CN116665424A - Fire automatic alarm fire-fighting linkage system - Google Patents
Fire automatic alarm fire-fighting linkage system Download PDFInfo
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- 238000012545 processing Methods 0.000 claims abstract description 23
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 98
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 49
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 49
- 239000001569 carbon dioxide Substances 0.000 claims description 49
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- 238000002485 combustion reaction Methods 0.000 claims description 3
- 239000003550 marker Substances 0.000 claims 3
- RWSOTUBLDIXVET-UHFFFAOYSA-N Dihydrogen sulfide Chemical compound S RWSOTUBLDIXVET-UHFFFAOYSA-N 0.000 description 6
- 229910000037 hydrogen sulfide Inorganic materials 0.000 description 6
- 230000032683 aging Effects 0.000 description 5
- 238000007689 inspection Methods 0.000 description 5
- 239000000779 smoke Substances 0.000 description 5
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
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- G—PHYSICS
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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Abstract
The invention belongs to the technical field of fire monitoring, and discloses an automatic fire alarming and fire fighting linkage system, which comprises a first data acquisition module, a first data acquisition module and a second data acquisition module, wherein the first data acquisition module is used for acquiring environmental data of a power utilization circuit in a monitoring area; the second data acquisition module acquires the information of the electric line used in the monitoring area; the first data processing module is used for analyzing and processing the environmental data, generating an environmental influence coefficient corresponding to the monitoring area, comparing and analyzing the environmental influence coefficient with a preset gradient threshold value, and generating an environmental influence mark; the second data processing module is used for receiving the power utilization line information, analyzing and processing the power utilization line information, generating a fire symptom influence coefficient corresponding to the power utilization line, comparing the fire symptom influence coefficient with a preset fire symptom influence coefficient gradient reference value, analyzing and generating a fire symptom mark; and the early warning generation module is used for generating fire warning information for the corresponding monitoring areas according to the environmental impact marks and the fire symptom marks of the monitoring areas.
Description
Technical Field
The invention relates to the technical field of fire monitoring, in particular to an automatic fire alarming and fire fighting linkage system.
Background
The fire monitoring and fire fighting linkage system is a system comprehensively utilizing modern technology and equipment, and aims to monitor fire conditions in real time and reduce loss and harm of fire through linkage control of fire fighting equipment and the system.
Most of the prior art solves the problem that the sensitivity of the related fire monitoring equipment is improved, so that a large number of alarms are triggered by mistake.
In view of the above, the present invention proposes an automatic fire alarm fire protection linkage system to solve the above-mentioned problems.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present invention provide an automatic fire alarm fire protection linkage system.
In order to achieve the above purpose, the present invention provides the following technical solutions: an automatic fire alarm fire protection linkage system comprising:
the first data acquisition module acquires environment data of the power utilization circuit in the monitoring area, wherein the environment data is surrounding environment data of the power utilization wire Lewis combustion area;
the second data acquisition module acquires the information of the electric line used in the monitoring area;
the first data processing module is used for analyzing and processing the environmental data, generating an environmental influence coefficient corresponding to the monitoring area according to the environmental influence coefficient, comparing and analyzing the environmental influence coefficient with a preset gradient threshold value, and generating an environmental influence mark;
the second data processing module is used for receiving the power utilization line information, analyzing and processing the power utilization line information, generating a fire symptom influence coefficient corresponding to the power utilization line according to the power utilization line information, comparing the fire symptom influence coefficient with a preset fire symptom influence coefficient gradient reference value, analyzing and generating a fire symptom mark;
the early warning generation module is used for generating fire warning information for the corresponding monitoring areas according to the environmental impact marks and the fire symptom marks of the monitoring areas;
and the fire-fighting linkage module forwards fire early-warning information to the information receiving end, the information receiving end displays the fire early-warning information, and the fire early-warning information at least comprises corresponding monitoring area places.
Preferably, the inflammable region comprises a connecting end region and a bending region, and the environmental data comprises temperature change data, carbon dioxide concentration change data and oxygen concentration change data.
Preferably, the power line information includes a current influence value and a voltage influence value.
Preferably, the temperature change data is obtained as follows:
acquiring n pieces of temperature data in a time period before the current moment, wherein the n pieces of temperature data are equal in acquisition interval time, calculating the temperature average value of the n pieces of temperature data, subtracting the temperature average value from the temperature value at the current moment to obtain a temperature difference value, and dividing the temperature difference value by the temperature average value to obtain temperature change data;
the carbon dioxide concentration variation data is obtained as follows:
acquiring n carbon dioxide concentration data in a time period before the current moment, wherein the acquisition interval time of the n carbon dioxide concentration data is equal, calculating the carbon dioxide concentration mean value of the n carbon dioxide concentration data, subtracting the carbon dioxide concentration mean value from the carbon dioxide concentration data at the current moment to obtain a carbon dioxide concentration difference value, and dividing the carbon dioxide concentration difference value by the carbon dioxide concentration mean value to obtain carbon dioxide concentration change data;
the oxygen concentration variation data is obtained as follows:
acquiring n pieces of oxygen concentration data in a time period before the current moment, wherein the n pieces of oxygen concentration data are equal in acquisition interval time, calculating an oxygen concentration average value of the n pieces of oxygen concentration data, subtracting the oxygen concentration average value from the current moment of oxygen concentration data to obtain an oxygen concentration difference value, and dividing the oxygen concentration difference value by the oxygen concentration average value to obtain oxygen concentration change data.
Preferably, the current influence value calculation process is as follows:
marking the duration time that the actual current value of each time of the power utilization line is larger than the rated current value of the power utilization line as overcurrent time, and marking the sum of the overcurrent time marked each time as a current influence value;
the voltage influence value is calculated as follows:
the duration time that the actual voltage value of each time of the power utilization line is larger than the rated voltage value of the power utilization line is marked as the over-voltage time, and the sum of the over-voltage times marked each time is marked as the voltage influence value.
Preferably, the environmental impact indicia includes a primary environmental indicia, a secondary environmental indicia, and a tertiary environmental indicia;
and when the environmental influence coefficient is larger than or equal to the maximum value of the gradient threshold value, generating a first-level environmental mark for the corresponding monitoring area, when the environmental influence coefficient is smaller than the maximum value of the gradient threshold value and larger than the minimum value of the gradient threshold value, generating a second-level environmental mark for the corresponding monitoring area, and when the environmental influence coefficient is smaller than or equal to the minimum value of the gradient threshold value, generating a third-level environmental mark for the corresponding monitoring area.
Preferably, the fire symptom indicia includes a primary symptom indicia, a secondary symptom indicia and a tertiary symptom indicia;
the fire symptom influence coefficient gradient reference values comprise Yz1 and Yz2, and Yz2 is larger than Yz1; when the fire symptom influence coefficient is larger than the gradient reference value Yz2, generating a first-level symptom mark for the corresponding monitoring area; when the fire symptom influence coefficient is smaller than or equal to the gradient reference value Yz2 and is larger than or equal to the gradient reference value Yz1, generating a secondary symptom mark for the corresponding monitoring area; when the fire symptom influence coefficient is smaller than the gradient reference value Yz1, a three-level symptom mark is generated for the corresponding monitoring area.
Preferably, the fire early-warning information includes emergency patrol information, intermediate patrol information and general patrol information,
the fire early warning information generation process comprises the following steps:
generating a situation A by the first-level symptom mark and the first-level environment mark of the monitoring area at the same moment;
b, generating a situation B by using a first-level symptom mark and a second-level environment mark on a monitoring area at the same moment; generating a C situation by using a first-level symptom mark and a third-level environment mark on a monitoring area at the same moment; generating a D situation by using a secondary sign mark and a primary environment mark in a monitoring area at the same moment; generating an E situation by using a secondary symptom mark and a secondary environment mark on a monitoring area at the same moment; generating an F situation by using a secondary sign mark and a tertiary environment mark on a monitoring area at the same moment; g situations are generated for any situation except the A situation, the B situation, the C situation, the D situation, the E situation and the F situation in the monitoring area at the same moment;
generating emergency patrol information for the monitoring area with the situation A or the situation B; generating intermediate patrol information for a monitoring area with a C situation, a D situation, an E situation or an F situation; general patrol information is generated for a monitoring area having a G situation.
Preferably, the method further comprises:
the fire disaster recognition training module is used for acquiring a plurality of groups of fire disaster recognition characteristic training data in a plurality of groups of monitoring areas;
each set of fire identification characteristic training data comprises temperature change data, carbon dioxide concentration change data, oxygen concentration change data, current influence values and voltage influence values, and each set of fire identification characteristic training data corresponds to the impending or non-occurrence of a fire;
training a machine learning model of a fire identification result based on the fire identification training data, wherein the fire identification result comprises the impending fire and the non-occurrence of the fire;
the fire disaster prediction module is used for obtaining a real-time fire disaster identification result based on real-time temperature change data, carbon dioxide concentration change data, oxygen concentration change data, current influence value, voltage influence value and a machine learning model, and sending the fire disaster identification result to an information receiving end as a monitoring area place corresponding to the impending fire disaster.
The invention relates to a technical effect and advantages of an automatic fire alarming and fire fighting linkage system:
according to the fire monitoring and fire fighting linkage system, the hidden danger points which mainly cause the fire are monitored, and the related data of the fire caused by overload of the electric line are monitored, so that the hidden danger of the fire can be eliminated in time, the fire caused by the hidden danger of the fire is avoided, the hidden danger of the fire can be eliminated in time in the sprouting stage of the fire, early warning linkage is performed in advance, and the loss caused by the fire is effectively reduced.
Drawings
FIG. 1 is a schematic diagram of a fire monitoring fire protection linkage system in embodiment 1 of the present invention;
fig. 2 is a schematic diagram of a fire monitoring fire protection linkage system in embodiment 2 of 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.
Example 1
Referring to fig. 1, the fire automatic alarm fire-fighting linkage system according to the present embodiment includes a first data acquisition module, a second data acquisition module, a first data processing module, a second data processing module, an early warning generation module, and a fire-fighting linkage module, where the modules are connected by a wired and/or wireless manner.
The first data acquisition module acquires the environmental data of the power utilization circuit in the monitoring area and sends the environmental data to the first data processing module, wherein the environmental data is the environmental data around the power utilization circuit ignition area, and the inflammable area comprises a connecting end area and a bending area; the environmental data includes temperature change data, carbon dioxide concentration change data, and oxygen concentration change data of the monitored area.
Wherein, the temperature change data acquisition process is as follows:
acquiring n pieces of temperature data in a time period before the current moment, wherein the n pieces of temperature data are equal in acquisition interval time, calculating the temperature average value of the n pieces of temperature data, subtracting the temperature average value from the temperature value at the current moment to obtain a temperature difference value, and dividing the temperature difference value by the temperature average value to obtain temperature change data. The temperature change data is used as one of prediction data of fire prediction, so that a temperature sensor in the fire monitoring equipment can be better adapted to different monitoring environments, and the later fire prediction accuracy reduction caused by natural temperature rise of the monitoring area environment is reduced.
The carbon dioxide concentration variation data is obtained as follows:
acquiring n carbon dioxide concentration data in a time period before the current moment, wherein the acquisition interval time of the n carbon dioxide concentration data is equal, calculating the carbon dioxide concentration mean value of the n carbon dioxide concentration data, subtracting the carbon dioxide concentration mean value from the carbon dioxide concentration data at the current moment to obtain a carbon dioxide concentration difference value, and dividing the carbon dioxide concentration difference value by the carbon dioxide concentration mean value to obtain carbon dioxide concentration change data. The carbon dioxide concentration change data is used as one of prediction data of fire prediction, so that the carbon dioxide sensor in the fire monitoring equipment can be better adapted to different monitoring area environments, and the later-stage fire prediction accuracy reduction caused by the natural rising of carbon dioxide in the monitoring environment is reduced.
The oxygen concentration variation data is obtained as follows:
acquiring n pieces of oxygen concentration data in a time period before the current moment, wherein the n pieces of oxygen concentration data are equal in acquisition interval time, calculating an oxygen concentration average value of the n pieces of oxygen concentration data, subtracting the oxygen concentration average value from the current moment of oxygen concentration data to obtain an oxygen concentration difference value, and dividing the oxygen concentration difference value by the oxygen concentration average value to obtain oxygen concentration change data. The oxygen concentration change data is used as one of prediction data of fire prediction, so that an oxygen sensor in the fire monitoring equipment can be better adapted to different monitoring environments, and the later fire prediction accuracy reduction caused by natural reduction of oxygen in the monitoring area environment is reduced.
The temperature data, the carbon dioxide concentration data and the oxygen concentration data are all obtained by monitoring related sensors in the fire monitoring equipment and are correspondingly arranged in the connecting end area and the bending area of the electric circuit, when the electric circuit is overloaded, heat and energy in the connecting end area of the electric circuit and the electric equipment and in the bending area of the electric circuit are easy to accumulate, so that insulating materials of the electric circuit in the areas are heated, and can be melted or burnt due to overhigh temperature, the inflammable area in the electric circuit is monitored, the pertinence is stronger, and the accuracy of fire monitoring can be improved.
The second data acquisition module acquires power consumption line information in the monitoring area, the power consumption line information comprises current influence values and voltage influence values, the power consumption line information comprises power consumption main line information and power consumption branch line information, the acquired current influence values and voltage influence values are generated to the second data processing module, and the current influence values are calculated as follows:
and marking the duration time that the actual current value of each time of the power utilization line is larger than the rated current value of the power utilization line as the overcurrent time, and marking the sum of the overcurrent time marked each time as the current influence value.
The voltage influence value is calculated as follows:
the duration time that the actual voltage value of each time of the power utilization line is larger than the rated voltage value of the power utilization line is marked as the over-voltage time, and the sum of the over-voltage times marked each time is marked as the voltage influence value.
The actual current value and the actual voltage value of the electric wire are respectively obtained through monitoring of the corresponding current sensor and the corresponding voltage sensor, the rated current value and the rated voltage value of the electric wire are set according to the electric wires with different specifications, specific values are provided by electric wire manufacturers, the current sensor and the voltage sensor are arranged on the electric main circuit and the electric wire branch circuit in the implementation, the electric main circuit is electrically connected with the electric wire branch circuit, and the electric wire branch circuit is electrically connected with electric equipment, namely the electric main circuit and the electric wire branch circuit, which correspond to the current influence value and the voltage influence value respectively in the embodiment.
Therefore, the abnormal electricity consumption of the corresponding electric equipment can be correspondingly known, and the electric circuit and the electric equipment can be conveniently and pertinently maintained in the later stage.
It should be noted that, as the service life of the power line increases, the insulation sheath gradually ages. Excessive voltage and current can generate overheat phenomenon in the power utilization line, the overheat causes high temperature, the high temperature can cause expansion, softening and decomposition of insulating materials, aging and degradation speed are caused, the excessive voltage exceeds the rated voltage value of the power utilization line, the excessive current exceeds the rated current value of the power utilization line, the service time of the power utilization line is prolonged, and fire is more liable to be caused when the excessive condition occurs.
The first data processing module is used for receiving temperature change data, carbon dioxide concentration change data and oxygen concentration change data in the environmental data, analyzing and processing the environmental data, generating an environmental influence coefficient corresponding to the monitoring area according to the environmental change data, and the environmental influence coefficient generating process comprises the following steps:
marking the number of monitoring areas asAccording to the above, the method comprises, according to,
；
obtaining environmental impact coefficient，/>For temperature change data, +.>For carbon dioxide concentration change data, +.>For oxygen concentration variation data, +.>Weight factor for temperature change data, +.>Weight factor for carbon dioxide concentration variation data, +.>Weight factor for oxygen concentration variation data, +.>、/>、/>The average value is larger than 0, and the weight factors in the formula are used for balancing the proportion of each item of data in the formula, so that accuracy of a calculation result is promoted, and the specific value of the weight factors can be adjusted and set by a user or generated by fitting an analysis function.
The environmental impact coefficientThe larger the probability of fire occurrence in the corresponding monitoring area.
Presetting and environmental impact coefficientsGradient threshold of (2) environmental influence coefficient +.>Comparing the gradient threshold value with the gradient threshold value for analysis to generate an environmental impact mark, wherein the environmental impact mark comprises a primary environmental mark, a secondary environmental mark and a tertiary environmental mark;
coefficient of environmental influenceIf the gradient threshold value is larger than or equal to the maximum value of the gradient threshold value, generating a first-level environment mark for the corresponding monitoring area, and when the environment influence coefficient is +>If the gradient is smaller than the maximum value of the gradient threshold and larger than the minimum value of the gradient threshold, generating a secondary environment mark for the corresponding monitoring area, and when the environment influence coefficient is +.>And if the gradient threshold value is smaller than or equal to the minimum value of the gradient threshold value, generating three-level environment marks for the corresponding monitoring area.
The probability of fire occurrence in the monitoring areas corresponding to the primary environmental markers, the secondary environmental markers and the tertiary environmental markers is gradually reduced.
The second data processing module is used for receiving the current influence value and the voltage influence value, analyzing and processing the current influence value and the voltage influence value, generating a fire symptom influence coefficient corresponding to the power utilization line according to the current influence value and the voltage influence value, and the fire symptom influence coefficient generating process comprises the following steps:
marking the current influence value and the voltage influence value as respectively、/>According to the formula:
；
obtaining the influence coefficient of fire symptom，/>Weight factor for the current influencing value, +.>Weight factor for the voltage influencing value, +.>>/>>0，/>The specific values may be set by user adjustment or generated by an analytical function fit.
The fire symptom influence coefficient is described as followsThe smaller the expression value of (c) represents the smaller the corresponding aging and degradation degree of the electric line, the smaller the probability of fire occurrence due to aging of the electric line.
By monitoring the power line information, the aging condition of the power line can be predicted, and the fire caused by the aging of the power line can be reduced.
Setting gradient reference values Yz1 and Yz2 of fire symptom influence coefficients, wherein Yz1 is less than Yz2, comparing the fire symptom influence coefficients with the gradient reference values Yz1 and Yz2, and analyzing to generate fire symptom marks, wherein the fire symptom marks comprise a first-stage symptom mark, a second-stage symptom mark and a third-stage symptom mark;
when the fire symptom affects the coefficientWhen the gradient reference value Yz2 is larger than the gradient reference value Yz2, generating a first-level symptom mark for the corresponding monitoring area; when the fire symptom influence coefficient->Is less than or equal to the gradient reference value Yz2, and the fire symptom influence coefficient +.>When the gradient reference value Yz1 is larger than or equal to the gradient reference value Yz1, generating a secondary symptom mark for the corresponding monitoring area; when the fire symptom influence coefficient->When the gradient reference value Yz1 is smaller than the gradient reference value Yz1, generating a three-level symptom mark for the corresponding monitoring area; first-level sign mark and second-level sign markThe signs of the fire disaster in the monitoring areas corresponding to the signs of the third level signs are gradually not obvious.
The early warning generation module generates fire warning information for the corresponding monitoring areas according to the environmental impact marks and the fire symptom marks of the monitoring areas and sends the fire warning information to the fire-fighting linkage module, the fire warning information comprises emergency inspection information, intermediate inspection information and general inspection information,
the fire early warning information generation process comprises the following steps:
generating a situation A by the first-level symptom mark and the first-level environment mark of the monitoring area at the same moment;
b, generating a situation B by using a first-level symptom mark and a second-level environment mark on a monitoring area at the same moment; generating a C situation by using a first-level symptom mark and a third-level environment mark on a monitoring area at the same moment; generating a D situation by using a secondary sign mark and a primary environment mark in a monitoring area at the same moment; generating an E situation by using a secondary symptom mark and a secondary environment mark on a monitoring area at the same moment; generating an F situation by using a secondary sign mark and a tertiary environment mark on a monitoring area at the same moment; g situations are generated for any situation except the A situation, the B situation, the C situation, the D situation, the E situation and the F situation in the monitoring area at the same moment;
generating emergency patrol information for the monitoring area with the situation A or the situation B; generating intermediate patrol information for a monitoring area with a C situation, a D situation, an E situation or an F situation; general patrol information is generated for a monitoring area having a G situation.
The fire control linkage module forwards fire disaster early warning information to the information receiving end, the fire control linkage module is in wireless connection with the information receiving end, information transmission is achieved, corresponding patrol personnel carry the information receiving end, the fire disaster early warning information at least comprises corresponding monitoring area places, the patrol personnel conduct targeted patrol on the corresponding monitoring areas according to the monitoring area places, fire hazards are eliminated in time, and fire hazards are avoided.
And the fire-fighting linkage module generates overhaul information of the electric equipment and the electric wire to the corresponding monitoring area according to the fire early warning information, so that fire hidden danger points are better eliminated.
The emergency patrol information, the medium-level patrol information and the general patrol information are gradually reduced in patrol priority, and the monitoring area with larger fire hazard can be preferentially patrol under the condition that the number of patrol personnel is limited, so that the occurrence of fire is further reduced.
According to the fire monitoring and fire fighting linkage system, related data of fire caused by overload of the power line can be monitored, fire hazards can be eliminated in time, the fire hazards are avoided, the fire hazards can be eliminated in time in a sprouting stage (namely, a non-sprouting stage or an initial stage) of the fire, early warning linkage is performed in advance, and loss caused by the fire is effectively reduced.
And secondly, corresponding fire early warning information is generated for each monitoring area, so that compared with regular or sequential inspection, the method can conduct targeted inspection, can more accurately eliminate fire hidden danger and avoid fire occurrence.
Example 2
Referring to fig. 2, the present embodiment is a further improved design based on the first embodiment, except that the present embodiment provides an automatic fire alarm fire-fighting linkage system, and further includes a fire identification training module and a fire prediction module;
the fire identification training module acquires a plurality of groups of fire identification characteristic training data in the monitoring area,
each set of fire identification characteristic training data comprises temperature change data, carbon dioxide concentration change data, oxygen concentration change data, current influence values and voltage influence values, and each set of fire identification characteristic training data corresponds to the impending or non-occurrence of a fire;
training a machine learning model of a fire identification result based on the fire identification training data, wherein the fire identification result comprises the impending fire and the non-occurrence of the fire;
the fire disaster prediction module obtains a real-time fire disaster identification result based on real-time temperature change data, carbon dioxide concentration change data, oxygen concentration change data, current influence value, voltage influence value and a machine learning model, and sends the fire disaster identification result to an information receiving end as a monitoring area place corresponding to the impending fire disaster.
The machine learning model is trained by collecting fire identification feature training data of a period of time before a fire disaster occurs in a monitoring area and a large amount of fire identification feature training data of a fire disaster which does not occur, the period of time before the fire disaster can be 10 minutes or 5 minutes and the like, the fire hazard processing speed is set based on patrol personnel, specific data of the fire identification feature training data are obtained based on the mode described in the embodiment 1, the data accuracy is better, the prediction accuracy of the trained machine learning model is higher, and the accuracy of fire disaster early warning is further improved.
The mode of training a machine learning model for predicting fire identification results is as follows:
taking fire identification characteristic training data as a sample set, and dividing 70% as a characteristic training set and 30% as a characteristic test set; the feature training set is used as input data of a machine learning model, and is input into the feature training set for training so as to obtain an initial training machine learning model; inputting the feature test set into an initial training machine learning model for testing so as to output the initial training machine learning model meeting the accuracy of a preset fire identification result as a machine learning model; the machine learning model is any one of a deep neural network model or a deep belief network model.
Example 3
The embodiment is further improved and designed based on the first embodiment, and the difference is that in the automatic fire alarm and fire control linkage system provided by the implementation, the first data acquisition module also acquires hydrogen sulfide concentration data and smoke concentration data in a monitoring area, the hydrogen sulfide concentration data and the smoke concentration data are obtained by monitoring related sensors in fire monitoring equipment, and hydrogen sulfide is generated by using an electric line insulating layer under the condition of high-temperature combustion or generated by burning other combustible objects in the monitoring area.
The first data processing module is used for generating a hazard degree coefficient according to the hydrogen sulfide concentration data and the smoke concentration data, presetting a hazard degree coefficient threshold, generating alarm information when the hazard degree coefficient is larger than or equal to the preset hazard degree coefficient threshold, enabling the alarm information to occur to the fire-fighting linkage module, enabling the fire-fighting linkage module to control fire-fighting equipment to be started, for example, starting a water pump corresponding to a spray pipe in a monitoring area, and then spraying water by the spray pipe to extinguish a fire.
The hazard degree coefficient generation process comprises the following steps:
the hydrogen sulfide concentration data and the smoke concentration data are multiplied by corresponding preset weight factors respectively and added, and the added sum is marked as a hazard degree coefficient.
When the fire monitoring fire-fighting linkage system of the embodiment is applied to a high-rise residential building, the fire-fighting linkage module sends alarm information according to the pre-stored resident contact information of the corresponding high-rise residential building, wherein the alarm information comprises fire floor positions and evacuation routes.
And determining the fire floor according to the positions of the fire monitoring devices of the hydrogen sulfide concentration data and the smoke concentration data, for example, when each fire monitoring device is installed, storing the fire floor in association with the corresponding floor position, and calling the floor position associated with the fire monitoring device when the alarm information is generated.
The evacuation route is determined according to the fire floor position as follows:
the distance data of each fire floor position from each fire channel is stored in advance, the fire channel closest to the fire floor position is eliminated, the rest fire channels are used as evacuation routes, namely the fire channel with the fastest fire spread is eliminated, the smoothness of the evacuation routes of residents can be guaranteed to the greatest extent, and the life and property safety of personnel can be protected to the greatest extent.
The above formulas are all the dimensionality removal and numerical calculation, the formulas are formulas which can be obtained by acquiring a large amount of data and performing software simulation to obtain the latest real situation, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center over a wired network or a wireless network. The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely one, and there may be additional divisions in actual implementation, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. An automatic fire alarm fire protection linkage system, comprising:
the first data acquisition module acquires environment data of the power utilization circuit in the monitoring area, wherein the environment data is surrounding environment data of the power utilization wire Lewis combustion area;
the second data acquisition module acquires the information of the electric line used in the monitoring area;
the first data processing module is used for analyzing and processing the environmental data, generating an environmental influence coefficient corresponding to the monitoring area according to the environmental influence coefficient, comparing and analyzing the environmental influence coefficient with a preset gradient threshold value, and generating an environmental influence mark;
the second data processing module is used for receiving the power utilization line information, analyzing and processing the power utilization line information, generating a fire symptom influence coefficient corresponding to the power utilization line according to the power utilization line information, comparing the fire symptom influence coefficient with a preset fire symptom influence coefficient gradient reference value, analyzing and generating a fire symptom mark;
the early warning generation module is used for generating fire warning information for the corresponding monitoring areas according to the environmental impact marks and the fire symptom marks of the monitoring areas;
and the fire-fighting linkage module forwards fire early-warning information to the information receiving end, the information receiving end displays the fire early-warning information, and the fire early-warning information at least comprises corresponding monitoring area places.
2. The automatic fire alarm and control linkage system according to claim 1, wherein the inflammable area comprises a connecting end area and a bending area, and the environmental data comprises temperature change data, carbon dioxide concentration change data and oxygen concentration change data.
3. The automatic fire alarm and control linkage system according to claim 2, wherein the power line information includes a current impact value and a voltage impact value.
4. A fire automatic alarm fire protection linkage system according to claim 3, wherein the temperature change data is obtained by:
acquiring n pieces of temperature data in a time period before the current moment, wherein the n pieces of temperature data are equal in acquisition interval time, calculating the temperature average value of the n pieces of temperature data, subtracting the temperature average value from the temperature value at the current moment to obtain a temperature difference value, and dividing the temperature difference value by the temperature average value to obtain temperature change data;
the carbon dioxide concentration variation data is obtained as follows:
acquiring n carbon dioxide concentration data in a time period before the current moment, wherein the acquisition interval time of the n carbon dioxide concentration data is equal, calculating the carbon dioxide concentration mean value of the n carbon dioxide concentration data, subtracting the carbon dioxide concentration mean value from the carbon dioxide concentration data at the current moment to obtain a carbon dioxide concentration difference value, and dividing the carbon dioxide concentration difference value by the carbon dioxide concentration mean value to obtain carbon dioxide concentration change data;
the oxygen concentration variation data is obtained as follows:
acquiring n pieces of oxygen concentration data in a time period before the current moment, wherein the n pieces of oxygen concentration data are equal in acquisition interval time, calculating an oxygen concentration average value of the n pieces of oxygen concentration data, subtracting the oxygen concentration average value from the current moment of oxygen concentration data to obtain an oxygen concentration difference value, and dividing the oxygen concentration difference value by the oxygen concentration average value to obtain oxygen concentration change data.
5. The automatic fire alarm and control linkage system according to claim 4, wherein the current influence value is calculated as follows:
marking the duration time that the actual current value of each time of the power utilization line is larger than the rated current value of the power utilization line as overcurrent time, and marking the sum of the overcurrent time marked each time as a current influence value;
the voltage influence value is calculated as follows:
the duration time that the actual voltage value of each time of the power utilization line is larger than the rated voltage value of the power utilization line is marked as the over-voltage time, and the sum of the over-voltage times marked each time is marked as the voltage influence value.
6. The automatic fire alarm and fire control linkage system according to claim 5, wherein the environmental impact markers include a primary environmental marker, a secondary environmental marker and a tertiary environmental marker;
and when the environmental influence coefficient is larger than or equal to the maximum value of the gradient threshold value, generating a first-level environmental mark for the corresponding monitoring area, when the environmental influence coefficient is smaller than the maximum value of the gradient threshold value and larger than the minimum value of the gradient threshold value, generating a second-level environmental mark for the corresponding monitoring area, and when the environmental influence coefficient is smaller than or equal to the minimum value of the gradient threshold value, generating a third-level environmental mark for the corresponding monitoring area.
7. The automatic fire alarm and control linkage system according to claim 6, wherein the fire symptom marks comprise a primary symptom mark, a secondary symptom mark and a tertiary symptom mark;
the fire symptom influence coefficient gradient reference values comprise Yz1 and Yz2, and Yz2 is larger than Yz1; when the fire symptom influence coefficient is larger than the gradient reference value Yz2, generating a first-level symptom mark for the corresponding monitoring area; when the fire symptom influence coefficient is smaller than or equal to the gradient reference value Yz2 and is larger than or equal to the gradient reference value Yz1, generating a secondary symptom mark for the corresponding monitoring area; when the fire symptom influence coefficient is smaller than the gradient reference value Yz1, a three-level symptom mark is generated for the corresponding monitoring area.
8. The fire automatic alarm fire protection linkage system according to claim 7, wherein the fire early-warning information includes emergency patrol information, intermediate patrol information and general patrol information,
the fire early warning information generation process comprises the following steps:
generating a situation A by the first-level symptom mark and the first-level environment mark of the monitoring area at the same moment;
b, generating a situation B by using a first-level symptom mark and a second-level environment mark on a monitoring area at the same moment; generating a C situation by using a first-level symptom mark and a third-level environment mark on a monitoring area at the same moment; generating a D situation by using a secondary sign mark and a primary environment mark in a monitoring area at the same moment; generating an E situation by using a secondary symptom mark and a secondary environment mark on a monitoring area at the same moment; generating an F situation by using a secondary sign mark and a tertiary environment mark on a monitoring area at the same moment; g situations are generated for any situation except the A situation, the B situation, the C situation, the D situation, the E situation and the F situation in the monitoring area at the same moment;
generating emergency patrol information for the monitoring area with the situation A or the situation B; generating intermediate patrol information for a monitoring area with a C situation, a D situation, an E situation or an F situation; general patrol information is generated for a monitoring area having a G situation.
9. The automatic fire alarm and fire protection linkage system according to claim 8, further comprising:
the fire disaster recognition training module is used for acquiring a plurality of groups of fire disaster recognition characteristic training data in a plurality of groups of monitoring areas;
each set of fire identification characteristic training data comprises temperature change data, carbon dioxide concentration change data, oxygen concentration change data, current influence values and voltage influence values, and each set of fire identification characteristic training data corresponds to the impending or non-occurrence of a fire;
training a machine learning model of a fire identification result based on the fire identification training data, wherein the fire identification result comprises the impending fire and the non-occurrence of the fire;
the fire disaster prediction module is used for obtaining a real-time fire disaster identification result based on real-time temperature change data, carbon dioxide concentration change data, oxygen concentration change data, current influence value, voltage influence value and a machine learning model, and sending the fire disaster identification result to an information receiving end as a monitoring area place corresponding to the impending fire disaster.
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