Display method and system applied to digital cultural relics presentation of tourism and travel and video display

Abstract

This invention provides a display method, system, and video display for presenting digital cultural relics in the cultural and tourism sector, belonging to the field of image data processing technology. The method includes: generating a digital cultural relic model based on the three-dimensional data of the cultural relic; evolving the digital cultural relic model according to its spatiotemporal characteristics and displaying the evolution video stream; and providing interactive evolution based on the video stream. This invention's display method, system, and video display for presenting digital cultural relics in the cultural and tourism sector, by introducing a "spatiotemporal characteristic" driving mechanism, enables the digital cultural relic model to automatically simulate the changes in its physical state over time based on its historical period and geographical changes, significantly enhancing the realism and narrative of the digital display. Simultaneously, users can create two-way feedback through real-time control of the evolution video stream, greatly improving public participation and cultural dissemination effects.

Description

Technical Field

[0001] This invention relates to the field of image data processing technology, and in particular to display methods, systems and video displays for presenting digital cultural relics in cultural tourism. Background Technology

[0002] Existing digital museums or 3D artifact display systems mostly remain at the level of static model display, only providing 3D browsing from a fixed perspective. They cannot reflect the evolution of artifacts over the course of history (such as material aging, corrosion, and the accumulation of signs of use), and lack dynamic expression in the dimension of time. Moreover, most systems only support basic operations such as rotation and zoom, and users cannot intervene in or trace the historical evolution of artifacts. The level of interaction is shallow, and the immersive experience and educational value are limited.

[0003] In view of this, there is an urgent need for display methods, systems and video displays for the presentation of digital cultural relics in cultural tourism, in order to at least address the above-mentioned shortcomings. Summary of the Invention

[0004] One of the objectives of this invention is to provide a display method, system, and video display for presenting digital cultural relics in cultural tourism. By introducing a "spatiotemporal feature" driving mechanism, the digital cultural relic model can automatically simulate the changes in its physical state over time based on its historical period and geographical changes, significantly enhancing the realism and narrative of the digital display. Simultaneously, users can create two-way feedback by controlling the evolving video stream in real time, greatly improving public participation and the effectiveness of cultural dissemination.

[0005] The display method for presenting digital cultural relics in cultural tourism, provided in this embodiment of the invention, includes: Digital cultural relic models are generated based on 3D data of cultural relics and artifacts. The evolution of digital cultural relic models is based on the spatiotemporal characteristics of cultural relics, and the evolution video stream is displayed. Evolutionary interaction based on evolutionary video streams.

[0006] Preferably, methods for acquiring 3D data include: structured light scanning, laser scanning, photogrammetry, CT scanning, and handheld scanner scanning.

[0007] Preferably, digital cultural relic models are generated based on 3D data of cultural relics, including: Perform point cloud preprocessing on 3D data and convert discrete point clouds into continuous triangular mesh models; The true colors and surface textures of cultural relics are UV-unwrapped with triangular mesh models and automatically mapped, and finally structured and packaged into digital cultural relic models.

[0008] Preferably, the evolution of digital cultural relic models is based on the spatiotemporal characteristics of cultural relics, and the evolution video stream is displayed, including: Based on the preset feature extraction templates for different types of cultural relics, extract the cultural relic feature set of the digital cultural relic model; Based on spatiotemporal characteristics, the evolutionary relationships of features of different cultural relics are obtained; Using the set of cultural relic features as the evolutionary result, an evolutionary video stream was obtained.

[0009] Preferably, based on spatiotemporal characteristics, the evolutionary relationships of features of different cultural relics are obtained, including: Based on different types of cultural relics characteristics, feature evolution templates are constructed; Based on spatiotemporal characteristics, obtain the historical narrative of cultural relics and tourism artifacts; Based on historical narratives and the types of cultural relics, determine the evolution coefficients of the template positions of the corresponding feature evolution templates; Once all evolution coefficients in the feature evolution template are determined, the relationship described by the corresponding feature evolution template is taken as the feature evolution relationship.

[0010] Preferably, based on historical narratives and types of cultural relic characteristics, the evolution coefficients of the template positions of the corresponding feature evolution templates are determined, including: Identify spatiotemporal event nodes based on historical narratives; Analyze the event information of spatiotemporal event nodes and extract key information. Based on the information keywords, determine the impact characteristic types of spatiotemporal events and the corresponding key impact parameters; Based on the pre-defined level fuzzification rules for key impact parameters, the quantified value of the first key impact parameter is determined according to information keywords. Based on the evidence quantification function, the quantification value of the second key influence parameter is determined according to the event evidence of the spatiotemporal event; Based on the confidence level, the quantitative value of the first key influence parameter, the strength of event evidence, and the quantitative value of the second key influence parameter extracted from the keywords, the quantitative value of the target key influence parameter corresponding to the type of influence feature is determined. Based on the correlation between the evolution coefficients of key impact parameters and impact feature types, the evolution coefficients are set according to the quantified values ​​of the target key impact parameters.

[0011] Preferably, evolutionary interaction based on evolutionary video streams includes: Based on the changes in the evolutionary direction during the evolutionary interaction process, determine the local backtracking video stream; Based on the associated spatiotemporal characteristics of local retrospective video streams, historical knowledge is indexed; Based on the user's gaze flow information in the local retrospective video stream, identify the user's interest in historical knowledge. Map the knowledge that the user is interested in to the model location associated with the knowledge that the user is interested in in the current video frame.

[0012] Preferably, based on the user's gaze flow information in the local retrospective video stream, the user's interest in historical knowledge is determined, including: Based on the user's gaze flow information, determine the distribution of the first gaze point, the boundary of the gaze point, and the distribution of the second gaze point; Determine whether the evolutionary model corresponding to the user's gaze flow information spans time and space; If it spans time and space, determine the target process evolution model based on the boundary of the line of sight; The first local model is determined based on the first local distribution corresponding to the target process evolution model that traces back from the first line of sight landing point distribution; The second local model is determined based on the second local distribution corresponding to the target process evolution model reviewed in the second line of sight distribution; Based on the differences in presentation between the first and second local models, the knowledge of interest to users in historical knowledge is determined.

[0013] The display system for presenting digital cultural relics in cultural tourism provided in this embodiment of the invention includes: The generation module is used to generate digital cultural relic models based on the 3D data of cultural and tourism relics; The display module is used to evolve digital cultural relic models based on the spatiotemporal characteristics of cultural relics and to display the evolution video stream. The interaction module is used for evolutionary interaction based on the evolutionary video stream.

[0014] This invention provides a video display that uses the display method described above to display digital cultural relics for cultural tourism.

[0015] The beneficial effects of this invention are as follows: This invention introduces a "spatiotemporal feature" driving mechanism, enabling digital cultural relic models to automatically simulate the changes in their physical state over time based on their historical period and geographical variations, significantly enhancing the realism and narrative quality of digital displays. Simultaneously, users can create two-way feedback through real-time control of the evolving video stream, greatly improving public participation and cultural dissemination effectiveness.

[0016] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in this application.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of a display method applied to the presentation of digital cultural relics in cultural tourism, as described in an embodiment of the present invention. Figure 2 This is a schematic diagram of a display system applied to the presentation of digital cultural relics in cultural tourism, as described in an embodiment of the present invention. Detailed Implementation

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0020] This invention provides a display method for presenting digital cultural relics in the cultural tourism industry, such as... Figure 1 As shown, it includes: Step 1: Generate digital cultural relic models based on the 3D data of cultural relics.

[0021] In this embodiment, the cultural relics are those that need to be digitally displayed, and the 3D data is the 3D scan data of the cultural relics. The digital cultural relic model is a 3D model reconstructed based on the 3D scan data.

[0022] Step 2: Based on the spatiotemporal characteristics of cultural relics, evolve the digital cultural relic model and display the evolution video stream; In this embodiment, spatiotemporal features are a characteristic representation of the composite attributes of cultural relics in the time dimension (historical evolution process) and the spatial dimension (physical location). Time features include the chronological changes of cultural relics, such as: experiencing the Xia, Shang, and Zhou dynasties; spatial features include the geographical location of cultural relics; spatiotemporal features are the alignment of time features and spatial features, such as: a composite description of the dynasties that cultural relics have experienced and the corresponding geographical locations they have passed through.

[0023] In this embodiment, evolution refers to the dynamic simulation process of the digital model based on spatiotemporal characteristics. During the simulation, the model state changes over time through keyframe animation or a physics engine. For example, based on the spatiotemporal characteristics of a bronze tripod, the model evolves its color changes and corrosion from the Shang and Zhou dynasties to the present day. The evolution video stream is a continuous sequence of video frames rendered in real time during the above evolution process.

[0024] Specifically, step 2: Based on the spatiotemporal characteristics of cultural relics, the digital cultural relic model is evolved, and the evolution video stream is displayed, including: Step 21: Extract the cultural relic feature set of the digital cultural relic model according to the preset feature extraction templates for different cultural relic feature types.

[0025] In this embodiment, the cultural relic feature type refers to the physical or cultural attribute category that can be structurally classified during the digitization process of cultural and tourism cultural relics, including material feature type, texture feature type, color feature type and functional feature type.

[0026] In this embodiment, the preset feature extraction template is a set of algorithm rules for extracting quantitative data of specific "cultural relic feature types" from the digital model. For example, for the template of "material feature type": based on the curvature analysis algorithm of PCL, inputting bronze point cloud data, outputting "corrosion area heat map".

[0027] In this embodiment, the cultural relic feature set is a set of structured feature data output from the "digital cultural relic model" through the "feature extraction template", and is stored in the form of feature vectors (such as JSON arrays). Each vector corresponds to a quantized value of "cultural relic feature type".

[0028] Step 22: Based on spatiotemporal characteristics, obtain the feature evolution relationship of different cultural relic feature types.

[0029] In this embodiment, the feature evolution relationship is: a mathematical mapping rule describing the dynamic changes of different "cultural relic feature types" in the historical process. The mathematical mapping rule is the correlation between feature values ​​and time / space variables.

[0030] Step 23: Using the artifact feature set as the evolution result, obtain the evolution video stream.

[0031] In this embodiment, since the current features (artifact feature set) and feature evolution relationship are known, in order to obtain the evolution process, the evolution process can be obtained by deducing from the result, and then the evolution video stream can be obtained.

[0032] Step 3: Perform evolutionary interaction based on the evolutionary video stream.

[0033] In this embodiment, evolutionary interaction refers to the user's real-time control of the evolutionary video stream through input devices (such as touch screens or gesture recognition), including pausing, rewinding, or adjusting the viewpoint, forming two-way feedback.

[0034] The working principle and beneficial effects of the above technical solution are as follows: This invention introduces a "spatiotemporal feature" driving mechanism, enabling digital cultural relic models to automatically simulate the changes in their physical state over time based on their historical period and geographical variations, significantly enhancing the realism and narrative quality of digital displays. Simultaneously, users can create two-way feedback through real-time control of the evolving video stream, greatly improving public participation and cultural dissemination effectiveness.

[0035] In one embodiment, step 22: Based on spatiotemporal features, obtain the feature evolution relationship of different cultural relic feature types, including: Step 221: Construct feature evolution templates based on different types of cultural relics.

[0036] In this embodiment, the constructed feature evolution template is essentially a description matrix that describes the evolution process of cultural relic features. During construction, one type of cultural relic feature corresponds to one matrix position (template position), and the specific position correspondence is preset manually.

[0037] Step 222: Obtain the historical narrative of cultural relics based on spatiotemporal characteristics.

[0038] In this embodiment, the historical narrative is: a historical storyline of cultural relics generated based on spatiotemporal features, which transforms abstract spatiotemporal data into a coherent sequence of events with cultural logic, including key turning points (such as dynastic changes and geographical migrations).

[0039] Step 223: Determine the evolution coefficient of the template position of the corresponding feature evolution template based on the historical narrative and the type of cultural relic characteristics.

[0040] In this embodiment, the evolution coefficient is a specific value that needs to be filled in at the template position. This specific value quantifies the rate or degree of change of the feature type at that position. For example, when the artifact feature type is a material feature type, the parameter at the template position is the corrosion area. , It is the evolution coefficient; if the historical narrative mentions "long-term damp burial", then k=0.75 (high corrosion rate); if the historical narrative mentions "preservation in a dry environment", then k=0.2 (low corrosion rate).

[0041] Step 224: Once all evolution coefficients in the feature evolution template are determined, the relationship described by the corresponding feature evolution template is taken as the feature evolution relationship.

[0042] In this embodiment, the feature evolution relationship is the evolution relationship description matrix formed after filling all evolution coefficients into the feature evolution template. It can drive the dynamic rendering of the digital model and is essentially an executable mapping from spatiotemporal features to visual changes.

[0043] The working principle and beneficial effects of the above technical solution are as follows: This invention first constructs standardized feature evolution templates based on pre-defined cultural relic characteristics such as material, texture, color, and function. The construction process relies on statistical patterns from historical relic databases: the system analyzes common change patterns among similar cultural relics (e.g., 100 bronze artifacts) and extracts the functional form of the templates (e.g., exponential decay or linear growth). Based on spatiotemporal features, historical narratives are retrieved, and the system cross-analyzes these narratives with the relic feature types, dynamically calculating the evolution coefficients at each template position within the feature evolution template. Once all evolution coefficients in the feature evolution template are determined, the template is instantiated into a feature evolution relationship (an executable spatiotemporal driving matrix). This uses abstract historical narratives to drive the dynamic evolution of cultural relics, solving the problem of unobservable historical processes. Users can immerse themselves in the shaping effect of time on cultural relics, resulting in a superior user experience.

[0044] In one embodiment, step 223: determining the evolution coefficient of the template position of the corresponding feature evolution template based on the historical narrative and the type of cultural relic characteristics, including: Step 2231: Identify spatiotemporal event nodes based on historical narrative.

[0045] In this embodiment, spatiotemporal event nodes are turning points in historical narratives with precise spatiotemporal coordinates, comprising three elements: time interval, geographical location, and event nature. For example, time coordinates: Warring States period, spatial coordinates: Haojing, Shaanxi, event nature: burial.

[0046] Step 2232: Analyze the event information of the spatiotemporal event nodes and extract information keywords.

[0047] In this embodiment, information keywords are core semantic units extracted from event information that can characterize the mechanism by which the event affects cultural relics. They are then structured using NLP technology. For example, the original description is: "Buried in a damp environment due to war during the Warring States period." The extracted keywords are: buried (verb), war (cause), and damp.

[0048] Step 2233: Based on the information keywords, determine the impact characteristic type of the spatiotemporal event and the corresponding key impact parameters.

[0049] In this embodiment, the influencing feature type refers to the type of cultural relic feature that can be affected by spatiotemporal events. For example, when a spatiotemporal event involves information related to temperature and humidity changes, the material feature type and color feature type are the influencing feature types of the corresponding event; when a spatiotemporal event involves information related to regime change or ritual reform, the functional feature type is the influencing feature type of the corresponding event. The corresponding key influencing parameters are the specific parameters that produce an impact in the spatiotemporal event. Continuing with the above examples, the key influencing parameters corresponding to the material feature type and color feature type are temperature and humidity.

[0050] Step 2234: Based on the preset level fuzzification rules of key impact parameters, determine the quantitative value of the first key impact parameter according to the information keywords.

[0051] In this embodiment, the preset level fuzzification rule is a set of preset rules that map qualitative descriptions in natural language to quantified levels. For example, if the information keyword is "relatively humid," the first key influencing parameter quantization value is level 2 humidity; if the information keyword is "very humid," the first key influencing parameter quantization value is level 4 humidity.

[0052] Step 2235: Based on the evidence quantification function, determine the quantification value of the second key influence parameter according to the event evidence of the spatiotemporal event.

[0053] In this embodiment, the event evidence of a spatiotemporal event is evidence that confirms the event, such as evidence that cultural relics were buried in a humid environment (carbon dating reports, literature records showing groundwater levels greater than 2 meters). The evidence quantification function is a function that quantifies the event evidence of a spatiotemporal event into corresponding key influencing parameters, such as a function that quantifies environmental humidity based on groundwater levels.

[0054] Step 2236: Extract confidence level, first key influence parameter quantification value, event evidence strength and second key influence parameter quantification value based on keywords, and determine the target key influence parameter quantification value corresponding to the influence feature type.

[0055] In this embodiment, the keyword extraction confidence level is based on NLP entity recognition and ranges from 0 to 1. The event evidence strength is preset according to the type of evidence, for example: carbon dating report = 0.9, literature record = 0.6. When determining the quantitative value of the target key impact parameter, the keyword extraction confidence level and the first key impact parameter quantitative value are multiplied together, the event evidence strength and the second key impact parameter quantitative value are multiplied together, and then summed to obtain the target key impact parameter quantitative value.

[0056] Step 2237: Based on the correlation setting relationship between the evolution coefficients of key impact parameters and impact feature types, set the evolution coefficients according to the quantified values ​​of the target key impact parameters.

[0057] In this embodiment, the association setting is the correspondence between key influencing parameter values ​​and evolution coefficient values, determined based on historical experimental data. For example, when the humidity is determined to be level 4.2 based on the quantified humidity rule in the above example, the parameter determined through multiple experiments is the rust area. In the equation, k=0.75; when the humidity level is 2, k=0.2. Therefore, a humidity level of 4.2 is associated with an evolution coefficient value of k=0.75, and a humidity level of 2 is associated with an evolution coefficient value of k=0.2. When setting the evolution coefficient based on the quantified value of the target key impact parameter, the evolution coefficient corresponding to the parameter that is consistent with the quantified value of the target key impact parameter is determined from the association setting relationship; this is the set evolution coefficient.

[0058] The working principle and beneficial effects of the above technical solution are as follows: While existing technologies can use NLP to extract keywords from historical narratives and cross-analyze them with artifact feature types to determine evolutionary impacts, they have not established a specific mathematical correlation between historical narratives and evolutionary coefficients. The accuracy of these evolutionary coefficients significantly influences subsequent evolutionary outcomes.

[0059] Therefore, after extracting information keywords, this invention determines the impact feature types of spatiotemporal events and their corresponding key impact parameters. For the NLP recognition results, fuzzy processing is employed to improve the efficiency of determining the quantification value of the first key impact parameter. Furthermore, considering the support of event evidence for the spatiotemporal event, an evidence quantification function is introduced to quantify the second key impact parameter, improving the accuracy of the subsequently determined target key impact parameter quantification values. Moreover, the accuracy of the evolution coefficients determined based on the target key impact parameter quantification values ​​is also higher.

[0060] In one embodiment, step 3: performing evolutionary interaction based on the evolutionary video stream includes: Step 31: Determine the local backtracking video stream based on the changes in the evolutionary direction of the evolutionary interaction process.

[0061] In this embodiment, the evolutionary interaction process is a two-way interactive process between the user and the video stream of the dynamic evolution of cultural relics, including operations such as time axis control, perspective adjustment, pause / playback, etc.

[0062] In this embodiment, the change in evolution direction refers to the user actively altering the timeline of the artifact's evolution during the interaction process. This typically indicates the user's desire for in-depth exploration of a specific historical period. For example, when the bronze ding (a type of ancient Chinese cooking vessel) evolved to the mid-Western Zhou Dynasty, the user swiped the timeline to the left.

[0063] In this embodiment, the local retrospective video stream is a deep exploration evolution video corresponding to the change in evolutionary direction. Continuing from the example above, the user slides the timeline to the left to retrospectively review the evolution video of the bronze ding from the early Western Zhou Dynasty to the mid-Western Zhou Dynasty. The retrospective evolution video of the bronze ding from the early Western Zhou Dynasty to the mid-Western Zhou Dynasty is the local retrospective video stream.

[0064] Step 32: Index historical knowledge based on the associated spatiotemporal characteristics of the local retrospective video stream.

[0065] In this embodiment, the associated spatiotemporal features are a set of spatiotemporal attributes that strictly correspond to the local retrospective video stream, including precise time coordinates (such as dynasty, year range) and spatial coordinates (such as geographical location, cultural region).

[0066] Step 33: Based on the user's gaze flow information in the local retrospective video stream, determine the knowledge that the user is interested in from the historical knowledge.

[0067] In this embodiment, the user's gaze flow information refers to the change in the user's gaze point over time from initially viewing the evolutionary process video corresponding to the partial retrospective video stream to retrospectively viewing the partial retrospective video stream. For example, the change in the user's gaze point over time from initially viewing the evolutionary video of the bronze ding (a type of ancient Chinese cooking vessel) from the early to the mid-Western Zhou Dynasty to retrospectively viewing the partial retrospective video stream (the user spends 1 second initially viewing the decorative pattern area on the ding's legs, and 3 seconds later when retrospectively viewing the decorative pattern area on the ding's legs).

[0068] Step 34: Map the knowledge that the user is interested in to the model location associated with the knowledge that the user is interested in in the current video frame.

[0069] In this embodiment, the knowledge of interest to the user is specific knowledge content that highly matches the user's current focus, selected from the historical knowledge base based on the user's gaze flow information. For example, continuing the above example, when reviewing the tripod leg decoration area, since the tripod leg decoration area in the mid-Western Zhou Dynasty is significantly different from that in the early Western Zhou Dynasty, the knowledge of interest to the user is the knowledge of the replacement of tripod leg decorations in the Western Zhou Dynasty, and the associated model position is the blank model display area next to the tripod legs.

[0070] The working principle and beneficial effects of the above technical solution are as follows: This invention monitors user interaction with an evolving video stream in real time, detecting local retrospective video streams by analyzing changes in evolutionary direction. It analyzes the spatiotemporal context of the local retrospective video streams to perform precise knowledge retrieval. Combining user gaze flow information, it identifies user-interested knowledge within historical contexts. By projecting this user-interested knowledge onto corresponding locations and performing precise spatial anchoring, it achieves visual fusion of knowledge and artifacts, realizing a complete drive from user behavior to precise knowledge delivery and multimodal fusion, thus enhancing the user experience.

[0071] In one embodiment, step 33: Based on the user gaze flow information of the local retrospective video stream, determine the user-interested knowledge in the historical knowledge, including: Step 331: Based on the user's gaze flow information, determine the distribution of the first gaze point, the boundary of the gaze point, and the distribution of the second gaze point.

[0072] In this embodiment, the first gaze point distribution is the gaze point distribution corresponding to the local retrospective video before the rewind. For example, the gaze point when a user first views the evolution video of the digital bronze ding from the early to the middle Western Zhou Dynasty.

[0073] In this embodiment, the boundary of the gaze point is the minimum convex hull boundary of the user's gaze concentration area identified by a spatial clustering algorithm. It is the backtracking gaze point used to precisely define the scope of the area of ​​interest. For example, the minimum convex hull boundary of the leg decoration area of ​​a digital bronze ding from the mid-Western Zhou Dynasty.

[0074] In this embodiment, the second viewpoint distribution is the viewpoint distribution when reviewing a local retrospective video stream. For example, the viewpoint distribution when a user views the leg decoration of a digital bronze ding from the mid-Western Zhou period and then views the evolution video of the digital bronze ding from the early to mid-Western Zhou period.

[0075] Step 332: Determine whether the process evolution model corresponding to the user's gaze flow information spans time and space.

[0076] In this embodiment, the process evolution model is a digital cultural relic model corresponding to the flow of user gaze information.

[0077] In this embodiment, "across time and space" refers to the user's visual behavior spanning significantly different historical periods or geographical spaces, and the time span exceeding a preset threshold (such as 500 years), indicating that the user is conducting a comparative exploration across historical stages.

[0078] Step 333: If it spans time and space, determine the target process evolution model based on the boundary of the line of sight.

[0079] In this embodiment, the target process evolution model is two digital artifact models that are compared across time and space, such as a digital bronze ding from the early Western Zhou Dynasty and a digital bronze ding from the middle Western Zhou Dynasty.

[0080] Step 334: Determine the first local model based on the first local distribution corresponding to the target process evolution model that traces back to the beginning in the first line of sight landing point distribution.

[0081] In this embodiment, the first local distribution is the distribution of the first line of sight falling on the initial target process evolution model. The first local model is the smallest local backtracking target process evolution model containing the first local distribution, for example: the tripod leg area of ​​a digital bronze ding from the mid-Western Zhou Dynasty.

[0082] Step 335: Determine the second local model based on the second local distribution corresponding to the target process evolution model reviewed in the second line of sight distribution.

[0083] In this embodiment, the second local distribution refers to the distribution of the second line-of-sight points falling within the target process evolution model of the retrospective viewing. The second local model is the smallest local retrospective viewing target process evolution model that includes the second local distribution, such as the leg area of ​​a digital bronze ding from the early Western Zhou Dynasty.

[0084] Step 336: Based on the differences in presentation between the first local model and the second local model, determine the knowledge of interest to the user in the historical knowledge.

[0085] In this embodiment, the difference is presented as the model difference between the first local model and the second local model, such as the difference in the patterns on the legs of a digital bronze ding from the early Western Zhou Dynasty and the legs of a digital bronze ding from the mid-Western Zhou Dynasty. Based on the difference in presentation, the user-interested knowledge in historical knowledge is determined as knowledge related to the difference in presentation, such as knowledge describing the cultural background of the changes in the leg decorations from the early to mid-Western Zhou Dynasty.

[0086] The working principle and beneficial effects of the above technical solution are as follows: This invention overcomes the limitations of existing single-point line-of-sight analysis by decoupling the first and second line-of-sight distributions over time, enabling dynamic capture of the evolution of user interests. The differences between the first and second local models provide accurate evidence for historical knowledge matching, refining the knowledge association unit from the entire artifact to local areas, greatly improving the accuracy of pushing relevant knowledge.

[0087] This invention provides a display system for presenting digital cultural relics in the cultural tourism industry, such as... Figure 2 As shown, it includes: Module 1 is used to generate digital cultural relic models based on the 3D data of cultural relics. Display module 2 is used to evolve the digital cultural relic model according to the spatiotemporal characteristics of cultural relics and display the evolution video stream; Interaction module 3 is used for evolutionary interaction based on the evolutionary video stream.

[0088] This invention provides a video display that utilizes the display method described in the above embodiments for displaying digital cultural relics for cultural tourism.

[0089] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A display method applied to the presentation of digital cultural relics in cultural tourism, characterized in that, include: Digital cultural relic models are generated based on 3D data of cultural relics and artifacts. The evolution of digital cultural relic models is based on the spatiotemporal characteristics of cultural relics, and the evolution video stream is displayed. Evolutionary interaction based on evolutionary video streams.

2. The display method for presenting digital cultural relics in cultural tourism as described in claim 1, characterized in that, Methods for acquiring 3D data include: structured light scanning, laser scanning, photogrammetry, CT scanning, and handheld scanner scanning.

3. The display method for presenting digital cultural relics in cultural tourism as described in claim 1, characterized in that, Based on the 3D data of cultural relics and artifacts, digital models of cultural relics are generated, including: Perform point cloud preprocessing on 3D data and convert discrete point clouds into continuous triangular mesh models; The true colors and surface textures of cultural relics are UV-unwrapped with triangular mesh models and automatically mapped, and finally structured and packaged into digital cultural relic models.

4. The display method for presenting digital cultural relics in cultural tourism as described in claim 1, characterized in that, The evolution of digital cultural relic models is based on the spatiotemporal characteristics of cultural relics, and the evolution video stream is displayed, including: Based on the preset feature extraction templates for different types of cultural relics, extract the cultural relic feature set of the digital cultural relic model; Based on spatiotemporal characteristics, the evolutionary relationships of features of different cultural relics are obtained; Using the set of cultural relic features as the evolutionary result, an evolutionary video stream was obtained.

5. The display method for presenting digital cultural relics in cultural tourism as described in claim 4, characterized in that, Based on spatiotemporal characteristics, the evolutionary relationships of features of different cultural relic types are obtained, including: Based on different types of cultural relics characteristics, feature evolution templates are constructed; Based on spatiotemporal characteristics, obtain the historical narrative of cultural relics and tourism artifacts; Based on historical narratives and the types of cultural relics, determine the evolution coefficients of the template positions of the corresponding feature evolution templates; Once all evolution coefficients in the feature evolution template are determined, the relationship described by the corresponding feature evolution template is taken as the feature evolution relationship.

6. The display method for presenting digital cultural relics in cultural tourism as described in claim 5, characterized in that, Based on historical narratives and the types of cultural relic characteristics, determine the evolution coefficients of the template positions for the corresponding feature evolution templates, including: Identify spatiotemporal event nodes based on historical narratives; Analyze the event information of spatiotemporal event nodes and extract key information. Based on the information keywords, determine the impact characteristic types of spatiotemporal events and the corresponding key impact parameters; Based on the pre-defined level fuzzification rules for key impact parameters, the quantified value of the first key impact parameter is determined according to information keywords. Based on the evidence quantification function, the quantification value of the second key influence parameter is determined according to the event evidence of the spatiotemporal event; Based on the confidence level, the quantitative value of the first key influence parameter, the strength of event evidence, and the quantitative value of the second key influence parameter extracted from the keywords, the quantitative value of the target key influence parameter corresponding to the type of influence feature is determined. Based on the correlation between the evolution coefficients of key impact parameters and impact feature types, the evolution coefficients are set according to the quantified values ​​of the target key impact parameters.

7. The display method for presenting digital cultural relics in cultural tourism as described in claim 1, characterized in that, Evolutionary interaction based on evolutionary video streams includes: Based on the changes in the evolutionary direction during the evolutionary interaction process, determine the local backtracking video stream; Based on the associated spatiotemporal characteristics of local retrospective video streams, historical knowledge is indexed; Based on the user's gaze flow information in the local retrospective video stream, identify the user's interest in historical knowledge. Map the knowledge that the user is interested in to the model location associated with the knowledge that the user is interested in in the current video frame.

8. The display method for presenting digital cultural relics in cultural tourism as described in claim 7, characterized in that, Based on user gaze flow information from partial retrospective video streams, identify user-interested knowledge within historical knowledge, including: Based on the user's gaze flow information, determine the distribution of the first gaze point, the boundary of the gaze point, and the distribution of the second gaze point; Determine whether the evolutionary model corresponding to the user's gaze flow information spans time and space; If it spans time and space, determine the target process evolution model based on the boundary of the line of sight; The first local model is determined based on the first local distribution corresponding to the target process evolution model that traces back from the first line of sight landing point distribution; The second local model is determined based on the second local distribution corresponding to the target process evolution model reviewed in the second line of sight distribution; Based on the differences in presentation between the first and second local models, the knowledge of interest to users in historical knowledge is determined.

9. A display system applied to the presentation of digital cultural relics in cultural tourism, characterized in that, include: The generation module is used to generate digital cultural relic models based on the 3D data of cultural and tourism relics; The display module is used to evolve digital cultural relic models based on the spatiotemporal characteristics of cultural relics and to display the evolution video stream. The interaction module is used for evolutionary interaction based on the evolutionary video stream.

10. A video display, characterized in that, The display method described in claims 1-8 is used to display digital cultural relics for cultural tourism.

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