CN115049904A - Image recognition-based industrial control system intelligent cabinet temperature control method - Google Patents
Image recognition-based industrial control system intelligent cabinet temperature control method Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 16
- 230000007246 mechanism Effects 0.000 claims description 27
- 238000006073 displacement reaction Methods 0.000 claims description 15
- 238000009434 installation Methods 0.000 claims description 14
- 230000011218 segmentation Effects 0.000 claims description 7
- 238000010586 diagram Methods 0.000 claims description 6
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 238000001816 cooling Methods 0.000 abstract description 8
- 238000005057 refrigeration Methods 0.000 abstract description 8
- 230000009467 reduction Effects 0.000 abstract description 4
- 238000004378 air conditioning Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 238000013021 overheating Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 125000003003 spiro group Chemical group 0.000 description 2
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- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
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- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
- H05K7/00—Constructional details common to different types of electric apparatus
- H05K7/20—Modifications to facilitate cooling, ventilating, or heating
- H05K7/20009—Modifications to facilitate cooling, ventilating, or heating using a gaseous coolant in electronic enclosures
- H05K7/20209—Thermal management, e.g. fan control
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Abstract
The application discloses industrial control system intelligence rack temperature control method based on image recognition relates to rack temperature control technical field, including rack, high definition digtal camera, infrared camera, air conditioner supply air duct, Mask-RCNN model, air outlet, removal subassembly and control chip, high definition digtal camera and infrared camera set up on the removal subassembly of rack inside dead ahead to and include following step: s1: the air conditioner air supply pipeline penetrates through the cabinet, and each air outlet corresponds to key network equipment comprising a switch and a server in the cabinet; s2: respectively acquiring a color image and a thermal image of network equipment in the cabinet through a high-definition camera and an infrared camera; the collected color image and the thermal image correspond to the same cabinet; s3: and identifying and segmenting the color image by adopting a Mask-RCNN model. This application is convenient for pinpoint the super high equipment of temperature in the rack, realizes accurate cooling, avoids the temperature unbalance and the refrigeration efficiency reduction that the cooling of full rack brought.
Description
Technical Field
The invention relates to the technical field of cabinet temperature control, in particular to an intelligent cabinet temperature control method of an industrial control system based on image recognition.
Background
In the basic requirements of safety protection of an industrial control system and network safety level protection, physical environment safety is the basis of safe operation of the system, and the requirements on the temperature of network equipment of a machine room are included. In an industrial control system, a plurality of devices are arranged in a cabinet, and part of the devices have the characteristics of long running time, frequent information interaction, high power and the like, so that the temperature of the devices is easily overhigh.
The existing cabinet temperature control mainly adopts an internal air conditioning system, an air inlet and an air outlet are arranged at the same time, the front part of the cabinet is an air inlet, and the rear part of the cabinet is an air outlet, so that heat is taken away.
The existing integrated cabinet air conditioning system adopts a mode of pervasively pushing cold air to adjust the temperature of a cabinet. Whether the internal temperature exceeds the standard or not is detected by deploying a plurality of temperature sensors, and when one temperature sensor detects that the temperature exceeds a set value, the whole temperature of the cabinet is reduced by reducing the set temperature of the air conditioner or increasing the positive cooling power, so that the whole area is cooled. And when the temperature is restored to the reasonable temperature range, the original operation state is restored.
The existing technical scheme is full-area refrigeration cooling, in order to cool a local high-heat area, the temperature of the whole air conditioning system is often required to be adjusted to be very low, so that the refrigeration efficiency of the air conditioner in a machine room is low, and the PUE value standard of a green data center cannot be reached. At present, a cabinet air conditioning system in actual operation is usually set to be in a working mode of ' forced refrigeration, 25 ℃ or ' automatic, 28 ℃ starting ', air conditioners in a part of machine rooms are even started all year round, the temperature of a certain part or equipment in the cabinet cannot be timely, pertinently and accurately adjusted, overheating of a part of areas is easily caused, a large number of local hot spots occur, the phenomenon of electric energy waste is common and serious, and the refrigeration efficiency of the machine room air conditioners is reduced.
Therefore, the existing method cannot realize the internal temperature balance of the cabinet, and due to the difference of the operating power and the operating frequency of different equipment, the phenomenon of overheating of partial areas or equipment is easily caused, even the shutdown of individual equipment due to overheating is caused, and the normal operation of the system is influenced.
Based on the above, an industrial control system intelligent cabinet temperature control method based on image recognition is provided.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides an intelligent cabinet temperature control method of an industrial control system based on image recognition, which is convenient for accurately positioning equipment with ultrahigh temperature in a cabinet, realizes accurate cooling and avoids temperature imbalance and refrigeration efficiency reduction caused by cooling of the whole cabinet.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
the utility model provides an industrial control system intelligence rack temperature control method based on image recognition, includes rack, high definition digtal camera, infrared camera, air conditioner supply air duct, Mask-RCNN model, air outlet, removal subassembly and control chip, high definition digtal camera and infrared camera set up on the removal subassembly of rack inside dead ahead to and include following step:
s1: the air conditioner air supply pipeline penetrates through the cabinet, and each air outlet corresponds to key network equipment comprising a switch and a server in the cabinet;
s2: respectively acquiring a color image and a thermal image of network equipment in the cabinet through a high-definition camera and an infrared camera; the collected color image and the thermal image correspond to the same cabinet;
s3: adopting a Mask-RCNN model to identify and segment the color image;
s4: inputting an equipment image in a cabinet for binarization preprocessing, and inputting the equipment image into a pre-trained neural network to obtain a corresponding characteristic diagram;
s5: setting a fixed number of ROI (region of interest) at each pixel position of the feature map, then sending the ROI region into an RPN (resilient packet network) to perform secondary classification and coordinate regression, and filtering out a part of ROI;
s6: performing ROI Align operation on the rest ROI;
s7: classifying the ROIs, and generating BB regression and Mask to obtain semantic segmentation images of equipment in the cabinet;
s8: the semantic segmentation image of the equipment in the cabinet is used as a mask to cut the thermal image to obtain a thermal image with corresponding equipment, and then corresponding temperature is obtained;
s9: comparing the temperature with the temperature related requirement in the physical environment of the machine room, and judging whether the temperature is ultrahigh;
s10: when the temperature is judged to be ultrahigh, the control chip controls the moving assembly to move the high-definition camera and the infrared camera to a high-temperature device, the actions from the step S2 to the step S9 are repeated, and the recheck is carried out;
s11: and when the temperature is judged to be ultrahigh through rechecking, opening the corresponding air outlet for reducing the temperature of the related equipment.
Preferably, the mobile assembly includes vertical movement mechanism and lateral shifting mechanism, vertical movement mechanism and lateral shifting mechanism all comprise screw lead screw, displacement piece, positive and negative motor, installation piece and frame of rotating, high definition digtal camera and infrared camera and control chip electrical output connection, positive and negative motor and control chip electrical input connection, clear digtal camera and infrared camera are installed on the installation piece in lateral shifting mechanism.
Based on above-mentioned technical characteristics, be convenient for fixed the installation of high definition digtal camera and infrared camera on the removal subassembly.
Preferably, the threaded screw rod is rotatably connected to an inner cavity of the frame, the forward and reverse rotating motor is installed at one end of the frame, and a power output end of the forward and reverse rotating motor is connected with one end of the threaded screw rod.
Based on the technical characteristics, the threaded screw rod is convenient to drive to rotate.
Preferably, displacement piece sliding connection is in the inner chamber of frame, and displacement piece and screw thread lead screw looks spiro union, installation piece and displacement piece looks rigid coupling, and the installation piece is located the frame and presses close to one side of rack center department.
Based on the technical characteristics, the installation block and the displacement block are conveniently driven to move together.
Preferably, a frame in the vertical moving mechanism is fixedly connected with the cabinet, and a frame in the horizontal moving mechanism is fixedly connected to the mounting block in the vertical moving mechanism.
Based on the technical characteristics, the transverse moving mechanism can be conveniently moved up and down in the vertical direction.
In summary, the invention includes at least one of the following advantages:
firstly, utilize image recognition technology to come the super high equipment of accurate positioning rack temperature, realize accurate cooling, avoid the unbalanced temperature and the refrigeration efficiency reduction that the cooling of whole rack brought.
Secondly, a Mask-RCNN model is used, and the problem that the recognition accuracy rate of the traditional image recognition technology is reduced under the conditions of weak light and shielding is avoided.
Drawings
FIG. 1 is a flow chart of the temperature control method of an intelligent cabinet of an industrial control system based on image recognition;
FIG. 2 is a schematic structural diagram of the cabinet of the present invention;
FIG. 3 is a right side cross-sectional view of FIG. 2 of the present invention;
FIG. 4 is a schematic structural diagram of a moving assembly of the present invention;
FIG. 5 is a functional block diagram of the control system of the present invention;
in the drawings, the components represented by the respective reference numerals are listed below:
the device comprises a cabinet 1, a high-definition camera 2, an infrared camera 3, a moving assembly 4, a threaded screw rod 401, a displacement block 402, a positive and negative rotation motor 403, a mounting block 404 and a control chip 5.
Detailed Description
The present invention is described in further detail below with reference to figures 1-5.
The embodiment provided by the invention comprises the following steps: as shown in fig. 1, fig. 2, fig. 3 and fig. 5, an industrial control system intelligent cabinet temperature control method based on image recognition comprises a cabinet 1, a high-definition camera 2, an infrared camera 3, an air conditioning air supply pipeline, a Mask-RCNN model, an air outlet, a mobile assembly 4 and a control chip 5, wherein the high-definition camera 2 and the infrared camera 3 are electrically output and connected with the control chip 5, the high-definition camera 2 and the infrared camera 3 are arranged on the mobile assembly 4 right in front of the cabinet 1, the infrared camera 3 is responsible for collecting a hot image in the cabinet, the high-definition camera 2 is responsible for collecting a cabinet image picture for image recognition, and the high-heat part or equipment in the cabinet is accurately recognized.
As shown in fig. 2 and 4, the moving assembly 4 includes a vertical moving mechanism and a horizontal moving mechanism, the vertical moving mechanism and the horizontal moving mechanism are both composed of a threaded screw 401, a displacement block 402, a forward and reverse rotation motor 403, an installation block 404 and a frame 405, the forward and reverse rotation motor 403 is electrically connected with the control chip 5 in an input manner, the high definition camera 2 and the infrared camera 3 are installed on the installation block 404 in the horizontal moving mechanism, and the high definition camera 2 and the infrared camera 3 are conveniently installed and fixed on the moving assembly 4.
As shown in fig. 2 and 4, the threaded screw 401 is rotatably connected to an inner cavity of the frame 405, the forward and reverse rotation motor 403 is installed at one end of the frame 405, and a power output end of the forward and reverse rotation motor 403 is connected to one end of the threaded screw 401, so that the forward and reverse rotation motor 403 works to drive the threaded screw 401 to rotate. Displacement piece 402 sliding connection is in the inner chamber of frame 405, and displacement piece 402 and screw thread lead screw 401 looks spiro union, and installation piece 404 and displacement piece 402 looks rigid coupling, and installation piece 404 is located frame 405 and presses close to one side of rack 1 center department, when screw thread lead screw 401 rotates, is convenient for drive installation piece 404 and displacement piece 402 carry out the position together and removes.
As shown in fig. 2 and 4, a frame 405 in the vertical moving mechanism is fixedly connected with the cabinet 1, the frame 405 in the horizontal moving mechanism is fixedly connected with a mounting block 404 in the vertical moving mechanism, the horizontal moving mechanism, the high-definition camera 2 and the infrared camera 3 can be conveniently moved up and down in the vertical direction together through forward and reverse rotation of a forward and reverse rotation motor 403, and then in the horizontal moving mechanism, the high-definition camera 2 and the infrared camera 3 can be conveniently moved back and forth in the horizontal direction together through forward and reverse rotation of the forward and reverse rotation motor 403, so that the high-definition camera 2 and the infrared camera 3 can be conveniently moved at any position in a vertical plane.
And comprising the steps of:
s1: the air conditioner air supply pipeline penetrates through the cabinet 1, and each air outlet corresponds to key network equipment including a switch and a server in the cabinet 1 and is used for reducing the temperature of the relevant equipment;
s2: respectively acquiring color images and thermal images of network equipment in the cabinet 1 through the high-definition camera 2 and the infrared camera 3; the collected color image and the thermal image correspond to the same cabinet;
s3: adopting a Mask-RCNN model to identify and segment the color image;
s4: inputting an equipment image in a cabinet for binarization preprocessing, and inputting the equipment image into a pre-trained neural network to obtain a corresponding characteristic diagram;
s5: setting a fixed number of ROI (region of interest) at each pixel position of the feature map, then sending the ROI region into an RPN (resilient packet network) to perform secondary classification and coordinate regression, and filtering out a part of ROI;
s6: performing ROI Align operation on the rest ROI;
s7: classifying the ROIs, and generating BB regression and Mask to obtain semantic segmentation images of equipment in the cabinet;
s8: the semantic segmentation image of the equipment in the cabinet is used as a mask to cut out the thermal image to obtain the thermal image with the corresponding equipment, and then the corresponding temperature is obtained;
s9: comparing the temperature with the temperature-related requirement in the physical environment of the machine room, and judging whether the temperature is ultrahigh;
s10: when the temperature is judged to be ultrahigh, the control chip 5 controls the moving assembly 4 to move the high-definition camera 2 and the infrared camera 3 at any position in a vertical plane, so that the high-definition camera 2 and the infrared camera 3 move to equipment with high temperature, and the actions from the step S2 to the step S9 are repeated for rechecking;
s11: and when the temperature is judged to be ultrahigh through rechecking, opening the corresponding air outlet for reducing the temperature of the related equipment.
Whether this application detects the part that surpasss the temperature standard in the rack through infrared camera 3, and rethread high definition digtal camera 2 utilizes image recognition technology to find corresponding equipment, then opens the air conditioner supply-air outlet that corresponds the part, realizes the accurate refrigeration of rack air conditioner, solves the problem that current rack air conditioning system is difficult to the accurate high hot zone temperature of reduction rack portion. According to the image recognition method and device, the Mask-RCNN model is adopted to carry out image recognition and segmentation, the problem that the recognition accuracy rate is reduced under the conditions of weak light and shielding in the traditional image recognition technology is avoided, the problems that equipment in a cabinet is stacked and shielded or light rays exist and misjudgment on the equipment is easily caused by the traditional image recognition scheme are solved.
The above are all preferred embodiments of the present invention, and the protection scope of the present invention is not limited thereby, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.
Claims (5)
1. The utility model provides an industrial control system intelligence rack temperature control method based on image recognition, includes rack (1), high definition digtal camera (2), infrared camera (3), air conditioner supply air duct, Mask-RCNN model, air outlet, removes subassembly (4) and control chip (5), high definition digtal camera (2) and infrared camera (3) set up on the removal subassembly (4) of rack (1) inside dead ahead, its characterized in that: and comprising the steps of:
s1: the air conditioner air supply pipeline penetrates through the cabinet (1), and each air outlet corresponds to key network equipment comprising an exchanger and a server in the cabinet (1);
s2: respectively acquiring color images and thermal images of network equipment in the cabinet (1) through the high-definition camera (2) and the infrared camera (3); the collected color image and the thermal image correspond to the same cabinet;
s3: adopting a Mask-RCNN model to identify and segment the color image;
s4: inputting an equipment image in a cabinet for binarization preprocessing, and inputting the equipment image into a pre-trained neural network to obtain a corresponding characteristic diagram;
s5: setting a fixed number of ROI (region of interest) at each pixel position of the feature map, then sending the ROI region into an RPN (resilient packet network) to perform secondary classification and coordinate regression, and filtering out a part of ROI;
s6: performing ROI Align operation on the rest ROI;
s7: classifying the ROIs, and generating BB regression and Mask to obtain semantic segmentation images of equipment in the cabinet;
s8: the semantic segmentation image of the equipment in the cabinet is used as a mask to cut the thermal image to obtain a thermal image with corresponding equipment, and then corresponding temperature is obtained;
s9: comparing the temperature with the temperature-related requirement in the physical environment of the machine room, and judging whether the temperature is ultrahigh;
s10: when the temperature is judged to be ultrahigh, the control chip (5) controls the moving assembly (4) to move the high-definition camera (2) and the infrared camera (3) to enable the high-definition camera (2) and the infrared camera (3) to move to equipment with high temperature, and the actions from the step S2 to the step S9 are repeated for rechecking;
s11: and when the temperature is judged to be ultrahigh through rechecking, opening the corresponding air outlet for reducing the temperature of the related equipment.
2. The image recognition-based industrial control system intelligent cabinet temperature control method according to claim 1, characterized in that: remove subassembly (4) including vertical moving mechanism and lateral shifting mechanism, vertical moving mechanism and lateral shifting mechanism all comprise screw lead screw (401), displacement piece (402), just reverse motor (403), installation piece (404) and frame (405), high definition digtal camera (2) and infrared camera (3) and control chip (5) electrical output connection, just reverse motor (403) and control chip (5) electrical input connection, install on installation piece (404) in lateral shifting mechanism clear camera (2) and infrared camera (3).
3. The image recognition-based industrial control system intelligent cabinet temperature control method according to claim 2, characterized in that: the screw thread lead screw (401) is rotatably connected to an inner cavity of the frame (405), the forward and reverse rotation motor (403) is installed at one end of the frame (405), and a power output end of the forward and reverse rotation motor (403) is connected with one end of the screw thread lead screw (401).
4. The image recognition-based industrial control system intelligent cabinet temperature control method according to claim 2, characterized in that: the displacement block (402) is connected to the inner cavity of the frame (405) in a sliding mode, the displacement block (402) is in threaded connection with the threaded screw rod (401), the mounting block (404) is fixedly connected with the displacement block (402), and the mounting block (404) is located on one side, close to the center of the cabinet (1), of the frame (405).
5. The image recognition-based industrial control system intelligent cabinet temperature control method according to claim 2, characterized in that: and a frame (405) in the vertical moving mechanism is fixedly connected with the cabinet (1), and a frame (405) in the transverse moving mechanism is fixedly connected with an installation block (404) in the vertical moving mechanism.
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