CN111243214A - Industrial machine room fire monitoring system and method based on machine learning - Google Patents
Industrial machine room fire monitoring system and method based on machine learning Download PDFInfo
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Abstract
The invention relates to a machine learning-based industrial machine room fire monitoring system and method, which comprises the following steps: step 201, randomly arranging N sensor groups at the position where a machine is placed in a machine room; step 202, respectively obtaining a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D measured by each sensor group; step 203, substituting the measured value in the step 202 into a fire prediction model to obtain a fire intention factor E of the corresponding Nth sensor group; step 204, sending message information representing fire hazard to a central machine room for the sensor group with the fire intention factor exceeding the threshold value H; and step 205, the central computer room predicts the intention degree of the fire according to the distribution condition of the sensor groups sending the message information of the fire hidden danger, and displays the intention degree of the fire and the sensor groups with the fire hidden danger. The fire disaster warning system can give an alarm to remind workers to perform prevention regulation and control in time when fire disaster intentions exist.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a machine learning-based fire monitoring system and method for an industrial machine room.
Background
Industrial fire risk management is an organizational management activity of industrial enterprises for forecasting, preventing, controlling and putting out fires for fire prevention and fire control. The purpose is as follows: one is to prevent a fire. Secondly, after the fire disaster occurs, the damage and the loss of the fire disaster are controlled within the minimum limit as much as possible.
In the current industrial application, a worker often determines whether a fire exists according to the value of the sensor only by arranging the sensor at a critical position and displaying the value of the sensor in a striking manner, so that the worker is over dependent on the value of the sensor, and forgets to pay attention to the fire for a while due to negligence of the worker, so that the optimal prevention period of the fire may be missed, and unnecessary loss may be caused.
Disclosure of Invention
The invention aims to solve the defects of the prior art and provides an industrial machine room fire monitoring system and method based on machine learning.
In order to achieve the purpose, the invention adopts the following technical scheme:
the utility model provides an industry computer lab fire monitoring system based on machine learning, includes:
n sequentially numbered sensor groups, the sensor groups comprising,
a carbon monoxide concentration measuring unit for acquiring a carbon monoxide concentration in the vicinity of the sensor group,
a carbon dioxide concentration measuring unit for acquiring a concentration of the two-sampled carbon in the vicinity of the sensor group,
a temperature measuring unit for acquiring a temperature in the vicinity of the sensor group,
a humidity measurement unit for acquiring humidity near the sensor group;
the message information sending unit is used for sending message information representing fire hidden danger to the central machine room when the sensor group has fire intention;
the fire intention measuring module is used for calculating whether the sensor group has fire intention according to the numerical value measured by the sensor group;
the fire intention degree prediction module is used for predicting the fire intention degree according to the distribution condition of a sensor group which sends message information representing fire hidden danger;
and the display alarm module is used for displaying the degree of the fire intention and the sensor group with the fire hidden danger and giving an alarm when the fire intention exists.
The invention also provides a machine learning-based fire monitoring method for the industrial machine room, which is characterized by comprising the following steps:
step 201, randomly arranging N sensor groups at the position where a machine is placed in a machine room, wherein each sensor group comprises a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a temperature sensor and a humidity sensor;
step 202, respectively obtaining a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D measured by each sensor group;
step 203, substituting the measured value in the step 202 into a fire prediction model to obtain a fire intention factor E of the corresponding Nth sensor group;
step 204, sending message information representing fire hazard to a central machine room for the sensor group with the fire intention factor exceeding the threshold value H;
and step 205, the central computer room predicts the intention degree of the fire according to the distribution condition of the sensor groups sending the message information of the fire hidden danger, and displays the intention degree of the fire and the sensor groups with the fire hidden danger.
Further, the fire monitoring model in step 202 is obtained by:
301, constructing a target layer and a criterion layer, wherein the target layer is an output fire intention factor E, and the criterion layer comprises a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D which are measured by a sensor group;
step 302, constructing a judgment matrix P as follows:
wherein P isi-jRepresenting the relative weight of i in pairwise comparison with j, i being taken to [ A, D]J is taken as [ A, D ]]；
Step 303, calculating to obtain the maximum characteristic root of the judgment matrix P
And the maximum eigenvector WP；
304, carrying out consistency check to obtain the weight of each factor of a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D;
step 305, obtaining a fire monitoring model according to the weight as follows:
E＝QA*WA+QB*WB+QC*WC+QD*WD；
wherein Q isA、QB、QCAnd QDRespectively representing the corresponding scores of the sensor values.
Further, the judgment matrix P in the step 204 is specifically:
and corresponding WP＝(0.0622、0.1835、0.4432、0.3111)TWherein W isA＝0.0622，WB＝0.1835，WC＝0.4432，WD＝0.3111。
Further, the predicting the degree of intention of the fire by the central computer room according to the distribution of the sensor group sending the message information of the fire hazard in step 205 specifically includes the following steps:
step 501, calculating and predicting the adjacent degree of a sensor group with fire hazard, wherein the adjacent degree is obtained by the ratio of the number of adjacent sensors to the total number of the sensors;
step 502, judging whether the adjacent degree is higher than a first threshold value, if so, judging the adjacent degree as a high-risk fire hazard, judging whether the adjacent degree is between a second threshold value and the first threshold value, if so, judging the adjacent degree as a medium-risk fire hazard, judging whether the adjacent degree is lower than the second threshold value, and if so, judging the adjacent degree as a low-risk fire hazard.
Further, the value of the threshold H is [0.7, 1.0], the first threshold is 0.3, and the second threshold is 0.1.
Further, the industrial machine room fire monitoring method further comprises the following steps:
when there is a failure, a log file is generated, which contains,
the number of the sensor group with the fire hazard, the level of the intention of the fire, the current time and the current date.
The invention can obtain the following beneficial effects when adopting the system and the method:
according to the invention, the plurality of sensor groups are arranged in the industrial machine room, the fire hazard is monitored according to the data measured by the sensor groups, and the alarm is given to remind workers to perform prevention regulation and control in time when the fire intention exists.
Drawings
Fig. 1 is a flow chart of a fire monitoring method for an industrial machine room based on machine learning according to the present invention.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The same reference numbers will be used throughout the drawings to refer to the same or like parts.
Referring to fig. 1, the invention provides a machine learning-based fire monitoring system for an industrial machine room, which comprises:
n sequentially numbered sensor groups, the sensor groups comprising,
a carbon monoxide concentration measuring unit for acquiring a carbon monoxide concentration in the vicinity of the sensor group,
a carbon dioxide concentration measuring unit for acquiring a concentration of the two-sampled carbon in the vicinity of the sensor group,
a temperature measuring unit for acquiring a temperature in the vicinity of the sensor group,
a humidity measurement unit for acquiring humidity near the sensor group;
the message information sending unit is used for sending message information representing fire hidden danger to the central machine room when the sensor group has fire intention;
the fire intention measuring module is used for calculating whether the sensor group has fire intention according to the numerical value measured by the sensor group;
the fire intention degree prediction module is used for predicting the fire intention degree according to the distribution condition of a sensor group which sends message information representing fire hidden danger;
and the display alarm module is used for displaying the degree of the fire intention and the sensor group with the fire hidden danger and giving an alarm when the fire intention exists.
The invention also provides a machine learning-based fire monitoring method for the industrial machine room, which is characterized by comprising the following steps:
step 201, randomly arranging N sensor groups at the position where a machine is placed in a machine room, wherein each sensor group comprises a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a temperature sensor and a humidity sensor;
step 202, respectively obtaining a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D measured by each sensor group;
step 203, substituting the measured value in the step 202 into a fire prediction model to obtain a fire intention factor E of the corresponding Nth sensor group;
step 204, sending message information representing fire hazard to a central machine room for the sensor group with the fire intention factor exceeding the threshold value H;
and step 205, the central computer room predicts the intention degree of the fire according to the distribution condition of the sensor groups sending the message information of the fire hidden danger, and displays the intention degree of the fire and the sensor groups with the fire hidden danger.
As a preferred embodiment of the present invention, the fire monitoring model in step 202 is obtained by:
301, constructing a target layer and a criterion layer, wherein the target layer is an output fire intention factor E, and the criterion layer comprises a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D which are measured by a sensor group;
step 302, constructing a judgment matrix P as follows:
wherein P isi-jRepresenting the relative weight of i in pairwise comparison with j, i being taken to [ A, D]J is taken as [ A, D ]]；
Step 303, calculating to obtain the maximum characteristic root of the judgment matrix P
And the maximum eigenvector WP；
304, carrying out consistency check to obtain the weight of each factor of a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D;
step 305, obtaining a fire monitoring model according to the weight as follows:
E＝QA*WA+QB*WB+QC*WC+QD*WD；
wherein Q isA、QB、QCAnd QDRespectively representing the corresponding scores of the sensor values.
As a preferred embodiment of this embodiment, the determination matrix P in step 204 is specifically:
and corresponding WP＝(0.0622、0.1835、0.4432、0.3111)TWherein W isA＝0.0622，WB＝0.1835，WC＝0.4432，WD＝0.3111。
As a preferred embodiment of this solution, in step 205, the predicting the degree of intention of fire by the central computer room according to the distribution of the sensor group that sends the message information of the fire hazard specifically includes the following steps:
step 501, calculating and predicting the adjacent degree of a sensor group with fire hazard, wherein the adjacent degree is obtained by the ratio of the number of adjacent sensors to the total number of the sensors;
step 502, judging whether the adjacent degree is higher than a first threshold value, if so, judging the adjacent degree as a high-risk fire hazard, judging whether the adjacent degree is between a second threshold value and the first threshold value, if so, judging the adjacent degree as a medium-risk fire hazard, judging whether the adjacent degree is lower than the second threshold value, and if so, judging the adjacent degree as a low-risk fire hazard.
As a preferred embodiment of the present disclosure, the value of the threshold H is between [0.7 and 1.0], the first threshold is 0.3, and the second threshold is 0.1.
As a preferred embodiment of this solution, the method for monitoring fire in an industrial machine room further includes:
when there is a failure, a log file is generated, which contains,
the number of the sensor group with the fire hazard, the level of the intention of the fire, the current time and the current date.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the above-described method embodiments when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
While the present invention has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed as effectively covering the intended scope of the invention by providing a broad, potential interpretation of such claims in view of the prior art with reference to the appended claims. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and the present invention shall fall within the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (7)
1. The utility model provides an industry computer lab fire monitoring system based on machine learning which characterized in that includes:
n sequentially numbered sensor groups, the sensor groups comprising,
a carbon monoxide concentration measuring unit for acquiring a carbon monoxide concentration in the vicinity of the sensor group,
a carbon dioxide concentration measuring unit for acquiring a concentration of the two-sampled carbon in the vicinity of the sensor group,
a temperature measuring unit for acquiring a temperature in the vicinity of the sensor group,
a humidity measurement unit for acquiring humidity near the sensor group;
the message information sending unit is used for sending message information representing fire hidden danger to the central machine room when the sensor group has fire intention;
the fire intention measuring module is used for calculating whether the sensor group has fire intention according to the numerical value measured by the sensor group;
the fire intention degree prediction module is used for predicting the fire intention degree according to the distribution condition of a sensor group which sends message information representing fire hidden danger;
and the display alarm module is used for displaying the degree of the fire intention and the sensor group with the fire hidden danger and giving an alarm when the fire intention exists.
2. A machine learning-based industrial machine room fire monitoring method is characterized by comprising the following steps:
step 201, randomly arranging N sensor groups at the position where a machine is placed in a machine room, wherein each sensor group comprises a carbon monoxide concentration sensor, a carbon dioxide concentration sensor, a temperature sensor and a humidity sensor;
step 202, respectively obtaining a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D measured by each sensor group;
step 203, substituting the measured value in the step 202 into a fire prediction model to obtain a fire intention factor E of the corresponding Nth sensor group;
step 204, sending message information representing fire hazard to a central machine room for the sensor group with the fire intention factor exceeding the threshold value H;
and step 205, the central computer room predicts the intention degree of the fire according to the distribution condition of the sensor groups sending the message information of the fire hidden danger, and displays the intention degree of the fire and the sensor groups with the fire hidden danger.
3. The method for fire monitoring in industrial machine room based on machine learning as claimed in claim 2, wherein the fire monitoring model in step 202 is obtained by:
301, constructing a target layer and a criterion layer, wherein the target layer is an output fire intention factor E, and the criterion layer comprises a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D which are measured by a sensor group;
step 302, constructing a judgment matrix P as follows:
wherein P isi-jRepresenting the relative weight of i in pairwise comparison with j, i being taken to [ A, D]J is taken as [ A, D ]]；
Step 303, calculating to obtain the maximum characteristic root of the judgment matrix P
And the maximum eigenvector WP；
304, carrying out consistency check to obtain the weight of each factor of a carbon monoxide concentration value A, a carbon dioxide concentration value B, a temperature value C and a humidity value D;
step 305, obtaining a fire monitoring model according to the weight as follows:
E＝QA*WA+QB*WB+QC*WC+QD*WD；
wherein Q isA、QB、QCAnd QDRespectively representing the corresponding scores of the sensor values.
4. The method according to claim 2, wherein the determination matrix P in step 204 is specifically:
and corresponding WP＝(0.0622、0.1835、0.4432、0.3111)TWherein W isA＝0.0622，WB＝0.1835，WC＝0.4432，WD＝0.3111。
5. The method for monitoring fire in industrial computer room based on machine learning as claimed in claim 2, wherein the step 205 of predicting the degree of intention of fire by the central computer room according to the distribution of the sensor group sending the message information of the hidden fire hazard includes the following steps:
step 501, calculating and predicting the adjacent degree of a sensor group with fire hazard, wherein the adjacent degree is obtained by the ratio of the number of adjacent sensors to the total number of the sensors;
step 502, judging whether the adjacent degree is higher than a first threshold value, if so, judging the adjacent degree as a high-risk fire hazard, judging whether the adjacent degree is between a second threshold value and the first threshold value, if so, judging the adjacent degree as a medium-risk fire hazard, judging whether the adjacent degree is lower than the second threshold value, and if so, judging the adjacent degree as a low-risk fire hazard.
6. The machine learning-based fire monitoring method for the industrial machine room according to claim 5, wherein the threshold H is between [0.7 and 1.0], the first threshold is 0.3, and the second threshold is 0.1.
7. The machine learning-based industrial machine room fire monitoring method according to claim 2, further comprising:
when there is a failure, a log file is generated, which contains,
the number of the sensor group with the fire hazard, the level of the intention of the fire, the current time and the current date.
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