CN112634572A - Fire prediction method and device based on smoke temperature sensing and electronic equipment - Google Patents
Fire prediction method and device based on smoke temperature sensing and electronic equipment Download PDFInfo
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Abstract
One or more embodiments of the present specification provide a fire prediction method, an apparatus, and an electronic device based on smoke temperature sensing, where the fire prediction method includes acquiring an original alarm data set; inputting an original alarm data set into a first detection classification model to obtain a predicted value of normal alarm and false alarm probability; inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment; inputting the original alarm data set into a third detection classification model to obtain a predicted alarm time range; and outputting a prediction result according to the prediction values of the normal alarm probability and the false alarm probability, the predicted alarm area, the alarm equipment and the predicted alarm time range. According to the embodiment of the invention, the prediction data of the time, the place, the influence range and the probability of the fire are obtained through a machine learning algorithm, and the processing scheme of fire early warning is obtained through analyzing the prediction data, so that the occurrence rate of the fire is effectively reduced in time, and the loss caused by the occurrence of the fire is avoided.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of fire safety technologies, and in particular, to a fire prediction method and apparatus based on smoke temperature sensing, and an electronic device.
Background
The smoke vibration and temperature sensing detection is that a smoke sensor and a semiconductor temperature sensor are combined from a process structure and a circuit structure, abnormal smoke and temperature in the surrounding environment are detected, received smoke signals and temperature signals are converted into electric signals, and alarm processing is carried out.
At present, for the alarm problem of smoke temperature, an alarm is mainly used for alarming, passive emergency treatment is carried out in a manual judgment mode, and due to the fact that the basis of complete data statistical analysis and a mature processing scheme are not available, fire hazard treatment efficiency is low when smoke temperature is alarmed, and even serious economic loss is brought.
Disclosure of Invention
In view of the above, one or more embodiments of the present disclosure are directed to a fire prediction method, a fire prediction apparatus, and an electronic device based on smoke temperature sensing, so as to solve the problem of slow analysis and processing of smoke temperature sensing alarms.
In view of the above, one or more embodiments of the present disclosure provide a fire prediction method based on smoke temperature, including:
acquiring an original alarm data set;
inputting the original alarm data set into a first detection classification model to obtain a predicted value of the probability of normal alarm and false alarm;
inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment;
inputting the original alarm data set into a third detection classification model to obtain a predicted alarm time range;
and outputting a prediction result according to the prediction value of the normal alarm probability and the false alarm probability, the predicted alarm area, the alarm equipment and the predicted alarm time range.
As an optional implementation, the first detection classification model is a GBDT strong learner model;
the training method of the first detection classification model comprises the following steps:
acquiring a training set, wherein the training set comprises historical alarm time, historical alarm false alarm times, historical normal alarm times and historical alarm places;
inputting the training set into a GBDT initialization weak learner model for iterative operation until a preset iteration number is reached;
and obtaining a well-trained GBDT strong learner model.
As an alternative embodiment, the second detection classification model is an ID3 model;
the training method of the second detection classification model comprises the following steps:
acquiring a training set, wherein the training set comprises historical alarm time, historical alarm false alarm times, historical normal alarm times and historical alarm places;
respectively taking the contents in the training set as feature sets, taking the optimal feature set as a first leaf node, taking other feature sets as a second leaf node or a third leaf node, and traversing until all the leaf nodes are output or no data exists;
a trained ID3 model is obtained.
As an alternative embodiment, the optimal feature set is the feature set with the largest information gain.
As an alternative embodiment, the third detection classification model is an LSTM model.
As an optional implementation, the prediction result includes: the alarm bell is started and/or the elevator is shut down and/or the fire power supply is started and/or personnel and/or equipment are dredged for maintenance.
As an optional implementation, the processing scheme includes: starting an alarm bell, turning off an elevator, starting a fire-fighting power supply, dredging personnel and maintaining equipment.
As an optional implementation mode, the fire prediction method further comprises a model display, and the model display comprises a step of displaying the predicted alarm area and the alarm device by using a three-dimensional data model.
Corresponding to the fire prediction method, the embodiment of the invention also provides a fire prediction device based on smoke temperature, which comprises the following steps:
the acquisition module is used for acquiring an original alarm data set;
the first calculation module is used for inputting the original alarm data set into a first detection classification model to obtain a predicted value of the probability of normal alarm and false alarm;
the second calculation module is used for inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment;
the third calculation module is used for inputting the original alarm data set into a third detection classification model to obtain a prediction time range of the alarm;
and the output module is used for outputting a prediction result according to the prediction value of the normal alarm and the false alarm probability, the predicted alarm area, the prediction time range of the alarm equipment and the alarm.
Corresponding to the fire prediction method, the embodiment of the invention also provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor executes the program to realize the method.
As can be seen from the foregoing, one or more embodiments of the present disclosure provide a fire prediction method, device and electronic device based on smoke temperature, which are different from the conventional fire prevention and control processing, and obtain prediction data of time, place, influence range and probability of a fire by using a machine learning algorithm, obtain a processing scheme of fire early warning by performing statistical analysis on the prediction data, execute an early warning processing scheme in time, and effectively reduce the occurrence rate of a fire, thereby avoiding economic loss caused by the occurrence of a fire.
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In order to more clearly illustrate one or more embodiments or prior art solutions of the present specification, the drawings that are needed in the description of the embodiments or prior art will be briefly described below, and it is obvious that the drawings in the following description are only one or more embodiments of the present specification, and that other drawings may be obtained by those skilled in the art without inventive effort from these drawings.
FIG. 1 is a schematic diagram of a fire prediction method according to one or more embodiments of the present disclosure;
FIG. 2 is a schematic diagram of a fire prediction device according to one or more embodiments of the present disclosure;
fig. 3 is a schematic diagram of fire prediction electronics in accordance with one or more embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the present disclosure more apparent, the present disclosure is further described in detail below with reference to specific embodiments.
In order to achieve the above object, an embodiment of the present invention provides a fire prediction method based on smoke temperature, including:
acquiring an original alarm data set;
inputting the original alarm data set into a first detection classification model to obtain a predicted value of the probability of normal alarm and false alarm;
inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment;
inputting the original alarm data set into a third detection classification model to obtain a predicted alarm time range;
and outputting a prediction result according to the prediction value of the normal alarm probability and the false alarm probability, the predicted alarm area, the alarm equipment and the predicted alarm time range.
In the embodiment of the invention, aiming at the prevention of smoke temperature sensing, the time, the place, the influence range and the probability of a fire disaster are respectively predicted through a machine learning algorithm, the predicted result is input into a system, the system analyzes data according to the input result, and the predicted result is output. In the embodiment of the specification, the machine learning algorithm is adopted to respectively obtain the prediction data of the time, the place, the influence range and the probability of the fire, the processing scheme of fire early warning is obtained through statistical analysis of the prediction value data, the early warning processing scheme is executed in time, the occurrence rate of the fire is effectively reduced, and therefore economic loss caused by the occurrence of the fire is avoided.
Referring to fig. 1, an embodiment of the present invention provides a fire prediction method based on smoke temperature, including:
s100, acquiring an original alarm data set.
Optionally, the raw data set is obtained by extracting historical alarm events stored in the system.
S200, inputting the original alarm data set into a first detection classification model to obtain a predicted value of the probability of normal alarm and false alarm.
As an optional implementation, the first detection classification model is a GBDT strong learner model;
the training method of the first detection classification model comprises the following steps:
acquiring a training set, wherein the training set comprises historical alarm time, historical alarm false alarm times, historical normal alarm times and historical alarm places;
inputting the training set into a GBDT initialization weak learner model for iterative operation until a preset iteration number is reached;
and obtaining a well-trained GBDT strong learner model.
Optionally, when a concurrent alarm event is predicted, priority processing may be performed according to a probability obtained by an algorithm.
S300, inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment.
As an alternative embodiment, the second detection classification model is an ID3 model;
the training method of the second detection classification model comprises the following steps:
acquiring a training set, wherein the training set comprises historical alarm time, historical alarm false alarm times, historical normal alarm times and historical alarm places;
respectively taking the contents in the training set as feature sets, taking the optimal feature set as a first leaf node, taking other feature sets as a second leaf node or a third leaf node, and traversing until all the leaf nodes are output or no data exists;
a trained ID3 model is obtained.
As an alternative embodiment, the optimal feature set is the feature set with the largest information gain.
Optionally, the information gain is selected by using an information entropy principle.
S400, inputting the original alarm data set into a third detection classification model to obtain a predicted alarm time range.
As an alternative embodiment, the third detection classification model is an LSTM model.
S500, outputting a prediction result according to the prediction value of the normal alarm probability and the false alarm probability, the predicted alarm area, the alarm equipment and the predicted alarm time range.
As an optional implementation, the prediction result includes: the time, location, extent of impact, probability and treatment plan of the fire are predicted.
As an optional implementation, the processing scheme includes: the alarm bell is started and/or the elevator is shut down and/or the fire power supply is started and/or personnel and/or equipment are dredged for maintenance.
As an optional implementation mode, the fire prediction method further comprises a model display, and the model display comprises a step of displaying the predicted alarm area and the alarm device by using a three-dimensional data model.
Alternatively, the calculation and transmission of data is performed by using a 5G communication technology.
Optionally, the three-dimensional data model is communicated with the actual alarm area and the alarm device through a java technology.
It should be noted that the method of one or more embodiments of the present disclosure may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the devices may perform only one or more steps of the method of one or more embodiments of the present disclosure, and the devices may interact with each other to complete the method.
Based on any one of the embodiments of the fire prediction method based on smoke temperature, the present invention further provides a fire prediction device based on smoke temperature, as shown in fig. 2, including:
an obtaining module 10, configured to obtain an original alarm data set;
a first calculating module 20, configured to input the original alarm data set into a first detection classification model, so as to obtain a predicted value of the probability of normal alarm and false alarm;
a second calculation module 30, configured to input the original alarm data set into a second detection classification model, so as to obtain a predicted alarm region and alarm devices;
a third calculation module 40, configured to input the original alarm data set into a third detection classification model, so as to obtain a predicted time range of an alarm;
and the output module 50 is used for outputting a prediction result according to the prediction value of the normal alarm and the false alarm probability, the predicted alarm area, the prediction time range of the alarm equipment and the alarm.
In the embodiment of the invention, aiming at the prevention of smoke temperature sensing, the time, the place, the influence range and the probability of a fire disaster are respectively predicted through a machine learning algorithm, the predicted result is input into a system, the system analyzes data according to the input result, and the predicted result is output. In the embodiment of the specification, the machine learning algorithm is adopted to respectively obtain the prediction data of the time, the place, the influence range and the probability of the fire, the processing scheme of fire early warning is obtained through statistical analysis of the prediction value data, the early warning processing scheme is executed in time, the occurrence rate of the fire is effectively reduced, and therefore economic loss caused by the occurrence of the fire is avoided.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present specification should have the ordinary meaning as understood by those of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in one or more embodiments of the specification is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
Based on any one of the above embodiments of the method for obtaining the structured data of the pole and tower signboard, the present invention further provides a more specific schematic diagram of a hardware structure of an electronic device, as shown in fig. 3, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the spirit of the present disclosure, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of different aspects of one or more embodiments of the present description as described above, which are not provided in detail for the sake of brevity.
It is intended that the one or more embodiments of the present specification embrace all such alternatives, modifications and variations as fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of one or more embodiments of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A fire prediction method based on smoke temperature sensing is characterized by comprising the following steps:
acquiring an original alarm data set;
inputting the original alarm data set into a first detection classification model to obtain a predicted value of the probability of normal alarm and false alarm;
inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment;
inputting the original alarm data set into a third detection classification model to obtain a predicted alarm time range;
and outputting a prediction result according to the prediction value of the normal alarm probability and the false alarm probability, the predicted alarm area, the alarm equipment and the predicted alarm time range.
2. The method of claim 1, wherein the first detection classification model is a GBDT strong learner model;
the training method of the first detection classification model comprises the following steps:
acquiring a training set, wherein the training set comprises historical alarm time, historical alarm false alarm times, historical normal alarm times and historical alarm places;
inputting the training set into a GBDT initialization weak learner model for iterative operation until a preset iteration number is reached;
and obtaining a well-trained GBDT strong learner model.
3. The fire prediction method based on smoke temperature sensing of claim 1, wherein the second detection classification model is an ID3 model;
the training method of the second detection classification model comprises the following steps:
acquiring a training set, wherein the training set comprises historical alarm time, historical alarm false alarm times, historical normal alarm times and historical alarm places;
respectively taking the contents in the training set as feature sets, taking the optimal feature set as a first leaf node, taking other feature sets as a second leaf node or a third leaf node, and traversing until all the leaf nodes are output or no data exists;
a trained ID3 model is obtained.
4. The method of claim 3, wherein the optimal feature set is the feature set with the highest information gain.
5. The method of claim 1, wherein the third detection classification model is an LSTM model.
6. The fire prediction method based on smoke temperature sensing according to claim 1, wherein the prediction result comprises: the time, location, extent of impact, probability and treatment plan of the fire are predicted.
7. The method of claim 6, wherein the processing scheme comprises: the alarm bell is started and/or the elevator is shut down and/or the fire power supply is started and/or personnel and/or equipment are dredged for maintenance.
8. The fire prediction method based on smoke sensation is characterized in that the fire prediction method further comprises model display, and the model display comprises displaying the predicted alarm area and the alarm device by using a three-dimensional data model.
9. A fire prediction device based on smoke temperature sensing, comprising:
the acquisition module is used for acquiring an original alarm data set;
the first calculation module is used for inputting the original alarm data set into a first detection classification model to obtain a predicted value of the probability of normal alarm and false alarm;
the second calculation module is used for inputting the original alarm data set into a second detection classification model to obtain a predicted alarm area and alarm equipment;
the third calculation module is used for inputting the original alarm data set into a third detection classification model to obtain a prediction time range of the alarm;
and the output module is used for outputting a prediction result according to the prediction value of the normal alarm and the false alarm probability, the predicted alarm area, the prediction time range of the alarm equipment and the alarm.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 8 when executing the program.
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