Cabinet fire extremely-early-stage detection and grading early warning system based on multi-dimensional perception

Abstract

The invention relates to the technical field of fire early warning, in particular to a cabinet fire extremely-early-stage detection and grading early warning system based on multi-dimensional perception. Comprising a distributed sensing unit used for collecting internal temperature data of a cabinet, a spectrum analysis unit used for detecting electromagnetic interference signals, a main controller used for processing data and an early warning unit used for outputting early warning information, and the distributed sensing unit is connected with the main controller through a data bus. The output end of the spectrum analysis unit is electrically connected with the input end of the main controller, and the output end of the main controller is electrically connected with the early warning unit; the system is characterized by further comprising a particle detection unit used for collecting the concentration of pyrolysis particles, an environment sensing unit used for monitoring humidity and air pressure parameters, and a power monitoring unit used for obtaining active power data; according to the technical scheme, the coupling relation between electromagnetic interference and material pyrolysis can be processed in a targeted mode, and the blank of extremely early detection is filled up.

Description

Technical Field

[0001] The present invention relates to the field of fire warning technology, and in particular to a cabinet fire very early detection and graded warning system based on multi-dimensional perception. Background Art

[0002] In the field of cabinet fire monitoring, existing technologies generally rely on single physical parameters such as temperature and smoke to achieve early warning. Its core logic is to determine whether the fire risk standard is met by setting a fixed threshold.

[0003] However, cabinets contain a large number of strong electromagnetic interference sources, such as high-frequency switching devices and motors. The broadband electromagnetic radiation generated by these devices can couple with insulating materials. Electromagnetic energy can accelerate the thermal motion of material molecules, causing them to undergo early pyrolysis before reaching traditional temperature thresholds, producing trace characteristic particles. Existing systems fail to consider this coupling relationship between electromagnetic interference and material pyrolysis and assess risk solely based on temperature or smoke concentration. This leads to two key issues: first, they fail to capture the very early pyrolysis signals under electromagnetic catalysis, causing detection to lag behind the actual development of risk; second, electromagnetic interference can easily overwhelm the weak signals of temperature sensors, leading to measurement distortion and further exacerbating false alarms or missed alarms. This neglect of the electromagnetic-thermal decomposition coupling effect makes it difficult for existing systems to provide effective early warning of fires and fail to meet the core needs of cabinets for early detection and early response to fire risks.

[0004] Therefore, there is an urgent need for a technical solution that can specifically deal with the coupling relationship between electromagnetic interference and material thermal decoupling to fill the gap in very early detection. Summary of the Invention

[0005] The purpose of the present application is to solve the problems existing in the prior art, and the proposed multi-dimensional perception-based cabinet fire very early detection and hierarchical warning system, comprising a distributed sensing unit for collecting cabinet internal temperature data, a spectrum analysis unit for detecting electromagnetic interference signals, a main controller for processing data, and a warning unit for outputting warning information, the distributed sensing unit is connected with the main controller through a data bus, the output end of the spectrum analysis unit is electrically connected with the input end of the main controller, and the output end of the main controller is electrically connected with the warning unit, further comprising a particle detection unit for collecting pyrolysis particle concentration, an environmental sensing unit for monitoring humidity and air pressure parameters, and a power monitoring unit for obtaining active power data, the output end of the particle detection unit is electrically connected with the input end of the main controller, the environmental sensing unit is connected with the main controller through an SPI interface, and the signal output end of the power monitoring unit is connected with the signal input end of the main controller, the main controller calls the stored algorithm program, realizes very early pyrolysis particle concentration prediction, dynamic weight hierarchical warning calculation and temperature signal correction after interference suppression according to the data obtained by the distributed sensing unit, the spectrum analysis unit, the particle detection unit, the environmental sensing unit and the power monitoring unit, and outputs the hierarchical warning result through the warning unit; the spectrum analysis unit is used for detecting the energy spectrum density of the electromagnetic interference signals in the cabinet, and transmitting the energy spectrum density to the main controller to quantify the catalytic effect of electromagnetic interference on pyrolysis particle generation; the main controller calculates the electromagnetic-pyrolysis coupling coefficient according to the energy spectrum density and the pyrolysis particle concentration collected by the particle detection unit.

[0006] Preferably, the distributed sensing unit comprises fiber grating temperature sensors arranged at intervals along the height direction of the cabinet, the wavelength drift amount of each fiber grating temperature sensor is converted into temperature data through a fiber demodulation module, the output end of the fiber demodulation module is connected with the main controller through an RS485 bus, and the fiber grating temperature sensor and the fiber demodulation module are connected through an optical fiber.

[0007] Further preferably, the spectrum analysis unit comprises an electromagnetic interference receiving antenna, a low-noise amplifier, a band-pass filter and an FFT processing circuit connected in sequence, the electromagnetic interference receiving antenna is used for receiving electromagnetic interference signals in the cabinet, the low-noise amplifier amplifies the received electromagnetic interference signals, the band-pass filter filters the amplified electromagnetic interference signals, and the FFT processing circuit performs spectrum analysis on the filtered electromagnetic interference signals, and the output end of the FFT processing circuit is electrically connected with the input end of the main controller.

[0008] Further preferably, the main controller comprises a microprocessor, a memory and a clock module, the microprocessor is connected with the memory through an internal bus, the output of the clock module is connected with the external interrupt interface of the microprocessor, the memory is used for storing algorithm program and collected data, the clock module provides timing reference for the system, and the microprocessor realizes data processing by calling the algorithm program stored in the memory.

[0009] Further preferably, when realizing the early pyrolysis particle concentration prediction, the main controller adopts a pyrolysis particle concentration formula to calculate the pyrolysis particle concentration:

[0010] wherein, is the pyrolysis particle concentration at time t; is a material response coefficient; is time; is a joule heat influence coefficient; is is the local joule heat power density at time t; is a temperature influence coefficient; is is the real-time temperature at time t; is an electromagnetic interference energy influence coefficient; is is the electromagnetic interference energy spectrum density at time t; is a temperature sensitive coefficient; is the ambient temperature.

[0011] Further preferably, when realizing the dynamic weight grading early warning calculation, the main controller adopts an early warning weight formula to calculate the early warning value: ; wherein, is the early warning value at time t; is a temperature weight coefficient; is the corrected temperature at time t; is a pyrolysis particle concentration weight coefficient; is the pyrolysis particle concentration at time t; is a humidity / particle composite index weight coefficient; is the humidity / particle composite index at time t, dimensionless; is the active power fluctuation gradient at time t; is the air pressure altitude self-adaptive compensation factor at time t, dimensionless, and , is the altitude, in m, , , is a dimensionless normalization coefficient.

[0012] Further preferably, the main controller calculates the corrected temperature signal after implementing interference suppression using the following formula: ; Wherein, is the corrected temperature signal at time t; is the original temperature signal at time t; is the interference amplitude compensation parameter; is the characteristic frequency of the electromagnetic interference signal; is the lower limit of the effective frequency band of the thermal signal; is the frequency spectrum distribution function of the electromagnetic interference signal; is the frequency variable; is the phase compensation parameter.

[0013] Further preferably, the particle detection unit includes a light source module, a gas chamber, and a photodetector module, the light source module uses a laser diode, the gas chamber inlet is connected to sampling points in different areas inside the cabinet through a pipeline, the photodetector module includes a photodiode and a signal amplification circuit, the photodiode is used to receive light signals scattered by pyrolysis particles, the signal amplification circuit amplifies signals output by the photodiode, the output end of the photodetector module is electrically connected to the input end of the main controller, and the output light signal of the light source module enters the gas chamber.

[0014] Further preferably, the environmental sensing unit includes a humidity sensor and an air pressure sensor, both the humidity sensor and the air pressure sensor are connected to the main controller through an I 2 C bus, the humidity sensor is used to collect humidity data inside the cabinet, the air pressure sensor is used to collect air pressure data around the cabinet, and the environmental sensing unit performs data sampling at a set period.

[0015] Further preferably, the early warning unit includes an audible and visual alarm module and a communication module, the audible and visual alarm module includes a red LED indicator and a buzzer, the flashing frequency of the LED indicator increases with the increase of the early warning level, and the buzzer is used to emit an alarm sound, the communication module uses LoRa or NB-IoT communication mode, the output end of the communication module is connected to a remote monitoring center, and is used to transmit early warning information to the remote monitoring center.

[0016] Technical effects: The present application quantitatively couples the electromagnetic interference and material pyrolysis by integrating the electromagnetic interference signal capturing spectrum analysis unit, the pyrolysis particle monitoring particle detection unit, and the main controller for the fusion processing of multi-source data. The scheme effectively captures the extremely early pyrolysis signal under electromagnetic catalysis, eliminates the temperature measurement distortion caused by electromagnetic interference, solves the detection lag and false alarm and missed alarm problems caused by ignoring the electromagnetic and pyrolysis coupling effect in the background technology, and realizes the extremely early accurate early warning of the cabinet fire. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The figure is the overall structure and connection relationship diagram of the cabinet fire extremely early detection and hierarchical early warning system based on multi-dimensional perception of the present application. Figure 2 The figure is the structure and data transmission path diagram of the distributed sensing unit of the present application. Figure 3 The figure is the signal processing flow and connection relationship diagram of the spectrum analysis unit of the present application. Figure 4 The figure is the data processing and algorithm execution flow chart of the main controller of the present application. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0019] The conventional technical scheme has the following technical problems: The existing cabinet fire detection system mainly relies on a single temperature or smoke sensor, and the monitoring dimension is limited to a single physical quantity, which cannot capture the multi-parameter coupling characteristics of the extremely early fire, such as the correlation between electromagnetic interference and material pyrolysis, the cross influence of humidity and pressure on detection accuracy, etc., resulting in low sensitivity to weak signals in the smoldering stage, high false alarm rate and high false alarm rate, and difficulty in realizing hierarchical early warning. At the same time, the existing system lacks a cooperative processing mechanism for multi-source data, and cannot deeply fuse parameters such as temperature, electromagnetic interference, and pyrolysis particles, making it difficult to meet the extremely early detection requirements in the complex electromagnetic environment of the cabinet.

[0020] Based on this, please refer to Figure 1The embodiment provides a cabinet fire extremely early detection and hierarchical early warning system based on multi-dimensional perception, which comprises a distributed sensing unit for collecting temperature data inside the cabinet, a spectrum analysis unit for detecting electromagnetic interference signals, a main controller for processing data, and an early warning unit for outputting early warning information, the distributed sensing unit is connected with the main controller through a data bus, the output end of the spectrum analysis unit is electrically connected with the input end of the main controller, and the output end of the main controller is electrically connected with the early warning unit, and the system further comprises a particle detection unit for collecting pyrolysis particle concentration, an environmental sensing unit for monitoring humidity and air pressure parameters, and a power monitoring unit for acquiring active power data, the output end of the particle detection unit is electrically connected with the input end of the main controller, the environmental sensing unit is connected with the main controller through an SPI interface, and the signal output end of the power monitoring unit is connected with the signal input end of the main controller, the main controller calls a stored algorithm program, realizes extremely early pyrolysis particle concentration prediction, dynamic weight hierarchical early warning calculation and temperature signal correction after interference suppression according to the data acquired by the distributed sensing unit, the spectrum analysis unit, the particle detection unit, the environmental sensing unit and the power monitoring unit, and outputs the hierarchical early warning result through the early warning unit.

[0021] The scheme breaks through the limitation of traditional single parameter monitoring by constructing a multi-dimensional perception network of temperature, electromagnetic interference, pyrolysis particles, environmental parameters and power, and forms a collaborative interaction mechanism of each sensing unit and the main controller: the distributed sensing unit provides global temperature distribution data, the spectrum analysis unit captures electromagnetic interference characteristics, the particle detection unit identifies extremely early pyrolysis products, and the environmental sensing unit and the power monitoring unit supplement environmental and load influencing factors. The main controller fuses and processes multi-source data through an algorithm program, realizes the whole process cooperation from signal acquisition to early warning output, and solves the detection lag problem caused by single parameter and isolated data of the existing system.

[0022] The technical effects achieved by the above embodiments include that the multi-dimensional perception network can synchronously capture the multi-physical field changes in the extremely early stage of fire, such as the coupling characteristics of material pyrolysis and electromagnetic interference, the influence of humidity on pyrolysis particle diffusion, and the like, to provide rich data support for extremely early detection; the multi-algorithm collaborative processing mechanism of the main controller realizes effective suppression of interference signals, dynamic prediction of pyrolysis particle concentration, and accurate output of hierarchical early warning, avoiding the limitations of single parameter threshold judgment; each unit is connected with the main controller through a standardized interface, ensuring the real-time and reliability of data transmission, so that the system can stably work in a complex electromagnetic environment of a cabinet and realize full-stage monitoring and hierarchical early warning of fire from the extremely early stage to the development stage, thereby providing decision basis for early disposal of cabinet fire.

[0023] The conventional technical solutions have the following technical problems: the existing cabinet temperature monitoring mostly uses a single-point temperature sensor, which can only obtain local temperature data and cannot reflect the temperature gradient distribution along the height direction inside the cabinet, making it difficult to locate the heat source position, and the single-point data is easily affected by local electromagnetic interference or air flow disturbance, resulting in temperature measurement deviation and affecting the accuracy of early fire judgment. Meanwhile, the connection mode of the existing temperature sensing unit and processing module lacks anti-interference design, and data transmission errors are prone to occur in a strong electromagnetic environment of a cabinet, further reducing the reliability of temperature monitoring.

[0024] Based on this, please refer to Figure 2 The distributed sensing unit includes fiber grating temperature sensors arranged at intervals along the height direction of the cabinet, the wavelength shift amount of each fiber grating temperature sensor is converted into temperature data through a fiber demodulation module, the output end of the fiber demodulation module is connected with the main controller through an RS485 bus, and the fiber grating temperature sensor and the fiber demodulation module are connected through an optical fiber.

[0025] This scheme constructs a spatial temperature monitoring network through the distributed arrangement of fiber grating temperature sensors, which are arranged at intervals along the height direction of the cabinet, can capture temperature changes at different height levels, reflect temperature gradient distribution characteristics, and solve the limitations of single-point temperature measurement. The fiber grating sensor transmits temperature information by using optical signals and has anti-electromagnetic interference capability, avoiding the signal distortion problem of conventional electrical signal sensors in a strong electromagnetic environment. The fiber demodulation module converts the wavelength shift amount into temperature data, and the main controller is connected through an RS485 bus. This bus has differential transmission characteristics, further enhancing the anti-interference capability of data transmission and ensuring the stable transmission of temperature data.

[0026] The technical effects achieved by the above embodiment include that the distributed fiber grating sensor can realize global monitoring of the temperature inside the cabinet, the arrangement mode along the height direction can accurately capture the temperature abnormal point corresponding to the heat source position, and provide spatial dimension data for early fire positioning; the combination of fiber transmission and RS485 bus significantly improves the reliability of temperature data in a strong electromagnetic environment and reduces measurement errors caused by interference; the high-precision conversion of the wavelength shift amount by the fiber demodulation module ensures the measurement accuracy of the temperature data, provides reliable temperature reference data for subsequent interference suppression and pyrolysis particle concentration prediction, and improves the recognition ability of the system for extremely early temperature abnormalities.

[0027] The conventional technical solution has the following technical problems: the existing cabinet fire detection system does not consider the influence of electromagnetic interference on the monitoring signal, high-frequency switching power supply, motors and other equipment inside the cabinet can produce wideband electromagnetic interference, and these interference signals can drown out weak monitoring signals such as temperature and particles, resulting in distorted sensor data. At the same time, the existing system lacks feature analysis of electromagnetic interference itself, cannot distinguish between interference signals and real fire signals, and is difficult to achieve targeted suppression of interference, affecting detection accuracy.

[0028] Based on this, please refer to Figure 3 , the spectrum analysis unit includes electromagnetic interference receiving antennas, low-noise amplifiers, band-pass filters and FFT processing circuits connected in sequence, the electromagnetic interference receiving antennas are used to receive electromagnetic interference signals inside the cabinet, the low-noise amplifiers amplify the received electromagnetic interference signals, the band-pass filters filter the amplified electromagnetic interference signals, and the FFT processing circuits perform spectrum analysis on the filtered electromagnetic interference signals, and the output end of the FFT processing circuit is electrically connected with the input end of the main controller.

[0029] This scheme realizes complete analysis of electromagnetic interference signals through multiple processing circuits: the electromagnetic interference receiving antennas capture electromagnetic signals inside the cabinet, the low-noise amplifiers reduce the introduction of their own noise while amplifying the signals, ensuring the effective extraction of weak interference signals; the band-pass filter selects the interference frequency band related to fire monitoring, avoiding the interference of irrelevant frequency band signals; the FFT processing circuit performs spectrum analysis on the filtered signals, obtains the frequency, amplitude, phase and other characteristic parameters of the interference signals, and provides data support for subsequent interference suppression. The components are connected in sequence to form a complete signal processing link, ensuring accurate extraction of electromagnetic interference characteristics.

[0030] The technical effects achieved by the above embodiment include that the spectrum analysis unit can comprehensively capture the electromagnetic interference characteristics inside the cabinet, providing a basis for distinguishing interference signals from real fire signals; the combination of low-noise amplification and band-pass filtering ensures the effective extraction and purification of weak interference signals, avoiding signal distortion; the spectrum feature data output by the FFT processing circuit provides accurate interference model parameters for the interference suppression algorithm of the main controller, so that the temperature signal correction algorithm can counteract the influence of electromagnetic interference, improve the measurement accuracy of temperature, particle and other key parameters, and reduce false alarms and missed alarms caused by interference.

[0031] The conventional technical solution has the following technical problems: the processing unit of the existing cabinet fire detection system mostly uses a single microprocessor, lacks a collaborative design with a memory and a clock module, is difficult to process massive data of multiple source sensors, has low data storage and calling efficiency, and lacks a stable timing reference, resulting in inaccurate data timestamp and affecting time correlation analysis of multiple parameters. Meanwhile, the interaction logic between the processing unit and each sensor is simple, cannot realize efficient calling of algorithm programs and real-time processing of data, and is difficult to meet the requirement of early detection on data processing speed.

[0032] Therefore, please refer to Figure 4 The main controller includes a microprocessor, a memory and a clock module, the microprocessor is connected with the memory through an internal bus, an output end of the clock module is connected with an external interrupt interface of the microprocessor, the memory is used for storing algorithm programs and collected data, the clock module provides a timing reference for the system, and the microprocessor realizes data processing by calling the algorithm programs stored in the memory.

[0033] The scheme builds an efficient data processing core through the collaborative architecture of the microprocessor, the memory and the clock module: the microprocessor serves as a control center, realizes high-speed interaction with the memory through an internal bus, ensures fast loading of algorithm programs and real-time storage of collected data, and solves the data processing bottleneck of a single processor; the clock module provides a precise timing reference for the microprocessor through an external interrupt interface, ensures synchronization of timestamps of data of each sensor, and lays a foundation for time correlation analysis of multiple parameters; the memory stores algorithm programs and collected data in a classified manner, improves the efficiency of data calling and algorithm execution, and enables the main controller to quickly respond to data input of multiple source sensors.

[0034] The technical effects achieved by the above embodiments include: the collaborative architecture of the main controller improves the processing efficiency of multi-source data, meeting the real-time requirements of very early detection; the efficient storage and call mechanism of the memory ensures the rapid operation of complex algorithms such as pyrolysis particle concentration prediction and dynamic weight calculation; the precise timing reference provided by the clock module realizes the time synchronization of each sensor data, so that the multi-parameter fusion analysis can accurately capture the temporal correlation between parameters, such as the temporal coupling relationship between electromagnetic interference and temperature changes, thereby improving the system's recognition accuracy of very early fire characteristics and providing reliable processing results for graded early warning.

[0035] Traditional solutions suffer from the following technical issues: Existing systems often use threshold-based methods to monitor pyrolytic particle concentrations in the very early stages of a fire. These systems trigger an alarm based solely on whether the particle concentration exceeds a fixed threshold. These methods fail to consider the coupling between the pyrolysis process and factors such as Joule heating, temperature, and electromagnetic interference. They are unable to quantify the correlation between the generation rate of pyrolytic particles and environmental parameters, resulting in delayed predictions of the very early pyrolysis stage and difficulty achieving truly early warnings. Furthermore, existing methods fail to factor in the influence of ambient temperature, making concentration estimation biases prone to occur at varying temperatures, impacting the accuracy of early warnings.

[0036] Based on this, the main controller uses the following formula to calculate the pyrolysis particle concentration when realizing the very early pyrolysis particle concentration prediction:

[0037] in, is the concentration of pyrolysis particles at time t; is the material response coefficient; For time; is the Joule heat influence coefficient; for The local Joule heat power density at the moment; is the temperature influence coefficient; for Real-time temperature at the moment; is the electromagnetic interference energy influence coefficient; for Electromagnetic interference energy spectrum density at the moment; is the temperature sensitivity coefficient; is the ambient temperature; the integral interval is from the initial time 0 to the current time t, reflecting the cumulative effect of pyrolysis particles over time.

[0038] in the formula It is not a simple superposition of density + temperature + electromagnetic, but rather reflects the time-accumulated coupling effect of multiple factors through integration: α·P_cu(t′) reflects the driving effect of cable Joule heat on material pyrolysis. The higher the power density, the more significant the pyrolysis acceleration. Reflects the promotion of temperature rise on molecular thermal motion; δ·Φ_EMI(t′)Quantifies the catalytic effect of electromagnetic interference energy on the breaking of material chemical bonds, and the electromagnetic energy accelerates pyrolysis through molecular vibration; Exponential term Used to correct the influence of ambient temperature on pyrolysis rate, when the real-time temperature is lower than the ambient temperature, the pyrolysis is inhibited.

[0039] The formula is used to dynamically predict the concentration of pyrolysis particles of insulating materials in the cabinet in the very early stage of fire, and the core quantifies the cumulative effect of the pyrolysis process through the mechanism of multi-physical field coupling.

[0040] The left side of the formula represents the concentration of pyrolysis particles at time t, which is a key indicator reflecting the degree of material pyrolysis and is directly related to the material change in the early stage of fire.

[0041] The integral term on the right side of the formula Reflects the time accumulation characteristics of the pyrolysis process, the integral operation from the initial time (0) to the current time (t), which can capture the dynamic generation process of pyrolysis particles with time, avoiding the limitations of single time point measurement.

[0042] The inside of the integral term, through the product of the joule heat influence coefficient and the local joule heat power density , quantifies the driving effect of joule heat generated by cable overload on material pyrolysis—joule heat is a common heat source in the cabinet, its continuous effect will accelerate the decomposition of insulating materials, and this part is directly related to the pyrolysis risk caused by abnormal load of electrical equipment.

[0043] The term is multiplied by the real-time temperature through the product of the temperature influence coefficient , reflecting the acceleration effect of ambient temperature rise on pyrolysis reaction, the higher the temperature, the more intense the molecular thermal motion, the faster the rate of chemical bond breaking of materials, and the corresponding increase in the generation of pyrolysis particles.

[0044] The term introduces the influence of electromagnetic interference energy, through the product of the electromagnetic interference energy influence coefficient and the electromagnetic interference energy spectrum density , quantifies the catalytic effect of high-frequency electromagnetic radiation on the molecular structure of materials—electromagnetic interference generated by high-frequency switching equipment in the cabinet will exacerbate the oxidative decomposition of materials, and this term is a key innovation point that distinguishes it from traditional temperature-dominated models.

[0045] Exponential term As an ambient temperature inhibition factor, through the product of the temperature-sensitive coefficient and the ambient temperature With real-time temperature The difference between the real-time temperature and the ambient temperature is used to correct the pyrolysis rate deviation under different environmental benchmarks: when the real-time temperature is lower than the ambient temperature, this factor will suppress the calculated pyrolysis rate to avoid misjudgment due to ambient temperature fluctuations; when the real-time temperature is higher than the ambient temperature, this factor will enhance the cumulative effect of the pyrolysis rate, which is in line with the physical laws of material pyrolysis.

[0046] The overall formula is obtained by using the material response coefficient Normalization calibration is performed to ensure that the calculated results are consistent with the dimension of the actual particle concentration.

[0047] This model comprehensively considers the coupling effects of multiple fields, including electricity, heat, and electromagnetics, breaking through the limitations of traditional single-temperature-driven pyrolysis models. It enables technicians in this field to accurately capture weak pyrolysis signals in the very early stages of a fire, providing a quantitative basis for very early warning.

[0048] This formula constructs a dynamic prediction model for pyrolysis particle concentration in an integral form, comprehensively considering the coupling effects of multiple factors such as local Joule heat power density, real-time temperature, and electromagnetic interference energy spectrum density: The term reflects the driving effect of the Joule heat generated by cable overload on the pyrolysis of the material. The term reflects the accelerating effect of increasing temperature on the pyrolysis reaction. The term quantifies the catalytic effect of electromagnetic interference energy on material pyrolysis, and the index term The difference between ambient temperature and real-time temperature is introduced as a suppression factor to correct for deviations in pyrolysis rates at different ambient temperatures. Each parameter is calibrated using material properties and experimental data to ensure model accuracy.

[0049] The technical effects achieved by the above embodiments include: the formula realizes dynamic quantitative prediction of pyrolysis particle concentration, breaking through the limitations of traditional threshold judgment and being able to capture the concentration change trend in the very early pyrolysis stage; the multi-factor coupling modeling method accurately reflects the complex mechanism of the pyrolysis process inside the cabinet, such as the material decomposition characteristics under the synergistic effect of electromagnetic interference and Joule heat, and improves the scientific nature of concentration prediction; the introduction of the ambient temperature suppression factor enables the model to maintain prediction accuracy under different environmental conditions, providing a quantitative particle concentration basis for very early fire warning, identifying fire risks in advance, and buying time for disposal.

[0050] The traditional technical solution has the following technical problems: the grading warning mechanism of the existing cabinet fire warning system adopts fixed threshold or single parameter weight, and cannot dynamically adjust the influence weight of each parameter according to the change of the internal environment of the cabinet, for example, the weight proportion of the humidity parameter is not increased when the humidity increases, and there is no corresponding compensation mechanism when the air pressure changes, resulting in a large deviation between the warning result and the actual fire risk. At the same time, the existing system does not include the key dynamic factor of active power fluctuation in the warning calculation, and it is difficult to capture the fire hazards caused by load mutation, the accuracy of the grading warning is insufficient, and it cannot meet the warning needs in different scenes.

[0051] Based on this, when implementing dynamic weight grading warning calculation, the main controller calculates the warning value by using the following formula: ; Among them, is the warning value at time t; is the temperature weight coefficient; is the corrected temperature at time t; is the pyrolysis particle concentration weight coefficient; is the pyrolysis particle concentration at time t; is the humidity / particle composite index weight coefficient; is the humidity / particle composite index at time t, dimensionless; is the active power fluctuation gradient at time t; is the air pressure-altitude adaptive compensation factor at time t, dimensionless, and , is the altitude, , , is the dimensionless normalization coefficient.

[0052] The formula is used to calculate the dynamic weight value of the cabinet fire risk, to realize the quantitative decision of the grading warning, and the core is to solve the problem of poor adaptability of the traditional fixed threshold warning through the multi-parameter dynamic weighting and scene compensation mechanism.

[0053] The on the left side of the formula represents the warning value at time t, and the numerical value directly reflects the fire risk level, which provides a basis for the grading output of the warning unit.

[0054] The first term on the right side of the formula is the core parameter weighting term, which realizes the adaptive fusion of multiple parameters through dynamic weight coefficients. Among them, , , These are the weight coefficients of temperature, pyrolysis particle concentration, and humidity / particulate matter composite index. Their values ​​can be dynamically adjusted based on the real-time environment of the cabinet and historical fault data. For example, in a high humidity environment, the inhibitory effect of humidity on material pyrolysis is enhanced. will automatically increase to enhance the influence weight of humidity parameters; when the concentration of pyrolysis particles increases significantly, It will be improved accordingly, highlighting the contribution of very early characteristic parameters.

[0055] Formula through dynamic weighting 、 、 Realize multi-parameter adaptive fusion, for example: when electromagnetic interference is strong, It is automatically increased to highlight the very early characteristics of pyrolytic particles; ΔP(t)·ϵ(t) is used to capture the combined effects of load mutation and altitude pressure on fire risk.

[0056] In the molecule It is the real-time temperature after interference correction and is a direct representation of fire development; is the concentration of pyrolysis particles, reflecting the very early material changes; It is a humidity / particulate matter composite index that comprehensively reflects the synergistic effects of ambient humidity and suspended particulate matter. The introduction of this parameter can correct the warning lag problem caused by the slow diffusion of pyrolysis particles in a high humidity environment.

[0057] Denominator By normalizing the weights, the calculation deviation caused by the dimension difference of different parameters is eliminated, ensuring the rationality of the weighted results. is a dynamic risk compensation term used to capture the impact of sudden load changes and geographical environment on fire risk. Active power fluctuation gradient, quantifying the degree of sudden change in the load of electrical equipment in the cabinet. A sudden increase in load will cause a sharp increase in Joule heating, which is a major cause of fire. is the pressure-altitude adaptive compensation factor, which is related to the altitude Inversely proportional to ( ), which is used to correct the characteristics of accelerated diffusion of pyrolysis particles and lower combustion threshold in high-altitude and low-pressure environments, ensuring the consistency of early warning standards in different geographical environments.

[0058] This formula combines dynamic weight distribution with scenario compensation, so that the warning value can reflect the real risk status of the cabinet in real time. It not only considers the cumulative effects of core parameters such as temperature and pyrolytic particles, but also takes into account dynamic factors such as load mutations and ambient air pressure, solving the problems of false alarms and missed alarms in traditional fixed threshold warnings in complex scenarios.

[0059] This system is based on the warning weight value It is divided into three levels of warning: Primary warning ≤0.3: slight increase in pyrolysis particle concentration or electromagnetic interference anomaly, LED indicator flashes once every 3 seconds, no alarm from the buzzer, only push observation information to the remote monitoring center; Secondary warning 0.3 ≤0.6: continuous increase in pyrolysis particle concentration and temperature anomaly, LED indicator flashes once every 1 second, buzzer alarms every 2 seconds, remote push local inspection instruction; Tertiary warning >0.6: significant early fire characteristics, LED indicator constant flashing, frequency 5Hz, buzzer continuous alarm, remote push emergency disposal instruction and linkage cabinet power cut-off device.

[0060] The formula realizes precise grading warning through dynamic weight allocation and multi-factor coupling mechanism: the numerator part adjusts the contribution of temperature, pyrolysis particle concentration, and humidity / particle composite index in the warning through three dynamic weight coefficients, which can be automatically adjusted according to historical data and real-time environment, for example, automatically increased in high humidity environment; the denominator part ensures weight normalization to avoid parameter magnitude difference affecting the result; the additional term combines active power fluctuation gradient and air pressure-altitude compensation factor, which captures fire risks caused by load mutation and corrects the influence of different altitude air pressure on warning threshold, so that the weight value can truly reflect the fire risk level of the cabinet. 、 、

[0061] The above embodiments achieve the following technical effects: dynamic weight coefficients enable the system to flexibly adjust the influence weight of each parameter according to environmental changes, solving the problem of fixed weight not adapting to complex environments; the introduction of active power fluctuation gradient realizes real-time response to fire hazards caused by load mutation; the air pressure-altitude compensation factor ensures the consistency of the system's warning in different geographical environments, avoiding misjudgment in high-altitude low-pressure environments; the warning value calculated by the overall formula can quantitatively reflect the dynamic changes of fire risk, providing precise numerical basis for grading warning, matching the warning level with the actual risk, and improving the reliability and practicality of the warning.

[0062] ​​​Traditional solutions present the following technical challenges: Existing cabinet temperature monitoring systems fail to specifically suppress strong electromagnetic interference within the cabinet when processing temperature signals. This interference signal easily overlaps with the temperature signal, distorting the temperature measurement and failing to accurately reflect the true temperature changes within the cabinet. Existing filtering methods, which often employ fixed-parameter low-pass or high-pass filtering, struggle to adapt to the broadband characteristics and dynamic variations of electromagnetic interference. They are unable to effectively separate the temperature signal from the interference signal, leading to deviations in subsequent pyrolysis particle concentration prediction and early warning calculations, impacting the system's very early detection accuracy.

[0063] Based on this, when the main controller implements the temperature signal correction after interference suppression, it uses the following formula to calculate the corrected temperature signal: ; in, is the corrected temperature signal at time t; is the original temperature signal at time t; is the interference amplitude compensation parameter; is the characteristic frequency of the electromagnetic interference signal; is the lower limit of the effective frequency band of the thermal signal; is the spectrum distribution function of the electromagnetic interference signal; is a frequency variable; is the phase compensation parameter.

[0064] This formula is used to eliminate the influence of electromagnetic interference on the temperature signal and obtain the real internal temperature of the cabinet. The core solves the problem of temperature measurement distortion in a strong electromagnetic environment through interference signal reconstruction and reverse cancellation mechanism. The temperature signal after correction is the basic data for subsequent pyrolysis particle concentration prediction and warning weight calculation. Its accuracy directly affects the detection accuracy of the entire system.

[0065] The formula reconstructs the time domain waveform of the electromagnetic interference signal through integration, based on the spectrum analysis unit , and reversely offset it from the original temperature signal to eliminate the distortion effect of high-frequency electromagnetic on temperature measurement, where ξ is the interference amplitude compensation parameter, which is positively correlated with the intensity of the electromagnetic source. is the phase compensation parameter, matching the transmission delay of the interference signal.

[0066] The first term on the right side of the formula This is the raw temperature signal collected by the distributed sensing unit. This signal can be superimposed with electromagnetic interference noise in the cabinet's strong electromagnetic environment, such as interference from high-frequency switching power supplies and motors, causing the measured value to deviate from the true temperature. The second term, interference compensation, purifies the temperature signal by reconstructing and subtracting the electromagnetic interference component.

[0067] Integral in the interference compensation term is the core operation, which is used to reconstruct the time-domain waveform of electromagnetic interference in a specific frequency range. The lower limit of the integral is the lower limit of the effective frequency band of the thermal signal, and the upper limit is the characteristic frequency of the electromagnetic interference signal. The selection of this frequency range is based on the spectral characteristics of electromagnetic interference in the cabinet. The thermal signal is usually distributed in the low frequency band, while the electromagnetic interference is mostly concentrated in the high frequency band. By limiting the integral interval, the interference component overlapping with the temperature signal can be accurately extracted, avoiding the false elimination of the effective temperature signal.

[0068] is the internal integral is the spectral distribution function of the electromagnetic interference signal, which is obtained in real time by the spectral analysis unit through FFT processing, reflecting the interference amplitude characteristics at different frequencies; is the time-domain phase model of the interference signal, where represents the angular frequency characteristics of the sinusoidal signal, is the phase compensation parameter, which is used to match the phase offset of the actual interference signal. The electromagnetic interference will produce phase changes due to path differences during transmission. This parameter is calibrated through the historical interference template library to ensure that the reconstructed interference phase is consistent with the real interference.

[0069] is the interference amplitude adjustment factor, where is the interference amplitude compensation parameter, which is based on the type of interference source, such as switch power supply, motor calibration; reflects the attenuation characteristics of high-frequency interference. The energy distribution of high-frequency interference is more dispersed, and its impact on the temperature signal decreases with increasing frequency. This factor can avoid overcompensation of high-frequency interference. By subtracting the reconstructed interference component from the original temperature signal , this formula can effectively separate the temperature signal and the electromagnetic interference, solving the limitations of traditional filtering methods that cannot handle wideband and dynamic interference.

[0070] This formula realizes accurate correction of the temperature signal through spectral analysis and interference reconstruction: the second term on the right side of the formula is the interference compensation term, where partly based on the electromagnetic interference spectrum provided by the spectral analysis unit , outside the effective frequency band of the thermal signal, from to reconstruct the time-domain waveform of the interference signal, ensure that the phase of the reconstructed interference is consistent with the original interference; the term dynamically adjusts the compensation amplitude according to the characteristic frequency of the interference. The amplitude automatically decreases for high-frequency interference, avoiding overcompensation; by subtracting the reconstructed interference signal from the original temperature signal , the corrected real temperature signal is obtained, effectively eliminating the impact of electromagnetic interference.

[0071] The technical effects achieved by the above embodiments include: the interference compensation term can counteract electromagnetic interference of different frequencies and amplitudes, solving the problem that fixed filtering cannot adapt to wide-frequency dynamic interference; interference reconstruction based on frequency spectrum distribution ensures the accuracy of interference signal separation and avoids distortion of the temperature signal; the phase compensation parameter makes the reconstructed interference match the original interference in phase, improving the thoroughness of interference suppression; the corrected temperature signal can truly reflect the temperature changes inside the cabinet, providing reliable basic data for subsequent pyrolysis particle concentration prediction, dynamic weight early warning calculation, and the like, reducing false positives and omissions caused by interference, and improving the detection accuracy of the system in a strong electromagnetic environment.

[0072] The traditional technical solutions have the following technical problems: the particle detection unit of the existing cabinet fire detection system mostly uses a single light source and a simple air chamber structure, and the wavelength of the light source and the design of the air chamber are not optimized for the characteristics of the pyrolysis particles in the cabinet, resulting in weak pyrolysis particle scattering signals, low detection sensitivity, and difficulty in capturing low-concentration pyrolysis particles in the early stage. At the same time, the signal processing link of the photoelectric detection module is simple, and the weak signal characteristics of the particle scattering light are not considered, which is easily disturbed by environmental light and circuit noise, resulting in low particle concentration measurement accuracy and being unable to provide effective data support for early warning.

[0073] Therefore, the particle detection unit includes a light source module, an air chamber, and a photoelectric detection module. The light source module uses a laser diode. The air inlet of the air chamber is connected to sampling points in different areas inside the cabinet through a pipeline. The photoelectric detection module includes a photodiode and a signal amplification circuit. The photodiode is used to receive light signals scattered by pyrolysis particles. The signal amplification circuit amplifies the signals output by the photodiode. The output end of the photoelectric detection module is electrically connected to the input end of the main controller. The output light signal of the light source module enters the air chamber.

[0074] This scheme realizes high-sensitivity detection of pyrolysis particles through modular design and a collaborative working mechanism: the light source module uses a laser diode, which has good monochromaticity and strong directivity, can produce scattering light matching the size of pyrolysis particles, and enhances the scattering signal strength; the air chamber connects sampling points in different areas of the cabinet through a pipeline, can collect air samples at multiple positions, ensures the comprehensiveness of particle detection, and the air chamber volume and light path design can prolong the action path of light and particles, improving the scattering efficiency; in the photoelectric detection module, the photodiode receives the scattering light signal, its spectral response characteristics match the wavelength of the laser diode, and the light signal conversion efficiency is improved; the signal amplification circuit amplifies the weak photoelectric signal, ensuring that the scattering signal of low-concentration particles can be effectively identified; the modules work collaboratively, forming a complete particle detection link from particle sampling, light scattering to signal conversion and amplification.

[0075] The technical effects achieved by the above embodiment include: the optimized design of the laser diode and the gas chamber enhances the scattering signal of pyrolysis particles, and improves the detection sensitivity of extremely early low-concentration particles; the multi-sampling-point gas chamber design realizes the monitoring of particle distribution in different areas inside the cabinet, avoiding the limitations of single-point sampling; the cooperation of the photodiode and the signal amplification circuit ensures the effective conversion and amplification of weak scattering signals and reduces the interference of ambient light and circuit noise; the signals output by the particle detection unit can accurately reflect the changes in pyrolysis particle concentration, providing reliable input data for the extremely early pyrolysis particle concentration prediction formula, so that the system can capture the particle concentration anomaly in the extremely early stage of fire and provide a basis for early warning.

[0076] The traditional technical solution has the following technical problems: the existing cabinet fire warning system does not comprehensively monitor environmental parameters, and only monitors a single parameter of temperature or humidity, without combining humidity and air pressure parameters for analysis, so it cannot reflect the synergistic effect of the two parameters on the development of cabinet fire, such as the influence of air pressure changes in a high-humidity environment on the diffusion of pyrolysis particles. At the same time, the connection mode of the environmental sensing unit and the main controller is not optimized for low power consumption and real-time performance, the sampling period is fixed, and the sampling frequency cannot be dynamically adjusted according to environmental changes, resulting in data redundancy or missing of critical data, affecting the supporting role of environmental parameters on warning calculation.

[0077] Therefore, the environmental sensing unit includes a humidity sensor and an air pressure sensor, both of which are connected to the main controller through an I 2 C bus. The humidity sensor is used to collect humidity data inside the cabinet, and the air pressure sensor is used to collect air pressure data around the cabinet. The environmental sensing unit samples data at a set period. This solution realizes accurate acquisition of environmental parameters through dual-parameter monitoring and bus connection: the environmental sensing unit integrates a humidity sensor and an air pressure sensor, simultaneously collecting humidity data inside the cabinet and air pressure data around the cabinet, which reflect the influence of the environment on the development of fire, such as high humidity slowing down pyrolysis reaction and low air pressure accelerating particle diffusion; the I 2 C bus is used to connect with the main controller, which has the characteristics of two-way transmission and multiple device mounting, can reduce wiring complexity, reduce system power consumption, and ensure data transmission stability and real-time performance; data sampling is performed at a set period, and the sampling period can be dynamically adjusted according to the instructions of the main controller, such as shortening the sampling period when environmental parameters change dramatically, and lengthening the period when the parameters are stable, to balance data real-time performance and system power consumption.

[0078] The technical effects achieved by the above embodiment include: the synergistic monitoring of humidity and air pressure provides a humidity / particle composite index and air pressure-altitude compensation factor for the dynamic weight warning formula, which provides complete environmental data, enabling the warning calculation to fully consider the influence of environmental factors;2 The C bus connection mode reduces the wiring complexity and power consumption of the system, is suitable for installation in the compact space inside the cabinet, the adjustable sampling period enables the system to reduce invalid data collection and reduce the processing pressure of the main controller on the premise of ensuring that key data is not missing, and the humidity and air pressure data provided by the environmental sensing unit are complementary to the temperature, particle concentration and other parameters to form a multi-dimensional environmental feature description and improve the adaptability of the system to complex environments, thereby providing comprehensive environmental basis for early warning.

[0079] The traditional technical solution has the following technical problems: the existing cabinet fire warning system uses a single audible and visual alarm mode for the warning unit, the alarm signal has low distinguishability, cannot intuitively reflect the classification of fire risk, and makes it difficult for operation and maintenance personnel to quickly determine the emergency level of the fire.

[0080] At the same time, the remote communication function is weak, and wired communication or low-reliability wireless methods are mostly used. In the scene where cabinets are densely distributed or the environment is complex, communication interruption is prone to occur, the warning information cannot be transmitted to the remote monitoring center in time, the timely disposal of the fire is affected, and the power consumption control of the communication module is not good, which increases the energy burden of the cabinet.

[0081] Therefore, the warning unit includes an audible and visual alarm module and a communication module, the audible and visual alarm module includes a red LED indicator light and a buzzer, the flashing frequency of the LED indicator light increases with the increase of the warning level, the buzzer is used to emit an alarm sound, the communication module adopts LoRa or NB-IoT communication mode, and the output end of the communication module is connected with the remote monitoring center and is used to transmit the warning information to the remote monitoring center.

[0082] The scheme realizes effective transmission of warning information through hierarchical audible and visual alarm and reliable communication: in the audible and visual alarm module, the flashing frequency of the red LED indicator light dynamically changes with the warning level, the low-frequency flashing occurs at the first-level warning, and the high-frequency flashing occurs at the high-level warning, thereby intuitively reflecting the risk level; the alarm sound emitted by the buzzer is synchronized with the flashing frequency, and the alarm is double prompted through sound and light, thereby ensuring that the operation and maintenance personnel can perceive the alarm in different environments; the communication module adopts two low-power wide-area network technologies of LoRa and NB-IoT, LoRa is suitable for near-distance dense deployment scenes, and NB-IoT is suitable for wide-area coverage scenes, and the two methods can be selected according to the distribution of the cabinet, thereby ensuring that the warning information can be stably transmitted to the remote monitoring center; the connection of the communication module and the remote monitoring center realizes real-time pushing of the warning information, and facilitates centralized management and rapid response.

[0083] The technical effects achieved by the above embodiment include: the hierarchical flashing LED indicator light and the synchronous buzzer alarm enable the operation and maintenance personnel to quickly identify the fire risk level and take corresponding disposal measures, solving the problem of low distinguishability of single alarm mode; the selection of LoRa or NB-IoT communication mode adapts to different cabinet deployment scenes, ensures the stability and reliability of remote communication, and avoids information transmission interruption; the low-power communication technology reduces the energy consumption of the system and is suitable for long-term continuous operation; the early warning unit realizes the cooperation of local sound and light alarm and remote information pushing, which not only meets the on-site rapid response demand, but also is convenient for centralized management and scheduling of the remote monitoring center, improves the efficiency of fire disposal, and gains time for early control of cabinet fire.

[0084] The above is only a preferred embodiment of the present application, and does not limit the present application in other forms. Any person skilled in the art can use the disclosed technical content to make changes or modifications to equivalent embodiments applied to other fields, but any simple modification, equivalent change and modification made to the above embodiments without departing from the technical solution content of the present application, in accordance with the technical essence of the present application, still belongs to the protection scope of the technical solution of the present application.

Claims

1. A cabinet fire early detection and graded warning system based on multi-dimensional perception, including: A distributed sensing unit for collecting temperature data inside the cabinet, a spectrum analysis unit for detecting electromagnetic interference signals, a main controller for processing data, and an early warning unit for outputting early warning information, wherein the distributed sensing unit is connected to the main controller via a data bus, the output end of the spectrum analysis unit is electrically connected to the input end of the main controller, and the output end of the main controller is electrically connected to the early warning unit; It is characterized by further comprising a particle detection unit for collecting the concentration of pyrolysis particles, an environmental sensing unit for monitoring humidity and air pressure parameters, and a power monitoring unit for obtaining active power data; The output end of the particle detection unit is electrically connected to the input end of the main controller, the environment sensing unit is connected to the main controller via an SPI interface, and the signal output end of the power monitoring unit is connected to the signal input end of the main controller; The main controller calls the stored algorithm program and realizes very early prediction of pyrolysis particle concentration, dynamic weight graded warning calculation and temperature signal correction after interference suppression based on the data obtained by the distributed sensing unit, spectrum analysis unit, particle detection unit, environmental sensing unit and power monitoring unit, and outputs the graded warning result through the warning unit; the spectrum analysis unit is used to detect the energy spectrum density of the electromagnetic interference signal inside the cabinet and transmit the energy spectrum density to the main controller to quantify the catalytic effect of electromagnetic interference on the generation of pyrolysis particles; the main controller calculates the electromagnetic-thermal decoupling coefficient based on the energy spectrum density and the pyrolysis particle concentration collected by the particle detection unit; the electromagnetic-thermal decoupling coefficient is the ratio of the pyrolysis particle concentration to the electromagnetic interference energy spectrum density multiplied by the electromagnetic sensitivity coefficient of the material.

2. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: The distributed sensing unit includes fiber Bragg grating temperature sensors arranged at intervals along the height direction of the cabinet. The wavelength drift of each fiber Bragg grating temperature sensor is converted into temperature data by a fiber demodulation module. The output end of the fiber demodulation module is connected to the main controller via an RS485 bus, and the fiber Bragg grating temperature sensor is connected to the fiber demodulation module via an optical fiber.

3. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: The spectrum analysis unit includes an electromagnetic interference receiving antenna, a low-noise amplifier, a bandpass filter and an FFT processing circuit connected in sequence. The electromagnetic interference receiving antenna is used to receive the electromagnetic interference signal inside the cabinet. The low-noise amplifier amplifies the received electromagnetic interference signal. The bandpass filter filters the amplified electromagnetic interference signal. The FFT processing circuit performs spectrum analysis on the filtered electromagnetic interference signal. The output end of the FFT processing circuit is electrically connected to the input end of the main controller.

4. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: The main controller includes a microprocessor, a memory and a clock module. The microprocessor is connected to the memory through an internal bus. The output end of the clock module is connected to the external interrupt interface of the microprocessor. The memory is used to store algorithm programs and collected data. The clock module provides a timing reference for the system. The microprocessor realizes data processing by calling the algorithm programs stored in the memory.

5. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: When realizing the very early prediction of the pyrolysis particle concentration, the main controller uses the pyrolysis particle concentration formula to calculate the pyrolysis particle concentration: ； in, is the concentration of pyrolysis particles at time t, in units of ; is the material response coefficient, in units of , characterize the pyrolysis activity of materials; is the Joule heat influence coefficient, in units of , related to the cable material; is the local Joule heat power density at time t', in units of ; is the temperature influence coefficient, in units of , reflecting the accelerating effect of temperature on pyrolysis; is the real-time temperature at time t', in units of ; is the electromagnetic interference energy influence coefficient, in units of , quantifying the catalytic effect of electromagnetic energy; is the electromagnetic interference energy spectrum density at time t', in units of ; is the temperature sensitivity coefficient, in units of ; is the ambient temperature, in units of ; Integration interval : reflects the cumulative effect of pyrolysis particles over time, with the dimension of , reflecting the energy density, and After multiplication, we get the concentration dimension .

6. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: When the main controller implements dynamic weighted graded warning calculation, it uses the warning weight formula to calculate the warning value: ； in, is the warning value at time t, dimensionless; is the temperature weight coefficient, dimensionless; is the corrected temperature at time t, in °C; is the weight coefficient of pyrolysis particle concentration, dimensionless; is the concentration of pyrolysis particles at time t, in mg / m 3 ; is the weight coefficient of the humidity / particulate matter composite index, dimensionless; is the humidity / particulate matter composite index at time t, dimensionless; is the active power fluctuation gradient at time t, in kW / s; is the pressure altitude adaptive compensation factor at time t, dimensionless, and , is the altitude above sea level in m, 、 、 is the dimensionless normalization coefficient.

7. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: When the main controller corrects the temperature signal after interference suppression, it uses the following formula to calculate the corrected temperature signal: ； in, is the corrected temperature signal at time t, in °C; is the original temperature signal at time t, in °C; is the interference amplitude compensation parameter, the unit is ; is the characteristic frequency of the electromagnetic interference signal, in Hz; is the lower limit of the effective frequency band of the thermal signal, in units of ; is the spectrum distribution function of the electromagnetic interference signal, in V / Hz; is the frequency variable; is the phase compensation parameter, unit is °C.

8. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: The particle detection unit includes a light source module, an air chamber and a photoelectric detection module. The light source module uses a laser diode. The air inlet of the air chamber is connected to sampling points in different areas inside the cabinet through a pipe. The photoelectric detection module includes a photodiode and a signal amplification circuit. The photodiode is used to receive the light signal scattered by the pyrolysis particles. The signal amplification circuit amplifies the signal output by the photodiode. The output end of the photoelectric detection module is electrically connected to the input end of the main controller, and the output light signal of the light source module is emitted into the air chamber.

9. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: The environmental sensing unit includes a humidity sensor and an air pressure sensor, both of which are connected to the 2 The C bus is connected to the main controller, the humidity sensor is used to collect humidity data inside the cabinet, the air pressure sensor is used to collect air pressure data around the cabinet, and the environment sensing unit performs data sampling according to a set period.

10. The cabinet fire early detection and graded warning system based on multi-dimensional perception according to claim 1 is characterized in that: The early warning unit includes an audible and visual alarm module and a communication module. The audible and visual alarm module includes a red LED indicator light and a buzzer. The flashing frequency of the LED indicator light increases with the increase of the early warning level. The buzzer is used to emit an alarm sound. The communication module adopts LoRa or NB-IoT communication mode. The output end of the communication module is connected to the remote monitoring center for transmitting early warning information to the remote monitoring center.

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