Scenic Spot-based Monitoring of Cultural Relics Protection Status and Preventive Protection Method

Abstract

The present invention relates to the field of cultural relics protection, and discloses a method for monitoring the protection status and preventive protection of cultural relics based on scenic spots, which is used to solve the problem that when the cultural relics in the scenic spot have been damaged and repaired, the parameters previously selected to evaluate the status of the cultural relics will no longer be representative. The method includes: recording the images of cultural relics in real time, collecting the historical maintenance data of cultural relics, analyzing the images of cultural relics through image recognition technology to obtain the historical damage degree of cultural relics, comprehensively evaluating the re-damage coefficient of cultural relics according to the cultural relics maintenance information, collecting the real-time temperature and humidity data near the cultural relics, calculating the environmental impact coefficient, collecting the tourist carrying capacity of the scenic spot during the detection period, calculating the tourist impact coefficient, comprehensively evaluating the cultural relics damage index, and carrying out early warning management of cultural relics damage according to the cultural relics damage index, effectively realizing the real-time status monitoring of cultural relics in scenic spots and improving the accuracy of cultural relics status evaluation.

Description

Technical Field

[0001] The present invention relates to the field of cultural relics protection, and more particularly to a method for monitoring the protection status and preventive protection of cultural relics based on scenic spots. Background Art

[0002] The monitoring of the state of cultural relics is a systematic process aimed at regularly evaluating and recording various physical, chemical, and biological characteristics of cultural relics, as well as the environmental factors associated with them, in order to promptly detect potential damage, degradation, or other problems and take necessary measures for protection and maintenance. Monitoring the state of cultural relics can help detect potential damage or destruction at an early stage, take necessary protection measures to avoid further damage to cultural relics, and rationally allocate human, material, and financial resources based on the monitoring results of the state of cultural relics to improve management efficiency.

[0003] The existing methods for monitoring the state of cultural relics and preventive protection are to monitor the state of cultural relics through physical detection, chemical analysis, and environmental detection of cultural relics. However, this detection method will lead to the problem that when the cultural relics in the scenic spot have been damaged and repaired before, the parameters previously selected to evaluate the state of the cultural relics are no longer representative.

[0004] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method for monitoring the protection status and preventive protection of cultural relics based on scenic spots to solve the problems existing in the above-mentioned background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for monitoring the protection status and preventive protection of cultural relics based on scenic spots, comprising the following steps:

[0008] Set up a high-definition camera device near the cultural relics in the scenic spot for real-time video recording, collect images of the cultural relics during each maintenance, and save them in the maintenance status database;

[0009] Collect historical maintenance data of the cultural relics, the maintenance data including the start time of maintenance, the end time of maintenance, maintenance personnel information, and cultural relic status images, and the maintenance personnel information including name, obtained professional certificates, and working age;

[0010] Collect the images of the cultural relics during the last maintenance from the maintenance status database, analyze the cultural relic images through image recognition technology to obtain the historical damage degree of the cultural relics;

[0011] Calculate the professional degree of the maintenance personnel according to the information of the maintenance personnel, and its calculation formula is PL = log(NUM C((PL + 1) × log(AE + 1)), where PL represents the professional level of the maintenance personnel, NUM C represents the number of professional certificates obtained by the maintenance personnel, and AE represents the working age of the maintenance personnel;

[0012] Calculate the time used for the most recent maintenance based on the maintenance data. The calculation formula is MT = T 结束 - T 开始 , where MT represents the time used for the most recent maintenance, T 结束 represents the end time of the maintenance, and T 开始 represents the start time of the maintenance;

[0013] Calculate the coefficient of the cultural relics' re - damage based on the damage degree of historical cultural relics, the professional level of maintenance personnel, and the time used for the most recent maintenance. The calculation formula is where DA represents the coefficient of the cultural relics' re - damage, ED represents the damage degree of historical cultural relics, MT represents the time used for the previous maintenance, and PL represents the professional level of the maintenance personnel;

[0014] Collect the real - time temperature and humidity data near the cultural relics, calculate the real - time temperature change rate and humidity change rate, and calculate the environmental impact coefficient based on the real - time temperature change rate and humidity change rate;

[0015] Collect the tourist carrying capacity of the scenic area during the detection period and calculate the tourist impact coefficient;

[0016] Comprehensively evaluate and calculate the cultural relics damage index based on the coefficient of the cultural relics' re - damage, the environmental impact coefficient, and the tourist impact coefficient. The calculation formula is HS = a1×DA + a2×EI + a3×TI, where HS represents the cultural relics damage index, DA represents the coefficient of the cultural relics' re - damage, EI represents the environmental impact coefficient, TI represents the tourist impact coefficient, and a1, a2, a3 represent the weight coefficients of the coefficient of the cultural relics' re - damage, the environmental impact coefficient, and the tourist impact coefficient;

[0017] Conduct early warning of cultural relics damage according to the cultural relics damage index, and remind the staff to repair and manage the cultural relics in time.

[0018] Preferably, the steps of analyzing the cultural relics image through image recognition technology to obtain the damage degree of historical cultural relics are as follows:

[0019] Collect the cultural relics image during the previous maintenance, and use the edge detection method to separate the cultural relics body image from the background;

[0020] Extract the separated cultural relics body image, divide the image into n equal parts on average, and record them as sub - cultural relics body images;

[0021] Using image recognition technology, identify the number of damages in each sub-cultural relic ontology image, which is recorded as the damage quantity dataset. The number of damages in the damage quantity dataset is the data point;

[0022] Perform clustering processing on the number of damages in each sub-cultural relic ontology image, and calculate the damage degree of historical cultural relics according to the clustering result.

[0023] Preferably, the step of separating the cultural relic ontology image from the background using the edge detection method is as follows:

[0024] Convert the color image to a grayscale image and perform Gaussian blur on the image;

[0025] Use the Sobel operator to calculate the gradients of the image in the x and y directions, convolve the Sobel operator with the grayscale image to obtain the gradient of the image in the x direction, and its calculation formula is I x =I gray *G x where I x represents the gradient of the image in the x direction, I y represents the gradient of the image in the y direction, I gray represents the input grayscale image, and * represents the convolution operation;

[0026] According to the horizontal and vertical gradients, calculate the gradient magnitude of each pixel, and its calculation formula is Calculate the gradient direction of each pixel, and its calculation formula is

[0027] Quantize the gradient direction, set a high threshold and a low threshold, and use double-threshold detection to divide the edges into strong edges and weak edges;

[0028] Traverse the image, for each pixel marked as a weak edge, check its neighboring pixels. If at least one neighboring pixel is a strong edge, then retain the weak edge pixel as an edge. If the weak edge pixel is not connected to any strong edge, then set it to 0, ensure that the weak edge is connected to the strong edge, remove the isolated weak edges, and obtain the edge line between the cultural relic ontology image and the background

[0029] Preferably, the step of using image recognition technology to identify the number of damages in each sub-cultural relic ontology image is as follows:

[0030] Collect a dataset containing damaged and undamaged sub-cultural relic ontology images, annotate the collected images, annotate the categories of damaged or not, preprocess the damaged pictures, and divide the preprocessed damaged pictures into a training set, a validation set, and a test set;

[0031] Use the annotated dataset to train the convolutional neural network model to obtain the final model;

[0032] Use the final model to identify each sub-cultural relic ontology image, and obtain the number of damaged areas in each sub-cultural relic ontology image according to the identification results.

[0033] Preferably, the steps of training the convolutional neural network maze using the labeled data set are as follows:

[0034] Step 1: Define the initial model;

[0035] Step 2: Input the images in the training set into the model to obtain the prediction results, and calculate the loss function value according to the prediction results and the true labels. The calculation formula is where Loss represents the loss function value, represents the label predicted by the model, and y i represents the true label, and N represents the number of samples;

[0036] Step 3: Use the backpropagation algorithm to update the model parameters to gradually reduce the loss function value, and use the stochastic gradient descent method to update the model parameters;

[0037] Step 4: Repeat Step 3 until the loss function converges;

[0038] Step 5: Use the validation set to evaluate the trained model, evaluate the accuracy, precision, recall, and F1 score metrics of the model, calculate the model accuracy according to the evaluation results, compare the model accuracy with the preset threshold. If the model accuracy is greater than the preset threshold, output the current model as the final model. If the model accuracy is less than the preset threshold, return to Step 2 to continue model training.

[0039] Preferably, the steps of clustering the number of damages in each sub-cultural relic ontology image and calculating the damage degree of historical cultural relics according to the clustering results are as follows:

[0040] Step 1: Use the elbow method to determine the optimal number of clustering clusters;

[0041] Step 2: Use the K-means clustering method to cluster the number of damages in each sub-cultural relic ontology image;

[0042] Step 3: According to the optimal number of clustering clusters, randomly select the corresponding number of data points from the data points as the initial clustering centers of the clustering. Calculate the distance between each sample and each initial clustering center point using the Euclidean distance, and assign the data points to the cluster to which the nearest clustering center belongs;

[0043] Step 4: For each cluster, calculate the mean of all its data points and use this mean as the new clustering center;

[0044] Step 5: Repeat Steps 3 and 4 until the cluster centers no longer change, and obtain the final clustering clusters and the final cluster centers;

[0045] Step 6: Count the number of data points in each final clustering cluster, calculate the ratio of the number of data points in each final clustering cluster to the total number of data points in the dataset, and obtain the weight coefficient of each final clustering cluster;

[0046] Step 7: Calculate the degree of damage to historical relics based on the cluster centers and weight coefficients of each final clustering cluster.

[0047] Preferably, the calculation steps of the environmental impact coefficient are as follows:

[0048] Set the detection time period, collect the temperature data at the current time point and the time points before the monitoring time period, and calculate the temperature change rate within the detection time period;

[0049] Collect the humidity data at the current time point and the time points before the monitoring time period, and calculate the humidity change rate within the detection time period;

[0050] Use the maximum-minimum normalization method to convert the temperature change rate and the humidity change rate into values between 0 and 1;

[0051] Calculate the environmental impact coefficient based on the normalized temperature change rate and humidity change rate.

[0052] Preferably, the steps of collecting the tourist carrying capacity in the scenic area during the detection time period and calculating the tourist impact coefficient are as follows:

[0053] Taking the unit buildings in the scenic area as the calculation unit, the unit buildings include halls, roads, and courtyards. Obtain the total area of the unit buildings, take a set proportion of the total area of the unit buildings as the effective area, and calculate the instantaneous carrying capacity of the unit buildings. The calculation formula is CY 瞬时 = GA × 80% × QP, where CY 瞬时 represents the instantaneous carrying capacity, GA represents the total area of the unit buildings, and QP represents the number of tourists carried per unit area;

[0054] Obtain the effective opening hours of the scenic area every day and the average visiting time of each tourist in the scenic area, and calculate the daily average turnover rate. The calculation formula is where DT represents the daily average turnover rate, T 开放 represents the effective opening hours of the scenic area every day, and T 游览 represents the average visiting time of each tourist in the scenic area;

[0055] Calculate the maximum carrying capacity based on the instantaneous carrying capacity and the daily average turnover rate. The calculation formula is CY 最大 = CY 瞬时×DT, where CY 最大 is expressed as the maximum carrying capacity, CY 瞬时 is expressed as the instantaneous carrying capacity, and DT is expressed as the daily average turnover rate;

[0056] Monitor the real-time number of tourists in the scenic area, and cumulatively count the real-time cumulative number of tourists on the same day. Calculate the tourist impact coefficient based on the real-time number of tourists, the real-time cumulative number of tourists, the instantaneous carrying capacity, and the maximum carrying capacity. The calculation formula is TI = (CY 瞬时 - NUM 实时 ) + (CY 最大 - NUM 累计 ), where TI is expressed as the tourist impact coefficient, CY 瞬时 is expressed as the instantaneous carrying capacity, NUM 实时 is expressed as the real-time number of tourists, CY 最大 is expressed as the maximum carrying capacity, and NUM 累计 is expressed as the real-time cumulative number of tourists.

[0057] Preferably, the steps for warning against cultural relic damage based on the cultural relic damage index are as follows: Compare the cultural relic damage index with a preset threshold. If the cultural relic damage index is less than the preset threshold, it is determined that the current degree of cultural relic damage is small and no warning is processed. If the cultural relic damage index is greater than the preset threshold, it is determined that the current degree of cultural relic damage is large and a warning is processed.

[0058] The technical effects and advantages of the present invention:

[0059] Record the images of cultural relics in real time, collect the historical maintenance data of cultural relics, analyze the images of cultural relics through image recognition technology to obtain the historical damage degree of cultural relics, comprehensively evaluate the cultural relics to obtain the coefficient of re-damage of cultural relics according to the cultural relic maintenance information, collect the real-time temperature and humidity data near the cultural relics, calculate the environmental impact coefficient, collect the tourist carrying capacity of the scenic area during the detection period, calculate the tourist impact coefficient, comprehensively evaluate to obtain the cultural relic damage index, and conduct warning management of cultural relic damage based on the cultural relic damage index, effectively realizing the real-time status monitoring of cultural relics in the scenic area and improving the accuracy of cultural relic status evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is the overall flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0061] The following will clearly and completely describe the technical solutions in the present invention in conjunction with the accompanying drawings in the present invention. Additionally, the forms of each structure described in the following embodiments are merely examples. The method for monitoring the protection status and preventive protection of cultural relics based on scenic spots in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0062] The present invention provides a method for monitoring the protection status and preventive protection of cultural relics based on scenic spots, including the following steps:

[0063] Set up a high-definition camera device near the cultural relics in the scenic spot for real-time video recording, collect images of the cultural relics during each maintenance, and save them in the maintenance status database;

[0064] Collect historical maintenance data of the cultural relics. The maintenance data includes the start time of maintenance, the end time of maintenance, maintenance personnel information, and the image of the cultural relic status. The maintenance personnel information includes name, obtained professional certificates, and working age;

[0065] Collect the images of the cultural relics during the last maintenance from the maintenance status database, analyze the images of the cultural relics through image recognition technology to obtain the historical damage degree of the cultural relics. The image recognition technology is a technology that uses computer vision and artificial intelligence algorithms to analyze and understand the content of images. It has important applications in the protection of cultural relics, especially in evaluating the damage degree of historical cultural relics;

[0066] Calculate the professional degree of the maintenance personnel according to the information of the maintenance personnel. The calculation formula is PL = log(NUM C + 1) × log(AE + 1), where PL represents the professional degree of the maintenance personnel, NUM C represents the number of professional certificates obtained by the maintenance personnel, and AE represents the working age of the maintenance personnel;

[0067] Calculate the time used for the last maintenance according to the maintenance data. The calculation formula is MT = T 结束 - T 开始 , where MT represents the time used for the last maintenance, T 结束 represents the end time of maintenance, and T 开始 represents the start time of maintenance. If the damage to the cultural relics is very serious, such as structural damage, extensive corrosion or erosion, then the time required for maintenance is usually longer because the repair process may require more steps, more complex technologies, and more meticulous work;

[0068] Calculate the coefficient of re-damage of the cultural relics according to the historical damage degree of the cultural relics, the professional degree of the maintenance personnel, and the time used for the last maintenance. The calculation formula is Among them, DA represents the coefficient of re - damage to cultural relics, ED represents the degree of damage to historical cultural relics. The degree of damage to historical cultural relics is used as the numerator, representing the initial risk of damage. MT represents the time used for the last maintenance. The square root of the time used for maintenance reflects the role of maintenance time in reducing the risk of re - damage. However, considering the diminishing marginal effect of time on risk reduction, the square root is used to smooth this impact. PL represents the professional level of maintenance personnel. The professional level of maintenance personnel is used as the denominator, indicating that the higher the professional level, the lower the risk of re - damage;

[0069] Collect the real - time temperature and humidity data near the cultural relics, calculate the real - time change rates of temperature and humidity, and calculate the environmental impact coefficient based on the real - time temperature and humidity data and the change rates of temperature and humidity;

[0070] Collect the tourist carrying capacity of the scenic area during the detection period and calculate the tourist impact coefficient;

[0071] Comprehensively evaluate and calculate the cultural relic damage index based on the coefficient of re - damage to cultural relics, environmental impact coefficient, and tourist impact coefficient. Its calculation formula is HS = a1×DA + a2×EI + a3×TI, where HS represents the cultural relic damage index, DA represents the coefficient of re - damage to cultural relics. The coefficient of re - damage to cultural relics represents the past damage situation of cultural relics. The past damage situation of cultural relics has a certain impact on its current state. Cultural relics that have suffered greater historical damage may be more vulnerable to further damage. The environmental conditions where cultural relics are located have an important impact on the degree of their damage. If cultural relics are in harsh environmental conditions for a long time, such as high humidity, high temperature, or strong ultraviolet radiation, then their degree of historical damage may be higher, and there may be a closer relationship with the current damage index. EI represents the environmental impact coefficient. The environmental conditions where cultural relics are located (such as temperature, humidity, light, etc.) are important factors for cultural relic damage. Harsh environmental conditions may accelerate the corrosion, fading, or physical damage process of cultural relics, resulting in an increase in the damage index of cultural relics. If the environmental impact coefficient is higher, it means that the environmental conditions have a greater impact on the damage of cultural relics, and the damage index of cultural relics will also increase accordingly. TI represents the tourist impact coefficient. Tourist activities may cause various damages to cultural relics, such as touching, friction, trampling, collision, etc. These activities may cause damages such as wear, scratches, and fading on the surface of cultural relics, thereby affecting the integrity and beauty of cultural relics. The tourist impact coefficient can be regarded as a quantitative indicator of the direct impact degree of tourist activities on the cultural relic damage index. If the tourist impact coefficient is higher, it means that tourist activities have a greater impact on the damage of cultural relics, and the damage index of cultural relics will also increase accordingly. a1, a2, a3 represent the weight coefficients of the coefficient of re - damage to cultural relics, environmental impact coefficient, and tourist impact coefficient, and in this embodiment, the specific values of a1, a2, a3 are not specifically calculated;

[0072] Based on the cultural relic damage index, issue a warning for cultural relic damage to remind the staff to promptly carry out cultural relic restoration and management.

[0073] In this embodiment, it should be specifically noted that the steps of analyzing the cultural relic image through image recognition technology to obtain the damage degree of historical cultural relics are as follows:

[0074] Collect the cultural relic image during the last maintenance. Use the edge detection method to separate the cultural relic body image from the background. The edge detection method is a technology used in image processing and computer vision to identify areas where pixel values change significantly in an image. These areas usually correspond to the boundaries of objects. Edge detection is particularly useful in segmenting the cultural relic body from the background and can help accurately extract the contour of the cultural relic. Commonly used edge detection algorithms include the Sobel operator, Canny operator, Prewitt operator, and Laplacian operator;

[0075] Extract the separated cultural relic body image and divide the image into n equal parts, denoted as sub-cultural relic body images;

[0076] Use image recognition technology to identify the number of damages in each sub-cultural relic body image, denoted as the damage quantity dataset. The number of damages in the damage quantity dataset is the data point;

[0077] Perform clustering processing on the number of damages in each sub-cultural relic body image and calculate the damage degree of historical cultural relics based on the clustering results.

[0078] In this embodiment, it should be specifically noted that the steps of using the edge detection method to separate the cultural relic body image from the background are as follows:

[0079] Convert the color image to a grayscale image to simplify the calculation and reduce the processing complexity. The conversion formula is I gray = 0.299×R + 0.587×G + 0.114×B, where I gray represents the grayscale image, and R, G, B are the red, blue, and green channel values of the image respectively;

[0080] Perform Gaussian blur on the image to reduce image noise. The Gaussian blur expression is where x represents the horizontal coordinate of the pixel point relative to the center of the Gaussian kernel, y represents the vertical coordinate of the pixel point relative to the center of the Gaussian kernel, G(x,y) represents the value of the Gaussian function at the pixel point (x,y), σ represents the standard deviation of the Gaussian function, which determines the degree of Gaussian blur, and 2πσ 2 represents the normalization factor to ensure that the sum of the Gaussian kernel is 1;

[0081] Calculate the gradients of the image in the x and y directions using the Sobel operator. The Sobel operator consists of two 3x3 convolution kernels, one for calculating the horizontal gradient G x and the other for calculating the vertical gradient G y , where the horizontal gradient is Vertical gradient

[0082] Convolve the Sobel operator with the grayscale image to obtain the gradient of the image in the x direction. Its calculation formula is I x = I gray * G x , where I x represents the gradient of the image in the x direction, I y represents the gradient of the image in the y direction I gray represents the input grayscale image, and * represents the convolution operation;

[0083] According to the horizontal and vertical gradients, calculate the gradient magnitude of each pixel. Its calculation formula is Calculate the gradient direction of each pixel. Its calculation formula is

[0084] Quantize the gradient direction into four main directions (0°, 45°, 90°, 135°). These directions correspond to eight directions (horizontal, vertical, and two diagonal directions) in the gradient image. Compare the gradient magnitude of the current pixel with the gradient magnitudes of its two neighboring pixels along the gradient direction. If the current pixel is not a local maximum, set its gradient magnitude to 0;

[0085] Set high and low thresholds, and use double-threshold detection to divide the edges into strong edges and weak edges;

[0086] Traverse the image. For each pixel marked as a weak edge, check its 8 neighboring pixels. If at least one neighboring pixel is a strong edge, retain the weak edge pixel as an edge. If the weak edge pixel is not connected to any strong edge, set it to 0 to ensure that weak edges are connected to strong edges and remove isolated weak edges.

[0087] In this embodiment, it should be specifically noted that the steps for using image recognition technology to identify the number of damages in each sub-cultural relic ontology image are as follows:

[0088] Collect a dataset containing damaged and undamaged sub-cultural relic ontology images, annotate the collected images to label the categories of damaged or not, preprocess the damaged images. The preprocessing includes grayscaling, denoising, and edge detection operations, and divide the preprocessed damaged images into a training set, a validation set, and a test set;

[0089] Train a convolutional neural network model using the labeled dataset to obtain the final model. The convolutional neural network model is a deep learning model specialized for tasks dealing with data having a grid structure, such as image recognition, image classification, object detection, etc. The convolutional neural network model has achieved great success in the field of image processing and has taken the leading position in many computer vision tasks;

[0090] Use the final model to identify each sub-cultural relic ontology image, and obtain the number of damaged areas in each sub-cultural relic ontology image according to the identification results.

[0091] In this embodiment, it should be specifically noted that the steps of training the convolutional neural network maze using the labeled dataset are as follows:

[0092] Step 1: Define structures such as convolutional layers, pooling layers, fully connected layers, etc., and determine the activation function and loss function, and initialize the weight and bias parameters of the model;

[0093] Step 2: Input the images of the training set into the model to obtain the prediction results, and calculate the loss function value according to the prediction results and the true labels. The calculation formula is where Loss represents the loss function value, represents the label predicted by the model, y i represents the true label, and N represents the number of samples;

[0094] Step 3: Use the backpropagation algorithm to update the model parameters to gradually reduce the loss function value, and use the stochastic gradient descent method to update the parameters of the model;

[0095] Step 4: Repeat Step 3 until the loss function converges;

[0096] Step 5: Use the validation set to evaluate the trained model, evaluate the accuracy, precision, recall, and F1 score metrics of the model, calculate the model accuracy according to the evaluation results. The calculation formula is ZQ = f(Z1, Z2, Z3, Z4), where ZQ represents the model accuracy, and Z1, Z2, Z3, Z4 represent the accuracy, precision, recall, and F1 score metrics of the model. Compare the model accuracy with the preset threshold. If the model accuracy is greater than the preset threshold, output the current model as the final model. If the model accuracy is less than the preset threshold, return to Step 2 to continue training the model. The calculation of the accuracy, precision, recall, and F1 score of the model is prior art, and this embodiment does not specifically describe the specific calculation steps.

[0097] In this embodiment, it should be specifically noted that the steps of clustering the number of damages in each sub-cultural relic ontology image and calculating the damage degree of historical cultural relics according to the clustering results are as follows:

[0098] Step 1: Use the elbow method to determine the optimal number of clustering clusters. The elbow method is a heuristic method for determining the optimal number of clusters in a clustering algorithm;

[0099] Step 2: Use the K-means clustering method to cluster the number of damages in each sub-cultural relic ontology image. The K-means clustering method is a commonly used clustering algorithm for dividing a data set into K different categories. The algorithm optimizes the clustering results by iteratively assigning data points to the nearest cluster center and updating the cluster center;

[0100] Step 3: According to the optimal number of clustering clusters, randomly select the corresponding number of data points from the data points as the initial cluster centers of the clustering. Use the Euclidean distance to calculate the distance between each sample and each initial cluster center point. For example, calculate the distance d(x i and the initial cluster center c j ), and its calculation formula is i ,c j ), where x represents the value of the data point x ik in the k-th clustering cluster, c i represents the value of the cluster center c jk in the k-th clustering cluster, n represents the number of features of the data point x j and the cluster center c i , and assign the data point to the cluster to which the nearest cluster center belongs. In the K-means clustering algorithm, the Euclidean distance is commonly used to measure the distance between each sample and the cluster center point. Calculating the Euclidean distance between each sample and each initial cluster center point can help determine the nearest cluster center to which each sample belongs, so as to perform clustering assignment; j

[0101] Step 4: For each cluster, calculate the mean value of all its data points and use this mean value as the new cluster center. Its calculation formula is where S j represents the set of data points in the j-th cluster, c' j represents the new cluster center of the j-th cluster, |S j | represents the number of data points in the j-th cluster, and x i represents the i-th data point;

[0102] Step 5: Repeat Steps 3 and 4 until the cluster centers no longer change, and obtain the final clustering clusters and the final cluster centers;

[0103] ​Step 6: Count the number of data points in each final clustering cluster, calculate the ratio of the number of data points in each final clustering cluster to the total number of data points in the dataset to obtain the weight coefficient of each final clustering cluster, and its calculation formula is where X j represents the weight coefficient of the j-th final clustering cluster, NUM 总 represents the total number of data points in the dataset, NUM j represents the number of data points in the j-th final clustering cluster;

[0104] Step 7: Calculate the damage degree of historical relics based on the clustering center and weight coefficient of each final clustering cluster, and its calculation formula is where ED represents the damage degree of historical relics, X j represents the weight coefficient of the j-th final clustering cluster, L j represents the j-th final clustering center.

[0105] In this embodiment, it should be specifically noted that the calculation steps of the environmental impact coefficient are as follows:

[0106] Set the detection time period, collect the temperature data at the current time point and the time point before the monitoring time period, and calculate the temperature change rate within the detection time period. Its calculation formula is where CT represents the temperature change rate, C2 represents the temperature data at the current time point, C1 represents the temperature data at the time point before the monitoring time period, and T represents the detection time period;

[0107] Collect the humidity data at the current time point and the time point before the monitoring time period, and calculate the humidity change rate within the detection time period. Its calculation formula is where CH represents the humidity change rate, H2 represents the humidity data at the current time point, H1 represents the humidity data at the time point before the monitoring time period, and T represents the detection time period;

[0108] Use the maximum-minimum normalization method to convert the temperature change rate and humidity change rate into values between 0 and 1. The maximum-minimum normalization is a data preprocessing technique used to linearly transform data into a specified range, usually between 0 and 1. This method ensures that the relative size of the data remains unchanged, but scales the actual range of the data. Since the normalization process converts the data into unitless values, it eliminates the influence of different dimensions on the data analysis results;

[0109] Calculate the environmental impact coefficient based on the normalized temperature change rate and humidity change rate. Its calculation formula is where EI represents the environmental impact coefficient, CT represents the temperature change rate, and CH represents the humidity change rate.

[0110] In this embodiment, it should be specifically noted that for the tourist carrying capacity of the scenic area during the collection and detection time period, the steps for calculating the tourist influence coefficient are as follows:

[0111] Taking the unit building of the scenic area as the calculation unit, the unit building includes halls, roads, courtyards, etc. Obtain the total area of the unit building, take 80% of the total area of the unit building as the effective area, and calculate the instantaneous carrying capacity of the unit building. Its calculation formula is CY 瞬时 = GA × 80% × QP, where CY 瞬时 represents the instantaneous carrying capacity, GA represents the total area of the unit building, QP represents the number of tourists carried per unit area. In this embodiment, QP is taken as 1.1 m 2 / person;

[0112] Obtain the effective opening time of the scenic area every day and the average visiting time of each tourist in the scenic area, and calculate the daily average turnover rate. Its calculation formula is where DT represents the daily average turnover rate, T 开放 represents the effective opening time of the scenic area every day, T 游览 represents the average visiting time of each tourist in the scenic area;

[0113] Calculate the maximum carrying capacity based on the instantaneous carrying capacity and the daily average turnover rate. Its calculation formula is CY 最大 = CY 瞬时 × DT, where CY 最大 represents the maximum carrying capacity, CY 瞬时 represents the instantaneous carrying capacity, and DT represents the daily average turnover rate;

[0114] Monitor the real-time number of tourists in the scenic area, and cumulatively count the real-time cumulative number of tourists on the same day. Evaluate and calculate the tourist influence coefficient based on the real-time number of tourists, the real-time cumulative number of tourists, the instantaneous carrying capacity, and the maximum carrying capacity. Its calculation formula is TI = (CY 瞬时 - NUM 实时 ) + (CY 最大 - NUM 累计 ), where TI represents the tourist influence coefficient, CY 瞬时 represents the instantaneous carrying capacity, NUM 实时 represents the real-time number of tourists, CY 最大 represents the maximum carrying capacity, and NUM 累计 represents the real-time cumulative number of tourists.

[0115] The tourist impact coefficient provides real-time data, enabling managers to promptly detect situations of excessive tourists in the scenic area and prevent overcrowding. Excessive tourists may cause damage to cultural relics and the natural environment. By monitoring and regulating the number of tourists in real time, such damage can be reduced. A high number of tourists may have a negative impact on the ecological environment of the scenic area, and timely adjustment can protect the natural resources of the scenic area. It provides data support for scenic area managers to help them make more scientific and effective management decisions. By analyzing historical tourist impact coefficient data, the patterns and trends of tourist flow can be discovered for more long-term planning and resource allocation.

[0116] Evaluating and controlling the tourist carrying capacity can prevent a large number of tourists from gathering near cultural relics in a short period of time, reducing physical damage caused by behaviors such as collisions and frictions. Appropriately restricting the number of tourists can reduce the frequency of tourists directly contacting cultural relics and reduce the damage to cultural relics caused by behaviors such as touching and taking pictures. Excessive tourists may bring problems such as garbage and air pollution. Evaluating and controlling the carrying capacity can mitigate the impact of such pollution on cultural relics. A large number of tourists may cause changes in humidity and temperature within the scenic area, having an adverse impact on the preservation environment of cultural relics. Appropriately controlling the number of tourists can better maintain the environmental stability of cultural relics.

[0117] Excessive tourists may lead to frequent ground vibrations, causing long-term hidden damage to the structure of cultural relics. By evaluating and controlling the number of tourists, such impact can be reduced. The gathering of a large number of tourists may cause additional stress on the bearing structures of cultural relics (such as ancient bridges and ancient buildings). Appropriately restricting the number of tourists can protect these structures from excessive stress. Evaluating the tourist carrying capacity can avoid the overuse and over-visiting of cultural relics, ensuring that cultural relics get sufficient rest and protection.

[0118] In this embodiment, it should be specifically noted that the step of warning about cultural relic damage according to the cultural relic damage index is to compare the cultural relic damage index with a preset threshold. If the cultural relic damage index is less than the preset threshold, it is determined that the current degree of cultural relic damage is small and no warning is processed. If the cultural relic damage index is greater than the preset threshold, it is determined that the current degree of cultural relic damage is large and warning is processed.

[0119] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

[0120] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or replacements, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claimed rights.

Claims

1. A method for monitoring and preventing cultural relics in scenic areas, characterized in that: The following steps are involved: Set up high-definition video cameras near the cultural relics in the scenic area to record in real time, collect images of the cultural relics every time they are maintained, and save them in the maintenance status database; Collecting historical maintenance data of cultural relics, the maintenance data including maintenance start time, maintenance end time, maintenance personnel information and cultural relic status image, the maintenance personnel information including name, obtained professional certificate and working age; Collect the images of cultural relics during the last maintenance from the maintenance status database, analyze the images of cultural relics through image recognition technology, and obtain the degree of damage of historical cultural relics; The professional level of the maintenance personnel is calculated based on the maintenance personnel's information. The calculation formula is PL = log (NUM C +1)×log(AE+1), where PL represents the professional level of the maintenance personnel, NUM C It represents the number of professional certificates obtained by the maintenance personnel, and AE represents the maintenance personnel’s working age; Calculate the time taken for the most recent maintenance based on the maintenance data. The calculation formula is MT = T 结束 -T 开始 , where MT represents the time taken for the most recent maintenance. T End indicates the maintenance end time. T Start indicates the maintenance start time; The re-damage coefficient of cultural relics is calculated based on the degree of damage to the historical relics, the professional level of the maintenance personnel, and the time taken for the most recent maintenance. The calculation formula is: DA is the re-damage coefficient of cultural relics, ED is the degree of damage to historical cultural relics, MT is the time taken for the last maintenance, and PL is the professional level of the maintenance personnel. Collect real-time temperature and humidity data near the cultural relics, calculate the real-time temperature change rate and humidity change rate, and calculate the environmental impact coefficient based on the real-time temperature change rate and humidity change rate; Collect the tourist carrying capacity of the scenic spot during the detection period and calculate the tourist impact coefficient; The cultural relic damage index is calculated based on the comprehensive evaluation of the cultural relic re-damage coefficient, environmental impact coefficient and tourist impact coefficient. The calculation formula is HS = a1 × DA + a2 × EI + a3 × TI, where HS represents the cultural relic damage index, DA represents the cultural relic re-damage coefficient, EI represents the environmental impact coefficient, TI represents the tourist impact coefficient, and a1, a2, and a3 represent the weight coefficients of the cultural relic re-damage coefficient, the environmental impact coefficient and the tourist impact coefficient. Issue cultural relics damage warnings based on the cultural relics damage index to remind staff to carry out cultural relics repair and management in a timely manner.

2. The method for monitoring and preventing cultural relics in scenic areas according to claim 1 is characterized in that: The steps of analyzing the cultural relic images by image recognition technology to obtain the degree of damage of the historical cultural relics are as follows: Collect the image of the cultural relic at the time of the last maintenance, and use the edge detection method to separate the image of the cultural relic from the background; Extract the separated cultural relic main body image, divide the image into n parts equally, and record them as sub-cultural relic main body images; Using image recognition technology, the number of damages in each sub-cultural relic body image is identified and recorded as a damage number data set, and the number of damages in the damage number data set is a data point; The number of damaged parts in each sub-cultural relic body image is clustered, and the degree of damage to the historical cultural relic is calculated based on the clustering results.

3. The method for monitoring and preventing the protection of cultural relics in scenic areas according to claim 2 is characterized in that: The steps of separating the cultural relic body image from the background using the edge detection method are as follows: Convert the color image to a grayscale image and perform Gaussian blur on the image; The Sobel operator is used to calculate the gradient of the image in the x and y directions. The Sobel operator is convolved with the grayscale image to obtain the gradient of the image in the x direction. The calculation formula is I x =I gray *G x , where I x It is represented as the gradient of the image in the x direction, and * is represented as the convolution operation; According to the horizontal and vertical gradients, the gradient amplitude of each pixel is calculated, and the calculation formula is: Calculate the gradient direction of each pixel, and the calculation formula is Among them, I y Represented as the gradient of the image in the y direction I gray Represented as the input grayscale image; Quantify the gradient direction, set high threshold and low threshold, and use double threshold detection to divide the edges into strong edges and weak edges; Traverse the image and check the neighboring pixels for each pixel marked as a weak edge. If at least one neighboring pixel is a strong edge, keep the weak edge pixel as an edge. If the weak edge pixel is not connected to any strong edge, set it to 0 to ensure that the weak edge is connected to the strong edge. Remove the isolated weak edges and obtain the edge line between the artifact image and the background.

4. The method for monitoring and preventing the protection of cultural relics in scenic areas according to claim 2 is characterized in that: The steps of using image recognition technology to identify the amount of damage in each sub-cultural relic body image are as follows: Collect a dataset containing damaged and undamaged sub-cultural relics images, annotate the collected images, mark the categories of damage or not, preprocess the damaged images, and divide the preprocessed damaged images into training set, verification set and test set; Use the labeled data set to train the convolutional neural network model to obtain the final model; The final model is used to identify each sub-cultural relic body image, and the number of damaged areas in each sub-cultural relic body image is obtained based on the identification results.

5. The method for monitoring and preventing the protection of cultural relics in scenic areas according to claim 4 is characterized in that: The steps of training the convolutional neural network model using the labeled data set are: Step 1: Define the initialization model; Step 2: Input the images of the training set into the model to obtain the prediction results, and calculate the loss function value based on the prediction results and the true labels. The calculation formula is: Where Loss is the loss function value. Represents the label predicted by the model, y i Represents the true label, and N represents the number of samples; Step 3: Use the back propagation algorithm to update the model parameters so that the loss function value gradually decreases, and use the stochastic gradient descent method to update the model parameters; Step 4: Repeat step 3 until the loss function converges; Step 5: Use the validation set to evaluate the trained model, evaluate the model's accuracy, precision, recall rate, and F1 score indicators, calculate the model accuracy based on the evaluation results, and compare the model accuracy with the preset threshold. If the model accuracy is greater than the preset threshold, output the current model as the final model. If the model accuracy is less than the preset threshold, return to step 2 to continue model training.

6. The method for monitoring and preventing the protection of cultural relics in scenic areas according to claim 2 is characterized in that: The steps of clustering the number of damages in each sub-cultural relic body image and calculating the degree of damage to the historical cultural relic based on the clustering result are as follows: Step 1: Use the elbow method to determine the optimal number of clusters; Step 2: Use K-means clustering method to cluster the number of damages in each sub-cultural relic body image; Step 3: According to the optimal number of clusters, randomly select the corresponding number of data points from the data points as the initial cluster centers of the cluster, use the Euclidean distance to calculate the distance between each sample and each initial cluster center point, and assign the data points to the cluster to which the nearest cluster center belongs; Step 4: For each cluster, calculate the mean of all its data points and use the mean as the new cluster center; Step 5: Repeat steps 3 and 4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center; Step 6: Count the number of data points in each final cluster, calculate the ratio of the number of data points in each final cluster to the total number of data points in the data set, and obtain the weight coefficient of each final cluster; Step 7: Calculate the degree of damage to historical relics based on the cluster center and weight coefficient of each final cluster.

7. The method for monitoring and preventing cultural relics in scenic areas according to claim 1 is characterized in that: The environmental impact coefficient calculation steps are: Set the detection time period, collect temperature data at the current time point and the time point before the monitoring time period, and calculate the temperature change rate within the detection time period; Collect humidity data at the current time point and at the time point before the monitoring period, and calculate the humidity change rate during the detection period; The temperature change rate and humidity change rate are converted into values ​​between 0 and 1 using the maximum-minimum normalization method; The environmental impact coefficient is calculated based on the standardized temperature change rate and humidity change rate.

8. The method for monitoring and preventing cultural relics in scenic areas according to claim 1 is characterized in that: The steps of collecting the tourist carrying capacity of the scenic spot during the detection period and calculating the tourist impact coefficient are as follows: The unit building of the scenic area is used as the calculation unit. The unit building includes halls, roads and courtyards. The total area of ​​the unit building is obtained, and the set proportion of the total area of ​​the unit building is taken as the effective area. The instantaneous carrying capacity of the unit building is calculated. The calculation formula is CY 瞬时 =GA×80%×QP, where CY 瞬时 It is expressed as the instantaneous carrying capacity, GA is expressed as the total area of ​​the unit building, and QP is expressed as the number of tourists per unit area; Obtain the effective opening hours of the scenic spot every day and the average visiting time of each tourist in the scenic spot, and calculate the daily average turnover rate. The calculation formula is: Where DT represents the daily average turnover rate, T 开放 It is the effective opening time of the scenic spot every day, T 游览 It is expressed as the average visiting time of each tourist in the scenic spot; The maximum carrying capacity is calculated based on the instantaneous carrying capacity and the daily average turnover rate. The calculation formula is CY 最大 =CY 瞬时 ×DT, where CY 最大 It is expressed as the maximum carrying capacity, CY is expressed as the instantaneous carrying capacity, and DT is expressed as the daily average turnover rate; Monitor the real-time number of tourists in the scenic area, and accumulate the real-time cumulative number of tourists on the day. According to the real-time number of tourists, the real-time cumulative number of tourists, the instantaneous carrying capacity and the maximum carrying capacity, the tourist impact coefficient is calculated. The calculation formula is TI = (CY 瞬时 -NUM 实时 )+(CY 最大 -NUM 累计 ), where TI is the tourist impact coefficient, CY 瞬时 Expressed as instantaneous load, NUM 实时 Expressed as the real-time number of tourists, CY 最大 Expressed as maximum load, NUM 累计 Expressed as the real-time cumulative number of visitors.

9. The method for monitoring and preventing cultural relics in scenic areas according to claim 1 is characterized in that: The step of issuing a warning for damage to cultural relics based on the damage index of cultural relics is to compare the damage index of cultural relics with a preset threshold value. If the damage index of cultural relics is less than the preset threshold value, it is determined that the current degree of damage to the cultural relics is small and no warning is issued. If the damage index of cultural relics is greater than the preset threshold value, it is determined that the current degree of damage to the cultural relics is large and a warning is issued.

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