CN116992067A - Non-heritable heritage digital display system and method - Google Patents
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Abstract
The invention discloses a non-heritable cultural heritage digital display system and a method, which belong to the technical field of cultural protection and comprise a user terminal, a display platform, a non-heritable data collection module, a classification marking module, a feature extraction module, a 3D reconstruction module, an intelligent correction module, a voice broadcasting module and a block storage module, wherein the user terminal is used for being in communication connection with the display platform, displaying 3D scene animation and providing an interactive browsing function; according to the invention, the randomness of the training sample can be improved, the accuracy of anomaly detection can be effectively improved, meanwhile, the 3D model with errors can be automatically corrected, the manual correction time is saved, the workload of staff is reduced, the use experience of the staff is improved, the non-heritage cultural heritage can be accurately displayed for the user, and the user's look and feel is increased.
Description
Technical Field
The invention relates to the technical field of cultural protection, in particular to a non-heritable cultural heritage digital display system and method.
Background
The non-material cultural heritage is abbreviated as non-heritage, which is opposite to the material cultural heritage. In China, the non-material cultural heritage refers to various traditional cultural manifestations which are transmitted by people from generation to generation and are regarded as components of the cultural heritage, and real objects and places related to the traditional cultural manifestations. The non-material cultural heritage is an important component of the most vigor in cultural diversity, is the crystallization of human civilization and the most precious common wealth, and carries the wisdom of human beings and the civilization and magnificence of human histories. In the modern process of globalization and world high-speed development, the proposal of the non-material cultural heritage concept is a historical necessity and an epoch requirement, meets the requirement of the historical development, and has practical significance for effectively protecting the non-material cultural heritage.
Through retrieval, chinese patent number CN109144275A discloses a digital display system and a digital display method for non-material cultural heritage, the digital display system is deeply shocked and impression of the non-material cultural heritage though being deeply communicated with visitors, but only can manually correct a 3D model with errors, thereby increasing the workload of staff, needing to spend a great deal of time, being incapable of accurately displaying the non-heritage cultural heritage for users and reducing the appearance of the users; therefore, we propose a non-heritable cultural heritage digital display system and method.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a non-heritage digital display system and a method.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a non-heritable heritage digital display system comprises a user terminal, a display platform, a non-heritable data collection module, a classification marking module, a feature extraction module, a 3D reconstruction module, an intelligent correction module, a voice broadcasting module and a block storage module;
the user terminal is used for being in communication connection with the display platform, displaying 3D scene animation and providing an interactive browsing function;
the display platform is used for 3D reproduction of non-heritable heritage and feeding back the generated 3D model to the user terminal;
the non-genetic data collection module is used for collecting known non-genetic cultural heritage data;
the classification marking module is used for carrying out classification analysis on the collected non-heritable cultural heritage data and marking the collected non-heritable cultural heritage data;
the feature extraction module is used for extracting feature information in the non-heritable cultural heritage data;
the 3D reconstruction module is used for reproducing the corresponding 3D scene and the non-genetic culture making process according to the characteristic information;
the intelligent correction module is used for analyzing the non-heritable cultural heritage data and detecting and adjusting the 3D model established by the 3D reconstruction module;
the voice broadcasting module is used for introducing various information of related non-heritable cultural heritage to visitors;
the block storage module is used for storing various collected non-heritable cultural heritage data.
As a further scheme of the invention, the user terminal specifically comprises an intelligent collection device, a tablet computer, a desktop computer and a notebook computer.
As a further scheme of the invention, the classification marking module specifically comprises the following steps of:
step one: after receiving each group of non-heritable cultural heritage data, the classifying and marking module classifies each group of heritage data according to a specified non-material cultural heritage directory system, and classifies the classified heritage data according to specific categories of the heritage data;
step two: sorting the groups of heritage data according to the order of the first letters A to Z of the non-heritage names, and classifying the sorted groups of heritage data information according to the text information, the picture information and the video information.
As a further scheme of the invention, the classification standard of the non-material cultural heritage directory system is specifically country, province, city and county;
the non-heritable cultural heritage category specifically comprises traditional spoken literature and languages, traditional art, handwriting, music, dance, drama, melody and acrobatics, traditional skills, medicine and calendar, traditional etiquette, festival celebration folks, traditional sports and recreation and other non-matter cultural heritages as carriers thereof.
As a further scheme of the invention, the specific extraction steps of the characteristic information of the characteristic extraction module are as follows:
step (1): the feature extraction module collects video information and picture information in each heritage data, then processes the video information frame by frame to obtain a plurality of groups of picture information, then performs blocking processing according to the display proportion of each group of picture information, and then removes high-frequency components in the blocked picture information through Fourier transformation;
step (2): setting Gaussian smoothing filter parameters, calculating weight occupied by each filter, smoothing the picture information through each weighted anisotropic filter, carrying out nonlinear transformation on the processed image, and carrying out weighting treatment on the sum of results obtained by the nonlinear transformation to obtain final picture information;
step (3): selecting windows meeting the conditions to move in each group of image information, calculating gray level co-occurrence matrixes under the windows at each time, calculating texture features in related image information from the gray level co-occurrence matrixes, separating a target from a background according to the calculated texture features, carrying out normalization processing on a target image, and extracting feature data of the target image;
step (4): the global attitude features of each group of image information are obtained through a plurality of times of ShuffleBlock of an acquisition network, then the global attitude features are returned to the key point feature map through deconvolution operation, then the key point feature map is decoded, and two-dimensional key points of the human body generated after decoding are collected.
As a further scheme of the invention, the intelligent correction module detects and adjusts the specific steps as follows:
step I: the intelligent correction module receives the characteristic data and the key point information of each group to construct a detection data set, calculates the standard deviation of the detection data set, and screens out abnormal data in the detection data set according to the calculated standard deviation;
step II: inputting the detection data set into a convolutional neural network, performing convolutional operation to obtain a feature map meeting requirements, repeatedly adopting two groups of convolutional layers and a group of maximum pooling layers to process the feature map, performing deconvolution operation in an expansion channel to halve the dimension of the feature map, re-forming a feature map with 2 times of the dimension, adopting two groups of convolutional layers, repeating the structure, mapping the feature map obtained in the last layer into a 6-dimensional output feature map at a final output layer, collecting the output feature map obtained through forward propagation, and converting linear prediction values of all targets into probability values through a softmax function;
step III: calculating a loss value between real data and detection probability by using a loss function, updating parameters in the convolutional neural network layer by layer, calculating corresponding loss values, stopping training when the loss value reaches a certain threshold value, taking the parameters as optimal parameters, and outputting a correction model;
step IV: inputting the 3D model into a correction model, setting a detection label by the correction model, convoluting, pooling and fully connecting the 3D model, confirming the key point positions of the abnormal model, and correcting the abnormal model.
A non-heritable heritage digital display method specifically comprises the following steps:
(1) Collecting non-heritable heritage data and classifying according to the regulations;
(2) Extracting various non-heritable heritage characteristic information and constructing a 3D model;
(3) Generating various non-heritable heritage literal descriptions and matching with the 3D model;
(4) Correcting the 3D model and feeding back a correction result of the staff model;
(5) The user logs in the display platform and selects a related 3D model for viewing;
(6) And storing each group of non-heritable heritage 3D models and optimizing and displaying the performance of the platform.
As a further scheme of the invention, the specific steps of optimizing the performance of the display platform in the step (6) are as follows:
step I: generating a starting chain table for each group of functional interfaces of the display platform, and further linking each group of starting chain tables according to the sequence of the LRU chain tables from less to more according to the number of times each group is accessed;
step II: according to the interactive information of each group of functional interfaces, updating data of each group of pages in each group of starting linked lists in real time, sequentially selecting the functional interface starting linked list with the least accessed times from the head of the LRU linked list to select the victim page, and stopping until enough victim pages are recovered;
and III, step III: combining the selected victim page into a block and marking, waking up a compression driver program to analyze the marked block, obtaining a physical page belonging to the block, copying the physical page into a buffer area, then calling a compression algorithm to compress the physical page in the buffer area into a compression block, and storing the compression block into a compression area of a platform optimization module.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, each group of characteristic data and key point information are received through an intelligent correction module to construct a detection data set, the detection data set is preprocessed and then is input into a convolutional neural network to be convolved, pooled and deconvoluted to obtain an output characteristic diagram, the output characteristic diagram obtained through forward propagation is collected, linear predicted values of all targets are converted into probability values through a softmax function, loss values between real data and detection probability are calculated through a loss function, parameters in the convolutional neural network are updated layer by layer, corresponding loss values are calculated, training is stopped when the loss values reach a certain threshold value, the parameters are used as optimal parameters and are output to a correction model, a 3D model is input into the correction model, the correction model is provided with a detection label, the 3D model is convolved, pooled and fully connected to confirm the key point positions of the abnormal model, and corrected, so that the randomness of the training sample can be improved, the accuracy of abnormal detection can be effectively improved, meanwhile, the correction of the 3D model with errors can be automatically carried out, the manual correction time can be saved, the work load of staff can be reduced, the work experience of the staff can be accurately shown by the user, and the user can not feel the user can be shown.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
FIG. 1 is a system block diagram of a non-heritable heritage digital display system according to the present invention;
fig. 2 is a block flow diagram of a non-heritable heritage digital display method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments.
Example 1
Referring to fig. 1, a non-heritable heritage digital display system comprises a user terminal, a display platform, a non-heritable data collection module, a classification marking module, a feature extraction module, a 3D reconstruction module, an intelligent correction module, a voice broadcasting module and a block storage module.
The user terminal is used for being in communication connection with the display platform, displaying 3D scene animation and providing an interactive browsing function; the display platform is used for 3D reproduction of non-heritable cultural heritage and feeding the generated 3D model back to the user terminal.
It should be further noted that the user terminal specifically includes an intelligent collection, a tablet computer, a desktop computer, and a notebook computer.
The non-heritable data collection module is used for collecting known non-heritable cultural heritable data; the classification marking module is used for carrying out classification analysis on the collected non-heritable cultural heritage data and marking the collected non-heritable cultural heritage data.
Specifically, after receiving each group of non-heritable heritage data, the classification marking module classifies each group of heritage data according to a specified non-material culture heritage directory system, classifies the classified heritage data according to specific categories thereof, sorts each group of heritage data according to the order of first letters A to Z of each non-heritable heritage name, and classifies the sorted heritage data information according to text information, picture information and video information.
In the embodiment, the classification standard of the non-material cultural heritage directory system is specifically country, province, city and county; the category of non-heritable heritage specifically includes traditional spoken literature, traditional art, handwriting, music, dance, drama, melody and acrobatics, traditional skills, medicine and calendar, traditional etiquette, festival celebration folks, traditional sports and recreation, and other non-material cultural heritages as their carriers.
The feature extraction module is used for extracting feature information in the non-heritable cultural heritage data.
Specifically, the feature extraction module collects video information and picture information in each heritage data, then processes the video information frame by frame to obtain multiple groups of picture information, then carries out blocking processing according to the display proportion of each group of picture information, then removes high-frequency components in the blocked picture information through Fourier transformation, sets Gaussian smoothing filter parameters, calculates weight occupied by each filter, carries out smoothing processing on the picture information through each weighted group of anisotropic filters, carries out nonlinear transformation on the processed image, carries out weighting processing on the sum of results obtained by the nonlinear transformation to obtain final picture information, selects a window meeting the condition to move in each group of picture information, calculates a gray matrix under the window at each movement, calculates texture features in the relevant picture information from the gray co-occurrence matrix every time, then separates a target from a background according to the calculated texture features, carries out normalization processing on the target image, simultaneously extracts the characteristic data of the target image, carries out global feature of each group of picture information through acquisition network block for multiple times, carries out global feature rolling back to enable global feature of the global feature to be carried out inverse rolling operation to the global feature map, then carries out key point decoding, and then carries out two-dimensional feature decoding on the key point.
The 3D reconstruction module is used for reproducing the corresponding 3D scene and the non-genetic culture making process according to the characteristic information; the intelligent correction module is used for analyzing the non-heritable cultural heritage data and detecting and adjusting the 3D model established by the 3D reconstruction module.
Specifically, the intelligent correction module receives feature data of each group and key point information to construct a detection data set, then calculates standard deviation of the detection data set, screens abnormal data in the detection data set according to the calculated standard deviation, inputs the detection data set into a convolutional neural network, firstly carries out convolutional operation to obtain feature images meeting requirements, repeatedly adopts two groups of convolutional layers and a group of structure processing feature images of a maximum pooling layer, then carries out deconvolution operation in an expansion channel to halve dimensions of the feature images, then reconstructs 2-fold dimension feature images, then adopts two groups of convolutional layers, and repeats the structure, then maps the feature images obtained in the last layer into a 6-dimensional output feature image, collects output feature images obtained through forward propagation, converts linear prediction values of all targets into probability values through a softmax function, calculates loss values between real data and the detection probability, then updates parameters in the convolutional neural network layer by layer, calculates corresponding loss values, stops training after the loss values reach a certain threshold, simultaneously takes the parameters as optimal parameters, sets a correction model, and carries out correction on the model, and then carries out full-label correction, and the model D is connected with the model, and the model is subjected to correction model D is subjected to the correction model, and the model is subjected to the correction model position correction model is subjected to the full-label processing.
The voice broadcasting module is used for introducing various information of related non-heritable heritage to visitors; the block storage module is used for storing various collected non-heritable cultural heritage data.
Example 2
Referring to fig. 2, a non-heritable heritage digital display method specifically comprises the following steps:
non-cultural heritage data is collected and classified according to the regulations.
And extracting various non-heritable heritage characteristic information and constructing a 3D model.
Various non-heritable heritage literal descriptions are generated and matched with the 3D model.
And correcting the 3D model and correcting the result to the feedback staff model.
The user logs into the display platform and selects the relevant 3D model for viewing.
And storing each group of non-heritable heritage 3D models and optimizing and displaying the performance of the platform.
Specifically, a starting chain table is generated for each group of functional interfaces of the display platform, each group of starting chain tables is further linked according to the sequence of the LRU chain tables from less to more times of access, each group of pages in each group of starting chain tables are updated in real time according to the interaction information of each group of functional interfaces, the functional interface starting chain table with the least times of access is sequentially selected from the head of the LRU chain table to conduct victim page selection until enough victim pages are recovered, the selected victim pages are combined into a block and marked, then a compression driver is awakened to analyze the marked block, physical pages belonging to the block are obtained, the physical pages are copied into a buffer zone, then a compression algorithm is called to compress the physical pages in the buffer zone into a compression block, and the compression block is stored into a compression zone of the platform optimization module.
Claims (8)
1. The non-heritable heritage digital display system is characterized by comprising a user terminal, a display platform, a non-heritable data collection module, a classification marking module, a feature extraction module, a 3D reconstruction module, an intelligent correction module, a voice broadcasting module and a block storage module;
the user terminal is used for being in communication connection with the display platform, displaying 3D scene animation and providing an interactive browsing function;
the display platform is used for 3D reproduction of non-heritable heritage and feeding back the generated 3D model to the user terminal;
the non-genetic data collection module is used for collecting known non-genetic cultural heritage data;
the classification marking module is used for carrying out classification analysis on the collected non-heritable cultural heritage data and marking the collected non-heritable cultural heritage data;
the feature extraction module is used for extracting feature information in the non-heritable cultural heritage data;
the 3D reconstruction module is used for reproducing the corresponding 3D scene and the non-genetic culture making process according to the characteristic information;
the intelligent correction module is used for analyzing the non-heritable cultural heritage data and detecting and adjusting the 3D model established by the 3D reconstruction module;
the voice broadcasting module is used for introducing various information of related non-heritable cultural heritage to visitors;
the block storage module is used for storing various collected non-heritable cultural heritage data.
2. The non-heritable heritage digital display system of claim 1, wherein said user terminal specifically comprises an intelligent collection, a tablet computer, a desktop computer, and a notebook computer.
3. The non-heritable heritage digital display system of claim 1, wherein said classification marking module category analysis comprises the specific steps of:
step one: after receiving each group of non-heritable cultural heritage data, the classifying and marking module classifies each group of heritage data according to a specified non-material cultural heritage directory system, and classifies the classified heritage data according to specific categories of the heritage data;
step two: sorting the groups of heritage data according to the order of the first letters A to Z of the non-heritage names, and classifying the sorted groups of heritage data information according to the text information, the picture information and the video information.
4. The system of claim 3, wherein the non-material cultural heritage directory system classification criteria of step one are country, province, city and county;
the non-heritable cultural heritage category specifically comprises traditional spoken literature and languages, traditional art, handwriting, music, dance, drama, melody and acrobatics, traditional skills, medicine and calendar, traditional etiquette, festival celebration folks, traditional sports and recreation and other non-matter cultural heritages as carriers thereof.
5. The non-heritable heritage digital display system of claim 3, wherein said feature extraction module feature information specifically extracts the following steps:
step (1): the feature extraction module collects video information and picture information in each heritage data, then processes the video information frame by frame to obtain a plurality of groups of picture information, then performs blocking processing according to the display proportion of each group of picture information, and then removes high-frequency components in the blocked picture information through Fourier transformation;
step (2): setting Gaussian smoothing filter parameters, calculating weight occupied by each filter, smoothing the picture information through each weighted anisotropic filter, carrying out nonlinear transformation on the processed image, and carrying out weighting treatment on the sum of results obtained by the nonlinear transformation to obtain final picture information;
step (3): selecting windows meeting the conditions to move in each group of image information, calculating gray level co-occurrence matrixes under the windows at each time, calculating texture features in related image information from the gray level co-occurrence matrixes, separating a target from a background according to the calculated texture features, carrying out normalization processing on a target image, and extracting feature data of the target image;
step (4): the global attitude features of each group of image information are obtained through a plurality of times of ShuffleBlock of an acquisition network, then the global attitude features are returned to the key point feature map through deconvolution operation, then the key point feature map is decoded, and two-dimensional key points of the human body generated after decoding are collected.
6. The non-heritable heritage digital display system of claim 5, wherein said intelligent correction module detects and adjusts the specific steps as follows:
step I: the intelligent correction module receives the characteristic data and the key point information of each group to construct a detection data set, calculates the standard deviation of the detection data set, and screens out abnormal data in the detection data set according to the calculated standard deviation;
step II: inputting the detection data set into a convolutional neural network, performing convolutional operation to obtain a feature map meeting requirements, repeatedly adopting two groups of convolutional layers and a group of maximum pooling layers to process the feature map, performing deconvolution operation in an expansion channel to halve the dimension of the feature map, re-forming a feature map with 2 times of the dimension, adopting two groups of convolutional layers, repeating the structure, mapping the feature map obtained in the last layer into a 6-dimensional output feature map at a final output layer, collecting the output feature map obtained through forward propagation, and converting linear prediction values of all targets into probability values through a softmax function;
step III: calculating a loss value between real data and detection probability by using a loss function, updating parameters in the convolutional neural network layer by layer, calculating corresponding loss values, stopping training when the loss value reaches a certain threshold value, taking the parameters as optimal parameters, and outputting a correction model;
step IV: inputting the 3D model into a correction model, setting a detection label by the correction model, convoluting, pooling and fully connecting the 3D model, confirming the key point positions of the abnormal model, and correcting the abnormal model.
7. The non-heritable heritage digital display method is characterized by comprising the following steps of:
(1) Collecting non-heritable heritage data and classifying according to the regulations;
(2) Extracting various non-heritable heritage characteristic information and constructing a 3D model;
(3) Generating various non-heritable heritage literal descriptions and matching with the 3D model;
(4) Correcting the 3D model and feeding back a correction result of the staff model;
(5) The user logs in the display platform and selects a related 3D model for viewing;
(6) And storing each group of non-heritable heritage 3D models and optimizing and displaying the performance of the platform.
8. The method for digitally displaying non-heritable heritage according to claim 7, wherein the specific step of optimizing the performance of the display platform in the step (6) is as follows:
step I: generating a starting chain table for each group of functional interfaces of the display platform, and further linking each group of starting chain tables according to the sequence of the LRU chain tables from less to more according to the number of times each group is accessed;
step II: according to the interactive information of each group of functional interfaces, updating data of each group of pages in each group of starting linked lists in real time, sequentially selecting the functional interface starting linked list with the least accessed times from the head of the LRU linked list to select the victim page, and stopping until enough victim pages are recovered;
and III, step III: combining the selected victim page into a block and marking, waking up a compression driver program to analyze the marked block, obtaining a physical page belonging to the block, copying the physical page into a buffer area, then calling a compression algorithm to compress the physical page in the buffer area into a compression block, and storing the compression block into a compression area of a platform optimization module.
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| CN117556065A (en) * | 2024-01-11 | 2024-02-13 | 江苏古卓科技有限公司 | Deep learning-based large model data management system and method |
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| CN117556065A (en) * | 2024-01-11 | 2024-02-13 | 江苏古卓科技有限公司 | Deep learning-based large model data management system and method |
| CN117556065B (en) * | 2024-01-11 | 2024-03-26 | 江苏古卓科技有限公司 | Deep learning-based large model data management system and method |
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