Display screen data display method and system based on Internet of Things

Abstract

The invention relates to the technical field of data display, and particularly discloses a display screen data display method and system based on the Internet of Things, and the method comprises the steps: determining the scene identification of each camera according to the installation position of each camera, and determining a plurality of passing tracks based on the scene identification of each camera; comparing each path of camera monitoring video data obtained in real time with a preset silent image; and filtering the obtained monitoring video data of each path of camera based on the comparison result, pre-adjusting the definition of each path of camera based on the filtered video data and the plurality of path tracks, and displaying and storing the filtered real-time video data on a display screen. The video data in the completely silent state is deleted, so that the situation that a hard disk memory is occupied, definition adjustment is performed along with movement of a monitored picture organism can be avoided, and the monitored video can be clearer by improving the definition while the hard disk memory can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of data display, and in particular to a display screen data display method and system based on the Internet of Things. Background Art

[0002] In daily life, we will find that in order to avoid disputes and property losses, business premises usually have a certain number of cameras deployed. The cameras collect surveillance videos of equipment in the area in real time, project them through the display screen in the monitoring room, and store them on the hard disk. When a dispute arises or property losses occur, the surveillance videos can be retrieved for viewing.

[0003] However, due to the large area of ​​the business premises, the number of cameras is correspondingly large, and the video images of the corresponding cameras projected on the display screen are correspondingly small. The large number of images makes manual monitoring difficult, and leads to a lag in the investigation of property losses. The safety of personnel and property in the business premises cannot be guaranteed. Summary of the invention

[0004] The purpose of the present invention is to provide a display screen data display method and system based on the Internet of Things to solve the problems raised in the above background technology.

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

[0006] Obtaining the installation location of each camera, determining the scene identification of each camera according to the installation location of each camera, and determining multiple passing trajectories based on the scene identification of each camera;

[0007] Acquire the surveillance video data of each camera in real time, and compare the surveillance video data of each camera acquired in real time with the preset silent image;

[0008] Based on the comparison result, the acquired surveillance video data of each camera is filtered to obtain filtered video data;

[0009] Pre-adjust the clarity of each camera based on filtered video data and multiple path trajectories;

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

[0011] As a further solution of the present invention, the step of determining multiple path trajectories based on the scene identification of each camera specifically includes:

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

[0013] Determine a path trajectory based on the number of occurrences of the biological body's movement trajectory, wherein the path trajectory is a trajectory timeline of multiple cameras;

[0014] The passing interval between cameras is determined based on the passing trajectory.

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

[0016] Obtaining silent images from each camera, extracting frame by frame the real-time surveillance video data from each camera to obtain a number of image data;

[0017] Comparing the obtained image data with the silent image;

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

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

[0020] When the obtained image data is successfully compared with the silent image, the real-time surveillance video data of each camera is deleted;

[0021] When the comparison between the obtained image data and the silent image fails, the real-time surveillance video data of each camera is divided into the video data of the changing area and the video data of the static environment area;

[0022] The video data of the static environment area is deleted, and the video data of the changing area is used as the filtered video data.

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

[0024] Get the camera installation location of the filtered video data;

[0025] Determine the camera scene identification on the passing track based on the camera installation position of the filtered video data;

[0026] The clarity of the next camera on the passing track is adjusted in advance based on the passing interval.

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

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

[0029] Obtaining the daily storage data size from the start of storage on the hard disk to the current day and the daily attributes of the hard disk from the start of storage on the current day, wherein the daily attributes include working days, statutory holidays, and rest days;

[0030] Determine the average daily storage data size for working days, statutory holidays, and rest days based on the daily storage data size;

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

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

[0033] A trajectory determination module is used to obtain the installation position of each camera, determine the scene identification of each camera according to the installation position of each camera, and determine multiple passing trajectories based on the scene identification of each camera;

[0034] The comparison module is used to obtain the monitoring video data of each camera in real time, and compare the monitoring video data of each camera obtained in real time with the preset silent image;

[0035] A filtering module is used to filter the acquired surveillance video data of each camera based on the comparison result to obtain filtered video data;

[0036] A definition adjustment module is used to pre-adjust the definition 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 a display screen.

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

[0039] A historical data acquisition unit, used to acquire historical monitoring video data and determine the movement trajectory of the organism based on the historical monitoring video data;

[0040] A passing track determination unit, used to determine the passing track based on the number of occurrences of the biological body movement track, wherein the passing track is a track timeline of multiple cameras;

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

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

[0043] The extraction unit is used to obtain the silent images of each camera, and extract the real-time monitoring video data of each camera frame by frame to obtain a number of image data;

[0044] A comparison unit, used for comparing the obtained plurality of image data with the silent image;

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

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows: based on the consideration of storage space, the camera surveillance video in the business premises will generally be overwritten after 3 months. After a short period of data overwriting, the video cannot be traced back after 3 months. By acquiring real-time camera surveillance video data, and by comparing the real-time acquired camera surveillance video data with the preset silent image, the video data in a completely silent state can be deleted, and the hard disk memory can be avoided from being occupied, thereby extending the surveillance video coverage time, which is beneficial for the regulatory authority to retrieve the surveillance video, and can also reduce the burden on the staff in the monitoring room. At the same time, the clarity is adjusted according to the trajectory, that is, the clarity is adjusted according to the movement of the organism in the monitoring screen, and a lower clarity is used when no organism passes by, thereby reducing the hard disk memory. In the case of the presence of organisms, the surveillance video can be made clearer by improving the clarity, which is convenient for the staff to identify in time and to respond to emergencies in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0048] Figure 1 A flowchart of a display screen data display method based on the Internet of Things provided in an embodiment of the present invention.

[0049] Figure 2 A flowchart of the steps of determining the scene identification of each camera according to the installation position of each camera and determining multiple passing trajectories based on the scene identification of each camera provided in an embodiment of the present invention.

[0050] Figure 3 A flowchart of the steps of comparing the real-time acquired surveillance video data of each camera with a preset silent image provided in an embodiment of the present invention.

[0051] Figure 4A flowchart of the steps of filtering the acquired surveillance video data of each camera based on the comparison results provided in an embodiment of the present invention.

[0052] Figure 5 A flowchart of the steps of pre-adjusting the clarity of each camera based on filtered video data and multiple path trajectories provided in an embodiment of the present invention.

[0053] Figure 6 A structural block diagram of a display screen data presentation system based on the Internet of Things provided in an embodiment of the present invention.

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

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

[0056] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] Figure 1 The flowchart of a display screen data display method based on the Internet of Things is shown in FIG. In an embodiment of the present invention, a display screen data display method based on the Internet of Things includes:

[0058] Step S100, obtaining the installation position of each camera, determining the scene identification of each camera according to the installation position of each camera, and determining multiple passing trajectories based on the scene identification of each camera;

[0059] Step S200, acquiring the surveillance video data of each camera in real time, and comparing the surveillance video data of each camera acquired in real time with a preset silent image;

[0060] Step S300, filtering the acquired surveillance video data of each camera based on the comparison result to obtain filtered video data;

[0061] Step S400, pre-adjusting the definition of each camera based on the filtered video data and multiple path trajectories;

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

[0063] In this embodiment, the scene identification corresponding to each camera is determined by first obtaining the installation position of the camera. Taking the business premises of a hotel as an example, the installation position of the camera is the hotel corridor, that is, the scene identification corresponding to the camera is the corridor at the room entrance. Other scene identifications may be the hotel registration office, stairs, elevator, dining area, and hotel entrance. Multiple paths may be determined through the above scene identifications. For example, there are the following paths: hotel entrance-hotel registration office-stairs or elevator-room entrance corridor, room entrance corridor-dining area-stairs or elevator-hotel registration office-hotel entrance, room entrance corridor-stairs or elevator-hotel registration office-hotel entrance. The camera surveillance video in the business premises is generally overwritten after 3 months based on storage space considerations. A short-term data overwriting may cause the video to be lost in 3 months. It cannot be traced back after months. By acquiring real-time camera surveillance video data and comparing the real-time camera surveillance video data with the preset silent image, the video data in a completely silent state can be deleted, which can avoid occupying hard disk memory, thereby extending the surveillance video coverage time, which is beneficial for the regulatory authorities to retrieve surveillance videos, and can also reduce the burden on staff in the monitoring room. At the same time, the clarity is adjusted according to the trajectory, that is, the clarity is adjusted according to the movement of the organism in the surveillance picture, and a lower clarity is used when no organism passes by, thereby reducing hard disk memory. In the case of the presence of organisms, the surveillance video can be made clearer by improving the clarity, which is convenient for staff to identify in time and respond to emergencies in time.

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

[0065] Step S101, acquiring historical monitoring video data, and determining the movement trajectory of the organism based on the historical monitoring video data;

[0066] Step S102, determining a path trajectory based on the number of occurrences of the biological body movement trajectory, wherein the path trajectory is a trajectory timeline of multiple cameras;

[0067] Step S103, determining the passing interval between cameras based on the passing trajectory.

[0068] In this embodiment, historical surveillance video data is obtained, surveillance videos within a period of time are selected from the historical surveillance video data, and people in the surveillance videos are identified. The movement trajectory of the person and the time point when the specified person appears on each camera are obtained according to the surveillance video data of each camera, a certain number of movement trajectories of the person are selected, and they are classified according to the movement trajectory route, and a number threshold is pre-set. The number of times a certain movement trajectory route appears is compared with the threshold. If the number of times a certain movement trajectory route appears is greater than or equal to the threshold, the movement trajectory route is determined as a passing trajectory, and the time points when the person appears on each camera in different people in a certain movement trajectory route are obtained. The time points when the person appears on each camera determine the maximum value and the minimum value of the passing interval time of adjacent cameras. The passing trajectory includes the camera scene identifier passed by the trajectory, and the passing interval represents the time interval from the beginning of the specified person just appearing in one camera to the beginning of the person appearing in the next camera.

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

[0070] Step S201, obtaining a silent image of each camera, and extracting frame by frame the real-time monitoring video data of each camera to obtain a number of image data;

[0071] Step S202, comparing the obtained image data with the silent image;

[0072] Step S203: updating the silent images of the cameras based on the surveillance video data of the cameras.

[0073] In this embodiment, the real-time surveillance video data of each camera is extracted frame by frame, so as to convert the video data into image data, and then the image data is compared with the silent image. The silent image is actually a scene environment image in the initial state. When the real-time surveillance video data of the camera remains completely consistent within a certain period of time, the static image data of any frame is obtained, and the silent image is updated to the static image data at this time. In the hotel's night surveillance data, for a considerable period of time, the surveillance video inside the elevator, the stairs and the corridor at the door of the room remains static, that is, no organisms pass by, and the organisms include humans, cats, dogs, etc. In the prior art, the surveillance video at this time is still stored, resulting in a waste of storage space, and is still displayed on the display screen of the monitoring room, resulting in more video areas on the display screen of the monitoring room, causing difficulty for the staff of the monitoring room to monitor.

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

[0075] Step S301, when the obtained image data is successfully compared with the silent image, the real-time surveillance video data of each camera is deleted;

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

[0077] Step S303: The video data in the static environment area is deleted, and the video data in the changing area is used 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 the storage space is overwritten, so that the hard disk can retain the surveillance video for a longer time. After the comparison fails, it means that a biological body 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 display screen, thereby reducing the monitoring burden of the staff in the monitoring room, and the area where the biological body has passed can be directly located. Secondly, cutting the surveillance video can also reduce the space occupied by the hard disk.

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

[0080] Step S401, obtaining the camera installation position of the filtered video data;

[0081] Step S402, determining the camera scene identifier on the passing track based on the camera installation position of the filtered video data;

[0082] Step S403, pre-adjusting the definition of the next camera on the passing track based on the passing interval.

[0083] In this embodiment, the filtered video data is video data in which the movement of the organism occurs. For example, in a hotel business environment, the camera scene identification of the organism is generally first obtained as the hotel entrance. In the path of hotel entrance-hotel registration office-stairs or elevator-room door corridor, the next camera scene identification should be the hotel registration office, so the clarity of the next camera is pre-adjusted, and the camera clarity adjustment interval is the minimum value of the passing interval time. At the same time, the time that the specified person stays in a single camera in each camera is obtained, and the maximum value of the stay time is selected as the stay time. The duration of the clarity t i , t i =a i -b i +e i , where t i is the clarity duration of the i-th camera, a i is the maximum time interval between the previous camera and the previous camera, b i is the minimum time interval between the previous camera and the previous camera. i The dwell time is based on which the clarity can be adjusted when a biological body passes by, thereby improving the clarity of the video display on the display screen, making it easier to identify events occurring in the video.

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

[0085] In this embodiment, by obtaining the remaining storage space of the hard disk, it is possible to generate an estimated coverage date for video data, and to selectively copy important data to other mobile hard disks for storage.

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

[0087] Obtaining the daily storage data size from the start of storage on the hard disk to the current day and the daily attributes of the hard disk from the start of storage on the current day, wherein the daily attributes include working days, statutory holidays, and rest days;

[0088] Determine the average daily storage data size for working days, statutory holidays, and rest days based on the daily storage data size;

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

[0090] In this embodiment, by distinguishing daily attributes, the hard disk coverage date can be accurately estimated. It can be known that the flow of people on working days, statutory holidays, and rest days in the business premises 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 working days, statutory holidays, and rest days. The number of working days, statutory holidays, and rest days is multiplied by the corresponding daily storage data, and the coverage date is estimated based on the product result.

[0091] like Figure 6 As shown, an embodiment of the present invention further 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 position of each camera, determine the scene identification of each camera according to the installation position of each camera, and determine multiple passing trajectories based on the scene identification of each camera;

[0093] The comparison module 200 is used to obtain the surveillance video data of each camera in real time, and compare the surveillance video data of each camera obtained in real time with the preset silent image;

[0094] The filtering module 300 is used to filter the acquired surveillance video data of each camera based on the comparison result to obtain filtered video data;

[0095] A definition adjustment module 400 is used to pre-adjust the definition 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 a display screen.

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

[0098] A historical data acquisition unit 101 is used to acquire historical monitoring video data and determine the movement trajectory of the organism based on the historical monitoring video data;

[0099] A passing track determination unit 102, used to determine a passing track based on the number of occurrences of the biological body movement track, wherein the passing track is a track timeline of multiple cameras;

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

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

[0102] The extraction unit 201 is used to obtain the silent images of each camera, and extract the real-time monitoring video data of each camera frame by frame to obtain a plurality of image data;

[0103] A comparison unit 202, configured to compare the obtained plurality of image data with the silent image;

[0104] The updating unit 203 is used to update the silent images of each camera based on the monitoring 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 in the protection scope of the present invention.

Claims

1. A display screen data display method based on the Internet of Things, characterized in that: The method comprises: Obtaining the installation location of each camera, determining the scene identification of each camera according to the installation location of each camera, and determining multiple passing trajectories based on the scene identification of each camera; Acquire the surveillance video data of each camera in real time, and compare the surveillance video data of each camera acquired in real time with the preset silent image; Based on the comparison result, the acquired surveillance video data of each camera is filtered to obtain filtered video data; Pre-adjust the clarity of each camera based on filtered video data and multiple path trajectories; The filtered real-time video data is displayed on the display screen and stored.

2. A display screen data display method based on the Internet of Things according to claim 1, characterized in that: The step of determining multiple path trajectories based on the scene identification of each camera specifically includes: Acquire historical surveillance video data, and determine the movement trajectory of the organism based on the historical surveillance video data; Determine a path trajectory based on the number of occurrences of the biological body's movement trajectory, wherein the path trajectory is a trajectory timeline of multiple cameras; The passing interval between cameras is determined based on the passing trajectory.

3. The display screen data display method based on the Internet of Things according to claim 1, characterized in that: The step of comparing the real-time acquired surveillance video data of each camera with the preset silent image specifically includes: Obtaining silent images from each camera, extracting frame by frame the real-time surveillance video data from each camera to obtain a number of image data; Comparing the obtained image data with the silent image; The silent images of each camera are updated based on the surveillance video data of each camera.

4. The method for display screen data display based on the Internet of Things according to claim 3, characterized in that: The step of filtering the acquired surveillance video data of each camera based on the comparison result to obtain the filtered video data specifically includes: When the obtained image data is successfully compared with the silent image, the real-time surveillance video data of each camera is deleted; When the comparison between the obtained image data and the silent image fails, the real-time surveillance video data of each camera is divided into the video data of the changing area and the video data of the static environment area; The video data of the static environment area is deleted, and the video data of the changing area is used as the filtered video data.

5. The display screen data display method based on the Internet of Things according to claim 2 is characterized in that: The step of pre-adjusting the definition of each camera based on the filtered video data and multiple path trajectories specifically includes: Get the camera installation location of the filtered video data; Determine the camera scene identification on the passing track based on the camera installation position of the filtered video data; The clarity of the next camera on the passing track is adjusted in advance based on the passing interval.

6. The method for display screen data display based on the Internet of Things according to claim 1, characterized in that: Obtain the remaining storage space of the hard disk in real time, and generate an estimated coverage date based on the remaining storage space of the hard disk.

7. A display screen data display method based on the Internet of Things according to claim 6, characterized in that: The step of generating the estimated data coverage date based on the remaining storage space of the hard disk specifically includes: Obtaining the daily storage data size from the start of storage on the hard disk to the current day and the daily attributes of the hard disk from the start of storage on the current day, wherein the daily attributes include working days, statutory holidays, and rest days; Determine the average daily storage data size for working days, statutory holidays, and rest days based on the daily storage data size; The estimated data coverage date is determined based on the average daily storage data size on working days, statutory holidays, and rest days.

8. A display screen data display system based on the Internet of Things, used to implement a display screen data display method based on the Internet of Things as described in any one of claims 1 to 7, characterized in that: The system comprises: A trajectory determination module is used to obtain the installation position of each camera, determine the scene identification of each camera according to the installation position of each camera, and determine multiple passing trajectories based on the scene identification of each camera; The comparison module is used to obtain the monitoring video data of each camera in real time, and compare the monitoring video data of each camera obtained in real time with the preset silent image; A filtering module is used to filter the acquired surveillance video data of each camera based on the comparison result to obtain filtered video data; A definition adjustment module is used to pre-adjust the definition of each camera based on the filtered video data and multiple path trajectories; The display module is used to display and store the filtered real-time video data on a display screen.

9. The display screen data display system based on the Internet of Things according to claim 8, characterized in that: The trajectory determination module specifically includes: A historical data acquisition unit, used to acquire historical monitoring video data and determine the movement trajectory of the organism based on the historical monitoring video data; A passing track determination unit, used to determine the passing track based on the number of occurrences of the biological body movement track, wherein the passing track is a track timeline of multiple cameras; An interval determination unit is used to determine the passing interval between cameras based on the passing trajectory.

10. The display screen data display system based on the Internet of Things according to claim 8, characterized in that: The comparison module specifically includes: The extraction unit is used to obtain the silent images of each camera, and extract the real-time monitoring video data of each camera frame by frame to obtain a number of image data; A comparison unit, used for comparing the obtained plurality of image data with the silent image; The updating unit is used to update the silent images of each camera based on the monitoring video data of each camera.

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