A video surveillance data integrity protection method

Abstract

The present invention belongs to the technical field related to video processing, and relates to a method for protecting the integrity of video surveillance data. The method performs segmentation processing on a target surveillance video stream, constructs a transmission sequence of each batch of surveillance video segments according to the network bandwidth status for batch transmission, matches a corresponding data verification algorithm combination for each surveillance video segment to carry out data integrity verification after the target surveillance video stream transmission operation is completed, locates the abnormal data position of each verification abnormal surveillance video segment, performs local data retransmission processing, waits for the target surveillance video stream transmission verification to be completed, performs data reorganization operations in sequence according to index tags, verifies the data integrity of the segments to be reorganized in each data reorganization operation, investigates the causes of the abnormal segments to be reorganized, and provides corresponding solutions, so as to comprehensively guarantee the accuracy and integrity of the target surveillance video stream constructed by the final transmission, and help better meet the requirements for data quality in the field of video surveillance.

Description

Technical Field

[0001] The invention belongs to the technical field related to video processing, and relates to a method for protecting the integrity of video monitoring data. Background Art

[0002] With the widespread application of surveillance technology and the continuous improvement of security needs, whether it is public safety, corporate security or personal privacy protection, higher requirements have been placed on video surveillance data. However, due to factors such as network attacks, equipment failures, and human tampering, video surveillance data is at risk of being destroyed or tampered with, affecting the integrity of surveillance video data. Therefore, developing a reliable method for protecting the integrity of video surveillance data has become an important issue to ensure the reliable operation of the surveillance system.

[0003] In the prior art, there are also some solutions related to video integrity protection. For example, a streaming video integrity protection method with Chinese patent publication number CN110366049A generates integrity description data to detect video integrity, defines an additional real-time integrity ES stream in the TS stream, and customizes it as a data description format Integrity_set, which includes the number of Integrity_descriptors and the corresponding Integrity_descriptor descriptor content array. The TS stream includes several data ES streams and a real-time integrity ES stream, wherein the content of the data ES stream and the Integrity_descriptor both include PID, PTS, and Hash value. The real-time integrity ES stream performs integrity protection on the data ES stream in the TS stream. The receiving end parses the corresponding real-time integrity ES stream in the TS stream, caches it, parses the data ES stream in the TS stream, and searches for the same PID and PTS in the cache. If the Hash value in the data ES stream is consistent with the Hash value in the cache, the data is considered to be complete, otherwise the video is considered to be tampered with, and frame-level audio and video protection is provided without increasing playback delay, which is suitable for TCP and UDP transmission.

[0004] Another Chinese patent publication number CN118075518A is a method and device for implementing the integrity of video surveillance record data storage. It receives network traffic transmitted by an IPC network camera through a video security device 1, and forwards it to an NVR network video recorder after performing a video signature on the network traffic. Video security device 2 receives network traffic transmitted by the NVR network video recorder, performs video signature verification on the network traffic, and forwards it to the video playback end after the signature verification is passed. Otherwise, the log alarm will not be forwarded. It can maintain the existing video surveillance equipment and network structure without affecting the normal use of the video surveillance equipment, so that the original video surveillance system can quickly, cost-effectively and efficiently meet the requirements of using cryptographic technology to ensure the integrity of video surveillance record data storage.

[0005] Although the above-mentioned solutions involve video integrity protection, the existing technology still has limitations. Specifically, the existing technology mainly focuses on the use of data signatures, matching ports or verification algorithms to carry out video integrity protection verification. However, in the process of network transmission of large video data, the protection of data integrity details in the segmentation and reassembly links is neglected. Specifically, the existing technology focuses on the data integrity status before and after transmission, but does not fully consider the characteristics of large-scale video data transmission that requires segmentation and reassembly operations and impose effective protection measures. Even if the final verification result shows that the data does not have integrity, it is difficult to accurately determine the specific problem link. It only stays at the verification of data integrity, but fails to effectively perform the substantive function of data protection, and cannot take targeted prevention and repair measures before or when the data integrity is damaged, which greatly reduces the comprehensiveness and effectiveness of data protection. Summary of the invention

[0006] In view of this, in order to solve the problems raised in the above background technology, a method for protecting the integrity of video surveillance data is now proposed.

[0007] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides a method for protecting the integrity of video surveillance data, including: S1. Segmenting the target surveillance video stream to obtain each surveillance video segment, and adding a corresponding index tag to each surveillance video segment.

[0008] S2. Evaluate the data importance of each surveillance video segment, and use this to match the corresponding data verification algorithm combination for each surveillance video segment.

[0009] S3. Construct a segmented transmission sequence for each batch of surveillance video according to the network bandwidth conditions, and execute the target surveillance video stream transmission operation batch by batch.

[0010] S4. After the target surveillance video stream transmission operation is completed, perform data integrity check on each surveillance video segment, select each surveillance video segment with check abnormalities and locate its abnormal data position, and perform local data retransmission processing on the abnormal data position of each surveillance video segment with check abnormalities.

[0011] S5. After the target surveillance video stream transmission verification is completed, perform data reorganization operations in sequence according to the index tags, verify the data integrity of the segments to be reorganized in each data reorganization operation, find out the reasons for the abnormal segments to be reorganized and provide corresponding solutions to completely construct the target surveillance video stream.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention facilitates the precise positioning and operation of the target monitoring video stream by segmenting the target monitoring video stream and adding index tags, which is beneficial to the targeted development of subsequent data processing, transmission and verification, and improves the overall management efficiency and flexibility.

[0013] (2) The present invention matches the verification algorithm combination according to the data importance assessment results, and can perform more rigorous and adaptive verification on key data to ensure the accuracy of important information, while avoiding the waste of resources caused by excessive verification of unimportant data and optimizing resource allocation.

[0014] (3) The present invention constructs a segmented transmission sequence for each batch of surveillance video according to the network bandwidth, which can make full use of network resources, make video stream transmission more efficient and smooth, adapt to different network environments, reduce transmission interruptions or delays caused by bandwidth problems, and thus improve the reliability and real-time performance of transmission.

[0015] (4) The present invention can quickly locate and solve data anomalies by performing data integrity verification and abnormal local retransmission processing on each surveillance video segment, reduce the overall data processing volume, ensure the accuracy and integrity of the data, and thus improve the stability and fault tolerance of the system.

[0016] (5) The present invention performs integrity verification and exception troubleshooting during the data reorganization phase, which can effectively avoid data errors or losses caused by the reorganization process, thereby ensuring the quality of the final reorganized data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0019] Figure 2 The figure is a logical schematic diagram of segmented processing of a target monitoring video stream according to the present invention.

[0020] Figure 3 The present invention is a logical schematic diagram of performing local data retransmission processing on the abnormal data position of each abnormal monitoring video segment checked. 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] See also Figure 1 As shown, the present invention provides a method for protecting the integrity of video surveillance data, comprising: S1. performing segmentation processing on a target surveillance video stream to obtain each surveillance video segment, and adding a corresponding index tag to each surveillance video segment.

[0023] See also Figure 2 As shown, specifically, the segmented processing for the target monitoring video stream includes: testing the current network transmission environment of the target monitoring video stream, and collecting test values ​​of various network indicators of the current network transmission environment of the target monitoring video stream.

[0024] It should be noted that the network indicators of the above-mentioned network transmission environment include but are not limited to network delay, packet loss rate, network bandwidth, etc.

[0025] Get the values ​​of basic attribute parameters of the target monitoring video stream, where the basic attribute parameters include resolution, frame rate and data encoding format compression ratio. According to the preset intervals of the basic attribute parameters of the monitoring video stream stored in the WEB cloud corresponding to the threshold values ​​of the network indicators of the network transmission environment, get the threshold values ​​of the network indicators of the network transmission environment corresponding to the preset intervals of the basic attribute parameters of the target monitoring video stream. Compare the threshold values ​​of the same network indicators of the network transmission environment corresponding to the preset intervals of the basic attribute parameters of the target monitoring video stream. Filter the maximum value as the comprehensive requirement of the network indicator of the network transmission environment corresponding to the basic attribute parameters of the target monitoring video stream, so as to obtain the comprehensive requirement threshold of the network indicators of the network transmission environment corresponding to the basic attribute parameters of the target monitoring video stream. ,in is the number of each network indicator of the network transmission environment, , and the test values ​​of various network indicators of the current network transmission environment of the target monitoring video stream For comparison, the formula Analyze the transmission quality adaptability of the current network transmission environment relative to the target monitoring video stream, is the number of network indicators of the network transmission environment, thereby determining the amount of segmented data of the target monitoring video stream.

[0026] It should be noted that the specific process of determining the segmented data volume of the above-mentioned target monitoring video stream includes: extracting the planned segmented data volume corresponding to the benchmark transmission quality adaptability of the network transmission environment stored in the WEB cloud relative to the monitoring video stream, performing a ratio analysis on the transmission quality adaptability of the current network transmission environment relative to the target monitoring video stream and the benchmark transmission quality adaptability of the network transmission environment relative to the monitoring video stream, and multiplying the ratio analysis result by the planned segmented data volume corresponding to the benchmark transmission quality adaptability of the network transmission environment relative to the monitoring video stream to obtain the segmented data volume of the target monitoring video stream.

[0027] Identify the focus elements pre-recorded in the corresponding monitoring scene of the target monitoring video stream, compare the change rate of each focus element in each video frame in the target monitoring video stream relative to its previous video frame, if the change rate of any focus element in a video frame in the target monitoring video stream relative to its previous video frame is greater than or equal to the preset monitoring scene focus element warning change rate threshold stored in the WEB cloud, then the video frame is used as the segmentation starting point, thereby determining the starting points of each segment in the target monitoring video stream.

[0028] It should be noted that the above-mentioned focus elements include but are not limited to people, vehicles, etc.

[0029] It should also be noted that the specific comparison process of the change rate of each attention element in each video frame in the above-mentioned target monitoring video stream relative to its previous video frame includes: judging whether each attention element in each video frame in the target monitoring video stream is a newly added attention element relative to its previous video frame; if it is judged that a certain attention element in a certain video frame in the target monitoring video stream is a newly added attention element relative to its previous video frame, then assigning the change rate of the attention element in the video frame in the target monitoring video stream relative to its previous video frame as If it is determined that a certain focus element in a certain video frame in the target monitoring video stream is not a newly added focus element relative to its previous video frame, then the position coordinate change of the focus element in the video frame relative to its previous video frame is obtained, and a ratio analysis is performed with the preset monitoring position coordinate allowable deviation change to obtain the change rate of the focus element in the video frame in the target monitoring video stream relative to its previous video frame.

[0030] Specifically, the segmentation processing for the target monitoring video stream also includes: cutting the target monitoring video stream into monitoring video partitions according to the starting points of each segment, assigning each monitoring video partition a sequential number according to the video frame timestamp corresponding to the starting point of each segment, extracting video frames frame by frame from the segment starting point in each monitoring video partition, accumulating the data amount of each video frame extracted frame by frame until the accumulated video frame data amount reaches the segmented data amount of the target monitoring video stream, terminating the frame-by-frame extraction action, thereby generating each monitoring video segment in each monitoring video partition, and based on the sequential numbering of each monitoring video partition, performing a secondary sequential numbering on each monitoring video segment in each monitoring video partition, integrating each monitoring video segment of the target monitoring video stream, and using its corresponding sequential number as an index label.

[0031] It should be noted that the specific analysis process of the data amount of each video frame extracted frame by frame is as follows: the color depth of each video frame is collected, and the color depth of each video frame is multiplied by the resolution of the target monitoring video stream and the data encoding format compression ratio. The value corresponding to the preset proportional fraction of this product is further analyzed with the data encoding format compression ratio of the target monitoring video stream, so as to obtain the data amount of each video frame extracted frame by frame.

[0032] The embodiment of the present invention facilitates accurate positioning and operation of the target monitoring video stream by segmenting the target monitoring video stream and adding index tags, which is beneficial to the targeted development of subsequent data processing, transmission and verification, and improves overall management efficiency and flexibility.

[0033] S2. Evaluate the data importance of each surveillance video segment, and use this to match the corresponding data verification algorithm combination for each surveillance video segment.

[0034] Specifically, the evaluation of the data importance of each monitoring video segment includes: tracking the focus elements of each video frame in each monitoring video segment, obtaining the number of video frames where each focus element appears and the length of the position change trajectory in each monitoring video segment, analyzing the activity level of each focus element in each monitoring video segment, and using the cumulative activity level of the focus elements in each monitoring video segment as a data importance evaluation indicator.

[0035] It should be noted that the specific analysis process of the activity level of each focus element in each of the above-mentioned surveillance video segments is as follows: performing a ratio analysis on the number of video frames in which each focus element in each surveillance video segment appears and the total number of video frames in the corresponding surveillance video segment to obtain the appearance frame ratio of each focus element in each surveillance video segment, performing a ratio analysis on the position change trajectory length of each focus element in each surveillance video segment and the diagonal position trajectory length of the unit video frame of the corresponding surveillance video segment to obtain the position change ratio of each focus element in each surveillance video segment, and accumulating the appearance frame ratio and the position change ratio with the product of their corresponding preset weights respectively to obtain the activity level of each focus element in each surveillance video segment.

[0036] Specifically, the matching of each surveillance video segment to a corresponding data verification algorithm combination includes: grading the data importance evaluation index of each surveillance video segment, obtaining the data importance level of each surveillance video segment, and matching the data verification algorithm combination according to the data importance level stored in the WEB cloud, wherein the data verification algorithm combination includes a primary data verification algorithm and a secondary data verification algorithm, and matching each surveillance video segment to a corresponding data verification algorithm combination according to its data importance level.

[0037] It should be noted that the above-mentioned classification of the data importance evaluation index for each surveillance video segment is carried out in accordance with the data importance evaluation index interval corresponding to each data importance level stored in the WEB cloud.

[0038] It should also be noted that the data verification algorithms adopted in the present invention specifically include CRC, MD5 and SHA, and the data importance levels specifically include first-level data importance level, second-level data importance level and third-level data importance level. The data verification algorithm combinations corresponding to each data importance level are: the first-level data importance level corresponds to CRC and MD5, the second-level data importance level corresponds to CRC and SHA, and the third-level data importance level corresponds to MD5 and SHA.

[0039] The embodiment of the present invention matches the verification algorithm combination according to the data importance evaluation result, and can perform more rigorous and adaptive verification on key data to ensure the accuracy of important information, while avoiding the waste of resources caused by excessive verification of unimportant data and optimizing resource allocation.

[0040] S3. Construct a segmented transmission sequence for each batch of surveillance video according to the network bandwidth conditions, and execute the target surveillance video stream transmission operation batch by batch.

[0041] Specifically, the method of constructing each batch of surveillance video segment transmission sequences according to the network bandwidth conditions includes: setting the transmission duration of a single batch of data surveillance video segment transmission sequence, multiplying it by the network bandwidth value collected by the current network environment monitoring of the target surveillance video stream, and selecting the value corresponding to the preset proportional fraction of the calculated product as the transmission data volume of the current batch of surveillance video segment transmission sequence.

[0042] Sort each surveillance video segment in ascending order according to the data importance evaluation index, traverse each surveillance video segment one by one in order and perform data volume accumulation operation. When the accumulated data volume of a certain surveillance video traversed is less than or equal to the transmission data volume of the current batch of surveillance video segment transmission sequence and the corresponding accumulated data volume of the next surveillance video traversed to the surveillance video is greater than the transmission data volume of the current batch of surveillance video segment transmission sequence, then the surveillance videos before the surveillance video are integrated into the current batch of surveillance video segment transmission sequence. After the current batch of surveillance video segment transmission sequence is transmitted, re-evaluate the transmission data volume of the next batch of surveillance video segment transmission sequence and repeat the above operation to realize the construction of each batch of surveillance video segment transmission sequence according to the network bandwidth condition.

[0043] The embodiment of the present invention constructs a segmented transmission sequence for each batch of surveillance video according to the network bandwidth, which can make full use of network resources, make video stream transmission more efficient and smooth, adapt to different network environments, reduce transmission interruptions or delays caused by bandwidth problems, and thus improve the reliability and real-time performance of transmission.

[0044] S4. After the target surveillance video stream transmission operation is completed, perform data integrity check on each surveillance video segment, select each surveillance video segment with check abnormalities and locate its abnormal data position, and perform local data retransmission processing on the abnormal data position of each surveillance video segment with check abnormalities.

[0045] Specifically, the screening condition of each abnormal monitoring video segment is satisfied and the verification result output of any data verification algorithm in the data verification algorithm combination is 0.

[0046] It should be noted that if the verification result output of the above data verification algorithm is 0, it means that there is an abnormality in the verification.

[0047] See also Figure 3As shown, specifically, the specific process of the local data retransmission processing includes: expanding the multi-network channel retransmission processing of the data packets corresponding to the abnormal data positions of each verified abnormal monitoring video segment, assigning the data packets corresponding to the abnormal data positions of each verified abnormal monitoring video segment to a preset data importance level and a corresponding data verification algorithm combination before the retransmission processing, and verifying the data packets corresponding to the abnormal data positions of each verified abnormal monitoring video segment after the retransmission processing of each network channel. If the number of network channels whose data verification results of the data packets corresponding to the abnormal data positions of a certain verified abnormal monitoring video segment are output as 1 is greater than or equal to the preset number, it means that the retransmission processing of the data packets corresponding to the abnormal data positions of the verified abnormal monitoring video segment is successful, and extracting the transmission data packets corresponding to the network channels whose data verification results are output as 1 as the filling data packets corresponding to the abnormal data positions of the verified abnormal monitoring video segment. If the number of network channels whose data verification results of the data packets corresponding to the abnormal data positions of a certain verified abnormal monitoring video segment are output as 1 is less than the preset number, it means that the retransmission processing of the data packets corresponding to the abnormal data positions of the verified abnormal monitoring video segment has failed, and the multi-network channel retransmission processing needs to be performed again.

[0048] The embodiment of the present invention can quickly locate and solve data anomaly problems by performing data integrity verification and abnormal local retransmission processing on each monitoring video segment, reduce the overall data processing volume, ensure data accuracy and integrity, and thus improve the stability and fault tolerance of the system.

[0049] S5. After the target surveillance video stream transmission verification is completed, perform data reorganization operations in sequence according to the index tags, verify the data integrity of the segments to be reorganized in each data reorganization operation, find out the reasons for the abnormal segments to be reorganized and provide corresponding solutions to completely construct the target surveillance video stream.

[0050] Specifically, the data integrity of the to-be-reassembled segments is verified in each data reorganization operation, including: retrieving a cloud backup target monitoring video stream to perform the same segmentation processing and the same reorganization processing, performing video frame comparison analysis on the to-be-reassembled segments corresponding to each data reorganization operation and the backup to-be-reassembled segments, evaluating the image feature similarity between the to-be-reassembled segments corresponding to each data reorganization operation and the backup to-be-reassembled segments, if the image feature similarity between the to-be-reassembled segments corresponding to a certain data reorganization operation and the backup to-be-reassembled segments is greater than or equal to a preset image feature similarity threshold, then the data integrity verification result of the to-be-reassembled segments corresponding to the data reorganization operation is output as 1, otherwise it is output as 0, thereby obtaining the data integrity verification results of the to-be-reassembled segments corresponding to each data reorganization operation.

[0051] It should be noted that the specific evaluation process of the image feature similarity between the segments to be reassembled corresponding to each of the above-mentioned data reassembly operations and the spare segments to be reassembled is as follows: extract each image feature point of each video frame image in the segments to be reassembled corresponding to each data reassembly operation and the spare segments to be reassembled, and generate a corresponding feature description vector, analyze the similarity of each image feature point of each video frame image between the segments to be reassembled corresponding to each data reassembly operation and the spare segments to be reassembled through the cosine similarity calculation formula, and calculate the image feature similarity between the segments to be reassembled corresponding to each data reassembly operation and the spare segments to be reassembled by the mean.

[0052] Specifically, the reasons for the abnormal segments to be reassembled include index abnormality reasons and data abnormality reasons.

[0053] It should be noted that the specific analysis process of checking the causes of abnormal segments to be reassembled and providing corresponding solutions is as follows: retrieve the index tags of each surveillance video segment that did not participate in the reassembly process. If the surveillance video segments that did not participate in the reassembly process have the same index tags as the abnormal segments to be reassembled, it means that the index of the abnormal segments to be reassembled is abnormal. Retrieve the image feature similarity between each surveillance video segment in the cloud backup target surveillance video stream and the abnormal segments to be reassembled. Use the index tag of the surveillance video segment corresponding to the maximum image feature similarity in the cloud backup target surveillance video stream as the index tag of the abnormal segments to be reassembled.

[0054] Recalculate the data volume of the abnormal segment to be reassembled, compare it with the data volume before it participates in the reassembly process, and re-perform data verification according to the data verification algorithm combination corresponding to the abnormal segment to be reassembled. If the data volume of the abnormal segment to be reassembled deviates from the data volume before it participates in the reassembly process and the data verification result output is 0, it means that the data of the abnormal segment to be reassembled is abnormal, and the corresponding surveillance video segment with the same index label in the backup target surveillance video stream on the cloud is retrieved for replacement. It should be noted that the process of troubleshooting the cause of the abnormal segment to be reassembled requires first checking the index anomaly and then checking the data anomaly.

[0055] The embodiment of the present invention performs integrity verification and abnormality troubleshooting during the data reorganization phase, which can effectively avoid data errors or losses caused by the reorganization process, thereby ensuring the quality of the final reorganized data.

[0056] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A method for protecting the integrity of video surveillance data, characterized in that: include: S1. Segment the target surveillance video stream to obtain each surveillance video segment, and add a corresponding index tag to each surveillance video segment; S2. Evaluate the importance of data of each surveillance video segment, and match the corresponding data verification algorithm combination for each surveillance video segment; wherein the data verification algorithm combination includes a primary data verification algorithm and a secondary data verification algorithm; S3. Construct each batch of surveillance video segment transmission sequence according to the network bandwidth status, and perform the target surveillance video stream transmission operation batch by batch; S4. After the target surveillance video stream transmission operation is completed, the data integrity check is performed on each surveillance video segment, each check abnormal surveillance video segment is screened and the abnormal data position is located, and the abnormal data position of each check abnormal surveillance video segment is retransmitted locally; S5. After the target surveillance video stream transmission verification is completed, perform data reorganization operations in sequence according to the index tags, verify the data integrity of the segments to be reorganized based on each data reorganization operation, find out the reasons for the abnormal segments to be reorganized and provide corresponding solutions to completely construct the target surveillance video stream.

2. A method for protecting the integrity of video surveillance data according to claim 1, characterized in that: The segmented processing for the target monitoring video stream includes: testing the current network transmission environment of the target monitoring video stream, collecting the test values ​​of various network indicators of the current network transmission environment of the target monitoring video stream; Obtain the values ​​of basic attribute parameters of the target monitoring video stream, where the basic attribute parameters include resolution, frame rate and data encoding format compression ratio. According to the preset intervals of the basic attribute parameters of the monitoring video stream stored in the WEB cloud, the thresholds of the network indicators of the network transmission environment are corresponding to the standard. Analyze the transmission quality adaptability of the current network transmission environment relative to the target monitoring video stream, thereby determining the segmented data volume of the target monitoring video stream. Identify the focus elements pre-recorded in the corresponding monitoring scene of the target monitoring video stream, compare the change rate of each focus element in each video frame in the target monitoring video stream relative to its previous video frame, if the change rate of any focus element in a video frame in the target monitoring video stream relative to its previous video frame is greater than or equal to the preset monitoring scene focus element warning change rate threshold stored in the WEB cloud, then the video frame is used as the segmentation starting point, thereby determining the starting points of each segment in the target monitoring video stream.

3. A method for protecting the integrity of video surveillance data according to claim 2, characterized in that: The segmentation processing for the target monitoring video stream also includes: cutting the target monitoring video stream into monitoring video partitions according to the starting points of each segment, assigning each monitoring video partition a sequential number according to the video frame timestamp corresponding to the starting point of each segment, extracting video frames frame by frame from the segment starting point in each monitoring video partition, accumulating the data amount of each video frame extracted frame by frame until the accumulated video frame data amount reaches the segmented data amount of the target monitoring video stream, terminating the frame-by-frame extraction action, thereby generating each monitoring video segment in each monitoring video partition, and based on the sequential numbering of each monitoring video partition, performing a secondary sequential numbering on each monitoring video segment in each monitoring video partition, integrating each monitoring video segment of the target monitoring video stream, and using its corresponding sequential number as an index tag.

4. A method for protecting the integrity of video surveillance data according to claim 1, characterized in that: The evaluation of the data importance of each monitoring video segment includes: tracking the focus elements of each video frame in each monitoring video segment, obtaining the number of video frames where each focus element appears and the length of the position change trajectory in each monitoring video segment, analyzing the activity level of each focus element in each monitoring video segment, and using the cumulative activity level of the focus elements in each monitoring video segment as a data importance evaluation indicator.

5. A method for protecting the integrity of video surveillance data according to claim 4, characterized in that: The method for matching each surveillance video segment with a corresponding data verification algorithm combination includes: grading the data importance evaluation index of each surveillance video segment, obtaining the data importance level of each surveillance video segment, and matching the data verification algorithm combination corresponding to the data importance level of each surveillance video segment according to the data verification algorithm combination corresponding to each data importance level stored in the WEB cloud.

6. A method for protecting the integrity of video surveillance data according to claim 4, characterized in that: The method of constructing each batch of surveillance video segment transmission sequence according to the network bandwidth status includes: setting the transmission duration of a single batch of surveillance video segment transmission sequence, multiplying it by the network bandwidth value collected by the current network environment monitoring of the target surveillance video stream, and selecting the value corresponding to the preset proportion fraction of the calculated product as the transmission data volume of the current batch of surveillance video segment transmission sequence; Sort each surveillance video segment in ascending order according to the data importance evaluation index, traverse each surveillance video segment one by one in order and perform data volume accumulation operation. When the accumulated data volume of a certain surveillance video traversed is less than or equal to the transmission data volume of the current batch of surveillance video segment transmission sequence and the corresponding accumulated data volume of the next surveillance video traversed to the surveillance video is greater than the transmission data volume of the current batch of surveillance video segment transmission sequence, then the surveillance videos before the surveillance video are integrated into the current batch of surveillance video segment transmission sequence. After the current batch of surveillance video segment transmission sequence is transmitted, re-evaluate the transmission data volume of the next batch of surveillance video segment transmission sequence and repeat the above operation to realize the construction of each batch of surveillance video segment transmission sequence according to the network bandwidth condition.

7. A method for protecting the integrity of video surveillance data according to claim 5, characterized in that: The screening conditions of each verification abnormal monitoring video segment satisfy the verification result output of any data verification algorithm in the data verification algorithm combination as 0.

8. A method for protecting the integrity of video surveillance data according to claim 5, characterized in that: The specific process of the local data retransmission processing includes: launching multi-network channel retransmission processing of the data packets corresponding to the abnormal data positions of each verified abnormal monitoring video segment, assigning a preset data importance level corresponding to the data packets corresponding to the abnormal data positions of each verified abnormal monitoring video segment before retransmission processing, and verifying the data packets corresponding to the abnormal data positions of each verified abnormal monitoring video segment after retransmission processing of each network channel. If the number of network channels whose data verification results of the data packets corresponding to the abnormal data positions of a certain verified abnormal monitoring video segment are output as 1 is greater than or equal to the preset number, it means that the retransmission processing of the data packets corresponding to the abnormal data positions of the verified abnormal monitoring video segment is successful, and extracting the transmission data packets corresponding to the network channels whose data verification results are output as 1 as the filling data packets corresponding to the abnormal data positions of the verified abnormal monitoring video segment. If the number of network channels whose data verification results of the data packets corresponding to the abnormal data positions of a certain verified abnormal monitoring video segment are output as 1 is less than the preset number, it means that the retransmission processing of the data packets corresponding to the abnormal data positions of the verified abnormal monitoring video segment has failed, and multi-network channel retransmission processing needs to be performed again.

9. A method for protecting the integrity of video surveillance data according to claim 1, characterized in that: The method verifies the data integrity of the to-be-reassembled segments based on each data reorganization operation, including: retrieving a cloud backup target monitoring video stream to perform the same segmentation processing and the same reorganization processing, performing video frame comparison analysis on the to-be-reassembled segments corresponding to each data reorganization operation and the backup to-be-reassembled segments, evaluating the image feature similarity between the to-be-reassembled segments corresponding to each data reorganization operation and the backup to-be-reassembled segments, and if the image feature similarity between the to-be-reassembled segments corresponding to a certain data reorganization operation and the backup to-be-reassembled segments is greater than or equal to a preset image feature similarity threshold, then outputting the data integrity verification result of the to-be-reassembled segments corresponding to the data reorganization operation as 1, otherwise outputting as 0, thereby obtaining the data integrity verification results of the to-be-reassembled segments corresponding to each data reorganization operation.

10. A method for protecting the integrity of video surveillance data according to claim 1, characterized in that: The reasons for the abnormal segments to be reassembled include index abnormality reasons and data abnormality reasons.

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