Intelligent analysis method and system for realizing video stream based on neural network

Abstract

The invention relates to the technical field of image processing, in particular to an intelligent analysis method and system for achieving video streams based on a neural network, and the method comprises the steps: confirming to receive a video decomposition instruction from a video decomposition unit, analyzing the video decomposition instruction, obtaining a first decomposition frame number, obtaining a decomposition image time sequence through the first decomposition frame number and an initial video stream, and obtaining a second decomposition frame number; obtaining a decomposed video time point set based on the decomposed image time sequence, obtaining a target video stream set based on the decomposed video time point set and the initial video stream, obtaining a second decomposed frame number based on the target video stream, and determining an abnormal image set based on the second decomposed frame number and the target video stream, and sending the abnormal image set to an initiating end of an analysis instruction by using a result feedback unit to realize intelligent analysis of the initial video stream. According to the method and the device, the accuracy and the intelligent degree of analyzing the video stream can be improved, and resources required for analyzing the video stream are reduced.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method and system for realizing intelligent analysis of video streams based on a neural network. Background Art

[0002] With the popularization of Internet of Things technology, more and more surveillance is being used in daily life. Through surveillance, real-time shooting of different areas can be achieved. If an emergency occurs in the area, the captured video will serve as important video evidence. Correspondingly, how to intelligently and resource-savingly realize the analysis of video streams is of great significance to confirm the accuracy of the initial video stream.

[0003] Currently, most of the analysis of video streams is done manually frame by frame to filter out possible abnormal images from the video stream.

[0004] Although the above method can realize the analysis of video streams, it consumes a lot of human resources when realizing frame-by-frame analysis of video streams, and the judgment of abnormal images in the video mostly depends on personal experience, which may cause inaccurate screening of abnormal images. Therefore, how to realize the analysis of video streams intelligently, accurately and energy-saving has become an urgent problem to be solved. Summary of the invention

[0005] The present invention provides a method for realizing intelligent analysis of video stream based on neural network and a computer-readable storage medium, the main purpose of which is to improve the accuracy and intelligence of analyzing video stream and reduce the resources required for analyzing video stream.

[0006] To achieve the above object, the present invention provides a method for intelligent analysis of video streams based on a neural network, comprising: Receiving an analysis instruction, and confirming an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit, and a result feedback unit; Confirming receipt of a video decomposition instruction from a video decomposition unit, parsing the video decomposition instruction to obtain a first decomposition frame number, and obtaining a decomposition image time sequence using the first decomposition frame number and an initial video stream, wherein the decomposition image time sequence includes a plurality of decomposition image nodes, and the decomposition image nodes include decomposition images and image time; A decomposed video time point set is obtained based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0; a target video stream set is obtained based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and the following operations are performed on each target video stream in the target video stream set: A second decomposition frame number is obtained based on the target video stream, and an abnormal image set is confirmed based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction by using a result feedback unit to realize intelligent analysis of the initial video stream.

[0007] Optionally, acquiring a decomposed video time point set based on the decomposed image time sequence includes: Based on the decomposed image sequence, an analysis grayscale image sequence is obtained, and the following operations are performed on each analysis grayscale image in the analysis grayscale image sequence: Acquire an analysis grayscale mean based on the analysis grayscale image, wherein the analysis grayscale mean is the mean of multiple grayscale values ​​corresponding to the analysis grayscale image; Associating the analyzed grayscale mean and image time to obtain initial analysis nodes, and summarizing the initial analysis nodes to obtain an initial analysis node set; Obtain one or more target analysis node sets using a pre-built clustering model and an initial analysis node set; Based on one or more target analysis node sets, a set of decomposed video time points is identified in the initial video stream.

[0008] Optionally, obtaining a second decomposition frame number based on the target video stream includes: Obtaining an initial number of frames and a video duration of a target video stream, obtaining a number of images based on the initial number of frames and the video duration, obtaining an image extraction gradient set for extracting images, and extracting an image time sequence from the target video stream based on the number of images and the image extraction gradient set, wherein the image extraction gradient set includes a plurality of image extraction ratios, and the image time sequence includes a plurality of initial images; Confirming receipt of an image recognition instruction from an image recognition unit, confirming an image recognition model set based on the image recognition instruction, wherein the image recognition model set includes a plurality of image recognition models, sequentially extracting an initial image from the image sequence, and performing the following operations on the extracted initial image: Randomly extracting two image recognition models from the image recognition model set to obtain a first recognition model and a second recognition model, recognizing the extracted initial image based on the first recognition model and the second recognition model to obtain a first recognition name set and a second recognition name set, wherein the first recognition name set includes a plurality of first recognition nodes, wherein the first recognition nodes include a first recognition name and a first recognition quantity, and the second recognition name set includes a plurality of second recognition nodes, wherein the second recognition nodes include a second recognition name and a second recognition quantity; After the target identification name set is confirmed by using the first identification name set and the second identification name set, wherein the target identification name set includes a plurality of target identification nodes, and the target identification node includes a target identification name and a target identification quantity, the following operations are performed on the target identification names in the target identification name set: A local area image corresponding to a target recognition name is identified in the initial image, the local area image is labeled using the target recognition name and the target recognition number to obtain a target image with a serial number, the target images are aggregated to obtain a target image set, and the target image set is used to obtain a second decomposition frame number.

[0009] Optionally, the using the first identification name set and the second identification name set to identify a target identification name set includes: The following operations are performed for each first identification node in the first identification name set: Determine whether there is a second identification node in the second identification name set that is the same as the first identification node; If there is no second recognition node in the second recognition name set that is the same as the first recognition node, then randomly extracting a plurality of initial image recognition models from the image recognition model set, wherein the number of the plurality of initial image recognition models is a preset number of extractions, and obtaining a plurality of initial recognition name sets using the plurality of initial image recognition models and the initial image, wherein the initial image recognition models correspond to the initial recognition name sets one by one, and the initial recognition name sets include a plurality of initial recognition nodes; Combining the multiple initial identification name sets in a combined form to obtain multiple combined identification name sets, wherein the combined identification name set includes two initial identification name sets, and identifying one or more fused identification name sets in the multiple combined identification name sets, wherein the two initial identification name sets in the fused identification name set are the same; Eliminate any one of the initial identification name sets corresponding to each fused identification name set in one or more fused identification name sets from the multiple initial identification name sets to obtain an updated identification name set, use the updated identification name set as multiple initial identification name sets, and return to the step of combining the multiple initial identification name sets in a combined form until one or more initial fused name sets are obtained, wherein the multiple initial identification name sets corresponding to the initial fused name set are all the same; Respectively counting the number of initial identification name sets corresponding to each of the one or more initial fusion name sets to obtain one or more initial fusion numbers, wherein the initial fusion numbers correspond to the initial fusion name sets one by one; Extracting the largest initial fusion number from one or more initial fusion numbers to obtain a target verification number, calculating the ratio of the target verification number to the extracted number to obtain a correct ratio, and when the correct ratio is greater than or equal to a preset ratio threshold, taking the fusion recognition name set corresponding to the target verification number as the target recognition name set; Otherwise, return to the step of randomly extracting a plurality of initial image recognition models from the image recognition model set until a target recognition name set is obtained.

[0010] Optionally, the obtaining a second decomposition frame number by using the target image set includes: Summarizing the target image sets to obtain multiple target image sets, and obtaining the target image time sequence using the target identification name, serial number and target image; Extracting initial analysis images from the target image sequence in sequence, and performing the following operations on the extracted initial analysis images: Based on the initial analysis image, identifying a target analysis image in the target image time sequence, wherein the target analysis image is adjacent to the initial analysis image and lags behind the initial analysis image in the target image time sequence; Acquire an initial coordinate point set and a target coordinate point set based on an initial analysis image and a target analysis image; Mapping the initial coordinate points in the initial coordinate point set and the target coordinate points in the target coordinate point set to a pre-constructed reference coordinate system to obtain a mapping coordinate point set, wherein the mapping coordinate point set includes a plurality of mapping coordinate points; The number of reference coordinate points corresponding to each mapping coordinate point in the mapping coordinate point set is counted to obtain a reference quantity set, wherein the reference coordinate point is an initial coordinate point or a target coordinate point, and the reference quantity set includes multiple reference quantities. The reference quantity in the reference quantity set is counted as 2 to obtain a target reference quantity. The number of mapping coordinate points in the mapping coordinate point set is counted to obtain a comprehensive quantity. The ratio of the target reference quantity to the comprehensive quantity is calculated to obtain a reference quantity ratio. If the reference quantity ratio is less than or equal to a preset reference ratio threshold, the target analysis image is taken as the initial analysis image, and the step of confirming the target analysis image in the target image time sequence based on the initial analysis image is returned. If the reference quantity ratios of the reference quantity set corresponding to the target image time sequence are all less than or equal to the reference ratio threshold, the target image is identified as a background image, otherwise, the target image is identified as an initial detection image. After confirming that the initial detection image is a preset target detection image, the target detection image is used to identify an adjacent image set in the initial image, wherein the adjacent image set includes one or more adjacent images, and the adjacent image is a target detection image or a background image. The second decomposition frame number is obtained based on the adjacent image set and the detection image.

[0011] Optionally, confirming that the initial detection image is a preset target detection image includes: Acquire a detection image sequence according to the target recognition name, serial number and initial detection image, wherein the detection image sequence includes multiple initial detection images, perform a grayscale operation on each initial detection image in the detection image sequence, and obtain a grayscale detection image sequence, and extract analysis image groups in sequence from the grayscale detection image sequence based on a preset sliding window, wherein the analysis image group includes two grayscale detection images; The following operations are performed for each grayscale detection image in the analysis image group: The image center coordinates are calculated based on the grayscale detection image. The calculation formula is as follows: , in, Respectively represent the horizontal and vertical coordinates of the image center coordinates, Indicates that there are a total of pixels, Indicates the grayscale detection image The gray value corresponding to each pixel point, Respectively represent the grayscale detection image The horizontal and vertical coordinates corresponding to the pixel points; Summarizing the image center coordinates to obtain an image center coordinate set, calculating a comprehensive image change degree based on the image center coordinate set, and determining whether the comprehensive image change degree is equal to a preset comprehensive change degree threshold; If the comprehensive image change degree is greater than or equal to the comprehensive change degree threshold, the initial detection image is confirmed to be a target detection image.

[0012] Optionally, the calculating the comprehensive image change degree based on the image center coordinate set includes: Based on the image center coordinate set, an image center coordinate time series is obtained, and the sliding window is used to sequentially extract analysis coordinate groups from the image center coordinate time series, wherein the analysis coordinate group includes two image center coordinates, and the Euclidean distance between the two image center coordinates in the analysis coordinate group is obtained to obtain the moving distance; Extract the first image center coordinate and the last image center coordinate in the image center coordinate time series, obtain the initial center coordinate and the target center coordinate, and obtain the evaluation distance based on the initial center coordinate and the target center coordinate; Summarizing the moving distances to obtain a moving distance set, and using the moving distance set to obtain a moving distance variance, wherein the moving distance variance is a variance of multiple moving distances in the moving distance set; The comprehensive image change degree is calculated based on the moving distance variance and the evaluation distance. The calculation formula is as follows: , in, represents the comprehensive image change degree, are all preset coefficients. represents the evaluation distance, represents the variance of moving distance.

[0013] Optionally, acquiring the second decomposition frame number based on the adjacent image set and the detection image includes: Based on the adjacent image set and the detection image, a set of adjacent grayscale images and a detection grayscale image are obtained, one or more local area images are extracted from the detection grayscale image using a pre-constructed region growing algorithm and a pre-constructed image grayscale gradient set, and one or more target area images are identified in the one or more local area images, wherein the image grayscale gradient set includes a plurality of image gradient grayscale values, and the target area image is adjacent to at least one adjacent grayscale image in the adjacent grayscale image set; The following operations are performed on each of the one or more target area images: Based on the target area image, a local area grayscale mean is obtained, wherein the local area grayscale mean is the mean of multiple grayscale values ​​corresponding to the target area image, and the target area image and adjacent grayscale images adjacent to the target area image are associated to obtain an associated image set; Taking the local region grayscale mean as a starting point, extracting a related region image from the related image set using the region growing algorithm, and identifying a target discrimination image in the related region image, wherein the target discrimination image is a region of a grayscale image adjacent to the target region image in the related image set; The target discrimination mean is obtained based on the target discrimination image, and the discrimination frame number is calculated according to the target discrimination mean and the local area grayscale mean. The calculation formula is as follows: , , in, Indicates the number of frames to be judged. Indicates the initial frame number, represents the target discrimination mean, represents the grayscale mean of the local area, represents the preset coefficient, represents the preset coefficient, Indicates the rounding symbol; The determination frame numbers are summarized to obtain a determination frame number set, and a second decomposition frame number is determined based on the determination frame number set, wherein the second decomposition frame number is the smallest determination frame number in the determination frame number set.

[0014] Optionally, the identifying an abnormal image set based on the second decomposed frame number and the target video stream includes: A decomposed image sequence is extracted from the target video stream using the second decomposed frame number, wherein the decomposed image sequence includes a plurality of initial decomposed images, initial decomposed images are sequentially extracted from the decomposed image sequence, and the following operations are performed on the extracted initial decomposed images: Based on the initial decomposition image, a target decomposition image is identified in the decomposition image time sequence, wherein the target decomposition image is adjacent to the initial decomposition image and lags behind the extracted initial decomposition image; Confirming receiving an image comparison instruction from an image comparison unit, parsing the image comparison instruction, obtaining a local name database, and using the image recognition model set and the local name database to respectively identify a target identification decomposition image set and an initial identification decomposition image set in the target decomposition image and the initial decomposition image, wherein the target identification decomposition image set includes a plurality of target identification decomposition images, and the initial identification decomposition image set includes a plurality of initial identification decomposition images; Matching the target identification decomposition image in the target identification decomposition image set and the initial identification decomposition image in the initial identification decomposition image set to obtain a plurality of identification decomposition nodes, wherein the identification decomposition nodes include the initial identification decomposition image and the target identification decomposition image; The following operations are performed on each of the multiple identity decomposition nodes: Obtaining local offset distances based on the identified decomposition nodes, summarizing the local offset distances to obtain a local offset distance set, extracting the maximum local offset distance from the local offset distance set to obtain a target evaluation distance, and comparing the target evaluation distance with a preset evaluation distance threshold; If the target evaluation distance is greater than or equal to the evaluation distance threshold, both the extracted initial decomposition image and the target decomposition image are confirmed as abnormal images, and the target decomposition image is used as the extracted initial decomposition image, and the process returns to the step of confirming the target decomposition image in the decomposition image time sequence based on the initial decomposition image; Otherwise, the local offset distance variance is obtained based on the local offset distance set, and after confirming that the local offset distance variance is greater than or equal to the preset local offset distance threshold, the extracted initial decomposition image and the target decomposition image are both confirmed as abnormal images, and the target decomposition image is used as the extracted initial decomposition image, and the process returns to the step of confirming the target decomposition image in the decomposition image time sequence based on the initial decomposition image; The abnormal images are aggregated to obtain an abnormal image set.

[0015] To achieve the above object, the present invention also provides a system for intelligently analyzing video streams based on a neural network, comprising: An analysis environment confirmation module is used to receive an analysis instruction and confirm an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit and a result feedback unit; An initial frame number confirmation module is used to confirm receiving a video decomposition instruction from a video decomposition unit, parse the video decomposition instruction, obtain a first decomposition frame number, and obtain a decomposition image sequence using the first decomposition frame number and an initial video stream, wherein the decomposition image sequence includes a plurality of decomposition image nodes, and the decomposition image node includes a decomposition image and an image time; The initial video partitioning module is used to obtain a decomposed video time point set based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0, and obtain a target video stream set based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and perform the following operations on each target video stream in the target video stream set: The abnormal image recognition module is used to obtain a second decomposition frame number based on a target video stream, and to identify an abnormal image set based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction using a result feedback unit to realize intelligent analysis of the initial video stream.

[0016] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising: A memory storing at least one instruction; and a processor executing the instruction stored in the memory to implement the above-mentioned method for intelligent analysis of video streams based on neural network.

[0017] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned intelligent analysis method of video stream based on neural network.

[0018] The present invention is to solve the problem described in the background technology. The present invention confirms receiving a video decomposition instruction from a video decomposition unit, parses the video decomposition instruction, obtains a first decomposition frame number, uses the first decomposition frame number and an initial video stream to obtain a decomposition image timing, wherein the decomposition image timing includes multiple decomposition image nodes, and the decomposition image node includes a decomposition image and an image time, obtains a decomposition video time point set based on the decomposition image timing, wherein the decomposition video time point set includes M decomposition video time points, and M is an integer greater than or equal to 0, obtains a target video stream set based on the decomposition video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams. It can be seen that the present invention considers different situations that may exist in the initial video stream before analyzing the initial video stream. Therefore, the initial video stream is divided to obtain a target video stream set, thereby improving the accuracy of analyzing the initial video stream and improving the intelligence of analyzing the initial video stream. The present invention obtains the second decomposition frame number based on the target video stream, and confirms the abnormal image set based on the second decomposition frame number and the target video stream. It can be seen that the present invention combines the characteristics corresponding to each target video stream to obtain the second decomposition frame number for analyzing the target video stream. Obtaining different second decomposition frame numbers through different characteristics can save the resources required for analyzing the target video stream, thereby improving the intelligence level of the embodiment of the present invention. Therefore, the present invention can improve the accuracy and intelligence level of analyzing the video stream and reduce the resources required for analyzing the video stream. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic diagram of a process for realizing intelligent analysis of video streams based on a neural network provided by an embodiment of the present invention; Figure 2 A functional module diagram of a system for intelligently analyzing video streams based on a neural network provided by an embodiment of the present invention; Figure 3A schematic diagram of the structure of an electronic device for implementing the method for intelligent analysis of video streams based on a neural network provided in one embodiment of the present invention.

[0020] Description of reference numerals: 1. Electronic device; 10. Processor; 11. Memory; 12. Bus.

[0021] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0023] The embodiment of the present application provides a method for intelligent analysis of video streams based on neural networks. The execution subject of the method for intelligent analysis of video streams based on neural networks includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for intelligent analysis of video streams based on neural networks can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0024] Reference Figure 1 FIG. 1 is a flow chart of a method for realizing intelligent analysis of video streams based on a neural network according to an embodiment of the present invention. In this embodiment, the method for realizing intelligent analysis of video streams based on a neural network includes: S1. Receive an analysis instruction, and confirm an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit, and a result feedback unit.

[0025] It should be explained that the analysis instruction is an instruction for realizing video analysis, and the intelligent analysis environment is a necessary environment for realizing intelligent analysis of the video, and the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit, and a result feedback unit. For the specific application of the units, please refer to the subsequent embodiments. Generally speaking, when analyzing the initial video stream, the object to be analyzed can be selected in the initial video stream to reduce the resources required for analyzing the initial video stream.

[0026] It is understandable that the initial video stream refers to the video to be intelligently analyzed. The embodiment of the present invention mainly realizes the recognition and analysis of the modified content in the video stream, thereby ensuring the security and correctness of the video stream, and when analyzing the initial video stream, considering setting different frame numbers according to the specific content of the video, thereby saving the resources required for analyzing the initial video stream.

[0027] Exemplarily, in order to determine whether the video stream used as evidence has been tampered with, the holder of the video stream issues the analysis instruction and confirms the intelligent analysis environment based on the analysis instruction. Here, the video stream used as evidence is the initial video stream.

[0028] S2. Confirm receipt of a video decomposition instruction from a video decomposition unit, parse the video decomposition instruction, obtain a first decomposition frame number, and use the first decomposition frame number and an initial video stream to obtain a decomposition image timing sequence, wherein the decomposition image timing sequence includes a plurality of decomposition image nodes, and the decomposition image node includes a decomposition image and an image time.

[0029] It should be explained that the first decomposition frame number refers to the preset frame number for dividing the initial video stream. Optionally, the first decomposition frame number is obtained by artificial setting. The decomposition image timing refers to the sequence corresponding to the decomposition images extracted from the initial video stream according to the first decomposition frame number. For example, the initial video stream includes a total of 1000 images, and the preset first decomposition frame number is 10, then the 10th image, the 20th image, the 30th image, ..., the 990th image and the 1000th image are extracted from the initial video stream respectively by the first decomposition frame number, and the extracted images are sorted from the first to the last according to the time corresponding to the image when shooting, and the decomposition image timing is obtained, wherein the extracted image is the decomposition image, and the time corresponding to the image when shooting is the image time. Generally, the setting of the first decomposition frame number can be set in combination with the frame number and time length of the initial video stream.

[0030] S3. Based on the decomposed image timing, a decomposed video time point set is obtained, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0; based on the decomposed video time point set and the initial video stream, a target video stream set is obtained, wherein the target video stream set includes one or more target video streams.

[0031] It should be understood that the step of obtaining a decomposed video time point set based on the decomposed image timing includes: Based on the decomposed image sequence, an analysis grayscale image sequence is obtained, and the following operations are performed on each analysis grayscale image in the analysis grayscale image sequence: Acquire an analysis grayscale mean based on the analysis grayscale image, wherein the analysis grayscale mean is the mean of multiple grayscale values ​​corresponding to the analysis grayscale image; Associating the analyzed grayscale mean and image time to obtain initial analysis nodes, and summarizing the initial analysis nodes to obtain an initial analysis node set; Obtain one or more target analysis node sets using a pre-built clustering model and an initial analysis node set; Based on one or more target analysis node sets, a set of decomposed video time points is identified in the initial video stream.

[0032] Furthermore, a grayscale transformation is performed on each decomposed image in the decomposed image sequence to obtain the analyzed grayscale image sequence. The grayscale transformation refers to the operation of converting a color image into a grayscale image, and the grayscale transformation is a prior art and will not be described in detail here. Optionally, the k-means clustering algorithm is used as the clustering model, and other technologies can achieve the same effect, which will not be described in detail here. The clustering model can combine the characteristics of time and image to realize the classification of the initial video stream, where the characteristics of the image refer to the mean of the grayscale value corresponding to the image. For example, the k-means clustering algorithm is used to cluster 10 initial analysis nodes to obtain three clusters, wherein the initial analysis nodes contained in each cluster constitute the target analysis node set.

[0033] It should be explained that each target analysis node set includes a plurality of different initial analysis nodes, and each initial analysis node corresponds to an image time. Therefore, based on one or more target analysis node sets, each target analysis node set can identify a time period, and the dividing point of adjacent time periods can be used as the decomposed video time point. For example, there are three target analysis node sets, where the time period corresponding to the first target analysis node set is 10 to 15.8 seconds, the time period corresponding to the second target analysis node set is 15.9 to 20 seconds, and the time period corresponding to the third target analysis node set is 0 to 9.9 seconds. Then, two decomposed video time points can be identified using these three target analysis node sets, where the two decomposed video time points are: 9.9 seconds and 15.8 seconds, respectively.

[0034] It is understandable that obtaining the target video stream set based on the decomposed video time point set and the initial video stream means dividing the initial video stream using each decomposed video time point in the decomposed video time point set, and the divided initial video stream is the target video stream.

[0035] S4. Obtain a second decomposed frame number based on the target video stream, and confirm an abnormal image set based on the second decomposed frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0, and use a result feedback unit to send the abnormal image set to the initiator of the analysis instruction to realize intelligent analysis of the initial video stream.

[0036] It is understandable that the obtaining of the second decomposition frame number based on the target video stream includes: Obtaining an initial number of frames and a video duration of a target video stream, obtaining a number of images based on the initial number of frames and the video duration, obtaining an image extraction gradient set for extracting images, and extracting an image time sequence from the target video stream based on the number of images and the image extraction gradient set, wherein the image extraction gradient set includes a plurality of image extraction ratios, and the image time sequence includes a plurality of initial images; Confirming receipt of an image recognition instruction from an image recognition unit, confirming an image recognition model set based on the image recognition instruction, wherein the image recognition model set includes a plurality of image recognition models, sequentially extracting an initial image from the image sequence, and performing the following operations on the extracted initial image: Randomly extracting two image recognition models from the image recognition model set to obtain a first recognition model and a second recognition model, recognizing the extracted initial image based on the first recognition model and the second recognition model to obtain a first recognition name set and a second recognition name set, wherein the first recognition name set includes a plurality of first recognition nodes, wherein the first recognition nodes include a first recognition name and a first recognition quantity, and the second recognition name set includes a plurality of second recognition nodes, wherein the second recognition nodes include a second recognition name and a second recognition quantity; After the target identification name set is confirmed by using the first identification name set and the second identification name set, wherein the target identification name set includes a plurality of target identification nodes, and the target identification node includes a target identification name and a target identification quantity, the following operations are performed on the target identification names in the target identification name set: A local area image corresponding to a target recognition name is identified in the initial image, the local area image is labeled using the target recognition name and the target recognition number to obtain a target image with a serial number, the target images are aggregated to obtain a target image set, and the target image set is used to obtain a second decomposition frame number.

[0037] Further, the initial frame number refers to the frame number of the target video stream, the video duration refers to the duration of the target video stream, the number of images refers to the number of images included in the target video stream, and the number of images is the product of the initial frame number and the video duration. The image extraction ratio refers to the ratio used to realize the division of the number of images. For example: the frame number of the target video stream is 24FPS, the video duration is 10 seconds, then the number of images is 240, the image extraction gradient set includes multiple image extraction ratios, and the multiple image extraction ratios are respectively: 0.2, 0.4, 0.6, 0.8, then the image extraction ratio is used to calculate that the 48th frame image, the 96th frame image, the 144th frame image and the 192nd frame image need to be extracted from the initial video stream, and the image sequence is composed of the 48th frame image, the 96th frame image, the 144th frame image and the 192nd frame image, and each frame image in the image sequence is the initial image.

[0038] It should be understood that the image recognition model refers to a model used to realize the recognition of information in an image. Optionally, a pre-trained neural network model is used as the image recognition model. The first recognition name refers to the name of the information recognized in the initial image using the first recognition model, the second recognition name refers to the name of the information recognized in the initial image using the second recognition model, the first recognition quantity refers to the quantity corresponding to the first recognition name, and the second recognition quantity refers to the quantity corresponding to the second recognition name. For example: an image includes: 5 pedestrians and 3 vehicles. In an ideal case, the neural network model can be used to identify two recognition nodes. Here, the recognition node is the first recognition node or the second recognition node, wherein the recognition name and recognition quantity corresponding to the first recognition node are: person and 5, respectively, and the recognition name and recognition quantity corresponding to the second recognition node are: vehicle and 3, respectively.

[0039] It should be explained that the local area image refers to the area of ​​the image corresponding to the target recognition name. Generally speaking, the recognition of the initial image and the division of the local area image can be realized by using a neural network model. The same effect can be achieved by using other technologies, which will not be repeated here. The purpose of using the target recognition name to identify the local area image is to distinguish the content of the image corresponding to different local areas in the initial image, and in order to achieve better distinction between different areas, when identifying different contents corresponding to the same recognition name, the form of identifying the serial number can be adopted. The acquisition method of this serial number can be used from left to right or sorted from top to bottom, and the largest number in the serial number is the number of target recognition. For example: if there are 3 people in an image, the area of ​​each of the 3 people is identified in the initial image, and the area is identified as a person, and the area corresponding to the initial image with a person is identified as person-1, person-2, person-3, and the number of target recognitions corresponding to the target recognition node is 3. Generally speaking, when identifying the same information, the same serial number should be used to facilitate the analysis of the same image.

[0040] Further, the using the first identification name set and the second identification name set to identify the target identification name set includes: The following operations are performed for each first identification node in the first identification name set: Determine whether there is a second identification node in the second identification name set that is the same as the first identification node; If there is no second recognition node in the second recognition name set that is the same as the first recognition node, then randomly extracting a plurality of initial image recognition models from the image recognition model set, wherein the number of the plurality of initial image recognition models is a preset number of extractions, and obtaining a plurality of initial recognition name sets using the plurality of initial image recognition models and the initial image, wherein the initial image recognition models correspond to the initial recognition name sets one by one, and the initial recognition name sets include a plurality of initial recognition nodes; Combining the multiple initial identification name sets in a combined form to obtain multiple combined identification name sets, wherein the combined identification name set includes two initial identification name sets, and identifying one or more fused identification name sets in the multiple combined identification name sets, wherein the two initial identification name sets in the fused identification name set are the same; Eliminate any one of the initial identification name sets corresponding to each fused identification name set in one or more fused identification name sets from the multiple initial identification name sets to obtain an updated identification name set, use the updated identification name set as multiple initial identification name sets, and return to the step of combining the multiple initial identification name sets in a combined form until one or more initial fused name sets are obtained, wherein the multiple initial identification name sets corresponding to the initial fused name set are all the same; Respectively counting the number of initial identification name sets corresponding to each of the one or more initial fusion name sets to obtain one or more initial fusion numbers, wherein the initial fusion numbers correspond to the initial fusion name sets one by one; Extracting the largest initial fusion number from one or more initial fusion numbers to obtain a target verification number, calculating the ratio of the target verification number to the extracted number to obtain a correct ratio, and when the correct ratio is greater than or equal to a preset ratio threshold, taking the fusion recognition name set corresponding to the target verification number as the target recognition name set; Otherwise, return to the step of randomly extracting a plurality of initial image recognition models from the image recognition model set until a target recognition name set is obtained.

[0041] It is understandable that when there is a second identification node in the second identification name set that is different from the first identification node, the first identification name set is different from the second identification name set, that is, the results of using different image recognition models to identify the initial image are different. For example, if the preset extraction number is 5, then 5 initial image recognition models are randomly extracted from the image recognition model set, and the 5 initial image recognition models are used to identify the initial image respectively to obtain 5 initial recognition name sets. Through combination, the 5 initial image recognition models can be combined into 10 combined recognition name sets. If there are two initial recognition name sets corresponding to the combined recognition name sets in the 10 combined recognition name sets that are the same, then the combined recognition name set is recorded as a fused recognition name set. Generally speaking, when the specific application scenarios are different, different methods can be used to realize the construction of the neural network model, thereby improving the accuracy of using the neural network model to recognize information in the image.

[0042] It should be explained that different neural network models may produce different results when used to identify the same object. Therefore, in the embodiment of the present invention, a loop is adopted to merge the same initial recognition name sets together, and in the process of the loop, the same initial recognition name sets are continuously eliminated from the combined subject to save the resources required for the loop. Here, the step of continuously updating the combined subject refers to: using a more updated recognition name set as the multiple initial recognition name sets.

[0043] It should be understood that different initial image recognition models may recognize different information for the same content in the image. Therefore, the combination method can classify the image recognition results of the initial image recognition model as much as possible. When the correct ratio is greater than or equal to the preset ratio threshold, it indicates that the result of the initial image recognition model for recognizing the initial image is relatively accurate. Otherwise, the result of recognizing the initial image may have a large error.

[0044] Furthermore, when the correct ratio is greater than or equal to the ratio threshold, a relatively accurate target recognition name set is obtained, and then, the use of a relatively accurate target recognition name set can improve the accuracy of obtaining the second decomposed frame number, thereby saving the energy consumption required for analyzing the initial video stream. For the implementation of the specific beneficial effects, please refer to the subsequent embodiments.

[0045] It should be explained that the step of obtaining the second decomposition frame number by using the target image set includes: Summarizing the target image sets to obtain multiple target image sets, and obtaining the target image time sequence using the target identification name, serial number and target image; Extracting initial analysis images from the target image sequence in sequence, and performing the following operations on the extracted initial analysis images: Based on the initial analysis image, identifying a target analysis image in the target image time sequence, wherein the target analysis image is adjacent to the initial analysis image and lags behind the initial analysis image in the target image time sequence; Acquire an initial coordinate point set and a target coordinate point set based on an initial analysis image and a target analysis image; Mapping the initial coordinate points in the initial coordinate point set and the target coordinate points in the target coordinate point set to a pre-constructed reference coordinate system to obtain a mapping coordinate point set, wherein the mapping coordinate point set includes a plurality of mapping coordinate points; The number of reference coordinate points corresponding to each mapping coordinate point in the mapping coordinate point set is counted to obtain a reference quantity set, wherein the reference coordinate point is an initial coordinate point or a target coordinate point, and the reference quantity set includes multiple reference quantities. The reference quantity in the reference quantity set is counted as 2 to obtain a target reference quantity. The number of mapping coordinate points in the mapping coordinate point set is counted to obtain a comprehensive quantity. The ratio of the target reference quantity to the comprehensive quantity is calculated to obtain a reference quantity ratio. If the reference quantity ratio is less than or equal to a preset reference ratio threshold, the target analysis image is taken as the initial analysis image, and the step of confirming the target analysis image in the target image time sequence based on the initial analysis image is returned. If the reference quantity ratios of the reference quantity set corresponding to the target image time sequence are all less than or equal to the reference ratio threshold, the target image is identified as a background image, otherwise, the target image is identified as an initial detection image. After confirming that the initial detection image is a preset target detection image, the target detection image is used to identify an adjacent image set in the initial image, wherein the adjacent image set includes one or more adjacent images, and the adjacent image is a target detection image or a background image. The second decomposition frame number is obtained based on the adjacent image set and the detection image.

[0046] It is understandable that the target image time sequence refers to the image sequence corresponding to different initial images of the same object. For example, a person numbered 1 is identified in each target image in the target image set, and the target images in the target image set are sorted in order from the earliest to the latest corresponding time to obtain the target image time sequence.

[0047] It should be understood that the initial coordinate point set refers to the set of pixel coordinates corresponding to each pixel point in the initial analysis image. The target coordinate point set is obtained in the same manner as the initial coordinate point set, which will not be repeated here. Optionally, the image coordinate system is used as the reference coordinate system, and other technologies can achieve the same effect, which will not be repeated here. Generally speaking, if the initial coordinate points in the initial coordinate point set are the same as the target coordinate points in the target coordinate point set, it indicates that the object corresponding to the target image is stationary. Therefore, when the reference numbers corresponding to the target image sequence are all 2, it can be shown that the object corresponding to the target image in the initial video stream is stationary. Here, the number of reference number sets corresponding to the target image sequence is related to the number of target images contained in the target image sequence. For example, if the target image sequence includes 5 target images, 4 reference number sets can be obtained using 5 target images, and the 4 reference number sets are respectively obtained by the first target image and the second target image, the second target image and the third target image, the third target image and the fourth target image, and the fourth target image and the fifth target image. When the reference quantity ratio is greater than the reference ratio threshold, it indicates that the object corresponding to the target image is moving, so it is necessary to determine whether the object corresponding to the target image is in a moving state. Generally speaking, when the object is in a moving state, it is meaningful to analyze the object.

[0048] Further, the confirming that the initial detection image is a preset target detection image includes: Acquire a detection image sequence according to the target recognition name, serial number and initial detection image, wherein the detection image sequence includes multiple initial detection images, perform a grayscale operation on each initial detection image in the detection image sequence, and obtain a grayscale detection image sequence, and extract analysis image groups in sequence from the grayscale detection image sequence based on a preset sliding window, wherein the analysis image group includes two grayscale detection images; The following operations are performed for each grayscale detection image in the analysis image group: The image center coordinates are calculated based on the grayscale detection image. The calculation formula is as follows: , in, Respectively represent the horizontal and vertical coordinates of the image center coordinates, Indicates that there are a total of pixels, Indicates the grayscale detection image The gray value corresponding to each pixel point, Respectively represent the grayscale detection image The horizontal and vertical coordinates corresponding to the pixel points; Summarizing the image center coordinates to obtain an image center coordinate set, calculating a comprehensive image change degree based on the image center coordinate set, and determining whether the comprehensive image change degree is equal to a preset comprehensive change degree threshold; If the comprehensive image change degree is greater than or equal to the comprehensive change degree threshold, the initial detection image is confirmed to be a target detection image.

[0049] Furthermore, the sliding window refers to a window of fixed size, and the sliding step size of the sliding window is set. The grayscale operation and the grayscale transformation can achieve the same effect, which will not be repeated here. The method for obtaining the detection image time sequence is the same as the method for obtaining the target image time sequence, which will not be repeated here.

[0050] It should be explained that the calculation of the comprehensive image change degree based on the image center coordinate set includes: Based on the image center coordinate set, an image center coordinate time series is obtained, and the sliding window is used to sequentially extract analysis coordinate groups from the image center coordinate time series, wherein the analysis coordinate group includes two image center coordinates, and the Euclidean distance between the two image center coordinates in the analysis coordinate group is obtained to obtain the moving distance; Extract the first image center coordinate and the last image center coordinate in the image center coordinate time series, obtain the initial center coordinate and the target center coordinate, and obtain the evaluation distance based on the initial center coordinate and the target center coordinate; Summarizing the moving distances to obtain a moving distance set, and using the moving distance set to obtain a moving distance variance, wherein the moving distance variance is a variance of multiple moving distances in the moving distance set; The comprehensive image change degree is calculated based on the moving distance variance and the evaluation distance. The calculation formula is as follows: , in, represents the comprehensive image change degree, are all preset coefficients. represents the evaluation distance, represents the variance of moving distance.

[0051] It is understandable that the method of obtaining the evaluation distance based on the initial center coordinates and the target center coordinates is the same as the method of obtaining the moving distance, and will not be repeated here. Generally speaking, there may be moving objects in the initial video stream but they belong to the background. For example, the leaves of trees blown by the wind, here, the leaves are moving, and the trees are stationary. Therefore, through the comprehensive image change degree, the dynamic but background objects can be distinguished from the moving objects that are actually to be analyzed, thereby saving energy consumption for analyzing the objects that actually need to be analyzed and improving the timeliness of analyzing the objects that actually need to be analyzed.

[0052] Furthermore, the obtaining of the second decomposition frame number based on the adjacent image set and the detection image includes: Based on the adjacent image set and the detection image, a set of adjacent grayscale images and a detection grayscale image are obtained, one or more local area images are extracted from the detection grayscale image using a pre-constructed region growing algorithm and a pre-constructed image grayscale gradient set, and one or more target area images are identified in the one or more local area images, wherein the image grayscale gradient set includes a plurality of image gradient grayscale values, and the target area image is adjacent to at least one adjacent grayscale image in the adjacent grayscale image set; The following operations are performed on each of the one or more target area images: Based on the target area image, a local area grayscale mean is obtained, wherein the local area grayscale mean is the mean of multiple grayscale values ​​corresponding to the target area image, and the target area image and adjacent grayscale images adjacent to the target area image are associated to obtain an associated image set; Taking the local region grayscale mean as a starting point, extracting a related region image from the related image set using the region growing algorithm, and identifying a target discrimination image in the related region image, wherein the target discrimination image is a region of a grayscale image adjacent to the target region image in the related image set; The target discrimination mean is obtained based on the target discrimination image, and the discrimination frame number is calculated according to the target discrimination mean and the local area grayscale mean. The calculation formula is as follows: , , in, Indicates the number of frames to be judged. Indicates the initial frame number, represents the target discrimination mean, represents the grayscale mean of the local area, represents the preset coefficient, represents the preset coefficient, Indicates the rounding symbol; The determination frame numbers are summarized to obtain a determination frame number set, and a second decomposition frame number is determined based on the determination frame number set, wherein the second decomposition frame number is the smallest determination frame number in the determination frame number set.

[0053] It should be explained that the grayscale operation is performed on the adjacent images and the detection image in the adjacent image set respectively to obtain the adjacent grayscale image set and the detection grayscale image. The image grayscale image set includes multiple image gradient grayscale values. The method of extracting one or more local area images in the detection grayscale image using the region growing algorithm and the image grayscale gradient set is: extracting the image gradient grayscale values ​​from the image grayscale gradient set in sequence, and performing the following operations on the extracted image gradient grayscale values: Taking the image gradient grayscale value as the starting point, a search is performed in the detection grayscale image using a region growing algorithm to obtain an initial search area, and the number of pixels corresponding to the initial search area is counted to obtain a statistical quantity. If the statistical quantity is greater than or equal to a preset statistical threshold, the initial search area is recorded as a local area image, and the local area images are aggregated to obtain one or more local area images.

[0054] Furthermore, the technology of using a fixed gray value as the starting point of the region growing algorithm and using the region growing algorithm to search in the image is a prior art and will not be described in detail here. Generally speaking, there may be abnormal factors such as noise in the image. Therefore, by setting a statistical threshold, the noise in the image can be removed to improve the accuracy of obtaining the target area image. Generally speaking, the current regional image is adjacent to the adjacent grayscale image means that the regions corresponding to the two images are adjacent.

[0055] It should be understood that the target discrimination image refers to an image region with a similar grayscale value to the target region image. The higher the similarity between the target discrimination image and the target region image, the smaller the number of second decomposition frames required. For example, when there is a large difference between the target discrimination image and the target region image, it is easy to distinguish the target discrimination image from the target region image in the initial video stream. The target discrimination mean refers to the mean of multiple grayscale values ​​corresponding to the target discrimination image.

[0056] It should be understood that the step of identifying an abnormal image set based on the second decomposed frame number and the target video stream includes: A decomposed image sequence is extracted from the target video stream using the second decomposed frame number, wherein the decomposed image sequence includes a plurality of initial decomposed images, initial decomposed images are sequentially extracted from the decomposed image sequence, and the following operations are performed on the extracted initial decomposed images: Based on the initial decomposition image, a target decomposition image is identified in the decomposition image time sequence, wherein the target decomposition image is adjacent to the initial decomposition image and lags behind the extracted initial decomposition image; Confirming receiving an image comparison instruction from an image comparison unit, parsing the image comparison instruction, obtaining a local name database, and using the image recognition model set and the local name database to respectively identify a target identification decomposition image set and an initial identification decomposition image set in the target decomposition image and the initial decomposition image, wherein the target identification decomposition image set includes a plurality of target identification decomposition images, and the initial identification decomposition image set includes a plurality of initial identification decomposition images; Matching the target identification decomposition image in the target identification decomposition image set and the initial identification decomposition image in the initial identification decomposition image set to obtain a plurality of identification decomposition nodes, wherein the identification decomposition nodes include the initial identification decomposition image and the target identification decomposition image; The following operations are performed on each of the multiple identity decomposition nodes: Obtaining local offset distances based on the identified decomposition nodes, summarizing the local offset distances to obtain a local offset distance set, extracting the maximum local offset distance from the local offset distance set to obtain a target evaluation distance, and comparing the target evaluation distance with a preset evaluation distance threshold; If the target evaluation distance is greater than or equal to the evaluation distance threshold, both the extracted initial decomposition image and the target decomposition image are confirmed as abnormal images, and the target decomposition image is used as the extracted initial decomposition image, and the process returns to the step of confirming the target decomposition image in the decomposition image time sequence based on the initial decomposition image; Otherwise, the local offset distance variance is obtained based on the local offset distance set, and after confirming that the local offset distance variance is greater than or equal to the preset local offset distance threshold, the extracted initial decomposition image and the target decomposition image are both confirmed as abnormal images, and the target decomposition image is used as the extracted initial decomposition image, and the process returns to the step of confirming the target decomposition image in the decomposition image time sequence based on the initial decomposition image; The abnormal images are aggregated to obtain an abnormal image set.

[0057] It should be explained that the acquisition method of the decomposition image time sequence is the same as the acquisition method of the detection image time sequence, which will not be repeated here. The local name database refers to a database storing multiple local names. For example, the local names corresponding to people may include: hands, arms, mouths, eyes, etc. The method of using the image recognition model set and the local name database to confirm the target identification decomposition image set and the initial identification decomposition image set in the target decomposition image and the initial decomposition image respectively is the same as the method of obtaining the target image, which will not be repeated here. Matching the target identification decomposition image and the initial identification decomposition image refers to matching the target identification decomposition image and the initial identification decomposition image using local names and serial numbers. For example: the target identification decomposition image set includes hand-1 and hand-2, and the initial identification decomposition image set includes hand-1 and hand-2. Then, the target identification decomposition image set and the initial identification decomposition image set can be matched with hand-1 and hand-1, hand-2 and hand-2 by local names and serial numbers to obtain two identification decomposition nodes. The acquisition method of the local offset distance is the same as the acquisition method of the evaluation distance, which will not be repeated here. The local offset distance variance refers to the variance of multiple local offset distances corresponding to the local offset distance set. Generally speaking, when the target evaluation distance is greater than or equal to the evaluation distance threshold, it indicates that there are traces of local area modification in the initial video stream, and when the local offset distance variance is greater than or equal to the local offset distance threshold, it indicates that the initial video stream may have traces of editing. Therefore, the initial decomposed image and the target decomposed image that meet the conditions are confirmed as abnormal images.

[0058] It is understandable that in the same target video stream, there may be clipping or modification of the target video stream. When a part of the target video stream is modified, the target evaluation distance will be greater than or equal to the evaluation distance threshold. When the target video stream is clipped, spliced, etc., the local offset distance variance will be greater than or equal to the local offset distance threshold, that is, the target video stream will have a visual effect of two frames being split. Therefore, after sending the abnormal image set to the initiator of the analysis instruction, the abnormal image frame in the initial video stream can be identified.

[0059] The present invention is to solve the problem described in the background technology. The present invention confirms receiving a video decomposition instruction from a video decomposition unit, parses the video decomposition instruction, obtains a first decomposition frame number, uses the first decomposition frame number and an initial video stream to obtain a decomposition image timing, wherein the decomposition image timing includes multiple decomposition image nodes, and the decomposition image node includes a decomposition image and an image time, obtains a decomposition video time point set based on the decomposition image timing, wherein the decomposition video time point set includes M decomposition video time points, and M is an integer greater than or equal to 0, obtains a target video stream set based on the decomposition video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams. It can be seen that the present invention considers different situations that may exist in the initial video stream before analyzing the initial video stream. Therefore, the initial video stream is divided to obtain a target video stream set, thereby improving the accuracy of analyzing the initial video stream and improving the intelligence of analyzing the initial video stream. The present invention obtains the second decomposition frame number based on the target video stream, and confirms the abnormal image set based on the second decomposition frame number and the target video stream. It can be seen that the present invention combines the characteristics corresponding to each target video stream to obtain the second decomposition frame number for analyzing the target video stream. Obtaining different second decomposition frame numbers through different characteristics can save the resources required for analyzing the target video stream, thereby improving the intelligence level of the embodiment of the present invention. Therefore, the present invention can improve the accuracy and intelligence level of analyzing the video stream and reduce the resources required for analyzing the video stream.

[0060] like Figure 2 , which is a functional module diagram of a system for intelligently analyzing video streams based on a neural network provided by an embodiment of the present invention.

[0061] The intelligent analysis system 100 for realizing video stream based on neural network of the present invention can be installed in an electronic device. According to the functions realized, the intelligent analysis system 100 for realizing video stream based on neural network can include an analysis environment confirmation module 101, an initial frame number confirmation module 102, an initial video division module 103 and an abnormal image recognition module 104. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by a processor of an electronic device and can complete fixed functions, which are stored in the memory of the electronic device.

[0062] The analysis environment confirmation module 101 is used to receive an analysis instruction and confirm an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit and a result feedback unit; The initial frame number confirmation module 102 is used to confirm the video decomposition instruction received from the video decomposition unit, parse the video decomposition instruction, obtain a first decomposition frame number, and obtain a decomposition image sequence using the first decomposition frame number and the initial video stream, wherein the decomposition image sequence includes a plurality of decomposition image nodes, and the decomposition image node includes a decomposition image and an image time; The initial video segmentation module 103 is used to obtain a decomposed video time point set based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0, and obtain a target video stream set based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and perform the following operations on each target video stream in the target video stream set: The abnormal image recognition module 104 is used to obtain a second decomposition frame number based on the target video stream, and to identify an abnormal image set based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction using a result feedback unit to realize intelligent analysis of the initial video stream.

[0063] In detail, the modules in the system 100 for realizing intelligent analysis of video streams based on neural networks in the embodiment of the present invention are used in the same manner as described above. Figure 1 The same technical means as the method for realizing intelligent analysis of video stream based on neural network described in the text can produce the same technical effect, so I will not go into details here.

[0064] like Figure 3 , is a schematic diagram of the structure of an electronic device for implementing a method for intelligently analyzing video streams based on a neural network provided by an embodiment of the present invention.

[0065] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a program for an intelligent analysis method for video streams based on a neural network.

[0066] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the program of the intelligent analysis method for video streams based on neural networks, but also can be used to temporarily store data that has been output or is to be output.

[0067] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, a program for intelligent analysis of video streams based on neural networks, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0068] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0069] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0070] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0071] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0072] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0073] The program of the method for realizing intelligent analysis of video stream based on neural network stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, the following can be realized: Receiving an analysis instruction, and confirming an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit, and a result feedback unit; Confirming receipt of a video decomposition instruction from a video decomposition unit, parsing the video decomposition instruction to obtain a first decomposition frame number, and obtaining a decomposition image time sequence using the first decomposition frame number and an initial video stream, wherein the decomposition image time sequence includes a plurality of decomposition image nodes, and the decomposition image nodes include decomposition images and image time; A decomposed video time point set is obtained based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0; a target video stream set is obtained based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and the following operations are performed on each target video stream in the target video stream set: A second decomposition frame number is obtained based on the target video stream, and an abnormal image set is confirmed based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction by using a result feedback unit to realize intelligent analysis of the initial video stream.

[0074] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0075] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or system that can carry the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0076] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement: Receiving an analysis instruction, and confirming an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit, and a result feedback unit; Confirming receipt of a video decomposition instruction from a video decomposition unit, parsing the video decomposition instruction to obtain a first decomposition frame number, and obtaining a decomposition image time sequence using the first decomposition frame number and an initial video stream, wherein the decomposition image time sequence includes a plurality of decomposition image nodes, and the decomposition image nodes include decomposition images and image time; A decomposed video time point set is obtained based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0; a target video stream set is obtained based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and the following operations are performed on each target video stream in the target video stream set: A second decomposition frame number is obtained based on the target video stream, and an abnormal image set is confirmed based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction by using a result feedback unit to realize intelligent analysis of the initial video stream.

[0077] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0078] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0079] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0080] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for intelligent analysis of video streams based on neural networks, characterized in that: The method comprises: Receiving an analysis instruction, and confirming an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit, and a result feedback unit; Confirming receipt of a video decomposition instruction from a video decomposition unit, parsing the video decomposition instruction to obtain a first decomposition frame number, and obtaining a decomposition image time sequence using the first decomposition frame number and an initial video stream, wherein the decomposition image time sequence includes a plurality of decomposition image nodes, and the decomposition image nodes include decomposition images and image time; A decomposed video time point set is obtained based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0; a target video stream set is obtained based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and the following operations are performed on each target video stream in the target video stream set: A second decomposition frame number is obtained based on the target video stream, and an abnormal image set is confirmed based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction by using a result feedback unit to realize intelligent analysis of the initial video stream.

2. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 1, characterized in that: The step of obtaining a decomposed video time point set based on the decomposed image time sequence comprises: Based on the decomposed image sequence, an analysis grayscale image sequence is obtained, and the following operations are performed on each analysis grayscale image in the analysis grayscale image sequence: Acquire an analysis grayscale mean based on the analysis grayscale image, wherein the analysis grayscale mean is the mean of multiple grayscale values ​​corresponding to the analysis grayscale image; Associating the analyzed grayscale mean and image time to obtain initial analysis nodes, and summarizing the initial analysis nodes to obtain an initial analysis node set; Obtain one or more target analysis node sets using a pre-built clustering model and an initial analysis node set; Based on one or more target analysis node sets, a set of decomposed video time points is identified in the initial video stream.

3. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 2, characterized in that: The obtaining of the second decomposition frame number based on the target video stream includes: Obtaining an initial number of frames and a video duration of a target video stream, obtaining a number of images based on the initial number of frames and the video duration, obtaining an image extraction gradient set for extracting images, and extracting an image time sequence from the target video stream based on the number of images and the image extraction gradient set, wherein the image extraction gradient set includes a plurality of image extraction ratios, and the image time sequence includes a plurality of initial images; Confirming receipt of an image recognition instruction from an image recognition unit, confirming an image recognition model set based on the image recognition instruction, wherein the image recognition model set includes a plurality of image recognition models, sequentially extracting an initial image from the image sequence, and performing the following operations on the extracted initial image: Randomly extracting two image recognition models from the image recognition model set to obtain a first recognition model and a second recognition model, recognizing the extracted initial image based on the first recognition model and the second recognition model to obtain a first recognition name set and a second recognition name set, wherein the first recognition name set includes a plurality of first recognition nodes, wherein the first recognition nodes include a first recognition name and a first recognition quantity, and the second recognition name set includes a plurality of second recognition nodes, wherein the second recognition nodes include a second recognition name and a second recognition quantity; After the target identification name set is confirmed by using the first identification name set and the second identification name set, wherein the target identification name set includes a plurality of target identification nodes, and the target identification node includes a target identification name and a target identification quantity, the following operations are performed on the target identification names in the target identification name set: A local area image corresponding to a target recognition name is identified in the initial image, the local area image is labeled using the target recognition name and the target recognition number to obtain a target image with a serial number, the target images are aggregated to obtain a target image set, and the target image set is used to obtain a second decomposition frame number.

4. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 3, characterized in that: The step of using the first identification name set and the second identification name set to identify a target identification name set includes: The following operations are performed for each first identification node in the first identification name set: Determine whether there is a second identification node in the second identification name set that is the same as the first identification node; If there is no second recognition node in the second recognition name set that is the same as the first recognition node, then randomly extracting a plurality of initial image recognition models from the image recognition model set, wherein the number of the plurality of initial image recognition models is a preset number of extractions, and obtaining a plurality of initial recognition name sets using the plurality of initial image recognition models and the initial image, wherein the initial image recognition models correspond to the initial recognition name sets one by one, and the initial recognition name sets include a plurality of initial recognition nodes; Combining the multiple initial identification name sets in a combined form to obtain multiple combined identification name sets, wherein the combined identification name set includes two initial identification name sets, and identifying one or more fused identification name sets in the multiple combined identification name sets, wherein the two initial identification name sets in the fused identification name set are the same; Eliminate any one of the initial identification name sets corresponding to each fused identification name set in one or more fused identification name sets from the multiple initial identification name sets to obtain an updated identification name set, use the updated identification name set as multiple initial identification name sets, and return to the step of combining the multiple initial identification name sets in a combined form until one or more initial fused name sets are obtained, wherein the multiple initial identification name sets corresponding to the initial fused name set are all the same; Respectively counting the number of initial identification name sets corresponding to each of the one or more initial fusion name sets to obtain one or more initial fusion numbers, wherein the initial fusion numbers correspond to the initial fusion name sets one by one; Extracting the largest initial fusion number from one or more initial fusion numbers to obtain a target verification number, calculating the ratio of the target verification number to the extraction number to obtain a correct ratio, and when the correct ratio is greater than or equal to a preset ratio threshold, taking the fusion recognition name set corresponding to the target verification number as the target recognition name set; Otherwise, return to the step of randomly extracting a plurality of initial image recognition models from the image recognition model set until a target recognition name set is obtained.

5. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 4, characterized in that: The step of obtaining a second decomposition frame number by using the target image set includes: Summarizing the target image sets to obtain multiple target image sets, and obtaining the target image time sequence using the target identification name, serial number and target image; Extracting initial analysis images from the target image sequence in sequence, and performing the following operations on the extracted initial analysis images: Based on the initial analysis image, identifying a target analysis image in the target image time sequence, wherein the target analysis image is adjacent to the initial analysis image and lags behind the initial analysis image in the target image time sequence; Acquire an initial coordinate point set and a target coordinate point set based on an initial analysis image and a target analysis image; Mapping the initial coordinate points in the initial coordinate point set and the target coordinate points in the target coordinate point set to a pre-constructed reference coordinate system to obtain a mapping coordinate point set, wherein the mapping coordinate point set includes a plurality of mapping coordinate points; The number of reference coordinate points corresponding to each mapping coordinate point in the mapping coordinate point set is counted to obtain a reference quantity set, wherein the reference coordinate point is an initial coordinate point or a target coordinate point, and the reference quantity set includes multiple reference quantities. The reference quantity in the reference quantity set is counted as 2 to obtain a target reference quantity. The number of mapping coordinate points in the mapping coordinate point set is counted to obtain a comprehensive quantity. The ratio of the target reference quantity to the comprehensive quantity is calculated to obtain a reference quantity ratio. If the reference quantity ratio is less than or equal to a preset reference ratio threshold, the target analysis image is taken as the initial analysis image, and the step of confirming the target analysis image in the target image time sequence based on the initial analysis image is returned. If the reference quantity ratios of the reference quantity set corresponding to the target image time sequence are all less than or equal to the reference ratio threshold, the target image is identified as a background image, otherwise, the target image is identified as an initial detection image. After confirming that the initial detection image is a preset target detection image, the target detection image is used to identify an adjacent image set in the initial image, wherein the adjacent image set includes one or more adjacent images, and the adjacent image is a target detection image or a background image. The second decomposition frame number is obtained based on the adjacent image set and the detection image.

6. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 5, characterized in that: The step of confirming that the initial detection image is a preset target detection image includes: Acquire a detection image sequence according to the target recognition name, serial number and initial detection image, wherein the detection image sequence includes multiple initial detection images, perform a grayscale operation on each initial detection image in the detection image sequence, and obtain a grayscale detection image sequence, and extract analysis image groups in sequence from the grayscale detection image sequence based on a preset sliding window, wherein the analysis image group includes two grayscale detection images; The following operations are performed for each grayscale detection image in the analysis image group: The image center coordinates are calculated based on the grayscale detection image. The calculation formula is as follows: ， in, Respectively represent the horizontal and vertical coordinates of the image center coordinates, Indicates that there are a total of pixels, Indicates the grayscale detection image The gray value corresponding to each pixel point, Respectively represent the grayscale detection image The horizontal and vertical coordinates corresponding to the pixel points; Summarizing the image center coordinates to obtain an image center coordinate set, calculating a comprehensive image change degree based on the image center coordinate set, and determining whether the comprehensive image change degree is equal to a preset comprehensive change degree threshold; If the comprehensive image change degree is greater than or equal to the comprehensive change degree threshold, the initial detection image is confirmed to be a target detection image.

7. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 6, characterized in that: The calculating of the comprehensive image change degree based on the image center coordinate set includes: Based on the image center coordinate set, an image center coordinate time series is obtained, and the sliding window is used to sequentially extract analysis coordinate groups from the image center coordinate time series, wherein the analysis coordinate group includes two image center coordinates, and the Euclidean distance between the two image center coordinates in the analysis coordinate group is obtained to obtain the moving distance; Extract the first image center coordinate and the last image center coordinate in the image center coordinate sequence, obtain the initial center coordinate and the target center coordinate, and obtain the evaluation distance based on the initial center coordinate and the target center coordinate; Summarizing the moving distances to obtain a moving distance set, and using the moving distance set to obtain a moving distance variance, wherein the moving distance variance is a variance of multiple moving distances in the moving distance set; The comprehensive image change degree is calculated based on the moving distance variance and the evaluation distance. The calculation formula is as follows: ， in, represents the comprehensive image change degree, are all preset coefficients. represents the evaluation distance, represents the variance of moving distance.

8. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 7, characterized in that: The step of obtaining a second decomposed frame number based on the adjacent image set and the detection image includes: Based on the adjacent image set and the detection image, a set of adjacent grayscale images and a detection grayscale image are obtained, one or more local area images are extracted from the detection grayscale image using a pre-constructed region growing algorithm and a pre-constructed image grayscale gradient set, and one or more target area images are identified in the one or more local area images, wherein the image grayscale gradient set includes a plurality of image gradient grayscale values, and the target area image is adjacent to at least one adjacent grayscale image in the adjacent grayscale image set; The following operations are performed on each of the one or more target area images: Based on the target area image, a local area grayscale mean is obtained, wherein the local area grayscale mean is the mean of multiple grayscale values ​​corresponding to the target area image, and the target area image and adjacent grayscale images adjacent to the target area image are associated to obtain an associated image set; Taking the local region grayscale mean as a starting point, extracting a related region image from the related image set using the region growing algorithm, and identifying a target discrimination image in the related region image, wherein the target discrimination image is a region of an adjacent grayscale image in the related image set that is adjacent to the target region image; The target discrimination mean is obtained based on the target discrimination image, and the discrimination frame number is calculated according to the target discrimination mean and the local area grayscale mean. The calculation formula is as follows: ， ， in, Indicates the number of frames to be judged. Indicates the initial frame number, represents the target discrimination mean, represents the grayscale mean of the local area, represents the preset coefficient, represents the preset coefficient, Indicates the rounding symbol; The determination frame numbers are summarized to obtain a determination frame number set, and a second decomposition frame number is determined based on the determination frame number set, wherein the second decomposition frame number is the smallest determination frame number in the determination frame number set.

9. The method for realizing intelligent analysis of video stream based on neural network as claimed in claim 8, characterized in that: The step of identifying an abnormal image set based on the second decomposed frame number and the target video stream includes: A decomposed image sequence is extracted from the target video stream using the second decomposed frame number, wherein the decomposed image sequence includes a plurality of initial decomposed images, initial decomposed images are sequentially extracted from the decomposed image sequence, and the following operations are performed on the extracted initial decomposed images: Based on the initial decomposition image, a target decomposition image is identified in the decomposition image time sequence, wherein the target decomposition image is adjacent to the initial decomposition image and lags behind the extracted initial decomposition image; Confirming receiving an image comparison instruction from an image comparison unit, parsing the image comparison instruction, obtaining a local name database, and using the image recognition model set and the local name database to respectively identify a target identification decomposition image set and an initial identification decomposition image set in the target decomposition image and the initial decomposition image, wherein the target identification decomposition image set includes a plurality of target identification decomposition images, and the initial identification decomposition image set includes a plurality of initial identification decomposition images; Matching the target identification decomposition image in the target identification decomposition image set and the initial identification decomposition image in the initial identification decomposition image set to obtain a plurality of identification decomposition nodes, wherein the identification decomposition nodes include the initial identification decomposition image and the target identification decomposition image; The following operations are performed on each of the multiple identity decomposition nodes: Obtaining local offset distances based on the identified decomposition nodes, summarizing the local offset distances to obtain a local offset distance set, extracting the maximum local offset distance from the local offset distance set to obtain a target evaluation distance, and comparing the target evaluation distance with a preset evaluation distance threshold; If the target evaluation distance is greater than or equal to the evaluation distance threshold, both the extracted initial decomposition image and the target decomposition image are confirmed as abnormal images, and the target decomposition image is used as the extracted initial decomposition image, and the process returns to the step of confirming the target decomposition image in the decomposition image time sequence based on the initial decomposition image; Otherwise, the local offset distance variance is obtained based on the local offset distance set, and after confirming that the local offset distance variance is greater than or equal to the preset local offset distance threshold, the extracted initial decomposition image and the target decomposition image are both confirmed as abnormal images, and the target decomposition image is used as the extracted initial decomposition image, and the process returns to the step of confirming the target decomposition image in the decomposition image time sequence based on the initial decomposition image; The abnormal images are aggregated to obtain an abnormal image set.

10. An intelligent analysis system for video streams based on neural networks, characterized in that: The system comprises: An analysis environment confirmation module is used to receive an analysis instruction and confirm an intelligent analysis environment based on the analysis instruction, wherein the intelligent analysis environment includes an initial video stream and a video analysis system, wherein the video analysis system includes: a video decomposition unit, an image recognition unit, an image comparison unit and a result feedback unit; An initial frame number confirmation module is used to confirm receiving a video decomposition instruction from a video decomposition unit, parse the video decomposition instruction, obtain a first decomposition frame number, and obtain a decomposition image sequence using the first decomposition frame number and an initial video stream, wherein the decomposition image sequence includes a plurality of decomposition image nodes, and the decomposition image node includes a decomposition image and an image time; The initial video partitioning module is used to obtain a decomposed video time point set based on the decomposed image time sequence, wherein the decomposed video time point set includes M decomposed video time points, and M is an integer greater than or equal to 0, and obtain a target video stream set based on the decomposed video time point set and the initial video stream, wherein the target video stream set includes one or more target video streams, and perform the following operations on each target video stream in the target video stream set: The abnormal image recognition module is used to obtain a second decomposition frame number based on a target video stream, and to identify an abnormal image set based on the second decomposition frame number and the target video stream, wherein the abnormal image set includes N abnormal images, and N is an integer greater than or equal to 0. The abnormal image set is sent to the initiator of the analysis instruction using a result feedback unit to realize intelligent analysis of the initial video stream.

Images