A Study on the Re-creation of Sense of Place in Non-Heritage Images Based on Imaging Technology and Mathematical Modeling
Published Online: Mar 24, 2025
Received: Oct 31, 2024
Accepted: Feb 12, 2025
DOI: https://doi.org/10.2478/amns-2025-0775
Keywords
© 2025 Xueyuan Fang, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
With the wave of globalization, people’s awareness of “sense of place” continues to diminish, and the global similarity of physical space has aroused people’s concern. Intangible cultural heritage (ICH), as iconic cultural landscapes, is an important link between people and places, and has a positive significance in the process of remodeling and spreading the sense of place [1-3].
As the crystallization of human civilization and the most precious common wealth, intangible cultural heritage is rooted in a specific regional culture, carrying the wisdom of human beings, the civilization and glory of human history, and the unique emotions and connections between people and places, and this “human-place” relationship is reflected in human geography as a sense of place [4-6]. However, with the advance of urbanization and modernization, the connotation of place has changed, and modern society has been able to develop through standardized spatial production, which has weakened or even destroyed the authenticity and richness of place, bringing about the disintegration of the habitual sense of place as a living space and the disappearance of identity, and subjecting the excellent traditional Chinese culture represented by intangible cultural heritage to strong challenges and impacts [7-9]. How to recreate the sense of place? Established studies have paid less attention to the role of media content, especially intangible cultural heritage, in shaping the sense of place [10].
Intangible cultural heritage, as an important part of the history and culture of a country and a nation, carries rich historical information and profound cultural values. It not only reflects the wisdom and creativity accumulated by human society in the process of long-term development, but also is an important symbol of national identity and cultural diversity [11-12]. However, with the acceleration of globalization and rapid changes in modern society, NRH is facing unprecedented challenges, including problems such as loss of skills and cultural homogenization, which is not only a threat to the heritage of individual cultures, but also a challenge to the diversity of cultural heritage of the whole mankind [13-14]. In this context, the protection and transmission of non-heritage is particularly important. Multiple forces such as national and local governments, cultural institutions, and the media have intervened with the aim of preserving and promoting these precious cultural assets through various means. Documentary, as a powerful media tool, has become an important way of NH heritage inheritance due to its unique artistic expression and wide dissemination range [15-17]. Non-heritage documentaries not only record the non-heritage programs themselves, but more importantly, they pass the deep regional culture and historical background behind the non-heritage to the general audience through the way of video narrative. Non-heritage documentaries are not only an important way for the transmission of non-heritage culture, but also an important window for the study of the dissemination and protection of non-heritage culture [18-19]. Through the study of regional culture image narrative style in these documentaries, it will provide new perspectives and methods for the protection and dissemination of non-heritage and contribute to the sustainable development of non-heritage culture [20-21].
This paper describes the use and function of AR technology in the creation of non-heritage images, and deduces the necessity and feasibility of AR technology applied to non-heritage images. With the help of 3DS MAX, MAYA, and other three-dimensional software, the practical program design of non-heritage images based on AR technology has been completed. Area C is selected as the research subject, after completing the data collection work of this research. In order to test the practicality of its program, 20 satisfaction test items and 16 sense of place evaluation indexes are used as the basis of research and analysis, and KNO model and mathematical evaluation model based on hierarchical analysis-fuzzy comprehensive evaluation are used to evaluate the program of this paper.
Imaging technology includes VR technology, AR technology, image processing technology, etc., while this study mainly uses AR technology to explore the sense of place re-creation of non-heritage images.AR is the abbreviation of Augmented Reality (AR), which is a technology that can combine virtual content with the real world to provide a richer experience. Specifically, AR technology is a technology that accurately “superimposes” computer-generated two-dimensional or three-dimensional virtual digital content information (text, graphics, animation, sound, video, virtual three-dimensional objects, etc.) onto the real environment that users want to experience with the help of information technology, a variety of sensing technologies, computer vision technology and multimedia technology. A technology that aims to enhance the user’s perception of the real environment by integrating computer-generated virtual digital content with the real environment.
Compared to traditional image creation, the use of AR technology can overlap the three aspects of information, image, and interaction, which can provide more dimensional information, thus making non-heritage works more artistically infectious.
AR technology can enrich the content and effect of non-heritage images and enhance the sense of rhythm and visual impact. For example, virtual 3D images can be added to the image creation process to make it more three-dimensional, which can express the structural characteristics, texture characteristics, and color characteristics of non-heritage works more comprehensively. It not only makes the art work more vivid, but also gives the audience a more intuitive experience.
AR technology can also add more background knowledge of non-heritage culture to achieve better “cultural popularization”. In the process of introducing non-legacy, it is easy to add the historical background, development process and protection status of non-legacy culture, so that the audience can fully understand the non-legacy culture and gain more relevant background knowledge, laying a foundation for the subsequent appreciation. This kind of learning context has a broad imaginable space, which broadens the scope of human cognition, and can not only reproduce the real situation, but also conceptualize objectively non-existent or even impossible situations. It makes the learner feel that he or she has entered a “real” world and has a deep impression of the knowledge conveyed.
AR technology can give the audience a new sensory experience through virtual dynamic graphics, build a richer form of expression, and make the audience more easily immersed in it. For example, various virtual elements such as sound effects, pyrotechnics, and placement of spiral ascension can be added to the non-heritage images, so that people can feel the unique atmosphere of the scene and thus have a stronger desire to watch. Introducing the concept of immersive experience and activities in art creation and aesthetics can let the subject get a pleasant aesthetic experience, and even cause the extension of thoughts and emotions in the process of subject-object interaction, which is an ideal realm of art creation and the goal of art exhibition.
AR technology can make non-heritage images interactive, making it easier for viewers to interact with the content in the images. For example, AR can be utilized in the creation of non-legacy images to allow users to interact with the real world through virtual content, such as 3D models, virtual games, virtual environments, etc., so as to better learn and experience the unique charm of non-legacy culture. So that users can feel the fun in the process of cultural consumption and have a more in-depth interpretation of the cultural connotation of the genus, providing a new interactive experience for future users.
In the current visualization and preservation, it is mainly television stations, newspapers, and relevant government departments that play a role. However, contemporary young people are already surrounded by social software and popular culture, and it is difficult for them to pay attention to these contents, especially when they are also related to rituals, labor life, and customs. In fact, due to the development of urbanization, the traditional way of working life and customs have changed dramatically, and it is difficult for young people to experience the richness of non-heritage. Compared with the previous image-based preservation techniques, AR technology can combine people’s real feelings with the wonderful situations in the virtual world, which can more conveniently allow more viewers to participate in it and get a richer experience. Of course, AR technology is a trendy emerging technology that can satisfy young people’s curiosity about cutting-edge technology. It is because of the advantages mentioned above that we need to utilize AR technology in the practice of creating non-heritage images.
The use of AR technology in non-heritage images mainly involves two elements: the acquisition of original non-heritage materials, and the re-creation based on AR technology. At present, there are no obstacles to acquiring original images and AR creation tools.
In terms of the acquisition of original information, a large amount of information has been acquired during the declaration of non-heritage culture and placed on the non-heritage culture platform, which can be accessed and downloaded by the public. On the other hand, the author contacted the non-heritage bearers and interviewed them, obtaining a lot of precious materials. In addition, I have followed and filmed for a long time and studied with non-hereditary inheritors many times. I have filmed more than thirty typical stories among the 108 traditional stories, obtaining first-hand materials. The non-heritage image material library has been basically established.
In terms of tools for AR creation, many mature AR production software have emerged on the market, including Unity, Vuforia, ARCore, ARKit, etc. Unity is a real-time 3D interactive content creation and operation platform, a powerful AR development tool that can be used to create a wide range of AR applications.Vuforia, a subsidiary of PTC, is a platform for developing Vuforia is a platform for developing augmented reality software under PTC, which is a professional AR development tool.ARCore is an AR development tool launched by Google, which can be used to create AR applications on the Android platform.ARKit is an AR development tool launched by Apple, which can be used to create AR applications on the iOS platform. The promotion of these development tools has made it possible to create based on AR technology.
The ultimate purpose of the creation of non-heritage images based on AR technology is to present them in a comprehensive and three-dimensional interactive way on mobile devices, and the production process of non-heritage images mainly utilizes 3DS MAX software and Unity3d software. Firstly, the modeling work is completed by 3DS MAX, and then Unity3d is imported to carry out interactive settings after the modeling part is finished. Finally, the audience can view the video on mobile devices.
Mainly with the help of 3DS MAX, MAYA and other three-dimensional software for the construction of three-dimensional models, the collected images of non-heritage as a view imported into the three-dimensional software, the images of non-heritage as a reference to assist the construction of the model. According to the aspect ratio of the content, set the size parameter in the 3D software, what is common in the picture does not need to be made repeatedly, after screening the content of the picture, choose the content that can express the mood of the picture to be made. After the construction of the 3D model is completed, it is imported into Unity3D for secondary production, and finally the information is bound and the screen is adjusted.
3DS MAX is a PC-based software for 3D modeling and animation, rendering, and it can be said to be one of the most used 3D software around the world today. 3DS MAX is loved by many professional companies and digital workers because of its strong professionalism and excellent user interface. The software offers a comprehensive range of 3D modeling, animation, rendering, and compositing solutions for a diverse range of applications for individuals who work in digital media art, urban planning, 3D games, and other visual design.
We build 3D models with the help of this software, using polygonal modeling. We use polygon modeling in accordance with the required proportion of reasonable modeling. In doing to highlight the main body, the first to create a new plane, and then collapse the plane into poly, the use of paint Defcrmaton made into the background of the trend, with a moderate relax for smooth processing, plus some details on the control of its overall presentation of the form, the proportion of a certain degree of control, and then select the appropriate mood of the material mapping, of course, but also according to the need to adjust the appropriate light and shadow, according to the need for reasonable modeling ratio. But also according to the needs of light and shadow adjustment of the appropriate lighting, lighting effects is an important element of the impact of authenticity. According to different weather conditions, we can adjust the brightness of the light, grasp the direction of the light, and imitate the most realistic lighting effects.
Unity3D software is a cross-platform game engine, which can develop games on multiple platforms such as PC, Mac Os, iOS, Android, etc. Of course it can also develop online games on multiple browsers. Many famous games at home and abroad are made using this game software, such as Hero of Tanks, Hearthstone Legends, Temple Run, etc. The existence of Unity3D provides strong technical support for AR technology-based real-life restoration, and it provides a choice of interaction methods for the presentation of the mobile device afterwards. The software supports different lighting effects and can be used to import models directly into the 3DS MAX format as well as to load dynamic external models using the Asset Bundle mode. Designing the camera code is also an important part of the program, which involves the knowledge of the relevant programming categories, Unity3D currently supports three scripting languages Java Script, C++, Boo to control the scene, objects.
After the completion of the modeling imported into Unity3d to continue editing, in the import session you need to collect the previous non-legacy images for import and with the physical object for information physical model for seamless overlay, we need to identify the scene in the form of pictures into Unity3D in the new directory, in the directory of a new json file, using the imported model, Easy AR Camera into the panel, edit a script for the display and disappearance of the model, and then set up the path, size and other information for the recognition. After completing the above steps, there is some testing to be done.
We use Unity3D’s ability to call the camera and collect information to provide a favorable support for the next step with the use of handheld devices, we are borrowing its camera function to achieve the final presentation of the combination of real and virtual AR technology.
The important thing about non-heritage images based on AR technology is the perfect integration of virtual scenery and real environment. AR recognition with the help of special signs in the existing attractions, because of the imperfection of the technology, the recognition may fail due to weather, light, distance and other reasons. For the design of interactive devices, several different interactive devices can be selected according to different purposes. Handheld devices are indispensable because of their convenience. Viewers can download the corresponding application software on their cell phones and use the camera scanning function of the cell phone to recognize the environment in front of them, so that they can match and observe the results of the combination of reality and reality in real time on their cell phones. Head-mounted devices can be installed at each attraction as part of the audience’s travel experience, utilizing their immersive features to greatly enhance the audience’s travel experience.
Sense of place is a relatively general concept, researchers try to split and interpret it from many angles, although there is a lack of unified expression about the current definition of sense of place, but due to the humanistic and subjective characteristics of local research, then sense of place can be an inclusive concept, which can be understood as people’s emotional attachment and identification with a specific place. Combined with the experts’ opinions and relevant reference materials, 16 secondary indicators and 4 first-level indicators are screened from 50 indicators as the evaluation index system of sense of place for NRL images, and the first-level indicators are resource value H1, protection status H2, tourism development H3 and social cognition H4, and the second-level indicators are historical value J1, artistic value J2, scarcity J3, innovative development J4 and preservation integrity J5, Real-time effect of protection measures J6, training and selection mechanism of inheritors J7, financial investment and support J8, viewability J9, participation J10, tourism route design J11, tourism product and service development J12, public understanding J13, media exposure J14, international exchange J15, cultural identity J16.The evaluation index system of NRH image place is shown in Figure 1.
Local evaluation index system of non-legacy image
Hierarchical analysis is a systematic approach to decision analysis that aims to dismantle complex decision objectives layer by layer, compare elements within the same level two by two, and quantitatively assess the relative importance between them [22-24]. The algorithmic process is as follows:
Establish a hierarchical model. Systematically sort out and score the decision goal, the factors involved (i.e., decision criteria) and the decision object, and construct a clear hierarchical model based on the internal logical relationship between them, which is divided into the highest level, the middle level and the lowest level from top to bottom. Constructing a judgment matrix P. Comparing the elements of the same level two by two, this comparison process aims to construct a judgment matrix based on the relative importance of the elements, so as to provide a quantitative basis for the subsequent decision analysis. In order to achieve this goal, experts in related fields were invited to score the indicators meticulously using the nine-level scale method. Finding the weight vector. After constructing the judgment matrix, the eigenvectors and maximum eigenvalues of the judgment matrix are calculated, and to ensure the reasonableness and accuracy of the weight vectors, the eigenvectors are normalized, and then the corresponding weight vectors are derived. Consistency test. By solving the eigenvectors of the judgment matrix, the priority weight of each element of each level to an element of the previous level is derived, and the consistency test is performed. The primary objective of the consistency test is to ensure that the weights and ordering of each level have internal consistency, avoid logical contradictions, and improve the reliability and effectiveness of final decision-making results.
After the weights of the indicators at all levels are determined, the specific according values of each evaluation indicator need to be derived with the help of the fuzzy comprehensive evaluation method. The fuzzy comprehensive evaluation method uses fuzzy mathematics to deal with the uncertainty information in the evaluation process, which is especially suitable for dealing with the factors that have inconsistent expert opinions or are difficult to quantify [25-26]. The method first determines the evaluation factors and constructs a fuzzy relationship matrix, which describes the importance and status of the factors as a fuzzy set. Applied to the process of data security risk assessment and analysis, it is mainly used to build the weight fuzzy matrix and relationship fuzzy matrix by establishing the set of risk factors and the evaluation set, using the affiliation function, and then the product of the two as the result. The determination of weights assigns a value to the relative influence of each factor, followed by the synthesis of single-factor evaluation through fuzzy matrix operations to arrive at the total evaluation result. The fuzzy synthesis evaluation method is effective in quantifying qualitative evaluation and managing uncertainty in the assessment of the sense of place of non-heritage images.
The KANO model uses a structured and standardized questionnaire to conduct research, with the help of positive and negative questions, to reveal the user’s demand for service functions and clearly express the potential attributes of service quality. By observing the feedback and needs of users in the face of intangible cultural heritage images with or without specific functions or services, the model uses a 5-level Likert scale to classify the problems into satisfaction, including “satisfied”, “taken for granted”, “indifferent”, “reluctant to accept” and “dissatisfied”. According to the relationship between the different characteristics of intangible cultural heritage images and user satisfaction, the KANO model divides the characteristics of intangible cultural heritage images into five categories: essential demand (M), desired demand (O), charismatic demand (A), undifferentiated demand (I) and reverse demand (R), as shown in Figure 2. These classifications help intangible cultural heritage images to meet the needs of users more deeply and guide the design and improvement of intangible cultural heritage images.
KNO model
The Worse index was used to measure the sensitivity of the impact of the level of unmet needs on user satisfaction, with a particular focus on user dissatisfaction. The value of this index ranges from -1 to 0. If the Worse Index is close to -1, it indicates that users are extremely sensitive to the unfulfillment of a need; in other words, the lower the Worse value, the more significant the unfulfillment contributes to user dissatisfaction. On the contrary, when the Worse index gradually close to 0, it means that the user’s sensitivity to whether the demand is satisfied or not has been weakened, i.e., the impact of this demand on the user’s dissatisfaction has tended to be weak. The formula is:
Non-legacy image satisfaction survey is a comprehensive process involving multiple factors, which aims to assess the degree of satisfaction of viewers or participants with the content, form, and dissemination effect of non-legacy images.20 non-legacy image satisfaction test items, richness K1, authenticity K2, artistry K3, education K4, interactivity K5, diversity of dissemination channels K6, accessibility K7, frequency of updating K8, Explanation quality K9, Visual effect K10, Auditory effect K11, Duration K12, Pace K13, Innovativeness K14, International influence K15, Social benefit K16, Economic effect K17, Professional review K18, Improvement aspects K19, Long-term planning K20.Based on KANO model, the results of the questionnaire were distributed and survey data were counted, and the questionnaire data were analyzed using SPSS. The data was tested for reliability and validity, and the user satisfaction coefficient was calculated using the KANO model.
The KANO attribute analysis of satisfaction test items is shown in Table 1, from the KANO model questionnaire survey users’ answers to positive and negative questions, comprehensive KANO two-dimensional attribute categorization, take the highest frequency method to determine the KANO attributes of the nonfiction image satisfaction test items, and derive the KANO attribute table of the nonfiction image satisfaction test items. Through this table, the meaning of the item for the user can be analyzed, and the user’s necessary, expected, charming, indifferent, and annoying needs can be derived. In order to facilitate the observation of the data, this paper integrates the classification of the attributes of the non-legacy image satisfaction test items.
KANO attribute analysis of satisfaction test items
| Project | M | O | A | I | R | Attribution type |
|---|---|---|---|---|---|---|
| K1 | 0.085 | 0.052 | 0.364 | 0.322 | 0.177 | A |
| K2 | 0.029 | 0.1 | 0.368 | 0.356 | 0.147 | A |
| K3 | 0.061 | 0.101 | 0.331 | 0.387 | 0.12 | I |
| K4 | 0.098 | 0.072 | 0.366 | 0.391 | 0.073 | I |
| K5 | 0.115 | 0.095 | 0.397 | 0.339 | 0.054 | A |
| K6 | 0.108 | 0.15 | 0.383 | 0.333 | 0.026 | A |
| K7 | 0.034 | 0.143 | 0.311 | 0.363 | 0.149 | I |
| K8 | 0.045 | 0.129 | 0.302 | 0.321 | 0.203 | I |
| K9 | 0.119 | 0.06 | 0.329 | 0.337 | 0.155 | I |
| K10 | 0.07 | 0.113 | 0.354 | 0.317 | 0.146 | A |
| K11 | 0.023 | 0.084 | 0.371 | 0.361 | 0.161 | A |
| K12 | 0.084 | 0.116 | 0.309 | 0.324 | 0.167 | I |
| K13 | 0.096 | 0.071 | 0.396 | 0.395 | 0.042 | A |
| K14 | 0.027 | 0.051 | 0.35 | 0.326 | 0.246 | A |
| K15 | 0.077 | 0.054 | 0.334 | 0.376 | 0.159 | I |
| K16 | 0.102 | 0.088 | 0.335 | 0.388 | 0.087 | I |
| K17 | 0.026 | 0.149 | 0.378 | 0.378 | 0.048 | A |
| K18 | 0.029 | 0.144 | 0.359 | 0.377 | 0.091 | I |
| K19 | 0.066 | 0.138 | 0.398 | 0.307 | 0.091 | A |
| K20 | 0.042 | 0.123 | 0.346 | 0.362 | 0.127 | I |
A summary of the KANO attribute categorization of the satisfaction items of the NRI images is shown in Table 2. It can be clearly seen that the 20 satisfaction items were classified as charismatic (K1, K2, K4, K5, K10, K11, K13, K14, K17, K19), and undifferentiated (K3, K4, K6, K7, K8, K9, K10, K12, K15, K16, K18, K20) in the two types of the classification results are relatively homogeneous, and therefore it is still necessary to use the Better-Worse coefficients for further analysis.
Classification and summary of KANO attributes
| Attribution type | Item number |
|---|---|
| M | No |
| O | No |
| A | K1, K2, K4, K5, K10, K11, K13, K14, K17, K19 |
| I | K3, K4, K6, K7, K8, K9, K10, K12, K15, K16, K18, K20 |
| R | No |
The original KANO theoretical model has some limitations in establishing attribute attribution, as it relies on the most frequent method to determine the KANO category corresponding to each service feature, and this traditional strategy does not take into account the overall distribution of functional demand attributes, the total amount of data, and the possible evolution of KANO attributes over time. In this case, the advantages of this method are not prominent, which makes the results of user needs analysis based on this method ineffective in helping cultural heritage-related organizations to explore user needs in depth. Therefore, in order to make up for its limitations, this survey calculates the percentage between different attribute ratings of each functional requirement item with each other, and adopts the formula to calculate the Better-Worse coefficient, so as to derive the satisfaction index of non-heritage images based on the integration of AR technology, and the results of the satisfaction coefficient calculation are shown in Table 3. The Better values of each item are distributed between 0.5 and 0.8, while the Worse values are in the range of -0.5 to -0.2.
Satisfaction coefficient calculation results
| Project | Better | Worse |
|---|---|---|
| K1 | 0.663 | -0.246 |
| K2 | 0.783 | -0.439 |
| K3 | 0.676 | -0.218 |
| K4 | 0.775 | -0.49 |
| K5 | 0.518 | -0.493 |
| K6 | 0.753 | -0.256 |
| K7 | 0.791 | -0.244 |
| K8 | 0.579 | -0.415 |
| K9 | 0.654 | -0.467 |
| K10 | 0.551 | -0.241 |
| K11 | 0.626 | -0.394 |
| K12 | 0.618 | -0.419 |
| K13 | 0.521 | -0.268 |
| K14 | 0.517 | -0.304 |
| K15 | 0.797 | -0.346 |
| K16 | 0.67 | -0.248 |
| K17 | 0.765 | -0.426 |
| K18 | 0.635 | -0.214 |
| K19 | 0.679 | -0.427 |
| K20 | 0.681 | -0.479 |
In order to gain a deeper understanding of the distribution of the various satisfaction items, the data were analyzed and processed in depth using the Excel tool based on the values of the Better-Worse coefficients provided in the table. By calculating the mean value of the satisfaction items on the Worse index (-0.3517) and on the Better index (0.6626), it indicates that the users are basically satisfied with the non-heritage images integrating AR technology, and then these 20 test items are plotted according to their coefficient values into the Better-Worse coefficient four-quadrant diagram, and Figure 3 shows the Better -Worse coefficient four-quadrant diagram. It can be more intuitively seen that charismatic needs (A) contain K14, K13, K10, K18, essential needs (M) are K9, K20, K4, K2, K17, K19, undifferentiated needs (I) have K5, K12, K8, K12, K11, and lastly expectancy needs (O) encompasses K1, K16, K6, K7, K15, K3. The division according to this type is more relevant to the actual situation and more conducive to the satisfaction of NRM images.
Better-Worse coefficient quadrants
Calculation of weights of secondary indicators under resource value H1 With the help of hierarchical analysis algorithm, the weight of secondary indicators under resource value H1 is calculated, and Table 4 shows the weight values of secondary indicators under resource value H1. The relative weights of historical value J1, artistic value J2, scarcity J3, innovative development J4 are 0.3032, 0.2411, 0.2146, 0.2411, and the weights of the indicators pass the consistency test. Calculation of the weights of secondary indicators under the protection status H2 Adopting the same method described above, the weights of the secondary indicators under the protection status H2 are determined, and the results of the calculation of the weights of the secondary indicators under the protection status H2 are shown in Table 5. The relative weights of the secondary indicators under the protection status H2 are 0.2233, 0.3155, 0.2536, 0.2077, and the corresponding CR values are 0.018, 0.022, 0.025, 0.031, which indicates that the calculation results meet the requirements of consistency test. Calculation of weights of secondary indicators under tourism development H3 Calculate the relative weights of secondary indicators under tourism development H3, and the relative weights and consistency test results are shown in Table 6. The relative weights of viewability J9, participation J10, tourism route design J11, and tourism product and service development J12 are 0.2394, 0.1956, 0.2848, and 0.2802, and they also satisfy the judgment condition of CR<0.1. Calculation of weights of secondary indicators under social cognition H4 Table 7 presents the relative weights and consistency test values of the secondary indicators subordinate to social cognition H4. The data exhibits four secondary indicators with values of 0.2828, 0.2441, 0.2214, and 0.2517, and CR values of 0.039, 0.045, 0.041, and 0.066, none of which exceed <0.1, ensuring the scientific validity of the subsequent results. Calculation of relative weights of first-level indicators After calculating the relative weights of the 16 second-level indicators, the four first-level indicators relative weights and CR values were finally calculated, and the relative weights of the first-level indicators and the consistency test are shown in Table 8. The relative weights of the indicators are 0.1980, 0.2798, 0.3097, 0.2125, and also meet the consistency test.
The value of the resource value H1 is the value of the secondary index
| Index | J1 | J2 | J3 | J4 | Weighting | CR |
|---|---|---|---|---|---|---|
| J1 | 1 | 0.5 | 0.25 | 0.333 | 0.3032 | 0.051 |
| J2 | 0.2 | 1 | 0.25 | 0.333 | 0.2411 | 0.053 |
| J3 | 0.125 | 0.167 | 1 | 0.5 | 0.2146 | 0.037 |
| J4 | 0.333 | 0.2 | 0.25 | 1 | 0.2411 | 0.042 |
Protection status H2 subordinate secondary index weight calculation results
| Index | J5 | J6 | J7 | J8 | Weighting | CR |
|---|---|---|---|---|---|---|
| J5 | 1 | 0.2 | 0.167 | 0.25 | 0.2233 | 0.018 |
| J6 | 0.333 | 1 | 0.2 | 0.5 | 0.3155 | 0.022 |
| J7 | 0.25 | 0.167 | 1 | 0.333 | 0.2535 | 0.025 |
| J8 | 0.125 | 0.2 | 0.25 | 1 | 0.2077 | 0.031 |
Relative weight and consistency test results
| Index | J9 | J10 | J11 | J12 | Weighting | CR |
|---|---|---|---|---|---|---|
| J9 | 1 | 0.333 | 0.125 | 0.2 | 0.2394 | 0.042 |
| J10 | 0.2 | 1 | 0.167 | 0.111 | 0.1956 | 0.061 |
| J11 | 0.2 | 0.167 | 1 | 0.5 | 0.2848 | 0.072 |
| J12 | 0.5 | 0.125 | 0.25 | 1 | 0.2802 | 0.059 |
Social recognition H4 subordinate secondary index weight calculation
| Index | J9 | J10 | J11 | J12 | Weighting | CR |
|---|---|---|---|---|---|---|
| J9 | 1 | 0.5 | 0.2 | 0.333 | 0.2828 | 0.039 |
| J10 | 0.333 | 1 | 0.5 | 0.111 | 0.2441 | 0.045 |
| J11 | 0.125 | 0.2 | 1 | 0.5 | 0.2214 | 0.041 |
| J12 | 0.167 | 0.5 | 0.25 | 1 | 0.2517 | 0.066 |
First level index relative weight and consistency test
| Index | J9 | J10 | J11 | J12 | Weighting | CR |
|---|---|---|---|---|---|---|
| J9 | 1 | 0.5 | 0.125 | 0.167 | 0.1980 | 0.044 |
| J10 | 0.25 | 1 | 0.5 | 0.333 | 0.2798 | 0.049 |
| J11 | 0.5 | 0.25 | 1 | 0.5 | 0.3097 | 0.033 |
| J12 | 0.333 | 0.125 | 0.333 | 1 | 0.2125 | 0.025 |
On the basis of the known relative weights, the absolute weights of each indicator are calculated, and the results of the absolute weights of each indicator are shown in Table 9. The absolute weight is determined by multiplying the relative weight of this layer by the relative weight of the previous layer, using J1 as an example. The calculation process is as follows:
The absolute weight of each indicator is the result
| Primary index | Weighting | Secondary index | Relative weight | Absolute weight |
|---|---|---|---|---|
| H1 | 0.1980 | J1 | 0.3032 | 0.0600 |
| J2 | 0.2411 | 0.0477 | ||
| J3 | 0.2146 | 0.0425 | ||
| J4 | 0.2411 | 0.0477 | ||
| H2 | 0.2798 | J5 | 0.2233 | 0.0625 |
| J6 | 0.3155 | 0.0883 | ||
| J7 | 0.2535 | 0.0709 | ||
| J8 | 0.2077 | 0.0581 | ||
| H3 | 0.3097 | J9 | 0.2394 | 0.0741 |
| J10 | 0.1956 | 0.0606 | ||
| J11 | 0.2848 | 0.0882 | ||
| J12 | 0.2802 | 0.0868 | ||
| H4 | 0.2125 | J13 | 0.2828 | 0.0601 |
| J14 | 0.2441 | 0.0519 | ||
| J15 | 0.2214 | 0.0470 | ||
| J16 | 0.2517 | 0.0535 |
The same applies to the remaining indicators.
The evaluation index system weights are obtained by statistically summarizing the results of previous calculations, and Table 10 displays the results of the evaluation index system weights. It can be clearly seen that in the secondary indicators there are J3 (0.0425) < J15 (0.047) < J2 (0.0477) < J4 (0.0477) < J14 (0.0519) < J16 (0.0535) < J8 (0.0581) < J1 (0.06) < J13 (0.0601) < J10 (0.0606) < J5 ( 0.0625) <J7 (0.0709) <J9 (0.0741) <J12 (0.0868) <J11 (0.0882) <J6 (0.0883), while the first level indicators are H1 (0.1980) <H4 (0.2125) <H2 (0.2798) <H3 (0.3097).
Evaluation index system weight
| Primary index | Weighting | Secondary index | Weight |
|---|---|---|---|
| H1 | 0.1980 | J1 | 0.0600 |
| J2 | 0.0477 | ||
| J3 | 0.0425 | ||
| J4 | 0.0477 | ||
| H2 | 0.2798 | J5 | 0.0625 |
| J6 | 0.0883 | ||
| J7 | 0.0709 | ||
| J8 | 0.0581 | ||
| H3 | 0.3097 | J9 | 0.0741 |
| J10 | 0.0606 | ||
| J11 | 0.0882 | ||
| J12 | 0.0868 | ||
| H4 | 0.2125 | J13 | 0.0601 |
| J14 | 0.0519 | ||
| J15 | 0.0470 | ||
| J16 | 0.0535 |
Taking region C as the object of this study, the research data were obtained through the distribution of questionnaires, and then the evaluation affiliation degree was set up, which was excellent, good, average, poor, and poor in five cases, with the corresponding values of 5, 4, 3, 2, and 1, respectively.On the basis of the index weights and the data of questionnaires, the fuzzy comprehensive evaluation method was utilized to derive the comprehensive evaluation matrix of NRM images, and the NRM images comprehensive evaluation matrix was as shown in Table 11. The evaluation results of AR technology-driven non-heritage image sense of place are (3.5804, 2.0325, 1.4579, 1.5178, 1.4104), and based on the principle of maximum affiliation, the final AR technology-driven non-heritage image sense of place recreation effect is excellent, which indicates that AR technology plays a very good role in the protection and inheritance of the non-heritage culture, and arouses people’s traditional cultural spirit.
Comprehensive evaluation matrix of non-legacy image
| Secondary index | Outstanding | Good | Normal | Range | Poor |
|---|---|---|---|---|---|
| J1 | 0.3 | 0.06 | 0.06 | 0.12 | 0.06 |
| J2 | 0.1908 | 0.0954 | 0.0954 | 0.0477 | 0.0477 |
| J3 | 0.17 | 0.085 | 0.085 | 0.0425 | 0.0425 |
| J4 | 0.2385 | 0.0954 | 0.0477 | 0.0477 | 0.0477 |
| J5 | 0.125 | 0.1875 | 0.125 | 0.0625 | 0.125 |
| J6 | 0.2649 | 0.2649 | 0.0883 | 0.1766 | 0.0883 |
| J7 | 0.2836 | 0.1418 | 0.0709 | 0.1418 | 0.0709 |
| J8 | 0.1743 | 0.1162 | 0.1162 | 0.1162 | 0.0581 |
| J9 | 0.2223 | 0.2223 | 0.0741 | 0.0741 | 0.1482 |
| J10 | 0.303 | 0.1212 | 0.0606 | 0.0606 | 0.0606 |
| J11 | 0.1764 | 0.1764 | 0.1764 | 0.1764 | 0.1764 |
| J12 | 0.434 | 0.0868 | 0.0868 | 0.0868 | 0.1736 |
| J13 | 0.2404 | 0.1202 | 0.1202 | 0.0601 | 0.0601 |
| J14 | 0.1557 | 0.0519 | 0.1038 | 0.1038 | 0.1038 |
| J15 | 0.141 | 0.047 | 0.094 | 0.094 | 0.094 |
| J16 | 0.1605 | 0.1605 | 0.0535 | 0.107 | 0.0535 |
Under the vision of image technology, the article proposes a practical program for creating non-heritage images using AR technology, based on the performance of AR technology in the creation of non-heritage images. The KNO model is first used to analyze the satisfaction of the program, and the average value on the Worse index of satisfaction is -0.3517, while the Better index is 0.6626, indicating that the audience’s attitude towards the non-heritage images integrating AR technology is basically satisfied. In order to improve the credibility of the research results, the sense of place of non-heritage images was evaluated using hierarchical analysis and fuzzy comprehensive evaluation, and the results were (3.5804, 2.0325, 1.4579, 1.5178, 1.4104), and according to the principle of the maximum degree of affiliation, the AR technology has an excellent facilitating role in the sense of place reengineering of non-heritage images.