CN115311607B - Epoxy resin production monitoring data management method - Google Patents

Epoxy resin production monitoring data management method Download PDF

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CN115311607B
CN115311607B CN202211231069.6A CN202211231069A CN115311607B CN 115311607 B CN115311607 B CN 115311607B CN 202211231069 A CN202211231069 A CN 202211231069A CN 115311607 B CN115311607 B CN 115311607B
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CN115311607A (en
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成唯
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Nantong Lanxi New Materials Co ltd
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Abstract

The invention relates to the technical field of production data processing, in particular to a management method of epoxy resin production monitoring data. The method comprises the following steps: obtaining dynamic frames in all video frames; calculating the similarity of two moving objects in the two types of dynamic frames to obtain the moving objects belonging to the same moving object in the various types of dynamic frames, and recording as penetrating moving objects; obtaining a random frame, and partitioning the monitoring video data by using the random frame to obtain monitoring video data of different intervals; selecting a moving object with a random path and the largest average characteristic value from the monitoring video data in an interval, and recording the moving object as a key generation moving object; generating a key matrix by using all pixel points in a mark connected domain in an image corresponding to a first frame in all random frames corresponding to a moving object in monitoring video data by using a key; and encrypting the monitoring video data of each interval by using the key matrix corresponding to the monitoring video data of each interval. The invention can improve the safety of monitoring video data.

Description

Epoxy resin production monitoring data management method
Technical Field
The invention relates to the technical field of production data processing, in particular to an epoxy resin production monitoring data management method.
Background
Epoxy resin is an important thermosetting resin. Has good chemical properties and physical properties, and can be widely applied to various industries. In the process of epoxy resin production, wherein production monitoring data, such as production monitoring videos, contain private information of enterprises, such as unique formulas and operation practices of epoxy resin production, information leakage in the monitoring videos can cause relatively large influence on production data of the enterprises, so that encryption processing is required.
The conventional monitoring video encryption method is to utilize the existing algorithm to generate a key to encrypt the whole video, and the encryption mode is that the whole is generally used with the key, and the algorithm of the generated key is not truly random, so that the key is easily cracked violently when being invaded by the outside, thereby causing the monitoring video data loss in the production process of epoxy resin and causing the leakage of production data of enterprises.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a method for managing epoxy resin production monitoring data, which adopts the following technical scheme:
one embodiment of the invention provides a management method for monitoring data in epoxy resin production, which comprises the following steps: collecting monitoring video data in the production process of epoxy resin; obtaining the information quantity of each video frame in the monitoring video data; obtaining the information quantity difference value of every two adjacent video frames, and judging the dynamic frame in all the video frames based on the information quantity difference value, wherein the dynamic frame refers to the video frame with object motion in the monitoring video data;
dividing dynamic frames which are continuous frames into one type, and obtaining a mark connected domain in an image corresponding to the dynamic frame in each type of dynamic frame by using a frame difference method; calculating to obtain the object characteristics of the marked connected domain based on a two-bit data set consisting of gray values of pixel points in the marked connected domain and pixel points in the neighborhood in the image corresponding to each dynamic frame; obtaining the motion trail of the same moving object in each type of dynamic frame based on the object characteristics of the marked connected domain; obtaining the average characteristic value of each moving object in each type of dynamic frame, calculating the similarity of two moving objects in the two types of dynamic frames, obtaining the moving objects belonging to the same moving object in the various types of dynamic frames, and recording as penetrating moving objects;
classifying the motion tracks of the penetrating motion objects in each type of dynamic frame to obtain a random path penetrating the motion objects, wherein the dynamic frame corresponding to the random path is a random frame; partitioning the monitoring video data by using random frames to obtain monitoring video data of different intervals; selecting a moving object with a random path and the largest average characteristic value from the monitoring video data in an interval, and recording the moving object as a key generation moving object; generating a key matrix by using all pixel points in a mark connected domain in an image corresponding to a first frame in all random frames corresponding to a moving object in monitoring video data by using a key; and encrypting the monitoring video data of each interval by using the key matrix corresponding to the monitoring video data of each interval.
Preferably, the amount of information of each video frame is:
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wherein, the first and the second end of the pipe are connected with each other,
Figure 585649DEST_PATH_IMAGE002
an information amount indicating an nth frame video frame; />
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Represents the gray value of the pixel point, and is greater or less than>
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Represents a gray value of->
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In the first or second direction>
Figure 512967DEST_PATH_IMAGE005
Probability of occurrence in the image corresponding to the frame video frame.
Preferably, obtaining an information amount difference value between every two adjacent video frames, and determining a dynamic frame in all the video frames based on the information amount difference value comprises: and if the difference value of the information quantity of every two adjacent video frames is not equal to 0, the video frame of the next frame in every two adjacent video frames is a dynamic frame.
Preferably, the object characteristic of the labeled connected components is:
Figure 853688DEST_PATH_IMAGE006
wherein the content of the first and second substances,
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representing the object characteristics of the m-th mark connected domain in the n-th frame dynamic frame; />
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Indicates the th->
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The fifth of the flag connected field>
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A pixel point is selected and/or judged>
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The number of all pixel points in the mark connected domain; />
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Indicates the th or fifth in the class>
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The fifth of the flag connected field>
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Peripheral 8 neighborhood pixels of each pixel, and->
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Indicates the fifth->
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The gray value and the fifth or fifth degree of the pixel points>
Figure 534571DEST_PATH_IMAGE008
The second-bit data group of the average value of the gray values of 8 pixel points in the neighborhood around each pixel point is at the fifth or fifth judgment>
Figure 720701DEST_PATH_IMAGE009
The probability of occurrence in all the two-bit data sets in the connected component field is marked.
Preferably, obtaining the average feature value of each moving object in each type of dynamic frame, calculating the similarity of two moving objects in two types of dynamic frames, and obtaining the moving objects belonging to the same moving object in each type of dynamic frames comprises:
the average characteristic value is the average value of the object characteristics of all corresponding mark connected domains of a moving object in a dynamic frame; the ratio of the average characteristic values of the two moving objects in different types of dynamic frames is the similarity of the two moving objects in the two types of dynamic frames, and if the similarity of the two moving objects in the two types of dynamic frames is larger than a preset threshold value, the two moving objects in the two types of dynamic frames are the same moving object.
Preferably, classifying the motion trajectory of each type of dynamic frame of the penetrating moving object to obtain a random path penetrating the moving object, where the dynamic frame corresponding to the random path is a random frame and includes: and analyzing the motion track penetrating through the moving object in each type of dynamic frame by using an LOF algorithm to obtain outliers, wherein the path corresponding to the outliers is a random path.
Preferably, generating the key matrix comprises: each element in the key matrix is used for generating a gray value of a pixel point in a mark connected domain of a moving object in an image corresponding to a first frame in all random frames corresponding to the monitoring video data for the key.
The embodiment of the invention at least has the following beneficial effects: the invention utilizes the epoxy resin to produce the moving object which moves randomly in the monitoring video data to quantize and set a secret key for encryption, because the behavior of the moving object which moves randomly in the epoxy resin production monitoring video data moving object is influenced by external factors, the behavior does not have regularity and is random in the true sense, the monitoring video data produced by the epoxy resin is continuously encrypted by utilizing the characteristic, the randomness of the secret key is truly achieved, and the secret key is different and the encryption result is different because the random dynamic frames selected by each section of monitoring video are different.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a method for managing monitoring data of epoxy resin production according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention for achieving the predetermined objects, the following detailed description of the method for managing the monitoring data of the epoxy resin production according to the present invention, the specific implementation, structure, features and effects thereof will be provided in conjunction with the accompanying drawings and the preferred embodiments. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The following describes a specific scheme of the epoxy resin production monitoring data management method provided by the invention in detail with reference to the accompanying drawings.
Example (b):
the main application scenarios of the invention are as follows: in the process of epoxy resin production, a monitoring video records a plurality of private enterprise production data, so that encryption is needed to be carried out on the monitoring video.
The main purposes of the invention are: the method comprises the steps of extracting dynamic frames by using an epoxy resin monitoring video, identifying moving objects in all the dynamic frames, analyzing different moving tracks of all the moving objects in different dynamic frames, selecting a random dynamic frame where the random moving track is located to partition the whole monitoring video, calculating a key of a current interval for the video of each interval according to carrying information of the moving objects, and finally encrypting the video of the current interval through the key and completing transmission and storage to realize epoxy resin production monitoring data management.
Referring to fig. 1, a flowchart of a method for managing monitoring data of epoxy resin production according to an embodiment of the present invention is shown, where the method includes the following steps:
s1, collecting monitoring video data in the production process of epoxy resin; obtaining the information content of each video frame in the monitoring video data; and obtaining the information quantity difference value of every two adjacent video frames, and judging the dynamic frame in all the video frames based on the information quantity difference value, wherein the dynamic frame refers to the video frame with object motion in the monitoring video data.
The method needs to encrypt the monitoring video data of epoxy resin production, so the monitoring video data of epoxy resin production needs to be collected, the specific collection process is to collect the monitoring video data of epoxy resin production by using a monitoring camera in an epoxy resin production workshop, and the specific arrangement position method of the monitoring camera is arranged by an implementer.
Further, the difference value of the information carrying amount of the image of each frame in the monitoring video data produced by epoxy resin is used for extracting the dynamic frame.
The monitoring video of the epoxy resin is obtained, and the monitoring video is divided into static frames and dynamic frames due to the particularity (scene invariance), wherein the static frames are video frames without object motion in the monitoring video, and the dynamic frames are video frames with object motion in the monitoring video. The information carrying amount in all the static frames is the same, and the information carrying amount of the video in the dynamic frame is different, so that the static frames and the dynamic frames of the monitoring video are divided according to the characteristic, and the dynamic frame video is extracted, which is specifically shown as follows:
to a first order
Figure 473894DEST_PATH_IMAGE005
For example, the method for determining whether the frame video is a dynamic frame is as follows:
firstly, calculating the information carrying quantity of the first frame video
Figure 355262DEST_PATH_IMAGE002
：
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In the formula:
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an information quantity representing a video frame of an nth frame, based on a predetermined criterion>
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Represents the gray value of the pixel point, ("is or is)>
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），/>
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Represents a gray value of +>
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Is at the fifth->
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The probability of occurrence in the frame image is calculated in such a way that the gray value is ≥>
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Is at the first of the pixel points
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The frequency of occurrence in the frame image is divided by the number of pixels of the overall image.
Formula logic: first, the
Figure 654842DEST_PATH_IMAGE005
The frame image is a static picture, the carried information is expressed by the gray values of different pixel points as visual information, and the carried information is constant because the frame image is a static picture, namely the distribution of the expressed gray values of the pixel points is constant, so the carried information is quantized by utilizing the calculation mode of the information entropy.
Then calculate the
Figure 920739DEST_PATH_IMAGE015
Information carrying amount of the image of->
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Based on the calculation mode and the ^ th->
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Information carrying amount of frame image
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The same way of calculation.
Then calculate the first
Figure 55606DEST_PATH_IMAGE005
Frame image and ^ th->
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The difference value of the information carrying amount of the frame image is used for judging the first time
Figure 191238DEST_PATH_IMAGE005
Whether a frame image is a dynamic image, first->
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The frame image and the ^ h->
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The difference value of the information carrying quantity of the frame image->
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The calculation is as follows:
Figure 310318DEST_PATH_IMAGE018
formula logic: by the first
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Frame imageInformation carrying quantity of image>
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And a fifth +>
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The difference in the information carrying amount of the frame image represents the ^ h>
Figure 115101DEST_PATH_IMAGE005
Whether the frame image is a dynamic frame or not is judged, wherein the dynamic frame refers to that dynamic movement of different objects appears in two continuous frames of images in the production monitoring video of the epoxy resin, namely that the dynamic in the dynamic frame is relative to the previous frame of image. The surveillance video has particularity in all videos, because the fixed surveillance camera shooting is realized, the shooting angle is constant, and when no dynamic article exists, the video of each frame shot by the surveillance camera shooting is the same, so the difference value is greater or less than the preset value>
Figure 548356DEST_PATH_IMAGE017
Zero indicates a fifth or fifth decision>
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Frame image is compared with the fifth->
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The frame image does not have any difference, i.e., is in the ^ h/er in the production surveillance video of the epoxy>
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If no dynamic object appears in the frame image, even if the dynamic object appears in the frame image, the frame image can be changed in the angle of the monitoring camera after being moved, the gray value of the dynamic object can be changed according to the optical principle, the gray value is expressed in the above formula, the difference value is not 0, and therefore the judgment of the position/value of the position/area based on the gray value is judged by the above method>
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Whether the frame image is a dynamic frame. />
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Then explain the
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The frame image is a static frame image, and>
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then it indicates a fifth->
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The frame image is a dynamic frame image.
To this end, the
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And finishing the judgment of the dynamic frame and the static frame of the frame image. By utilizing the method, the videos of all the frames shot in the epoxy resin monitoring video are judged, whether the monitoring videos of all the frames are dynamic frames or not can be obtained, and then the dynamic frames are marked and extracted. And at this point, extracting dynamic frames of all the epoxy resin monitoring videos.
S2, dividing the dynamic frames which are continuous frames into one type, and obtaining a mark connected domain in an image corresponding to the dynamic frame in each type of dynamic frame by using a frame difference method; calculating to obtain the object characteristics of the marked connected domain based on a two-bit data set consisting of gray values of pixel points in the marked connected domain and pixel points in the neighborhood in the image corresponding to each dynamic frame; obtaining the motion trail of the same moving object in each type of dynamic frame based on the object characteristics of the marked connected domain; and obtaining the average characteristic value of each moving object in each type of dynamic frame, calculating the similarity of the two moving objects in the two types of dynamic frames, obtaining the moving objects belonging to the same moving object in the various types of dynamic frames, and recording as penetrating moving objects.
The method comprises the steps of S1, obtaining dynamic frames in all epoxy resin production monitoring videos, extracting random dynamic frames from all dynamic frame images, carrying out interval division on the whole epoxy resin production monitoring videos by using the random dynamic frames, and calculating encryption keys of the epoxy resin monitoring videos in corresponding intervals by using characteristic parameters of the random dynamic frames in each interval.
The purpose is as follows: in the monitoring video of epoxy resin, the dynamic objects appear in two states, namely, mechanical repeated motion (motion of a conveyor belt, motion of workers on and off duty and the like) and non-mechanical motion of randomly appearing objects (a certain staff or leader performs workshop inspection, abnormal motion of the conveyor belt and the like). The latter state is unexpected factor in the production process of epoxy, uncontrollable, it is totally random relatively with holistic surveillance video, so utilize its characteristic to carry out encryption to epoxy's production surveillance video, what its real sense was accomplished is random of key, and because the difference of the random dynamic frame that every section surveillance video selected, its key also just also is different, the encryption result also is different, compare in current pseudo-random encryption and whole encryption, its security is higher, be difficult to be cracked more.
Quantizing the characteristics and motion tracks of the moving object by using the dynamic frame image; the purpose of the feature quantization of the moving object in the dynamic frame image is to determine whether the motion within the continuous dynamic frames is the same object, and the purpose of the quantization of the motion trajectory of the moving object in the dynamic frame is to determine the motion trajectory of the same moving object.
All the dynamic frames are obtained, and the motion trail of the same moving object is quantized. The specific process is to cluster the objects according to whether the objects are continuous frames or not (the motion representation of the objects is continuous in the monitored video, so the objects are clustered according to the characteristics, for example, the 1,2,3,7,8,9 and 10 frames are continuous frames, and the clustering result is that 1,2,3 frames are of the same type, 7,8,9 and 10 frames are of the same type). Then, each frame image in each class is quantized with the moving object characteristics and the moving characteristics, taking continuous dynamic frame images of any class as an example.
The quantization of its features is as follows: firstly, detecting all moving object boundaries in two continuous frame images by using a frame difference method for the dynamic frame images of two adjacent frames, and marking the detected moving object boundaries on the two continuous frame images to obtain a marked connected domain.
Then, the object characteristics in each connected domain in each frame image are quantified, and the first one in a certain class is used
Figure 118064DEST_PATH_IMAGE005
In the frame image
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Individual connected fields are taken as an example, the object characteristics thereof->
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The specific calculation method is as follows:
Figure 526676DEST_PATH_IMAGE021
in the formula (I), the compound is shown in the specification,
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representing the object characteristics of the m-th mark connected domain in the n-th frame dynamic frame; />
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Indicates the th or fifth in the class>
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A fifth of a plurality of flag connected fields>
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Multiple pixel point (based on whether or not the device is on>
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Wherein->
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The number of all the pixel points in the marked connected domain), and (or) the number of the pixel points in the marked connected domain>
Figure 567685DEST_PATH_IMAGE011
Indicates the th or fifth in the class>
Figure 637272DEST_PATH_IMAGE009
A fifth of a plurality of flag connected fields>
Figure 444823DEST_PATH_IMAGE008
Peripheral 8 neighborhood pixels of each pixel, and->
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Represents a fifth or fifth party>
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The gray value and the fifth or fifth degree of the pixel points>
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The probability of the two-bit data group of the average value of the gray values of 8 pixel points in the neighborhood around each pixel point appearing in all the two-bit data groups in the first marking connected domain is calculated in the manner of ^ h>
Figure 275058DEST_PATH_IMAGE008
Gray value and the second of each pixel point
Figure 307DEST_PATH_IMAGE008
Two-bit data of the average value of gray values of 8 pixel points in the neighborhood around each pixel point is judged as being ^ h or greater on the whole>
Figure 87211DEST_PATH_IMAGE009
The frequency count occurring in each connected field is divided by the number of all the two-bit data sets.
Formula logic: because only the boundary of a moving object between two continuous dynamic frames is detected by the frame difference method, when a plurality of same objects move simultaneously, the corresponding objects cannot be detected. So the quantization is performed in the above-mentioned manner
Figure 623235DEST_PATH_IMAGE009
The characteristics of the object in each connected domain not only consider the information carried by the gray value of each pixel point in the connected domain, but also consider the information carried by the gray value of the pixel points in 8 neighborhoods around each pixel point and the pixel point to be integrally distributed, and the characteristics of the moving object in the connected domain are quantified in the spatial distribution mode.
In the above manner to
Figure 795590DEST_PATH_IMAGE005
Frame dynamic frame and/or->
Figure 950628DEST_PATH_IMAGE023
All connected domains in the frame dynamic frame are subjected to characteristic quantization of the moving object and then are judged as being ^ h>
Figure 501826DEST_PATH_IMAGE023
Matching the characteristic values of all connected domains in the frame dynamic frame image to calculate the matching degree so as to judge whether the connected domains are matched or not>
Figure 349697DEST_PATH_IMAGE005
Frame first->
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A communication field and a ^ th->
Figure 561552DEST_PATH_IMAGE023
The frame is at the ^ h>
Figure 623049DEST_PATH_IMAGE024
Several connected fields as an example, a connected field matching degree>
Figure 484563DEST_PATH_IMAGE025
The calculation is as follows:
Figure 365932DEST_PATH_IMAGE026
in the formula:
Figure 252985DEST_PATH_IMAGE027
is the first->
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Fifth in a frame dynamic frame>
Figure 702869DEST_PATH_IMAGE024
Quantized characteristic values of moving objects of a connected field>
Figure 71534DEST_PATH_IMAGE007
Is a first->
Figure 372065DEST_PATH_IMAGE005
The ^ th or ^ th in the frame dynamic frame>
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And the quantized characteristic values of the moving objects of the connected domains.
Formula logic: in order to effectively prevent the problem that correct matching cannot be carried out under the condition that the connected domains of a plurality of moving objects are relatively the same (the connected domains of a plurality of moving people are nearly the same), the matching is carried out by utilizing the difference value of the space distribution characteristics of moving objects of two connected domains in two continuous frames.
Then setting a matching threshold
Figure 627783DEST_PATH_IMAGE028
For two connected fields corresponding to a match greater than the threshold, the same moving object is considered (an empirically matched threshold is ≥ er)>
Figure 218164DEST_PATH_IMAGE029
）。
By using the above method to match different connected domains in all the frame dynamic frames in the category by using the characteristic values, the distribution of the same moving object in all the dynamic frame images in the category can be obtained.
Then, the moving track of each moving object is quantifiedIn any sports article
Figure 430709DEST_PATH_IMAGE030
For example, its movement locus->
Figure 321304DEST_PATH_IMAGE031
The quantization is as follows:
locating the corresponding connected domain appearing in each frame in the category, and then obtaining the centroid coordinates of the connected domain corresponding to the moving object in each frame by utilizing the prior art, wherein the centroid coordinates are respectively as follows:
Figure 711834DEST_PATH_IMAGE032
the coordinates of the centroid position are the motion track of the moving object
Figure 789512DEST_PATH_IMAGE033
。
The tracks of all moving objects are quantized by the method, so that the moving tracks of all moving objects can be obtained.
At this point, the motion trail of the same object in all the dynamic frames and the feature quantization of the moving object are completed.
Selecting a random dynamic frame through the characteristics and the motion track of a quantized moving object and partitioning a monitoring video by utilizing the random dynamic frame; the specific logic is that similarity calculation is carried out on all moving objects in all epoxy resin production monitoring videos through the quantized characteristics and quantized tracks of all the moving objects, and continuous dynamic frames where moving objects which appear for many times but have different motion tracks are located are screened out and considered to be random dynamic frames. And then partitioning all the overall monitoring videos by using the random dynamic frame. The specific method is as follows:
firstly, performing overall moving object identification on all moving objects identified in the above step according to the feature quantization values of the moving objects, wherein the identification mode is as follows whether different types of moving objects are the same moving object or not:
since the feature quantization values of each moving object in each category dynamic frame are slightly different due to the motion, the average value of the feature quantization values of each moving object in each category dynamic frame is calculated first to reduce the influence of the difference, and then
Figure 41633DEST_PATH_IMAGE034
Moving item in a category>
Figure 521156DEST_PATH_IMAGE030
For example, the specific manner is as follows:
Figure 223532DEST_PATH_IMAGE035
in the formula (I), the compound is shown in the specification,
Figure 913140DEST_PATH_IMAGE036
representing a moving item>
Figure 93585DEST_PATH_IMAGE030
In the fifth or fifth place>
Figure 67095DEST_PATH_IMAGE034
Mean characteristic value in each classification, < > >>
Figure 674794DEST_PATH_IMAGE037
Indicates that the item is moved>
Figure 992643DEST_PATH_IMAGE030
In the fifth or fifth place>
Figure 101413DEST_PATH_IMAGE034
The ^ th ^ of consecutive frames in a classification>
Figure 289949DEST_PATH_IMAGE005
Corresponding connected components count for a frameCalculated characteristic quantization value +>
Figure 209495DEST_PATH_IMAGE038
Indicates that the item is moved>
Figure 749060DEST_PATH_IMAGE030
At the fifth place>
Figure 395942DEST_PATH_IMAGE034
Total number of consecutive total frames present in each category.
By performing the average feature value calculation on all the different categories of moving objects in the above manner, the average feature value of each object in each category can be obtained.
Then, the similarity calculation of the average characteristic value is utilized for the articles in each category to determine whether the moving articles in all the categories are the same moving article or not, and the second step is to calculate the similarity of the average characteristic values
Figure 970143DEST_PATH_IMAGE040
Moving object in each category pick>
Figure 919645DEST_PATH_IMAGE041
And the fifth->
Figure 320408DEST_PATH_IMAGE034
Moving object in each category pick>
Figure 380768DEST_PATH_IMAGE030
For example, the similarity is calculated as follows:
Figure 934109DEST_PATH_IMAGE042
in the formula (I), the compound is shown in the specification,
Figure 320091DEST_PATH_IMAGE043
represents a fifth or fifth party>
Figure 834249DEST_PATH_IMAGE044
Moving object in each category pick>
Figure 573666DEST_PATH_IMAGE041
Is greater than or equal to>
Figure 856880DEST_PATH_IMAGE036
Represents a fifth or fifth party>
Figure 538397DEST_PATH_IMAGE034
Moving object in a category>
Figure 274272DEST_PATH_IMAGE030
Average eigenvalues of (d).
Figure 327633DEST_PATH_IMAGE045
The closer to 1, the greater the ^ th ^ is>
Figure 465354DEST_PATH_IMAGE044
Moving object in each category pick>
Figure 52193DEST_PATH_IMAGE041
And a first +>
Figure 540943DEST_PATH_IMAGE034
Moving object in each category pick>
Figure 746796DEST_PATH_IMAGE030
The greater the likelihood of being the same item in motion, for>
Figure 879969DEST_PATH_IMAGE046
The two articles are considered as the same sports article.
All the above-mentioned modes are used
Figure 778654DEST_PATH_IMAGE047
Similarity calculation is carried out on all moving objects in each category, and then the similarity is utilized to carry out similarity calculation on moving objects in all categoriesAnd identifying, namely identifying the same moving object in all the moving objects in all the categories and marking as a penetrating moving object.
S3, classifying the motion tracks of the penetrating motion objects in each type of dynamic frames to obtain random paths penetrating the motion objects, wherein the dynamic frames corresponding to the random paths are random frames; partitioning the monitoring video data by using random frames to obtain monitoring video data of different intervals; selecting a moving object with a random path and the largest average characteristic value from the monitoring video data in an interval, and recording the moving object as a key generation moving object; generating a key matrix by using all pixel points in a mark connected domain in an image corresponding to a first frame in all random frames corresponding to a moving object in monitoring video data by using a key; and encrypting the monitoring video data of each interval by using the key matrix corresponding to the monitoring video data of each interval.
Then classifying the motion tracks of the penetrating moving objects in different categories to find out different motion tracks, wherein most operations and courses in the production process are repeated mechanical motions, so that the theoretical motion tracks of the same object are the same, and each penetrating moving object changes in the motion tracks due to the influence of various external factors in the motion process to generate random behaviors, so that the random behaviors are detected by utilizing the motion tracks to detect the random behaviors
Figure 613755DEST_PATH_IMAGE030
For example, the random behavior is detected by first extracting the motion trace quantization value appearing in each category.
And then, analyzing the motion track quantization values which appear in all the categories of the penetrating motion object by utilizing LOF algorithm model outliers, and selecting each outlier, wherein the corresponding path is a random path.
All the penetrating moving objects are subjected to random behavior path acquisition in the mode, and the frames of the random paths are random dynamic frames which can be acquired
Figure 623300DEST_PATH_IMAGE048
A random frame. (a plurality of random paths penetrating the moving object are in the same frame and are calculated once), then the whole monitoring video is segmented by using the random frames, and the classification process is as follows:
first, the position of the random frame (which is also a continuous frame because the path is obtained by continuous frame analysis) in the whole epoxy resin surveillance video is retrieved; and then segmenting the whole epoxy resin monitoring video by using random frames. So far, the data partitioning ends.
And calculating an encryption key of each interval according to the random dynamic frame of each interval and carrying out interval encryption by using the encryption key. In the above, the partition of the whole monitoring video is obtained by using the random frame, the encryption key is calculated for each interval and the encryption key is used for the interval encryption, so as to
Figure 735612DEST_PATH_IMAGE049
Taking a partition of a surveillance video as an example, its key @>
Figure 913521DEST_PATH_IMAGE050
The calculation of (c) is as follows:
firstly, searching the average value of the characteristic quantization values of the moving objects with random paths in the random frame in the interval, and selecting the moving object corresponding to the maximum average characteristic quantization value
Figure 111285DEST_PATH_IMAGE051
And recording as a key generation moving object.
Then using the key to generate the moving object
Figure 49154DEST_PATH_IMAGE049
All of the ≦ in the connected component (obtained by the mid-frame difference method described above) that appears in the first frame of the random frames>
Figure 15973DEST_PATH_IMAGE010
Individual pixel point becomes key matrix->
Figure 131827DEST_PATH_IMAGE050
Each element of the key matrix being this->
Figure 816886DEST_PATH_IMAGE010
The gray value of each pixel point and the size of the key matrix are->
Figure 168233DEST_PATH_IMAGE052
Wherein->
Figure 114193DEST_PATH_IMAGE053
And &>
Figure 791162DEST_PATH_IMAGE054
Are respectively based on>
Figure 71839DEST_PATH_IMAGE010
The largest two prime factors of (a).
Then use
Figure 289194DEST_PATH_IMAGE050
To a fifth->
Figure 496184DEST_PATH_IMAGE049
Encrypting each frame of video of each surveillance video partition to obtain encrypted ciphertext
Figure 953842DEST_PATH_IMAGE055
The encryption mode is to utilize>
Figure 144651DEST_PATH_IMAGE050
And a fifth +>
Figure 680544DEST_PATH_IMAGE049
Each frame of each surveillance video partition is subjected to convolution operation.
By utilizing the method, each frame of image in the epoxy resin monitoring video is encrypted, and the encrypted data of the epoxy resin monitoring video after being encrypted integrally can be obtained. At this point, the encryption of the epoxy monitoring video is completed. And acquiring encrypted data of the epoxy resin monitoring video data, transmitting the transmission value to a monitoring video storage terminal, and storing.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. A management method for monitoring data of epoxy resin production is characterized by comprising the following steps:
collecting monitoring video data in the production process of epoxy resin; obtaining the information content of each video frame in the monitoring video data; obtaining the information quantity difference value of every two adjacent video frames, and judging the dynamic frame in all the video frames based on the information quantity difference value, wherein the dynamic frame refers to the video frame with object motion in the monitoring video data;
dividing dynamic frames which are continuous frames into one class, and obtaining a mark connected domain in an image corresponding to the dynamic frame in each class of dynamic frames by using a frame difference method; calculating to obtain object characteristics of the marked connected domain based on a two-bit data set consisting of gray values of pixel points in the marked connected domain and pixel points in the neighborhood in an image corresponding to each dynamic frame; obtaining the motion trail of the same moving object in each type of dynamic frame based on the object characteristics of the marked connected domain; obtaining the average characteristic value of each moving object in each type of dynamic frame, calculating the similarity of two moving objects in the two types of dynamic frames, obtaining the moving objects belonging to the same moving object in the various types of dynamic frames, and recording as penetrating moving objects;
classifying the motion tracks of the through moving objects in each type of dynamic frames to obtain random paths through the moving objects, wherein the dynamic frames corresponding to the random paths are random frames; partitioning the monitoring video data by using random frames to obtain monitoring video data of different intervals; selecting a moving object with a random path and the largest average characteristic value from the monitoring video data of one interval, and recording the moving object as a key generation moving object; generating a key matrix by using all pixel points in a mark connected domain in an image corresponding to a first frame in all random frames corresponding to a moving object in monitoring video data by using a key; encrypting the monitoring video data of each interval by using a key matrix corresponding to the monitoring video data of each interval;
the information content of each video frame is as follows:
Figure DEST_PATH_IMAGE002
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE004
an information amount indicating an nth frame video frame; />
Figure DEST_PATH_IMAGE006
Represents the gray value of a pixel point, is combined with the gray value of the pixel point>
Figure DEST_PATH_IMAGE008
Represents a gray value of->
Figure 265484DEST_PATH_IMAGE006
Is at the fifth->
Figure DEST_PATH_IMAGE010
Image corresponding to frame video frameThe probability of occurrence;
the object characteristics of the marked connected domain are as follows:
Figure DEST_PATH_IMAGE012
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE014
representing the object characteristics of the m-th mark connected domain in the n-th frame dynamic frame; />
Figure DEST_PATH_IMAGE016
Indicates the th or fifth in the class>
Figure DEST_PATH_IMAGE018
The fifth of the flag connected field>
Figure 550666DEST_PATH_IMAGE016
A plurality of pixel points>
Figure DEST_PATH_IMAGE020
The number of all pixel points in the marked connected domain; />
Figure DEST_PATH_IMAGE022
Indicates the th or fifth in the class>
Figure 81136DEST_PATH_IMAGE018
The fifth of the flag connected field>
Figure 538662DEST_PATH_IMAGE016
Peripheral 8 neighborhood pixels of each pixel, and->
Figure DEST_PATH_IMAGE024
Indicates the fifth->
Figure 225471DEST_PATH_IMAGE016
The gray value and the second of each pixel point
Figure 126562DEST_PATH_IMAGE016
The second-bit data group of the average value of the gray values of 8 pixel points in the neighborhood around each pixel point is at the fifth or fifth judgment>
Figure 455912DEST_PATH_IMAGE018
The probability of occurrence in all the two-bit data sets in the connected component field is marked.
2. The epoxy resin production monitoring data management method according to claim 1, wherein the obtaining the information amount difference value of every two adjacent video frames and the determining the dynamic frames in all the video frames based on the information amount difference value comprises: and if the difference value of the information quantity of every two adjacent video frames is not equal to 0, the video frame of the next frame in every two adjacent video frames is a dynamic frame.
3. The epoxy resin production monitoring data management method according to claim 1, wherein the obtaining of the average feature value of each moving object in each type of dynamic frame, the calculating of the similarity of two moving objects in two types of dynamic frames, and the obtaining of moving objects belonging to the same moving object in various types of dynamic frames comprises:
the average characteristic value is the average value of the object characteristics of all corresponding mark connected domains of a moving object in a class of dynamic frames; the ratio of the average characteristic values of the two moving objects in different types of dynamic frames is the similarity of the two moving objects in the two types of dynamic frames, and if the similarity of the two moving objects in the two types of dynamic frames is larger than a preset threshold value, the two moving objects in the two types of dynamic frames are the same moving object.
4. The epoxy resin production monitoring data management method according to claim 1, wherein the classifying the motion trajectories of the motion penetrating objects in each type of dynamic frame to obtain a random path penetrating the motion penetrating objects, and the dynamic frame corresponding to the random path is a random frame including: and analyzing the motion track penetrating through the moving object in each type of dynamic frame by using an LOF algorithm to obtain outliers, wherein the path corresponding to the outliers is a random path.
5. The epoxy production monitoring data management method of claim 1, wherein the generating a key matrix comprises: each element in the key matrix is used as a key to generate a gray value of a pixel point in a mark connected domain of a moving object in an image corresponding to a first frame in all random frames corresponding to the monitored video data.
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