CN116311750B - Intelligent fireproof system and method for control cabinet based on Internet of things - Google Patents
Intelligent fireproof system and method for control cabinet based on Internet of things Download PDFInfo
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- CN116311750B CN116311750B CN202310275652.5A CN202310275652A CN116311750B CN 116311750 B CN116311750 B CN 116311750B CN 202310275652 A CN202310275652 A CN 202310275652A CN 116311750 B CN116311750 B CN 116311750B
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- 238000000034 method Methods 0.000 title claims abstract description 38
- 230000007613 environmental effect Effects 0.000 claims abstract description 35
- 238000004458 analytical method Methods 0.000 claims abstract description 19
- 238000010248 power generation Methods 0.000 claims abstract description 15
- 238000009826 distribution Methods 0.000 claims abstract description 9
- 238000012544 monitoring process Methods 0.000 claims abstract description 9
- 238000004146 energy storage Methods 0.000 claims abstract description 5
- 238000004891 communication Methods 0.000 claims abstract description 4
- 230000008569 process Effects 0.000 claims description 28
- 230000017525 heat dissipation Effects 0.000 claims description 22
- 230000020169 heat generation Effects 0.000 claims description 16
- 238000005286 illumination Methods 0.000 claims description 15
- 238000006243 chemical reaction Methods 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000006855 networking Effects 0.000 claims description 2
- 238000012163 sequencing technique Methods 0.000 claims 1
- 230000002265 prevention Effects 0.000 abstract description 6
- 238000001816 cooling Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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- 238000004519 manufacturing process Methods 0.000 description 1
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- 238000012360 testing method Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch
-
- A—HUMAN NECESSITIES
- A62—LIFE-SAVING; FIRE-FIGHTING
- A62C—FIRE-FIGHTING
- A62C3/00—Fire prevention, containment or extinguishing specially adapted for particular objects or places
- A62C3/16—Fire prevention, containment or extinguishing specially adapted for particular objects or places in electrical installations, e.g. cableways
Abstract
The invention relates to the technical field of control cabinet fire prevention, and particularly discloses an intelligent control cabinet fire prevention system and method based on the Internet of things, wherein the system comprises the following components: the temperature sensor comprises a plurality of groups, wherein one group is arranged outside the control cabinet, and the other groups are arranged in the control cabinet and are used for monitoring environmental temperature data and temperature data of all positions in the control cabinet; the solar energy component is arranged at the top of the distribution box and is used for supplying energy to the system through solar energy; the energy storage battery is used for storing electric energy generated by the solar energy component; the analysis control module is used for analyzing the fire risk of the control cabinet according to the temperature data monitored by each group of temperature sensors and the real-time power generation power of the solar module to obtain an early warning signal; the communication module is used for sending the early warning signal to the management end; the system eliminates the influence of environmental factors on the judgment result of the temperature in the control box, thereby improving the judgment accuracy.
Description
Technical Field
The invention relates to the technical field of control cabinet fire prevention, in particular to an intelligent control cabinet fire prevention system and method based on the Internet of things.
Background
The electric control cabinet is a bridge for connecting the electric power system with equipment, and the common control cabinet has the protection functions of overload, short circuit, phase failure protection and the like; the device has compact structure, stable work and complete functions; the intelligent control system can be combined according to the actual control scale, can realize the automatic control of a single cabinet, and can also realize that a plurality of cabinets form a Distributed Control System (DCS) through an industrial Ethernet or an industrial field bus network; can be suitable for industrial automation control occasions with various sizes and scales; the outdoor control cabinet can generate heat in the use process, especially when partial electric components and parts break down, the generated heat is increased sharply, and fire hidden danger is generated easily.
The existing power control cabinet generally performs corresponding operations according to the magnitude of the monitored temperature value by arranging a temperature sensor inside and comparing the temperature value of the sensor with the early warning temperature point and the power-off temperature point.
However, existing control cabinet monitoring systems are integrated with the control cabinet, so when an electrical fire occurs, there is a risk of failure of the monitoring system; meanwhile, aiming at the outdoor control cabinet, the temperature of the outdoor control cabinet is influenced by environmental factors, so that the temperature monitored by the indoor control cabinet is independently used for judging, erroneous judgment results are easy to generate, and the management process of the control cabinet is further influenced.
Disclosure of Invention
The invention aims to provide an intelligent fireproof system and method for a control cabinet based on the Internet of things, which solve the following technical problems:
how to improve the accuracy and the effectiveness of the electric fire monitoring in the control cabinet.
The aim of the invention can be achieved by the following technical scheme:
control cabinet intelligent fireproof system based on thing networking, the system includes:
the temperature sensor comprises a plurality of groups, wherein one group is arranged outside the control cabinet, and the other groups are arranged in the control cabinet and are used for monitoring environmental temperature data and temperature data of all positions in the control cabinet;
the solar energy component is arranged at the top of the distribution box and is used for supplying energy to the system through solar energy;
the energy storage battery is used for storing electric energy generated by the solar energy component;
the analysis control module is used for analyzing the fire risk of the control cabinet according to the temperature data monitored by each group of temperature sensors and the real-time power generation power of the solar module to obtain an early warning signal;
and the communication module is used for sending the early warning signal to the management end.
Further, the working process of the analysis control module comprises:
predicting the current illumination intensity according to the real-time power generation power of the solar module and the environmental temperature data;
analyzing and obtaining environmental heat generation quantity according to the current illumination intensity and the environmental temperature data;
and analyzing the fire risk of the control cabinet according to the temperature data of each position in the control cabinet and the environmental heat generation quantity.
Further, the process of analyzing the fire risk of the control cabinet comprises the following steps:
maximum value T in temperature data in control cabinet max And a temperature threshold T thr And (3) performing comparison:
if T max ≥T thr Early warning is carried out;
otherwise, performing fire risk early warning analysis, wherein the early warning analysis process comprises the following steps:
by the formula Calculating to obtain fire risk value R at current t moment T (t)；
Wherein M is the number of temperature sensors, i E [1, M]；T i (t) is the temperature value monitored by the ith temperature sensor at time t; t (T) e (t) analyzing at the moment t to obtain environmental heat generation quantity; sigma is an adjustment coefficient; n is the number of temperature sensors with temperature data higher than the average temperature value, j is E [1, N]；t 0 For a first preset period of time, T max (t) is the maximum value of temperature; x is x 1 、x 2 Is a preset coefficient;
according to the fire risk value R T (t) early warning the fire risk of the current control cabinet.
Further, the fire risk early warning analysis process comprises the following steps:
will fire risk value R T (t) and risk threshold R thr And (3) performing comparison:
if R is T (t)≥R thr Early warning is carried out;
otherwise, the normal operation is maintained.
Further, the environment generates heat T e The process of (t) calculation includes:
acquiring a light energy conversion efficiency function under the corresponding temperature condition according to the environmental temperature data;
inputting the real-time power generation power into a light energy conversion efficiency function to obtain a current illumination intensity value Lux (t);
by the formulaCalculating and obtaining environmental heat generation quantity T e (t)；
Wherein f x Presetting a conversion function; t is t 1 A second preset time period; t (T) o (t) is an ambient temperature value; y is 1 、y 2 Is a preset coefficient; h is a reference function.
Further, the system also comprises a heat dissipation module;
the heat dissipation module is used for dissipating heat inside the control cabinet.
Further, the heat dissipation process of the heat dissipation module includes:
by the formulaCalculating the heat value Q of the current control cabinet T (t)；
Will Q T (t) and a preset threshold interval [ Q ] 1 ，Q 2 ]And (3) performing comparison:
if Q T (t)＜Q 1 Then heat dissipation is not performed;
if Q T (t)∈[Q 1 ，Q 2 ]Radiating through the radiating module, wherein the working power of the radiating module is as follows
If Q T (t)≥Q 2 Radiating heat with maximum power and performing early warning;
wherein p is preset base power; d, d 1 Euler between a temperature sensor corresponding to a maximum temperature value and a second temperature sensor ordered by temperature valueDistance.
The intelligent fireproof method for the control cabinet based on the Internet of things is realized through the intelligent fireproof system for the control cabinet based on the Internet of things. The invention has the beneficial effects that:
(1) According to the invention, an independent power supply system is arranged to realize independent monitoring and fire prevention of the system, on the basis that the temperature sensor monitors the internal temperature of the power distribution cabinet, the real-time power generation power of the solar module and the temperature sensor arranged outside can be combined to comprehensively judge the temperature state of the environment, so that the influence of environmental factors on the judgment result of the internal temperature of the control box is eliminated, and the judgment accuracy is improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a schematic block diagram of an intelligent fire protection system for a control cabinet according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, in one embodiment, an intelligent fire protection system for a control cabinet based on the internet of things is provided, the system includes:
the temperature sensor comprises a plurality of groups, wherein one group is arranged outside the control cabinet, and the other groups are arranged in the control cabinet and are used for monitoring environmental temperature data and temperature data of all positions in the control cabinet;
the solar energy component is arranged at the top of the distribution box and is used for supplying energy to the system through solar energy;
the energy storage battery is used for storing electric energy generated by the solar energy component;
the analysis control module is used for analyzing the fire risk of the control cabinet according to the temperature data monitored by each group of temperature sensors and the real-time power generation power of the solar module to obtain an early warning signal;
and the communication module is used for sending the early warning signal to the management end.
Through above-mentioned technical scheme, this embodiment is through setting up independent power supply system, realizes the independent monitoring fire prevention process of system, provides the electric energy through setting up solar module and energy storage battery, simultaneously, on temperature sensor monitors the basis to the inside temperature of switch board, can also combine solar module's real-time power generation and set up and synthesize the judgement in the temperature state of outside temperature sensor to the environment, eliminates the influence of environmental factor to the inside temperature judgement result of control box, and then the accuracy of the judgement of improvement.
As an embodiment of the present invention, the operation of the analysis control module includes:
predicting the current illumination intensity according to the real-time power generation power of the solar module and the environmental temperature data;
analyzing and obtaining environmental heat generation quantity according to the current illumination intensity and the environmental temperature data;
and analyzing the fire risk of the control cabinet according to the temperature data of each position in the control cabinet and the environmental heat generation quantity.
Through the above technical scheme, this embodiment provides the working process of analysis control module, at first predicts current illumination intensity according to solar module's real-time power generation and ambient temperature data, obtains the environment heat generation volume according to current illumination intensity and ambient temperature data analysis again, and finally analyzes the conflagration risk of switch board according to the temperature data and the environment heat generation volume of each position in the switch board, through above-mentioned process, can realize the judgement to real-time illumination state, combines ambient temperature data, and then can judge the influence of environment to the inside temperature of switch board, and then in subsequent judgement in-process, guarantees the accuracy of judgement result.
In the above technical solution, the determination process of the illumination intensity is determined according to factors such as the specification and performance of the solar module, which will not be further described in detail in this embodiment.
As one embodiment of the present invention, the process of analyzing the fire risk of the control cabinet includes:
maximum value T in temperature data in control cabinet max And a temperature threshold T thr And (3) performing comparison:
if T max ≥T thr Early warning is carried out;
otherwise, performing fire risk early warning analysis, wherein the early warning analysis process comprises the following steps:
by the formula Calculating to obtain fire risk value R at current t moment T (t)；
Wherein M is the number of temperature sensors, i E [1, M]；T i (t) is the temperature value monitored by the ith temperature sensor at time t; t (T) e (t) analyzing at the moment t to obtain environmental heat generation quantity; sigma is an adjustment coefficient; n is the number of temperature sensors with temperature data higher than the average temperature value, j is E [1, N]；t 0 For a first preset period of time, T max (t) is the maximum value of temperature; x is x 1 、x 2 Is a preset coefficient;
according to the fire risk value R T (t) early warning the fire risk of the current control cabinet.
The fire risk early warning analysis process comprises the following steps:
will fire risk value R T (t) and risk threshold R thr And (3) performing comparison:
if R is T (t)≥R thr Early warning is carried out;
otherwise, the normal operation is maintained.
Through the above technical scheme, this embodiment provides a specific process of fire risk analysis, which includes first determining the maximum value T in the temperature data in the control cabinet max And a temperature threshold T thr Proceeding withComparison, if T max ≥T thr The fact that the temperature in the power distribution cabinet is too high is indicated, so that early warning is directly carried out, and management staff is reminded to check and overhaul the power distribution cabinet; otherwise, further analysis and judgment are carried out, and the formula is passed through Calculating to obtain fire risk value R at current t moment T (t) in the above calculation process, the overall state, the accumulated state and the out-of-tolerance state of the relative average value of the temperature in the control cabinet are integrated, and at the same time, the influence of the environmental factors on the temperature state in the control cabinet is eliminated, so that the obtained fire risk value R T (t) and risk threshold R thr And then the early warning of fire risk is realized.
In the above-mentioned embodiment 1, the adjustment coefficient σ and the preset coefficient x 1 、x 2 Are obtained according to empirical data fitting; a first preset period t 0 Selecting a setting according to the experience data; and will not be described in further detail herein.
As one embodiment of the invention, the environment generates heat T e The process of (t) calculation includes:
acquiring a light energy conversion efficiency function under the corresponding temperature condition according to the environmental temperature data;
inputting the real-time power generation power into a light energy conversion efficiency function to obtain a current illumination intensity value Lux (t);
by the formulaCalculating and obtaining environmental heat generation quantity T e (t)；
Wherein f x Presetting a conversion function; t is t 1 A second preset time period; t (T) o (t) is an ambient temperature value; y is 1 、y 2 Is a preset coefficient; h is a reference function.
Through the technical proposal, the implementationExample gives the environmental heat production T e The calculation process of (t) is to obtain the light energy conversion efficiency function under the corresponding temperature condition according to the ambient temperature data; inputting the real-time power generation power into a light energy conversion efficiency function to obtain a current illumination intensity value Lux (t); by the formulaCalculating and obtaining environmental heat generation quantity T e (t) wherein the transformation function f is preset x Preset coefficient y 1 、y 2 And the reference function H is determined according to fitting of test data, and a second preset period t 1 The setting is selected based on empirical data, and thus the environmental heat generation quantity T is obtained e And (t) combining the detected temperature and the influence of illumination on the temperature of the control cabinet to judge the influence of the environmental temperature on the temperature inside the control cabinet.
As an embodiment of the present invention, the system further comprises a heat dissipation module;
the heat dissipation module is used for dissipating heat inside the control cabinet.
The heat dissipation process of the heat dissipation module comprises the following steps:
by the formulaCalculating the heat value Q of the current control cabinet T (t)；
Will Q T (t) and a preset threshold interval [ Q ] 1 ，Q 2 ]And (3) performing comparison:
if Q T (t)＜Q 1 Then heat dissipation is not performed;
if Q T (t)∈[Q 1 ，Q 2 ]Radiating through the radiating module, wherein the working power of the radiating module is as follows
If Q T (t)≥Q 2 Radiating heat with maximum power and performing early warning;
wherein p is preset base power; d, d 1 Is the most temperatureThe large value corresponds to the Euler distance between the temperature sensor and the second temperature sensor of the temperature value sequence.
Through the above technical scheme, the system in this embodiment further sets a heat dissipation module to dissipate heat inside the control cabinet, and the heat dissipation process mainly passes through the formula Calculating the heat value Q of the current control cabinet T (t) the calculation process integrates the accumulated heat, the temperature extreme value and the temperature concentration in the power distribution cabinet to carry out comprehensive judgment, and then the integrated judgment is carried out through Q T (t) and a preset threshold interval [ Q ] 1 ，Q 2 ]Performing comparison to execute corresponding cooling strategy when Q T (t)＜Q 1 When in use, heat dissipation is not carried out; when Q is T (t)∈[Q 1 ，Q 2 ]When the heat dissipation module is used for heat dissipation, the working power of the heat dissipation module is +.> When Q is T (t)≥Q 2 When the system is in operation, heat dissipation is carried out with maximum power, and early warning is carried out; through the analysis and judgment process, the cooling requirement of the control cabinet can be met adaptively.
In one embodiment, the intelligent fireproof method of the control cabinet based on the Internet of things is realized through the intelligent fireproof system of the control cabinet based on the Internet of things, on the basis that the temperature sensor monitors the internal temperature of the power distribution cabinet, the real-time power generation of the solar module and the temperature sensor arranged outside can be combined to comprehensively judge the temperature state of the environment, the influence of environmental factors on the judgment result of the internal temperature of the control cabinet is eliminated, and the judgment accuracy is improved.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (4)
1. Control cabinet intelligence fire protection system based on thing networking, its characterized in that, the system includes:
the temperature sensor comprises a plurality of groups, wherein one group is arranged outside the control cabinet, and the other groups are arranged in the control cabinet and are used for monitoring environmental temperature data and temperature data of all positions in the control cabinet;
the solar energy component is arranged at the top of the distribution box and is used for supplying energy to the system through solar energy;
the energy storage battery is used for storing electric energy generated by the solar energy component;
the analysis control module is used for analyzing the fire risk of the control cabinet according to the temperature data monitored by each group of temperature sensors and the real-time power generation power of the solar module to obtain an early warning signal;
the communication module is used for sending the early warning signal to the management end;
the working process of the analysis control module comprises the following steps:
predicting the current illumination intensity according to the real-time power generation power of the solar module and the environmental temperature data;
analyzing and obtaining environmental heat generation quantity according to the current illumination intensity and the environmental temperature data;
analyzing fire risks of the control cabinet according to temperature data of each position in the control cabinet and environmental heat generation quantity;
the process of analyzing the fire risk of the control cabinet comprises the following steps:
maximum value T in temperature data in control cabinet max And a temperature threshold T thr And (3) performing comparison:
if T max ≥T thr Early warning is carried out;
otherwise, performing fire risk early warning analysis, wherein the early warning analysis process comprises the following steps:
by the formula Calculating to obtain fire risk value R at current t moment T (t)；
Wherein M is the number of temperature sensors, i E [1, M]；T i (t) is the temperature value monitored by the ith temperature sensor at time t; t (T) e (t) analyzing at the moment t to obtain environmental heat generation quantity; sigma is an adjustment coefficient; n is the number of temperature sensors with temperature data higher than the average temperature value, j is E [1, N]；t 0 For a first preset period of time, T max (t) is the maximum value of temperature; x is x 1 、x 2 Is a preset coefficient;
according to the fire risk value R T (t) early warning the fire risk of the current control cabinet;
the fire risk early warning analysis process comprises the following steps:
will fire risk value R T (t) and risk threshold R thr And (3) performing comparison:
if R is T (t)≥R thr Early warning is carried out;
otherwise, keeping normal operation;
the environment generates heat T e The process of (t) calculation includes:
acquiring a light energy conversion efficiency function under the corresponding temperature condition according to the environmental temperature data;
inputting the real-time power generation power into a light energy conversion efficiency function to obtain a current illumination intensity value Lux (t);
by the formulaCalculating and obtaining environmental heat generation quantity T e (t)；
Wherein f x Presetting a conversion function; t is t 1 A second preset time period; t (T) o (t) is an ambient temperature value; y is 1 、y 2 Is a preset coefficient; h is a reference function.
2. The intelligent fireproof system of a control cabinet based on the internet of things according to claim 1, wherein the system further comprises a heat dissipation module;
the heat dissipation module is used for dissipating heat inside the control cabinet.
3. The intelligent fireproof system of a control cabinet based on the internet of things according to claim 2, wherein the heat dissipation module performs heat dissipation, comprising:
by the formulaCalculating the heat value Q of the current control cabinet T (t)；
Will Q T (t) and a preset threshold interval [ Q ] 1 ，Q 2 ]And (3) performing comparison:
if Q T (t)＜Q 1 Then heat dissipation is not performed;
if Q T (t)∈[Q 1 ，Q 2 ]Radiating through the radiating module, wherein the working power of the radiating module is as follows
If Q T (t)≥Q 2 Radiating heat with maximum power and performing early warning;
wherein p is preset base power; d, d 1 And sequencing the Euler distance between the second temperature sensor and the corresponding temperature sensor of the temperature maximum value.
4. An intelligent fireproof method for a control cabinet based on the Internet of things, which is characterized by being realized by the intelligent fireproof system for the control cabinet based on the Internet of things, wherein the intelligent fireproof method for the control cabinet is realized by the intelligent fireproof system for the control cabinet based on the Internet of things.
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