Monitoring data uploading method and device based on link load

Abstract

The invention discloses a monitoring data uploading method and device based on a link load, and relates to the technical field of data transmission. By monitoring the load of an uploading link, the available bandwidth capable of sending the monitoring video is determined, and the original monitoring video is processed, so that the size of the original monitoring video meets the real-time transmission condition, namely, the low-delay real-time uploading of the data is realized by reducing the original data under the condition that the bandwidth is insufficient. And meanwhile, content analysis is performed on the video, which data needs to be successfully uploaded is determined, and the camera is enabled to know which data is lost through the design of the application layer, so that the lost important data is retransmitted, and an effective security guarantee is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission, and in particular to a monitoring data uploading method and device based on link load. Background Art

[0002] With the increasing demand for home security and communication, video surveillance technology has gradually become an important reliance for family life. Traditional monitoring systems often rely on local storage devices. Although they can ensure data security to a certain extent, they have defects such as data loss, insufficient storage space, and inability to access in real time. Therefore, cloud video surveillance came into being.

[0003] With the help of home broadband network, surveillance cameras can transmit surveillance videos to cloud servers. Considering the usage restrictions of home broadband resources, the existing cloud storage method often uploads local historical video data to the cloud when home broadband resources are sufficient; and for users' real-time viewing and communication needs, the UDP protocol is used for transmission to achieve as low latency as possible, but the UDP protocol is unreliable and data packets may be lost, which will cause video missing or freezing, resulting in a poor user viewing experience. When users do not watch in real time, and the camera and storage medium are damaged, the surveillance data cannot be uploaded to the cloud in real time, which will result in the loss of key evidence and cannot be played back from the cloud, which makes home monitoring meaningless. The existing surveillance data upload method has lags and lacks effective guarantees. Summary of the invention

[0004] The purpose of the present invention is to solve the problem of hysteresis and lack of effective guarantee in the uploading method mentioned in the above background technology, and to propose a monitoring data uploading method and device based on link load.

[0005] A first aspect of the present invention provides a method for uploading monitoring data based on link load, the method comprising: Acquire video surveillance data for a first preset time period; Obtain the current load data of the uplink and confirm the effective bandwidth; If the effective bandwidth is not greater than a first preset threshold, storing the video surveillance data as backup data in a local memory; Reducing the video surveillance data to obtain target uploaded data; Send the target upload data to the cloud; Performing importance analysis on the video surveillance data to obtain important time periods; Obtaining feedback data from the cloud; the feedback data includes a packet loss rate and a sequence number of a lost data packet; According to the sequence number, it is determined whether the lost data packet belongs to the important time period, and if so, the lost data packet is retransmitted.

[0006] Optionally, the obtaining of uplink load data at the current moment and confirming the effective bandwidth includes: Get the currently used bandwidth of the uplink; The effective bandwidth is calculated based on the used bandwidth, the total bandwidth of the uplink, and the packet loss rate fed back by the cloud in the previous stage:

[0007] Wherein, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; is the correction coefficient; PBW is the upload bandwidth used by the video surveillance data in the previous stage; r is the packet loss rate fed back by the cloud; rs and ts are the packet loss rate threshold and delay threshold respectively.

[0008] Optionally, the reducing the video surveillance data to obtain target upload data includes: According to the effective bandwidth, query a preset video quality comparison table to obtain a target frame rate and a target resolution; Adjusting the frame rate and resolution of the video surveillance data to the target frame rate and the target resolution, respectively, to obtain a first target video frame group; The first target video frame group is encoded, compressed and segmented to obtain target upload data.

[0009] Optionally, performing importance analysis on the video surveillance data to obtain important time periods includes: Extracting a second target video frame group from the video surveillance data according to a preset sampling frequency; Performing motion detection on the second target video frame group using a frame difference method to obtain a motion detection result; the motion detection result includes motion and stillness; If the motion detection result is motion, the motion detection result also includes the start time and end time of the motion; Extracting a third target video frame group from the second target video frame group according to the start time and the end time; Using each frame in the third target video frame group as an input of a pre-trained target detection model to determine whether there is a person in the picture of the third target video frame group; If there is a person in the picture of the third target video frame group, the time period between the start time and the end time is taken as the important time period.

[0010] Optionally, the method further includes: If the effective bandwidth is greater than the first preset threshold and not greater than the second preset threshold, uploading the video surveillance data to the cloud at the original quality; If the effective bandwidth is greater than the second preset threshold, the backup data in the local storage is uploaded to the cloud using the remaining bandwidth while uploading the video surveillance data to the cloud.

[0011] A second aspect of the present invention provides a monitoring data uploading device based on link load, the device comprising: A source data acquisition module, used to acquire video surveillance data for a first preset period of time; The resource acquisition module is used to obtain the load data of the uplink at the current moment and confirm the effective bandwidth; A low-speed processing module, configured to store the video surveillance data as backup data in a local memory if the effective bandwidth is not greater than a first preset threshold; A data volume reduction module, used to reduce the video surveillance data to obtain target uploaded data; A sending module, used for sending the target uploaded data to the cloud; A content analysis module, used to perform importance analysis on the video surveillance data to obtain important time periods; A feedback information receiving module, used to obtain feedback data from the cloud; the feedback data includes a packet loss rate and a sequence number of a lost data packet; The retransmission judgment module is used to judge whether the lost data packet belongs to the important time period according to the sequence number, and if so, retransmit the lost data packet.

[0012] Optionally, the resource acquisition module includes: The bandwidth monitoring module is used to obtain the currently used bandwidth of the uplink; The effective bandwidth calculation module is used to calculate the effective bandwidth according to the used bandwidth, the total bandwidth of the uplink and the packet loss rate fed back by the cloud in the previous stage:

[0013] Wherein, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; is the correction coefficient; PBW is the upload bandwidth used by the video surveillance data in the previous stage; r is the packet loss rate fed back by the cloud; rs and ts are the packet loss rate threshold and delay threshold respectively.

[0014] Optionally, the data volume reduction module includes: A quality determination module, configured to query a preset video quality comparison table according to the effective bandwidth to obtain a target frame rate and a target resolution; A downsampling module, used to adjust the frame rate and resolution of the video surveillance data to the target frame rate and the target resolution, respectively, to obtain a first target video frame group; The encoding module is used to perform encoding compression and segmentation processing on the first target video frame group to obtain target upload data.

[0015] Optionally, the content analysis module includes: A first frame extraction module, used for extracting a second target video frame group from the video surveillance data according to a preset sampling frequency; A motion detection module, used to perform motion detection on the second target video frame group using a frame difference method to obtain a motion detection result; the motion detection result includes motion and stillness; if the motion detection result is motion, the motion detection result also includes a start time and an end time of the motion; A second frame extraction module, configured to extract a third target video frame group from the second target video frame group according to the start time and the end time; A human body detection module, used to use each frame in the third target video frame group as an input of a pre-trained target detection model to determine whether there is a person in the picture of the third target video frame group; The important segment determination module is used to take the period between the start time and the end time as the important period if there is a person in the picture of the third target video frame group.

[0016] Optionally, the device further comprises: A stable uploading module, configured to upload the video surveillance data to the cloud at original quality if the effective bandwidth is greater than the first preset threshold and not greater than a second preset threshold; The quality update module is configured to, if the effective bandwidth is greater than the second preset threshold, upload the backup data in the local storage to the cloud using the remaining bandwidth while uploading the video surveillance data to the cloud.

[0017] Beneficial effects of the present invention: The present invention proposes a monitoring data uploading method based on link load, which includes: obtaining video monitoring data of a first preset time period; obtaining load data of an uplink at a current moment to confirm an effective bandwidth; if the effective bandwidth is not greater than a first preset threshold, storing the video monitoring data as backup data in a local memory; reducing the video monitoring data to obtain target upload data; sending the target upload data to a cloud; performing importance analysis on the video monitoring data to obtain an important time period; obtaining feedback data from the cloud; the feedback data includes a packet loss rate and a sequence number of a lost data packet; judging whether the lost data packet belongs to an important time period according to the sequence number, and if so, retransmitting the lost data packet.

[0018] In the case of insufficient bandwidth, the transmission burden is reduced by reducing the original data, and low-latency real-time upload of data is achieved. While transmitting data, the video content is analyzed to determine which data needs to be successfully uploaded, so that important data that may be lost can be retransmitted to achieve effective security protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Figure 1 A flowchart of a method for uploading monitoring data based on link load is provided for an embodiment of the present invention; Figure 2 A structural diagram of a monitoring data uploading device based on link load is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] The embodiment of the present invention provides a monitoring data uploading method based on link load. Figure 1 , Figure 1 A flowchart of a method for uploading monitoring data based on link load provided by an embodiment of the present invention. The method comprises the following steps: S101, obtaining video surveillance data of a first preset time period.

[0023] S102, obtaining the current load data of the uplink and confirming the effective bandwidth.

[0024] S103: If the effective bandwidth is not greater than the first preset threshold, the video surveillance data is stored in the local memory as backup data.

[0025] S104, reducing the video surveillance data to obtain target upload data.

[0026] S105, sending the target upload data to the cloud.

[0027] S106, performing importance analysis on the video surveillance data to obtain important time periods.

[0028] S107, obtaining feedback data from the cloud.

[0029] S108, judging whether the lost data packet belongs to an important period according to the sequence number, and if so, retransmitting the lost data packet.

[0030] The feedback data includes the packet loss rate and the sequence number of the lost data packet.

[0031] A monitoring data uploading method based on link load provided by an embodiment of the present invention reduces the transmission burden by reducing the original data in the case of insufficient bandwidth, thereby realizing low-latency real-time uploading of data; and while transmitting the data, the video content is analyzed to determine which data needs to be ensured to be successfully uploaded, so that important data that may be lost is retransmitted to achieve effective security protection.

[0032] In one implementation, the first preset time period may be 2 seconds or 3 seconds before the current moment. A shorter time period may effectively reduce the amount of data that needs to be processed and uploaded, thereby reducing bandwidth consumption.

[0033] In one implementation, the importance analysis process can be run concurrently with the data upload process to improve computing efficiency.

[0034] In one implementation, data packets are transmitted in accordance with the UDP protocol at the transport layer, and a packet loss feedback mechanism can be designed at the application layer.

[0035] In one embodiment, step S102 includes: Step 1: Obtain the currently used bandwidth of the uplink.

[0036] Step 1: Calculate the effective bandwidth based on the used bandwidth, the total bandwidth of the uplink, and the packet loss rate reported by the cloud in the previous stage:

[0037] Among them, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; is the correction coefficient; PBW is the upload bandwidth used by the video surveillance data in the previous stage; r is the packet loss rate fed back by the cloud; rs and ts are the preset packet loss rate threshold and delay threshold, respectively.

[0038] In one implementation, the correction factor It can be 0.9 or 0.95. It is a redundancy factor set to prevent possible bandwidth fluctuations, so that when uploading, there is a certain amount of bandwidth left for other tasks to use, without competing with the upload task, to ensure that the monitoring data can be successfully uploaded.

[0039] In one implementation, the packet loss rate threshold can be set between 0.1% and 1%, such as 0.3%. The delay threshold can be set between 20ms and 100ms, such as 50ms. When both the packet loss rate and the delay are greater than the corresponding thresholds, the network can be considered congested. When calculating the effective bandwidth, not only the bandwidth of the access link (such as WIFI) should be considered, but also the Internet congestion, so as to ensure that there will not be excessive packet loss when the network load is high.

[0040] In one embodiment, step S104 includes: Step 1: According to the effective bandwidth, query the preset video quality comparison table to obtain the target frame rate and target resolution.

[0041] Step 2: Adjust the frame rate and resolution of the video surveillance data to the target frame rate and target resolution respectively to obtain a first target video frame group.

[0042] Step three, encoding, compressing and segmenting the first target video frame group to obtain target upload data.

[0043] In one implementation, adjusting the frame rate and resolution of video surveillance data to target values ​​can effectively reduce the amount of data, make the upload process more efficient, and reduce the upload time. In the case of high network load, it can improve the upload success rate, reduce the risk of packet loss, and ensure that data can be transmitted smoothly.

[0044] In one implementation, the video quality comparison table may be set as shown in Table 1.

[0045] Table 1

[0046] Table 1 is a feasible implementation method, which can reasonably set the target frame rate and resolution while ensuring the upload efficiency, maintain a certain video quality, and meet the monitoring requirements.

[0047] In one embodiment, step S106 includes: Step 1: extracting a second target video frame group from the video surveillance data according to a preset sampling frequency; Step 2, performing motion detection on the second target video frame group using a frame difference method to obtain a motion detection result; Step 3: if the motion detection result is motion, extracting a third target video frame group from the second target video frame group according to the start time and the end time; Step 4, using each frame in the third target video frame group as an input of a pre-trained target detection model to determine whether there is a person in the picture of the third target video frame group; Step 5: If there is a person in the picture of the third target video frame group, the time period between the start time and the end time is taken as the important time period.

[0048] The motion detection result includes motion and stillness; if the motion detection result is motion, the motion detection result also includes the start time and end time of the motion.

[0049] In one implementation, the method only considers the time periods with human activities as important time periods, avoiding redundant transmission of static background.

[0050] In one embodiment, a monitoring data uploading method based on link load provided by an embodiment of the present invention further includes: Step 1: if the effective bandwidth is greater than a first preset threshold and not greater than a second preset threshold, the video surveillance data is uploaded to the cloud at the original quality; Step 2: If the effective bandwidth is greater than the second preset threshold, while uploading the video surveillance data to the cloud, the backup data in the local storage is uploaded to the cloud using the remaining bandwidth.

[0051] In one implementation, the first preset threshold may be 200 Mbps, and the second preset threshold may be 350 Mbps. By flexibly adjusting the upload strategy according to changes in the effective bandwidth, network resources can be utilized to the maximum extent, and upload efficiency can be improved.

[0052] In one implementation, the original version of past historical monitoring data can be uploaded to the cloud through idle bandwidth to replace the low-quality version uploaded in the past, thereby improving the clarity of the monitoring video and the user's viewing experience.

[0053] The embodiment of the present invention provides a monitoring data uploading device based on link load. Figure 2 , Figure 2 A structural diagram of a monitoring data uploading device based on link load provided by an embodiment of the present invention. The device includes: The source data acquisition module is used to acquire the video surveillance data of a first preset time period.

[0054] The resource acquisition module is used to obtain the load data of the uplink at the current moment and confirm the effective bandwidth.

[0055] The low-speed processing module is used to store the video surveillance data as backup data in a local memory if the effective bandwidth is not greater than a first preset threshold.

[0056] The data volume reduction module is used to reduce the video surveillance data to obtain the target uploaded data.

[0057] The sending module is used to send the target uploaded data to the cloud.

[0058] The content analysis module is used to analyze the importance of video surveillance data and obtain important time periods.

[0059] The feedback information receiving module is used to obtain feedback data from the cloud.

[0060] The retransmission judgment module is used to judge whether the lost data packet belongs to an important period according to the sequence number. If it does, the lost data packet will be retransmitted.

[0061] The feedback data includes the packet loss rate and the sequence number of the lost data packet.

[0062] A monitoring data uploading device based on link load provided by an embodiment of the present invention reduces the transmission burden by reducing the original data in the case of insufficient bandwidth, thereby realizing low-latency real-time uploading of data; and while transmitting data, performing content analysis on the video to determine which data needs to be ensured to be successfully uploaded, thereby retransmitting important data that may be lost, thereby achieving effective security protection.

[0063] In one embodiment, the resource acquisition module includes: The bandwidth monitoring module is used to obtain the currently used bandwidth of the uplink.

[0064] The effective bandwidth calculation module is used to calculate the effective bandwidth based on the used bandwidth, the total bandwidth of the uplink, and the packet loss rate fed back by the cloud in the previous stage:

[0065] Among them, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; is the correction coefficient; PBW is the upload bandwidth used by the video surveillance data in the previous stage; r is the packet loss rate fed back by the cloud; rs and ts are the packet loss rate threshold and delay threshold, respectively.

[0066] In one embodiment, the data volume reduction module includes: The quality determination module is used to query a preset video quality comparison table according to the effective bandwidth to obtain a target frame rate and a target resolution.

[0067] The down-sampling module is used to adjust the frame rate and resolution of the video surveillance data to a target frame rate and a target resolution respectively, so as to obtain a first target video frame group.

[0068] The encoding module is used to perform encoding compression and segmentation processing on the first target video frame group to obtain target upload data.

[0069] In one embodiment, the content analysis module includes: The first frame extraction module is used to extract a second target video frame group from the video monitoring data according to a preset sampling frequency.

[0070] The motion detection module is used to perform motion detection on the second target video frame group using a frame difference method to obtain a motion detection result.

[0071] The second frame extraction module is used to extract a third target video frame group from the second target video frame group according to the start time and the end time.

[0072] The human body detection module is used to use each frame in the third target video frame group as the input of the pre-trained target detection model to determine whether there is a person in the picture of the third target video frame group.

[0073] The important segment determination module is used to take the period between the start time and the end time as the important period if there is a person in the picture of the third target video frame group.

[0074] The motion detection result includes motion and stillness; if the motion detection result is motion, the motion detection result also includes the start time and end time of the motion.

[0075] In one embodiment, the apparatus further comprises: The stable uploading module is used to upload the video surveillance data to the cloud at the original quality if the effective bandwidth is greater than a first preset threshold and not greater than a second preset threshold.

[0076] The quality update module is used to upload the backup data in the local storage to the cloud using the remaining bandwidth while uploading the video surveillance data to the cloud if the effective bandwidth is greater than the second preset threshold.

[0077] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A monitoring data uploading method based on link load, characterized in that: The method comprises: Acquire video surveillance data for a first preset time period; Obtain the current load data of the uplink and confirm the effective bandwidth; If the effective bandwidth is not greater than a first preset threshold, storing the video surveillance data as backup data in a local memory; Reducing the video surveillance data to obtain target uploaded data; Send the target upload data to the cloud; Performing importance analysis on the video surveillance data to obtain important time periods; Obtaining feedback data from the cloud; the feedback data includes a packet loss rate and a sequence number of a lost data packet; According to the sequence number, it is determined whether the lost data packet belongs to the important time period, and if so, the lost data packet is retransmitted.

2. A monitoring data uploading method based on link load according to claim 1, characterized in that: The obtaining of the load data of the uplink at the current moment and confirming the effective bandwidth includes: Get the currently used bandwidth of the uplink; The effective bandwidth is calculated based on the used bandwidth, the total bandwidth of the uplink, and the packet loss rate fed back by the cloud in the previous stage: Wherein, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; is the correction coefficient; PBW is the upload bandwidth used by the video surveillance data in the previous stage; r is the packet loss rate fed back by the cloud; rs and ts are the packet loss rate threshold and delay threshold respectively.

3. The method for uploading monitoring data based on link load according to claim 1, characterized in that: The reducing the video surveillance data to obtain the target uploaded data comprises: According to the effective bandwidth, query a preset video quality comparison table to obtain a target frame rate and a target resolution; Adjusting the frame rate and resolution of the video surveillance data to the target frame rate and the target resolution, respectively, to obtain a first target video frame group; The first target video frame group is encoded, compressed and segmented to obtain target upload data.

4. The method for uploading monitoring data based on link load according to claim 1, characterized in that: The importance analysis of the video surveillance data is performed to obtain important time periods including: Extracting a second target video frame group from the video surveillance data according to a preset sampling frequency; Performing motion detection on the second target video frame group using a frame difference method to obtain a motion detection result; the motion detection result includes motion and stillness; If the motion detection result is motion, the motion detection result also includes the start time and end time of the motion; Extracting a third target video frame group from the second target video frame group according to the start time and the end time; Using each frame in the third target video frame group as an input of a pre-trained target detection model to determine whether there is a person in the picture of the third target video frame group; If there is a person in the picture of the third target video frame group, the time period between the start time and the end time is taken as the important time period.

5. The method for uploading monitoring data based on link load according to claim 1, characterized in that: The method further comprises: If the effective bandwidth is greater than the first preset threshold and not greater than the second preset threshold, uploading the video surveillance data to the cloud at the original quality; If the effective bandwidth is greater than the second preset threshold, the backup data in the local storage is uploaded to the cloud using the remaining bandwidth while uploading the video surveillance data to the cloud.

6. A monitoring data uploading device based on link load, characterized in that: The device comprises: A source data acquisition module, used to acquire video surveillance data for a first preset period of time; The resource acquisition module is used to obtain the load data of the uplink at the current moment and confirm the effective bandwidth; A low-speed processing module, configured to store the video surveillance data as backup data in a local memory if the effective bandwidth is not greater than a first preset threshold; A data volume reduction module, used to reduce the video surveillance data to obtain target uploaded data; A sending module, used for sending the target uploaded data to the cloud; A content analysis module, used to perform importance analysis on the video surveillance data to obtain important time periods; A feedback information receiving module, used to obtain feedback data from the cloud; the feedback data includes a packet loss rate and a sequence number of a lost data packet; The retransmission judgment module is used to judge whether the lost data packet belongs to the important time period according to the sequence number, and if so, retransmit the lost data packet.

7. The monitoring data uploading device based on link load according to claim 6, characterized in that: The resource acquisition module includes: The bandwidth monitoring module is used to obtain the currently used bandwidth of the uplink; The effective bandwidth calculation module is used to calculate the effective bandwidth according to the used bandwidth, the total bandwidth of the uplink and the packet loss rate fed back by the cloud in the previous stage: Wherein, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; is the correction coefficient; PBW is the upload bandwidth used by the video surveillance data in the previous stage; r is the packet loss rate fed back by the cloud; rs and ts are the packet loss rate threshold and delay threshold respectively.

8. The monitoring data uploading device based on link load according to claim 6, characterized in that: The data volume reduction module comprises: A quality determination module, configured to query a preset video quality comparison table according to the effective bandwidth to obtain a target frame rate and a target resolution; A downsampling module, used to adjust the frame rate and resolution of the video surveillance data to the target frame rate and the target resolution, respectively, to obtain a first target video frame group; The encoding module is used to perform encoding compression and segmentation processing on the first target video frame group to obtain target upload data.

9. The monitoring data uploading device based on link load according to claim 6, characterized in that: The content analysis module includes: A first frame extraction module, used for extracting a second target video frame group from the video surveillance data according to a preset sampling frequency; A motion detection module, used to perform motion detection on the second target video frame group using a frame difference method to obtain a motion detection result; the motion detection result includes motion and stillness; if the motion detection result is motion, the motion detection result also includes a start time and an end time of the motion; A second frame extraction module, configured to extract a third target video frame group from the second target video frame group according to the start time and the end time; A human body detection module, used to use each frame in the third target video frame group as an input of a pre-trained target detection model to determine whether there is a person in the picture of the third target video frame group; The important segment determination module is used to take the period between the start time and the end time as the important period if there is a person in the picture of the third target video frame group.

10. The monitoring data uploading device based on link load according to claim 6, characterized in that: The device also includes: A stable uploading module, configured to upload the video surveillance data to the cloud at original quality if the effective bandwidth is greater than the first preset threshold and not greater than a second preset threshold; The quality update module is configured to, if the effective bandwidth is greater than the second preset threshold, upload the backup data in the local storage to the cloud using the remaining bandwidth while uploading the video surveillance data to the cloud.

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