CN115050160A - Intelligent safety supervision device for industrial unmanned production field - Google Patents
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Abstract
The invention discloses an intelligent safety supervision device for an industrial unmanned production field. The method comprises the following steps: an image pickup unit and an edge calculation unit; the computing module of the edge computing unit executes: p1, reading a risk correspondence table file, a configuration file and a risk grade table file of the storage module, and executing a target detection program; p2, when the target detection program finds the risk source, determining the type of the risk source S; p3, executing a risk assessment module code to obtain a risk level P of a risk source; p4, performing risk handling according to the category of risk sources; p5, circularly executing P2 to P4 until the target detection module finds that the risk source disappears; the P6 and the event management module write the video clip of the risk event, the occurrence time, the end time, the risk source type and the risk level into the storage module; according to the invention, the risk source is locked, the risk factors are evaluated and the corresponding is carried out through edge calculation, so that integrated risk identification and management and control are carried out more quickly and accurately, and the risk is eliminated at the first time.
Description
Technical Field
The invention relates to an intelligent safety supervision device for an industrial unmanned production field. This device is calculated through the edge and is held in order locking the risk source, appraises the risk factor and deal with, compares with the tradition and carries out video identification and the artifical mode of developing emergency management of carrying out the distal end, can more swiftly, carry out integration risk identification and management and control accurately, gets rid of the risk in the very first time.
Background
Safety is a constant theme of industrial processes. With the development of the production scale of enterprises, it becomes more and more important to ensure the safety and high-efficiency production of the enterprises through digitization, informatization and fine management. However, in the current industry, a plurality of enterprises lack of real-time and efficient intelligent installation and supervision means, and the existing supervision technology generally has the following problems:
1) the traditional video monitoring system cannot be used efficiently, and abnormal information can be obtained only through online synchronous observation or playback inquiry of personnel and manual whole-course analysis and judgment. Not only needs to invest a great deal of time and energy, but also cannot comprehensively master the field safety condition in real time.
2) It is difficult to monitor the working positions, operation specifications, and action trajectories of unmanned production areas, vehicles near core production facilities, and operation devices in real time; it is difficult to timely identify and intervene to sudden personnel mistake entry or foreign body intrusion.
3) It is difficult to judge and deal with emergency situations such as mistaken entry into a dangerous area, non-compliant operation, sudden disaster and the like in time.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide an intelligent safety supervision device for an industrial unmanned production field.
In order to achieve the purpose, the invention relates to an intelligent safety supervision device for an industrial unmanned production field, which comprises
The camera shooting unit consists of an optical camera for collecting video signals and a rotatable device, and the rotatable device can drive the camera to rotate in all directions from top to bottom, left to right under the driving of signals;
the edge calculation unit consists of a storage module and a calculation module; wherein
The storage module receives a video file of the camera unit in real time;
a calculation module: the system comprises a main program module, a target detection module, a risk identification module, a knowledge retrieval module, a risk handling module and an event management module; wherein the content of the first and second substances,
the target detection module completes capture and identification of a risk source possibly existing in a picture through real-time analysis of the video;
the risk identification module judges the risk level of the risk source;
the knowledge retrieval module retrieves the risk handling table file according to the type of the risk source and the current state to obtain a risk handling suggestion;
the risk handling module converts the risk handling suggestion into alarm information and transmits the alarm information to an alarm unit; the event management module records the risk event, including event occurrence time, ending time, risk elements and the like, and adds the video data into the storage module;
the computing module of the edge computing unit executes:
p1, reading a risk correspondence table file, a configuration file and a risk grade table file of the storage module, and executing a target detection program;
p2, when the target detection program finds the risk source, determining the type of the risk source S;
p3, executing a risk assessment module code to obtain a risk level P of a risk source;
p4, risk handling according to the broad category of risk sources:
p4-1, if the source is the A-type risk source, executing a knowledge retrieval module, and obtaining the name of the audio file Z according to the S and the P; the risk processing module reads the audio file Z from the fourth storage module and sends the audio file Z to the alarm unit; the alarm unit plays an audio file Z;
p4-2, if the risk source is a B-type risk source, the risk processing module drives the audible and visual alarm of the alarm unit to generate a buzzing alarm sound according to the risk level and the sound intensity appointed by the code;
p5, circularly executing P2 to P4 until the target detection module finds that the risk source disappears;
the P6 and the event management module write the video clip of the risk event, the occurrence time, the end time, the risk source type and the risk level into the storage module;
further, a target tracking algorithm is added into the target detection module; and when the dangerous source is detected, automatically starting a target tracking algorithm, driving a rotatable device of the camera unit, and keeping the camera focused on the dangerous source to track the target.
Furthermore, the storage module comprises a first storage module, a second storage module, a third storage module, a fourth storage module and a fifth storage module; wherein the content of the first and second substances,
the first storage module group: storing a calculation program of the system;
the second storage module: storing a risk coping list file;
a third storage module: storing a target detection algorithm configuration file and a risk level table file;
the fourth storage module: storing a pre-recorded voice warning audio file;
a fifth storage module: storing video data from the camera unit;
wherein, the fifth storage module includes two partitions: 1) partitioning original data, and rolling and storing video data acquired by a camera according to a time sequence; 2) the evidence data partition is used for storing the video data in a first-in first-out mode when the target detects a dangerous source; and the event management module writes the video clip of the risk event, the occurrence time, the end time, the risk source type and the risk level into the evidence data partition of the fifth storage module.
The method is suitable for intelligent management of the industrial unmanned production site with risks, locks the risk source, evaluates risk factors and deals with the risk factors through edge calculation, and compared with the traditional mode of performing video identification and manual emergency management at a far end, the method can perform integrated risk identification and management control more quickly and accurately and eliminate the risks at the first time.
Drawings
FIG. 1 is a schematic diagram of an intelligent safety supervision device for an industrial unmanned production site according to the present invention;
fig. 2 is an explanatory diagram of the cooperation relationship of the units in the operation process of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings.
In the description of the present invention, it is to be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Fig. 1 is a schematic diagram of an intelligent safety supervision apparatus for an industrial unmanned production site according to the present invention, which structurally includes a main body component composed of a camera unit 1, an edge computing unit 2, a power supply unit 3, and an alarm unit 4, and an attachment component 5 having fixing, supporting, protecting, etc. effects required for mounting the main body component to a certain place.
The camera unit 1 is constituted by an optical camera and a rotatable device that collect video signals. The rotatable device can drive the camera to rotate in all directions up and down, left and right under the driving of signals.
The edge calculation unit 2 is composed of a first storage module 2-1, a second storage module 2-2, a third storage module 2-3, a fourth storage module 2-4, a fifth storage module 2-5 and a calculation module 2-6. The method specifically comprises the following steps:
the first storage module 2-1: storing the calculation program of the system.
The second storage module 2-2: and storing a risk handling list file.
The third storage module 2-3: and storing a target detection algorithm configuration file and a risk level table file.
Fourth memory module 2-4: and storing the pre-recorded voice warning audio file. The audio file is in wav format, and other audio formats such as wma or mp3 can be adopted.
The second, third and fourth memory modules share a high-speed TF card as a medium.
Fifth storage module 2-5: and storing the video data from the camera. Comprises two partitions: 1) and partitioning the original data, and rolling and storing the video data acquired by the camera according to a time sequence. 2) And the evidence data partition is used for storing the video data in a first-in first-out mode when the target detects a dangerous source. The fifth storage module adopts a high-capacity TF card as a medium.
A calculation module 2-6: the system comprises a main program module, a target detection module, a risk identification module, a knowledge retrieval module, a risk handling module and an event management module. The target detection module captures and identifies a risk source possibly existing in a picture through real-time analysis of a video; the risk identification module judges the risk level of the risk source; the knowledge retrieval module obtains a suggestion of risk disposal by inquiring a risk correspondence table; the risk handling module converts the risk handling suggestion into alarm information and transmits the alarm information to an alarm unit; and the event management module records the risk event, including event occurrence time, risk elements, treatment suggestions and end time, and adds the video data into the evidence data partition of the fifth storage module.
Optionally, a target tracking algorithm is further added to the target detection module. And when the target detects a dangerous source, simultaneously starting a target tracking algorithm, driving a rotatable device of the camera unit, and focusing the camera on the dangerous source for target tracking.
The alarm unit consists of an audio driving module, a loudspeaker and an audible and visual alarm.
The power supply unit supplies power to other units.
The following description will be given taking as an example the case where the apparatus is used for on-site management of a belt transfer work area in industrial production of steel, coal and the like. The attachment member is fixed in a reasonable position convenient for monitoring the tape transportation, such as in a corridor of an industrial tape machine, a tape splicing switching area, etc., according to the field situation.
Enumerating a risk source list S ═ S that needs prevention and control in a monitoring scene 1 S 2 …S n S risk source i It may be a person (e.g., a person entering an exclusion area), an object (e.g., a foreign object being caught in), or some event (e.g., a tape running off, a sudden tear). And respectively acquiring enough images of various listed risk sources, training by adopting a machine vision algorithm to obtain target detection model parameters, and storing the target detection model parameters into a second storage module in a configuration file mode.
1) Based on expert knowledge, sequentially aiming at risk sources S appearing in monitoring scene i The event is evaluated by taking a scene working condition C, a risk source position D and a risk source residence time T as conditions under the joint condition i Risk class f (S i | C ═ C) j ,D＝D k ,T＝T b ) And is abbreviated as P i|j,k,b In which C is j 、D k 、T b Sequentially carrying out quantitative acquirable values according to a preset rule on scene working condition C, risk source position D and risk source residence time T, wherein f is a risk evaluation rule, and generating a risk level sub-table P i . P of all i i The merger constitutes a risk level table P.
To P i Method for setting up risk of each element in the product i|j,k,b The various risk measures form a set O, and for each risk measure added to the set O, the risk measures are separately processedAnd generating an audio file with a unique name by means of speech synthesis or manual recording. It is noted that for different combinations (i, j, k, b) the risk handling methods employed may be the same, i.e. mapped to the same audio file.
Will risk the source S i Evaluation at risk level P i|j,k,b Risk disposition method taken at the time O i|j,k,b The corresponding audio file name is recorded as Z i|j,k,b 。S i 、P i|j,k,b And Z i|j,k,b And (4) constructing triples, wherein all the { S, P, Z } triples constitute a risk corresponding table and are stored in the second storage module.
The cooperation relationship of each unit in the operation process of the device is shown in fig. 2, and is specifically explained as follows:
s1, starting the power supply unit and keeping in a continuous working state;
s2, the camera unit collects video signals of the monitored scene and sends the video data to the original data of the fifth storage module for storage in a partition mode;
s3, the calculation module of the edge calculation unit reads program codes including a main program, a target detection module, a risk evaluation module, a knowledge retrieval module, a risk handling module and an event management module from the first storage module;
s4, under the drive of the main program, reading the risk correspondence table file of the second storage module and the configuration file and the risk grade table file of the third storage module, and executing the target detection module code;
s5, when the target detection program finds a risk source, judging the type S of the risk source;
optionally, a target tracking algorithm is started synchronously, a rotatable device of the camera unit is driven, and the camera is focused on a risk source for target tracking;
s6, executing a risk assessment module code, specifically, determining a scene condition C, a risk source location D, and a risk source retention time T, and looking up a risk level table according to the determination vector (C, D, T) ═ j, k, b), to obtain a risk level P of the risk source;
s7, performing risk handling according to the major categories of risk sources:
and S7-1, if the source is the A-type risk source, executing a knowledge retrieval module, and obtaining an audio file name Z according to the S and the P. And the risk handling module reads the audio file Z from the fourth storage module and sends the audio file Z to the alarm unit. The alarm unit plays the audio file through the loudspeaker by the audio driving module.
S7-2, if the risk source is B type risk source, the risk processing module drives the acousto-optic alarm of the alarm unit to send out buzzing alarm sound according to the risk level and the sound intensity appointed in the module.
S8, executing S5 to S7 in a circulating way until the target detection module finds that the risk source disappears;
and S9, the event management module writes the video clip of the risk event, the occurrence time, the end time, the risk source type and the risk level into an evidence data partition of the fifth storage module.
In the description herein, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present invention, and shall cover the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (3)
1. The utility model provides an industry unmanned production field intelligent security supervises device which characterized in that includes:
the camera shooting unit consists of an optical camera for collecting video signals and a rotatable device, and the rotatable device can drive the camera to rotate in all directions from top to bottom, left to right under the driving of signals;
the edge calculation unit consists of a storage module and a calculation module; wherein
The storage module receives a video file of the camera unit in real time;
a calculation module: the system comprises a main program module, a target detection module, a risk identification module, a knowledge retrieval module, a risk handling module and an event management module; wherein the content of the first and second substances,
the target detection module completes capture and identification of a risk source possibly existing in a picture through real-time analysis of a video;
the risk identification module judges the risk level of the risk source;
the knowledge retrieval module retrieves the risk handling table file according to the type of the risk source and the current state to obtain a risk handling suggestion;
the risk handling module converts the risk handling suggestion into alarm information and transmits the alarm information to an alarm unit; the event management module records the risk event, including event occurrence time, ending time, risk elements and the like, and adds the video data into the storage module;
the computing module of the edge computing unit executes:
p1, reading a risk correspondence table file, a configuration file and a risk grade table file of the storage module, and executing a target detection program;
p2, when the target detection program finds the risk source, determining the type of the risk source S;
p3, executing a risk assessment module code to obtain a risk level P of a risk source;
p4, risk handling according to the broad category of risk sources:
p4-1, if the source is the A-type risk source, executing a knowledge retrieval module, and obtaining the name of the audio file Z according to the S and the P; the risk processing module reads the audio file Z from the fourth storage module and sends the audio file Z to the alarm unit; the alarm unit plays an audio file Z;
p4-2, if the risk source is a B-type risk source, the risk processing module drives the audible and visual alarm of the alarm unit to generate a buzzing alarm sound according to the risk level and the sound intensity appointed by the code;
p5, circularly executing P2 to P4 until the target detection module finds that the risk source disappears;
and the P6 and the event management module write the video clip of the risk event, the occurrence time, the end time, the risk source type and the risk level into the storage module.
2. The intelligent safety supervision device for the industrial unmanned production field according to claim 1, characterized in that a target tracking algorithm is added in the target detection module; and when the dangerous source is detected, automatically starting a target tracking algorithm, driving a rotatable device of the camera unit, and keeping the camera focused on the dangerous source to track the target.
3. The intelligent safety supervision device for the industrial unmanned production field according to claim 1, wherein the storage module comprises a first storage module, a second storage module, a third storage module, a fourth storage module and a fifth storage module; wherein the content of the first and second substances,
the first storage module: storing a calculation program of the system;
the second storage module: storing a risk coping list file;
a third storage module: storing a target detection algorithm configuration file and a risk level table file;
the fourth storage module: storing a pre-recorded voice warning audio file;
a fifth storage module: storing video data from the camera unit;
wherein, the fifth storage module includes two partitions: 1) partitioning original data, and rolling and storing video data acquired by a camera according to a time sequence; 2) the evidence data partition is used for storing the video data in a first-in first-out mode when the target detects a dangerous source; and the event management module writes the video clip of the risk event, the occurrence time, the end time, the risk source type and the risk level into the evidence data partition of the fifth storage module.
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| CN202210606489.1A CN115050160B (en) | 2022-05-31 | 2022-05-31 | Intelligent safety supervision device for industrial unmanned production site |
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| CN202210606489.1A CN115050160B (en) | 2022-05-31 | 2022-05-31 | Intelligent safety supervision device for industrial unmanned production site |
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| CN115050160A true CN115050160A (en) | 2022-09-13 |
| CN115050160B CN115050160B (en) | 2024-02-23 |
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| WO2021016517A1 (en) * | 2019-07-25 | 2021-01-28 | Confluera, Inc. | Methods and system for identifying infrastructure attack progressions |
| CN113344326A (en) * | 2021-04-30 | 2021-09-03 | 西安交通大学 | System and method for identifying dynamic hidden danger and evaluating risk of multi-information fusion special operation site |
| CN114240116A (en) * | 2021-12-09 | 2022-03-25 | 北京华企动力信息技术有限公司 | Artificial intelligence risk monitoring and early warning system |
| CN114326625A (en) * | 2021-12-28 | 2022-04-12 | 毕马智能科技(上海)有限公司 | System and method for monitoring potential safety risks in power grid infrastructure construction |
| CN114358461A (en) * | 2021-11-02 | 2022-04-15 | 浙江宇联信息发展有限公司 | Risk early warning system and risk early warning method constructed based on multi-factor model |
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| WO2021016517A1 (en) * | 2019-07-25 | 2021-01-28 | Confluera, Inc. | Methods and system for identifying infrastructure attack progressions |
| CN113344326A (en) * | 2021-04-30 | 2021-09-03 | 西安交通大学 | System and method for identifying dynamic hidden danger and evaluating risk of multi-information fusion special operation site |
| CN114358461A (en) * | 2021-11-02 | 2022-04-15 | 浙江宇联信息发展有限公司 | Risk early warning system and risk early warning method constructed based on multi-factor model |
| CN114240116A (en) * | 2021-12-09 | 2022-03-25 | 北京华企动力信息技术有限公司 | Artificial intelligence risk monitoring and early warning system |
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