CN110704676A - Dynamic abnormal information video processing system and method - Google Patents
Dynamic abnormal information video processing system and method Download PDFInfo
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Abstract
The invention discloses a dynamic abnormal information video processing system and a method, which are mainly used for linking, storing and analyzing the change and the abnormal movement of production information and video monitoring in real time through the fusion of the production information and the video monitoring. The invention realizes the integration of production early warning and video monitoring, performs unified information display, visual information viewing and active video early warning, and achieves the purpose of active video monitoring in the production process. Meanwhile, the method abandons the storage of massive normal useless industrial production monitoring videos, accurately records the production process with real problems, reduces the cost of using the monitoring videos by managers for problem verification, and effectively saves the video storage space.
Description
Technical Field
The invention relates to the technical field of industrial production monitoring, in particular to a dynamic abnormal information video processing system and a method.
Background
Industrial processes often have several video monitoring devices placed on the production line in order to monitor anomalies in the production process in real time. However, due to the numerous monitoring devices, even if one or two production links are abnormal, managers are difficult to find the abnormal production from the numerous monitoring devices, and therefore the purpose of monitoring the abnormal production in real time cannot be achieved. At present, the production process monitoring is often passive, and the monitoring is used as a checking tool for checking problems after the problems occur, so that the functions of problem prediction and video early warning cannot be realized. Thus, the active monitoring function of the production process cannot be realized. The mass production monitoring video screen storage occupies a large amount of storage resources, is difficult to locate the production recording video with problems, and is inconvenient for problem checking.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the above and/or other problems occurring in the prior industrial production processes.
Therefore, one of the objectives of the present invention is to provide a dynamic abnormal information video processing system, which solves the problem of the splitting of the production system and the video monitoring in the industrial production process, realizes the analysis and complementation of the image data and the industrial production data, and helps the factory to quickly locate the abnormality from the image and the production data.
In order to solve the technical problems, the invention provides the following technical scheme: a dynamic abnormal information video processing system comprises a production data collection module, a rule configuration data modeling module, an early warning information uploading module and a video processing and alarming module;
the production data collection module collects related industrial production data through a DCS (distributed control system) and an online diagnosis system, is connected with the rule configuration data modeling module and can transmit an analysis data source to the rule configuration data modeling module;
the rule configuration data modeling module is used for setting video geographic position information and monitoring object preset bits, establishing an abnormal judgment rule, evaluating an early warning algorithm and storing the contents; the rule configuration data modeling module is simultaneously connected with the production data collection module and the early warning information uploading module, receives the production data from the production data collection module, and completes configuration, establishment and storage of early warning rules and models for the early warning information uploading module to use;
the early warning information uploading module is used for dynamically calculating a model and uploading early warning information, is simultaneously connected with the production data collecting module, the rule configuration data modeling module and the video processing and alarming module, receives industrial production data from the production data collecting module, calls rules and models stored by the rule configuration data modeling module, dynamically calculates and uploads equipment early warning information to the video processing and alarming module;
the video processing and alarming module is used for receiving the early warning information generated by the early warning information uploading module, and performing abnormal video highlighting, early warning information trend query, video inspection and intelligent storage of abnormal videos.
As a preferable solution of the dynamic abnormal information video processing system of the present invention, wherein: the dynamic abnormal information video processing system also comprises a storage module; the storage module uses a real-time database, a relational database and a local file; the real-time database is used for storing measuring point data; the relational database is used for storing information such as production business data, an abnormal analysis result, a preset threshold value, a video control instruction and the like; the local file stores an early warning video.
As a preferable solution of the dynamic abnormal information video processing system of the present invention, wherein: the production data acquisition module also comprises a production process early warning function; the production process early warning function establishes a daily production model, a measuring point threshold warning condition and a production process KPI threshold based on the normal operation state of the conventional industrial production, and inputs real-time production data during operation to obtain a difference residual between a predicted value set value and an actually measured value of the model, and the early warning module issues early warning information of the production process according to the difference through the size of the residual.
As a preferable solution of the dynamic abnormal information video processing system of the present invention, wherein: the rule configuration data modeling module also comprises a function of establishing an early warning model; the function of establishing the early warning model is as follows: establishing measuring points at each position of equipment and a process flow, and setting two-stage thresholds of the measuring points, wherein the two-stage thresholds are respectively a normal working threshold and a general deviation threshold; and acquiring data in the range of the historical normal working threshold of each measuring point from the production information collecting module, and carrying out combined clustering on the plurality of measuring points of one device to obtain an ultrahigh-dimension clustering model, wherein the distance between the edge point of a clustering cluster and the center of the cluster in the model is the device health threshold.
The invention also aims to provide a dynamic abnormal information video processing method, which solves the problem of the splitting of a production system and video monitoring in the industrial production process, realizes the analysis and complementation of image data and industrial production data, and helps a factory to quickly locate the abnormality from the image and the production data.
In order to solve the technical problems, the invention provides the following technical scheme: a dynamic abnormal information video processing method includes the following steps S1-S4:
s1: collecting related industrial production data;
s2: establishing video geographic position information and monitoring object preset positions corresponding to all positions of equipment and a process flow, establishing an early warning rule and an evaluation algorithm, and performing structured storage;
s3: calculating and analyzing the relevant industrial production data acquired in the step S1 in real time according to the algorithm and the rule in the step S2, and uploading early warning information to a video processing system;
s4: and the video processing system performs video highlighting and draws abnormal trends according to the early warning information provided in the step S3, and intelligently stores corresponding videos for post-examination.
The invention has the beneficial effects that: the invention realizes the integration of production early warning and video monitoring, performs unified information display, visual information viewing and active video early warning, and achieves the purpose of active video monitoring in the production process. Meanwhile, the method abandons the storage of massive normal useless industrial production monitoring videos, accurately records the production process with real problems, reduces the cost of using the monitoring videos by managers for problem verification, and effectively saves the video storage space.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise. Wherein:
FIG. 1 is a schematic flow diagram of the system of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Furthermore, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
Referring to fig. 1, a first embodiment of the present invention provides a dynamic exception information video processing system. The dynamic abnormal information video processing system comprises a production data collecting module, a rule configuration data modeling module, an early warning information uploading module and a video processing and alarming module.
The production data collection module collects related industrial production data through a DCS (distributed control system) and an online diagnosis system, is connected with the rule configuration data modeling module and can transmit an analysis data source to the rule configuration data modeling module;
the rule configuration data modeling module is used for setting video geographic position information and monitoring object preset bits, establishing an abnormal judgment rule, evaluating an early warning algorithm and storing the contents; the rule configuration data modeling module is simultaneously connected with the production data collection module and the early warning information uploading module, receives the production data from the production data collection module, and completes configuration, establishment and storage of early warning rules and models for the early warning information uploading module to use;
the early warning information uploading module is used for dynamically calculating a model and uploading early warning information, is simultaneously connected with the production data collecting module, the rule configuration data modeling module and the video processing and alarming module, receives industrial production data from the production data collecting module, calls rules and models stored by the rule configuration data modeling module, dynamically calculates and uploads equipment early warning information to the video processing and alarming module;
the video processing and alarming module is used for receiving the early warning information generated by the early warning information uploading module, and performing abnormal video highlighting, early warning information trend query, video inspection and intelligent storage of abnormal videos.
Further, the production data acquisition module further comprises a production process early warning function. Specifically, the production process early warning function establishes a daily production model, a measuring point threshold warning condition and a production process KPI threshold based on the normal operation state of the conventional industrial production, and inputs real-time production data in actual operation to obtain a difference residual between a predicted value set value and an actually measured value of the model, and the early warning module issues early warning information of the production process according to the difference through the size of the residual.
Further, the rule configuration data modeling module also comprises a function of establishing an early warning model; the function of establishing the early warning model is as follows: establishing measuring points at each position of equipment and a process flow, and setting two-stage thresholds of the measuring points, wherein the two-stage thresholds are respectively a normal working threshold and a general deviation threshold; and acquiring data in the range of the historical normal working threshold of each measuring point from the production information collecting module, and carrying out combined clustering on the plurality of measuring points of one device to obtain an ultrahigh-dimension clustering model, wherein the distance between the edge point of a clustering cluster and the center of the cluster in the model is the device health threshold. In practical application, a group of current values of the measuring points are input, the distance between the current values and the nearest clustering center is calculated, namely the current equipment health degree, and if the current health degree is lower than an equipment health degree threshold value, 3 measuring points with the largest residual error with the model measuring points in the current measuring point group are found, namely the 3 measuring points influencing the equipment health degree. The abnormity analysis module pushes the current equipment health degree and the abnormal measuring point information to the early warning information uploading module.
The system further comprises an information acquisition module, wherein the information acquisition module mainly depends on detectors arranged at each production link (measuring point) to obtain data of each measuring point.
The production process early warning system further comprises a production process early warning module, wherein the production process early warning module is used for establishing a daily production model based on the normal operation state of the previous industrial production, inputting real-time production data in actual operation to obtain the difference between a predicted value and an actually measured value of the model, and issuing early warning information of the production process through the difference.
The system further comprises a data processing and analyzing module, wherein the data analyzing module is mainly used for analyzing whether the data of each measuring point is normal or not. The module compares whether the measured value of the process production process, the production process early warning, the equipment energy management, the equipment on-line diagnosis and the equipment operation and maintenance inspection exceeds a preset threshold value, and if the measured value exceeds the preset threshold value, corresponding error information and position information are uploaded.
The system further comprises a storage module, wherein the storage module is used for storing information such as measuring point data, an abnormal analysis result, a preset threshold value, a video control instruction and the like.
The storage module uses a real-time database, a relational database and a local file; the real-time database is used for storing measuring point data; the relational database is used for storing information such as production business data, an abnormal analysis result, a preset threshold value, a video control instruction and the like; the local file stores an early warning video.
The invention also provides a dynamic abnormal information video processing method, which links, stores and analyzes the change and abnormal movement of the production information and the video monitoring in real time mainly through the fusion of the production information and the video monitoring. By the method, the problem of splitting of a production system and video monitoring in the industrial production process is solved, the analysis and complementation of image data and industrial production data are realized, and a factory is helped to quickly locate the abnormity from the image and the production data. In addition, the method effectively saves the storage space of the monitoring video in industrial production. The method can acquire production data of different dimensions from an industrial field system, and comprises the following steps: process production process data; production process early warning data; energy system data; device online diagnostic data; equipment operation and maintenance data, and the like.
The dynamic abnormal information video processing method comprises the following steps: s1: collecting related industrial production data; s2: setting the geographic position and preset position of a monitoring camera, establishing an early warning rule and an evaluation algorithm and storing the early warning rule and the evaluation algorithm into a system; s3: and calculating and analyzing the relevant industrial production data collected in the step S1 in real time according to the algorithm and the rule in the step S2. Uploading early warning information to a video processing system; s4: and the video processing system performs video highlighting and draws abnormal trends according to the early warning information provided in the step S3, and intelligently stores corresponding videos for post-examination.
Specifically, the method for processing the dynamic abnormal information video specifically comprises the following steps:
s1: and collecting related industrial production data including process production process data, production process early warning data, energy system data, equipment online diagnosis data and equipment operation and maintenance data. And acquiring process production process information, production process early warning information and process and equipment energy management information through a DCS (distributed control system). And acquiring early warning frequency statistics and daily average energy consumption statistics through a production management system. And acquiring equipment diagnosis information (such as equipment temperature and vibration change) through an online diagnosis system. And acquiring equipment operation and maintenance management information (such as equipment daily inspection record and equipment maintenance and repair record) through an equipment management system.
S2: rule configuration and data modeling. Video geographic position information and monitoring object preset positions corresponding to all positions of equipment and a process flow are established, rules corresponding to abnormal information of monitoring points are set, abnormal judgment rules are established, an abnormal early warning algorithm is evaluated and pre-warned, an abnormal early warning model is obtained, and the contents are stored in a structured mode.
S3: and (4) inputting the production process information collected in the step (S1) in real time through the abnormity early warning rule and the abnormity early warning model established in the step (S2) to perform dynamic calculation. And uploading the early warning information and the position information of the early warning point to a video processing system.
S4: the video processing system is used for searching and integrating abnormal equipment, monitoring equipment near a process flow and early warning information of the abnormal equipment, calling monitoring videos of related positions, displaying abnormal trends on a picture, and alarming and reminding in modes of video highlighting, alarm lamps, warning sound, video flashing and the like. And simultaneously storing the video files in a period of time before and after the early warning for later inspection.
The invention realizes the integration of production early warning and video monitoring, performs unified information display, visual information viewing and active video early warning, and achieves the purpose of active video monitoring in the production process.
Meanwhile, the method abandons the storage of massive normal useless industrial production monitoring videos, accurately records the production process with real problems, reduces the cost of using the monitoring videos by managers for problem verification, and effectively saves the video storage space.
It is important to note that the construction and arrangement of the present application as shown in the various exemplary embodiments is illustrative only. Although only a few embodiments have been described in detail in this disclosure, those skilled in the art who review this disclosure will readily appreciate that many modifications are possible (e.g., variations in sizes, dimensions, structures, shapes and proportions of the various elements, values of parameters (e.g., temperatures, pressures, etc.), mounting arrangements, use of materials, colors, orientations, etc.) without materially departing from the novel teachings and advantages of the subject matter recited in this application. For example, elements shown as integrally formed may be constructed of multiple parts or elements, the position of elements may be reversed or otherwise varied, and the nature or number of discrete elements or positions may be altered or varied. Accordingly, all such modifications are intended to be included within the scope of this invention. The order or sequence of any process or method steps may be varied or re-sequenced according to alternative embodiments. In the claims, any means-plus-function clause is intended to cover the structures described herein as performing the recited function and not only structural equivalents but also equivalent structures. Other substitutions, modifications, changes and omissions may be made in the design, operating conditions and arrangement of the exemplary embodiments without departing from the scope of the present inventions. Therefore, the present invention is not limited to a particular embodiment, but extends to various modifications that nevertheless fall within the scope of the appended claims.
Moreover, in an effort to provide a concise description of the exemplary embodiments, all features of an actual implementation may not be described (i.e., those unrelated to the presently contemplated best mode of carrying out the invention, or those unrelated to enabling the invention).
It should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions may be made. Such a development effort might be complex and time consuming, but would nevertheless be a routine undertaking of design, fabrication, and manufacture for those of ordinary skill having the benefit of this disclosure, without undue experimentation.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
Claims (5)
1. A dynamic exception information video processing system, comprising: the system comprises a production data collection module, a rule configuration data modeling module, an early warning information uploading module and a video processing and alarming module;
the production data collection module collects related industrial production data through a DCS (distributed control system) and an online diagnosis system, is connected with the rule configuration data modeling module and can transmit an analysis data source to the rule configuration data modeling module;
the rule configuration data modeling module is used for setting video geographic position information and monitoring object preset bits, establishing an abnormal judgment rule, evaluating an early warning algorithm and storing the contents; the rule configuration data modeling module is simultaneously connected with the production data collection module and the early warning information uploading module, receives the production data from the production data collection module, and completes configuration, establishment and storage of early warning rules and models for the early warning information uploading module to use;
the early warning information uploading module is used for dynamically calculating a model and uploading early warning information, is simultaneously connected with the production data collecting module, the rule configuration data modeling module and the video processing and alarming module, receives industrial production data from the production data collecting module, calls rules and models stored by the rule configuration data modeling module, dynamically calculates and uploads equipment early warning information to the video processing and alarming module;
the video processing and alarming module is used for receiving the early warning information generated by the early warning information uploading module, and performing abnormal video highlighting, early warning information trend query, video inspection and intelligent storage of abnormal videos.
2. The dynamic exception information video processing system of claim 1, wherein: the device also comprises a storage module;
the storage module uses a real-time database, a relational database and a local file; the real-time database is used for storing measuring point data; the relational database is used for storing information such as production business data, an abnormal analysis result, a preset threshold value, a video control instruction and the like; the local file stores an early warning video.
3. The dynamic exception information video processing system of claim 2, wherein: the production data acquisition module also comprises a production process early warning function;
the production process early warning function establishes a daily production model, a measuring point threshold warning condition and a production process KPI threshold based on the normal operation state of the conventional industrial production, and inputs real-time production data during operation to obtain a difference residual between a predicted value set value and an actually measured value of the model, and the early warning module issues early warning information of the production process according to the difference through the size of the residual.
4. The dynamic exception information video processing system of claim 3, wherein: the rule configuration data modeling module also comprises a function of establishing an early warning model; the function of establishing the early warning model is as follows:
establishing measuring points at each position of equipment and a process flow, and setting two-stage thresholds of the measuring points, wherein the two-stage thresholds are respectively a normal working threshold and a general deviation threshold; and acquiring data in the range of the historical normal working threshold of each measuring point from the production information collecting module, and carrying out combined clustering on the plurality of measuring points of one device to obtain an ultrahigh-dimension clustering model, wherein the distance between the edge point of a clustering cluster and the center of the cluster in the model is the device health threshold.
5. A dynamic abnormal information video processing method is characterized in that: comprises the steps of (a) preparing a mixture of a plurality of raw materials,
s1: collecting related industrial production data;
s2: establishing video geographic position information and monitoring object preset positions corresponding to all positions of equipment and a process flow, establishing an early warning rule and an evaluation algorithm, and performing structured storage;
s3: calculating and analyzing the relevant industrial production data acquired in the step S1 in real time according to the algorithm and the rule in the step S2, and uploading early warning information to a video processing system;
s4: and the video processing system performs video highlighting and draws abnormal trends according to the early warning information provided in the step S3, and intelligently stores corresponding videos for post-examination.
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