated to the cultural background of the target real-world attraction; Players' exploration activities in the virtual game map require the consumption of the basic action points.

8. A game interaction system based on real-world tourist attractions, characterized in that, include: First processing module: used to acquire real-time scene data of the target real-world scenic spot, the real-time scene data including environmental data and operational data; The second processing module is used to dynamically generate a game layer in a virtual game map associated with the target real-world attractions based on the real-time scene data, and to determine at least one game challenge task. The third processing module is used to respond to the player's exploration behavior in the virtual game map, triggering and guiding the player to learn cultural knowledge points related to the current exploration location or the game challenge task; The fourth processing module is used to convert the cultural knowledge points into cultural energy values ​​after the player completes the learning and verification operation of the cultural knowledge points, and to complete the game challenge task based on the cultural energy values. The fifth processing module is used to calculate and distribute game rewards and real-world incentives associated with the target real-world attraction based on the cultural energy value accumulated and / or effectively used by the player during the game.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 7.

Images