A monitoring data uploading method and device based on link load

Abstract

The present invention discloses a link load-based monitoring data upload method and device, relating to the technical field of data transmission. By monitoring the upload link load, the available bandwidth for transmitting monitoring video is determined, and the original monitoring video is processed to a size that meets the requirements for real-time transmission. In other words, when bandwidth is insufficient, low-latency real-time data upload is achieved by reducing the original data. Simultaneously, the video content is analyzed to determine which data must be successfully uploaded. Through application layer design, the camera is informed of lost data, allowing the retransmission of important lost data to achieve effective security.

Description

Technical Field

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

[0002] With the increasing demand for home security and communication, video surveillance technology has become an essential part of family life. Traditional surveillance systems often rely on local storage devices. While this can provide a certain degree of data security, it can also lead to drawbacks such as data loss, insufficient storage space, and a lack of real-time access. Therefore, cloud-based video surveillance has emerged.

[0003] Using a home broadband network, surveillance cameras can transmit surveillance video to a cloud server. Given the limited availability of home broadband resources, existing cloud storage methods often upload local historical video data to the cloud when sufficient home broadband resources are available. For users' real-time viewing and communication needs, UDP is used for transmission to minimize latency. However, the UDP protocol is unreliable and can result in packet loss, which can cause video interruptions or freezes, resulting in a poor viewing experience. If users are not watching in real time, and the camera and storage media are damaged, the surveillance data cannot be uploaded to the cloud in real time. This results in the loss of key evidence and the inability to replay it from the cloud, rendering home surveillance meaningless. Existing methods for uploading surveillance data are subject to lags and lack effective safeguards. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems 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:

[0006] Obtaining video surveillance data for a first preset time period;

[0007] Obtain the current load data of the uplink and confirm the effective bandwidth;

[0008] If the effective bandwidth is not greater than a first preset threshold, storing the video surveillance data as backup data in a local memory;

[0009] Reducing the video surveillance data to obtain target uploaded data;

[0010] Send the target upload data to the cloud;

[0011] Performing importance analysis on the video surveillance data to obtain important time periods;

[0012] Obtaining feedback data from the cloud; the feedback data includes packet loss rate and sequence numbers of lost data packets;

[0013] According to the sequence number, it is determined whether the lost data packet belongs to the important time period. If so, the lost data packet is retransmitted.

[0014] Optionally, obtaining the load data of the uplink at the current moment and confirming the effective bandwidth includes:

[0015] Get the currently used bandwidth of the uplink;

[0016] The effective bandwidth is calculated based on the used bandwidth, the total uplink bandwidth, and the packet loss rate reported by the cloud in the previous phase:

[0017]

[0018] 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.

[0019] Optionally, the reducing the video surveillance data to obtain target upload data includes:

[0020] According to the effective bandwidth, query a preset video quality comparison table to obtain a target frame rate and a target resolution;

[0021] 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;

[0022] The first target video frame group is coded, compressed, and segmented to obtain target upload data.

[0023] Optionally, performing importance analysis on the video surveillance data to obtain important time periods includes:

[0024] Extracting a second target video frame group from the video surveillance data according to a preset sampling frequency;

[0025] 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;

[0026] If the motion detection result is motion, the motion detection result further includes the start time and end time of the motion;

[0027] Extracting a third target video frame group from the second target video frame group according to the start time and the end time;

[0028] Using each frame in the third target video frame group as an input to a pre-trained object detection model, and determining whether there is a person in the picture of the third target video frame group;

[0029] 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.

[0030] Optionally, the method further includes:

[0031] 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;

[0032] 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.

[0033] A second aspect of the present invention provides a link load-based monitoring data uploading device, the device comprising:

[0034] A source data acquisition module, configured to acquire video surveillance data for a first preset period of time;

[0035] Resource acquisition module, used to obtain the current load data of the uplink and confirm the effective bandwidth;

[0036] 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;

[0037] A data reduction module, configured to reduce the video surveillance data to obtain target uploaded data;

[0038] A sending module, used for sending the target uploaded data to the cloud;

[0039] A content analysis module is used to analyze the importance of the video surveillance data to obtain important time periods;

[0040] A feedback information receiving module is used to obtain feedback data from the cloud; the feedback data includes packet loss rate and sequence number of lost data packets;

[0041] 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.

[0042] Optionally, the resource acquisition module includes:

[0043] The bandwidth monitoring module is used to obtain the currently used bandwidth of the uplink;

[0044] 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:

[0045]

[0046] 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.

[0047] Optionally, the data volume reduction module includes:

[0048] A 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;

[0049] a downsampling module, configured 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;

[0050] The encoding module is used to perform encoding compression and segmentation processing on the first target video frame group to obtain target upload data.

[0051] Optionally, the content analysis module includes:

[0052] a first frame extraction module, configured to extract a second target video frame group from the video surveillance data according to a preset sampling frequency;

[0053] a motion detection module, configured 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;

[0054] A second frame extraction module is 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;

[0055] a human detection module, configured to use each frame in the third target video frame group as an input to a pre-trained object detection model to determine whether a human is present in the picture of the third target video frame group;

[0056] The important segment determining module is configured to, if there is a person in the picture of the third target video frame group, take the period between the start time and the end time as the important period.

[0057] Optionally, the device further includes:

[0058] 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;

[0059] The quality update module is configured to, if the effective bandwidth is greater than the second preset threshold, simultaneously upload the video surveillance data to the cloud and, using the remaining bandwidth, upload the backup data in the local storage to the cloud.

[0060] Beneficial effects of the present invention:

[0061] 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 the uplink at the current moment and confirming the 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 the cloud; performing importance analysis on the video monitoring 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 the lost data packet; judging whether the lost data packet belongs to an important time period based on the sequence number, and if so, retransmitting the lost data packet.

[0062] In the case of insufficient bandwidth, by reducing the original data, the transmission burden is reduced 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

[0063] The present invention will be further described below with reference to the accompanying drawings.

[0064] Figure 1 A flowchart of a method for uploading monitoring data based on link load is provided for an embodiment of the present invention;

[0065] Figure 2 The present invention provides a structural diagram of a monitoring data uploading device based on link load. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making any creative efforts shall fall within the scope of protection of the present invention.

[0067] 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 is provided in an embodiment of the present invention. The method comprises the following steps:

[0068] S101, obtaining video surveillance data of a first preset time period.

[0069] S102: Obtain the current load data of the uplink and confirm the effective bandwidth.

[0070] S103: If the effective bandwidth is not greater than the first preset threshold, the video surveillance data is stored as backup data in the local memory.

[0071] S104: Reduce the video surveillance data to obtain target upload data.

[0072] S105, sending the target upload data to the cloud.

[0073] S106: Perform importance analysis on the video surveillance data to obtain important time periods.

[0074] S107, obtaining feedback data from the cloud.

[0075] 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.

[0076] The feedback data includes the packet loss rate and the sequence number of the lost data packet.

[0077] A link load-based monitoring data uploading method provided by an embodiment of the present invention reduces the transmission burden by reducing the original data when bandwidth is insufficient, thereby achieving low-latency real-time uploading of data. In addition, 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.

[0078] In one implementation, the first preset time period may be 2 seconds or 3 seconds before the current moment. A shorter time period can effectively reduce the amount of data that needs to be processed and uploaded, thereby reducing bandwidth consumption.

[0079] In one implementation, the importance analysis process can be run concurrently with the data upload process to improve computational efficiency.

[0080] In one implementation, data packets are transmitted at the transport layer using the UDP protocol, and a packet loss feedback mechanism can be designed at the application layer.

[0081] In one embodiment, step S102 includes:

[0082] Step 1: Get the currently used bandwidth of the uplink.

[0083] Step 1: Calculate the effective bandwidth based on the used bandwidth, the total uplink bandwidth, and the packet loss rate reported by the cloud in the previous phase:

[0084]

[0085] Where EBW is the required effective bandwidth; TBW is the total uplink bandwidth; 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.

[0086] 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. This ensures that there is a certain amount of bandwidth left for other tasks to use during upload, without competing with the upload task, thus ensuring that the monitoring data can be successfully uploaded.

[0087] In one implementation, the packet loss rate threshold can be set between 0.1% and 1%, for example, 0.3%. The latency threshold can be set between 20ms and 100ms, for example, 50ms. When both the packet loss rate and latency exceed their respective thresholds, the network is considered congested. When calculating effective bandwidth, not only the bandwidth of the incoming link (such as Wi-Fi) but also Internet congestion should be considered to ensure that excessive packet loss is avoided when the network is heavily loaded.

[0088] In one embodiment, step S104 includes:

[0089] Step 1: According to the effective bandwidth, query the preset video quality comparison table to obtain the target frame rate and target resolution.

[0090] 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.

[0091] Step three: perform encoding compression and segmentation processing on the first target video frame group to obtain target upload data.

[0092] 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 shorten the upload time. Under high network load conditions, it can increase the upload success rate, reduce the risk of packet loss, and ensure smooth data transmission.

[0093] In one implementation, the video quality comparison table may be configured as shown in Table 1.

[0094] Table 1

[0095]

[0096] Table 1 is a feasible implementation method that can reasonably set the target frame rate and resolution while ensuring upload efficiency, maintaining a certain video quality, and meeting monitoring requirements.

[0097] In one embodiment, step S106 includes:

[0098] Step 1: extracting a second target video frame group from the video surveillance data according to a preset sampling frequency;

[0099] Step 2: performing motion detection on the second target video frame group using a frame difference method to obtain a motion detection result;

[0100] Step 3: If the motion detection result is motion, extract a third target video frame group from the second target video frame group according to the start time and the end time;

[0101] Step 4: using each frame in the third target video frame group as input to the pre-trained object detection model to determine whether there is a person in the picture of the third target video frame group;

[0102] 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 regarded as the important time period.

[0103] 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.

[0104] In one implementation, the method only considers periods with human activities as important periods, avoiding redundant transmission of static background.

[0105] In one embodiment, a link load-based monitoring data uploading method provided by an embodiment of the present invention further includes:

[0106] 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;

[0107] 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.

[0108] 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 maximized and upload efficiency can be improved.

[0109] 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.

[0110] The embodiment of the present invention provides a monitoring data uploading device based on link load. Figure 2 , Figure 2 This is a structural diagram of a link load-based monitoring data upload device provided by an embodiment of the present invention. The device includes:

[0111] The source data acquisition module is used to acquire video surveillance data within a first preset time period.

[0112] The resource acquisition module is used to obtain the load data of the uplink at the current moment and confirm the effective bandwidth.

[0113] The low-speed processing module is 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.

[0114] The data volume reduction module is used to reduce the video surveillance data to obtain the target uploaded data.

[0115] The sending module is used to send the target uploaded data to the cloud.

[0116] The content analysis module is used to analyze the importance of video surveillance data and obtain important time periods.

[0117] The feedback information receiving module is used to obtain feedback data from the cloud.

[0118] 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.

[0119] The feedback data includes the packet loss rate and the sequence number of the lost data packet.

[0120] A link load-based monitoring data upload device provided by an embodiment of the present invention reduces the transmission burden by reducing the original data when bandwidth is insufficient, thereby achieving low-latency real-time uploading of data. In addition, 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.

[0121] In one embodiment, the resource acquisition module includes:

[0122] The bandwidth monitoring module is used to obtain the currently used bandwidth of the uplink.

[0123] The effective bandwidth calculation module is used to calculate the effective bandwidth based on the used bandwidth, the total uplink bandwidth, and the packet loss rate reported by the cloud in the previous stage:

[0124]

[0125] Where EBW is the required effective bandwidth; TBW is the total uplink bandwidth; 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.

[0126] In one embodiment, the data volume reduction module includes:

[0127] The quality determination module is used to query a preset video quality comparison table according to the effective bandwidth to obtain the target frame rate and target resolution.

[0128] The downsampling 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, to obtain a first target video frame group.

[0129] The encoding module is used to perform encoding compression and segmentation processing on the first target video frame group to obtain target upload data.

[0130] In one embodiment, the content analysis module includes:

[0131] The first frame extraction module is used to extract a second target video frame group from the video surveillance data according to a preset sampling frequency.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The important segment determining module is configured to determine 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.

[0136] 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.

[0137] In one embodiment, the apparatus further comprises:

[0138] 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.

[0139] The quality update module is configured to, if the effective bandwidth is greater than a second preset threshold, simultaneously upload the video surveillance data to the cloud and, at the same time, use the remaining bandwidth to upload the backup data in the local storage to the cloud.

[0140] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope 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: Obtaining video surveillance data for a first preset time period; Obtain the current load data of the uplink to confirm the effective bandwidth. Specifically, obtain the currently used bandwidth of the uplink. 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 phase: Wherein, EBW is the required effective bandwidth; TBW is the total bandwidth of the uplink; UBW is the used bandwidth; α is the correction coefficient; PBW is the upload bandwidth used for 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. 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 packet loss rate and sequence numbers of lost data packets; Determining, based on the sequence number, whether the lost data packet belongs to the important time period, and if so, retransmitting the lost data packet; 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 further 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 to a pre-trained object detection model, and determining 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.

2. 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 target uploaded 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 coded, compressed, and segmented to obtain target upload data.

3. 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.

4. A monitoring data uploading device based on link load, characterized in that: The device comprises: A source data acquisition module, configured to acquire video surveillance data for a first preset period of time; Resource acquisition module, used to obtain the current load data of the uplink 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 reduction module, configured 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 is used to analyze the importance of the video surveillance data to obtain important time periods; A feedback information receiving module is used to obtain feedback data from the cloud; the feedback data includes packet loss rate and sequence number of lost data packets; a retransmission judgment module, configured 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; 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 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; α is the correction coefficient; PBW is the upload bandwidth used for 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. The content analysis module includes: a first frame extraction module, configured to extract a second target video frame group from the video surveillance data according to a preset sampling frequency; a motion detection module, configured 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 is 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 detection module, configured to use each frame in the third target video frame group as an input to a pre-trained object detection model to determine whether a human is present in the picture of the third target video frame group; The important segment determining module is configured to, if there is a person in the picture of the third target video frame group, take the period between the start time and the end time as the important period.

5. The monitoring data uploading device based on link load according to claim 4 is characterized in that: The data volume reduction module includes: A 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; a downsampling module, configured 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.

6. The monitoring data uploading device based on link load according to claim 4, characterized in that: 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, simultaneously upload the video surveillance data to the cloud and, using the remaining bandwidth, upload the backup data in the local storage to the cloud.

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