Distribution box fire early warning and automatic power-off system based on AI

Abstract

The invention relates to the field of distribution box fire early warning and automatic power-off systems, in particular to an AI-based distribution box fire early warning and automatic power-off system, which comprises an environment monitoring module, a construction process monitoring module, a risk identification and early warning module and a safety assessment decision module, the environment monitoring module creates a cold region interchange overpass construction environment monitoring platform by using a'weather + geology 'linkage mode, and deepens the fusion of environment data and construction decisions; the construction process monitoring module creates a real-time monitoring platform for the construction process of the interchange overpass in the cold region by utilizing a'sensor + video monitoring 'linkage mode, and deepens the fusion of construction dynamics and safety management and control; powerful support is provided for construction safety management, the emergency plan making and drilling module can make a targeted emergency plan according to a risk assessment result and perform drilling, and the construction safety management level is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution box fire warning and automatic power-off system, in particular to an AI-based power distribution box fire warning and automatic power-off system. BACKGROUND

[0002] With the development of society and the improvement of people's living standards, the widespread application of power systems makes the power distribution box an indispensable device in daily life. However, the power distribution box may cause fire during operation due to electrical failure, equipment aging, environmental factors and other reasons, which poses a great threat to people's life and property safety. The traditional fire warning system and automatic power-off device have many shortcomings in actual application and are difficult to meet the needs of modern power distribution box fire prevention.

[0003] The traditional fire warning system mainly relies on sensors to monitor physical indicators such as flame or smoke. For electrical equipment such as power distribution boxes, the detection sensitivity of these sensors may not be sufficient, resulting in the neglect or delay of fire precursors. At the same time, there may be some common interference factors around the charging pile, such as vehicle exhaust, industrial dust, etc., which can easily lead to an increase in false positives. The existing fire warning mechanism is mostly large in size and difficult to install, and the internal detection components are easily attached by dust and other debris, thereby reducing its warning sensitivity. In addition, the traditional fire warning system may be affected by factors such as light, temperature, humidity, etc. in complex and variable indoor and outdoor environments, resulting in low warning accuracy or high false positive rate.

[0004] Therefore, an AI-based power distribution box fire warning and automatic power-off system is proposed to solve the above problems. SUMMARY

[0005] In order to make up for the shortcomings of the prior art, the problem of the traditional fire warning system mainly relying on sensors to monitor physical indicators such as flame or smoke, for electrical equipment such as power distribution boxes, the detection sensitivity of these sensors may not be sufficient, resulting in the neglect or delay of fire precursors. At the same time, there may be some common interference factors around the charging pile, such as vehicle exhaust, industrial dust, etc., which can easily lead to an increase in false positives. The existing fire warning mechanism is mostly large in size and difficult to install, and the internal detection components are easily attached by dust and other debris, thereby reducing its warning sensitivity. In addition, the traditional fire warning system may be affected by factors such as light, temperature, humidity, etc. in complex and variable indoor and outdoor environments, resulting in low warning accuracy or high false positive rate.

[0006] The technical scheme adopted by the present application to solve its technical problems is: the power distribution box fire warning and automatic power-off system based on AI, comprising an electrical parameter monitoring module, an environmental parameter monitoring module, a fire warning module and an automatic power-off control module; the electrical parameter monitoring module uses a "smart meter + current and voltage sensor" linkage mode to create a power distribution box electrical parameter real-time monitoring platform, deepening the integration of electrical data and fire warning; the environmental parameter monitoring module uses a "temperature and humidity sensor + smoke sensor" linkage mode to create a power distribution box environmental parameter real-time monitoring platform, deepening the integration of environmental perception and fire warning; the fire warning module uses a "big data analysis + machine learning algorithm" linkage mode to create a power distribution box fire intelligent warning platform, deepening the integration of fire risk perception and warning response; the automatic power-off control module uses a "smart circuit breaker + remote control unit" linkage mode to create a power distribution box fire automatic power-off control platform, deepening the integration of fire warning and power-off protection.

[0007] Preferably, the electrical parameter monitoring module comprises a current monitoring submodule and a voltage monitoring submodule.

[0008] Preferably, the current monitoring submodule comprises real-time current value monitoring and current fluctuation monitoring.

[0009] Preferably, the voltage monitoring submodule comprises real-time voltage value monitoring and voltage abnormal fluctuation monitoring.

[0010] Preferably, the environmental parameter monitoring module comprises a temperature monitoring submodule and a smoke concentration monitoring submodule.

[0011] Preferably, the fire warning module comprises fire feature database establishment and real-time fire warning signal generation.

[0012] Preferably, the automatic power-off control module comprises power-off instruction generation and power-off execution feedback.

[0013] Preferably, the fire feature database establishment comprises electrical fire feature data collection, environmental fire feature data collection and historical fire case data arrangement.

[0014] Preferably, the power-off instruction generation comprises generating power-off instructions of corresponding priority according to the intensity of the fire warning signal and a redundancy checking mechanism for the power-off instructions.

[0015] Preferably, the automatic power-off control module further comprises a power-off state monitoring and power supply recovery condition judging module for continuously monitoring the state of the power distribution box after power-off and restoring power supply when the safety conditions are met.

[0016] The present application has the advantages of:

[0017] 1. Multi-source data collaborative monitoring, improving the comprehensiveness of early warning: The system integrates electrical parameter monitoring modules and environmental parameter monitoring modules to build a dual-dimensional perception system of "electrical data + environmental data". The electrical parameter monitoring module uses the "smart meter + current and voltage sensor" mode to finely monitor the real-time values and fluctuation states of current and voltage, and can timely capture abnormal electrical signals such as overcurrent, undervoltage, and leakage. The environmental parameter monitoring module uses the "temperature and humidity sensor + smoke sensor" linkage to real-time perceive fire precursors such as sudden temperature change and smoke concentration rise. The two types of data complement each other, effectively avoiding single parameter misjudgment, and significantly improving the accuracy and reliability of fire warning.

[0018] 2. AI technology deeply empowered, strengthening risk prediction ability: The fire warning module introduces "big data analysis + machine learning algorithm", builds a fire feature database, integrates electrical fire feature data, environmental fire feature data, and historical cases, and enables the system to have self-learning ability. The algorithm can dynamically analyze monitoring data, mine potential fire risk patterns, and generate real-time fire warning signals in advance, realizing the transition from passive response to active prevention, and significantly improving the timeliness and forward-looking of warning compared with traditional threshold alarm methods.

[0019] 3. Intelligent power-off closed-loop control, ensuring the effectiveness of emergency handling: The automatic power-off control module uses the "intelligent circuit breaker + remote control unit" architecture, generates differentiated priority power-off instructions based on the intensity of fire warning signals, and ensures the reliability of the instructions through a redundancy verification mechanism. After power-off, the system continuously monitors the state of the distribution box and intelligently determines the power restoration opportunity based on preset safety conditions (such as temperature dropping to a safe threshold, smoke dissipating, etc.), forming a complete closed loop of "monitoring - warning - power-off - state monitoring - power restoration", effectively cutting off the fire spread path and minimizing unnecessary power-off time, ensuring the safety and continuity of power supply.

[0020] 4. Modular architecture design, enhancing system expandability and maintainability: Each functional module uses standardized interfaces and communication protocols, with high independence and expandability. Users can flexibly add or remove monitoring nodes or upgrade module performance according to actual needs, such as replacing high-precision sensors or optimizing AI algorithms. At the same time, modular design reduces system maintenance complexity, allowing quick localization and replacement when a module fails, significantly improving operation and maintenance efficiency and reducing operating costs. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0022] Figure 1 The frame structure diagram of the power distribution box fire warning and automatic power-off system of the present application AI.

[0023] 1, environmental monitoring module; 101, weather monitoring submodule 101; 102, geological monitoring submodule; 2, construction process monitoring module; 201, construction equipment operation monitoring; 202, construction personnel behavior monitoring; 3, risk identification and early warning module; 301, risk database establishment; 302, real-time early warning information release; 4, safety evaluation decision module; 401, safety index system construction; 402, construction scheme optimization suggestion. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0025] Embodiment one

[0026] Please refer to Figure 1 As shown in the figure, the power distribution box fire warning and automatic power-off system based on AI includes an electrical parameter monitoring module 1, an environmental parameter monitoring module 2, a fire warning module 3, and an automatic power-off control module 4.

[0027] The electrical parameter monitoring module 1 uses the "smart meter + current and voltage sensor" linkage mode to create a real-time monitoring platform for electrical parameters of the power distribution box, deepening the integration of electrical data and fire warning. The environmental parameter monitoring module 2 uses the "temperature and humidity sensor + smoke sensor" linkage mode to create a real-time monitoring platform for environmental parameters of the power distribution box, deepening the integration of environmental perception and fire warning. The fire warning module 3 uses the "big data analysis + machine learning algorithm" linkage mode to create an intelligent fire warning platform for the power distribution box, deepening the integration of fire risk perception and warning response. The automatic power-off control module 4 uses the "smart circuit breaker + remote control unit" linkage mode to create an automatic power-off control platform for the power distribution box, deepening the integration of fire warning and power-off protection.

[0028] Further, the electrical parameter monitoring module 1 includes a current monitoring submodule 101 and a voltage monitoring submodule 102.

[0029] Further, the current monitoring submodule 101 includes real-time current value monitoring and current fluctuation monitoring.

[0030] Further, the voltage monitoring submodule 102 includes real-time voltage value monitoring and voltage abnormal fluctuation monitoring. The cooperative work of the current monitoring submodule 101 and the voltage monitoring submodule 102 provides comprehensive electrical parameter monitoring for the power distribution box fire warning and automatic power-off system. These monitoring data not only help prevent fires, but also can be used to optimize the operation and maintenance strategies of the power distribution box, improving the safety and reliability of the entire power system. By monitoring the changes in current and voltage in real time, the system can timely discover and respond to electrical faults, thereby effectively reducing the risk of fire.

[0031] Further, the environmental parameter monitoring module 2 includes a temperature monitoring submodule 201 and a smoke concentration monitoring submodule 202. The environmental parameter monitoring module 2 provides key environmental parameter data for the power distribution box fire warning and automatic power-off system through the cooperative work of the temperature monitoring submodule 201 and the smoke concentration monitoring submodule 202. These data not only help to discover fire risks in a timely manner, but also can be used to optimize the operation and maintenance strategies of the power distribution box, improving the safety and reliability of the entire power system. By monitoring the changes in temperature and smoke concentration in real time, the system can timely discover and respond to fire risks, thereby effectively reducing the likelihood of fire.

[0032] Further, the fire warning module 3 includes fire feature database establishment 301 and real-time fire warning signal generation 302. The fire warning module 3 realizes real-time monitoring and warning of power distribution box fire risks through the cooperative work of the fire feature database establishment 301 and the real-time fire warning signal generation 302. This module can quickly and accurately identify fire risks and timely issue warning signals, thereby providing strong support for the prevention and control of power distribution box fires. Through this intelligent fire warning mechanism, the safety and reliability of the power distribution box can be significantly improved, reducing the occurrence of fire accidents.

[0033] Further, the automatic power-off control module 4 includes power-off instruction generation 401 and power-off execution feedback 402. Through the cooperation of power-off instruction generation 401 and power-off execution feedback 402, the automatic power-off control module 4 realizes the functions of automatic power-off and power restoration of the power distribution box fire warning and automatic power-off system. This module can quickly cut off the power supply when detecting fire risk, prevent the spread of fire, and restore power supply in time after the fire risk is eliminated, thereby effectively protecting the safety of the power distribution box and connected equipment and reducing the loss of fire accidents. Through this intelligent power-off control mechanism, the safety and reliability of the power distribution box can be significantly improved, ensuring the stable operation of the power system.

[0034] Further, the fire feature database establishment 301 includes electrical fire feature data collection, environmental fire feature data collection, and historical fire case data arrangement.

[0035] Further, the power-off instruction generation 401 includes generating power-off instructions with corresponding priority according to the intensity of the fire warning signal and a redundancy verification mechanism for power-off instructions.

[0036] Further, the automatic power-off control module 4 also includes a power-off state monitoring and power restoration condition judgment module for continuously monitoring the state of the power distribution box after power-off and restoring power supply when the safety conditions are met.

[0037] The basic principles, main features, and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. AI-based distribution box fire warning and automatic power-off system, characterized by: It includes an electrical parameter monitoring module (1), an environmental parameter monitoring module (2), a fire warning module (3) and an automatic power-off control module (4); The electrical parameter monitoring module (1) utilizes the linkage mode of "smart meter + current and voltage sensor" to create a real-time monitoring platform for electrical parameters of the distribution box, thereby deepening the integration of electrical data and fire warning. The environmental parameter monitoring module (2) utilizes the linkage mode of "temperature and humidity sensor + smoke sensor" to create a real-time monitoring platform for environmental parameters of the distribution box, thereby deepening the integration of environmental perception and fire warning. The fire warning module (3) utilizes the linkage mode of "big data analysis + machine learning algorithm" to create an intelligent early warning platform for fires in the distribution box, thereby deepening the integration of fire risk perception and early warning response. The automatic power-off control module (4) utilizes the linkage mode of "smart circuit breaker + remote control unit" to create an automatic power-off control platform for fires in the distribution box, thereby deepening the integration of fire warning and power-off protection.

2. The AI-based distribution box fire warning and automatic power-off system according to claim 1 is characterized by: The electrical parameter monitoring module (1) comprises a current monitoring submodule (101) and a voltage monitoring submodule (102).

3. The AI-based distribution box fire warning and automatic power-off system according to claim 2 is characterized by: The current monitoring submodule (101) includes real-time current value monitoring and current fluctuation monitoring.

4. The AI-based distribution box fire warning and automatic power-off system according to claim 2 is characterized by: The voltage monitoring submodule (102) includes real-time voltage value monitoring and abnormal voltage fluctuation monitoring.

5. The AI-based distribution box fire warning and automatic power-off system according to claim 1 is characterized by: The environmental parameter monitoring module (2) comprises a temperature monitoring submodule (201) and a smoke concentration monitoring submodule (202).

6. The AI-based distribution box fire warning and automatic power-off system according to claim 1 is characterized by: The fire warning module (3) includes fire feature database establishment (301) and real-time fire warning signal generation (302).

7. The AI-based distribution box fire warning and automatic power-off system according to claim 1 is characterized by: The automatic power-off control module (4) includes power-off instruction generation (401) and power-off execution feedback (402).

8. The AI-based distribution box fire warning and automatic power-off system according to claim 6 is characterized by: The fire characteristic database establishment (301) includes the collection of electrical fire characteristic data, the collection of environmental fire characteristic data and the collation of historical fire case data.

9. The AI-based distribution box fire warning and automatic power-off system according to claim 7 is characterized by: The power-off instruction generation (401) includes generating a power-off instruction of corresponding priority according to the intensity of the fire warning signal and a redundancy check mechanism for the power-off instruction.

10. The AI-based distribution box fire warning and automatic power-off system according to claim 7 is characterized by: The automatic power-off control module (4) also includes a post-power-off status monitoring and power supply restoration condition judgment module, which is used to continuously monitor the status of the distribution box after power off and restore power supply when safety conditions are met.

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