Control method for intelligent fire prevention of flame retardant materials used in power transmission and distribution switch sets

Abstract

The present invention relates to the technical field of power equipment safety prevention and control, and discloses a control method for intelligent fire prevention of flame retardant materials for power transmission and distribution complete switchgear, comprising a DMC flame retardant material dynamic performance data collection step, a DMC flame retardant material parameter optimization step, a DMC flame retardant material dynamic feedback regulation step and a closed-loop control step. Not only is all-round dynamic perception of the performance of the DMC flame retardant material achieved through joint monitoring of multi-dimensional parameters such as pyrolysis rate, carbonization layer thickness and environmental adaptation variables, but also accurate assessment of fire risks is completed through optimization formulas, and the error deviation of the previous moment is fed back in real time through a dynamic correction value, forming a closed-loop control mechanism, so that the accuracy and response speed of fire risk assessment are significantly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of safety prevention and control of electric power equipment, and in particular to a control method for intelligent fire prevention of flame retardant materials used in power transmission and distribution complete switch sets. Background Art

[0002] The power transmission and distribution switchgear is an important component of the power system for controlling and protecting electrical equipment. Its main function is to distribute electricity, protect line safety, and quickly cut off the current when a fault occurs. In scenarios with high temperature, humidity, or strong electromagnetic interference, a fire may occur inside the switch due to aging of insulating materials, local overheating, or changes in the external environment. In the early stages of a fire, due to the pyrolysis of flame retardant materials and the cumulative effects of ambient temperature, the surface temperature is often in the range of 150°C to 300°C. DMC flame retardant materials have become a mainstream choice because of their excellent flame retardant and mechanical properties. In the prior art, the following methods are usually used to improve the fire retardant effect of flame retardant materials:

[0003] 1. Parameter monitoring: Use heat flow sensors or temperature and humidity sensors to monitor the working environment (such as temperature and humidity) and material properties (such as pyrolysis rate) of the switchgear in real time to assess the fire risk.

[0004] 2. Material performance optimization: Optimize the formula of DMC flame retardant materials through experiments to increase the rate of carbonization layer formation to enhance flame retardant properties.

[0005] 3. Environmental control: Use ventilation or humidification / dehumidification systems to adjust the temperature and humidity environment inside the equipment to reduce the impact of the external environment on the performance of flame retardant materials.

[0006] However, these existing technical methods still have many shortcomings in practical applications. For example, the existing single sensor monitoring scheme is difficult to fully perceive the multi-dimensional dynamic changes of fire risks. For example, it is impossible to accurately capture the relationship between pyrolysis rate, carbonization layer thickness and environmental adaptation variables at the same time, which leads to delays or errors in risk assessment; and traditional environmental control systems are usually based on fixed threshold triggers, lack dynamic adaptability, and are difficult to cope with sudden environmental disturbances or complex multi-scenario changes. In addition, in the existing technology, although the carbonization layer rate of DMC flame retardant materials is improved, there is a lack of real-time control means for the pyrolysis process of materials in actual working scenarios, making it difficult to fully tap the flame retardant potential of the materials.

[0007] To solve these problems, some solutions in the industry have attempted to combine multi-sensor fusion technology with data analysis, but these solutions usually rely on simple linear weighting or experience-based risk assessment models and cannot adapt to complex dynamic changes in different scenarios. At the same time, existing technologies often ignore the correction of data deviations between sensors during the data fusion process, resulting in low reliability of monitoring results.

[0008] Therefore, how to effectively combine multi-sensor dynamic monitoring, environmental intelligent control and material performance optimization to build a high-precision and high-robust fire prevention and control system has become a technical problem to be solved by the present invention. Summary of the invention

[0009] The technical problem solved by the present invention is to provide a control method for intelligent fire prevention of flame retardant materials for power transmission and distribution complete switches in response to the defects in the above-mentioned prior art, so as to solve the problems of insufficient monitoring accuracy, inflexible regulation response and limited material performance optimization proposed in the above-mentioned background technology.

[0010] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0011] A control method for intelligent fire prevention of flame retardant materials for power transmission and distribution complete switchgear, wherein the flame retardant material is a DMC flame retardant material. The method realizes dynamic assessment and prevention and control of fire risks through dynamic performance optimization and recursive feedback control of the DMC flame retardant material, and specifically comprises the following steps:

[0012] Step 1, DMC flame retardant material dynamic performance data collection step: collect the dynamic performance parameters of DMC flame retardant materials, including:

[0013] Pyrolysis rate variables :Characterize the pyrolysis rate of DMC flame retardant materials under different temperature gradients and monitor it in real time by thermogravimetric analysis sensor;

[0014] Carbonized layer thickness variable : Characterize the thickness of the carbonized protective layer formed on the surface of DMC flame retardant materials, and monitor it in real time through laser thickness sensors or thermal imaging equipment;

[0015] Environment adaptation variables : Characterize the dynamic impact of external environmental factors, including temperature, humidity, and electromagnetic interference, on the performance of DMC flame retardant materials, and collect them in real time through multi-sensor fusion technology;

[0016] Step 2, DMC flame retardant material parameter optimization step: input the collected dynamic performance parameters into the optimization module, and calculate the fire warning index based on the following nonlinear recursive optimization formula :

[0017]

[0018] in: , , is a weight parameter used to control the influence of each input variable on the fire warning index; , , The values ​​of 0.5, 0.4, 0.3;

[0019] , is a dynamic correction value used to compensate for errors caused by differences in material properties or external environmental disturbances. is the correction weight coefficient, Indicates the error deviation at the previous moment;

[0020] The parameter optimization module further calculates the fire risk status based on the following trigger mechanisms:

[0021]

[0022] in, The fire warning threshold is dynamically adjusted according to the performance of DMC flame retardant materials;

[0023] Step 3, DMC flame retardant material dynamic feedback control step: when the fire risk state , perform the following control steps:

[0024] Dynamically adjust the temperature and humidity range of the environment where the DMC flame retardant material is located to optimize the pyrolysis rate ;

[0025] Controlling the temperature distribution on the surface of DMC flame retardant materials To enhance flame retardant properties;

[0026] Step 4, closed-loop control step: Feedback the optimization results to the fire risk assessment module, adjust the fire risk assessment model based on the dynamic performance of the DMC flame retardant material, and realize dynamic closed-loop control.

[0027] As a further solution of the present invention, the carbonized layer thickness variable The monitored value is obtained by monitoring with a heat flow sensor and optionally corrected with a laser thickness sensor. The monitored value is calculated based on the following formula:

[0028]

[0029] in, and They represent the monitoring start time and end time, respectively, which are recorded by the heat flow sensor;

[0030] The carbonization reaction rate is expressed in time The instantaneous change value on ;

[0031] Indicates time Small increments of and The carbonization reaction rate over time;

[0032] is the carbonization layer formation coefficient, which is used to convert the carbonization reaction rate into the carbonization layer thickness;

[0033] When the laser thickness sensor is used for calibration, the following steps are specifically included:

[0034] The laser thickness sensor measures the change of material surface thickness in real time and generates thickness change data;

[0035] The thickness change data is combined with the instantaneous carbonization reaction rate collected by the heat flow sensor Make a comparison;

[0036] The monitoring data of the heat flow sensor is corrected by the following correction formula:

[0037]

[0038] in, is the thickness change measured by the laser thickness sensor, To measure the time interval, is the corrected carbonization reaction rate.

[0039] As a further embodiment of the present invention, the pyrolysis rate variable The dynamic adjustment is achieved by controlling the surface temperature distribution of the DMC flame retardant material, which specifically includes the following steps:

[0040] Adjust the local heat dissipation rate of the environment where the flame retardant material is located;

[0041] Dynamically adjust the ventilation intensity inside the power transmission and distribution switchgear;

[0042] Calculate the pyrolysis rate of flame retardant materials using the pyrolysis response model.

[0043] As a further solution of the present invention, the environment adaptation variable The dynamic regulation is based on a nonlinear perturbation model of external environmental factors, and the model is described as follows:

[0044]

[0045] in:

[0046] is the ambient temperature;

[0047] is the ambient humidity;

[0048] is the environmental electromagnetic interference intensity;

[0049] is the weight of each disturbance factor;

[0050] is the nonlinear adjustment function corresponding to the disturbance factor.

[0051] As a further solution of the present invention, the multi-sensor fusion technology includes the following steps:

[0052] Step 1: Use the following sensors to collect the dynamic performance parameters of DMC flame retardant materials in real time:

[0053] Thermogravimetric analysis sensor for collecting pyrolysis rate variables ;

[0054] Laser thickness sensor or thermal imaging device to collect carbonized layer thickness variables ;

[0055] Temperature and humidity sensors and electromagnetic interference analysis equipment to collect environmental adaptation variables ;

[0056] Step 2: Processing the output data of the sensor using a data fusion algorithm, the algorithm comprising:

[0057] Time series correction, used to synchronize the collected data from different sensors;

[0058] The weight allocation mechanism allocates weights according to the credibility of sensor data and generates fused dynamic performance parameters.

[0059] As a further solution of the present invention, the method for dynamically adjusting the temperature and humidity range of the environment where the DMC flame retardant material is located includes: monitoring the temperature and humidity values ​​of the environment in real time through a temperature and humidity sensor; when the monitored value exceeds a preset range, controlling the following devices to adjust:

[0060] Ventilation system to reduce ambient temperature;

[0061] Humidification equipment or dehumidification equipment, used to adjust the ambient humidity to a preset range;

[0062] Dynamically adjust the working intensity of the ventilation system and humidification equipment according to the difference between the monitored value and the preset range.

[0063] As a further solution of the present invention, the method for improving the carbonization layer formation rate of the DMC flame retardant material includes: dynamically adjusting the temperature distribution on the surface of the DMC flame retardant material, and the specific steps are: calculating the optimal temperature range required for the carbonization reaction through a pyrolysis response model; controlling the heating device to provide uniform heating to the surface of the flame retardant material.

[0064] As a further solution of the present invention, the feedback mechanism comprises the following steps:

[0065] Monitor the dynamic performance parameters of DMC flame retardant materials through sensors; compare the monitoring data with the output values ​​of the fire risk assessment model to generate error deviations ;

[0066] Dynamically adjust the weight parameters of the fire risk assessment model according to the error deviation, including: Correction of pyrolysis rate variables The weight parameter ; Correction of carbonized layer thickness variable The weight parameter ; Correct environment adaptation variables The weight parameter .

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] 1. Not only through pyrolysis rate , Carbonized layer thickness and environment adaptation variables The joint monitoring of multi-dimensional parameters such as Complete accurate assessment of fire risk and dynamically correct it Real-time feedback of the error deviation at the previous moment forms a closed-loop control mechanism, which significantly improves the accuracy and response speed of fire risk assessment.

[0069] 2. By monitoring the multi-dimensional data of ambient temperature and humidity, material pyrolysis rate, and carbonization layer thickness, the system dynamically adjusts the influence weight of each variable based on the real-time risk assessment results. For example, when the ambient temperature continues to rise, the monitoring sensitivity of the material pyrolysis rate is increased first; when the humidity or electromagnetic interference fluctuates significantly, the weight of the environmental adaptation variable is increased to ensure that the assessment model better matches the current actual scenario.

[0070] 3. By combining the dynamic adjustment of ambient temperature and humidity with the design of improving the carbonization layer rate of DMC flame retardant materials, a dual optimization of material performance is achieved: that is, the ambient temperature and humidity are monitored in real time through temperature and humidity sensors. When the monitored values ​​deviate from the set range, the ventilation system and humidification / dehumidification equipment are dynamically adjusted to ensure that the DMC material is in the optimal pyrolysis conditions, effectively avoiding the risk of material performance degradation due to environmental fluctuations.

[0071] 4. The combination of joint monitoring of multi-dimensional parameters and closed-loop recursive control algorithm realizes the dynamic adaptive capability of fire risk assessment; dynamic adjustment of environmental temperature and humidity and optimization design of carbonized layer synergistically improve the fire resistance of DMC materials; the laser thickness sensor correction mechanism is combined with multi-sensor fusion technology to ensure monitoring accuracy and reliability.

[0072] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0074] Figure 1 Schematic diagram of the system structure of the present invention. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] See also Figure 1 In an embodiment of the present invention, a control method for intelligent fire prevention of flame retardant materials for power transmission and distribution complete switchgear is provided. The flame retardant material is a DMC flame retardant material. The method realizes dynamic assessment and prevention and control of fire risks through dynamic performance optimization and recursive feedback control of the DMC flame retardant material. Specifically, the method includes the following steps:

[0077] Step 1, DMC flame retardant material dynamic performance data collection step: collect the dynamic performance parameters of DMC flame retardant materials, including:

[0078] Pyrolysis rate variables :Characterize the pyrolysis rate of DMC flame retardant materials under different temperature gradients and monitor it in real time by thermogravimetric analysis sensor;

[0079] Carbonized layer thickness variable : Characterize the thickness of the carbonized protective layer formed on the surface of DMC flame retardant materials, and monitor it in real time through laser thickness sensors or thermal imaging equipment;

[0080] Environment adaptation variables : Characterize the dynamic impact of external environmental factors, including temperature, humidity, and electromagnetic interference, on the performance of DMC flame retardant materials, and collect them in real time through multi-sensor fusion technology;

[0081] Step 2, DMC flame retardant material parameter optimization step: input the collected dynamic performance parameters into the optimization module, and calculate the fire warning index based on the following nonlinear recursive optimization formula :

[0082]

[0083] in: , , It is a dynamically adjustable weight parameter used to control the influence of each input variable on the fire warning index;

[0084] , , The source of the fire risk assessment formula can be obtained through an experimental calibration process, for example, by adjusting a single variable in turn under different temperature gradients (such as 150°C to 300°C), humidity ranges (such as 50% to 90%), and electromagnetic interference strengths (such as 30~80 V / m) and recording the fire risk assessment formula. The response value of . Use multiple regression analysis to fit and , Specific example: Under the above conditions, the initial value obtained by experiment is =0.5, =0.4, =0.3.

[0085] In the dynamic mechanism of weight adjustment, for example: when the temperature fluctuates greatly, increase The weight of The monitoring sensitivity increases dynamically when humidity or electromagnetic interference exceeds the safety range. The weights of Impact on risk.

[0086] , is a dynamic correction value used to compensate for errors caused by differences in material properties or external environmental disturbances. is the correction weight coefficient, Indicates the error deviation at the previous moment;

[0087] The parameter optimization module further calculates the fire risk status based on the following trigger mechanisms:

[0088]

[0089] in, The fire warning threshold is dynamically adjusted according to the performance of DMC flame retardant materials;

[0090] Step 3, DMC flame retardant material dynamic feedback control step: when the fire risk state , perform the following control steps:

[0091] Dynamically adjust the temperature and humidity range of the environment where the DMC flame retardant material is located to optimize the pyrolysis rate ;

[0092] Controlling the temperature distribution on the surface of DMC flame retardant materials To enhance flame retardant properties;

[0093] Step 4, closed-loop control step: Feedback the optimization results to the fire risk assessment module, adjust the fire risk assessment model based on the dynamic performance of the DMC flame retardant material, and realize dynamic closed-loop control.

[0094] As a further solution of the present invention, the carbonized layer thickness variable The monitored value is obtained by monitoring with a heat flow sensor and optionally corrected with a laser thickness sensor. The monitored value is calculated based on the following formula:

[0095]

[0096] in, and They represent the monitoring start time and end time, respectively, which are recorded by the heat flow sensor;

[0097] The carbonization reaction rate is expressed in time The instantaneous change value on ;

[0098] Indicates time Small increments of and The carbonization reaction rate over time;

[0099] is the carbonization layer formation coefficient, which is used to convert the carbonization reaction rate into the carbonization layer thickness;

[0100] When the laser thickness sensor is used for calibration, the following steps are specifically included:

[0101] The laser thickness sensor measures the change of material surface thickness in real time and generates thickness change data;

[0102] The thickness change data is combined with the instantaneous carbonization reaction rate collected by the heat flow sensor Make a comparison;

[0103] The monitoring data of the heat flow sensor is corrected by the following correction formula:

[0104]

[0105] in, is the thickness change measured by the laser thickness sensor, To measure the time interval, is the corrected carbonization reaction rate.

[0106] As a further embodiment of the present invention, the pyrolysis rate variable The dynamic adjustment is achieved by controlling the surface temperature distribution of the DMC flame retardant material, which specifically includes the following steps:

[0107] Adjust the local heat dissipation rate of the environment where the flame retardant material is located;

[0108] Dynamically adjust the ventilation intensity inside the power transmission and distribution switchgear;

[0109] Calculate the pyrolysis rate of flame retardant materials using the pyrolysis response model.

[0110] As a further solution of the present invention, the environment adaptation variable The dynamic regulation is based on a nonlinear perturbation model of external environmental factors, and the model is described as follows:

[0111]

[0112] in: is the ambient temperature; is the ambient humidity; is the environmental electromagnetic interference intensity; is the weight of each disturbance factor; is the nonlinear adjustment function corresponding to the disturbance factor.

[0113] As a further solution of the present invention, the multi-sensor fusion technology includes the following steps:

[0114] Step 1: Use the following sensors to collect the dynamic performance parameters of DMC flame retardant materials in real time:

[0115] Thermogravimetric analysis sensor for collecting pyrolysis rate variables ;

[0116] Laser thickness sensor or thermal imaging device to collect carbonized layer thickness variables ;

[0117] Temperature and humidity sensors and electromagnetic interference analysis equipment to collect environmental adaptation variables ;

[0118] Step 2: Processing the output data of the sensor using a data fusion algorithm, the algorithm comprising:

[0119] Time series correction, used to synchronize the collected data from different sensors;

[0120] The weight allocation mechanism allocates weights according to the credibility of sensor data and generates fused dynamic performance parameters.

[0121] As a further solution of the present invention, the method for dynamically adjusting the temperature and humidity range of the environment where the DMC flame retardant material is located includes: monitoring the temperature and humidity values ​​of the environment in real time through a temperature and humidity sensor; when the monitored value exceeds a preset range, controlling the following devices to adjust:

[0122] Ventilation system to reduce ambient temperature;

[0123] Humidification equipment or dehumidification equipment, used to adjust the ambient humidity to a preset range;

[0124] Dynamically adjust the working intensity of the ventilation system and humidification equipment according to the difference between the monitored value and the preset range.

[0125] As a further solution of the present invention, the method for improving the carbonization layer formation rate of the DMC flame retardant material includes: dynamically adjusting the temperature distribution on the surface of the DMC flame retardant material, and the specific steps are: calculating the optimal temperature range required for the carbonization reaction through a pyrolysis response model; controlling the heating device to provide uniform heating to the surface of the flame retardant material.

[0126] As a further solution of the present invention, the feedback mechanism comprises the following steps:

[0127] Monitor the dynamic performance parameters of DMC flame retardant materials through sensors; compare the monitoring data with the output values ​​of the fire risk assessment model to generate error deviations ;

[0128] Dynamically adjust the weight parameters of the fire risk assessment model according to the error deviation, including: Correction of pyrolysis rate variables The weight parameter ; Correction of carbonized layer thickness variable The weight parameter ; Correct environment adaptation variables The weight parameter .

[0129] The present invention is used in power transmission and distribution switchgear, where early monitoring and prevention of fire risks are crucial. For example, since power transmission and distribution equipment will generate high temperatures under short-term overload or local hot spots, especially in the range of 200°C to 300°C, whether the internal flame retardant material (such as DMC) can form an effective carbonized layer is the core factor that determines whether the fire spreads. By introducing the variable of carbonized layer thickness, this technical solution solves the key problem that the prior art cannot accurately describe the dynamic properties of flame retardant materials and the risk of fire spread.

[0130] Specifically, the dynamic change of the thickness of the carbonized layer directly determines the thermal insulation effect and oxygen barrier performance of the material. For example, through experimental verification, under the condition of local overload inside the power transmission and distribution switch, for example: when the thickness of the carbonized layer formed by the DMC material reaches 0.8 mm, the heat diffusion rate inside the equipment is reduced by 50%, which significantly inhibits the further spread of the fire; and when the thickness of the carbonized layer is less than 0.3 mm, the material failure rate is accelerated and the fire risk increases rapidly; these experiments were completed under the conditions of heat flux intensity of 50 kW / m² and humidity (relative humidity (RH)) of 40%-60%. The data show that there is a clear linear relationship between the thickness of the carbonized layer and the fire risk assessment. In this way, compared with the traditional technical solution that only relies on heat flux or temperature sensors, the traditional solution has the shortcomings of lagging monitoring data and inability to describe the pyrolysis process of flame retardant materials, resulting in insufficient timeliness and accuracy of fire risk assessment. This technical solution introduces the thickness of the carbonized layer as a dynamic variable to make the fire prevention and control system of the power transmission and distribution switch more accurate and reliable.

[0131] And, the accuracy range of the laser thickness sensor is preferably within the high-precision requirement of ±1 µm to ensure that the dynamic changes of the thickness of the carbonized layer can be accurately measured. At the same time, it should have good environmental adaptability for the complex working conditions of the power transmission and distribution switchgear, such as high temperature resistance (adaptable to the range of 150°C to 350°C) and anti-electromagnetic interference ability to ensure stable operation in actual use scenarios. In this case, it is recommended to use blue light or infrared laser thickness sensors, which are more adaptable in high temperature and high reflective environments; at the same time, in order to cope with the influence of high temperature, a heat insulation device can be installed around the sensor, and for electromagnetic interference, a signal shielding device should be designed or an anti-interference algorithm should be used to compensate the collected data in real time to improve the accuracy and stability of the measurement data, which are all extended implementation methods known to ordinary technicians in this field.

[0132] At the same time, the technical solution of the present invention assesses the fire risk by real-time collection of multiple dynamic parameters (such as material pyrolysis rate, carbonization layer thickness and environmental adaptation variables). The core logic of the fire risk model is to convert these dynamic parameters into a comprehensive fire risk index to determine whether the system is in a safe state. If the risk exceeds the set threshold, the system will start the corresponding dynamic control measures. The design of the model makes each parameter have a clear role in assessing the risk. For example, the pyrolysis rate is used to describe the decomposition trend of the material at high temperature. When the pyrolysis rate is too fast, the fire risk will increase significantly; the thickness of the carbonization layer characterizes the flame retardant properties of the material. The thicker the carbonization layer, the lower the possibility of fire spread; environmental adaptation variables: consider the impact of the external environment (such as temperature, humidity, electromagnetic interference) on the material performance, and ensure that the risk assessment model can dynamically adapt to complex scenarios. By reasonably setting the weight ratio of these parameters, the model can be dynamically adjusted according to the specific scenario, which belongs to an extended implementation method known to ordinary technicians in this field.

[0133] In addition, the acquisition of dynamic parameters depends on a variety of sensors, such as thermogravimetric analysis sensors, laser thickness sensors, and environmental sensors. The specific implementation paths are as follows: Pyrolysis rate: The sensor monitors the change of material mass over time in real time, and the pyrolysis rate is obtained by calculating the rate of mass loss. This parameter directly reflects the decomposition behavior of the material and is a key indicator for early fire risk assessment; Carbonization layer thickness: The laser thickness sensor combines the data of the heat flow sensor to monitor the thickness change of the carbonization protective layer on the surface of the material. The data is automatically processed and input into the fire assessment model to determine whether the flame retardant performance of the material is within a safe range; Environmental adaptation variables: Environmental parameters (such as temperature, humidity, and electromagnetic interference intensity) are collected in real time through temperature and humidity sensors and electromagnetic interference analysis equipment, and converted into environmental adaptation variables. Environmental adaptation variables are used to correct the model evaluation results to ensure the reliability of risk assessment in a dynamic environment; the optimization process is mainly reflected in the dynamic adjustment of material properties and environmental parameters to improve the system's response speed to fire risks. Specific measures include material performance optimization: when the fire risk is detected to be increased, the system will enhance the material performance by regulating environmental conditions (such as temperature, humidity, and heat distribution). For example, increasing the ambient humidity can slow down the pyrolysis rate of the material and prolong the formation time of the carbonized layer, thereby reducing the risk of fire. Environmental optimization: The system uses ventilation equipment to reduce the temperature in high-temperature areas, or adjusts the humidity through humidification / dehumidification equipment to keep the material in the best working environment. These adjustments are triggered in real time by the control algorithm to ensure that the system can respond quickly to fire risks. Feedback control: The system will continuously monitor the effect of the regulation. If the risk is not effectively reduced, the regulation strategy will be further adjusted based on the monitoring data. This closed-loop feedback mechanism makes the optimization process more accurate and efficient.

[0134] And, the role of the adjustment function is to convert various disturbance factors in a complex environment into quantifiable variables, which are used to adjust the output results of the fire assessment model. For example, temperature, humidity and electromagnetic interference will affect the performance of materials. In order to accurately assess the risk in different environments, the adjustment function will set a set of adaptive parameters (such as temperature coefficient, humidity coefficient, etc.) according to experimental data. The setting of these parameters can be achieved in the following ways, such as experimental calibration: simulating various environmental conditions (such as high temperature, high humidity or strong electromagnetic interference) in the laboratory, recording the changes in material properties, and deriving the degree of influence of each environmental factor on the fire risk; dynamic adjustment: the adjustment function can adjust its output results according to real-time environmental data. For example, in a high humidity scenario, the weight of the humidity factor will increase to reflect the significant effect of humidity on the flame retardant properties of the material. The design of the adjustment function enables the system to dynamically adapt to complex environments and ensure the accuracy of fire risk assessment.

[0135] And, the core logic of multivariable adjustment is that the risk assessment model will analyze each dynamic parameter in real time and adjust the weight ratio according to the relationship between the parameters. For example, when the ambient temperature rises, the model will automatically increase the weight of the pyrolysis rate parameter in order to more sensitively monitor the material decomposition trend; if the humidity fluctuates greatly, the model will enhance the influence of the humidity parameter to reduce the interference of environmental factors on the assessment results; through such dynamic adjustment logic, the system can adaptively optimize the assessment results according to the needs of different scenarios, ensuring the accuracy of the assessment and the timeliness of the regulation, which are all extended implementation methods known to ordinary technicians in this field.

[0136] Embodiment 1:

[0137] In a large power transmission and distribution station in a coastal city, the power transmission and distribution switchgear is facing a great fire risk due to long-term high humidity, high salt fog and unstable electromagnetic environment. In this environment, the fire resistance of DMC flame retardant materials may be affected by many factors. For example, fluctuations in ambient temperature and humidity may lead to unstable pyrolysis rate of materials and uneven carbonization layer formation, which ultimately affects the flame retardant effect. The existing technology mainly achieves fire prevention and control through single parameter monitoring or fixed threshold control. This method is not adaptable enough to complex and changing environments and cannot comprehensively and effectively monitor fire risks.

[0138] In the above scenario, this technical solution targets the fire risk of switchgear in transmission and distribution stations, combines the dynamic characteristics of DMC flame-retardant materials, and achieves efficient fire prevention and control through multiple sensor linkage monitoring, intelligent data processing, and dynamic feedback control.

[0139] Thermal flow sensors, laser thickness sensors, temperature and humidity sensors, and electromagnetic interference analysis equipment are installed inside and around the switchgear. The thermal flow sensor collects real-time data on the pyrolysis rate of DMC materials during operation to determine their combustion or decomposition trends; the laser thickness sensor is used to monitor the thickness changes of the carbonized layer to compensate for the errors that may occur when the thermal flow sensor measures the thickness; the temperature and humidity sensor and electromagnetic interference analysis equipment monitor environmental conditions and record the temperature, humidity, and electromagnetic disturbances around the equipment. These data are fused through a time synchronization algorithm to form a complete dynamic monitoring system that can accurately capture multi-dimensional fire risk information in real time.

[0140] In order to deal with the problem of unstable material performance in complex environments, this technical solution uses a control system to dynamically adjust the environmental parameters of the equipment. When the temperature and humidity sensor detects that the humidity is too low, the humidification equipment automatically starts to increase the humidity around the equipment and reduce the risk of accelerated material pyrolysis rate due to insufficient humidity; when the ambient temperature rises close to the pyrolysis threshold of the DMC material, the ventilation system will actively lower the temperature to ensure that the equipment remains within a safe operating range. In addition, by obtaining the changes in the thickness of the carbonized layer through a laser thickness sensor, the system can automatically adjust the ambient temperature to make the carbonized layer of the DMC material more uniform and further enhance the flame retardant effect. This control method can respond in real time according to changes in the environment and materials, avoiding the fire hazards caused by the lag of traditional technologies.

[0141] For example, when electromagnetic interference in the transmission and distribution station equipment causes deviations in the monitoring data of the heat flow sensor, the data from the laser thickness sensor can provide a reliable reference value, thereby ensuring that the system correctly judges the material status.

[0142] Finally, all the collected dynamic parameters are processed by the system algorithm to form a fire risk assessment report, which can provide early warning when the fire risk first appears. At the same time, the control system further intelligently regulates the environment and materials according to the risk assessment results to achieve closed-loop optimization. In this way, the fireproof performance of the DMC material is fully utilized, the formation of the carbonized layer is more stable and uniform, and a significant fire prevention and control effect is achieved in high humidity and complex electromagnetic environments.

[0143] In practical applications, this technical solution can not only meet the needs of fire risk assessment in complex environments, but also dynamically adapt to environmental changes in multiple scenarios, achieving accuracy and stability that are difficult to achieve with traditional single monitoring solutions. This collaborative mechanism of multi-dimensional monitoring, intelligent regulation and feedback optimization not only significantly improves the efficiency of fire prevention and control, but also provides stronger guarantees for the safe operation of power transmission and distribution switchgear.

[0144] Embodiment 2:

[0145] This embodiment discloses a method for collecting dynamic performance parameters of DMC flame retardant materials and realizing dynamic optimization through multi-sensor fusion technology, which is used to improve the accuracy and response efficiency of fire prevention and control.

[0146] For example, for the dynamic performance parameter acquisition method, the pyrolysis rate variable Collection: Use thermogravimetric analysis sensor (TGA) to monitor the mass loss of DMC flame retardant materials in real time and record the dynamic mass change during pyrolysis and time interval , calculate the pyrolysis rate:

[0147]

[0148] The sensor forms a quality loss curve through multi-point monitoring to ensure data accuracy.

[0149] Carbonized layer thickness variable Acquisition: Acquiring pyrolysis rate data through heat flow sensor , combined with the carbonization layer formation coefficient Calculate the carbonized layer thickness:

[0150]

[0151] in: : represents the contribution rate of unit pyrolysis rate to the carbonized layer thickness, and its unit is mm·s / kg. Obtained through experimental calibration The specific method is: under the condition of constant temperature from 150°C to 350°C, the pyrolysis rate and carbonization capacity of DMC material are calibrated, that is, a series of pyrolysis rate are recorded by thermogravimetric analysis (TGA) and laser thickness measurement equipment. and the corresponding carbonized layer thickness , fitting , and during the experiment, the ambient humidity is controlled at 40%-60% to ensure data stability, which are all extended implementation methods known to ordinary technicians in this field.

[0152] , : Monitoring time interval, collected in real time by the sensor. Optionally, a laser thickness sensor is used to measure the actual change value of the carbonized layer thickness. ,right Correction of data:

[0153]

[0154] in It is the time interval between two samplings of the laser thickness sensor.

[0155] The environment adaptation variable For example, you can use temperature and humidity sensors and electromagnetic interference analysis equipment to collect ambient temperature ,humidity and electromagnetic interference intensity , and calculate the environment adaptation variables:

[0156]

[0157] in: : The weight of each disturbance factor is adjusted according to experimental data.

[0158] : Linear regulation function, defined as:

[0159]

[0160] , , , is the adjustment coefficient, obtained through experimental fitting.

[0161] In the dynamic performance parameter optimization method, the recursive optimization formula is applied: , , Enter the following fire risk assessment formula:

[0162]

[0163] in: , , : Weight parameter, which is adjusted through historical data fitting or dynamic adjustment. : Dynamic correction value, based on the fire risk error at the previous moment.

[0164] Multi-sensor data fusion: For example, the time series correction algorithm can be used to synchronize the data of the thermal flow sensor, laser thickness sensor and environmental sensor; the multi-sensor output data can be fused through the credibility weighting mechanism to generate unified dynamic performance parameters.

[0165] Embodiment 3:

[0166] This embodiment further illustrates how to optimize the performance of DMC flame retardant materials by dynamically adjusting environmental conditions and closed-loop feedback control, and solves the problems of dynamically adjusting temperature and humidity, increasing the carbonization layer rate, and realizing the feedback mechanism.

[0167] For example, in terms of methods for dynamically adjusting temperature and humidity, environmental monitoring: using temperature and humidity sensors to monitor ambient temperature in real time and humidity ; Its target value setting: set the target value according to the optimal fire prevention and control conditions calibrated by the experiment , , the adjustment formula is:

[0168]

[0169] in , To adjust the deviation.

[0170] In terms of dynamic adjustment, when the temperature and humidity deviate from the target values: for example, the ventilation system can be used to lower the ambient temperature; humidification or dehumidification equipment can be used to adjust the humidity; the adjustment equipment can achieve dynamic target tracking through PID control.

[0171] And, when improving the carbonization layer formation rate, for example, the carbonization layer temperature can be controlled. For example, experimental studies have shown that the carbonization rate of DMC flame retardant materials reaches a maximum value in the experimental temperature range of 150°C to 350°C. This temperature range is used to experimentally calibrate the carbonization layer formation coefficient. , not the actual working temperature zone. In actual applications, the ambient temperature is controlled within a range of no more than 150°C through ventilation systems and temperature control equipment to prevent premature pyrolysis, delay the carbonization process and optimize the formation of the carbonized layer. Control the temperature distribution of the external heating device to ensure uniform surface temperature of the material and avoid local overheating or overcooling that causes uneven carbonized layer.

[0172] And, the realization of dynamic feedback mechanism, such as error deviation calculation: the actual data collected by the sensor is compared with the output value of the fire risk model to calculate the error deviation:

[0173]

[0174] Weight adjustment: When the error deviation When the set threshold is exceeded, the weight parameter of the fire risk assessment model is dynamically adjusted. The adjustment formula is as follows:

[0175]

[0176] in , , is the weight adjustment coefficient.

[0177] And, closed-loop feedback control: the adjusted weight parameters are used for the next fire risk assessment to achieve closed-loop feedback control.

[0178] Embodiment 4:

[0179] The specific implementation of dynamic feedback control and multi-sensor data fusion is that, for example, in a transmission and distribution station in a high-altitude cold area, the ambient temperature fluctuates from -30°C to 10°C, and the humidity fluctuates from 20% to 90%. The experimental scene has strong electromagnetic interference (interference frequency is 50 Hz, and the amplitude varies randomly). The following equipment is used in this embodiment:

[0180] Heat flow sensor: monitors the pyrolysis rate of DMC flame retardant materials and generates a pyrolysis rate curve.

[0181] Laser thickness sensor: monitors the thickness of the carbonized layer in real time and is used to correct the heat flow sensor data.

[0182] Temperature and humidity sensor: collects the temperature and humidity parameters of the environment around the device.

[0183] Electromagnetic interference analysis equipment: monitors the intensity of electromagnetic interference and is used to adjust the credibility weight of data collection.

[0184] Ventilation system and humidification / dehumidification equipment: dynamically adjust ambient temperature and humidity to the target range.

[0185] On this basis, the multi-sensor data fusion process is:

[0186] Time series correction: synchronize the data of heat flow sensors, laser thickness sensors, and environmental sensors, and eliminate deviations caused by data delays or different sampling frequencies by unifying timestamps.

[0187] Credibility weighting mechanism: For the data fusion of the thermal flow sensor and the laser thickness sensor, when the electromagnetic interference intensity is high, the weight of the laser thickness sensor data is increased. The calculation formula is as follows:

[0188]

[0189] in: is the weight of the laser thickness sensor; is the baseline value of interference intensity; is the current interference intensity; is the sensitivity parameter (experimentally set to 0.1). After the weight is adjusted, the fusion results of pyrolysis rate and carbonization layer thickness are as follows:

[0190]

[0191] in: is the thickness data of the laser thickness sensor; Calculates the thickness of the heat flow sensor.

[0192] Fusion result output: The fused dynamic performance parameters are input into the optimization module for fire risk assessment.

[0193] In addition, in terms of the dynamic environmental control steps, such as target value setting: the temperature and humidity target values ​​are calibrated according to experiments: the temperature range is 0℃ to 5℃, and the humidity range is 60% to 70%. The electromagnetic interference target value is set to an interference amplitude of less than 20 V / m.

[0194] In its dynamic adjustment formula, the temperature and humidity adjustment uses the following formula to calculate the adjustment range:

[0195]

[0196]

[0197] in: and are the target values ​​of temperature and humidity respectively; and To regulate the coefficient, the experiment was set to 0.8 and 1.2.

[0198] When the electromagnetic interference intensity exceeds the target value, the sensor sensitivity is adjusted through the dynamic adjustment formula:

[0199]

[0200] in: is the raw data of the sensor; is the interference correction coefficient, which is set to 0.05 in the experiment.

[0201] When the device is executing, when the temperature is lower than the target value, the ventilation system operation intensity is increased; when the humidity is lower than the target value, the humidification equipment is started; when the electromagnetic interference intensity is too high, the impact of interference on sensor data is reduced through software filtering or signal suppression.

[0202] In its dynamic feedback mechanism, for example, error deviation calculation, the error deviation can be calculated based on the comparison between the real-time data collected by the sensor and the output value of the optimization model:

[0203]

[0204] in: To assess risk value in real time; Predict the value at risk for the model.

[0205] In terms of dynamic weight adjustment, for example, the following formula can be used to dynamically adjust the weight parameters in the optimization model:

[0206]

[0207] in: is the current weight; To dynamically adjust the coefficient, it is experimentally set to 0.05.

[0208] In addition, closed-loop feedback control can be implemented, that is, the adjusted weight parameters are input into the optimization module, the fire risk assessment results are updated, and dynamic closed-loop control is achieved.

[0209] Through the above implementation steps, the experimental results in high-altitude cold areas are as follows: 1. Accuracy of fire risk assessment: Combined with the credibility weighting mechanism and closed-loop feedback, the accuracy is increased to 97%, significantly higher than the 65% of the existing technology. 2. Dynamic response time: The average response time is shortened to 1.2 minutes; 3. Carbonized layer thickness uniformity: The thickness fluctuation range is reduced to ±2%, significantly better than the ±10% of the traditional solution; 4. Anti-interference ability: The sensor data error rate is reduced from ±15% to ±3%, ensuring reliability in high-interference environments.

[0210] Embodiment 5:

[0211] For example, when a power transmission and distribution switchgear under high temperature, high humidity and strong electromagnetic interference environment is selected as the experimental environment, the sensor data is susceptible to strong interference, and the material pyrolysis behavior and carbonization layer formation characteristics show significant nonlinearity under high temperature fluctuations. At the same time, environmental disturbances (severe fluctuations in humidity, electromagnetic interference) have a dynamic impact on material properties, and the accuracy of fire risk assessment needs to be ensured through weight adjustment and correction mechanisms.

[0212] Among them, in the dynamic performance parameters and weight adjustment, the weight parameter , , The physical meaning is: Controlling the Pyrolysis Rate Variable The weight of is applicable to the scene with significant fluctuation of ambient temperature; :Control the thickness of the carbonized layer The weight reflects the importance of material protection performance to fire assessment; : Control environment adaptation variables It is suitable for scenes with high humidity or strong electromagnetic interference.

[0213] The initial calibration of the weight parameters is as follows: The initial values ​​of the weight parameters are obtained by experimental calibration. For example, in the experimental environment, the temperature is 150℃~300℃; the humidity is 50%~90%; the electromagnetic interference intensity is 30~80 V / m; in the fitting process, a single variable is controlled (such as adjusting only the temperature, humidity or interference intensity), and the observation is made. The response value of ; the initial weight value is obtained through regression analysis: =0.5, =0.4, =0.3.

[0214] And, in the dynamic adjustment mechanism of weight parameters, in order to adapt to complex environments and dynamic characteristics of materials, weight parameters , , The dynamic adjustment mechanism is based on the adjustment of error deviation. The fire risk assessment error deviation Calculation formula:

[0215] Dynamically adjust weight parameters according to error deviation: ;in , , is the weight adjustment coefficient, set to 0.05-0.1.

[0216] And in the adjustment based on environmental conditions, when the ambient temperature exceeds the material pyrolysis threshold (such as 200°C), increase ; When the carbonized layer thickness fluctuation range exceeds ±3%, increase ; When the humidity exceeds 80% or the electromagnetic interference intensity is greater than 50 V / m, increase .

[0217] And, it can also be based on the optimization of multivariable interactions, and its dynamic optimization formula is:

[0218]

[0219] The correction It is used to compensate for errors caused by differences in material properties or external environmental disturbances. In its optimization mechanism, the correction term formula is:

[0220]

[0221] in is the dynamic correction factor:

[0222]

[0223] It is the standard deviation of the fire assessment index, which is used to limit the excessive fluctuation of the correction item. The temperature range is 150℃~300℃; the humidity range is 50%~90%; the electromagnetic interference intensity is 30~80 V / m; and the dynamically disturbed environmental parameters fluctuate at a rate of ±20%.

[0224] Through this experiment, in terms of fire risk assessment accuracy, the initial model assessment error was ±20%. After the introduction of dynamic weight adjustment, the error was reduced to ±5%; the average response time of its control system was 1.8 minutes, which was 75% shorter than the traditional solution; the fluctuation range of the carbonization layer thickness was reduced from ±8% to ±1%; and the data error rate of its anti-interference ability was reduced from ±15% to ±3%.

[0225] Embodiment 6:

[0226] In the actual operation scenario of a high-humidity transmission and distribution station in the coastal area, in order to verify the effectiveness of the dynamic control and multi-sensor fusion technology in this technical solution and compare the performance of the experimental group with that of the control group, a series of experiments were designed for the data in Tables 1 and 2, as detailed below:

[0227] Table 1 Comparative data of fire risk assessment and environmental adaptability of the present invention.

[0228]

[0229] Table 2 Comparison of fire prevention and control response efficiency and material performance of the present invention.

[0230]

[0231] To ensure the repeatability of experimental conditions and the reliability of data, the experimental equipment includes power transmission and distribution switchgear, heat flow sensor, laser thickness sensor, temperature and humidity sensor, electromagnetic interference analysis equipment, temperature and humidity control equipment and carbonized layer thickness detection device. The experimental group adopts the patented technical solution, including multi-sensor fusion monitoring, dynamic control and carbonized layer optimization functions; the control group adopts the existing technical solution, relying only on single sensor monitoring and fixed parameter control. In the experimental condition setting:

[0232] 1. The experiment simulates the following three typical scenarios:

[0233] High humidity environment (80% humidity): test the impact of environmental disturbances on the accuracy of fire risk assessment and control response time; low temperature environment (-20°C to 10°C fluctuation): verify the stability of flame retardant performance and the quality of carbonized layer formation in extreme low temperature scenarios; strong electromagnetic interference environment (50 Hz interference source): evaluate the reliability of sensor data and stable operation time.

[0234] 2. Experimental data collection and analysis methods:

[0235] Accuracy of fire risk assessment: The experimental group collected pyrolysis rate, carbonization layer thickness and environmental disturbance variables through multi-sensor fusion technology, and combined with the dynamic correction model to conduct fire risk assessment; the control group only directly monitored the pyrolysis rate through a single heat flux sensor. After data collection, the accuracy of risk assessment was calculated based on the exact number of times the experimental equipment actually triggered the fire alarm.

[0236] Dynamic control response time: When the environmental conditions change rapidly (such as humidity rising from 50% to 80%, temperature falling from 10°C to -20°C), record the average lag time from sensor data collection to the execution of control device actions in the experimental group and the control group. The data is obtained by recording the control system log timestamp.

[0237] Carbonized layer thickness uniformity: The carbonized layer thickness is monitored in real time by a laser thickness sensor, and the thickness fluctuation range after formation is recorded. The experimental group uses dynamic control technology to optimize the formation of the carbonized layer; the control group does not have this function and only relies on a fixed material reaction rate.

[0238] Flame retardant performance maintenance time: simulate the fire scene caused by continuous arc discharge and high temperature, and test the flame retardant performance maintenance time in low temperature and electromagnetic interference environment. Flame retardant performance is defined as the integrity maintenance time of the carbonized layer of DMC material, and the carbonized layer damage time point is synchronously detected using a heat flow sensor and an optical thickness analyzer.

[0239] Through the above experiments, in a high humidity environment, the fire risk assessment accuracy of the experimental group reached 93%, significantly higher than the 65% of the control group. The experimental group was able to accurately capture the impact of environmental disturbances on material properties and reduce data deviations through correction mechanisms (from ±20% of the control group to ±5%). In a low temperature environment, the dynamic control response time of the experimental group was 2 minutes, much lower than the 8 minutes of the control group. The experimental group avoided rapid fluctuations in the pyrolysis rate of the material through real-time control of temperature and humidity, and the uniformity fluctuation range of the carbonized layer thickness was reduced from ±15% to ±3%. In a strong electromagnetic interference environment, the stable operation time of the sensor in the experimental group was 50 hours, while the control group experienced data interruption or obvious deviation after 20 hours. This shows that the multi-sensor fusion correction mechanism in this technical solution significantly improves the reliability of monitoring data.

[0240] Also, in the flame retardant performance maintenance time experiment, the flame retardant performance of the experimental group can last for 12 hours in an extremely low temperature environment, while the control group can only last for 6 hours. The carbonized layer of the experimental group is more uniform due to dynamic regulation, avoiding local cracks or early breakage; through this embodiment, the significant superiority of this technical solution in multiple scenarios is verified, and it can be clearly seen from the experimental data that the dynamic regulation response and multi-sensor fusion technology of the experimental group have obvious advantages in fire risk assessment accuracy and environmental adaptability; in complex scenarios, the experimental group optimizes the performance of DMC flame retardant materials, which significantly improves the quality of carbonized layer formation and the stability of the flame retardant performance of the material.

[0241] Embodiment 7:

[0242] This embodiment demonstrates the optimization adjustment of completing dynamic acquisition, data correction and closed-loop feedback in a complex environment, which specifically includes the following steps:

[0243] 1. Dynamic weight adjustment: By monitoring the multi-dimensional data of ambient temperature and humidity, material pyrolysis rate and carbonization layer thickness, the system dynamically adjusts the influence weight of each variable according to the real-time risk assessment results. For example, when the ambient temperature continues to rise, the monitoring sensitivity of the material pyrolysis rate is increased first; when the humidity or electromagnetic interference fluctuates significantly, the weight of the environmental adaptation variable is increased to ensure that the assessment model better matches the current actual scenario.

[0244] 2. Multi-sensor data correction: To improve data accuracy, sensor monitoring data needs to be calibrated for credibility before being input into the evaluation model. When high humidity or strong electromagnetic interference affects certain sensor data, the system automatically lowers the priority of the interfered data and refers to the relevant data of other sensors for compensation. For example, when electromagnetic interference is strong, the data of the laser thickness sensor is preferentially used instead of the indirect derivation value of the heat flow sensor.

[0245] 3. Dynamic response of environmental control: When the system detects that the fire risk exceeds the threshold, the environmental control module is immediately triggered. For example, when the ambient temperature is high, the ventilation system is automatically activated to lower the temperature; when the humidity is too low, the humidification equipment is started; if the thickness of the carbonized layer of the material does not meet the safety standard, the formation rate of the carbonized layer is optimized by controlling the heating equipment. The operating intensity of the control equipment is adjusted in real time by the system to ensure that the material performance is always in the best state.

[0246] 4. Closed-loop feedback optimization: After all control measures are completed, the system will compare the control results with the new round of sensor monitoring data. If the risk is not effectively reduced, the system will automatically adjust the weight distribution and environmental control strategy in the risk assessment model until the fire risk is reduced to an acceptable range.

[0247] 5. Multi-scenario adaptive optimization: For different environmental conditions (such as high temperature, high humidity or strong electromagnetic interference), the system quickly adjusts the evaluation model and control strategy through pre-set scenario templates. Each template is experimentally calibrated and adaptively adjusted in combination with real-time data to ensure efficient operation of the system in complex dynamic environments.

[0248] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A control method for intelligent fire prevention of flame retardant materials used in power transmission and distribution switch sets, characterized in that: The flame retardant material is a DMC flame retardant material. The method realizes dynamic assessment and prevention and control of fire risks through dynamic performance optimization and recursive feedback control of the DMC flame retardant material. Specifically, the method includes the following steps: Step 1, DMC flame retardant material dynamic performance data collection step: collect the dynamic performance parameters of DMC flame retardant materials, including: Pyrolysis rate variables :Characterize the pyrolysis rate of DMC flame retardant materials under different temperature gradients and monitor it in real time by thermogravimetric analysis sensor; Carbonized layer thickness variable : Characterize the thickness of the carbonized protective layer formed on the surface of DMC flame retardant materials, and monitor it in real time through laser thickness sensors or thermal imaging equipment; Environment adaptation variables :Characterize the dynamic impact of external environmental factors, including temperature, humidity, and electromagnetic interference, on the performance of DMC flame retardant materials, collected through temperature and humidity sensors and electromagnetic interference analysis equipment; use data fusion algorithms to process the output data of the sensors and generate fused dynamic performance parameters; Step 2, DMC flame retardant material parameter optimization step: input the collected dynamic performance parameters into the fire risk assessment model, and calculate the fire warning index based on the following nonlinear recursive optimization formula : in: , , is a weight parameter used to control the influence of each input variable on the fire warning index; , , The values ​​of 0.5, 0.4, 0.3; , is a dynamic correction value used to compensate for errors caused by differences in material properties or external environmental disturbances. is the correction weight coefficient, Indicates the error deviation at the previous moment; The fire risk assessment model further calculates the fire risk status based on the following trigger mechanisms: in, The fire warning threshold is dynamically adjusted according to the performance of DMC flame retardant materials; Step 3, DMC flame retardant material dynamic feedback control step: when the fire risk state , perform the following control steps: Dynamically adjust the temperature and humidity range of the environment where the DMC flame retardant material is located to optimize the pyrolysis rate ; Controlling the thickness of the carbonized layer of DMC flame retardant materials To enhance flame retardant properties; Step 4, closed-loop control step: Feedback the optimization results to the fire risk assessment module, and adjust the fire risk assessment model based on the dynamic performance of the DMC flame retardant material to achieve dynamic closed-loop control.

2. A control method for intelligent fire prevention of flame retardant materials for power transmission and distribution complete switchgear according to claim 1, characterized in that: The carbonized layer thickness variable The value is obtained by monitoring with a heat flow sensor and optionally corrected with a laser thickness sensor. The value is calculated based on the following formula: in, and They represent the monitoring start time and end time, respectively, which are recorded by the heat flow sensor; The carbonization reaction rate is expressed in time The instantaneous change value on ; Indicates time Small increments of and The carbonization reaction rate over time; is the carbonization layer formation coefficient, which is used to convert the carbonization reaction rate into the carbonization layer thickness; When the laser thickness sensor is used for calibration, the following steps are specifically included: The laser thickness sensor measures the change of material surface thickness in real time and generates thickness change data; The thickness change data is combined with the instantaneous carbonization reaction rate collected by the heat flow sensor Make a comparison; The monitoring data of the heat flow sensor is corrected by the following correction formula: in, is the thickness change measured by the laser thickness sensor, To measure the time interval, is the corrected carbonization reaction rate.

3. The method for controlling the intelligent fire prevention of flame retardant materials for power transmission and distribution switchgear according to claim 1 is characterized in that: Step 3 includes the pyrolysis rate variable The dynamic adjustment is achieved by controlling the surface temperature distribution of the DMC flame retardant material, and specifically includes the following steps: adjusting the local heat dissipation rate of the environment where the flame retardant material is located; dynamically adjusting the ventilation intensity in the power transmission and distribution switchgear; and using the pyrolysis response model to calculate the pyrolysis rate of the flame retardant material.

4. A control method for intelligent fire prevention of flame retardant materials for power transmission and distribution switchgear according to claim 1, characterized in that: Step 3 includes environment adaptation variables The dynamic regulation is based on a nonlinear perturbation model of external environmental factors, and the model is described as follows: in: is the ambient temperature; is the ambient humidity; is the environmental electromagnetic interference intensity; is the weight of each disturbance factor; is the nonlinear adjustment function corresponding to the disturbance factor.

5. The method for controlling the intelligent fire prevention of flame retardant materials for power transmission and distribution switchgear according to claim 1 is characterized in that: The step of processing the output data of the sensor using a data fusion algorithm includes time series correction for time synchronization of collected data from different sensors; The weight allocation mechanism allocates weights according to the credibility of sensor data and generates fused dynamic performance parameters.

6. The method for controlling the intelligent fire prevention of flame retardant materials for power transmission and distribution switchgear according to claim 1 is characterized in that: The method for dynamically adjusting the temperature and humidity range of the environment where the DMC flame retardant material is located includes: monitoring the temperature and humidity values ​​of the environment in real time through a temperature and humidity sensor; when the monitored value exceeds a preset range, controlling the following devices to make adjustments: The ventilation system is used to lower the ambient temperature; the humidification equipment or dehumidification equipment is used to adjust the ambient humidity to a preset range; the working intensity of the ventilation system and the humidification equipment is dynamically adjusted according to the difference between the monitored value and the preset range.

7. A control method for intelligent fire prevention of flame retardant materials for power transmission and distribution switchgear according to claim 1, characterized in that: Step 3 includes increasing the formation rate of the carbonization layer of the DMC flame retardant material, and the method includes: dynamically adjusting the temperature distribution on the surface of the DMC flame retardant material, and the specific steps are: calculating the optimal temperature range required for the carbonization reaction through a pyrolysis response model; and controlling the heating device to provide uniform heating to the surface of the flame retardant material.

8. The method for controlling the intelligent fire prevention of flame retardant materials for power transmission and distribution switchgear according to claim 1 is characterized in that: Compare the fused dynamic performance parameters with the output values ​​of the fire risk assessment model to generate error deviations ; Dynamically adjust the weight parameters of the fire risk assessment model according to the error deviation, including: Correction of pyrolysis rate variables The weight parameter ; Correction of carbonized layer thickness variable The weight parameter ; Correct environment adaptation variables The weight parameter .

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