WO2021077472A1 - Comprehensive pyrolysis particle electrical fire monitoring method, device and system - Google Patents

Abstract

A comprehensive pyrolysis particle electrical fire monitoring method, device and system. The method comprises: acquiring the real-time values of seven parameters in a low-voltage power distribution cabinet, wherein the seven parameters respectively comprise a temperature T, a temperature change rate V_T, a thermally decomposed gas concentration C _gas, a thermally decomposed gas concentration change rate V_gas, a PM1.0 particle concentration C_1.0, a PM2.5 particle concentration C_2.5 and a PM10 particle concentration C₁₀; and acquiring threshold values d₁, d₂, d₃, d₄, d₅, d₆ and d₇ respectively corresponding to the seven parameters (S1); assigning different dynamic weights to the parameters, performing overall integration on the parameter values exceeding the threshold values in conjunction with the dynamic weights, and calculating a processing result (S2); and raising an alarm prompt according to the processing result (S3). The seven parameters are acquired and are comprehensively taken as the important parameters of early-warning determination for safety in the low-voltage power distribution cabinet, and the seven parameters are subjected to overall integration analysis according to threshold settings and dynamic weight analysis, thereby realizing the accurate early detection of electrical fire safety hazards in the low-voltage power distribution cabinet, and preventing alarm failure and false alarms.

Description

Comprehensive pyrolysis particle electrical fire monitoring method, device and system Technical field

The invention relates to the field of electrical fire safety monitoring, in particular to a comprehensive pyrolysis particle electrical fire monitoring method, device and system.

Background technique

At present, there are various types of electrical fire safety monitoring devices, but the detection parameters are single. For example, the temperature-measuring electrical fire safety monitoring device only detects temperature; the residual current electrical fire safety monitoring system only detects residual current; the smoke-sensing electrical fire safety monitoring device only detects Smoke concentration. Choosing these monitoring devices to realize the safety monitoring of low-voltage power distribution cabinets is not only costly but also false alarms occur from time to time. The most commonly used detectors in low-voltage power distribution cabinets are smoke detectors, but the wiring in the low-voltage power distribution cabinets is concentrated, and dust is easy to accumulate, which interferes with the detection of the detectors and is difficult to achieve effective prevention and control.

It can be seen that the above-mentioned existing electrical fire monitoring device, system and method obviously still have inconveniences and defects, and further improvement is urgently needed. How to create an electrical fire monitoring device, system and method that can accurately predict electrical fire safety hazards has become an urgent need for improvement in the industry.

Summary of the invention

The technical problem to be solved by the present invention is to provide an electrical fire monitoring method, device and system, which can realize accurate and early detection of electrical fire safety hazards in low-voltage power distribution cabinets, and avoid false alarms and false alarms.

In order to solve the above technical problems, the present invention adopts the following technical solutions:

On the one hand, the present invention provides a comprehensive pyrolysis particle electrical fire monitoring method, including:

S1. Obtain the real-time values of 7 parameters in the low-voltage power distribution cabinet, including temperature T, temperature change rate V _T , heated decomposition gas concentration C _gas , heated decomposition gas concentration change rate V _gas , PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ ;

_{Obtain the thresholds d 1} , d ₂ , d ₃ , d ₄ , d ₅ , d ₆ , and d ₇ corresponding to the 7 parameters respectively;

S2. Assign different dynamic weights to each parameter, and combine the value of each parameter that exceeds the threshold with the dynamic weight to integrate the calculation processing results as a whole;

S3. An alarm is issued according to the processing result.

As a further improvement of the present invention, in the S1: the thresholds d ₁ , d ₂ , d ₃ , d ₄ , d ₅ , d ₆ , and d ₇ are thresholds checked or modified according to actual applications.

Further, in the S1: the temperature T and the temperature change rate V _T are collected in real time by a temperature sensor; the heated decomposition gas concentration C _gas and the heated decomposition gas concentration change rate V _gas are collected in real time by a polluted gas sensor, the The PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ are collected by the laser dust particle sensor in real time.

Further, the S2 specifically includes:

S21. Construct a corresponding diagonal matrix S on the basis of obtaining the threshold, which participates in it as an activation matrix:

Where u(x) has a linear activation function:

S22: Calculate the initial alarm parameter A ₀ = (a ₁ a ₂ a ₃ a ₄ a ₅ a ₆ a ₇ ), the calculation is as follows:

The various parameters are expressed as follows:

x ₁ = T, x ₂ = V _T , x ₃ = C _gas ,

x ₄ ＝V _gas , x ₅ ＝C _1.0 , x ₆ ＝C _2.5 , x ₇ ＝C ₁₀

S23: Combine the initial alarm parameter A ₀ with the weight of each parameter, and the process is expressed as follows:

y=A ₀ ·W ₀

＝A ₀ ·(w ₁ w ₂ w ₃ w ₄ w ₅ w ₆ w ₇ ) ^T

Among them, W ₀ is the initial weight vector, w _i (i=1, 2,...7) represents the weight of each parameter, and the hazard level result y is a scalar;

S24: Normalization of parameter value numerical level:

Each element in the weight vector is multiplied by the corresponding magnification diagonal matrix Q, where only the diagonal elements q ₁₁ , q ₂₂ , q ₃₃ , q ₄₄ , q ₅₅ , q ₆₆ , and q _{77 are} non-zero, so they are normalized Weight vector W after transformation:

st w ₁ +w ₂ +w ₃ +w ₄ +w ₅ +w ₆ +w ₇ ≤1,

w ₂ +w ₄ ≥0.4

S25: Combine the real-time value with the weight, determine the optimal weight based on the weight deviation and the above limited conditions, and design the adaptive weight algorithm:

Among them, δ is the smoothing quantity, which is very important to obtain the optimal weight.

S26: Calculation result y=A ₀ ·W.

Further, in the S3, the alarm danger level is determined according to the processing result, so as to adjust the external alarm volume of the buzzer in decibels.

On the other hand, the present invention also provides a comprehensive pyrolysis particle electrical fire monitoring device, including one or more processors; a storage device, used to store one or more programs, when the one or more programs are The one or more processors execute, so that the one or more processors implement the above-mentioned comprehensive pyrolysis particle electrical fire monitoring method.

In another aspect, the present invention also provides a comprehensive pyrolysis particle electrical fire monitoring system, which applies the above-mentioned comprehensive pyrolysis particle electrical fire monitoring method, including a temperature sensor, a laser dust particle sensor, a polluted gas sensor, and real-time data Storage unit, threshold storage unit, weight analysis processing unit, result processing unit, alarm unit, including:

Temperature sensor, used to obtain temperature T, temperature change rate V _T and stored in the real-time data storage unit; laser dust particle sensor, used to obtain PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ and stored in the real-time data storage unit; the polluted gas sensor is used to obtain the gas concentration C _gas of the CH chain structure type and the gas concentration change rate V _{gas of the} CH chain structure type and store it in the real-time data storage unit; the threshold value storage unit is used The threshold value corresponding to each parameter is stored; the weight analysis processing unit obtains the parameter value stored in the real-time data storage unit and the threshold value in the threshold value storage unit and assigns different dynamic weights to each parameter; the result processing unit will exceed the threshold value Each parameter value is combined with the dynamic weight to integrate the calculation processing result as a whole; the alarm unit is used to give an alarm according to the processing result calculated by the result processing unit.

As a further improvement of the present invention, the system further includes a parameter setting display unit; the parameter setting display unit is connected to the threshold storage unit through a data interface; the default threshold data can be exported through the data interface for viewing, or modified after evaluating the actual application. Threshold data is re-imported and saved to the threshold storage unit.

Further, the alarm unit includes an alarm level discrimination unit and a buzzer; the alarm level discrimination unit delimits the alarm hazard level according to the processing result of the result processing unit and regulates the external alarm volume of the buzzer in decibels.

By adopting the above technical solutions, the present invention has at least the following advantages:

In the present invention, temperature T, temperature change rate V _T , heated decomposition gas concentration C _gas , heated decomposition gas concentration change rate V _gas , PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C are selected. ₁₀ These 7 parameters are comprehensively regarded as important parameters for the early warning and evaluation of safety in the low-voltage power distribution cabinet. According to the threshold setting and dynamic weight analysis, these 7 parameters are integrated and analyzed to realize the hidden danger of electrical fire in the low-voltage power distribution cabinet. The accurate early detection avoids false negatives and false positives.

Description of the drawings

The foregoing is only an overview of the technical solutions of the present invention. In order to better understand the technical means of the present invention, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

Figure 1 is a flow chart of a comprehensive pyrolysis particle electrical fire monitoring method according to an embodiment of the present invention;

Fig. 2 is a block diagram of a comprehensive pyrolysis particle electrical fire monitoring system according to an embodiment of the present invention.

Detailed ways

Aiming at the problem in the prior art that it is impossible to make an early and accurate judgment of potential electrical fire safety hazards in low-voltage power distribution cabinets, the present invention explores the essential causes of electrical safety hazards in low-voltage power distribution cabinets through in-depth research, and comprehensively analyzes multiple reference values, The accurate and early detection of electrical fire safety hazards in the low-voltage power distribution cabinet is realized, and false alarms and false alarms are avoided.

When there is a potential electrical fire safety hazard in the low-voltage power distribution cabinet, it is generally accompanied by changes in factors such as temperature, smoke, and light. The temperature change is an important inducing factor for other factors to change. Therefore, the present invention uses the real-time temperature value and the temperature change rate as one of the important parameters for the early warning and evaluation of safety in the low-voltage power distribution cabinet.

The low-voltage power distribution cabinet contains insulated wires, circuit breakers, circuit board substrates, etc., and its materials are mostly ABS materials, polyvinyl chloride, epoxy resin and other C-H chain synthetic materials. Once the external temperature is too high, the line current is too large, etc., the most direct impact on the interior of the low-voltage power distribution cabinet is that the material in the cabinet is heated to decompose particles. Therefore, the particle density in the free state can be detected as an important factor. Early warning reference amount. According to the particle diameter, the detection size is divided into: C-H chain structure and other gases, ultrafine particles PM1.0, particles less than or equal to 2.5 microns in diameter PM2.5, and inhalable particles less than 10 microns in diameter PM10. The detection methods are different for different particle types and diameters. Using the density of different types of pyrolysis particles is one of the important evaluation parameters for judging potential electrical safety hazards in the low-voltage power distribution cabinet of the present invention.

Figure 1 is a flow chart of a comprehensive pyrolysis particle electrical fire monitoring method according to an embodiment of the present invention, including:

S1. Obtain the real-time values of 7 parameters in the low-voltage power distribution cabinet, including temperature T, temperature change rate V _T , heated decomposition gas concentration C _gas , heated decomposition gas concentration change rate V _gas , PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ ;

_{Obtain the thresholds d 1} , d ₂ , d ₃ , d ₄ , d ₅ , d ₆ , and d ₇ corresponding to the 7 parameters respectively;

S2. Assign different dynamic weights to each parameter, and combine the value of each parameter that exceeds the threshold with the dynamic weight to integrate the calculation processing results as a whole;

S3. An alarm is issued according to the processing result.

The detailed description will be given below:

Step1: According to the analysis of the environmental conditions in the low-voltage power distribution cabinet, in order to achieve accurate prediction of electrical fire safety hazards, it is necessary to obtain multiple parameter values in the power distribution cabinet, including real-time temperature, temperature change rate, PM1.0 particle concentration, PM2 .5 The rate of change of particle concentration, PM10 particle concentration, CH chain structure and other thermally decomposed gas concentration, CH chain structure and other thermally decomposed gas concentration change rate. The setting methods of these parameters are as follows:

1) Obtain the temperature T. The real-time temperature in the environment is measured by the thermal effect of the thermistor of the temperature sensor, the start time is set, and the interval Δt is set to sample the ambient temperature. Assuming that the two temperatures in the interval Δt are T ₁ and T ₂ , then T=(T ₁ +T ₂ )/2;

2) Rate of temperature change. Taking the real-time temperature as the reference quantity, the temperature change rate is V _T =(T ₂ -T ₁ )/Δt.

3) C-H chain structure, VOC and other low-voltage power distribution cabinets and other substances in the heated decomposition gas concentration Cgas. The gases that can be generated by heating the materials in the low-voltage distribution cabinet include various C-H chain alkane compounds, hydrogen sulfide, and volatile compounds. Due to the adsorption and surface reaction of odor molecules, the resistance value of the specific semiconductor of the polluted gas sensor is sensitively changed, and the gas concentration in the low-voltage power distribution cabinet is detected with high sensitivity.

4) The rate of change of the thermal decomposition gas concentration Cgas concentration at the interval Δt is Vgas=(Cgas ₂ -Cgas ₁ )/Δt

5) The concentration of particulate matter PM1.0 is C _1.0 , the concentration of particulate matter PM2.5 is C _2.5 , and the concentration of particulate matter PM10 is C ₁₀ . The laser dust particle sensor is used for detection, and the light is scattered on the suspended particles in the air by laser irradiation, and the scattered light intensity collected in a certain angle range is linearly converted into a voltage, which is equivalent to the particle size based on the scattering theory And the number of particles of different particle sizes in a unit volume to obtain the PM1.0 concentration C _1.0 , the particulate matter PM2.5 concentration C _2.5 , and the particulate matter PM10 concentration C ₁₀ .

The calibration units for different measurement values are: temperature °C, temperature change rate °C/s, gas concentration ppm, gas concentration change rate ppm/s, and particle concentration of different particle sizes ug/m ³ .

Step2: Check or modify the corresponding threshold of each parameter. Low-voltage power distribution cabinets installed in different solar terms and locations have different values of conventional environmental parameters, so the thresholds have been adjusted. Wherein _{T, V T, Cgas, Vgas} , C 1.0, C 2.5, C 10 , including seven parameters corresponding to the threshold value _{d 1, d 2, d 3} , d 4, d 5, d 6, d 7, wherein D = (D ₁ d ₂ d ₃ d ₄ d ₅ d ₆ d ₇ ).

Step3: Construct the corresponding diagonal matrix S based on the successful threshold setting, which participates as an activation matrix:

Where u(x) has a linear activation function:

Step4: Calculate the initial alarm parameter A ₀ = (a ₁ a ₂ a ₃ a ₄ a ₅ a ₆ a ₇ ). The calculation is as follows:

The various parameters are expressed as follows:

x ₁ = T, x ₂ = V _T , x ₃ = C _gas ,

x ₄ ＝V _gas , x ₅ ＝C _1.0 , x ₆ ＝C _2.5 , x ₇ ＝C ₁₀

Step5: Based on the different hazard coefficients represented by the difference in the numerical level of each parameter, the degree of alarm represented is different. Therefore, the initial alarm parameter (row vector) A _{0 is} combined with the weight of each parameter, and the process is expressed as follows:

y=A ₀ ·W ₀

＝A ₀ ·(w ₁ w ₂ w ₃ w ₄ w ₅ w ₆ w ₇ ) ^T

Among them, W ₀ is the initial weight vector, w _i (i = 1, 2, ... 7) represents the weight of each parameter, and the hazard level result y is a scalar.

Step6: Normalize the numerical level of the parameter value.

Based on the value range of each parameter value, normalization processing is required to process multiple elements in the measured value X. The element in X has a corresponding relationship with the element in the set weight vector W, and the size range of each detection parameter is not uniform. Therefore, each element in the weight vector is multiplied by the corresponding magnification diagonal matrix Q (Q only has the diagonal element q ₁₁ , q ₂₂ , q ₃₃ , q ₄₄ , q ₅₅ , q ₆₆ , and q _{77 are} non-zero, and the size of each element refers to the detection range of the corresponding detection parameter), and the normalized weight vector W is obtained:

Step7: In order to accurately estimate the vector W, and make the final judgment result timely reflect the early safety hazards of electrical fire, the real-time measured value and the weight are combined, and the optimal weight is determined according to the deviation of the weight and the above restrictions. Therefore, the adaptive weighting algorithm is designed:

Among them, δ is the smoothing quantity, which is very important to obtain the optimal weight. According to the above conditions, the optimal weight value is obtained.

Step8: The calculation result y=A ₀ ·W.

Step9: Alarm based on the calculation result.

This embodiment also provides a comprehensive pyrolysis particle electrical fire monitoring device, including one or more processors; a storage device, for storing one or more programs, when the one or more programs are used by the one Or multiple processors execute, so that the one or more processors implement the above-mentioned comprehensive pyrolysis particle electrical fire monitoring method.

As shown in Figure 2, it is a block diagram of a comprehensive pyrolysis particle electrical fire monitoring system according to an embodiment of the present invention. The system includes: temperature sensor, laser dust particle sensor, polluted gas sensor, real-time data storage unit, threshold storage unit, weight analysis and processing unit, result processing unit, alarm unit, among them:

Temperature sensor, used to obtain temperature T, temperature change rate V _T and stored in the real-time data storage unit; laser dust particle sensor, used to obtain PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ and stored in the real-time data storage unit; the polluted gas sensor is used to obtain the gas concentration C _gas of the CH chain structure type and the gas concentration change rate V _{gas of the} CH chain structure type and store it in the real-time data storage unit; the threshold value storage unit is used The threshold value corresponding to each parameter is stored; the weight analysis processing unit obtains the parameter value stored in the real-time data storage unit and the threshold value in the threshold value storage unit and assigns different dynamic weights to each parameter; the result processing unit will exceed the threshold value Each parameter value is combined with the dynamic weight to integrate the calculation processing result; the alarm unit is used to give an alarm based on the processing result calculated by the above result processing unit, including an alarm level discrimination unit and a buzzer; the alarm level discrimination unit processes the unit's processing result according to the result Define the alarm hazard level and regulate the external alarm volume of the buzzer in decibels.

As a preferred solution, the above system also includes a parameter setting display unit; the parameter setting display unit is connected to the threshold storage unit through a data interface; the default threshold data can be exported through the data interface for viewing, or the threshold data can be modified and re-imported after evaluating the actual application. Save to the threshold storage unit. The adjustable threshold mode not only reduces the false alarm rate in some cases, but also can accurately predict the potential safety hazards of electrical fires in places with very strict safety requirements.

That is to say, the above system follows the three processes of data collection, data processing, and data storage in the execution process. Its data collection includes: real-time data (temperature, particle concentration of different sizes, concentration of heated decomposition gas), threshold D. Real-time data is collected in real time by temperature sensors, laser dust particles sensors, and polluted gas sensors. The threshold value D can be exported through the data interface to view the default threshold value data, or the threshold value data can be modified after evaluating the actual situation and re-imported and saved to the threshold value D storage unit. Data processing includes: weight analysis processing, processing of calculating the final result, which is completed by the weight analysis processing unit and the result processing unit. Based on the above process, its data storage includes the storage of real-time data and threshold data acquired by different sensors, namely real-time data storage unit and threshold storage unit. In terms of application, the alarm level judging unit delimits the alarm hazard level based on the transmission result of the result processing unit, thereby regulating the external alarm volume of the buzzer in decibels.

In summary, the present invention selects temperature T, temperature change rate V _T , heated decomposition gas concentration C _gas , heated decomposition gas concentration change rate V _gas , PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 The 7 parameters of PM10 and PM10 particle concentration C ₁₀ are integrated as important parameters for the early warning and evaluation of safety in the low-voltage power distribution cabinet. According to the threshold setting and dynamic weight analysis, these 7 parameters are integrated and analyzed to realize the low-voltage power distribution cabinet. Accurate early detection of internal electrical fire safety hazards avoids false alarms and false alarms, and is suitable for promotion and application.

The above are only preferred embodiments of the present invention, and are not intended to limit the present invention in any form. Those skilled in the art use the technical content disclosed above to make some simple modifications, equivalent changes or modifications, which fall within the present invention. Within the scope of protection of the invention.

Claims (9)

1. A comprehensive pyrolysis particle electrical fire monitoring method, which is characterized in that it comprises:

   S1. Obtain the real-time values of 7 parameters in the low-voltage power distribution cabinet, including temperature T, temperature change rate V _T , heated decomposition gas concentration C _gas , heated decomposition gas concentration change rate V _gas , PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ ;

   _{Obtain the thresholds d 1} , d ₂ , d ₃ , d ₄ , d ₅ , d ₆ , and d ₇ corresponding to the 7 parameters respectively;

   S2. Assign different dynamic weights to each parameter, and combine the value of each parameter that exceeds the threshold with the dynamic weight to integrate the calculation processing results as a whole;

   S3. An alarm is issued according to the processing result.
2. The comprehensive pyrolysis particle electrical fire monitoring method according to claim 1, wherein in the S1: the threshold values d ₁ , d ₂ , d ₃ , d ₄ , d ₅ , d ₆ , and d ₇ are Check or modify the threshold according to the actual application.
3. The comprehensive pyrolysis particle electrical fire monitoring method according to claim 1, wherein in the S1: the temperature T and the temperature change rate V _T are collected by a temperature sensor in real time; the thermal decomposition gas concentration C _gas The concentration change rate V _{gas of the} heated decomposition gas is collected in real time by the polluted gas sensor, and the PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ are collected in real time by the laser dust particle sensor.
4. The comprehensive pyrolysis particle electrical fire monitoring method according to any one of claims 1 to 3, wherein the S2 specifically includes:

   S21. Construct a corresponding diagonal matrix S on the basis of obtaining the threshold, which participates in it as an activation matrix:

   

   Where u(x) has a linear activation function:

   

   S22: Calculate the initial alarm parameter A ₀ = (a ₁ a ₂ a ₃ a ₄ a ₅ a ₆ a ₇ ), the calculation is as follows:

   

   The various parameters are expressed as follows:

   x ₁ = T, x ₂ = V _T , x ₃ = C _gas ,

   x ₄ ＝V _gas , x ₅ ＝C _1.0 , x ₆ ＝C _2.5 , x ₇ ＝C ₁₀

   S23: Combine the initial alarm parameter A ₀ with the weight of each parameter, and the process is expressed as follows:

   y=A ₀ ·W ₀

   ＝A ₀ ·(w ₁ w ₂ w ₃ w ₄ w ₅ w ₆ w ₇ ) ^T

   Among them, W ₀ is the initial weight vector, w _i (i=1, 2,...7) represents the weight of each parameter, and the hazard level result y is a scalar;

   S24: Normalization of parameter value numerical level:

   Each element in the weight vector is multiplied by the corresponding magnification diagonal matrix Q, where only the diagonal elements q ₁₁ , q ₂₂ , q ₃₃ , q ₄₄ , q ₅₅ , q ₆₆ , and q _{77 are} non-zero, so they are normalized Weight vector W after transformation:

   

   stw ₁ +w ₂ +w ₃ +w ₄ +w ₅ +w ₆ +w ₇ = 1,

   w ₂ +w ₄ ≥0.4

   S25: Combine the real-time value with the weight, determine the optimal weight based on the weight deviation and the above limited conditions, and design the adaptive weight algorithm:

   

   

   Among them, δ is the smoothing amount; according to the above conditions, obtain the optimal weight value;

   S26: Calculation result y=A ₀ ·W.
5. The comprehensive pyrolysis particle electrical fire monitoring method according to claim 1, wherein in said S3, an alarm hazard level is determined according to the processing result, so as to adjust the external alarm volume of the buzzer in decibels.
6. A comprehensive pyrolysis particle electrical fire monitoring device, which is characterized in that it comprises one or more processors;

   Storage device, used to store one or more programs,

   When the one or more programs are executed by the one or more processors, the one or more processors implement the comprehensive pyrolysis particle electrical fire monitoring method according to any one of claims 1 to 6 .
7. A comprehensive pyrolysis particle electrical fire monitoring system, characterized by applying the comprehensive pyrolysis particle electrical fire monitoring method of any one of claims 1-5, including a temperature sensor, a laser dust particle sensor, and a polluted gas sensor , Real-time data storage unit, threshold storage unit, weight analysis processing unit, result processing unit, alarm unit, including:

   The temperature sensor is used to obtain the temperature T and the temperature change rate V _T and store them in the real-time data storage unit;

   Laser dust particle sensor, used to obtain PM1.0 particle concentration C _1.0 , PM2.5 particle concentration C _2.5 and PM10 particle concentration C ₁₀ and store them in the real-time data storage unit;

   The polluted gas sensor is used to obtain the gas concentration C _gas of the CH chain structure type and the gas concentration change rate V _{gas of the} CH chain structure type and store it in the real-time data storage unit;

   The threshold value storage unit is used to store the threshold value corresponding to each acquired parameter;

   The weight analysis processing unit obtains the parameter value stored in the real-time data storage unit and the threshold value in the threshold storage unit and assigns different dynamic weights to each parameter;

   The result processing unit integrates each parameter value exceeding the threshold value with the dynamic weight to calculate the processing result as a whole;

   The alarm unit is used to give an alarm based on the processing result calculated by the result processing unit.
8. The comprehensive pyrolysis particle electrical fire monitoring system according to claim 7, further comprising a parameter setting display unit; the parameter setting display unit is connected to the threshold storage unit through a data interface; the default threshold can be derived through the data interface Check the data, or modify the threshold data after evaluating the actual application, and re-import and save it to the threshold storage unit.
9. The comprehensive pyrolysis particle electrical fire monitoring system according to claim 7, wherein the alarm unit includes an alarm level discrimination unit and a buzzer; the alarm level discrimination unit is defined according to the processing result of the result processing unit Alarm hazard level and adjust the external alarm volume of the buzzer in decibels.

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