CN117576857B - Intelligent safety monitoring system and method based on neural network model - Google Patents
Intelligent safety monitoring system and method based on neural network model Download PDFInfo
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- CN117576857B CN117576857B CN202410061533.4A CN202410061533A CN117576857B CN 117576857 B CN117576857 B CN 117576857B CN 202410061533 A CN202410061533 A CN 202410061533A CN 117576857 B CN117576857 B CN 117576857B
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention relates to an intelligent safety monitoring system and method based on a neural network model, which belong to the technical field of electric digital data processing, and can monitor the condition that a large number of heavy personnel enter a machine room to damage or collapse a floor accidentally through orderly matching actions among a machine room access control detection device, a personnel approach detection device, a personnel weight detection device, a personnel fall detection device, a floor deformation detection device and an emergency processing alarm device, so that related personnel can quickly know and take emergency measures, and the situation that an enterprise communication network collapses and larger economic loss are caused by overlong line damage time is avoided. In addition, most detection devices can be prevented from being in an invalid action state for a long time, the whole energy consumption of the system is reduced, and meanwhile, misoperation of the system can be avoided.
Description
Technical Field
The invention belongs to the technical field of electric digital data processing, and particularly relates to an intelligent safety monitoring system and method based on a neural network model.
Background
For information service enterprises, a machine room storing servers is used as a background support of enterprise IT services, wherein the larger the enterprise scale is, the larger the corresponding number of servers, communication lines and the like is, so that the machine room of most information service enterprises often has the condition of complicated lines in the machine room. For the above-mentioned situations, enterprises often place individual lines under the floor, conceal them, and avoid excessive exposure to the machine room.
However, we found in daily work that when related staff enter a machine room (especially when a large number of staff accidentally falls down in the machine room), the floor may be stepped down at a position where one floor is connected with another floor, or even the floor may be directly stepped on, so that the floor is extruded to a line arranged under the floor, and the enterprise communication network is extremely likely to be crashed, and large loss is caused; the current stage does not have a real-time monitoring scheme aiming at the conditions, and when the conditions occur, related staff cannot quickly know and take emergency measures.
Therefore, an intelligent safety monitoring system based on a neural network model needs to be designed at present to solve the above problems.
Disclosure of Invention
The invention aims to provide an intelligent safety monitoring system and method based on a neural network model, which are used for solving the technical problems in the prior art, and due to the problems of floor installation or floor quality, when related staff enter a machine room (especially when large-weight staff accidentally fall down in the machine room), the position where one floor is connected with the other floor can be stepped down, even the floor can be directly stepped down, so that the floor is extruded to a line arranged under the floor, the enterprise communication network is extremely likely to be crashed, and the like, and larger loss is caused; the current stage does not have a real-time monitoring scheme aiming at the conditions, and when the conditions occur, related staff cannot quickly know and take emergency measures.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
the intelligent safety monitoring system based on the neural network model comprises a machine room access control detection device, a personnel approaching detection device, a personnel weight detection device, a personnel falling detection device, a floor deformation detection device, an emergency treatment alarm device and a control device;
the machine room entrance guard detection device is used for detecting whether an entrance guard of the machine room is opened or not;
the personnel access detection device is used for detecting whether personnel enter from the entrance guard of the machine room;
the personnel weight detection device is used for detecting whether the real-time weight of the personnel entering the machine room accords with the preset weight;
the personnel falling detection device is used for detecting whether personnel falling phenomenon occurs in the machine room;
the floor deformation detection device is used for detecting whether the floor in the machine room deforms or not;
the emergency processing alarm device is used for alarming emergency processing personnel;
the control device controls the machine room entrance guard detection device to be in a normally open state, and controls the personnel approaching detection device, the personnel weight detection device, the personnel falling detection device, the floor deformation detection device and the emergency treatment alarm device to be in a normally closed state;
when the machine room entrance guard detection device detects that the entrance guard of the machine room is opened, the control device controls the personnel access detection device to be opened;
when the personnel access detection device detects that personnel enter from the entrance guard of the machine room, the control device controls the personnel weight detection device to be started;
when the personnel weight detection device detects that the real-time weight of the personnel entering the machine room accords with the preset weight, the control device controls the personnel falling detection device to be started;
when the personnel falling detection device detects that the personnel falling phenomenon occurs in the machine room, the control device controls the floor deformation detection device to be started;
when the floor deformation detection device detects that the floor in the machine room deforms, the control device controls the emergency treatment alarm device to be started.
Further, the automatic door closing device also comprises a door opening timing device and an automatic door closing device, and the control device is respectively connected with the door opening timing device and the automatic door closing device;
the door opening timing device is used for recording whether the real-time duration after the entrance guard of the machine room is opened meets the preset duration;
the automatic door closing device is used for automatically closing the entrance guard of the machine room;
the control device controls the door opening timing device and the automatic door closing device to be in a normally closed state;
when the door opening timing device records that the real-time duration after the entrance guard of the machine room is opened accords with the preset duration and the personnel proximity detection device detects that no personnel enter from the entrance guard of the machine room, the control device controls the automatic door closing device to open.
Further, the machine room entrance guard detection device comprises a first processor, a first memory and an opening sensor, wherein the first processor is respectively connected with the first memory, the opening sensor and the control device;
the opening sensor is used for detecting real-time opening data of the machine room door;
the first memory is used for storing preset opening data of a room door of the machine room;
and the first processor compares and analyzes the real-time opening data with the preset opening data, and if the real-time opening data and the preset opening data are matched, the first processor feeds back the opening of the access control of the machine room to the control device.
Further, the personnel proximity detection device comprises a human body proximity sensor, an infrared sensor and a second processor, and the second processor is respectively connected with the human body proximity sensor, the infrared sensor and the control device;
the human body proximity sensor is used for detecting whether a human body exists at an entrance guard of the machine room;
the infrared sensor is used for detecting whether personnel enter the entrance guard of the machine room or not;
the second processor controls the human body proximity sensor and the infrared sensor to be in a normally closed state;
when the control device controls the personnel proximity detection device to be started, the second processor controls the human body proximity sensor to be started;
when the human body proximity sensor detects that a human body exists at an access point of the machine room, the second processor controls the infrared sensor to be started;
when the infrared sensor detects that personnel enter the entrance guard of the machine room, the second processor feeds back the entrance guard of the machine room to the control device.
Further, the weight detection device for the personnel comprises a weight sensor, a third processor and a third memory, wherein the third processor is respectively connected with the weight sensor, the third memory and the control device;
the weight sensors cover the area where the personnel enter the machine room in a matrix distribution mode, and the weight sensors are used for detecting the real-time weight of the personnel entering the machine room;
the third memory is used for storing the preset weight of the personnel entering the machine room;
and the third processor compares and analyzes the real-time weight with the preset weight, and if the real-time weight and the preset weight are matched, the third processor feeds back to the control device that the real-time weight of the personnel entering the machine room accords with the preset weight.
Further, the personnel fall detection device comprises a height detection sensor, a fourth processor and a fourth memory, wherein the fourth processor is respectively connected with the height detection sensor, the fourth memory and the control device;
the height detection sensor is used for detecting real-time height data of personnel in the machine room;
the fourth memory is used for storing preset height data of personnel in the machine room;
and the fourth processor compares and analyzes the real-time height data with the preset height data, and if the real-time height data and the preset height data are matched, the fourth processor feeds back the falling phenomenon of personnel in the machine room to the control device.
Further, the floor deformation detection device comprises a high-definition camera, a fifth processor and a fifth memory, wherein the fifth processor is respectively connected with the high-definition camera, the fifth memory and the control device;
the high-definition camera is used for collecting real-time image data of floors in the machine room;
the fifth memory is used for storing preset image data of floors in the machine room;
and the fifth processor compares and analyzes the real-time image data with the preset image data, and if the real-time image data and the preset image data are not matched, the fifth processor feeds back the deformation of the floor in the machine room to the control device.
Further, the mobile terminal comprises a wireless communication device and a mobile terminal, and the control device is connected with the mobile terminal through the wireless communication device in a network mode.
The intelligent safety monitoring method based on the neural network model adopts the intelligent safety monitoring system based on the neural network model to conduct intelligent safety monitoring of the machine room.
Compared with the prior art, the invention has the following beneficial effects:
one of the beneficial effects of this scheme lies in, through the orderly cooperation action between computer lab entrance guard detection device, personnel approach detection device, personnel weight detection device, personnel fall down detection device, floor deformation detection device, the urgent processing alarm device, can monitor that the unexpected circumstances that causes the floor to damage or collapse of big weight personnel entering computer lab, relevant staff can fast know and take emergency measure, avoid the line impaired time overlength to arouse enterprise communication network collapse and great economic loss. In addition, most detection devices can be prevented from being in an invalid action state for a long time, the whole energy consumption of the system is reduced, and meanwhile, misoperation of the system can be avoided.
Drawings
Fig. 1 is a schematic diagram of the system operation principle of the embodiment.
Fig. 2 is a schematic diagram of another optimized operation principle of the system according to the embodiment.
Detailed Description
The following description of the embodiments of the present invention will be made more fully with reference to the accompanying drawings, 1-2, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an intelligent safety monitoring system based on a neural network model is provided, which comprises a machine room access control detection device, a personnel approaching detection device, a personnel weight detection device, a personnel falling detection device, a floor deformation detection device, an emergency treatment alarm device and a control device;
the machine room entrance guard detection device is used for detecting whether an entrance guard of the machine room is opened or not;
the personnel access detection device is used for detecting whether personnel enter from the entrance guard of the machine room;
the personnel weight detection device is used for detecting whether the real-time weight of the personnel entering the machine room accords with the preset weight;
the personnel falling detection device is used for detecting whether personnel falling phenomenon occurs in the machine room;
the floor deformation detection device is used for detecting whether the floor in the machine room deforms or not;
the emergency processing alarm device is used for alarming emergency processing personnel;
the control device controls the machine room entrance guard detection device to be in a normally open state, and controls the personnel approaching detection device, the personnel weight detection device, the personnel falling detection device, the floor deformation detection device and the emergency treatment alarm device to be in a normally closed state;
when the machine room entrance guard detection device detects that the entrance guard of the machine room is opened, the control device controls the personnel access detection device to be opened; the situation that the door of the machine room is unintentionally opened (no personnel enter) can be eliminated.
When the personnel access detection device detects that personnel enter from the entrance guard of the machine room, the control device controls the personnel weight detection device to be started; according to historical conditions, the probability that people with larger weight enter the machine room to cause the floor to be damaged or collapse is larger, and the probability that people with smaller weight enter the machine room to cause the floor to be damaged or collapse is extremely lower, so that a small probability event can be eliminated.
When the personnel weight detection device detects that the real-time weight of the personnel entering the machine room accords with the preset weight, the control device controls the personnel falling detection device to be started; the normal action is very difficult to cause the floor to be damaged or collapse after the personnel enter the machine room, and the probability of the floor being damaged or collapse is very high when the personnel with large weight fall accidentally, so that the personnel fall down to be detected.
When the personnel falling detection device detects that the personnel falling phenomenon occurs in the machine room, the control device controls the floor deformation detection device to be started;
when the floor deformation detection device detects that the floor in the machine room deforms, the control device controls the emergency treatment alarm device to be started.
Among the above-mentioned scheme, through the orderly cooperation action between computer lab entrance guard detection device, personnel approach detection device, personnel weight detection device, personnel fall down detection device, floor deformation detection device, the urgent processing alarm device, can monitor that the unexpected circumstances that causes the floor to damage or collapse of big weight personnel entering computer lab, relevant staff can know fast and take emergency measure, avoid the impaired time overlength of circuit to arouse enterprise communication network collapse and great economic loss. In addition, most detection devices can be prevented from being in an invalid action state for a long time, the whole energy consumption of the system is reduced, and meanwhile, misoperation of the system can be avoided.
Further, as shown in fig. 2, the door opening timing device and the automatic door closing device are further included, and the control device is respectively connected with the door opening timing device and the automatic door closing device;
the door opening timing device is used for recording whether the real-time duration after the entrance guard of the machine room is opened meets the preset duration;
the automatic door closing device is used for automatically closing the entrance guard of the machine room;
the control device controls the door opening timing device and the automatic door closing device to be in a normally closed state;
when the door opening timing device records that the real-time duration after the entrance guard of the machine room is opened accords with the preset duration and the personnel proximity detection device detects that no personnel enter from the entrance guard of the machine room, the control device controls the automatic door closing device to open.
In the scheme, after the door of the machine room is opened unintentionally (no personnel enter), the personnel access detection device can enter the continuous action, and if no personnel enter the machine room all the time, the system can continuously carry out the personnel access detection link; thus, a mating arrangement of the door opening timing device and the automatic door closing device is designed to overcome the above drawbacks.
Further, the machine room entrance guard detection device comprises a first processor, a first memory and an opening sensor, wherein the first processor is respectively connected with the first memory, the opening sensor and the control device;
the opening sensor is used for detecting real-time opening data of the machine room door;
the first memory is used for storing preset opening data of the machine room door (the preset opening data at least needs to meet the opening which can allow personnel to enter through the machine room door);
and the first processor compares and analyzes the real-time opening data with the preset opening data, and if the real-time opening data and the preset opening data are matched, the first processor feeds back the opening of the access control of the machine room to the control device.
Further, the personnel proximity detection device comprises a human body proximity sensor, an infrared sensor and a second processor, and the second processor is respectively connected with the human body proximity sensor, the infrared sensor and the control device;
the human body proximity sensor is used for detecting whether a human body exists at an entrance guard of the machine room;
the infrared sensor is used for detecting whether personnel enter the entrance guard of the machine room or not;
the second processor controls the human body proximity sensor and the infrared sensor to be in a normally closed state;
when the control device controls the personnel proximity detection device to be started, the second processor controls the human body proximity sensor to be started;
when the human body proximity sensor detects that a human body exists at an access point of the machine room, the second processor controls the infrared sensor to be started;
when the infrared sensor detects that personnel enter the entrance guard of the machine room, the second processor feeds back the entrance guard of the machine room to the control device.
Further, the weight detection device for the personnel comprises a weight sensor, a third processor and a third memory, wherein the third processor is respectively connected with the weight sensor, the third memory and the control device;
the weight sensors cover the area where the personnel enter the machine room in a matrix distribution mode, and the weight sensors are used for detecting the real-time weight of the personnel entering the machine room;
the third memory is used for storing the preset weight of the personnel entering the machine room;
and the third processor compares and analyzes the real-time weight with the preset weight, and if the real-time weight and the preset weight are matched, the third processor feeds back to the control device that the real-time weight of the personnel entering the machine room accords with the preset weight.
Further, the personnel fall detection device comprises a height detection sensor, a fourth processor and a fourth memory, wherein the fourth processor is respectively connected with the height detection sensor, the fourth memory and the control device;
the height detection sensor is used for detecting real-time height data of personnel in the machine room;
the fourth memory is used for storing preset height data of personnel in the machine room; moreover, the preset height data is obtained by training the historical heights of a large number of people when unbalance falls through the neural network model, so that the reliability of the subsequent fourth processor in performing height comparison analysis can be improved.
And the fourth processor compares and analyzes the real-time height data with the preset height data, and if the real-time height data and the preset height data are matched, the fourth processor feeds back the falling phenomenon of personnel in the machine room to the control device.
Further, the floor deformation detection device comprises a high-definition camera, a fifth processor and a fifth memory, wherein the fifth processor is respectively connected with the high-definition camera, the fifth memory and the control device;
the high-definition camera is used for collecting real-time image data of floors in the machine room;
the fifth memory is used for storing preset image data of floors in the machine room;
and the fifth processor compares and analyzes the real-time image data with the preset image data, and if the real-time image data and the preset image data are not matched, the fifth processor feeds back the deformation of the floor in the machine room to the control device.
Further, the system also comprises a wireless communication device and a mobile terminal, wherein the control device is connected with the mobile terminal through the wireless communication device in a network manner, so that remote data monitoring is realized.
The intelligent safety monitoring method based on the neural network model adopts the intelligent safety monitoring system based on the neural network model to conduct intelligent safety monitoring of the machine room.
The above is a preferred embodiment of the present invention, and all changes made according to the technical solution of the present invention belong to the protection scope of the present invention when the generated functional effects do not exceed the scope of the technical solution of the present invention.
Claims (9)
1. The intelligent safety monitoring system based on the neural network model is characterized by comprising a machine room access control detection device, a personnel approaching detection device, a personnel weight detection device, a personnel falling detection device, a floor deformation detection device, an emergency treatment alarm device and a control device;
the machine room entrance guard detection device is used for detecting whether an entrance guard of the machine room is opened or not;
the personnel access detection device is used for detecting whether personnel enter from the entrance guard of the machine room;
the personnel weight detection device is used for detecting whether the real-time weight of the personnel entering the machine room accords with the preset weight;
the personnel falling detection device is used for detecting whether personnel falling phenomenon occurs in the machine room;
the floor deformation detection device is used for detecting whether the floor in the machine room deforms or not;
the emergency processing alarm device is used for alarming emergency processing personnel;
the control device controls the machine room entrance guard detection device to be in a normally open state, and controls the personnel approaching detection device, the personnel weight detection device, the personnel falling detection device, the floor deformation detection device and the emergency treatment alarm device to be in a normally closed state;
when the machine room entrance guard detection device detects that the entrance guard of the machine room is opened, the control device controls the personnel access detection device to be opened;
when the personnel access detection device detects that personnel enter from the entrance guard of the machine room, the control device controls the personnel weight detection device to be started;
when the personnel weight detection device detects that the real-time weight of the personnel entering the machine room accords with the preset weight, the control device controls the personnel falling detection device to be started;
when the personnel falling detection device detects that the personnel falling phenomenon occurs in the machine room, the control device controls the floor deformation detection device to be started;
when the floor deformation detection device detects that the floor in the machine room deforms, the control device controls the emergency treatment alarm device to be started.
2. The intelligent safety monitoring system based on the neural network model according to claim 1, further comprising a door opening timing device and an automatic door closing device, wherein the control device is respectively connected with the door opening timing device and the automatic door closing device;
the door opening timing device is used for recording whether the real-time duration after the entrance guard of the machine room is opened meets the preset duration;
the automatic door closing device is used for automatically closing the entrance guard of the machine room;
the control device controls the door opening timing device and the automatic door closing device to be in a normally closed state;
when the door opening timing device records that the real-time duration after the entrance guard of the machine room is opened accords with the preset duration and the personnel proximity detection device detects that no personnel enter from the entrance guard of the machine room, the control device controls the automatic door closing device to open.
3. The intelligent security monitoring system based on the neural network model according to claim 2, wherein the machine room entrance guard detection device comprises a first processor, a first memory and an opening sensor, and the first processor is respectively connected with the first memory, the opening sensor and the control device;
the opening sensor is used for detecting real-time opening data of the machine room door;
the first memory is used for storing preset opening data of a room door of the machine room;
and the first processor compares and analyzes the real-time opening data with the preset opening data, and if the real-time opening data and the preset opening data are matched, the first processor feeds back the opening of the access control of the machine room to the control device.
4. The intelligent safety monitoring system based on the neural network model according to claim 3, wherein the personnel proximity detection device comprises a human body proximity sensor, an infrared sensor and a second processor, and the second processor is respectively connected with the human body proximity sensor, the infrared sensor and the control device;
the human body proximity sensor is used for detecting whether a human body exists at an entrance guard of the machine room;
the infrared sensor is used for detecting whether personnel enter the entrance guard of the machine room or not;
the second processor controls the human body proximity sensor and the infrared sensor to be in a normally closed state;
when the control device controls the personnel proximity detection device to be started, the second processor controls the human body proximity sensor to be started;
when the human body proximity sensor detects that a human body exists at an access point of the machine room, the second processor controls the infrared sensor to be started;
when the infrared sensor detects that personnel enter the entrance guard of the machine room, the second processor feeds back the entrance guard of the machine room to the control device.
5. The intelligent safety monitoring system based on the neural network model according to claim 4, wherein the personnel weight detection device comprises a weight sensor, a third processor and a third memory, and the third processor is respectively connected with the weight sensor, the third memory and the control device;
the weight sensors cover the area where the personnel enter the machine room in a matrix distribution mode, and the weight sensors are used for detecting the real-time weight of the personnel entering the machine room;
the third memory is used for storing the preset weight of the personnel entering the machine room;
and the third processor compares and analyzes the real-time weight with the preset weight, and if the real-time weight and the preset weight are matched, the third processor feeds back to the control device that the real-time weight of the personnel entering the machine room accords with the preset weight.
6. The intelligent safety monitoring system based on the neural network model according to claim 5, wherein the personal fall detection device comprises a height detection sensor, a fourth processor and a fourth memory, and the fourth processor is respectively connected with the height detection sensor, the fourth memory and the control device;
the height detection sensor is used for detecting real-time height data of personnel in the machine room;
the fourth memory is used for storing preset height data of personnel in the machine room;
and the fourth processor compares and analyzes the real-time height data with the preset height data, and if the real-time height data and the preset height data are matched, the fourth processor feeds back the falling phenomenon of personnel in the machine room to the control device.
7. The intelligent safety monitoring system based on the neural network model according to claim 6, wherein the floor deformation detection device comprises a high-definition camera, a fifth processor and a fifth memory, and the fifth processor is respectively connected with the high-definition camera, the fifth memory and the control device;
the high-definition camera is used for collecting real-time image data of floors in the machine room;
the fifth memory is used for storing preset image data of floors in the machine room;
and the fifth processor compares and analyzes the real-time image data with the preset image data, and if the real-time image data and the preset image data are not matched, the fifth processor feeds back the deformation of the floor in the machine room to the control device.
8. The neural network model-based intelligent security monitoring system of claim 7, further comprising a wireless communication device and a mobile terminal, wherein the control device is network-connected to the mobile terminal via the wireless communication device.
9. A neural network model-based intelligent security monitoring method, characterized in that the neural network model-based intelligent security monitoring system as claimed in any one of claims 1-7 is used for intelligent security monitoring of a machine room.
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| CN117576857A (en) | 2024-02-20 |
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