CN112637567B - Multi-node edge computing device-based cloud data uploading method and system - Google Patents
Multi-node edge computing device-based cloud data uploading method and system Download PDFInfo
- Publication number
- CN112637567B CN112637567B CN202011545944.9A CN202011545944A CN112637567B CN 112637567 B CN112637567 B CN 112637567B CN 202011545944 A CN202011545944 A CN 202011545944A CN 112637567 B CN112637567 B CN 112637567B
- Authority
- CN
- China
- Prior art keywords
- image data
- monitoring image
- uploading
- monitoring
- picture
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/50—Queue scheduling
- H04L47/62—Queue scheduling characterised by scheduling criteria
- H04L47/625—Queue scheduling characterised by scheduling criteria for service slots or service orders
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1097—Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/56—Provisioning of proxy services
- H04L67/565—Conversion or adaptation of application format or content
- H04L67/5651—Reducing the amount or size of exchanged application data
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
Landscapes
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
The invention provides a cloud data uploading method and system based on a multi-node edge computing device, which are used for determining image quality information of monitoring images generated by a plurality of monitors, thereby judging the uploading feasibility of the monitoring image data, generating a corresponding uploading queue according to the respective data volume of the monitoring image data with the uploading feasibility, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, thus, the data part which does not meet the corresponding resolution requirement in the monitoring image data can be removed in a targeted way, therefore, the data volume of the monitoring image data to be uploaded is effectively compressed, and in addition, the data uploading efficiency of the cloud terminal is improved and the situation of data uploading congestion is avoided by adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue.
Description
Technical Field
The invention relates to the technical field of computer data processing, in particular to a cloud data uploading method and system based on multi-node edge computing equipment.
Background
The distributed monitoring system obtains corresponding monitoring information by respectively arranging monitors in different monitoring areas, processes the monitoring information and uploads the processed monitoring information to the cloud terminal. The distributed monitoring system takes the monitors distributed in different monitoring areas as a computing node, and can greatly improve the monitoring comprehensiveness and the real-time performance of the distributed monitoring system. However, since the plurality of computing nodes need to upload the corresponding monitoring image data to the cloud terminal, and the monitoring image data monitored by all the computing nodes do not meet the corresponding resolution requirement, if all the monitoring image data are directly uploaded to the cloud terminal, the data processing workload of the cloud terminal is increased, and the data uploading of the cloud terminal is blocked, so that the data processing efficiency and the reliability of the cloud terminal are greatly reduced.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a cloud data uploading method and system based on multi-node edge computing equipment, the uploading feasibility of the monitoring image data is judged according to the image quality information by acquiring the monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas and determining the image quality information corresponding to the monitoring image data, and generating a set to be uploaded of the monitoring image data of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, according to the respective data quantity of the monitoring image data, an uploading queue of each monitoring image data in a set to be uploaded of the monitoring image data is generated, and then according to the current data receiving busy-free state of the cloud terminal, the uploading time interval between every two adjacent monitoring image data in the uploading queue is adjusted, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal; therefore, the multi-node edge computing device-based cloud data uploading method and system can determine the image quality information of the monitoring images generated by a plurality of monitors, thereby judging the uploading feasibility of the monitoring image data, generating a corresponding uploading queue according to the respective data volume of the monitoring image data with the uploading feasibility, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, thus, the data part which does not meet the corresponding resolution requirement in the monitoring image data can be removed in a targeted way, therefore, the data volume of the monitoring image data to be uploaded is effectively compressed, and in addition, the data uploading efficiency of the cloud terminal is improved and the situation of data uploading congestion is avoided by adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue.
The invention provides a cloud data uploading method based on multi-node edge computing equipment, which is characterized by comprising the following steps:
step S1, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitoring image data, and judging uploading feasibility of the monitoring image data according to the image quality information;
step S2, generating a monitoring image data to-be-uploaded set about all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an uploading queue about each monitoring image data in the monitoring image data to-be-uploaded set according to the respective data volume of the monitoring image data;
step S3, according to the current data receiving busy/idle state of the cloud terminal, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue, so as to upload all the monitoring image data in the uploading queue to the cloud terminal;
further, in step S1, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitoring image data, and determining the uploading feasibility of the monitoring image data according to the image quality information specifically includes:
step S101, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, and extracting a plurality of frames of monitoring pictures from the monitoring image data according to a preset time interval;
step S102, obtaining picture resolution values of a plurality of monitoring pictures for the same monitoring scene, and determining picture resolution fluctuation change values corresponding to the monitoring image data according to the picture resolution values;
step S103, comparing the picture resolution fluctuation variation value with a preset picture resolution fluctuation variation threshold, if the picture resolution fluctuation variation value is smaller than the preset picture resolution fluctuation variation threshold, determining that the corresponding dynamic monitoring image data has uploading feasibility, otherwise, determining that the corresponding dynamic monitoring image data does not have uploading feasibility;
further, in step S2, generating a set to be uploaded with respect to the monitoring image data of all the monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an upload queue with respect to each monitoring image data in the set to be uploaded with respect to the monitoring image data according to the respective data volume of the monitoring image data specifically includes:
step S201, from all the monitoring image data, the monitoring image data with uploading feasibility form a monitoring image data set to be uploaded;
step S202, obtaining respective data bit quantity of each monitoring image data included in the monitoring image data to-be-uploaded set, and performing ascending arrangement on all monitoring image data included in the monitoring image data to-be-uploaded set according to the size of the data bit quantity, so as to generate the uploading queue;
further, in step S3, adjusting an upload time interval between two adjacent monitor image data in the upload queue according to a current data receiving busy/idle state of the cloud terminal, so as to upload all the monitor image data in the upload queue to the cloud terminal specifically includes:
step S301, acquiring an actual data receiving rate corresponding to current receiving data of the cloud terminal, and determining a current actual data receiving bandwidth ratio of the cloud terminal according to the actual data receiving rate;
step S302, comparing the actual data receiving bandwidth ratio with a preset data receiving bandwidth ratio threshold, if the actual data receiving bandwidth ratio is smaller than the preset data receiving bandwidth ratio threshold, determining that the cloud terminal is currently in a data receiving idle state, otherwise, determining that the cloud terminal is currently in a data receiving busy state;
step S303, when the cloud terminal is currently in a data receiving idle state, reducing an uploading time interval between two adjacent monitoring image data in the uploading queue, so as to upload all the monitoring image data of the uploading queue to the cloud terminal, and when the cloud terminal is currently in a data receiving busy state, increasing the uploading time interval between two adjacent monitoring image data in the uploading queue, so as to upload all the monitoring image data of the uploading queue to the cloud terminal;
further, in step S102, obtaining picture resolution values of the plurality of monitored pictures for the same monitored scene, and determining the picture resolution fluctuation change value corresponding to the monitored image data according to the picture resolution values specifically includes scaling the pictures of the plurality of monitored pictures for the same monitored scene so that the scene sizes in the pictures are equal, analyzing and calculating the scaled pictures to obtain corresponding picture resolution values, analyzing the picture resolution values to obtain the picture resolution fluctuation change values, which specifically includes:
firstly, analyzing the pictures of the same monitored scenery according to the plurality of monitored pictures by using the following formula (1) to obtain the scaling ratio of the monitored pictures to the pictures of the same monitored scenery,
in the above formula (1), Ki(X) represents the scale of the ith monitor picture to the picture of the same monitored scenei,max,Yi,max) (X) coordinate value representing the topmost point of the scene in the picture of the ith monitoring picture relative to the same monitored scenei,min,Yi,min) The coordinate value of the ith monitoring picture relative to the scene bottommost in the pictures of the same monitored scene is shown, and n represents the total number of the monitoring pictures;
secondly, by using the following formula (2), the corresponding picture resolution value is obtained according to the scaling of the zoomed monitoring picture to the picture of the same monitoring scene,
in the above formula (2), PiRepresenting the resolution value of the ith monitored picture with respect to the picture of the same monitored scene, X representing the value before zoomingThe length pixel number of the monitoring picture to the picture of the same monitored scene is Y, the width pixel number of the monitoring picture to the picture of the same monitored scene before zooming is Y, and the diagonal length value of the monitoring picture to the picture of the same monitored scene before zooming is H;
thirdly, obtaining the picture resolution fluctuation value P according to the picture resolution value by using the following formula (3),
and the resolution fluctuation change value of the picture is utilized to integrate the resolution of all the monitored pictures for the picture of the same monitored scene to the maximum extent, so that the accuracy of the resolution fluctuation change value of the picture is ensured, and the reliable analysis on the subsequent judgment uploading feasibility is ensured.
The invention also provides a cloud data uploading system based on the multi-node edge computing equipment, which is characterized by comprising a monitoring image data acquisition module, a monitoring image data uploading feasibility judgment module, an uploading queue generation module and a monitoring image data uploading module; wherein the content of the first and second substances,
the monitoring image data acquisition module is used for acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas;
the monitoring image data uploading feasibility judgment module is used for determining image quality information corresponding to the monitoring image data and judging uploading feasibility of the monitoring image data according to the image quality information;
the uploading queue generating module is used for generating a monitoring image data to-be-uploaded set of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an uploading queue of each monitoring image data in the monitoring image data to-be-uploaded set according to the respective data volume of the monitoring image data;
the monitoring image data uploading module is used for adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, so that all the monitoring image data in the uploading queue are uploaded to the cloud terminal;
further, the acquiring of the monitoring image data by the monitoring image data acquiring module includes:
acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, and extracting a plurality of frames of monitoring pictures from the monitoring image data according to a preset time interval;
and the number of the first and second groups,
the monitoring image data uploading feasibility judgment module determines image quality information corresponding to the monitoring image data, and then judges the uploading feasibility of the monitoring image data according to the image quality information specifically comprises the following steps:
acquiring picture resolution values of a plurality of monitoring pictures for the same monitoring scene, and determining picture resolution fluctuation change values corresponding to the monitoring image data according to the picture resolution values;
comparing the picture resolution fluctuation variation value with a preset picture resolution fluctuation variation threshold, if the picture resolution fluctuation variation value is smaller than the preset picture resolution fluctuation variation threshold, determining that the corresponding dynamic monitoring image data has uploading feasibility, otherwise, determining that the corresponding dynamic monitoring image data does not have the uploading feasibility;
further, the uploading queue generating module generates a set to be uploaded of the monitoring image data of all the monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generates an uploading queue of each monitoring image data in the set to be uploaded of the monitoring image data according to the respective data volume of the monitoring image data specifically includes:
the monitoring image data with uploading feasibility form a monitoring image data to-be-uploaded set from all the monitoring image data;
acquiring the respective data bit quantity of each monitoring image data contained in the monitoring image data to-be-uploaded set, and performing ascending arrangement on all monitoring image data contained in the monitoring image data to-be-uploaded set according to the size of the data bit quantity so as to generate an uploading queue;
further, the monitoring image data uploading module adjusts an uploading time interval between two adjacent monitoring image data in the uploading queue according to a current data receiving busy/idle state of the cloud terminal, so that uploading all the monitoring image data of the uploading queue to the cloud terminal specifically includes:
acquiring an actual data receiving rate corresponding to the current receiving data of the cloud terminal, and determining the current actual data receiving bandwidth ratio of the cloud terminal according to the actual data receiving rate;
comparing the actual data receiving bandwidth ratio with a preset data receiving bandwidth ratio threshold, if the actual data receiving bandwidth ratio is smaller than the preset data receiving bandwidth ratio threshold, determining that the cloud terminal is currently in a data receiving idle state, otherwise, determining that the cloud terminal is currently in a data receiving busy state;
when the cloud terminal is in a data receiving idle state at present, the uploading time interval between two adjacent monitoring image data in the uploading queue is reduced, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal, and when the cloud terminal is in a data receiving busy state at present, the uploading time interval between two adjacent monitoring image data in the uploading queue is increased, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal.
Compared with the prior art, the multi-node edge computing device-based cloud data uploading method and system have the advantages that by acquiring the monitoring image data generated by the plurality of monitors in the respective corresponding monitoring areas, determining image quality information corresponding to the monitored image data, judging uploading feasibility of the monitored image data according to the image quality information, and generating a set to be uploaded of the monitoring image data of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, according to the respective data quantity of the monitoring image data, an uploading queue of each monitoring image data in a set to be uploaded of the monitoring image data is generated, and then according to the current data receiving busy-free state of the cloud terminal, the uploading time interval between every two adjacent monitoring image data in the uploading queue is adjusted, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal; therefore, the multi-node edge computing device-based cloud data uploading method and system can determine the image quality information of the monitoring images generated by a plurality of monitors, thereby judging the uploading feasibility of the monitoring image data, generating a corresponding uploading queue according to the respective data volume of the monitoring image data with the uploading feasibility, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, thus, the data part which does not meet the corresponding resolution requirement in the monitoring image data can be removed in a targeted way, therefore, the data volume of the monitoring image data to be uploaded is effectively compressed, and in addition, the data uploading efficiency of the cloud terminal is improved and the situation of data uploading congestion is avoided by adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a cloud data uploading method based on a multi-node edge computing device according to the present invention.
Fig. 2 is a schematic structural diagram of a cloud data uploading system based on a multi-node edge computing device according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a cloud data uploading method based on a multi-node edge computing device according to an embodiment of the present invention. The multi-node edge computing device-based cloud data uploading method comprises the following steps:
step S1, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitoring image data, and judging uploading feasibility of the monitoring image data according to the image quality information;
step S2, generating a set to be uploaded of the monitoring image data for all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an upload queue of each monitoring image data in the set to be uploaded of the monitoring image data according to the respective data amount of the monitoring image data;
step S3, adjusting an upload time interval between two adjacent monitor image data in the upload queue according to the current data receiving busy/idle state of the cloud terminal, so as to upload all the monitor image data in the upload queue to the cloud terminal.
The beneficial effects of the above technical scheme are: the multi-node edge computing device-based cloud data uploading method comprises the steps of determining image quality information of monitoring images generated by a plurality of monitors, thereby judging the uploading feasibility of the monitoring image data, generating a corresponding uploading queue according to the respective data volume of the monitoring image data with the uploading feasibility, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, thus, the data part which does not meet the corresponding resolution requirement in the monitoring image data can be removed in a targeted way, therefore, the data volume of the monitoring image data to be uploaded is effectively compressed, and in addition, the data uploading efficiency of the cloud terminal is improved and the situation of data uploading congestion is avoided by adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue.
Preferably, in step S1, the acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitoring image data, and determining uploading feasibility of the monitoring image data according to the image quality information specifically includes:
step S101, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, and extracting a plurality of frames of monitoring pictures from the monitoring image data according to a preset time interval;
step S102, obtaining picture resolution values of a plurality of monitoring pictures for the same monitored scenery respectively, and determining picture resolution fluctuation change values corresponding to the monitoring image data according to the picture resolution values;
step S103, comparing the picture resolution fluctuation variation value with a preset picture resolution fluctuation variation threshold, if the picture resolution fluctuation variation value is smaller than the preset picture resolution fluctuation variation threshold, determining that the corresponding dynamic monitoring image data has uploading feasibility, otherwise, determining that the corresponding dynamic monitoring image data does not have uploading feasibility.
The beneficial effects of the above technical scheme are: because a plurality of monitors arranged in a distributed mode only monitor the corresponding monitoring area, and are limited by the current monitoring condition of the monitoring area and the monitoring performance of the monitors, the monitoring image data generated by the monitors does not always keep proper data quality, the corresponding monitoring pictures have certain resolution fluctuation, if the resolution change of the monitoring pictures is too large, the monitoring image data is unstable, so that the monitoring image data does not have any data processing significance, and the monitoring image data which does not meet the corresponding resolution requirement can be effectively removed by comparing the picture resolution fluctuation change value with the preset picture resolution fluctuation change threshold value, so that the effectiveness and the transmission efficiency of the monitoring image data are improved.
Preferably, in step S2, the generating a set to be uploaded with respect to the monitoring image data of all the monitors according to the judgment result of the uploading feasibility of the monitoring image data, and the generating an upload queue with respect to each monitoring image data in the set to be uploaded with respect to the monitoring image data according to the respective data volume of the monitoring image data specifically includes:
step S201, from all the monitoring image data, the monitoring image data with uploading feasibility form a monitoring image data to-be-uploaded set;
step S202, obtaining respective data bit quantities of each monitoring image data included in the set to be uploaded of the monitoring image data, and performing ascending arrangement on all the monitoring image data included in the set to be uploaded of the monitoring image data according to the data bit quantities, thereby generating the uploading queue.
The beneficial effects of the above technical scheme are: because the data bit quantities of the monitoring image data generated by different monitors aiming at the corresponding monitoring areas are the same, and the uploading progress of the monitoring image data with larger data bit quantities is correspondingly longer, all the monitoring image data contained in the monitoring image data to-be-uploaded set are arranged in an ascending order according to the size of the data bit quantities, so that the uploading queue is generated, and the uploading efficiency of the monitoring image data with uploading feasibility can be optimized.
Preferably, in the step S3, adjusting an upload time interval between two adjacent monitor image data in the upload queue according to a current data receiving busy/idle state of the cloud terminal, so that uploading all the monitor image data in the upload queue to the cloud terminal specifically includes:
step S301, acquiring an actual data receiving rate corresponding to current receiving data of the cloud terminal, and determining a current actual data receiving bandwidth ratio of the cloud terminal according to the actual data receiving rate;
step S302, comparing the actual data receiving bandwidth ratio with a preset data receiving bandwidth ratio threshold, if the actual data receiving bandwidth ratio is smaller than the preset data receiving bandwidth ratio threshold, determining that the cloud terminal is currently in a data receiving idle state, otherwise, determining that the cloud terminal is currently in a data receiving busy state;
step S303, when the cloud terminal is currently in a data receiving idle state, reducing an upload time interval between two adjacent monitor image data in the upload queue, thereby uploading all the monitor image data of the upload queue to the cloud terminal, and when the cloud terminal is currently in a data receiving busy state, increasing the upload time interval between two adjacent monitor image data in the upload queue, thereby uploading all the monitor image data of the upload queue to the cloud terminal.
The beneficial effects of the above technical scheme are: because the cloud terminal is connected with the monitors in a communication way, the cloud terminal can receive all monitoring image data correspondingly contained in the uploading, if the current actual data receiving bandwidth ratio of the cloud terminal is too large, namely, the cloud terminal is currently in a busy data receiving state, and accordingly, the data receiving speed and efficiency of the cloud terminal are also reduced, so that by determining the busy data receiving state of the cloud terminal, and when the cloud terminal is currently in a data receiving idle state, reducing the uploading time interval between two adjacent monitoring image data in the uploading queue, and when the cloud terminal is in a busy data receiving state at present, increasing the uploading time interval between two adjacent monitoring image data in the uploading queue, and improving the data receiving efficiency of the cloud terminal to the maximum extent, so that the data of the uploading queue can be comprehensively received by the cloud terminal.
Preferably, in step S102, obtaining picture resolution values of a plurality of monitored pictures for a same monitored scene, and determining a picture resolution fluctuation change value corresponding to the monitored image data according to the picture resolution values specifically includes scaling the pictures of the plurality of monitored pictures for the same monitored scene so that the scene sizes in the pictures are equal, analyzing and calculating the scaled pictures to obtain corresponding picture resolution values, analyzing the picture resolution values to obtain the picture resolution fluctuation change value, which specifically includes:
firstly, using the following formula (1), analyzing the pictures of the same monitored scenery according to the plurality of monitored pictures respectively to obtain the scaling ratio of the monitored pictures to the pictures of the same monitored scenery,
in the above formula (1), KiIndicating the scale of the ith monitor picture to the picture of the same monitored scene, (X)i,max,Yi,max) (X) coordinate value representing the topmost point of the scene in the picture of the ith monitored picture relative to the same monitored scenei,min,Yi,min) The coordinate value of the ith monitoring picture relative to the bottommost end of the scenery in the pictures of the same monitored scenery is shown, and n represents the total number of the monitoring pictures;
secondly, by using the following formula (2), the corresponding picture resolution value is obtained according to the scaling of the zoomed monitoring picture to the picture of the same monitoring scene,
in the above formula (2), PiThe picture length value of the ith monitoring picture to the same monitored scenery is represented, X represents the number of length pixels of the monitoring picture to the picture of the same monitored scenery before zooming, Y represents the number of width pixels of the monitoring picture to the picture of the same monitored scenery before zooming, and H represents the diagonal length value of the monitoring picture to the picture of the same monitored scenery before zooming;
thirdly, obtaining the fluctuation value P of the resolution of the picture according to the resolution value of the picture by using the following formula (3),
the resolution fluctuation change value of the picture is utilized to integrate the resolution of all the monitored pictures for the picture of the same monitored scene to the maximum extent, thereby ensuring the accuracy of the resolution fluctuation change value of the picture and ensuring the reliable analysis of the subsequent judgment uploading feasibility.
The beneficial effects of the above technical scheme are: the method comprises the steps of obtaining the scaling ratio of a monitoring picture to the picture of the same monitoring scene by using a formula (1), so that the size of the scene in the picture is scaled consistently according to the scaling ratio, preventing errors caused by subsequent resolution calculation, obtaining a corresponding picture resolution value by using a formula (2), obtaining the resolution of the picture, providing conditions for the subsequent resolution fluctuation change value calculation of the picture, and finally obtaining a picture resolution fluctuation change value by using a formula (3), thereby integrating the resolution of all the monitoring pictures to the picture of the same monitoring scene to the maximum extent, ensuring the accuracy of the picture resolution fluctuation change value, and ensuring reliable analysis on the uploading feasibility of subsequent judgment.
Fig. 2 is a schematic structural diagram of a cloud data uploading system based on a multi-node edge computing device according to an embodiment of the present invention. The cloud data uploading system based on the multi-node edge computing device comprises a monitoring image data acquisition module, a monitoring image data uploading feasibility judgment module, an uploading queue generation module and a monitoring image data uploading module; wherein the content of the first and second substances,
the monitoring image data acquisition module is used for acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas;
the monitoring image data uploading feasibility judgment module is used for determining image quality information corresponding to the monitoring image data and judging the uploading feasibility of the monitoring image data according to the image quality information;
the uploading queue generating module is used for generating a monitoring image data to-be-uploaded set of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an uploading queue of each monitoring image data in the monitoring image data to-be-uploaded set according to the respective data volume of the monitoring image data;
the monitoring image data uploading module is used for adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, so that all the monitoring image data in the uploading queue are uploaded to the cloud terminal.
The beneficial effects of the above technical scheme are: the multi-node edge computing device-based cloud data uploading system determines the image quality information of monitoring images generated by a plurality of monitors, thereby judging the uploading feasibility of the monitoring image data, generating a corresponding uploading queue according to the respective data volume of the monitoring image data with the uploading feasibility, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, thus, the data part which does not meet the corresponding resolution requirement in the monitoring image data can be removed in a targeted way, therefore, the data volume of the monitoring image data to be uploaded is effectively compressed, and in addition, the data uploading efficiency of the cloud terminal is improved and the situation of data uploading congestion is avoided by adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue.
Preferably, the acquiring of the monitoring image data by the monitoring image data acquiring module includes:
acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, and extracting a plurality of frames of monitoring pictures from the monitoring image data according to a preset time interval;
and the number of the first and second groups,
the monitoring image data uploading feasibility judging module determines image quality information corresponding to the monitoring image data, and then judges the uploading feasibility of the monitoring image data according to the image quality information specifically comprises the following steps:
acquiring picture resolution values of a plurality of monitoring pictures for the same monitored scene respectively, and determining picture resolution fluctuation change values corresponding to the monitoring image data according to the picture resolution values;
and comparing the picture resolution fluctuation variation value with a preset picture resolution fluctuation variation threshold, if the picture resolution fluctuation variation value is smaller than the preset picture resolution fluctuation variation threshold, determining that the corresponding dynamic monitoring image data has uploading feasibility, otherwise, determining that the corresponding dynamic monitoring image data does not have the uploading feasibility.
The beneficial effects of the above technical scheme are: because a plurality of monitors arranged in a distributed mode only monitor the corresponding monitoring area, and are limited by the current monitoring condition of the monitoring area and the monitoring performance of the monitors, the monitoring image data generated by the monitors does not always keep proper data quality, the corresponding monitoring pictures have certain resolution fluctuation, if the resolution change of the monitoring pictures is too large, the monitoring image data is unstable, so that the monitoring image data does not have any data processing significance, and the monitoring image data which does not meet the corresponding resolution requirement can be effectively removed by comparing the picture resolution fluctuation change value with the preset picture resolution fluctuation change threshold value, so that the effectiveness and the transmission efficiency of the monitoring image data are improved.
Preferably, the uploading queue generating module generates a set to be uploaded of the monitoring image data about all the monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generates the uploading queue of each monitoring image data in the set to be uploaded of the monitoring image data according to the respective data volume of the monitoring image data specifically includes:
from all the monitoring image data, the monitoring image data with uploading feasibility form a monitoring image data set to be uploaded;
and then acquiring respective data bit quantity of each monitoring image data contained in the monitoring image data to-be-uploaded set, and performing ascending arrangement on all the monitoring image data contained in the monitoring image data to-be-uploaded set according to the size of the data bit quantity, thereby generating the uploading queue.
The beneficial effects of the above technical scheme are: because the data bit quantities of the monitoring image data generated by different monitors aiming at the corresponding monitoring areas are the same, and the uploading progress of the monitoring image data with larger data bit quantities is correspondingly longer, all the monitoring image data contained in the monitoring image data to-be-uploaded set are arranged in an ascending order according to the size of the data bit quantities, so that the uploading queue is generated, and the uploading efficiency of the monitoring image data with uploading feasibility can be optimized.
Preferably, the monitoring image data uploading module adjusts an uploading time interval between two adjacent monitoring image data in the uploading queue according to a current data receiving busy/idle state of the cloud terminal, so that uploading all the monitoring image data of the uploading queue to the cloud terminal specifically includes:
acquiring actual data receiving rate corresponding to the current receiving data of the cloud terminal, and determining the current actual data receiving bandwidth ratio of the cloud terminal according to the actual data receiving rate;
comparing the actual data receiving bandwidth ratio with a preset data receiving bandwidth ratio threshold, if the actual data receiving bandwidth ratio is smaller than the preset data receiving bandwidth ratio threshold, determining that the cloud terminal is currently in a data receiving idle state, otherwise, determining that the cloud terminal is currently in a data receiving busy state;
when the cloud terminal is in a data receiving idle state at present, the uploading time interval between two adjacent monitoring image data in the uploading queue is reduced, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal, and when the cloud terminal is in a data receiving busy state at present, the uploading time interval between two adjacent monitoring image data in the uploading queue is increased, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal.
The beneficial effects of the above technical scheme are: because the cloud terminal is connected with the monitors in a communication way, the cloud terminal can receive all monitoring image data correspondingly contained in the uploading, if the current actual data receiving bandwidth ratio of the cloud terminal is too large, namely, the cloud terminal is currently in a busy data receiving state, and accordingly, the data receiving speed and efficiency of the cloud terminal are also reduced, so that by determining the busy data receiving state of the cloud terminal, and when the cloud terminal is currently in a data receiving idle state, reducing the uploading time interval between two adjacent monitoring image data in the uploading queue, and when the cloud terminal is in a busy data receiving state at present, increasing the uploading time interval between two adjacent monitoring image data in the uploading queue, and improving the data receiving efficiency of the cloud terminal to the maximum extent, so that the data of the uploading queue can be comprehensively received by the cloud terminal.
From the content of the foregoing embodiments, the multi-node edge computing device-based cloud data uploading method and system acquire monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitored image data, judging uploading feasibility of the monitored image data according to the image quality information, and generating a set to be uploaded of the monitoring image data of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, according to the respective data quantity of the monitoring image data, an uploading queue of each monitoring image data in a set to be uploaded of the monitoring image data is generated, and then according to the current data receiving busy-free state of the cloud terminal, the uploading time interval between every two adjacent monitoring image data in the uploading queue is adjusted, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal; therefore, the multi-node edge computing device-based cloud data uploading method and system can determine the image quality information of the monitoring images generated by a plurality of monitors, thereby judging the uploading feasibility of the monitoring image data, generating a corresponding uploading queue according to the respective data volume of the monitoring image data with the uploading feasibility, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, thus, the data part which does not meet the corresponding resolution requirement in the monitoring image data can be removed in a targeted way, therefore, the data volume of the monitoring image data to be uploaded is effectively compressed, and in addition, the data uploading efficiency of the cloud terminal is improved and the situation of data uploading congestion is avoided by adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (6)
1. The cloud data uploading method based on the multi-node edge computing equipment is characterized by comprising the following steps:
step S1, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitoring image data, and judging uploading feasibility of the monitoring image data according to the image quality information;
step S2, generating a monitoring image data to-be-uploaded set about all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an uploading queue about each monitoring image data in the monitoring image data to-be-uploaded set according to the respective data volume of the monitoring image data;
step S3, according to the current data receiving busy/idle state of the cloud terminal, adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue, so as to upload all the monitoring image data in the uploading queue to the cloud terminal;
in step S1, the acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, determining image quality information corresponding to the monitoring image data, and determining the uploading feasibility of the monitoring image data according to the image quality information specifically includes:
step S101, acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, and extracting a plurality of frames of monitoring pictures from the monitoring image data according to a preset time interval;
step S102, obtaining picture resolution values of a plurality of monitoring pictures for the same monitoring scene, and determining picture resolution fluctuation change values corresponding to the monitoring image data according to the picture resolution values;
step S103, comparing the picture resolution fluctuation variation value with a preset picture resolution fluctuation variation threshold, if the picture resolution fluctuation variation value is smaller than the preset picture resolution fluctuation variation threshold, determining that the corresponding dynamic monitoring image data has uploading feasibility, otherwise, determining that the corresponding dynamic monitoring image data does not have uploading feasibility; in step S102, obtaining picture resolution values of a plurality of monitored pictures for a same monitored scene, and determining a picture resolution fluctuation change value corresponding to the monitored image data according to the picture resolution values specifically includes scaling the pictures of the same monitored scene of the plurality of monitored pictures so that the sizes of the scenes in the pictures are equal, analyzing and calculating the scaled pictures to obtain corresponding picture resolution values, and analyzing the picture resolution values to obtain the picture resolution fluctuation change value, which specifically includes:
firstly, analyzing the pictures of the same monitored scenery according to the plurality of monitored pictures by using the following formula (1) to obtain the scaling ratio of the monitored pictures to the pictures of the same monitored scenery,
in the above formula (1), KiIndicating the scale of the ith monitored picture to the picture of the same monitored scene, (X)i,max,Yi,max) (X) coordinate value representing the topmost point of the scene in the picture of the ith monitoring picture relative to the same monitored scenei,min,Yi,min) The coordinate value of the ith monitoring picture relative to the scene bottommost in the pictures of the same monitored scene is shown, and n represents the total number of the monitoring pictures;
secondly, by using the following formula (2), the corresponding picture resolution value is obtained according to the scaling of the zoomed monitoring picture to the picture of the same monitoring scene,
in the above formula (2), PiRepresenting the resolution value of the ith monitoring picture to the picture of the same monitored scene, X representing the number of length pixels of the monitoring picture to the picture of the same monitored scene before zooming, Y representing the number of width pixels of the monitoring picture to the picture of the same monitored scene before zooming, and H representing the diagonal length value of the monitoring picture to the picture of the same monitored scene before zooming;
thirdly, obtaining the picture resolution fluctuation value P according to the picture resolution value by using the following formula (3),
and the resolution fluctuation change value of the picture is utilized to integrate the resolution of all the monitored pictures for the picture of the same monitored scene to the maximum extent, so that the accuracy of the resolution fluctuation change value of the picture is ensured, and the reliable analysis on the subsequent judgment uploading feasibility is ensured.
2. The multi-node edge computing device-based cloud data uploading method of claim 1, wherein:
in step S2, generating a set to be uploaded of the monitoring image data for all the monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an upload queue of each monitoring image data in the set to be uploaded of the monitoring image data according to the respective data volume of the monitoring image data specifically includes:
step S201, from all the monitoring image data, the monitoring image data with uploading feasibility form a monitoring image data set to be uploaded;
step S202, obtaining respective data bit quantities of each monitoring image data included in the monitoring image data set to be uploaded, and performing ascending arrangement on all monitoring image data included in the monitoring image data set to be uploaded according to the data bit quantities, thereby generating the uploading queue.
3. The multi-node edge computing device-based cloud data uploading method of claim 2, wherein:
in step S3, adjusting an upload time interval between two adjacent monitor image data in the upload queue according to a current data receiving busy/idle state of the cloud terminal, so as to upload all the monitor image data in the upload queue to the cloud terminal specifically includes:
step S301, acquiring an actual data receiving rate corresponding to current receiving data of the cloud terminal, and determining a current actual data receiving bandwidth ratio of the cloud terminal according to the actual data receiving rate;
step S302, comparing the actual data receiving bandwidth ratio with a preset data receiving bandwidth ratio threshold, if the actual data receiving bandwidth ratio is smaller than the preset data receiving bandwidth ratio threshold, determining that the cloud terminal is currently in a data receiving idle state, otherwise, determining that the cloud terminal is currently in a data receiving busy state;
step S303, when the cloud terminal is currently in a data receiving idle state, reducing an uploading time interval between two adjacent monitoring image data in the uploading queue, so as to upload all the monitoring image data of the uploading queue to the cloud terminal, and when the cloud terminal is currently in a data receiving busy state, increasing the uploading time interval between two adjacent monitoring image data in the uploading queue, so as to upload all the monitoring image data of the uploading queue to the cloud terminal.
4. The cloud data uploading system based on the multi-node edge computing device is characterized by comprising a monitoring image data acquisition module, a monitoring image data uploading feasibility judgment module, an uploading queue generation module and a monitoring image data uploading module; wherein the content of the first and second substances,
the monitoring image data acquisition module is used for acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas;
the monitoring image data uploading feasibility judgment module is used for determining image quality information corresponding to the monitoring image data and judging uploading feasibility of the monitoring image data according to the image quality information;
the uploading queue generating module is used for generating a monitoring image data to-be-uploaded set of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generating an uploading queue of each monitoring image data in the monitoring image data to-be-uploaded set according to the respective data volume of the monitoring image data;
the monitoring image data uploading module is used for adjusting the uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, so that all the monitoring image data in the uploading queue are uploaded to the cloud terminal;
the acquiring of the monitoring image data generated by the plurality of monitors in the respective corresponding monitoring areas by the monitoring image data acquiring module specifically includes:
acquiring monitoring image data generated by a plurality of monitors in respective corresponding monitoring areas, and extracting a plurality of frames of monitoring pictures from the monitoring image data according to a preset time interval;
and the number of the first and second groups,
the monitoring image data uploading feasibility judgment module determines image quality information corresponding to the monitoring image data, and then judges the uploading feasibility of the monitoring image data according to the image quality information specifically comprises the following steps:
acquiring picture resolution values of a plurality of monitoring pictures for the same monitoring scene, and determining picture resolution fluctuation change values corresponding to the monitoring image data according to the picture resolution values;
comparing the picture resolution fluctuation variation value with a preset picture resolution fluctuation variation threshold, if the picture resolution fluctuation variation value is smaller than the preset picture resolution fluctuation variation threshold, determining that the corresponding dynamic monitoring image data has uploading feasibility, otherwise, determining that the corresponding dynamic monitoring image data does not have the uploading feasibility;
the obtaining of the picture resolution values of the plurality of monitored pictures for the same monitored scene respectively, and according to the plurality of picture resolution values, determining the picture resolution fluctuation change value corresponding to the monitored image data specifically includes scaling the plurality of monitored pictures for the same monitored scene respectively so that the sizes of the scenes in the pictures are equal, analyzing and calculating the scaled pictures to obtain the corresponding picture resolution values, and analyzing the picture resolution values to obtain the picture resolution fluctuation change value, which specifically includes:
firstly, analyzing the pictures of the same monitored scenery according to the plurality of monitored pictures by using the following formula (1) to obtain the scaling ratio of the monitored pictures to the pictures of the same monitored scenery,
in the above formula (1), KiIndicating the scale of the ith monitored picture to the picture of the same monitored scene, (X)i,max,Yi,max) (X) coordinate value representing the topmost point of the scene in the picture of the ith monitoring picture relative to the same monitored scenei,min,Yi,min) The coordinate value of the ith monitoring picture relative to the scene bottommost in the pictures of the same monitored scene is shown, and n represents the total number of the monitoring pictures;
secondly, by using the following formula (2), the corresponding picture resolution value is obtained according to the scaling of the zoomed monitoring picture to the picture of the same monitoring scene,
in the above formula (2), PiRepresenting the resolution value of the ith monitoring picture to the picture of the same monitored scene, X representing the number of length pixels of the monitoring picture to the picture of the same monitored scene before zooming, Y representing the number of width pixels of the monitoring picture to the picture of the same monitored scene before zooming, and H representing the diagonal length value of the monitoring picture to the picture of the same monitored scene before zooming;
thirdly, obtaining the picture resolution fluctuation value P according to the picture resolution value by using the following formula (3),
and the resolution fluctuation change value of the picture is utilized to integrate the resolution of all the monitored pictures for the picture of the same monitored scene to the maximum extent, so that the accuracy of the resolution fluctuation change value of the picture is ensured, and the reliable analysis on the subsequent judgment uploading feasibility is ensured.
5. The multi-node edge computing device based cloud data upload system of claim 4, wherein:
the uploading queue generating module generates a set to be uploaded of the monitoring image data of all monitors according to the judgment result of the uploading feasibility of the monitoring image data, and generates an uploading queue of each monitoring image data in the set to be uploaded of the monitoring image data according to the respective data volume of the monitoring image data, wherein the uploading queue specifically comprises:
the monitoring image data with uploading feasibility form a monitoring image data to-be-uploaded set from all the monitoring image data;
and then acquiring the respective data bit quantity of each monitoring image data contained in the monitoring image data to-be-uploaded set, and performing ascending arrangement on all the monitoring image data contained in the monitoring image data to-be-uploaded set according to the size of the data bit quantity, thereby generating the uploading queue.
6. The multi-node edge computing device based cloud data upload system of claim 5, wherein:
the monitoring image data uploading module adjusts an uploading time interval between two adjacent monitoring image data in the uploading queue according to the current data receiving busy/idle state of the cloud terminal, so that uploading all the monitoring image data of the uploading queue to the cloud terminal specifically comprises:
acquiring an actual data receiving rate corresponding to the current receiving data of the cloud terminal, and determining the current actual data receiving bandwidth ratio of the cloud terminal according to the actual data receiving rate; comparing the actual data receiving bandwidth ratio with a preset data receiving bandwidth ratio threshold, if the actual data receiving bandwidth ratio is smaller than the preset data receiving bandwidth ratio threshold, determining that the cloud terminal is currently in a data receiving idle state, otherwise, determining that the cloud terminal is currently in a data receiving busy state;
when the cloud terminal is in a data receiving idle state at present, the uploading time interval between two adjacent monitoring image data in the uploading queue is reduced, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal, and when the cloud terminal is in a data receiving busy state at present, the uploading time interval between two adjacent monitoring image data in the uploading queue is increased, so that all the monitoring image data of the uploading queue are uploaded to the cloud terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011545944.9A CN112637567B (en) | 2020-12-24 | 2020-12-24 | Multi-node edge computing device-based cloud data uploading method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011545944.9A CN112637567B (en) | 2020-12-24 | 2020-12-24 | Multi-node edge computing device-based cloud data uploading method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112637567A CN112637567A (en) | 2021-04-09 |
| CN112637567B true CN112637567B (en) | 2021-10-26 |
Family
ID=75324409
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011545944.9A Active CN112637567B (en) | 2020-12-24 | 2020-12-24 | Multi-node edge computing device-based cloud data uploading method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112637567B (en) |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2020100946A4 (en) * | 2019-10-18 | 2020-07-16 | Chang'an University | Multi-source traffic information sensing roadside device for smart highway |
| CN112104918A (en) * | 2020-11-04 | 2020-12-18 | 捷信（浙江）通信技术有限公司 | Image transmission method and device based on satellite network |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10616465B2 (en) * | 2015-09-16 | 2020-04-07 | Microsoft Technology Licensing, Llc | Bandwidth efficient video surveillance system |
| US10347102B2 (en) * | 2016-03-22 | 2019-07-09 | Sensormatic Electronics, LLC | Method and system for surveillance camera arbitration of uplink consumption |
| CN107346426B (en) * | 2017-07-10 | 2021-03-16 | 深圳市海清视讯科技有限公司 | Face information collection method based on camera face recognition |
| CN108494698B (en) * | 2017-12-13 | 2022-02-25 | 天地伟业技术有限公司 | Congestion control method based on transmission rate |
| CN109587264A (en) * | 2018-12-20 | 2019-04-05 | 新华三技术有限公司 | Data monitoring method, device and cloud platform server |
| CN109818789B (en) * | 2019-01-22 | 2022-03-11 | 苏州科达科技股份有限公司 | Picture transmission method and system, storage medium and electronic equipment |
| CN110427815B (en) * | 2019-06-24 | 2020-07-10 | 特斯联（北京）科技有限公司 | Video processing method and device for realizing interception of effective contents of entrance guard |
| CN110719438A (en) * | 2019-08-28 | 2020-01-21 | 北京大学 | Synchronous transmission control method for digital retina video stream and characteristic stream |
| CN111953939B (en) * | 2020-07-31 | 2021-06-22 | 中标慧安信息技术股份有限公司 | Method and system for improving monitoring video evidence storage accuracy |
-
2020
- 2020-12-24 CN CN202011545944.9A patent/CN112637567B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2020100946A4 (en) * | 2019-10-18 | 2020-07-16 | Chang'an University | Multi-source traffic information sensing roadside device for smart highway |
| CN112104918A (en) * | 2020-11-04 | 2020-12-18 | 捷信（浙江）通信技术有限公司 | Image transmission method and device based on satellite network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112637567A (en) | 2021-04-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11190715B2 (en) | System and method for event camera data processing | |
| CN107155093B (en) | Video preview method, device and equipment | |
| CN110012324B (en) | Code rate self-adaption method, WIFI camera, control device and code rate self-adaption system for embedded wireless video transmission | |
| CN111225254B (en) | Video transmission method, device, terminal equipment and storage medium | |
| CN112702429B (en) | Multi-dimensional data processing method and system based on multi-node edge computing equipment | |
| CN109996063B (en) | Video image screen splash detection method and device, computer equipment and storage medium | |
| CN112637567B (en) | Multi-node edge computing device-based cloud data uploading method and system | |
| CN113381898B (en) | Display module interference removing method and device, computer equipment and storage medium | |
| CN111970484B (en) | Operation and maintenance system and operation and maintenance method suitable for video monitoring equipment | |
| CN113660465A (en) | Image processing method, image processing device, readable medium and electronic equipment | |
| CN116761018B (en) | Real-time rendering system based on cloud platform | |
| CN112672155A (en) | Desktop sharing method and device based on sharing type discrimination and storage medium | |
| CN111278039B (en) | User perception suppression identification method, device, equipment and medium | |
| CN115643408A (en) | Image compression method, device, equipment and storage medium | |
| TWI586175B (en) | Method and system for managing bandwidth of video conference | |
| CN110659571A (en) | Streaming video face detection acceleration method based on frame buffer queue | |
| CN110213457B (en) | Image transmission method and device | |
| CN110971870B (en) | Data processing method for image display | |
| CN115484382A (en) | Parameter control method, electronic device, computer storage medium, and program product | |
| WO2024001970A1 (en) | Event data processing method and related device | |
| CN117591302B (en) | Project resource optimization allocation method based on unconstrained optimization algorithm | |
| CN114302125A (en) | Image processing method and device, and computer readable storage medium | |
| CN114827662B (en) | Video resolution adaptive adjustment method, device, equipment and storage medium | |
| CN114679559B (en) | Method for watching vehicle-mounted real-time video through intelligent network connection | |
| CN114827104B (en) | Time delay adjustment method, device, equipment and computer readable storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |