Field cultural relic prevention and control method, device and equipment and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to a field cultural relic prevention and control method, device and equipment and a storage medium, and the method comprises the steps: obtaining real-time water level detection information, and inputting the real-time water level detection information into a water level monitoring model to obtain water level monitoring information; performing face recognition processing and target recognition processing on the obtained real-time monitoring information to obtain personnel identity information and target behavior information; acquiring real-time temperature and humidity information and inputting the real-time temperature and humidity information into the temperature and humidity monitoring model to obtain temperature and humidity monitoring information; according to the water level monitoring information, the personnel identity information, the target behavior information and the temperature and humidity monitoring information, whether an alarm needs to be triggered is determined; if the alarm needs to be triggered, generating an alarm instruction based on a security encryption technology; according to the method disclosed by the invention, the water level monitoring information, the personnel identity information, the target behavior information and the temperature and humidity monitoring information are comprehensively analyzed to judge whether an alarm needs to be triggered, potential threats are responded in time, and the cultural relic prevention and control efficiency and effect are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a field cultural relics prevention and control method, device, equipment and storage medium. Background Art

[0002] Field cultural relics are an important part of human cultural heritage and an important carrier of Chinese civilization. The protection of field cultural relics is an important task in the current field of cultural relic protection technology.

[0003] In the field of field cultural relics protection and supervision, the challenges faced mainly include the difficulty of locating cultural relics, monitoring environmental changes, and preventing theft and vandalism; traditional monitoring systems mainly rely on regular manual inspections and basic video surveillance technology, which has significant limitations in resource allocation and efficiency improvement; traditional cultural relics monitoring systems mostly rely on manual inspections and basic video surveillance, which limits the timeliness and comprehensiveness of monitoring; for example, manual inspections cannot achieve all-weather coverage, and basic video surveillance has difficulty responding to environmental changes immediately, which may lead to a response that is not quick or effective enough in the event of emergencies such as theft or natural disasters, thereby causing irreparable loss of cultural relics; in addition, early cultural relics monitoring systems lacked advanced data processing capabilities and were unable to extract sufficient information from the collected data for risk assessment, which increased the uncertainty and risks of cultural relics protection work.

[0004] It can be seen that the existing technology still needs to be improved and enhanced. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a field cultural relics prevention and control method that can respond to potential threats in a timely manner and enhance the efficiency and effectiveness of cultural relics prevention and control.

[0006] The first aspect of the present invention provides a field cultural relics prevention and control method, including: obtaining real-time water level detection information, inputting the real-time water level detection information into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information; obtaining real-time monitoring information, performing face recognition processing on the real-time monitoring information to obtain facial feature information, and obtaining personnel identity information based on the facial feature information; performing target recognition processing on the real-time monitoring information to obtain target information, performing target tracking processing based on the target information to obtain target behavior information; obtaining real-time temperature and humidity information, inputting the real-time temperature and humidity information into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information; confirming whether it is necessary to trigger an alarm based on the water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information; if it is necessary to trigger an alarm, generating an alarm instruction based on security encryption technology.

[0007] Optionally, in a first implementation method of the first aspect of the present invention, the real-time water level detection information is obtained, and the real-time water level detection information is input into a water level monitoring model pre-constructed based on the YOLOv5 algorithm to obtain the water level monitoring information, including: obtaining real-time water level detection information, wherein the real-time water level detection information is a real-time water level image recording a water gauge; performing grayscale processing and denoising processing on the real-time water level image in sequence to obtain a denoised water level image; using the Canny edge detection algorithm to identify the edge of the water gauge from the denoised water level image, and using the Hough transform to identify the boundary line between the water gauge and the water surface baseline from the denoised water level image; using the OpenCV library to mark the identified boundary line between the water gauge and the water surface baseline to obtain a labeled water level image; inputting the labeled water level image into a water level monitoring model pre-constructed based on the YOLOv5 algorithm to obtain water level monitoring information, wherein the water level monitoring information is real-time water level data.

[0008] Optionally, in a second implementation of the first aspect of the present invention, the real-time monitoring information is obtained, face recognition processing is performed on the real-time monitoring information to obtain facial feature information, and personal identity information is obtained based on the facial feature information, including: obtaining real-time monitoring information, and using FFmpeg open source tools to decode the real-time monitoring information to obtain decoded monitoring information; inputting the decoded monitoring information into OpenCV's FaceRecognizer for face recognition processing to obtain facial feature information; inputting the facial feature information into a pre-trained FaceNet deep learning model to obtain a facial feature vector; comparing the facial feature vector with a known facial feature vector pre-stored in a database, and using a cosine similarity measurement method to calculate the similarity between the facial feature vector and the known facial feature vector; and obtaining personal identity information based on the similarity calculation result.

[0009] Optionally, in a third implementation method of the first aspect of the present invention, the real-time monitoring information is subjected to target recognition processing to obtain target information, and target tracking processing is performed based on the target information to obtain target behavior information, including: inputting the real-time monitoring information into a target detection model pre-built based on the YOLOv3 algorithm to obtain target area information; inputting the target area information into a feature extraction model built based on the ResNet-50 algorithm to obtain target feature information; based on the target feature information, target tracking processing is performed in combination with the Hungarian algorithm and a target re-identification method based on deep learning to obtain tracking information; and the tracking information is input into a behavior analysis model pre-built based on a convolutional neural network to obtain target behavior information, wherein the target behavior information includes normal target behavior and abnormal target behavior.

[0010] Optionally, in a fourth implementation method of the first aspect of the present invention, the real-time temperature and humidity information is obtained, and the real-time temperature and humidity information is input into a temperature and humidity monitoring model pre-constructed based on a convolutional neural network to obtain the temperature and humidity monitoring information, including: constructing a CNN temperature and humidity monitoring model based on a Python deep learning framework, the CNN temperature and humidity monitoring model including multiple convolutional layers and fully connected layers, and adding a batch normalization operation after each convolutional layer and each fully connected layer; obtaining real-time temperature and humidity information, and performing denoising, normalization and dimensionality reduction operations on the real-time temperature and humidity information in turn to obtain pre-processed temperature and humidity information; inputting the pre-processed temperature and humidity information into a pre-constructed CNN temperature and humidity monitoring model to obtain temperature and humidity monitoring information, wherein the temperature and humidity monitoring information is real-time temperature and humidity.

[0011] Optionally, in a fifth implementation of the first aspect of the present invention, the confirmation of whether to trigger an alarm based on water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information includes: obtaining a preset suitable water level range, and when the water level monitoring information indicates that the real-time water level data is lower than or higher than the suitable water level range, an alarm needs to be triggered; obtaining a preset staff information table, the staff information table including multiple staff identity information, and determining whether the staff identity information matches any staff identity information, and if none of them matches, an alarm needs to be triggered; obtaining a preset suitable temperature and humidity range, and when the temperature and humidity monitoring information indicates that the real-time temperature and humidity are lower than or higher than the suitable temperature and humidity range, an alarm needs to be triggered; and when the target behavior information indicates that the target behavior is abnormal, an alarm needs to be triggered.

[0012] Optionally, in a sixth implementation method of the first aspect of the present invention, if an alarm needs to be triggered, an alarm instruction is generated based on security encryption technology, including: when an alarm needs to be triggered, confirm the alarm type; obtain an alarm template according to the confirmed alarm type, embed monitoring information, personnel identity information, target behavior information or temperature and humidity monitoring information into the alarm template to obtain the alarm content; obtain pre-set sending configuration information, use an encryption module, and generate an alarm instruction based on the sending configuration information and the alarm content.

[0013] The second aspect of the present invention provides a field cultural relics prevention and control device, including: a water level monitoring module, which is used to obtain real-time water level detection information, and input the real-time water level detection information into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information; a face recognition module, which is used to obtain real-time monitoring information, perform face recognition processing on the real-time monitoring information, obtain facial feature information, and obtain personnel identity information based on the facial feature information; a behavior recognition module, which is used to perform target recognition processing on the real-time monitoring information to obtain target information, perform target tracking processing based on the target information to obtain target behavior information; a temperature and humidity monitoring module, which is used to obtain real-time temperature and humidity information, and input the real-time temperature and humidity information into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information; a confirmation module, which is used to confirm whether an alarm needs to be triggered based on the water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information; an early warning module, which is used to generate an alarm instruction based on security encryption technology if an alarm needs to be triggered.

[0014] The third aspect of the present invention provides a field cultural relics prevention and control device, which includes: a memory and at least one processor, wherein instructions are stored in the memory; at least one of the processors calls the instructions in the memory so that the field cultural relics prevention and control device executes each step of the field cultural relics prevention and control method described in any one of the above items.

[0015] The fourth aspect of the present invention provides a computer-readable storage medium, on which instructions are stored. When the instructions are executed by a processor, the various steps of any of the above-mentioned field cultural relics prevention and control methods are implemented.

[0016] In the technical solution of the present invention, water level monitoring information is obtained by acquiring real-time water level detection information and inputting it into a water level monitoring model; face recognition processing and target recognition processing are respectively performed on the acquired real-time monitoring information to obtain personnel identity information and target behavior information; real-time temperature and humidity information are acquired and input into the temperature and humidity monitoring model to obtain temperature and humidity monitoring information; whether an alarm needs to be triggered is confirmed based on the water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information; if an alarm needs to be triggered, an alarm instruction is generated based on security encryption technology; the method disclosed in the present application comprehensively analyzes water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information to determine whether an alarm needs to be triggered, responds to potential security threats in a timely manner, and enhances the prevention and control efficiency and effect of field cultural relics. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A first flow chart of a data processing method provided by an embodiment of the present invention;

[0018] Figure 2 A second flow chart of the data processing method provided by an embodiment of the present invention;

[0019] Figure 3 A third flow chart of the data processing method provided by an embodiment of the present invention;

[0020] Figure 4 A fourth flow chart of the data processing method provided by an embodiment of the present invention;

[0021] Figure 5 A fifth flow chart of the data processing method provided by the embodiment of the present invention;

[0022] Figure 6 A sixth flow chart of the data processing method provided by an embodiment of the present invention;

[0023] Figure 7 A seventh flow chart of the data processing method provided by the embodiment of the present invention;

[0024] Figure 8 A schematic diagram of the structure of a data processing device provided by an embodiment of the present invention;

[0025] Fig. 9 A schematic diagram of the structure of a data processing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] The present invention provides a method, device, equipment and storage medium for the prevention and control of field cultural relics. In the present invention, the terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 , an embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0028] 101. Obtain real-time water level detection information, and input the real-time water level detection information into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information;

[0029] In this embodiment, by acquiring real-time water level detection information and inputting it into a pre-built water level monitoring model, water level changes can be timely understood, and damage to cultural relics caused by abnormal water levels can be effectively prevented, thereby ensuring the safety of the environment in which field cultural relics are located.

[0030] 102. Acquire real-time monitoring information, perform face recognition processing on the real-time monitoring information to obtain facial feature information, and obtain person identity information based on the facial feature information;

[0031] 103. Perform target recognition processing on the real-time monitoring information to obtain target information, and perform target tracking processing based on the target information to obtain target behavior information;

[0032] In this embodiment, by performing face recognition processing and target recognition processing on real-time monitoring information respectively, it helps to accurately identify the identity and behavior of people entering the monitoring area, thereby effectively preventing illegal intrusion and vandalism and improving the level of security protection for field cultural relics.

[0033] 104. Acquire real-time temperature and humidity information, and input the real-time temperature and humidity information into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information;

[0034] In this embodiment, real-time temperature and humidity information is obtained and input into a pre-built temperature and humidity monitoring model, which can ensure the stability of the environment used to preserve field cultural relics, avoid damage to field cultural relics due to environmental changes, and ensure the suitability of the environment in which the field cultural relics are located.

[0035] 105. Confirm whether an alarm needs to be triggered based on water level monitoring information, personnel identity information, target behavior information, and temperature and humidity monitoring information;

[0036] 106. If an alarm needs to be triggered, an alarm instruction is generated based on security encryption technology.

[0037] The present application discloses a method for prevention and control of field cultural relics, which obtains water level monitoring information by acquiring real-time water level detection information and inputting it into a water level monitoring model; performs face recognition processing and target recognition processing on the acquired real-time monitoring information to obtain personnel identity information and target behavior information; acquires real-time temperature and humidity information and inputs it into a temperature and humidity monitoring model to obtain temperature and humidity monitoring information; confirms whether an alarm needs to be triggered based on the water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information; if an alarm needs to be triggered, generates an alarm instruction based on security encryption technology; the method disclosed in the present application comprehensively analyzes the water level monitoring information, personnel identity information, target behavior information and temperature and humidity monitoring information to determine whether an alarm needs to be triggered, responds to potential threats in a timely manner, and enhances the efficiency and effectiveness of prevention and control of cultural relics.

[0038] See also Figure 2 The second embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0039] 201. Acquire real-time water level detection information, where the real-time water level detection information is a real-time water level image recorded with a water gauge;

[0040] In this embodiment, a plurality of scales are engraved on the water ruler, and each scale corresponds to a straight line and a water level value.

[0041] 202. Perform grayscale processing and denoising processing on the real-time water level image in sequence to obtain a denoised water level image;

[0042] In this embodiment, a weighted average method is used to convert the real-time water level image into a grayscale image, and then a Gaussian filter denoising algorithm is applied to process the grayscale image to reduce the noise in the image and obtain a denoised water level image.

[0043] 203. Use the Canny edge detection algorithm to identify the edge of the water gauge from the denoised water level image, and use the Hough transform to identify the boundary between the water gauge and the water surface baseline from the denoised water level image;

[0044] In this embodiment, the Canny edge detection algorithm is applied to identify the edge of the water gauge. The Canny algorithm is a popular edge detection method, which includes the steps of Gaussian blur denoising, calculating gradient amplitude and direction, non-maximum suppression, and dual threshold detection and edge connection. Through these steps, a clear representation of the edge of the water gauge in the image can be obtained. Then, the Hough transform is used to identify the straight lines in the image. The Hough transform is a feature extraction technology that can detect straight lines or curves from an image. Specifically, it can detect the boundary line between the water gauge and the water surface baseline. Among the Hough transforms, the Hough line transform is usually used. The Hough line transform uses a voting mechanism to identify straight lines in an image. For each edge point, the algorithm calculates all the straight lines it may belong to and accumulates them in the parameter space. Finally, a threshold is set to determine which straight lines are significant, that is, they may correspond to the boundary line between the water gauge and the water surface.

[0045] 204. Use the OpenCV library to mark the boundary between the identified water gauge and the water surface baseline to obtain a marked water level image;

[0046] 205. Input the annotated water level image into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information, where the water level monitoring information is real-time water level data;

[0047] In this embodiment, building a water level monitoring model based on the YOLOv5 algorithm has the following advantages: the YOLOv5 algorithm can realize fast object detection, so that the water level monitoring system can capture and analyze image data in real time and respond to water level changes in time; the YOLOv5 algorithm shows a high accuracy in target detection tasks, and can accurately identify and locate water levels, reducing false alarms and missed alarms; YOLOv5 supports online learning and model updates, and can optimize model performance and adapt to environmental changes by continuously inputting new labeled water level images.

[0048] See also Figure 3 The third embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0049] 301. Obtain real-time monitoring information, and use FFmpeg open source tool to decode the real-time monitoring information to obtain decoded monitoring information;

[0050] In this embodiment, the real-time monitoring information is efficiently decoded and processed through the FFmpeg open source tool. Through the decoding process, the real-time monitoring information, that is, the monitoring data, can be converted into a usable format for further analysis and processing. This not only improves the processing speed of the monitoring data, but also enhances the availability of the data, allowing the prevention and control system to respond to various situations more quickly and accurately.

[0051] 302. Input the decoded monitoring information into the FaceRecognizer of OpenCV for face recognition processing to obtain facial feature information;

[0052] In this embodiment, FaceRecognizer matches the input decoded monitoring information, ie, the face image, according to the training data set, and returns the recognition result, ie, the facial feature information, which includes the label and confidence of the recognized face.

[0053] 303. Input the facial feature information into the pre-trained FaceNet deep learning model to obtain a facial feature vector;

[0054] In this embodiment, a pre-trained FaceNet model is loaded using a deep learning framework such as TensorFlow, and it is ensured that the input layer of the model matches the size of the preprocessed image; the facial feature information is input into the FaceNet deep learning model, and the model outputs a facial feature vector for each image; the obtained facial feature vector can be used for subsequent comparison, verification or recognition tasks.

[0055] 304. Compare the facial feature vector with known facial feature vectors pre-stored in the database, and calculate the similarity between the facial feature vector and the known facial feature vector using a cosine similarity measurement method;

[0056] In this embodiment, a database containing known facial feature vectors is pre-constructed in the prevention and control system. These feature vectors need to be pre-obtained from facial images of known individuals by the same feature extraction method. The cosine similarity measurement method is used to compare the similarity between the target facial feature vector and each known facial feature vector in the database. The cosine similarity is to determine the similarity between two vectors by measuring the cosine value of the angle between them. The calculation formula is:

[0057] Cosine similarity = (A·B) / (||A||*||B||);

[0058] Among them, A and B are the target facial feature vector and the known facial feature vector in the database respectively, A·B represents the dot product of the vectors, and ||A|| and ||B|| represent the norms of the vectors respectively.

[0059] 305. Obtaining personnel identity information according to the similarity calculation result;

[0060] In this embodiment, based on the calculated cosine similarity, the individual corresponding to the known facial feature vector with the highest similarity is selected as the recognition result, that is, as the output person identity information; usually a threshold is set, and the recognition is considered successful only when the similarity exceeds this threshold.

[0061] See also Figure 4 The fourth embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0062] 401. Input the real-time monitoring information into a target detection model pre-built based on the YOLOv3 algorithm to obtain target area information;

[0063] In this embodiment, real-time monitoring information is input into a target detection model pre-built based on the YOLOv3 algorithm, the bounding box coordinates and confidence scores output by the parsed model are analyzed, non-maximum suppression (NMS) is applied to filter overlapping bounding boxes, and then the detected target area information is output based on the filtering results.

[0064] 402. Input the target area information into a feature extraction model built based on the ResNet-50 algorithm to obtain target feature information;

[0065] In this embodiment, for each detected target area, cropping or resizing is performed to match the input requirements of the ResNet-50 model; the adjusted target area image is input into a feature extraction model built based on the ResNet-50 algorithm to obtain feature vectors output by the feature extraction model. These feature vectors represent the visual features of the target and can be used for subsequent analysis or processing, such as classification, recognition or other tasks.

[0066] 403. Based on the target feature information, the target tracking process is performed by combining the Hungarian algorithm and the target re-identification method based on deep learning to obtain tracking information;

[0067] In this embodiment, the extracted target feature information is used to perform feature matching through the Hungarian algorithm; the Hungarian algorithm is a combinatorial optimization algorithm that solves the allocation problem in polynomial time and can be used to match targets between different frames; further, by combining the target re-identification result obtained by the deep learning method and the matching result of the Hungarian algorithm, the target tracking information can be obtained, including the target trajectory, speed, direction, etc.

[0068] 404. Input the tracking information into a behavior analysis model pre-built based on a convolutional neural network to obtain target behavior information, wherein the target behavior information includes normal target behavior and abnormal target behavior.

[0069] To input the tracking information into the pre-built behavior analysis model based on the convolutional neural network and obtain the target behavior information (including normal and abnormal behavior), you can follow the following steps:

[0070] In this embodiment, the pre-built behavior analysis model can confirm whether the target's behavior is normal or abnormal, and integrate the trained model into the field cultural relics prevention and control system to ensure that the behavior analysis model can receive tracking information as input in real time; the real-time tracking information is input into the behavior analysis model, and the model will output the target behavior information, including determining whether the behavior is normal or abnormal.

[0071] See also Figure 5 The fifth embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0072] 501. Construct a CNN temperature and humidity monitoring model based on the Python deep learning framework, wherein the CNN temperature and humidity monitoring model includes multiple convolutional layers and fully connected layers, and a batch normalization operation is added after each convolutional layer and each fully connected layer;

[0073] In this embodiment, the required libraries and modules are first imported into the prevention and control system, including the TensorFlow deep learning framework, and then the CNN model structure is defined, including:

[0074] 1. Input layer: receiving pre-processed temperature and humidity data;

[0075] 2. Convolution layer: Use convolution kernels to extract data features. You can set multiple convolution layers and add batch normalization operations after each convolution layer.

[0076] 3. Activation layer: ReLU activation function is usually used after the convolution layer;

[0077] 4. Pooling layer: reduces data dimensions and improves computational efficiency;

[0078] 5. Fully connected layer: maps the features extracted by the convolutional layer to the final output, and batch normalization is also added after each fully connected layer;

[0079] 6. Output layer: Depending on the task requirements, it can be a regression layer or a classification layer.

[0080] In this embodiment, batch normalization operations are added after multiple convolutional layers and multiple fully connected layers, which helps to stabilize the distribution of inputs in each layer of the network, reduce internal covariate shifts caused by parameter updates, accelerate the model training process, and improve the generalization ability of the model.

[0081] 502. Acquire real-time temperature and humidity information, and perform denoising, normalization, and dimension reduction operations on the real-time temperature and humidity information in sequence to obtain pre-processed temperature and humidity information;

[0082] In this embodiment, first, the collected real-time temperature and humidity information is denoised by a median filter to eliminate possible random errors or outliers; then, the real-time temperature and humidity information is normalized by a linear transformation method. Normalization is to scale the data to a standard range, usually between 0 and 1; finally, in order to reduce the complexity of the data and extract the main features, the principal component analysis method can be applied to reduce the dimensionality of the real-time temperature and humidity information, which can greatly reduce the amount of calculation while retaining the main features of the data.

[0083] 503. Input the preprocessed temperature and humidity information into the pre-built CNN temperature and humidity monitoring model to obtain temperature and humidity monitoring information, where the temperature and humidity monitoring information is real-time temperature and humidity.

[0084] In this embodiment, the pre-built CNN temperature and humidity monitoring model has been trained and optimized for the temperature and humidity monitoring task.

[0085] See also Figure 6 The sixth embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0086] 601. Obtain a preset suitable water level range. When the water level monitoring information indicates that the real-time water level data is lower than or higher than the suitable water level range, an alarm needs to be triggered;

[0087] 602. Obtain a preset staff information table, the staff information table including multiple staff identity information, and determine whether the staff identity information matches any staff identity information. If none of them matches, an alarm needs to be triggered;

[0088] 603. Obtain a preset suitable range of temperature and humidity. When the temperature and humidity monitoring information indicates that the real-time temperature and humidity are lower than or higher than the suitable range, an alarm needs to be triggered;

[0089] 604. When the target behavior information indicates that the target behavior is abnormal, an alarm needs to be triggered.

[0090] In this embodiment, the suitable water level range and the suitable temperature and humidity range can be pre-set by the staff according to the environment in which the field cultural relics are located and the material of the field cultural relics; the staff information table can be pre-set by the staff according to the personnel information provided by the prevention and control company.

[0091] See also Figure 7 The seventh embodiment of the field cultural relics prevention and control method in the embodiment of the present invention includes:

[0092] 701. When an alarm needs to be triggered, confirm the alarm type;

[0093] In this embodiment, the alarm types include abnormal water level alarm, abnormal personnel identity alarm, abnormal personnel behavior alarm, and abnormal temperature and humidity alarm.

[0094] 702. Obtain an alarm template according to the confirmed alarm type, embed monitoring information, personnel identity information, target behavior information or temperature and humidity monitoring information into the alarm template, and obtain alarm content;

[0095] In this embodiment, according to the confirmed alarm type, a suitable alarm template is selected from a pre-built template library; the current monitoring information, personnel identity information, target behavior information or temperature and humidity monitoring information, etc. are obtained; the collected information is replaced according to the placeholders in the template to generate complete alarm content.

[0096] 703. Obtain preset sending configuration information, and use an encryption module to generate an alarm instruction according to the sending configuration information and the alarm content.

[0097] In this embodiment, the encryption module can be an AES encryption algorithm; the sending configuration information is preset, and the sending configuration information includes the recipient's contact information and the sending method; according to the preset sending configuration information and the alarm content, the alarm content is encrypted by the encryption module; and the encrypted alarm instruction is sent to the designated recipient via the set sending method.

[0098] In this embodiment, by configuring an encryption module in the prevention and control system, data security can be enhanced, privacy information can be protected, unauthorized access can be prevented, confidentiality and integrity of data transmission can be ensured, user trust can be improved, and data leakage and abuse can be prevented.

[0099] The above describes the field cultural relics prevention and control method in the embodiment of the present invention. The following describes the field cultural relics prevention and control device in the embodiment of the present invention. Figure 8 , an embodiment of the field cultural relics prevention and control device in the embodiment of the present invention includes:

[0100] The water level monitoring module 801 is used to obtain real-time water level detection information, and input the real-time water level detection information into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information;

[0101] The face recognition module 802 is used to obtain real-time monitoring information, perform face recognition processing on the real-time monitoring information, obtain facial feature information, and obtain person identity information based on the facial feature information;

[0102] The behavior recognition module 803 is used to perform target recognition processing on the real-time monitoring information to obtain target information, and perform target tracking processing based on the target information to obtain target behavior information;

[0103] The temperature and humidity monitoring module 804 is used to obtain real-time temperature and humidity information, and input the real-time temperature and humidity information into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information;

[0104] Confirmation module 805, used to confirm whether to trigger an alarm based on water level monitoring information, personnel identity information, target behavior information, and temperature and humidity monitoring information;

[0105] The early warning module 806 is used to generate an alarm instruction based on the security encryption technology if an alarm needs to be triggered.

[0106] above Figure 8 The field cultural relics prevention and control device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The field cultural relics prevention and control equipment in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0107] Fig. 9It is a structural schematic diagram of a field cultural relic prevention and control device provided by an embodiment of the present invention. The field cultural relic prevention and control device 900 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 (for example, one or more massive storage devices) storing application programs 933 or data 932. Among them, the memory 920 and the storage medium 930 can be short-term storage or persistent storage. The program stored in the storage medium 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the field cultural relic prevention and control device 900. Furthermore, the processor 910 can be configured to communicate with the storage medium 930, and execute a series of instruction operations in the storage medium 930 on the field cultural relic prevention and control device 900 to implement the steps of the field cultural relic prevention and control method provided by the above-mentioned method embodiments.

[0108] The field cultural relics prevention and control device 900 may also include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input and output interfaces 960, and / or one or more operating systems 931, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It can be understood by those skilled in the art that Fig. 9 The structure of the field cultural relics prevention and control equipment shown does not constitute a limitation of the field cultural relics prevention and control equipment, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0109] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the field cultural relics prevention and control method.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0112] Finally, it should be noted that the above description is only a preferred example of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for preventing and controlling field cultural relics, characterized in that: include: Obtain real-time water level detection information, and input the real-time water level detection information into the water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information; Acquire real-time monitoring information, perform face recognition processing on the real-time monitoring information to obtain facial feature information, and obtain person identity information based on the facial feature information; Perform target recognition processing on the real-time monitoring information to obtain target information, and perform target tracking processing based on the target information to obtain target behavior information; Obtain real-time temperature and humidity information, and input the real-time temperature and humidity information into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information; Determine whether to trigger an alarm based on water level monitoring information, personnel identity information, target behavior information, and temperature and humidity monitoring information; If an alarm needs to be triggered, an alarm instruction is generated based on secure encryption technology.

2. The field cultural relics prevention and control method according to claim 1 is characterized in that: The real-time water level detection information is obtained, and the real-time water level detection information is input into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information, including: Acquire real-time water level detection information, wherein the real-time water level detection information is a real-time water level image recorded with a water gauge; The real-time water level image is subjected to grayscale processing and denoising processing in sequence to obtain a denoised water level image; The Canny edge detection algorithm is used to identify the edge of the water gauge from the denoised water level image, and the Hough transform is used to identify the boundary between the water gauge and the water surface baseline from the denoised water level image. Use the OpenCV library to mark the boundary between the identified water gauge and the water surface baseline to obtain a marked water level image; The annotated water level image is input into a water level monitoring model pre-built based on the YOLOv5 algorithm to obtain water level monitoring information, which is real-time water level data.

3. The method for preventing and controlling field cultural relics according to claim 1, characterized in that: The real-time monitoring information is obtained, face recognition is performed on the real-time monitoring information to obtain facial feature information, and the identity information of the person is obtained according to the facial feature information, including: Obtain real-time monitoring information, and use the FFmpeg open source tool to decode the real-time monitoring information to obtain decoded monitoring information; Input the decoded monitoring information into OpenCV's FaceRecognizer for face recognition processing to obtain facial feature information; Input the facial feature information into the pre-trained FaceNet deep learning model to obtain the facial feature vector; Compare the facial feature vector with known facial feature vectors pre-stored in the database, and use the cosine similarity measurement method to calculate the similarity between the facial feature vector and the known facial feature vector; The person's identity information is obtained based on the similarity calculation results.

4. The method for preventing and controlling field cultural relics according to claim 1, characterized in that: The target identification processing is performed on the real-time monitoring information to obtain the target information, and the target tracking processing is performed based on the target information to obtain the target behavior information, including: Input the real-time monitoring information into the target detection model pre-built based on the YOLOv3 algorithm to obtain the target area information; Input the target area information into the feature extraction model built based on the ResNet-50 algorithm to obtain the target feature information; Based on the target feature information, the target tracking process is performed by combining the Hungarian algorithm and the target re-identification method based on deep learning to obtain the tracking information; The tracking information is input into a behavior analysis model pre-built based on a convolutional neural network to obtain target behavior information, which includes normal target behavior and abnormal target behavior.

5. The field cultural relics prevention and control method according to claim 1 is characterized in that: The real-time temperature and humidity information is obtained, and the real-time temperature and humidity information is input into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information, including: A CNN temperature and humidity monitoring model is constructed based on the Python deep learning framework. The CNN temperature and humidity monitoring model includes multiple convolutional layers and fully connected layers, and a batch normalization operation is added after each convolutional layer and each fully connected layer. Acquire real-time temperature and humidity information, perform denoising, normalization and dimension reduction operations on the real-time temperature and humidity information in sequence, and obtain pre-processed temperature and humidity information; The preprocessed temperature and humidity information is input into the pre-built CNN temperature and humidity monitoring model to obtain temperature and humidity monitoring information, which is real-time temperature and humidity.

6. The field cultural relics prevention and control method according to claim 1 is characterized in that: The step of confirming whether to trigger an alarm based on water level monitoring information, personnel identity information, target behavior information, and temperature and humidity monitoring information includes: Get the preset water level suitable range. When the water level monitoring information shows that the real-time water level data is lower or higher than the water level suitable range, an alarm needs to be triggered; Obtain a preset staff information table, the staff information table including multiple staff identity information, and determine whether the staff identity information matches any staff identity information. If none of them matches, an alarm needs to be triggered; Get the preset temperature and humidity suitable range. When the temperature and humidity monitoring information shows that the real-time temperature and humidity are lower or higher than the temperature and humidity suitable range, an alarm needs to be triggered; When the target behavior information indicates that the target behavior is abnormal, an alarm needs to be triggered.

7. The method for preventing and controlling field cultural relics according to claim 6, characterized in that: If an alarm needs to be triggered, an alarm instruction is generated based on the security encryption technology, including: When an alarm needs to be triggered, confirm the alarm type; Obtain an alarm template according to the confirmed alarm type, embed monitoring information, personnel identity information, target behavior information or temperature and humidity monitoring information into the alarm template, and obtain alarm content; Obtain the preset sending configuration information, use the encryption module, and generate an alarm instruction according to the sending configuration information and the alarm content.

8. A field cultural relic prevention and control device, characterized in that: include: The water level monitoring module is used to obtain real-time water level detection information, input the real-time water level detection information into the water level monitoring model pre-built based on the YOLOv5 algorithm, and obtain water level monitoring information; The face recognition module is used to obtain real-time monitoring information, perform face recognition processing on the real-time monitoring information, obtain facial feature information, and obtain person identity information based on the facial feature information; The behavior recognition module is used to perform target recognition processing on the real-time monitoring information to obtain target information, and perform target tracking processing based on the target information to obtain target behavior information; The temperature and humidity monitoring module is used to obtain real-time temperature and humidity information, and input the real-time temperature and humidity information into a temperature and humidity monitoring model pre-built based on a convolutional neural network to obtain temperature and humidity monitoring information; A confirmation module is used to confirm whether an alarm needs to be triggered based on water level monitoring information, personnel identity information, target behavior information, and temperature and humidity monitoring information; The early warning module is used to generate an alarm instruction based on security encryption technology if an alarm needs to be triggered.

9. A field cultural relic prevention and control device, characterized in that: The field cultural relics prevention and control equipment comprises: a memory and at least one processor, wherein the memory stores instructions; At least one of the processors calls the instructions in the memory so that the field cultural relics prevention and control equipment executes each step of the field cultural relics prevention and control method as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the field cultural relics prevention and control method as described in any one of claims 1-7 are implemented.

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