A method and system for displaying data on a screen based on the Internet of Things

Abstract

This invention relates to the field of data display technology, specifically disclosing a data display method and system based on the Internet of Things (IoT). The method includes determining scene identifiers for each camera based on their installation locations, determining multiple path trajectories based on these scene identifiers, comparing real-time acquired video data from each camera with preset silent images, filtering the acquired video data based on the comparison results, pre-adjusting the clarity of each camera based on the filtered video data and the multiple path trajectories, displaying and storing the filtered real-time video data on the display screen, and deleting completely silent video data. This avoids occupying hard drive memory. By adjusting the clarity according to the movement of living beings in the monitored scene, the method reduces hard drive memory usage while improving video clarity, thus making the monitoring video clearer.

Description

Technical Field

[0001] This invention relates to the field of data display technology, specifically a data display method and system based on the Internet of Things (IoT). Background Technology

[0002] In daily life, it is common to find that commercial establishments typically install a certain number of cameras to avoid disputes and property losses. These cameras collect real-time surveillance video of the equipment in the area, which is then displayed on a screen in the monitoring room and stored on a hard drive. This allows for review of the surveillance video in case of disputes or property losses.

[0003] However, due to the large area of ​​the business premises and the large number of cameras, the video images of the corresponding cameras projected on the display screen are relatively small. The large number of images makes manual monitoring difficult and leads to delays in investigating property losses. As a result, the safety of personnel and property in the business premises cannot be guaranteed. Summary of the Invention

[0004] The purpose of this invention is to provide a data display method and system based on the Internet of Things (IoT) to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a data display method for a display screen based on the Internet of Things, the method comprising:

[0006] Obtain the installation location of each camera, determine the scene identifier of each camera based on the installation location, and determine multiple paths based on the scene identifier of each camera.

[0007] The system acquires real-time video data from various cameras and compares the acquired video data with preset still images.

[0008] Based on the comparison results, the obtained video data from each camera is filtered to obtain filtered video data.

[0009] The clarity of each camera is pre-adjusted based on filtered video data and multiple path trajectories;

[0010] The filtered real-time video data is displayed on the screen and then stored.

[0011] As a further embodiment of the present invention, the step of determining multiple trajectories based on scene identifiers from various cameras specifically includes:

[0012] Acquire historical surveillance video data and determine the movement trajectory of organisms based on the historical surveillance video data;

[0013] The path is determined based on the frequency of occurrence of the organism's movement trajectory, and the path is a timeline of the trajectory from multiple cameras;

[0014] The distance between cameras is determined based on their travel trajectories.

[0015] As a further embodiment of the present invention, the step of comparing the real-time acquired video data from each camera with a preset silent image specifically includes:

[0016] The system acquires silent images from each camera and extracts several image data frames from the real-time monitoring video data of each camera.

[0017] The obtained image data was compared with the silent image.

[0018] The silent images of each camera are updated based on the surveillance video data from each camera.

[0019] As a further embodiment of the present invention, the step of filtering the acquired video data from each camera based on the comparison results to obtain the filtered video data specifically includes:

[0020] Once the obtained image data is successfully compared with the silent image, the real-time video data from each camera is deleted.

[0021] When the obtained image data fails to be compared with the silent image, the real-time video data from each camera is divided into video data of the changing area and video data of the static environment area.

[0022] Video data from static environmental areas is deleted, and video data from changing areas is used as the filtered video data.

[0023] As a further embodiment of the present invention, the step of pre-adjusting the clarity of each camera based on the filtered video data and multiple path trajectories specifically includes:

[0024] The installation location of the camera used to obtain filtered video data;

[0025] The camera scene identifiers on the trajectory are determined based on the camera installation location of the filtered video data.

[0026] The resolution of the next camera on the path is pre-adjusted based on the path interval.

[0027] As a further aspect of the present invention, the remaining storage space of the hard disk is obtained in real time, and a data overwrite date is generated based on the remaining storage space of the hard disk.

[0028] As a further embodiment of the present invention, the step of generating a data estimation overwrite date based on the remaining storage space of the hard disk specifically includes:

[0029] Obtain the daily data size from the start of storage on the hard drive to the current day, as well as the daily attributes from the start of storage on the hard drive to the current day. The daily attributes include weekdays, statutory holidays, and rest days.

[0030] The average daily storage data size for weekdays, statutory holidays, and rest days is determined based on the daily storage data size.

[0031] The estimated data coverage date is determined based on the average daily data storage size on weekdays, public holidays, and rest days.

[0032] The present invention also provides a data display system based on the Internet of Things, the system comprising:

[0033] The trajectory determination module is used to obtain the installation location of each camera, determine the scene identifier of each camera based on the installation location, and determine multiple paths based on the scene identifier of each camera.

[0034] The comparison module is used to acquire real-time monitoring video data from each camera and compare the real-time acquired monitoring video data from each camera with preset silent images.

[0035] The filtering module is used to filter the acquired video data from each camera based on the comparison results, and obtain the filtered video data.

[0036] The clarity adjustment module is used to pre-adjust the clarity of each camera based on the filtered video data and multiple path trajectories.

[0037] The display module is used to display and store the filtered real-time video data on the screen.

[0038] As a further embodiment of the present invention, the trajectory determination module specifically includes:

[0039] The historical data acquisition unit is used to acquire historical surveillance video data and determine the movement trajectory of organisms based on the historical surveillance video data.

[0040] The trajectory determination unit is used to determine the trajectory based on the number of times the organism's movement trajectory appears, wherein the trajectory is a timeline of the trajectory from multiple cameras;

[0041] An interval determination unit is used to determine the path interval between cameras based on the path trajectory.

[0042] As a further embodiment of the present invention, the comparison module specifically includes:

[0043] The extraction unit is used to acquire silent images from each camera and extract several image data frames by frame from the real-time monitoring video data of each camera.

[0044] The comparison unit is used to compare the obtained image data with the silent image.

[0045] The update unit is used to update the silent images of each camera based on the surveillance video data of each camera.

[0046] Compared with existing technologies, the beneficial effects of this invention are as follows: Due to storage space considerations, surveillance video from cameras in business premises is typically overwritten after three months. Short-term overwriting leads to the inability to trace the video after three months. By acquiring real-time camera surveillance video data and comparing it with preset silent images, completely silent video data can be deleted, avoiding hard drive memory usage and extending the surveillance video overwrite time. This facilitates the retrieval of surveillance video by regulatory authorities and reduces the workload of staff in the monitoring room. Furthermore, the clarity is adjusted based on the movement of any living organisms in the monitored area. Lower clarity is used when no living organisms are present, reducing hard drive memory usage. In cases where living organisms are present, increased clarity makes the surveillance video clearer, facilitating timely identification by staff and enabling prompt responses to emergencies. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0048] Figure 1 This is a flowchart illustrating a data display method for an Internet of Things (IoT) display screen, as provided in an embodiment of the present invention.

[0049] Figure 2 This is a flowchart illustrating the steps of determining the scene identifier of each camera based on its installation location and determining multiple trajectories based on the scene identifier of each camera, as provided in an embodiment of the present invention.

[0050] Figure 3 This is a flowchart illustrating the steps of comparing real-time acquired video data from various cameras with preset silent images, as provided in an embodiment of the present invention.

[0051] Figure 4This is a flowchart illustrating the steps for filtering the acquired surveillance video data from various cameras based on comparison results, as provided in an embodiment of the present invention.

[0052] Figure 5 This is a flowchart illustrating the steps for pre-adjusting the clarity of each camera based on filtered video data and multiple path trajectories, as provided in an embodiment of the present invention.

[0053] Figure 6 This is a structural block diagram of an Internet of Things (IoT) based display screen data display system provided in an embodiment of the present invention.

[0054] Figure 7 This is a block diagram illustrating the composition of the trajectory determination module provided in an embodiment of the present invention.

[0055] Figure 8 This is a block diagram illustrating the structural composition of the comparison module provided in an embodiment of the present invention. Detailed Implementation

[0056] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0057] Figure 1 This is a flowchart illustrating a data display method for an Internet of Things (IoT)-based display screen. In this embodiment of the invention, a data display method for an IoT-based display screen includes:

[0058] Step S100: Obtain the installation location of each camera, determine the scene identifier of each camera based on the installation location of each camera, and determine multiple paths based on the scene identifier of each camera.

[0059] Step S200: Acquire real-time monitoring video data from each camera and compare the real-time acquired monitoring video data from each camera with preset silent images.

[0060] Step S300: Based on the comparison results, filter the obtained video data from each camera to obtain filtered video data;

[0061] Step S400: Based on the filtered video data and multiple path trajectories, the clarity of each camera is pre-adjusted;

[0062] Step S500: Display the filtered real-time video data on the screen and store it.

[0063] In this embodiment, the scene identifiers corresponding to each camera are determined by first obtaining the installation location of the camera. Taking a hotel's business premises as an example, the camera's installation location is a hotel corridor, meaning the scene identifier corresponding to this camera is the corridor outside the room. Other scene identifiers can be the hotel registration office, stairs, elevator, dining area, and hotel entrance. Multiple path routes can be determined through these scene identifiers. For example, the following path routes exist: hotel entrance - hotel registration office - stairs or elevator - corridor outside the room; corridor outside the room - dining area - stairs or elevator - hotel registration office - hotel entrance; corridor outside the room - stairs or elevator - hotel registration office - hotel entrance. Due to storage space considerations, the video surveillance footage from the business premises is generally overwritten after 3 months. Short-term data overwriting will result in the video being lost after 3 months. Unable to trace back after one month, real-time camera surveillance video data is obtained. By comparing this data with preset silent images, completely silent video data can be deleted, avoiding hard drive memory usage and extending the surveillance video coverage time. This facilitates the retrieval of surveillance video by regulatory authorities and reduces the workload of staff in the monitoring room. Furthermore, the resolution is adjusted based on the movement of any living organisms in the monitored area. Lower resolution is used when no living organisms are present, reducing hard drive memory usage. In cases where living organisms are present, higher resolution makes the surveillance video clearer, allowing staff to identify them promptly and respond to emergencies in a timely manner.

[0064] like Figure 2 As shown, in a preferred embodiment of the present invention, the step of determining multiple trajectories based on scene identifiers from each camera specifically includes:

[0065] Step S101: Obtain historical surveillance video data and determine the movement trajectory of the organism based on the historical surveillance video data;

[0066] Step S102: Determine the path based on the number of times the organism's movement trajectory appears, wherein the path is a timeline of the trajectory from multiple cameras;

[0067] Step S103: Determine the path interval between cameras based on the path trajectory.

[0068] In this embodiment, historical surveillance video data is obtained, and surveillance videos within a certain period are selected from the historical surveillance video data. People in the surveillance videos are identified. The movement trajectories of people and the time points when a specified person appears on each camera are obtained based on the surveillance video data of each camera. A certain number of people's movement trajectories are selected and classified according to the movement trajectory routes. A frequency threshold is preset, and the frequency of a certain movement trajectory route is compared with the threshold. If the frequency of a certain movement trajectory route is greater than or equal to the threshold, the movement trajectory route is determined as a passing trajectory. The time points when different people appear on each camera in a certain movement trajectory route are obtained. The time points when people appear on each camera determine the maximum and minimum passing interval time between adjacent cameras. The passing trajectory includes the camera scene identifiers passed by the trajectory, and the passing interval represents the time interval from when a specified person just appears on one camera to when they appear on the next camera.

[0069] like Figure 3 As shown, in a preferred embodiment of the present invention, the step of comparing the real-time acquired video data from each camera with a preset still image specifically includes:

[0070] Step S201: Obtain the silent images of each camera and extract several image data frame by frame from the real-time monitoring video data of each camera.

[0071] Step S202: Compare the obtained image data with the silent image;

[0072] Step S203: Update the silent images of each camera based on the monitoring video data of each camera.

[0073] In this embodiment, the real-time video data from various cameras is extracted frame by frame to convert the video data into image data. This image data is then compared with a static image, which is initially a scene environment image. When the real-time video data from the cameras remains completely consistent over a certain period, a static image of any frame is acquired, and the static image is updated to reflect this static image. In the hotel's nighttime surveillance data, for a considerable period, the surveillance videos inside the elevator, on the stairs, and in the corridors outside the rooms remain static, meaning no living beings (including humans, cats, and dogs) pass by. In existing technologies, these videos are still stored, wasting storage space, and are still displayed on the monitor in the monitoring room, resulting in numerous video areas on the monitor and making it difficult for monitoring staff to monitor the footage.

[0074] like Figure 4As shown, in a preferred embodiment of the present invention, the step of filtering the acquired video data from each camera based on the comparison results to obtain filtered video data specifically includes:

[0075] Step S301: After the obtained image data is successfully compared with the silent image, the real-time obtained video data from each camera is deleted.

[0076] Step S302: When the obtained image data fails to be compared with the silent image, the real-time obtained video data from each camera is divided into video data of the changing area and video data of the static environment area.

[0077] Step S303: Delete the video data of the static environment area and use the video data of the changing area as the filtered video data.

[0078] In this embodiment, by deleting the surveillance video data after a successful comparison, the storage space occupied can be reduced, thereby extending the time that the storage space is overwritten, allowing the hard drive to retain surveillance videos for a longer period of time. When a comparison fails, it indicates that a living organism has passed through the surveillance video. At this time, the surveillance video is segmented, and the video data of the changed area is displayed on the screen, thereby reducing the monitoring burden on the staff in the monitoring room and enabling direct location of the area where the living organism passed. Secondly, segmenting the surveillance video can also reduce the space occupied on the hard drive.

[0079] like Figure 5 As shown, in a preferred embodiment of the present invention, the step of pre-adjusting the clarity of each camera based on the filtered video data and multiple path trajectories specifically includes:

[0080] Step S401: Obtain the camera installation location of the filtered video data;

[0081] Step S402: Determine the camera scene identifier on the path based on the camera installation location of the filtered video data;

[0082] Step S403: Adjust the resolution of the next camera on the path in advance based on the path interval.

[0083] In this embodiment, the filtered video data refers to video data showing the movement of a living organism. For example, in a hotel setting, the camera scene marker for the living organism is typically first the hotel entrance. Following the path from the hotel entrance to the hotel registration desk, then to the stairs or elevator, and finally to the room door corridor, the next camera scene marker should be the hotel registration desk. Therefore, the resolution of the next camera is pre-adjusted. The resolution adjustment interval is the minimum of the path interval time. Simultaneously, the time a designated person stays in a single camera is acquired, and the maximum stay time is selected as the dwell time. The duration t of the resolution is... i , t i =a i -b i +e i , where t i Let a be the duration of the sharpness of the i-th camera. i b is the maximum value of the time interval between the passing of the previous camera and the previous camera. i e is the minimum time interval between the passing of the previous camera and the previous camera. i Based on the dwell time, the clarity can be adjusted when a living organism passes by, thereby improving the clarity of the video display on the screen and making it easier to identify events occurring in the video.

[0084] As a preferred embodiment of the present invention, it further includes obtaining the remaining storage space of the hard disk in real time and generating a data estimated overwrite date based on the remaining storage space of the hard disk.

[0085] In this embodiment, by obtaining the remaining storage space on the hard drive, it is easy to generate an estimated overlay date for video data, and it is easy to selectively copy important data to other external hard drives for storage.

[0086] In a preferred embodiment of the present invention, the step of generating a data estimation overwrite date based on the remaining hard disk storage space specifically includes:

[0087] Obtain the daily data size from the start of storage on the hard drive to the current day, as well as the daily attributes from the start of storage on the hard drive to the current day. The daily attributes include weekdays, statutory holidays, and rest days.

[0088] The average daily storage data size for weekdays, statutory holidays, and rest days is determined based on the daily storage data size.

[0089] The estimated data coverage date is determined based on the average daily data storage size on weekdays, public holidays, and rest days.

[0090] In this embodiment, by distinguishing daily attributes, the hard drive overwrite date can be accurately estimated. It is known that the flow of people in a business premises on weekdays, statutory holidays, and rest days is completely different. Therefore, in this embodiment, the above attributes are distinguished and the average daily storage data is calculated separately to obtain the future weekdays, statutory holidays, and rest days. The number of weekdays, statutory holidays, and rest days is multiplied by the corresponding daily storage data, and the overwrite date is estimated based on the product result.

[0091] like Figure 6 As shown in the figure, this embodiment of the invention also provides a display screen data display system based on the Internet of Things, the system comprising:

[0092] The trajectory determination module 100 is used to obtain the installation location of each camera, determine the scene identifier of each camera based on the installation location of each camera, and determine multiple paths based on the scene identifier of each camera.

[0093] The comparison module 200 is used to acquire real-time monitoring video data from each camera and compare the real-time acquired monitoring video data from each camera with preset silent images.

[0094] The filtering module 300 is used to filter the acquired video data from each camera based on the comparison results to obtain filtered video data.

[0095] The clarity adjustment module 400 is used to pre-adjust the clarity of each camera based on the filtered video data and multiple path trajectories.

[0096] The display module 500 is used to display and store the filtered real-time video data on the screen.

[0097] like Figure 7 As shown, in a preferred embodiment of the present invention, the trajectory determination module 100 specifically includes:

[0098] The historical data acquisition unit 101 is used to acquire historical surveillance video data and determine the movement trajectory of the organism based on the historical surveillance video data.

[0099] The trajectory determination unit 102 is used to determine the trajectory based on the number of times the organism's movement trajectory appears, wherein the trajectory is a timeline of the trajectory from multiple cameras.

[0100] The interval determination unit 103 is used to determine the path interval between cameras based on the path trajectory.

[0101] like Figure 8 As shown, in a preferred embodiment of the present invention, the comparison module 200 specifically includes:

[0102] Extraction unit 201 is used to acquire silent images from each camera and extract several image data frames by frame from the real-time monitoring video data of each camera.

[0103] The comparison unit 202 is used to compare the obtained image data with the silent image;

[0104] The update unit 203 is used to update the silent images of each camera based on the surveillance video data of each camera.

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for displaying data on a screen based on the Internet of Things, characterized in that, The method includes: Obtain the installation location of each camera, determine the scene identifier of each camera based on the installation location, and determine multiple paths based on the scene identifier of each camera. The system acquires real-time video data from various cameras and compares the acquired video data with preset still images. Based on the comparison results, the obtained video data from each camera is filtered to obtain filtered video data. During the current acquisition process, the resolution of the next camera in the path of the current camera is pre-adjusted based on the filtered video data and multiple path trajectories. The filtered real-time video data is displayed on the screen and then stored. The steps for determining multiple trajectories based on scene identifiers from various cameras specifically include: Acquire historical surveillance video data, determine the movement trajectory of the organism based on the historical surveillance video data, wherein the historical surveillance video data includes historical surveillance image data and scene identifiers of each camera; The path is determined based on the frequency of occurrence of the organism's movement trajectory, and the path is a combination of cameras arranged in sequence based on the organism's movement trajectory. Determine the distance between cameras based on their trajectory; The step of comparing the real-time acquired video data from each camera with a preset silent image specifically includes: The system acquires silent images from each camera and extracts several image data frames from the real-time monitoring video data of each camera. The obtained image data was compared with the silent image. Update the silent images of each camera based on the surveillance video data of each camera; The step of filtering the acquired video data from each camera based on the comparison results to obtain the filtered video data specifically includes: Once the obtained image data is successfully compared with the silent image, the real-time video data from each camera is deleted. When the obtained image data fails to be compared with the silent image, the real-time video data from each camera is divided into video data of the changing area and video data of the static environment area. Video data from static environmental areas is deleted, and video data from changing areas is used as the filtered video data.

2. The method for displaying data on a screen based on the Internet of Things according to claim 1, characterized in that, The step of pre-adjusting the clarity of each camera based on filtered video data and multiple path trajectories during the current acquisition process specifically includes: The installation location of the camera used to obtain filtered video data; The current camera scene identifier on the trajectory is determined based on the camera installation location of the filtered video data. The resolution of the next camera in the path of the current camera is adjusted in advance based on the path interval.

3. The method for displaying data on a screen based on the Internet of Things according to claim 1, characterized in that, Get the remaining hard drive storage space in real time, and generate an estimated data overwrite date based on the remaining hard drive storage space.

4. The method for displaying data on a screen based on the Internet of Things according to claim 3, characterized in that, The step of generating a data estimate overwrite date based on the remaining hard disk storage space specifically includes: Obtain the daily data size from the start of storage on the hard drive to the current day, as well as the daily attributes from the start of storage on the hard drive to the current day. The daily attributes include weekdays, statutory holidays, and rest days. The average daily storage data size for weekdays, statutory holidays, and rest days is determined based on the daily storage data size. The estimated data coverage date is determined based on the average daily data storage size on weekdays, public holidays, and rest days.

5. A display screen data display system based on the Internet of Things (IoT), used to implement the display screen data display method based on the Internet of Things (IoT) as described in any one of claims 1-4, characterized in that, The system includes: The trajectory determination module is used to obtain the installation location of each camera, determine the scene identifier of each camera based on the installation location, and determine multiple paths based on the scene identifier of each camera. The comparison module is used to acquire real-time monitoring video data from each camera and compare the real-time acquired monitoring video data from each camera with preset silent images. The filtering module is used to filter the acquired video data from each camera based on the comparison results, and obtain the filtered video data. The clarity adjustment module is used to pre-adjust the clarity of the next camera in the path of the current camera based on the filtered video data and multiple path trajectories during the current acquisition process. The display module is used to display and store the filtered real-time video data on the screen.

6. A data display system based on the Internet of Things according to claim 5, characterized in that, The trajectory determination module specifically includes: The historical data acquisition unit is used to acquire historical surveillance video data and determine the movement trajectory of organisms based on the historical surveillance video data. A path determination unit is used to determine a path based on the number of times the organism's movement trajectory appears, wherein the path is a combination of cameras arranged in sequence based on the organism's movement trajectory. An interval determination unit is used to determine the path interval between cameras based on the path trajectory.

7. A data display system based on the Internet of Things according to claim 5, characterized in that, The comparison module specifically includes: The extraction unit is used to acquire silent images from each camera and extract several image data frames by frame from the real-time monitoring video data of each camera. The comparison unit is used to compare the obtained image data with the silent image. The update unit is used to update the silent images of each camera based on the surveillance video data of each camera.

Images