Museum monitoring visualization method and system, medium and product

Abstract

The invention discloses a museum monitoring visualization method and system, a medium and a product, and relates to the field of security monitoring, and the method comprises the steps: obtaining video pictures shot by a video camera in each region of a museum, and region pixel temperatures collected by a thermal imaging camera; based on the video picture, determining the crowding degree of the number of people in each region; based on the regional pixel temperature, determining the thermal activity degree and the thermal congestion degree of each region; determining a warning index of each region according to the crowding degree, the thermal activity degree and the thermal crowding degree of each region; and when the warning index exceeds a preset index threshold value, displaying the video picture of the corresponding region and the region pixel temperature on the display screen in an amplified manner. By implementing the method, the monitoring efficiency of the museum can be improved.

Description

Technical Field

[0001] The present application relates to the field of security monitoring, and in particular to a museum monitoring visualization method, system, medium and product. Background Art

[0002] As material conditions continue to improve, people are paying more and more attention to spiritual civilization. As a result, museums have become popular places for cultural exchanges, and it is particularly important to ensure the safety of personnel and facilities in the museum. During peak periods such as holidays, museums often face problems of crowded personnel and safety hazards. How to effectively monitor various areas in the museum has become an issue that needs to be solved urgently.

[0003] In the prior art, a combination of video surveillance and manual monitoring is usually used to manage the flow of people and safety in museums. By using fixed cameras to capture video footage of various areas of the museum, security personnel can observe the flow of people in each area and manually conduct personnel guidance and safety management.

[0004] However, existing technologies are prone to monitoring blind spots during peak hours and have difficulty accurately assessing the density and activity intensity of the crowd, making it difficult for security personnel to respond in a timely manner, resulting in low monitoring efficiency. Summary of the invention

[0005] The present application provides a museum monitoring visualization method, system, medium and product for improving the monitoring efficiency of the museum.

[0006] In a first aspect, the present application provides a museum monitoring visualization method, which is applied to a monitoring visualization system, and the method includes: obtaining video images captured by video cameras in various areas of the museum, and regional pixel temperatures collected by thermal imaging cameras; based on the video images, determining the crowding degree of each area, the crowding degree is used to indicate the crowding situation in the area; based on the regional pixel temperature, determining the thermal activity and thermal crowding degree of each area, the thermal activity is used to indicate the intensity of crowd activity in the area, and the thermal crowding degree is used to indicate the density of people in the area; determining a warning index for each area based on the crowding degree, thermal activity and thermal crowding degree of each area, the warning index is used to indicate the risk level of the area; when the warning index exceeds a preset index threshold, enlarging the video image and regional pixel temperature of the corresponding area on a display screen.

[0007] In the above embodiment, the monitoring visualization system determines the crowding degree, thermal activity degree and thermal crowding degree through the video screen and the regional pixel temperature. These three indicators provide real-time and dynamic monitoring information for museum managers, greatly improving the efficiency and safety of supervision. The crowding degree helps to determine which areas have too much traffic and may have safety hazards; the thermal activity degree can reflect the intensity of crowd activities and provide a basis for judging whether there are abnormal conditions in certain areas; the thermal crowding degree refers to the density of the crowd and is an important indicator in fire safety control. By combining these three indicators, the monitoring visualization system can comprehensively evaluate the warning index, so that potential risks can be discovered and responded to in a timely manner. When the warning index exceeds the preset index threshold, the monitoring visualization system can lock the area in the museum that needs to be focused on, and automatically enlarge the video screen and regional pixel temperature of the corresponding area on the display screen. This instant feedback mechanism provides museum managers with the ability to respond quickly, so that emergency evacuation or other safety measures can be taken, which greatly enhances the museum's security capabilities and creates a safer and more comfortable environment for visitors.

[0008] In combination with some embodiments of the first aspect, in some embodiments, the crowding degree of each area is determined based on the video image, and the crowding degree is used to indicate the crowding situation of people in the area, specifically including: performing human body detection on the video image to obtain the number of people in each area; obtaining the regional population density based on the area and the number of people in each area; and determining the crowding degree of each area based on the changing trends of the regional population density and the number of people.

[0009] In the above embodiment, the method of determining the crowding degree of the number of people in each area is elaborated in detail. First, the monitoring visualization system accurately counts the actual number of people in each area by detecting the human body in the video screen, avoiding the error and lag of traditional manual counting. Secondly, the monitoring visualization system calculates the regional population density in combination with the area of ​​each area, providing an objective measurement standard, so that the crowding degree of the number of people between areas of different sizes can be reasonably compared. In addition, this method not only considers the static regional population density, but also pays attention to the changing trend of the number of people, so that the monitoring visualization system can predict the potential crowding risk. For example, even if the current number of people has not reached the upper limit, if the number of people is increasing rapidly, the monitoring visualization system can also issue a warning in time. This dynamic evaluation method greatly improves the accuracy and timeliness of crowding warning, allowing museum managers to take early intervention measures to effectively prevent the occurrence of potential crowding risks, thereby improving the museum's safety management level and visitor experience.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the thermal activity and thermal crowding of each region are determined based on the regional pixel temperature, specifically including: determining the thermal activity of each region based on the regional pixel temperature, the thermal activity is related to the number of human pixels in each region and the sum of the temperatures of the human pixels; calculating the thermal crowding based on the total number of regional pixels obtained by the thermal imaging camera and the sum of the temperatures of the human pixels.

[0011] In the above embodiment, how to use the regional pixel temperature to determine the thermal activity and thermal crowding of each region is described in detail. First, the calculation of thermal activity takes into account the sum of the number of human pixels and the temperature of human pixels, which not only reflects the number of human bodies in the region, but also reflects the intensity of crowd activity. For example, even if the number of people is the same, if the crowd activity in an area is more intense (such as fast movement or gathering for discussion), its thermal activity will be higher, so it is easier to be identified by the monitoring visualization system as an area that needs to be focused on. Secondly, the calculation of thermal crowding cleverly combines the total number of regional pixels and the sum of the temperature of human pixels, providing a new angle to evaluate the density of the crowd. This method is more advantageous than simple video analysis and is still effective in the case of insufficient light or obstructed vision. Through the combined use of the two indicators of thermal activity and thermal crowding, the monitoring visualization system can evaluate the regional conditions more comprehensively and accurately, providing strong support for timely discovery of potential safety hazards, thereby greatly improving the safety management efficiency and emergency response capabilities of the museum.

[0012] In combination with some embodiments of the first aspect, in some embodiments, the method also includes: obtaining the real-time operating status of each device in the museum; calculating the failure risk index of each device based on the real-time operating status of each device; when the failure risk index of the target device exceeds the preset risk threshold, displaying early warning information on the display screen, the early warning information including the location information of the target device, the failure risk index and the inspection route.

[0013] In the above embodiment, the monitoring visualization system can timely discover potential failure risks by acquiring the real-time operating status of each device. When the failure risk index of a device exceeds the preset risk threshold, the monitoring visualization system will automatically display warning information on the display screen. This method greatly improves the safety of the overall operation of the museum by introducing equipment monitoring mechanism and failure warning mechanism.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of enlarging and displaying the video image and regional pixel temperature of the corresponding area on the display screen when the warning index exceeds the preset index threshold, the method also includes: real-time monitoring of the current visitor flow of the museum; if the current visitor flow reaches the maximum visitor capacity of the museum, a prompt for visitors to postpone entry is displayed on the display screen.

[0015] In the above embodiment, a passenger flow control function is introduced, which plays a key role in maintaining the safety of people in the museum and the quality of the visit. The monitoring visualization system monitors the current passenger flow of the museum in real time, so that the overall passenger flow situation can be accurately grasped. If the current passenger flow reaches the maximum capacity of the museum's visitors, it means that the safety of the people in the museum and the quality of the visit cannot be guaranteed. The monitoring visualization system will automatically display a reminder for visitors to postpone entry on the display screen, effectively preventing safety hazards caused by excessive crowding, such as stampedes, etc., and ensuring the comfort of the internal environment of the museum, avoiding a decrease in the visiting experience due to too many people. This method not only improves the safety management level of the museum, but also provides visitors with a better visiting experience, while also improving the museum's operating efficiency and reputation.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after the step of enlarging and displaying the video image and regional pixel temperature of the corresponding area on the display screen when the warning index exceeds the preset index threshold, the method also includes: performing face recognition through a high-definition camera to extract facial features; comparing the facial features with a preset blacklist database; if a blacklist person is detected, the real-time movement trajectory of the blacklist person is obtained and dynamically marked on the display screen.

[0017] In the above embodiment, face recognition and blacklist monitoring functions are introduced, which greatly enhances the security prevention capability of the museum. Through high-definition cameras for face recognition, the monitoring visualization system can quickly extract facial features. The facial features are compared with the preset blacklist database. Once a blacklist person is detected, the monitoring visualization system will track his movement trajectory in real time and mark it dynamically on the display screen, so that museum managers can grasp the location of suspicious persons at any time and effectively prevent them from committing illegal acts, greatly improving the efficiency of identifying and monitoring potential threat personnel and improving the efficiency of the overall security of the museum.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the method also includes: storing the detected abnormal events and corresponding processing measures in a to-do database; monitoring the processing status of to-do items in the to-do database in real time, the processing status including processing efficiency and processing quality; and generating a performance report based on the processing efficiency and processing quality of the to-do items.

[0019] In the above embodiment, the abnormal event management and performance evaluation functions are introduced, which plays an important role in improving the efficiency and service quality of the overall operation of the museum. The monitoring visualization system stores the abnormal events and corresponding processing measures in the to-do database to ensure that the abnormal events can be handled in a timely manner and are not missed. At the same time, the monitoring visualization system monitors the processing of the to-do items in real time, so that museum managers can understand the progress of solving each abnormal event in a timely manner. The performance report generated based on the processing efficiency and processing quality of the to-do items not only provides an objective basis for evaluating the performance of museum managers, but also helps to continuously improve the service level.

[0020] In a second aspect, an embodiment of the present application provides a monitoring visualization system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the monitoring visualization system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, which, when the computer program product is run on a monitoring visualization system, enables the monitoring visualization system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a monitoring visualization system, enables the monitoring visualization system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the monitoring visualization system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the use of multi-dimensional monitoring data fusion and intelligent analysis technology, the present invention can comprehensively and accurately evaluate the security status of each area in the museum, effectively solving the problem in the prior art that single video surveillance is difficult to accurately evaluate crowd density and activity intensity, thereby realizing the intelligent and refined museum security management.

[0025] 2. Due to the use of equipment failure warning and intelligent passenger flow control technology, the present invention can actively prevent the risk of equipment failure and crowding, effectively solving the safety hazards and low management efficiency caused by passive response in the prior art, thereby achieving a dual improvement in the safety of museum operations and the visiting experience.

[0026] 3. Due to the adoption of face recognition blacklist monitoring and abnormal event management methods, the present invention can timely identify potential threats and efficiently handle various abnormal situations, effectively solving the problems of passive security prevention and low event handling efficiency in the prior art, thereby realizing the active and systematic management of museum security. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a method for visualizing museum monitoring in an embodiment of the present application; Figure 2 is another flow chart of the museum monitoring visualization method in the embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of the monitoring visualization system in an embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] The following is a description of the process of the method provided by this implementation. Figure 1 , which is a flow chart of the museum monitoring visualization method in an embodiment of the present application.

[0031] S101, obtaining video images captured by video cameras in various areas of the museum and regional pixel temperatures collected by thermal imaging cameras; Regional pixel temperature refers to the temperature value corresponding to each human pixel captured by the thermal imaging camera, which is used to accurately represent the temperature distribution in the area.

[0032] The monitoring visualization system can obtain in real time the video images taken by the video cameras installed in various areas of the museum, as well as the regional pixel temperatures collected by the thermal imaging cameras.

[0033] It should be noted that, based on the size of the sensor in each thermal imaging camera, the number of pixels in the area that can be collected may be different.

[0034] For example, if the sensor in a thermal imaging camera is W pixels long and H pixels wide, the number of pixels in the area that can be collected can be W*H. The regional pixel temperature collected by a thermal imaging camera represents the temperature of the W*H pixels collected by the thermal imaging camera in the area. For example, the temperature of some pixels is 10°C, the temperature of some pixels is 38°C, and so on, which is not limited here.

[0035] S102, determining the crowding degree of each area based on the video image, where the crowding degree is used to indicate the crowding situation of people in the area; After the monitoring visualization system obtains the video images of each area, it can determine the crowding degree of each area. The crowding degree is used to indicate the crowding situation in the area, usually measured by the number of people per unit area, for example, 10 people per square meter in area A. In practical applications, the specific implementation of determining the crowding degree of each area can be achieved through a preset recognition algorithm, or it can be achieved by using an AI model, or it can be achieved by other video image analysis methods, which are not limited here. It can be understood that in an area, if the crowding degree is higher, it means that there are more people in the area and it is more likely to cause chaos.

[0036] Optionally, in general, based on the video screen, determining the crowding degree of each area can be achieved in the following ways: performing human body detection on the video screen to obtain the number of people in each area; obtaining the regional population density based on the area and the number of people in each area; and determining the crowding degree of each area based on the changing trends of the regional population density and the number of people.

[0037] Human detection refers to the process of using computer vision technology to identify and locate human bodies in video images, which is usually achieved using deep learning algorithms such as YOLO or Faster R-CNN. Regional population density refers to the number of people per unit area, which is used to indicate the concentration of a crowd, such as 3 people per square meter. Change trend refers to the increase or decrease in the number of people over a period of time, which may be rising, falling or stable.

[0038] The monitoring visualization system first continuously receives video images from various video cameras. For each frame of the image, the monitoring visualization system runs a human detection algorithm. This algorithm identifies and marks the location of each human body in the image, which may be represented by a bounding box. The monitoring visualization system counts each predefined area separately to obtain the number of human bodies in each area.

[0039] Next, the monitoring visualization system calls the pre-stored area of ​​each area. The area of ​​each area is usually measured and entered during configuration. The monitoring visualization system divides the number of people in each area by the area of ​​the corresponding area to calculate the regional population density of each area. This calculation is performed regularly, such as once every second or every few seconds.

[0040] The monitoring visualization system then analyzes the trend of the number of people. This usually involves analyzing the number of people within a certain time window (such as the past few minutes). The monitoring visualization system may calculate the growth rate of the number of people, or use more complex time series analysis methods to predict the trend of the number of people in the short term.

[0041] Finally, the monitoring visualization system will comprehensively consider the current regional population density and the trend of the number of people to determine the crowding degree of each area. For example, even if the current regional population density is low, if the trend of the number of people is increasing rapidly, the monitoring visualization system may also give a higher crowding degree.

[0042] The entire process is carried out in real time and in a cycle. The monitoring visualization system will continuously update various indicators to provide museum managers with dynamic crowd distribution and congestion information so that they can make corresponding crowd control decisions in a timely manner.

[0043] S103, determining the thermal activity and thermal crowding of each region based on the regional pixel temperature, where the thermal activity is used to indicate the intensity of crowd activity in the region, and the thermal crowding is used to indicate the density of crowds in the region; After the monitoring visualization system collects the regional pixel temperatures of each area, it can determine the thermal activity and thermal crowding of each area. The thermal activity reflects the average temperature of human pixels in the area, which is used to indicate the intensity of human activity in the area. For example, frequent movement of people or strenuous exercise will lead to higher thermal activity; the thermal crowding reflects the average temperature of pixels in the area, which is used to indicate the density of people in the area, taking into account the combined impact of human density and activity intensity on ambient temperature. For example, in crowded and active indoor places, the thermal crowding is usually higher.

[0044] In practical applications, the thermal activity and thermal crowding of each area can be determined directly by using a preset formula, or by using an AI model, or by using other pixel temperature analysis methods, which are not limited here.

[0045] It is understandable that when people feel uncomfortable in congestion or in a bad mood, their average temperature will also rise. Therefore, in an area, if the thermal activity is higher, it means that the proportion of visitors with greater emotional fluctuations in the area is greater, and the proportion of visitors who feel high temperature is also greater; if the thermal congestion is higher, it means that the emotional fluctuations of visitors in the area are greater and the temperature is higher.

[0046] In some embodiments, in the process of calculating and determining parameters such as crowding, thermal activity and thermal crowding, different parameters can be multiplied by different weighting coefficients based on the actual management needs of the museum to indicate the different levels of attention paid to their impact.

[0047] Optionally, in general, based on the regional pixel temperature, determining the thermal activity and thermal crowding of each region can be achieved in the following ways: based on the regional pixel temperature, determining the thermal activity of each region, the thermal activity is related to the number of human pixels in each region and the sum of the temperatures of the human pixels; calculating the thermal crowding based on the total number of regional pixels obtained by the thermal imaging camera and the sum of the temperatures of the human pixels.

[0048] Human pixels refer to pixels that are identified as human bodies in thermal imaging images. They are usually of higher temperature and have a specific shape. The total number of regional pixels refers to the sum of all pixels in the region, which is used to indicate the size of the entire region. The monitoring visualization system first continuously collects thermal imaging data of the area through a thermal imaging camera. After obtaining the thermal imaging data, the monitoring visualization system performs image processing and analysis. First, the image recognition algorithm is used to identify human pixels in the thermal imaging data. These human pixels usually have high temperature values ​​and specific shape characteristics. Then, the monitoring visualization system counts the number of human pixels in each area and calculates the sum of the temperatures of these human pixels. The calculation of thermal activity takes these two factors into account. For example, a weighted formula may be used, in which the number of human pixels reflects the number of people in the area, and the sum of the temperatures of the human pixels reflects the intensity of the activity. The more frequent the activity, the more heat is generated, and the higher the thermal activity.

[0049] Next, the monitoring visualization system calculates the thermal crowding. This step takes into account the total number of regional pixels (representing the area of ​​the region) and the sum of the temperatures of the human pixels in the entire area. The calculation formula may be the sum of the temperatures of the human pixels divided by the total number of regional pixels, multiplied by a correction factor. In this way, even if there are two areas of the same area, the area with more people or more intense activities will have a higher thermal crowding. The monitoring visualization system will update these calculation results in real time, providing dynamic thermal activity and thermal crowding, helping museum managers to timely evaluate the status of each area, and provide important basis for crowd control and safety management decisions.

[0050] S104, determining a warning index for each area according to the crowding degree, thermal activity degree, and thermal crowding degree of each area, the warning index being used to indicate the risk level of the area; The warning index is an indicator that comprehensively assesses the degree of regional risk. It is calculated by comprehensively considering the crowding degree, thermal activity degree and thermal crowding degree, and is used to guide museum managers to take corresponding safety measures.

[0051] After determining the three indicators of crowding, thermal activity and thermal crowding, the monitoring visualization system uses a preset algorithm or model to comprehensively consider the weights and mutual influence of these three indicators to calculate the warning index for each area. This warning index may be a numerical value, such as 30 points, 50 points and 80 points. The monitoring visualization system can issue corresponding levels of alarms to museum managers based on the level of the warning index and provide targeted response suggestions, such as evacuating the crowd, increasing ventilation or restricting entry. This process is usually ongoing in order to promptly detect and respond to potential safety risks.

[0052] S105. When the warning index exceeds a preset index threshold, the video image and the regional pixel temperature of the corresponding area are magnified and displayed on the display screen.

[0053] The preset index threshold represents the upper limit of the warning index determined in advance. If this value is exceeded, it is regarded as a high-risk state that requires special attention. For example, it may be set to 80 points (out of a total of 100 points); the display screen refers to the electronic screen used to display monitoring information in real time, which is usually installed in the control room or management center of the museum; the video screen refers to the dynamic image captured in real time by the video camera, which is used to intuitively display the on-site situation; the regional pixel temperature refers to the temperature distribution map obtained by the thermal imaging camera. Each human pixel corresponds to a temperature value in the actual area, which is used to accurately display the distribution of hotspots.

[0054] The monitoring visualization system will continuously monitor the warning index of each area. When the warning index of a certain area exceeds the preset index threshold, it means that the area is crowded or there are potential safety hazards. The monitoring visualization system will automatically trigger the alarm mechanism. First, the monitoring visualization system will automatically switch and enlarge the real-time video screen of the area on the main display screen, so that museum managers can quickly and intuitively understand the situation on the scene. At the same time, the monitoring visualization system will also superimpose the regional pixel temperature of the area on the display screen, and present the temperature distribution in the area in the form of pixel color depth or numerical annotation. This superimposed display not only shows the actual scene, but also highlights the potential hot spots, so that museum managers can quickly identify whether there are abnormally high temperatures, fire hazards or excessive gathering of people. In addition, the monitoring visualization system can also mark the warning index and possible risk reasons on the screen. Based on this intuitive information, museum managers can quickly make judgments and take corresponding countermeasures, such as evacuating the crowd, adding security personnel or adjusting environmental conditions. The whole process is automated, but human intervention is usually allowed. Museum managers can manually switch the screens of different areas or adjust display parameters as needed.

[0055] The monitoring visualization system determines the crowding degree, thermal activity degree and thermal crowding degree through video images and regional pixel temperatures. These three indicators provide museum managers with real-time and dynamic monitoring information, greatly improving supervision efficiency and safety. Crowding degree helps to determine which areas have too much traffic and may have safety hazards; thermal activity degree can reflect the intensity of crowd activities, providing a basis for judging whether there are abnormal conditions in certain areas; thermal crowding degree refers to the density of the crowd, which is an important indicator in fire safety control. By combining these three indicators, the monitoring visualization system can comprehensively evaluate the warning index, so that potential risks can be discovered and responded to in a timely manner. When the warning index exceeds the preset index threshold, the monitoring visualization system can lock the area in the museum that needs to be focused on, and automatically enlarge the video image and regional pixel temperature of the corresponding area on the display screen. This instant feedback mechanism provides museum managers with the ability to respond quickly, so that emergency evacuation or other safety measures can be taken, greatly enhancing the museum's security capabilities and creating a safer and more comfortable environment for visitors.

[0056] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the museum monitoring visualization method in the embodiment of the present application.

[0057] S201, obtaining video images captured by video cameras in various areas of the museum and regional pixel temperatures collected by thermal imaging cameras; For details, please refer to step S101, which will not be described in detail here.

[0058] S202: Determine the crowding degree of each area based on the video image, where the crowding degree is used to indicate the crowding situation of people in the area; For details, please refer to step S102, which will not be described in detail here.

[0059] S203, determining the thermal activity and thermal crowding of each region based on the regional pixel temperature, where the thermal activity is used to indicate the intensity of crowd activity in the region, and the thermal crowding is used to indicate the density of crowds in the region; For details, please refer to step S103, which will not be described in detail here.

[0060] S204, determining a warning index for each area according to the crowding degree, thermal activity degree, and thermal crowding degree of each area, the warning index being used to indicate the risk level of the area; For details, please refer to step S104, which will not be described in detail here.

[0061] S205: When the warning index exceeds a preset index threshold, the video image and the regional pixel temperature of the corresponding area are magnified and displayed on the display screen.

[0062] For details, please refer to step S105, which will not be described in detail here.

[0063] S206, performing face recognition through a high-definition camera to extract facial features; A high-definition camera refers to a video acquisition device that can capture high-resolution images, usually with a resolution of at least 1080p or higher, and is used to obtain clear facial images; face recognition refers to the process of automatically detecting, locating, and identifying faces using computer vision and machine learning techniques; facial features refer to the digital representation that can uniquely identify a face, usually including the position, shape, and texture information of key points such as eyes, nose, and mouth. For example, a feature vector containing 128 or 512 numerical values ​​may be extracted.

[0064] First, the surveillance visualization system continuously captures real-time video streams through high-definition cameras. These high-definition cameras are usually strategically placed at places where people must pass, such as entrances, passages, or key areas, to ensure that clear frontal facial images can be captured.

[0065] Next, the surveillance visualization system runs a face detection algorithm on each frame of the video stream or on selected key frames. The face detection algorithm quickly scans the entire image and identifies areas that may contain faces. Common methods include Haar cascade classifiers or deep learning-based detectors such as MTCNN.

[0066] Once a face is detected, the surveillance visualization system preprocesses the area containing the face. Preprocessing includes cropping, alignment (based on the location of eyes or other landmarks), adjusting brightness and contrast, etc. This step is intended to standardize the face image and improve the accuracy of subsequent facial feature extraction.

[0067] The monitoring visualization system then uses a facial feature extraction algorithm, such as a deep learning-based model (such as FaceNet, DeepFace, or ArcFace), to convert the preprocessed face image into a high-dimensional feature vector as a facial feature. This high-dimensional feature vector is usually an array containing hundreds of values, each of which represents an abstract feature of the face.

[0068] S207, comparing the facial features with a preset blacklist database; The preset blacklist database refers to a pre-established data set containing facial features of specific people, which is used to store individual information that requires special attention or restricted access, such as known criminal suspects or persons prohibited from entering.

[0069] When the monitoring visualization system obtains the facial features through the above steps, it will immediately start the comparison procedure. First, the monitoring visualization system will access the pre-stored blacklist database. This blacklist database is usually designed as an efficient index structure to support fast query and matching. The comparison process usually uses vector similarity calculation methods, such as cosine similarity or Euclidean distance. The monitoring visualization system will compare the newly acquired facial features with each record in the blacklist database. This process may be accelerated using parallel computing technology, especially when searching in large-scale databases.

[0070] The monitoring visualization system will set a similarity threshold. Only when the calculated similarity exceeds this similarity threshold will it be considered a potential match. The setting of the similarity threshold requires a balance between accuracy and recall, avoiding false positives (misjudging people who are not on the blacklist as blacklisted people) and trying not to miss real blacklisted people.

[0071] S208. If a blacklisted person is detected, the movement trajectory of the blacklisted person is recorded in real time and dynamically marked on the display screen; Blacklisted persons refer to individuals who are included in the blacklist database and require special attention or restrictions; movement trajectory represents the location information of blacklisted persons in the area over time, usually represented by a series of coordinate points or path lines; dynamic marking refers to a visualization technology that highlights blacklisted persons in real-time video footage, such as using boxes or arrows of different colors to mark the location of blacklisted persons.

[0072] When the monitoring visualization system detects a blacklisted person through face recognition technology, it immediately initiates the tracking procedure. First, the monitoring visualization system marks the blacklisted person at the initial position detected, possibly using a striking red box or other prominent visual elements. Next, the monitoring visualization system initiates a multi-target tracking algorithm, such as SORT (Simple Online and Realtime Tracking) or DeepSORT, to maintain identification and tracking of the target in consecutive video frames. As the blacklisted person moves in the area, the monitoring visualization system updates their location information in real time. These location information are converted into coordinate points in the site coordinate system to form a continuous movement trajectory. On the display screen, the monitoring visualization system dynamically updates the location mark of the blacklisted person. This may appear as a constantly moving mark box or a trajectory line extending over time. In order to facilitate museum managers to quickly locate, the monitoring visualization system may automatically switch to the video screen where the blacklisted person is located and mark the current location of the blacklisted person on the global map.

[0073] S209, obtaining the real-time operating status of each device in the museum; Equipment refers to various hardware facilities in the museum used to maintain normal operations, protect exhibits and serve visitors, such as environmental control equipment, lighting equipment, exhibit protection equipment, fire-fighting equipment, security equipment, etc.; real-time operating status indicates the current working status of the equipment, including whether it is turned on, operating parameters, fault information, etc.

[0074] First, each device will be equipped with corresponding sensors. For example, environmental control equipment may be equipped with temperature sensors and humidity sensors; lighting equipment may have light intensity sensors; security equipment may include motion detectors and camera status detectors. These sensors will continuously monitor various parameters of the equipment. Next, these devices are connected to the monitoring visualization system through a wired network (such as Ethernet) or a wireless network (such as Wi-Fi, ZigBee, or LoRaWAN). The monitoring visualization system regularly polls the real-time operating status of each device, which may include: the device's on / off status, operating time, current parameter values ​​(such as temperature, humidity, power, etc.), etc.

[0075] S210, calculating the failure risk index of each device according to the real-time operating status of each device; The failure risk index indicates the probability of equipment failure, usually expressed as a numerical value, where a higher value indicates a greater failure risk. In practical applications, the specific implementation of calculating the failure risk index of each device can be achieved through a preset recognition algorithm or an AI model, which is not limited here.

[0076] S211. When the failure risk index of the target device exceeds a preset risk threshold, a warning message is displayed on the display screen, wherein the warning message includes the location information of the target device, the failure risk index, and the inspection route; The target device refers to the specific device currently being monitored and evaluated; the preset risk threshold refers to the pre-set risk warning line used to determine whether early warning measures need to be taken; the early warning information refers to the notification generated by the monitoring visualization system to alert museum managers to potential problems; the location information indicates the specific location of the target device in the museum; the inspection route refers to the optimal travel path for museum managers from their current location to the target device.

[0077] When the monitoring visualization system calculates the failure risk index of a device, it will immediately compare it with the preset risk threshold, which can be dynamically adjusted according to the importance of the device, seasonal changes, or special events. If it is found that the failure risk index of a device exceeds the preset risk threshold, the monitoring visualization system will immediately start the early warning program and pop up a striking warning window on the main display to display the early warning information. This warning window usually uses warning colors such as red or yellow, and may be accompanied by flashing effects or sound prompts to ensure that museum managers can immediately notice it.

[0078] The content of the warning information will be automatically generated by the monitoring visualization system, including the following key parts: Basic information of the target device: including device name, model, function, etc.

[0079] Location information: may include floor, room number, or even specific coordinates.

[0080] Fault risk index: Displays the currently calculated fault risk index, which may be represented by both numbers and colors (e.g., 85 points, red indicates high risk). At the same time, the monitoring visualization system may display a historical trend chart of the fault risk index to help operators determine whether the risk is rising suddenly or accumulating continuously.

[0081] Inspection route: The monitoring visualization system will automatically plan an optimal inspection route based on the current location of the museum manager (which may be obtained through the employee positioning system). This inspection route will take into account the layout of the museum, possible obstacles, and whether it is necessary to pass through specific security checkpoints.

[0082] S212, real-time monitoring of the current visitor flow of the museum; The current passenger flow refers to the actual number of visitors in the museum during a preset time period. This step is usually carried out continuously during the museum's opening hours, and is especially important during holidays, special exhibitions, or other periods that may lead to a surge in passenger flow. Specifically, the museum will use a variety of technical means and methods to monitor the current passenger flow in real time, such as installing high-precision people counting equipment at the main entrances and exits of the museum. These people counting equipment may include: infrared sensors, 3D stereo vision sensors, thermal imaging cameras, etc., which are not limited here.

[0083] S213. If the current passenger flow reaches the maximum capacity of the museum's visitors, a prompt for visitors to temporarily postpone entry is displayed on the display screen; The maximum visitor capacity is used to indicate the maximum number of visitors that a museum can accommodate during a preset period of time. If the current passenger flow reaches the maximum visitor capacity of the museum, it means that there are too many people in the museum during the preset period of time, and the safety of the people and the quality of the visit cannot be guaranteed. Therefore, certain adjustments and guidance are needed to ensure the safety of the people and improve the visiting experience of the people in the museum. The monitoring visualization system displays a reminder for visitors to postpone entry on the display screen to inform museum managers that there are too many visitors at this time, and it is not appropriate to increase the number of visitors, and they need to postpone entry.

[0084] S214, storing the detected abnormal events and corresponding processing measures in a to-do database; An abnormal event refers to any situation or event that deviates from the normal operating state or expected behavior; a handling measure represents a specific action or strategy taken to resolve or mitigate an abnormal event; and a to-do database is used to store and manage abnormal events that need to be handled.

[0085] This step is usually executed immediately when the monitoring visualization system detects an abnormal event, and is a key link in tracking and managing abnormal events. Specifically, when the monitoring visualization system detects an abnormal event through various sensors, cameras or other monitoring devices, a series of automated processes will be triggered: (1) First, the monitoring visualization system performs preliminary analysis and classification of detected abnormal events. This may include: Equipment failure (such as abnormal air conditioning system, lighting equipment failure); Security threats (e.g., unauthorized entry, suspicious items); Environmental abnormalities (such as excessive temperature and humidity, and detection of harmful gases); Abnormal passenger flow (e.g. high density of people in a certain area); (2) Based on a preset rule base or machine learning algorithm, the monitoring visualization system automatically generates recommended treatment measures for each abnormal event. For example: For equipment failure: equipment restart, emergency repair or replacement of spare equipment may be recommended; For security threats: it may be recommended to dispatch security personnel, lock down specific areas, or notify relevant departments; For environmental anomalies: it may be recommended to adjust air conditioning parameters, start dehumidification equipment, or evacuate specific areas; (3) The monitoring visualization system will format abnormal events and corresponding processing measures into a standard data structure and then store them in the to-do database.

[0086] S215, real-time monitoring of the processing status of the to-do items in the to-do database, the processing status including processing efficiency and processing quality; There are many ways to monitor the processing of to-do items in the to-do database, such as calculating the time from creation to completion of each to-do item, and comparing it with the preset standard processing time to obtain the processing efficiency of the to-do items; collecting feedback information on the to-do items, which may include subsequent monitoring data of the affected areas, or evaluations of relevant personnel, so as to determine the processing quality of the to-do items, which is not limited here.

[0087] S216. Generate a performance report based on the processing efficiency and quality of the to-do items.

[0088] According to the preset scoring criteria, the monitoring visualization system comprehensively processes various indicators of efficiency and quality and calculates the overall performance score. The monitoring visualization system outputs the processing status and performance scores of the to-do items in the to-do database as a performance report. The specific content and presentation form of the performance report are not limited here.

[0089] The monitoring visualization system in the embodiment of the present invention is described below from the perspective of hardware processing. Figure 3 , which is a schematic diagram of a physical device structure of a monitoring visualization system in an embodiment of the present application.

[0090] It should be noted that Figure 3 The structure of the monitoring visualization system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0091] like Figure 3As shown, the monitoring visualization system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0092] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0093] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0094] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0095] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0096] Specifically, the monitoring visualization system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the museum monitoring visualization method provided by the above embodiment is implemented.

[0097] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the monitoring visualization system described in the above embodiment; or may exist independently without being assembled into the monitoring visualization system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the monitoring visualization system, the monitoring visualization system implements the museum monitoring visualization method provided in the above embodiment.

[0098] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0099] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0100] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A museum monitoring visualization method, characterized in that: Applied to a monitoring visualization system, the method comprises: Obtain the video images captured by the video cameras in each area of ​​the museum, and the regional pixel temperatures collected by the thermal imaging cameras; Based on the video images, determining the crowding degree of each area, wherein the crowding degree is used to indicate the crowding situation of people in the area; Based on the pixel temperature of the region, determining the thermal activity and thermal crowding of each region, wherein the thermal activity is used to indicate the intensity of crowd activity in the region, and the thermal crowding is used to indicate the density of crowds in the region; Determine a warning index for each area according to the crowding degree, thermal activity degree and thermal crowding degree of each area, wherein the warning index is used to indicate the risk level of the area; When the warning index exceeds a preset index threshold, the video image and the regional pixel temperature of the corresponding area are magnified and displayed on the display screen.

2. The method according to claim 1, characterized in that The determining of the crowding degree of each area based on the video image, wherein the crowding degree is used to indicate the crowding situation of people in the area, specifically includes: Performing human body detection on the video screen to obtain the number of human bodies in each area; According to the area and human population of each region, the regional population density is obtained; The crowding degree of each area is determined based on the changing trends of the population density and the number of people in the area.

3. The method according to claim 1, characterized in that Determining the thermal activity and thermal crowding of each region based on the regional pixel temperature specifically includes: Determining the thermal activity of each region based on the pixel temperature of the region, wherein the thermal activity is related to the number of human body pixels in each region and the sum of the temperatures of the human body pixels; The thermal crowding degree is calculated based on the sum of the total number of regional pixel points acquired by the thermal imaging camera and the temperature of the human body pixel points.

4. The method according to claim 1, characterized in that: The method further comprises: Get the real-time operating status of each device in the museum; Calculate the failure risk index of each device according to the real-time operating status of each device; When the failure risk index of the target device exceeds a preset risk threshold, a warning message is displayed on the display screen, wherein the warning message includes the location information of the target device, the failure risk index, and the inspection route.

5. The method according to claim 1, characterized in that After the step of magnifying and displaying the video image and the pixel temperature of the corresponding area on the display screen when the warning index exceeds the preset index threshold, the method further includes: Monitor the museum’s current visitor flow in real time; If the current passenger flow reaches the maximum visitor capacity of the museum, a prompt for visitors to postpone entry will be displayed on the display screen.

6. The method according to claim 1, characterized in that After the step of magnifying and displaying the video image and the pixel temperature of the corresponding area on the display screen when the warning index exceeds the preset index threshold, the method further includes: Perform face recognition and extract facial features through high-definition cameras; Comparing the facial features with a preset blacklist database; If a blacklisted person is detected, the movement trajectory of the blacklisted person is recorded in real time and dynamically marked on the display screen.

7. The method according to claim 1, characterized in that The method further comprises: Storing detected abnormal events and corresponding treatment measures in a to-do database; Real-time monitoring of the processing status of the to-do items in the to-do database, wherein the processing status includes processing efficiency and processing quality; Generate a performance report based on the processing efficiency and processing quality of the to-do items.

8. A monitoring visualization system, characterized in that: The monitoring visualization system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the monitoring visualization system to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a monitoring and visualization system, the monitoring and visualization system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is executed on a monitoring and visualization system, the monitoring and visualization system is enabled to execute the method according to any one of claims 1 to 7.

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