Power visual monitoring device and remote internet early warning system thereof

Abstract

The invention discloses a power visualization monitoring device and a remote internet early warning system thereof, and relates to the technical field of power environment monitoring, the power visualization monitoring device comprises a cabinet, a mounting plate is arranged in the cabinet, the top of the mounting plate is fixedly connected with a monitoring host, a fixing frame is arranged in the cabinet, the bottom of the fixing frame is fixedly connected with a support, and the bottom of the support is fixedly connected with the remote internet early warning system. A supporting plate is fixedly connected to the bottom of the support, a plurality of cooling fans are fixedly connected into the supporting plate, a wire pressing plate is fixedly connected to the bottom of the supporting plate, an air guide plate is arranged on the front side of the support, a supporting assembly is arranged on the top of the fixing frame, and an adjusting assembly is arranged on the rear side of the lower guide pipe. Meanwhile, the monitoring host and the wire harness are rapidly cooled, the cooling effect is improved, the remote internet early warning system provides a decision basis for the digital twin engine, and the closed-loop predictive maintenance function from data input to intelligent analysis to early warning output is achieved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, specifically a power visualization monitoring device and its remote internet early warning system. Background Technology

[0002] In existing power monitoring systems, the monitoring of equipment operating status relies heavily on traditional sensors and manual inspections, which results in problems such as incomplete data collection, poor real-time performance, and delayed fault warnings.

[0003] In the prior art, a Chinese patent, authorized publication number CN116300617A, describes an intelligent unattended environmental monitoring system, which includes a data sensing and acquisition module, a 3D visualization modeling and analysis module, and an automated control module. The data sensing and acquisition module collects data on power equipment and the computer room environment, as well as computer room security monitoring data, through hardware sensors, supporting subsequent visualization and intelligent analysis.

[0004] While the aforementioned system attempts to incorporate digital twins and artificial intelligence predictions during operation, it lacks closed-loop collaboration between physical devices and virtual models. Furthermore, the early warning mechanism is static and struggles to dynamically adapt to complex operating conditions. In addition, the existing monitoring devices have a simplistic heat dissipation design and chaotic wiring harness management, which can easily lead to overheating and signal interference, affecting system stability. Summary of the Invention

[0005] The purpose of this invention is to provide a power visualization monitoring device and its remote Internet early warning system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a power visualization monitoring device and its remote Internet early warning system, comprising a cabinet, an installation plate inside the cabinet, a monitoring host fixedly connected to the top of the installation plate, a fixing frame inside the cabinet, a bracket fixedly connected to the bottom of the fixing frame, a support plate fixedly connected to the bottom of the bracket, multiple cooling fans fixedly connected inside the support plate, a wire clamping plate fixedly connected to the bottom of the support plate, an air guide plate on the front side of the bracket, multiple limiting plates fixedly connected to the bottom of the air guide plate, a lower guide tube fixedly connected to the rear side of the support plate, the lower guide tube corresponding to the multiple cooling fans, a telescopic flexible plate fixedly connected to the top of the lower guide tube, an upper guide tube fixedly connected to the top of the telescopic flexible plate, the upper guide tube fixedly connected to the top of the cabinet interior, a support component on the top of the fixing frame, and an adjustment component on the rear side of the lower guide tube.

[0007] Preferably, both ends of the front side of the bracket are fixedly connected to slide rails, and a slider is slidably connected inside the slide rails. A vertical rod is fixedly connected to the front side of the slider. A first spring is provided inside the slide rail. The two ends of the first spring are respectively attached to the top of the inner wall of the slide rail and the top of the slider. The two vertical rods are respectively fixedly connected to both sides of the air guide plate, so that the vertical rods support the air guide plate, thereby supporting the multiple limiting plates.

[0008] Preferably, the support assembly includes a sliding sleeve, a piston plate, a sliding rod, a support block, and a second spring. An upper connecting block is fixedly connected to the top of the cabinet. The top of the sliding sleeve is hinged to the upper connecting block. The piston plate is slidably connected inside the sliding sleeve. The diameter of the piston plate is adapted to the inner diameter of the sliding sleeve, so that the gaps are well sealed when the piston plate moves inside the sliding sleeve, and the stability of the piston plate during movement is improved.

[0009] Preferably, the sliding sleeve is filled with buffer solution, the piston plate has multiple through holes arranged in a ring array inside, the sliding rod is fixedly connected to the bottom of the piston plate, the sliding rod passes through the bottom wall of the sliding sleeve and is slidably connected to the sliding sleeve, the support block is fixedly connected to the bottom end of the sliding rod, a second spring is sleeved on the outside of the sliding rod, the two ends of the second spring are fixedly connected to the sliding sleeve and the support block respectively, and a lower hinge block is hinged inside the support block. The lower hinge block is fixedly connected to the top of the fixed frame to form a damping effect and improve the buffering capacity.

[0010] Preferably, the adjustment assembly includes a guide rail, a bidirectional threaded rod, and a cover plate. A rear baffle is installed on the rear side of the cabinet. The guide rail is fixedly connected to the side of the rear baffle facing the inside of the cabinet. The bidirectional threaded rod is rotatably connected to the inside of the guide rail. The thread directions at both ends of the bidirectional threaded rod are set to opposite directions. A motor is fixedly connected to one end of the guide rail. The output end of the motor is fixedly connected to the bidirectional threaded rod. The motor starts and drives the bidirectional threaded rod to rotate, thereby driving the two guide blocks to move relative to each other.

[0011] Preferably, guide blocks are slidably connected to both sides of the inside of the guide rail. Two guide blocks are threaded to the outer sides of both ends of the bidirectional threaded rod. Side plates are fixedly connected to the front of each of the two guide blocks. Hinges are hinged to the opposite side of each of the two side plates. Support arms are hinged to the top of each of the two hinge plates. Mounting blocks are hinged to the front ends of each of the two support arms. Mounting blocks are fixedly connected to the rear side of the cover plate. The cover plate is snapped onto the rear side of the lower guide tube. When the guide blocks move, they drive the side plates and hinge plates to move, and the hinge plates drive the support arms to move, thereby extending the support arms forward or pulling them back, thus driving the lower guide tube to move back and forth.

[0012] Preferably, a protective door is installed on the front side of the cabinet, a monitor is fixedly connected to the front side of the protective door, a ventilation opening is provided inside the protective door, and mounting brackets are fixedly connected to both sides inside the cabinet. Multiple mounting slots are provided on the opposite side of each of the two mounting brackets, and the mounting plate is snapped into the mounting slot of the mounting bracket.

[0013] A dynamic visualization remote internet early warning system includes physical devices. The output terminals of the physical devices are connected to intelligent sensors. The output terminals of the intelligent sensors are connected to edge computing nodes. The output terminals of the edge computing nodes are connected to encrypted transmission. The output terminals of the encrypted transmission are connected to a cloud platform. The output terminals of the cloud platform are connected to a digital twin engine and an AI prediction engine. The output terminals of the digital twin engine and the AI ​​prediction engine are connected to a visual monitoring display screen. The output terminal of the visual monitoring display screen is connected to an early warning decision-making function. The output terminal of the early warning decision-making function is connected to automatic shutdown, SMS notification, and log recording. The automatic shutdown output terminal is connected to an alarm issuing function. The alarm issuing output terminal is connected to a synchronization status, and the synchronization status output terminal is connected to... The digital twin engine connects and collects equipment operation data in real time through smart sensors. After processing by edge computing nodes, the data is transmitted to the cloud platform via encrypted transmission. The digital twin engine simulates the operating status in real time, while the AI ​​prediction engine performs fault prediction and risk assessment. The processing results are displayed on a visual monitoring screen. When an anomaly is detected, the system automatically triggers a tiered warning. Based on the warning level, the system makes corresponding decisions. If the risk is low, a signal is sent to the log; if the risk is medium, a signal is sent to the SMS notification; if the risk is high, a signal is sent to automatically shut down the system and trigger an alarm. Then, a signal is sent to the synchronization status, which transmits the risk information to the digital twin engine.

[0014] Preferably, the digital twin engine includes a physical field simulation, the output of which is connected to a thermodynamic model, the output of which is connected to a real-time state mapping, the output of which is connected to a health score, and the output of which is connected to a visualization rendering. The digital twin engine receives sensor data in real time, constructs a multi-physics simulation model of the device, and dynamically maps the actual operating state of the device. The engine continuously compares the differences in operating parameters between the virtual model and the physical device, calculates the device health score, and performs visualization rendering based on real-time data. The results are then sent to a visual monitoring display screen to provide maintenance personnel with an intuitive display of the device status.

[0015] Preferably, the AI ​​prediction engine includes LSMT time-series prediction. The input of the LSMT time-series prediction is connected to historical data, and the output of the LSMT time-series prediction is connected to future value prediction and residual analysis. The output of the future value prediction is connected to fault probability calculation, and the output of the residual analysis is connected to random forest detection. The outputs of the fault probability calculation and random forest detection are connected to dynamic threshold adjustment. Historical data sends signals to the LSMT time-series prediction. The LSMT time-series prediction performs time-series analysis on the historical operating data of the equipment using an LSTM neural network, predicts the performance change trend for the next 72 hours using future values, and simultaneously uses a random forest algorithm to classify fault modes based on real-time collected multi-dimensional features such as vibration and temperature. The engine combines a lonely forest anomaly detection algorithm to identify potential risks and dynamically adjusts the warning threshold based on the equipment's operating conditions, ultimately outputting a fault probability assessment and risk level, providing decision-making basis for the digital twin engine, and realizing a closed-loop predictive maintenance function from "data input - intelligent analysis - warning output". Compared with the prior art, the beneficial effects of this invention are:

[0016] 1. In this application, when the fixed frame descends, it drives the bracket and support plate to descend, and the support plate drives the slide rails on both sides to descend, which in turn drives the slider and vertical rod to descend. The vertical rod drives the air guide plate and limit plate to descend, so that the limit plate contacts the top of the monitoring host and separates the wire harness inside multiple limit plates. After the limit plate contacts the monitoring host, the slide rail continues to descend, causing the slider to slide inside the slide rail and compress the first spring. At the same time, the support plate and the wire pressing plate continue to descend, and the wire pressing plate presses down on the wire harness to prevent the wire harness from shifting and getting tangled, making it easier to manage the wires.

[0017] 2. As the support plate continues to descend, the support plate moves relative to the air guide plate, aligning them to position the wiring harness and forming a ventilation channel above it. A power supply is installed inside the mounting bracket, and multiple cooling fans are electrically connected to the power supply. By starting the multiple cooling fans, the airflow in the ventilation channel is accelerated, thereby simultaneously and rapidly cooling the monitoring host and the wiring harness. During the cooling process, air flows along the cover plate and is exhausted from inside the cabinet, improving the cooling effect.

[0018] 3. The digital twin engine of this application receives sensor data in real time, constructs a multi-physics simulation model of the equipment, and dynamically maps the actual operating status of the equipment. The engine continuously compares the differences in operating parameters between the virtual model and the physical equipment, calculates the equipment health score, and performs visualization rendering based on real-time data. The results are sent to the visualization monitoring display screen to provide maintenance personnel with an intuitive display of the equipment status. At the same time, the simulation results are fed back to the AI ​​prediction engine to jointly complete fault warning and performance optimization, realizing closed-loop monitoring and intelligent decision support for the equipment from physical entity to digital model. LSMT time series prediction uses LSTM neural network to perform time series analysis on the historical operating data of the equipment, predicts the performance change trend of the next 72 hours through future values, and uses random forest algorithm to classify fault modes of multi-dimensional features such as vibration and temperature collected in real time. The engine combines the Lonely Forest anomaly detection algorithm to identify potential risks and dynamically adjusts the warning threshold based on the equipment operating conditions. Finally, it outputs fault probability assessment and risk level, providing decision basis for the digital twin engine and realizing closed-loop predictive maintenance function from "data input-intelligent analysis-warning output". Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall structure of the present invention;

[0020] Figure 2 This is a schematic diagram of the structure of the rear baffle of the present invention;

[0021] Figure 3 This is a schematic diagram of the mounting bracket of the present invention;

[0022] Figure 4 This is a schematic diagram of the mounting plate of the present invention;

[0023] Figure 5 This is a schematic diagram of the structure of the telescopic flexible board of the present invention;

[0024] Figure 6 This is a schematic diagram of the cover plate of the present invention;

[0025] Figure 7 This is a schematic diagram of the structure of the fixing frame of the present invention;

[0026] Figure 8 This is a schematic diagram of the structure of the lower guide tube of the present invention;

[0027] Figure 9 This is a schematic diagram of the structure of the limiting plate of the present invention;

[0028] Figure 10 This is a schematic diagram of the structure of the bracket of the present invention;

[0029] Figure 11 This is a schematic diagram of the piston plate of the present invention;

[0030] Figure 12 This is a schematic diagram of the guide rail structure of the present invention;

[0031] Figure 13 This is a schematic diagram of the slider of the present invention;

[0032] Figure 14 This is a schematic diagram of the dynamic visualization remote internet early warning system of the present invention; Figure 15 This is a schematic diagram of the digital twin engine of the present invention; Figure 16 This is a schematic diagram of the AI ​​prediction engine of the present invention.

[0033] The following are the labeling elements in the diagram: 1. Cabinet; 2. Mounting plate; 3. Monitoring host; 4. Fixing frame; 5. Bracket; 6. Support plate; 7. Cooling fan; 8. Cable clamping plate; 9. Air guide plate; 10. Limiting plate; 11. Slide rail; 12. Slider; 13. Vertical rod; 14. First spring; 15. Upper connecting block; 16. Sliding sleeve; 17. Piston plate; 18. Sliding rod; 19. Support block; 20. Second spring; 21. Lower hinge block; 22. Guide rail; 23. Two-way threaded rod; 24. Guide block; 25. Side plate; 26. Hinge plate; 27. Support arm; 28. Mounting block; 29. ​​Motor; 30. Cover plate; 31. Lower guide tube; 32. Telescopic flexible plate; 33. Upper guide tube; 34. Rear baffle; 35. Protective door; 36. Monitor; 37. Mounting bracket;

[0034] 100. Physical devices; 200. Smart sensors; 300. Edge computing nodes; 400. Encrypted transmission; 500. Cloud platform; 600. Digital twin engine; 601. Physical field simulation; 602. Thermodynamic model; 603. Real-time state mapping; 604. Health score; 605. Visual rendering; 700. AI prediction engine; 701. LSMT time series prediction; 702. Future value prediction; 703. Fault probability calculation; 704. Residual analysis; 705. Random forest detection; 706. Dynamic threshold adjustment; 800. Visual monitoring display screen; 900. Early warning decision; 1000. Automatic shutdown; 1100. Issue alarm; 1200. Synchronize status; 1300. SMS notification; 1400. Log recording; 1500. Historical data. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Example: Figures 1-13 As shown, the present invention provides a technical solution for a power visualization monitoring device, including a cabinet 1, an installation plate 2 inside the cabinet 1, a monitoring host 3 fixedly connected to the top of the installation plate 2, a protective door 35 installed on the front side of the cabinet 1, a monitor 36 fixedly connected to the front side of the protective door 35, a ventilation opening inside the protective door 35, mounting brackets 37 fixedly connected to both sides inside the cabinet 1, multiple mounting slots opened on opposite sides of the two mounting brackets 37, the installation plate 2 snaps into the mounting slots of the mounting brackets 37, a fixing bracket 4 inside the cabinet 1, a bracket 5 fixedly connected to the bottom of the fixing bracket 4, a support plate 6 fixedly connected to the bottom of the bracket 5, multiple cooling fans 7 fixedly connected inside the support plate 6, a wire clamping plate 8 fixedly connected to the bottom of the support plate 6, and an air guide plate 9 on the front side of the bracket 5. Both ends of the front side of the frame 5 are fixedly connected to slide rails 11. Slider 12 is slidably connected inside the slide rails 11. Vertical rods 13 are fixedly connected to the front of the slider 12. A first spring 14 is installed inside the slide rails 11. The two ends of the first spring 14 are respectively attached to the top of the inner wall of the slide rail 11 and the top of the slider 12. The two vertical rods 13 are fixedly connected to both sides of the air guide plate 9, so that the vertical rods 13 support the air guide plate 9, thereby supporting the multiple limiting plates 10. Multiple limiting plates 10 are fixedly connected to the bottom of the air guide plate 9. A lower guide tube 31 is fixedly connected to the rear side of the support plate 6. The lower guide tube 31 corresponds to multiple cooling fans 7. A telescopic flexible plate 32 is fixedly connected to the top of the lower guide tube 31. An upper guide tube 33 is fixedly connected to the top of the telescopic flexible plate 32. The upper guide tube 33 is fixedly connected to the top of the inside of the cabinet 1.

[0037] The top of the mounting bracket 4 is equipped with a support assembly, which includes a sliding sleeve 16, a piston plate 17, a sliding rod 18, a support block 19, and a second spring 20. An upper connecting block 15 is fixedly connected to the top of the cabinet 1. The top of the sliding sleeve 16 is hinged to the upper connecting block 15. The piston plate 17 is slidably connected inside the sliding sleeve 16. The diameter of the piston plate 17 matches the inner diameter of the sliding sleeve 16, ensuring good sealing at the gaps when the piston plate 17 moves within the sliding sleeve 16 and improving the stability of the piston plate 17 during movement. The sliding sleeve 16 is filled with a buffer... The piston plate 17 has multiple through holes arranged in a ring array inside. The slide rod 18 is fixedly connected to the bottom of the piston plate 17. The slide rod 18 passes through the bottom wall of the slide sleeve 16 and is slidably connected to the slide sleeve 16. The support block 19 is fixedly connected to the bottom end of the slide rod 18. A second spring 20 is sleeved on the outside of the slide rod 18. The two ends of the second spring 20 are fixedly connected to the slide sleeve 16 and the support block 19, respectively. A lower hinge block 21 is hinged inside the support block 19. The lower hinge block 21 is fixedly connected to the top of the fixed frame 4 to form a damping effect and improve the buffering capacity.

[0038] An adjustment assembly is provided on the rear side of the lower guide tube 31. The adjustment assembly includes a guide rail 22, a bidirectional threaded rod 23, and a cover plate 30. A rear baffle 34 is installed on the rear side of the cabinet 1. The guide rail 22 is fixedly connected to the side of the rear baffle 34 facing the inside of the cabinet 1. The bidirectional threaded rod 23 is rotatably connected to the inside of the guide rail 22. The thread directions at both ends of the bidirectional threaded rod 23 are set to opposite directions. A motor 29 is fixedly connected to one end of the guide rail 22. The output end of the motor 29 is fixedly connected to the bidirectional threaded rod 23. The motor 29 drives the bidirectional threaded rod 23 to rotate, thereby driving the two guide blocks 24 to move relative to each other. Guide blocks 24 are slidably connected to both sides inside the guide rail 22. The guide blocks 24 are threaded to the outer sides of both ends of the bidirectional threaded rod 23. The front sides of the two guide blocks 24 are fixedly connected to the side plates 25. The two side plates 25 are hinged to the opposite side of the two side plates 25. The top of the two hinge plates 26 is hinged to the support arm 27. The front end of the two support arms 27 is hinged to the mounting block 28. The two mounting blocks 28 are fixedly connected to the rear side of the cover plate 30. The cover plate 30 is snapped onto the rear side of the lower guide tube 31. When the guide blocks 24 move, they drive the side plates 25 and the hinge plates 26 to move, and the hinge plates 26 drive the support arms 27 to move. This allows the support arms 27 to extend forward or be pulled back, thereby driving the lower guide tube 31 to move back and forth.

[0039] In use, the mounting plate 2 is inserted into the mounting slot at the corresponding height inside the mounting bracket 37 to support it. The mounting plate 2 is then fixed inside the mounting bracket 37 using bolts or clips. Next, the wiring from the monitoring host 3 is placed above the monitoring host 3, and the wiring is pulled backward. The wiring is then passed through the opening at the bottom of the lower guide tube 31, through the lower guide tube 31, and out through the opening at the top of the cabinet 1 to connect to the wiring device. The cover plate 30 is then snapped into the lower guide tube 31 from the rear side of the cabinet 1. The cover plate 30 and the lower guide tube 31 are fixed using clips on both sides, and the wiring is clamped in the middle. Then, the motor 29 is started, causing the motor 29 to drive the bidirectional threaded rod 23 to rotate. The bidirectional threaded rod 23 moves the two guide blocks 24 towards the center, causing the guide blocks 24 to move the side plate 25 and the hinge plate 26. The hinge plate 26 then moves the support arm 27 and the mounting block 28, thereby moving the two mounting blocks 28 forward to move the cover plate. 30 moves forward, causing the cover plate 30 to move the lower guide tube 31 forward. During the forward movement of the lower guide tube 31, the support plate 6 and the fixing frame 4 also move forward. When the fixing frame 4 moves to the middle position, the sliding sleeve 16 remains vertical. At this time, the sliding sleeve 16 and the sliding rod 18 can be stretched to their maximum length. When the fixing frame 4 is at the rearmost position, the sliding sleeve 16 tilts backward. At this time, even when the sliding sleeve 16 and the sliding rod 18 are stretched to their maximum length, the pressure plate 8 will not contact the top of the monitoring host 3. After the fixing frame 4 moves forward... The fixed frame 4 will descend synchronously, causing the lower hinge block 21 to pull the support block 19 and the slide rod 18 down. The slide rod 18 will then drive the piston plate 17 down inside the sliding sleeve 16. When the sliding sleeve 16 slides inside the sliding sleeve 16, the buffer solution inside the sliding sleeve 16 flows through multiple through holes inside the piston plate 17, creating a damping effect. Combined with the elastic force of the second spring 20, this buffers the relative movement between the support block 19 and the sliding sleeve 16, thereby improving the stability of the fixed frame 4 during the descent process.

[0040] As the fixed frame 4 descends, it drives the bracket 5 and support plate 6 to descend, causing the support plate 6 to drive the slide rails 11 on both sides to descend. The slide rails 11 then drive the slider 12 and vertical rod 13 to descend, and the vertical rod 13 drives the air guide plate 9 and limiting plate 10 to descend. This causes the limiting plate 10 to contact the top of the monitoring host 3, separating the wire harness within multiple limiting plates 10. After the limiting plate 10 contacts the monitoring host 3, the slide rails 11 continue to descend, causing the slider 12 to slide within the slide rails 11 and compress the first spring 14. Simultaneously, the support plate 6 and wire pressing plate 8 continue to descend, and the wire pressing plate 8... The wire harness is pressed down to prevent it from shifting or getting tangled, making it easier to manage. As the support plate 6 continues to descend, it moves relative to the air guide plate 9, aligning them and positioning the wire harness to form a ventilation channel above it. The mounting bracket 4 contains a power supply, and multiple cooling fans 7 are electrically connected to the power supply. The activation of the multiple cooling fans 7 accelerates airflow within the ventilation channel, thereby simultaneously and rapidly cooling the monitoring host 3 and the wire harness. During the cooling process, air flows along the cover plate 30 and is exhausted from the cabinet 1, improving the cooling effect.

[0041] A dynamic visualization remote internet early warning system includes physical devices 100. The output of physical devices 100 is connected to intelligent sensors 200. The output of intelligent sensors 200 is connected to edge computing nodes 300. The output of edge computing nodes 300 is connected to encrypted transmission 400. The output of encrypted transmission 400 is connected to a cloud platform 500. The output of cloud platform 500 is connected to a digital twin engine 600 and an AI prediction engine 700. The outputs of digital twin engine 600 and AI prediction engine 700 are connected to a visual monitoring display screen 800. The output of visual monitoring display screen 800 is connected to an early warning decision 900. The output of early warning decision 900 is connected to automatic shutdown 1000, SMS notification 1300, and log recording 1400. The output of automatic shutdown 1000 is connected to alarm issuance 1100. The output of alarm issuance 1100 is connected to synchronization status 1200. The synchronization status 1200 output... The output end connects to the digital twin engine 600, collects device operation data in real time through smart sensors 200, processes it through edge computing nodes 300, and transmits it to the cloud platform 500 via encrypted transmission 400. The digital twin engine 600 simulates the operating status in real time, while the AI ​​prediction engine 700 performs fault prediction and risk assessment. The processing results are displayed on the visual monitoring screen 800. When an anomaly is detected, the system automatically triggers a graded warning. The warning decision 900 makes corresponding decisions based on the warning level. If the risk is low, a signal is sent to the log 1400. If the risk is medium, a signal is sent to the SMS notification 1300. If the risk is high, a signal is sent to the automatic shutdown 1000, which in turn sends a signal to the alarm 1100. Then, a signal is sent to the synchronization status 1200, which transmits the risk information to the digital twin engine 600.

[0042] The digital twin engine 600 includes a physical field simulation 601, the output of which is connected to a thermodynamic model 602. The output of the thermodynamic model 602 is connected to a real-time state mapping 603. The output of the real-time state mapping 603 is connected to a health score 604. The output of the health score 604 is connected to a visualization rendering 605. The digital twin engine 600 receives sensor data in real time to build a multi-physics simulation model of the device (including thermodynamic, vibration, and electromagnetic characteristics), dynamically mapping the actual operating state of the device. The engine continuously compares the differences in operating parameters between the virtual model and the physical device, calculates the device health score 604, and performs visualization rendering 605 based on real-time data. The results are then sent to a visualization monitoring display screen 800 to provide maintenance personnel with an intuitive display of the device status.

[0043] The AI ​​prediction engine 700 includes LSMT time series prediction 701. The input of LSMT time series prediction 701 is connected to historical data 1500. The output of LSMT time series prediction 701 is connected to future value prediction 702 and residual analysis 704. The output of future value prediction 702 is connected to fault probability calculation 703. The output of residual analysis 704 is connected to random forest detection 705. The outputs of fault probability calculation 703 and random forest detection 705 are connected to dynamic threshold adjustment 706. The historical data 1500 sends signals to LSMT time series prediction 701. The Time Series Prediction 701 uses an LSTM neural network to perform time series analysis on historical equipment operating data, and the Future Value Prediction 702 predicts the performance change trend for the next 72 hours. At the same time, the Random Forest algorithm is used to classify fault modes based on real-time collected multi-dimensional features such as vibration and temperature. The engine combines the Lonely Forest anomaly detection algorithm to identify potential risks and dynamically adjusts the warning threshold based on equipment operating conditions. Finally, it outputs a fault probability assessment and risk level, providing decision-making basis for the Digital Twin Engine 600, and realizing a closed-loop predictive maintenance function from "data input - intelligent analysis - warning output".

[0044] In this solution, the data detected by the intelligent sensor 200 from the physical device 100 is transmitted to the edge computing node 300. The intelligent sensor 200 collects real-time device operation data, which is then processed by the edge computing node 300 and transmitted via encrypted transmission 400 to the cloud platform 500. The digital twin engine 600 simulates the operating status in real time, while the AI ​​prediction engine 700 performs fault prediction and risk assessment. The processing results are displayed on the visual monitoring screen 800. When an anomaly is detected, the system automatically triggers a tiered warning. The warning decision 900 makes corresponding decisions based on the warning level: if the risk is low, a signal is sent to the log 1400; if the risk is medium, a signal is sent to the SMS notification 1300; if the risk is high, a signal is sent to the automatic shutdown 1000, which in turn sends a signal to the alarm 1100, and then a signal is sent to the synchronization status 1200, causing the synchronization status 1200 to transmit risk information to the digital twin engine 600. The digital twin engine 600, by receiving sensor data in real time, constructs a multi-physics simulation model of the device (including thermal simulation). The engine dynamically maps the actual operating status of the equipment (including its mechanical, vibration, and electromagnetic characteristics). It continuously compares the differences in operating parameters between the virtual model and the physical equipment, calculates the equipment health score (604), and performs visualization rendering based on real-time data (605). The results are sent to the visualization monitoring display screen (800) to provide maintenance personnel with an intuitive display of the equipment status. Simultaneously, the simulation results are fed back to the AI ​​prediction engine (700) to jointly complete fault warnings and performance optimization, achieving closed-loop monitoring and intelligent decision support from physical entity to digital model. The LSTM time-series prediction (701) uses an LSTM neural network to perform time-series analysis on historical operating data of the equipment, and predicts the performance change trend for the next 72 hours through future value prediction (702). It also uses a random forest algorithm to classify fault modes based on real-time collected vibration, temperature, and other multi-dimensional features. The engine combines a lonely forest anomaly detection algorithm to identify potential risks and dynamically adjusts the warning threshold based on the equipment's operating conditions, ultimately outputting a fault probability assessment and risk level, providing decision-making basis for the digital twin engine (600), and realizing a closed-loop predictive maintenance function from "data input - intelligent analysis - warning output".

[0045] LSTM (Long Short-Term Memory) neural networks are a special type of recurrent neural network that effectively solves the gradient vanishing problem of traditional recurrent neural networks through unique gating mechanisms (input gate, forget gate, output gate), and are particularly adept at handling long-term dependencies in time-series data. In this system, LSTM analyzes historical operating data of equipment (such as vibration, temperature, and other time series) to accurately capture the degradation trend and periodic patterns of equipment performance, enabling state prediction for the next 72 hours. This provides key time-series features for early fault warning. The design of its memory units allows it to learn both short-term fluctuation characteristics and retain long-term operating patterns, significantly improving the accuracy of predictive maintenance.

[0046] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A power visualization monitoring device, comprising a cabinet (1), wherein a mounting plate (2) is disposed inside the cabinet (1), and a monitoring host (3) is fixedly connected to the top of the mounting plate (2), characterized in that: The cabinet (1) is equipped with a fixed frame (4), the bottom of the fixed frame (4) is fixedly connected to a bracket (5), the bottom of the bracket (5) is fixedly connected to a support plate (6), the support plate (6) is fixedly connected to a plurality of cooling fans (7), the bottom of the support plate (6) is fixedly connected to a wire clamping plate (8), the front side of the bracket (5) is provided with an air guide plate (9), the bottom of the air guide plate (9) is fixedly connected to a plurality of limiting plates (10), the rear side of the support plate (6) is fixedly connected to a lower guide tube (31), the lower guide tube (31) corresponds to the plurality of cooling fans (7), the top of the lower guide tube (31) is fixedly connected to a telescopic flexible plate (32), the top of the telescopic flexible plate (32) is fixedly connected to an upper guide tube (33), the upper guide tube (33) is fixedly connected to the top of the cabinet (1), the top of the fixed frame (4) is provided with a support component, and the rear side of the lower guide tube (31) is provided with an adjustment component.

2. The power visualization monitoring device according to claim 1, characterized in that: The bracket (5) has slide rails (11) fixedly connected to both ends of the front side. A slider (12) is slidably connected inside the slide rail (11). A vertical rod (13) is fixedly connected to the front side of the slider (12). A first spring (14) is provided inside the slide rail (11). The two ends of the first spring (14) are respectively attached to the top of the inner wall of the slide rail (11) and the top of the slider (12). The two vertical rods (13) are respectively fixedly connected to both sides of the air guide plate (9).

3. The power visualization monitoring device according to claim 1, characterized in that: The support assembly includes a sliding sleeve (16), a piston plate (17), a sliding rod (18), a support block (19), and a second spring (20). An upper connecting block (15) is fixedly connected to the top of the cabinet (1). The top of the sliding sleeve (16) is hinged to the upper connecting block (15). The piston plate (17) is slidably connected inside the sliding sleeve (16). The diameter of the piston plate (17) is adapted to the inner diameter of the sliding sleeve (16).

4. The power visualization monitoring device according to claim 3, characterized in that: The sliding sleeve (16) is filled with buffer solution. The piston plate (17) has multiple through holes arranged in a ring array inside. The sliding rod (18) is fixedly connected to the bottom of the piston plate (17). The sliding rod (18) passes through the bottom wall of the sliding sleeve (16) and is slidably connected to the sliding sleeve (16). The support block (19) is fixedly connected to the bottom end of the sliding rod (18). A second spring (20) is sleeved on the outside of the sliding rod (18). The two ends of the second spring (20) are fixedly connected to the sliding sleeve (16) and the support block (19) respectively. A lower hinge block (21) is hinged inside the support block (19). The lower hinge block (21) is fixedly connected to the top of the fixing frame (4).

5. The power visualization monitoring device according to claim 1, characterized in that: The adjustment assembly includes a guide rail (22), a bidirectional threaded rod (23), and a cover plate (30). A rear baffle (34) is installed on the rear side of the cabinet (1). The guide rail (22) is fixedly connected to the side of the rear baffle (34) facing the inside of the cabinet (1). The bidirectional threaded rod (23) is rotatably connected to the inside of the guide rail (22). The thread directions at both ends of the bidirectional threaded rod (23) are set to opposite directions. A motor (29) is fixedly connected to one end of the guide rail (22). The output end of the motor (29) is fixedly connected to the bidirectional threaded rod (23).

6. The power visualization monitoring device according to claim 5, characterized in that: Guide blocks (24) are slidably connected to both sides inside the guide rail (22). The two guide blocks (24) are threaded to the outer sides of both ends of the bidirectional threaded rod (23). Side plates (25) are fixedly connected to the front side of the two guide blocks (24). Hinges (26) are hinged to the opposite side of the two side plates (25). Support arms (27) are hinged to the top of the two hinge plates (26). Mounting blocks (28) are hinged to the front end of the two support arms (27). The two mounting blocks (28) are fixedly connected to the rear side of the cover plate (30). The cover plate (30) is snapped onto the rear side of the lower guide tube (31).

7. A power visualization monitoring device according to claim 1, characterized in that: The cabinet (1) is equipped with a protective door (35) on the front side. A monitor (36) is fixedly connected to the front side of the protective door (35). A ventilation opening is provided inside the protective door (35). Mounting brackets (37) are fixedly connected to both sides inside the cabinet (1). Multiple mounting slots are provided on the opposite side of the two mounting brackets (37). The mounting plate (2) is snapped into the mounting slot of the mounting bracket (37).

8. A dynamic visualization remote internet early warning system, applicable to the dynamic visualization monitoring device as described in any one of claims 1-7, characterized in that: The system includes a physical device (100), the output of which is connected to a smart sensor (200). The output of the smart sensor (200) is connected to an edge computing node (300). The output of the edge computing node (300) is connected to an encrypted transmission (400). The output of the encrypted transmission (400) is connected to a cloud platform (500). The output of the cloud platform (500) is connected to a digital twin engine (600) and an AI prediction engine (700). The output end is connected to a visual monitoring display screen (800), the output end of which is connected to an early warning decision (900), the output end of which is connected to an automatic shutdown (1000), an SMS notification (1300), and a log recording (1400), the output end of which is connected to an alarm (1100), the output end of which is connected to a synchronization status (1200), and the output end of which is connected to a digital twin engine (600).

9. A dynamic visualization remote internet early warning system according to claim 8, characterized in that: The digital twin engine (600) includes a physics simulation (601), the output of which is connected to a thermodynamic model (602), the output of which is connected to a real-time state mapping (603), the output of which is connected to a health score (604), and the output of which is connected to a visualization rendering (605).

10. A dynamic visualization remote internet early warning system according to claim 8, characterized in that: The AI ​​prediction engine (700) includes LSMT time series prediction (701), the input of which is connected to historical data (1500), the output of which is connected to future value prediction (702) and residual analysis (704), the output of which is connected to fault probability calculation (703), the output of which is connected to random forest detection (705), and the outputs of which are connected to dynamic threshold adjustment (706).

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