Intelligent video stream batch processing optimization system
Through the intelligent video stream batch processing optimization system, the problems of insufficient priority evaluation of video stream data and single storage methods in the prior art are solved, efficient processing and secure storage of video stream data are realized, and timely processing of important data and confidentiality of data are ensured.
Patent Information
- Application Number
- CN202411789969.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-06
- Publication Date
- 2025-02-25
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The prior art is difficult to accurately judge the importance of video streaming data, and the lack of a scientific and reasonable priority evaluation system, which leads to delay processing of important video data and a single data storage method, which fails to effectively protect the confidentiality and integrity of video data.
Through the intelligent video stream batch processing optimization system, including video stream access module, priority evaluation module, processing scheduling module, index establishment module and video cleaning module, pre-processing, priority evaluation, sorting, storage and encryption processing of video stream data, as well as cleaning of storage databases.
It realizes accurate priority evaluation of video streaming data, ensures priority processing of important data, optimizes data processing order and storage efficiency, and improves the confidentiality of video data through multi-dimensional encryption, avoiding waste of storage resources and data leakage.
Smart Images
Figure CN119520744A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of video processing, and in particular to an intelligent video stream batch processing optimization system. Background Art
[0002] In today's security monitoring, intelligent management and many other fields, a large number of surveillance cameras are widely deployed in various areas, generating massive amounts of video stream data.
[0003] However, the existing technology still has the following deficiencies when processing the video stream data generated by each monitoring area: It is difficult to accurately judge the importance of video stream data based on the recent data performance of each monitoring area. There is a lack of a scientific and reasonable priority evaluation system, which makes it impossible to distinguish between critical and non-critical video data when allocating resources, resulting in delays in the processing of important video data. The data storage method is single and does not take into account the differences in storage requirements for video data of different priorities, resulting in a waste of storage resources. In addition, during the data storage process, existing encryption methods are usually used, which can be easily cracked by external attackers, leading to video data leakage. The confidentiality and integrity of the video content cannot be guaranteed, and there are limitations in processing optimization.
[0004] To this end, an intelligent video stream batch processing optimization system is launched. Summary of the invention
[0005] In view of this, the present invention provides an intelligent video stream batch processing optimization system to solve the problems raised by the above background technology.
[0006] The purpose of the present invention can be achieved by the following technical solution: an intelligent video stream batch processing optimization system, comprising: Video stream access module: receives video stream data from each monitoring area and performs preprocessing. After the preprocessing is completed, the video stream data of each monitoring area is sent to the priority evaluation module; Priority evaluation module: Taking the time point of currently receiving video stream data as the starting point, extract and analyze the abnormal parameters of each monitoring area within the preset time interval before the starting point, so as to determine the priority processing video segments corresponding to the video stream data of each monitoring area; the abnormal parameters include the number of abnormal occurrences of each monitoring area within the preset time interval and the time frame in which each abnormality occurs; Processing and scheduling module: receiving the priority processing video segments corresponding to the video stream data of each monitoring area, and performing corresponding operations to transfer the video stream data of each monitoring area to a pre-built storage database; Index establishment module: when storing the video stream data of each monitoring area, an index relationship with the video data storage location and metadata is established based on the monitoring area, time date and number corresponding to the video stream data; Video cleaning module: preset the cleaning time interval of the storage database. After the preset cleaning time interval is reached, the access parameters of each group of stored video data in the storage database are extracted and analyzed, and corresponding steps are executed to clean the storage database based on the analysis results; the stored video data includes each group of priority processing segments and pending segments; the access parameters include the number of accesses and the length of the access interval.
[0007] In some embodiments, the abnormal parameters of each monitoring area within a preset time interval before the starting point are extracted and analyzed, specifically: Predetermine the abnormality judgment criteria corresponding to each monitoring area in advance, and count the number of abnormalities occurring in each monitoring area within a preset time interval and the time frame in which each abnormality occurs; Preset the priority basic value corresponding to each monitoring area, extract the number of abnormal occurrences in each monitoring area within the preset time interval, preset the range of values of each group of times corresponding to the number of abnormal occurrences, set each group of number value ranges to correspond to a priority additional value, match the number of abnormal occurrences in each monitoring area within the preset time interval with the range of values of each group of times, so as to obtain the priority additional value of each monitoring area; multiply the priority basic value of each monitoring area by the corresponding matched priority additional value, and obtain the processing evaluation index of each monitoring area at the current time point.
[0008] In some embodiments, determining the priority processing video segments corresponding to the video stream data of each monitoring area is specifically as follows: Sort the data from large to small according to the processing evaluation index, and sort the video stream data of each monitoring area based on the sorting result of the processing evaluation index; After the sorting is completed, the time axis coordinate system corresponding to each monitoring area is established with the preset time interval as the boundary. On each set of established time axis coordinate systems, the time frame where each abnormality occurs in each monitoring area is sequentially mapped to the established time axis coordinate system, that is, the numerical point corresponding to the time frame where each abnormality occurs in the time axis coordinate system is drawn; The time axis coordinate system corresponding to the preset time interval is divided into three equal sub-time zones by dividing lines, and the number of abnormal occurrences in each sub-time zone corresponding to each monitoring area is counted, and the sub-time zone with the highest number of abnormal occurrences is used as the priority sub-time zone corresponding to each monitoring area. The numerical points located at the leftmost and rightmost ends of each priority sub-time zone are identified as construction points, and the time zone formed between the two groups of construction points in each priority sub-time zone is used as the priority processing video segment of each monitoring area.
[0009] In some embodiments, corresponding operations are performed to transmit the video stream data of each monitoring area to a pre-built storage database, specifically: Based on the priority processing video segments determined by each monitoring area, corresponding priority processing video segments are extracted from the video stream data of each monitoring area and integrated into a priority processing segment set; Mark the remaining video data of each monitoring area except the priority processed video segments as the video segments to be processed, and integrate them into a set of segments to be processed; Based on the sorting result of the processing evaluation index, sort each group of video data in the priority processing segment set and the to-be-processed segment set; preset resource allocation rules corresponding to the priority processing segment set and the to-be-processed segment set respectively; According to the sorting order of each group of video data in the priority processing segment set and the to-be-processed segment set, the priority processing segment set and the to-be-processed segment set are transmitted to a pre-constructed storage database in the sorting order using a corresponding preset resource allocation rule, and a priority storage area and a to-be-processed storage area corresponding to the priority processing segment set and the to-be-processed segment set are set in the storage database; After the transmission is completed, the priority processing fragments and the fragments to be processed in each monitoring area are encrypted based on the preset encryption rules.
[0010] In some embodiments, the priority processing segments and the to-be-processed segments of each monitoring area are encrypted based on a preset encryption rule, specifically: Each monitoring area is numbered, and the number is represented by i, where i=1, 2, ... k; k is the total number of monitoring areas; For the priority processing segments and the to-be-processed segments of each monitoring area, the processing evaluation index of each monitoring area is extracted, and each group of numbers of the processing evaluation index of each monitoring area is extracted and randomly shuffled to obtain a digital set, and the digital set of each monitoring area is spliced with the number of the corresponding monitoring area; wherein the number is in the first place; After the splicing is completed, the basic keys of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are obtained; then the segment durations of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are extracted; the values in the segment durations are used as additional keys and spliced with the basic keys, that is, the numbers of the additional keys are added to the end of the basic keys; Thus, the encryption keys of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are obtained. If the encryption key contains the number 0, the number 0 is converted into a preset value by default; The numerical values of each group of numbers in the encryption key are accumulated, and the accumulated value is compared with a preset reference value. If it is greater than the preset reference value, a graphic encryption signal is generated; if it is less than the preset reference value, a digital encryption signal is generated.
[0011] In some embodiments, encrypting the priority processing segments and the to-be-processed segments of each monitoring area based on a preset encryption rule further includes: If a graphic encryption signal is generated, a straight line is constructed, and numerical points equal to the number of encryption key digits are drawn at equal distances on the constructed straight line. A circle is constructed with each group of drawn numerical points as the center and the numerical value of each group as the radius of the circle. The construction is carried out from left to right according to the arrangement order of each group of digits in the encryption key. After the construction is completed, the image formed by each group of constructed circles is used as the graphic signaling key for the priority processing fragments and the fragments to be processed corresponding to each monitoring area; If digital encryption signaling is generated, each group of adjacent numbers in the encryption key will be summed up in pairs. After the summation is completed, a new group of numbers will be obtained as the digital key. The number of numbers in the digital key will be counted as the number of divisions. The pre-set square will be divided into sub-squares based on the number of divisions, and each group of numbers in the digital key will be randomly filled into each divided sub-square. After the filling is completed, the filled square will be used as the digital signaling key for the priority processing fragments and the fragments to be processed corresponding to each monitoring area.
[0012] In some embodiments, corresponding steps are performed based on the analysis results to clean up the storage database, specifically: The duration of the preset cleaning time window is based on the current time point, and the preset cleaning time window duration is moved forward, and the time point after the shift is marked as the split time point; Extracting each group of stored video data before the segmentation time point from the storage database as the video data to be cleaned; Obtain the time point of the most recent access to each group of video data to be cleaned before the current time point, and calculate the time difference to obtain the access interval duration of each group of video data to be cleaned; Count the number of accesses to each group of video data to be cleaned up before the current time point. In the process of counting the number of accesses, if the time interval between two adjacent accesses is less than the preset reference time, it is determined to be a repeated access, and the number of accesses to the corresponding video data to be cleaned up is reduced by one; Identify specific types of each group of video data to be cleaned, the specific types include priority processing segments and to-be-processed segments, and set a group of additional coefficients corresponding to the priority processing segments and the to-be-processed segments respectively; Preset the access interval duration corresponding to each group of duration value ranges, and set each group of duration value ranges to correspond to an access demand score; Match the access interval duration of each group of video data to be cleaned with the value range of each group duration, so as to obtain the access demand score of each group of video data to be cleaned; The weight coefficients corresponding to the preset access demand scores and access times are respectively determined. The access demand scores and access times of each group of video data to be cleaned are normalized and multiplied by the corresponding preset weight coefficients respectively. Then the sum is multiplied by the corresponding additional coefficient to obtain the cleaning evaluation index of each group of video data to be cleaned.
[0013] In some embodiments, executing corresponding steps to clean up the storage database based on the analysis results also includes: A reference threshold index corresponding to the cleaning evaluation index is set, and the video data to be cleaned whose cleaning evaluation index is higher than the reference threshold index is retained, and the video data to be cleaned whose cleaning evaluation index is lower than the reference threshold index is cleaned.
[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention takes the time point of currently receiving video stream data as the starting point, extracts the abnormal parameters of each monitoring area within the preset time interval before the starting point and calculates the processing evaluation index, sorts the video stream data of the monitoring area according to the index size, realizes accurate priority evaluation, and enables important video data to be processed first. At the same time, a time axis coordinate system is established with the preset time interval as the boundary, and the time frame of the abnormal occurrence is matched to the system. The priority sub-time zone is determined by dividing the sub-time zone and counting the number of abnormalities. The video clips to be processed first are determined with the leftmost and rightmost numerical points as the construction points. The range of video content that may contain important abnormal information can be accurately delineated, which solves the problem of the lack of a scientific and reasonable priority evaluation system in the prior art, which makes it impossible to distinguish between critical and non-critical video data when allocating resources, resulting in delays in the processing of important video data. Based on the priority evaluation result, the present invention divides the video data into a priority processing segment set and a to-be-processed segment set, and sorts them according to the processing evaluation index to ensure that important data is processed first and optimize the data processing order; resource allocation rules corresponding to different sets are preset, and data is transmitted to the priority storage area and the to-be-processed storage area in the storage database according to the sorting order using the rules, which meets the processing requirements of video data of different priorities and improves the transmission and storage efficiency; After the transmission is completed, the present invention uses a method of generating an encryption key based on multi-dimensional information such as the monitoring area number, the processing evaluation index and the segment duration to encrypt the priority processing segment and the to-be-processed segment, thereby increasing the uniqueness and randomness of the key, effectively protecting the confidentiality of the video data and preventing illegal acquisition and viewing; The present invention presets the cleaning time window duration according to the data volume of the storage database and business needs, determines the segmentation time point, screens out the video data to be cleaned, comprehensively considers the number of accesses, access interval duration, and data type to calculate the cleaning evaluation index, and compares it with the reference threshold index to decide whether to keep or discard the data, thereby avoiding the disadvantages of cleaning only based on a fixed retention period and improving the utilization efficiency of storage resources. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION
[0016] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete, and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.
[0017] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.
[0018] See also Figure 1 As shown, the intelligent video stream batch processing optimization system includes a video stream access module, a priority evaluation module, a processing scheduling module, an index building module and a video cleaning module; The video stream access module is used to receive and pre-process the video stream data from each monitoring area; the video stream data is collected and acquired through the monitoring cameras pre-deployed in each monitoring area. The pre-processing includes format conversion, screening and filtering of the received video stream data; after the pre-processing is completed, the video stream data of each monitoring area is sent to the priority evaluation module; After startup, the video stream access module begins to scan and connect to the surveillance cameras in each monitoring area. During the connection process, for each camera, first the device is identified to obtain its device model, manufacturer and other information, and then the pre-stored parameter configuration table is searched according to the device information to determine the communication parameters that match it. For example, for a specific model of camera of a certain brand, its default IP address range, port number, video encoding format and other parameters are obtained from the configuration table to attempt a connection; after a successful connection, the video stream receiving thread is started to start receiving video stream data; at the same time, the format conversion submodule is turned on to convert the received video stream into the system internal format according to the preset conversion rules. During the conversion process, the video stream data is preliminarily screened according to the preset rules. If the video stream with a frame rate lower than 5 frames / second is set as an invalid video stream, such video stream data that does not meet the set requirements is automatically filtered out.
[0019] The priority evaluation module is used to extract and analyze the abnormal parameters of each monitoring area within a preset time interval before the starting point, taking the time point of currently receiving the video stream data as the starting point, so as to determine the priority processing video segments corresponding to the video stream data of each monitoring area; the abnormal parameters include the number of abnormal occurrences of each monitoring area within the preset time interval and the time frame in which each abnormality occurs; Specifically: According to the monitoring purpose of each monitoring area, the abnormality judgment criteria corresponding to each monitoring area are pre-established; for example, in the personnel monitoring area, abnormalities may include sudden gathering, running, illegal intrusion and other behaviors of personnel; the number of abnormal occurrences in each monitoring area within the preset time interval and the time frame in which each abnormality occurs are counted; Preset the priority basic value corresponding to each monitoring area; the range is set to 1.294-1.485 by the technical staff based on the importance of each monitoring area; extract the number of abnormal occurrences in each monitoring area within the preset time interval, preset the number of abnormal occurrences corresponding to each group of number value ranges, and set each group of number value ranges to correspond to a priority additional value; the priority additional value range is set to 1.138-1.259, and the more abnormal occurrences, the higher the corresponding matching priority additional value; match the number of abnormal occurrences in each monitoring area within the preset time interval with the number value range of each group, so as to obtain the priority additional value of each monitoring area; multiply the priority basic value of each monitoring area by the corresponding matching priority additional value to obtain the processing evaluation index of each monitoring area at the current time point; It should be noted that the monitoring areas are sorted from large to small according to the size of the calculated processing evaluation index. The higher the processing evaluation index of the monitoring area, the higher the processing priority of the video stream data.
[0020] Sort the data from large to small according to the processing evaluation index, and sort the video stream data of each monitoring area based on the sorting result of the processing evaluation index; After the sorting is completed, the time axis coordinate system corresponding to each monitoring area is established with the preset time interval as the boundary. On each set of established time axis coordinate systems, the time frame where each abnormality occurs in each monitoring area is sequentially mapped to the established time axis coordinate system, that is, the numerical point corresponding to the time frame where each abnormality occurs in the time axis coordinate system is drawn; It should be noted that the specific time point of each abnormality in each monitoring area should be accurately placed at the corresponding position on the time axis. For example, if an abnormality occurred in a monitoring area at a specific time point (such as 10:30 am) in the past 24 hours, then a numerical point should be drawn at the position of 10:30 am on the time axis.
[0021] The time axis coordinate system corresponding to the preset time interval is divided into three equal sub-time zones by a dividing line, and the number of abnormal occurrences in each sub-time zone corresponding to each monitoring area is counted, and the sub-time zone with the highest number of abnormal occurrences is used as the priority sub-time zone corresponding to each monitoring area, and the numerical points located at the leftmost and rightmost ends of each priority sub-time zone are identified as construction points, and the time zone formed between the two groups of construction points in each priority sub-time zone is used as the priority processing video segment of each monitoring area; It should be noted that the leftmost and rightmost numerical points in the priority sub-time zone are identified as construction points to determine the priority video segments, so that the determined video segments can be closely centered around the time period when anomalies occur relatively concentratedly, and more accurately define the range of video content that is most likely to contain important anomaly information.
[0022] The processing scheduling module is used to receive the priority processing video segments corresponding to the video stream data of each monitoring area, and perform corresponding operations to transmit the video stream data of each monitoring area to a pre-built storage database; Specifically: Based on the priority processing video segments determined by each monitoring area, corresponding priority processing video segments are extracted from the video stream data of each monitoring area and integrated into a priority processing segment set; Mark the remaining video data of each monitoring area except the priority processed video segments as the video segments to be processed, and integrate them into a set of segments to be processed; Based on the sorting result of the processing evaluation index, the video data of each group in the priority processing segment set and the to-be-processed segment set are sorted; that is, the larger the processing evaluation index, the higher the sorting position of the corresponding priority processing segment and the to-be-processed segment in the corresponding set; The resource allocation rules corresponding to the priority processing fragment set and the to-be-processed fragment set are preset; the resource allocation rules are computing resource allocations set for the priority processing fragment set and the to-be-processed fragment set respectively, and the CPU usage allocation ratio and GPU resource allocation ratio in different computing resource allocations are different; According to the sorting order of each group of video data in the priority processing segment set and the to-be-processed segment set, the priority processing segment set and the to-be-processed segment set are transmitted to a pre-built storage database in the sorting order using a corresponding preset resource allocation rule, and a priority storage area and a to-be-processed storage area corresponding to the priority processing segment set and the to-be-processed segment set are set in the storage database; the priority storage area has the characteristics of low latency and high read and write speed, while the latency and read and write speed of the to-be-processed storage area are lower than those of the priority storage area; It should be noted that the specific operation of processing the priority processing segment set and the to-be-processed segment set according to different preset resource allocation rules can realize the reasonable and efficient processing and storage of the video stream data of each monitoring area, give full play to the role of system resources, and meet the processing requirements of video data of different priorities.
[0023] After the transmission is completed, the priority processing fragments and the fragments to be processed in each monitoring area are encrypted based on the preset encryption rules, specifically: Each monitoring area is numbered, and the number is represented by i, where i=1, 2, ... k; k is the total number of monitoring areas; For the priority processing segments and the to-be-processed segments of each monitoring area, the processing evaluation index of each monitoring area is extracted, and each group of numbers of the processing evaluation index of each monitoring area is extracted and randomly shuffled to obtain a digital set, and the digital set of each monitoring area is spliced with the number of the corresponding monitoring area; wherein the number is in the first place; After the splicing is completed, the basic keys of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are obtained; then the segment duration of the priority processing segments and the to-be-processed segments corresponding to each monitoring area is extracted; the unit is seconds; the value in the segment duration is used as the additional key and spliced with the basic key, that is, the number of the additional key is added to the end of the basic key; Thus, the encryption keys of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are obtained. If the encryption key contains the number 0, the number 0 is converted into a preset value by default; the preset value is a positive integer greater than 0, such as 3; The numerical values of each group of numbers in the encryption key are accumulated, and the accumulated value is compared with a preset reference value. If it is greater than the preset reference value, a graphic encryption signal is generated; if it is less than the preset reference value, a digital encryption signal is generated; If a graphic encryption signal is generated, a straight line is constructed, and numerical points equal to the number of encryption key digits are drawn at equal distances on the constructed straight line. A circle is constructed with each group of drawn numerical points as the center and the numerical value of each group as the radius of the circle. The construction is carried out from left to right according to the arrangement order of each group of digits in the encryption key. After the construction is completed, the image formed by each group of constructed circles is used as the graphic signaling key for the priority processing fragments and the fragments to be processed corresponding to each monitoring area; If digital encryption signaling is generated, each group of adjacent numbers in the encryption key is summed up in pairs, and after the summing is completed, a new group of numbers is obtained as the digital key, and the number of numbers in the digital key is counted as the number of divisions. The pre-set square will be divided into sub-squares based on the number of divisions, and each group of numbers in the digital key will be randomly filled into each divided sub-square. After the filling is completed, the filled square will be used as the digital signaling key corresponding to the priority processing segment and the to-be-processed segment of each monitoring area; It should be noted that the encryption key is generated by combining multi-dimensional information such as the monitoring area number, processing evaluation index, and clip length, and the numbers of the processing evaluation index are randomly shuffled. This makes the encryption keys of each monitoring area, and even video clips with different processing evaluation indexes and lengths in the same monitoring area, highly unique and random. This unique key generation method greatly increases the difficulty for external attackers to guess or crack the key through conventional means, effectively protecting the confidentiality of video data during storage and preventing illegal acquisition and viewing of video content.
[0024] The index establishment module is used to establish an index relationship with the video data storage location and metadata based on the monitoring area, time date and number corresponding to the video stream data when storing the video stream data of each monitoring area; When you need to find a video with a certain ID, you can quickly locate its physical location in the storage device through the index and obtain related metadata, including video format, resolution, and duration. The video cleaning module is used to preset the cleaning time interval of the storage database. After the preset cleaning time interval is reached, the access parameters of each group of stored video data in the storage database are extracted and analyzed, and corresponding steps are executed to clean up the storage database based on the analysis results; the stored video data includes each group of priority processing segments and to-be-processed segments; the access parameters include the number of accesses and the length of the access interval; Specifically: According to the amount of data stored in the database and business needs, the duration of the cleanup time window is preset. Taking the current time point as the benchmark, the preset cleanup time window is moved forward, and the time point after the shift is marked as the split time point; For example, if the current time is December 4, 2023, and the set time window is 6 months, then the split time point is June 4, 2023. This split time point can clearly distinguish the old data range that needs to be analyzed to see whether it can be cleaned up; Extracting each group of stored video data before the segmentation time point from the storage database as the video data to be cleaned; Obtain the time point of the most recent access to each group of video data to be cleaned before the current time point, and calculate the time difference to obtain the access interval duration of each group of video data to be cleaned; Count the number of accesses to each group of video data to be cleaned up before the current time point. In the process of counting the number of accesses, if the time interval between two adjacent accesses is less than the preset reference time, it is determined to be a repeated access, and the number of accesses to the corresponding video data to be cleaned up is reduced by one; Identify the specific type of each group of video data to be cleaned, including priority processing segments and to-be-processed segments, and set a group of additional coefficients corresponding to the priority processing segments and the to-be-processed segments respectively; the additional coefficients are set at 1.147-1.183, and the additional coefficients of the priority processing segments are higher than the additional coefficients of the to-be-processed segments, and the values of the additional coefficients are specifically set by the technicians; The preset access interval durations correspond to the duration value ranges of each group, and each duration value range is set to correspond to an access requirement score; the score range is set to 1-10, and the shorter the access interval, the higher the corresponding access requirement score; Match the access interval duration of each group of video data to be cleaned with the value range of each group duration, so as to obtain the access demand score of each group of video data to be cleaned; The weight coefficients corresponding to the preset access demand scores and access times are respectively normalized, and the access demand scores and access times of each group of video data to be cleaned are respectively multiplied by the corresponding preset weight coefficients, and then summed, and the summed value is multiplied by the corresponding additional coefficient to obtain the cleaning evaluation index of each group of video data to be cleaned; A reference threshold index corresponding to the cleaning evaluation index is set, video data to be cleaned whose cleaning evaluation index is higher than the reference threshold index is retained, and video data to be cleaned whose cleaning evaluation index is lower than the reference threshold index is cleaned; The cleaned video data is transferred to a recycling area in a storage database for retention for a set period of time; It should be noted that by presetting the cleaning time window and determining the segmentation time point based on it to filter out the video data to be cleaned, this method can focus on the relatively old data range for analysis and cleaning, avoiding indiscriminate scanning and judgment of all stored video data, effectively reducing the consumption of system resources, and improving the efficiency of cleaning work. Especially when the amount of data in the storage database is huge, accurately locating the old data that may need to be cleaned can release storage resources more reasonably, ensure that the system has enough space to continuously and stably store new video data, and optimize the overall utilization efficiency of storage resources.
[0025] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.
Claims
1. Intelligent video stream batch processing optimization system, characterized by: include: Video stream access module: receives video stream data from each monitoring area and performs preprocessing. After the preprocessing is completed, the video stream data of each monitoring area is sent to the priority evaluation module; Priority evaluation module: Taking the time point of currently receiving video stream data as the starting point, extract and analyze the abnormal parameters of each monitoring area within the preset time interval before the starting point, so as to determine the priority processing video segments corresponding to the video stream data of each monitoring area; The abnormal parameters include the number of abnormal occurrences in each monitoring area within a preset time interval and the time frame in which each abnormality occurs; Processing and scheduling module: receiving the priority processing video segments corresponding to the video stream data of each monitoring area, and performing corresponding operations to transfer the video stream data of each monitoring area to a pre-built storage database; Index establishment module: when storing the video stream data of each monitoring area, an index relationship with the video data storage location and metadata is established based on the monitoring area, time date and number corresponding to the video stream data; Video cleaning module: preset the cleaning time interval of the storage database. After the preset cleaning time interval is reached, the access parameters of each group of stored video data in the storage database are extracted and analyzed, and corresponding steps are executed to clean the storage database based on the analysis results; the stored video data includes each group of priority processing segments and pending segments; the access parameters include the number of accesses and the length of the access interval.
2. The intelligent video stream batch processing optimization system according to claim 1 is characterized in that: Extract the abnormal parameters of each monitoring area within the preset time interval before the starting point and analyze them, specifically: Predetermine the abnormality judgment criteria corresponding to each monitoring area in advance, and count the number of abnormalities occurring in each monitoring area within a preset time interval and the time frame in which each abnormality occurs; Preset the priority basic value corresponding to each monitoring area, extract the number of abnormal occurrences in each monitoring area within the preset time interval, preset the range of values of each group of times corresponding to the number of abnormal occurrences, set each group of number value ranges to correspond to a priority additional value, match the number of abnormal occurrences in each monitoring area within the preset time interval with the range of values of each group of times, so as to obtain the priority additional value of each monitoring area; multiply the priority basic value of each monitoring area by the corresponding matched priority additional value, and obtain the processing evaluation index of each monitoring area at the current time point.
3. The intelligent video stream batch processing optimization system according to claim 2 is characterized in that: Determine the priority processing video segments corresponding to the video stream data of each monitoring area, specifically: Sort the data from large to small according to the processing evaluation index, and sort the video stream data of each monitoring area based on the sorting result of the processing evaluation index; After the sorting is completed, the time axis coordinate system corresponding to each monitoring area is established with the preset time interval as the boundary. On each set of established time axis coordinate systems, the time frame where each abnormality occurs in each monitoring area is sequentially mapped to the established time axis coordinate system, that is, the numerical point corresponding to the time frame where each abnormality occurs in the time axis coordinate system is drawn; The time axis coordinate system corresponding to the preset time interval is divided into three equal sub-time zones by dividing lines, and the number of abnormal occurrences in each sub-time zone corresponding to each monitoring area is counted, and the sub-time zone with the highest number of abnormal occurrences is used as the priority sub-time zone corresponding to each monitoring area. The numerical points located at the leftmost and rightmost ends of each priority sub-time zone are identified as construction points, and the time zone formed between the two groups of construction points in each priority sub-time zone is used as the priority processing video segment of each monitoring area.
4. The intelligent video stream batch processing optimization system according to claim 3 is characterized in that: Execute corresponding operations to transfer the video stream data of each monitoring area to the pre-built storage database, specifically: Based on the priority processing video segments determined by each monitoring area, corresponding priority processing video segments are extracted from the video stream data of each monitoring area and integrated into a priority processing segment set; Mark the remaining video data of each monitoring area except the priority processed video segments as the video segments to be processed, and integrate them into a set of segments to be processed; Based on the sorting result of the processing evaluation index, sort each group of video data in the priority processing segment set and the to-be-processed segment set; preset resource allocation rules corresponding to the priority processing segment set and the to-be-processed segment set respectively; According to the sorting order of each group of video data in the priority processing segment set and the to-be-processed segment set, the priority processing segment set and the to-be-processed segment set are transmitted to a pre-constructed storage database in the sorting order using a corresponding preset resource allocation rule, and a priority storage area and a to-be-processed storage area corresponding to the priority processing segment set and the to-be-processed segment set are set in the storage database; After the transmission is completed, the priority processing fragments and the fragments to be processed in each monitoring area are encrypted based on the preset encryption rules.
5. The intelligent video stream batch processing optimization system according to claim 4 is characterized in that: The priority processing fragments and the fragments to be processed in each monitoring area are encrypted based on the preset encryption rules, specifically: Each monitoring area is numbered, and the number is represented by i, where i=1, 2, ... k; k is the total number of monitoring areas; For the priority processing segments and the to-be-processed segments of each monitoring area, the processing evaluation index of each monitoring area is extracted, and each group of numbers of the processing evaluation index of each monitoring area is extracted and randomly shuffled to obtain a digital set, and the digital set of each monitoring area is spliced with the number of the corresponding monitoring area; wherein the number is in the first place; After the splicing is completed, the basic keys of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are obtained; then the segment durations of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are extracted; the values in the segment durations are used as additional keys and spliced with the basic keys, that is, the numbers of the additional keys are added to the end of the basic keys; Thus, the encryption keys of the priority processing segments and the to-be-processed segments corresponding to each monitoring area are obtained. If the encryption key contains the number 0, the number 0 is converted into a preset value by default; The numerical values of each group of numbers in the encryption key are accumulated, and the accumulated value is compared with a preset reference value. If it is greater than the preset reference value, a graphic encryption signal is generated; if it is less than the preset reference value, a digital encryption signal is generated.
6. The intelligent video stream batch processing optimization system according to claim 5, characterized in that: Based on the preset encryption rules, the priority processing fragments and the to-be-processed fragments of each monitoring area are encrypted, and also include: If a graphic encryption signal is generated, a straight line is constructed, and numerical points equal to the number of encryption key digits are drawn at equal distances on the constructed straight line. A circle is constructed with each group of drawn numerical points as the center and the numerical value of each group as the radius of the circle. The construction is carried out from left to right according to the arrangement order of each group of digits in the encryption key. After the construction is completed, the image formed by each group of constructed circles is used as the graphic signaling key for the priority processing fragments and the fragments to be processed corresponding to each monitoring area; If digital encryption signaling is generated, each group of adjacent numbers in the encryption key will be summed up in pairs. After the summation is completed, a new group of numbers will be obtained as the digital key. The number of numbers in the digital key will be counted as the number of divisions. The pre-set square will be divided into sub-squares based on the number of divisions, and each group of numbers in the digital key will be randomly filled into each divided sub-square. After the filling is completed, the filled square will be used as the digital signaling key for the priority processing fragments and the fragments to be processed corresponding to each monitoring area.
7. The intelligent video stream batch processing optimization system according to claim 6 is characterized in that: Based on the analysis results, perform the corresponding steps to clean up the storage database, specifically: The duration of the preset cleaning time window is based on the current time point, and the preset cleaning time window duration is moved forward, and the time point after the shift is marked as the split time point; Extracting each group of stored video data before the segmentation time point from the storage database as the video data to be cleaned; Obtain the time point of the most recent access to each group of video data to be cleaned before the current time point, and calculate the time difference to obtain the access interval duration of each group of video data to be cleaned; Count the number of accesses to each group of video data to be cleaned up before the current time point. In the process of counting the number of accesses, if the time interval between two adjacent accesses is less than the preset reference time, it is determined to be a repeated access, and the number of accesses to the corresponding video data to be cleaned up is reduced by one; Identify specific types of each group of video data to be cleaned, the specific types include priority processing segments and to-be-processed segments, and set a group of additional coefficients corresponding to the priority processing segments and the to-be-processed segments respectively; Preset the access interval duration corresponding to each group of duration value ranges, and set each group of duration value ranges to correspond to an access demand score; Match the access interval duration of each group of video data to be cleaned with the value range of each group duration, so as to obtain the access demand score of each group of video data to be cleaned; The weight coefficients corresponding to the preset access demand scores and access times are respectively determined. The access demand scores and access times of each group of video data to be cleaned are normalized and multiplied by the corresponding preset weight coefficients respectively. Then the sum is multiplied by the corresponding additional coefficient to obtain the cleaning evaluation index of each group of video data to be cleaned.
8. The intelligent video stream batch processing optimization system according to claim 7, characterized in that: Based on the analysis results, corresponding steps are executed to clean up the storage database, including: A reference threshold index corresponding to the cleaning evaluation index is set, and the video data to be cleaned whose cleaning evaluation index is higher than the reference threshold index is retained, and the video data to be cleaned whose cleaning evaluation index is lower than the reference threshold index is cleaned.