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 PDF

Info

Publication number
CN112634572A
CN112634572A CN202011440743.2A CN202011440743A CN112634572A CN 112634572 A CN112634572 A CN 112634572A CN 202011440743 A CN202011440743 A CN 202011440743A CN 112634572 A CN112634572 A CN 112634572A
Authority
CN
China
Prior art keywords
alarm
predicted
fire
classification model
detection
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011440743.2A
Other languages
Chinese (zh)
Other versions
CN112634572B (en
Inventor
王奔
赵晴
林少波
邓卜侨
孙雷
李捷
代素敏
展敬宇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Zhongdian Feihua Communication Co Ltd
Original Assignee
State Grid Information and Telecommunication Co Ltd
Beijing Zhongdian Feihua Communication Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by State Grid Information and Telecommunication Co Ltd, Beijing Zhongdian Feihua Communication Co Ltd filed Critical State Grid Information and Telecommunication Co Ltd
Priority to CN202011440743.2A priority Critical patent/CN112634572B/en
Publication of CN112634572A publication Critical patent/CN112634572A/en
Application granted granted Critical
Publication of CN112634572B publication Critical patent/CN112634572B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/10—Actuation by presence of smoke or gases, e.g. automatic alarm devices for analysing flowing fluid materials by the use of optical means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/06—Electric actuation of the alarm, e.g. using a thermally-operated switch

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Mathematical Physics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Computing Systems (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Biomedical Technology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Molecular Biology (AREA)
  • Evolutionary Biology (AREA)
  • Business, Economics & Management (AREA)
  • Emergency Management (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Medical Informatics (AREA)
  • Fire Alarms (AREA)

Abstract

本说明书一个或多个实施例提供一种基于烟感温感的火灾预测方法、装置及电子设备，火灾预测方法包括获取原始告警数据集；将原始告警数据集输入第一检测分类模型，获得正常告警和误报的概率的预测值；将原始告警数据集输入第二检测分类模型，获得预测的告警区域和告警设备；将原始告警数据集输入第三检测分类模型，获得预测的告警时间范围；根据正常告警和误报的概率的预测值、预测的告警区域和告警设备和预测的告警时间范围，输出预测结果。本发明实施例通过机器学习算法得到火灾的时间、地点、影响范围、概率的预测数据，通过对预测数据的分析，获得火灾预警的处理方案，及时执行有效的降低了火灾的发生率，避免了因火灾的发生而带来的损失。

Figure 202011440743

One or more embodiments of this specification provide a fire prediction method, device, and electronic device based on smoke and temperature. The fire prediction method includes acquiring an original alarm data set; inputting the original alarm data set into a first detection and classification model to obtain a normal The predicted value of the probability of alarm and false alarm; input the original alarm data set into the second detection and classification model to obtain the predicted alarm area and alarm equipment; input the original alarm data set into the third detection and classification model to obtain the predicted alarm time range; According to the predicted value of the probability of normal alarm and false alarm, the predicted alarm area and alarm equipment, and the predicted alarm time range, the predicted result is output. The embodiment of the present invention obtains the prediction data of the time, place, influence range, and probability of the fire through the machine learning algorithm, and obtains the processing plan of the fire early warning through the analysis of the prediction data, and the timely execution effectively reduces the occurrence rate of the fire and avoids the loss due to fire.

Figure 202011440743

Description

Fire prediction method and device based on smoke temperature sensing and electronic equipment
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.
Drawings
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).
Bus 1050 includes a path that transfers information between various components of the device, such as processor 1010, memory 1020, input/output interface 1030, and communication interface 1040.
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)

1.一种基于烟感温感的火灾预测方法，其特征在于，包括：1. a fire prediction method based on smoke and temperature, is characterized in that, comprises: 获取原始告警数据集；Get the original alarm data set; 将所述原始告警数据集输入第一检测分类模型，获得正常告警和误报的概率的预测值；Inputting the original alarm data set into a first detection and classification model to obtain predicted values of the probability of normal alarms and false alarms; 将所述原始告警数据集输入第二检测分类模型，获得预测的告警区域和告警设备；Input the original alarm data set into the second detection and classification model to obtain the predicted alarm area and alarm device; 将所述原始告警数据集输入第三检测分类模型，获得预测的告警时间范围；Inputting the original alarm data set into a third detection and classification model to obtain a predicted alarm time range; 根据所述正常告警和误报的概率的预测值、预测的告警区域和告警设备和预测的告警时间范围，输出预测结果。According to the predicted value of the probability of normal alarm and false alarm, the predicted alarm area and alarm equipment, and the predicted alarm time range, the predicted result is output. 2.根据权利要求1所述的基于烟感温感的火灾预测方法，其特征在于，所述第一检测分类模型为GBDT强学习器模型；2. the fire prediction method based on smoke and temperature according to claim 1, is characterized in that, described first detection and classification model is GBDT strong learner model; 所述第一检测分类模型的训练方法为：The training method of the first detection and classification model is: 获取训练集，所述训练集包括历史告警时间、历史告警误报次数、历史正常告警次数和历史告警地点；obtaining a training set, where the training set includes the historical alarm time, the number of historical alarm false alarms, the number of historical normal alarms, and the historical alarm location; 将所述训练集输入GBDT初始化弱学习器模型中进行迭代运算，直至达到预设的迭代次数；The training set is input into the GBDT initialization weak learner model for iterative operation, until the preset number of iterations is reached; 获得训练好的GBDT强学习器模型。Get the trained GBDT strong learner model. 3.根据权利要求1所述的基于烟感温感的火灾预测方法，其特征在于，所述第二检测分类模型为ID3模型；3. The fire prediction method based on smoke and temperature according to claim 1, wherein the second detection and classification model is an ID3 model; 所述第二检测分类模型的训练方法为：The training method of the second detection and classification model is: 获取训练集，所述训练集包括历史告警时间、历史告警误报次数、历史正常告警次数和历史告警地点；obtaining a training set, where the training set includes the historical alarm time, the number of historical alarm false alarms, the number of historical normal alarms, and the historical alarm location; 将所述训练集中的内容分别作为特征集，以最优的特征集作为第一叶节点，以其它特征集作为第二叶节点或第三叶节点，直至叶节点全部输出或无数据遍历；The content in the training set is respectively used as a feature set, the optimal feature set is used as the first leaf node, and other feature sets are used as the second leaf node or the third leaf node, until all the leaf nodes are output or no data is traversed; 获得训练好的ID3模型。Get the trained ID3 model. 4.根据权利要求3所述的基于烟感温感的火灾预测方法，其特征在于，所述最优的特征集为信息增益最大的特征集。4 . The fire prediction method based on smoke and temperature according to claim 3 , wherein the optimal feature set is the feature set with the largest information gain. 5 . 5.根据权利要求1所述的基于烟感温感的火灾预测方法，其特征在于，所述第三检测分类模型为LSTM模型。5 . The fire prediction method based on smoke and temperature according to claim 1 , wherein the third detection and classification model is an LSTM model. 6 . 6.根据权利要求1所述的基于烟感温感的火灾预测方法，其特征在于，所述预测结果包括：预测到火灾的时间、地点、影响范围、概率和处理方案。6 . The fire prediction method based on smoke and temperature according to claim 1 , wherein the prediction result includes: time, location, influence range, probability and treatment plan when the fire is predicted. 7 . 7.根据权利要求6所述的基于烟感温感的火灾预测方法，其特征在于，所述处理方案包括：启动警铃和/或关闭电梯和/或启动消防电源和/或疏通人员和/或设备维修。7. The method for predicting fire based on smoke and temperature according to claim 6, wherein the processing scheme comprises: starting an alarm bell and/or closing an elevator and/or starting a fire-fighting power supply and/or dredging personnel and/or or equipment maintenance. 8.根据权利要求1所述的基于烟感温感的火灾预测方法，其特征在于，所述火灾预测方法还包括模型显示，所述模型显示包括:使用三维数据模型显示所述预测的告警区域和告警设备。8. The fire prediction method based on smoke and temperature according to claim 1, wherein the fire prediction method further comprises model display, and the model display comprises: using a three-dimensional data model to display the predicted warning area and alarm equipment. 9.一种基于烟感温感的火灾预测装置，其特征在于，包括：9. A fire prediction device based on smoke and temperature, characterized in that, comprising: 获取模块，用于获取原始告警数据集；The acquisition module is used to acquire the original alarm data set; 第一计算模块，用于将所述原始告警数据集输入第一检测分类模型，获得正常告警和误报的概率的预测值；a first calculation module, configured to input the original alarm data set into a first detection and classification model to obtain predicted values of the probability of normal alarms and false alarms; 第二计算模块，用于将所述原始告警数据集输入第二检测分类模型，获得预测的告警区域和告警设备；a second calculation module, configured to input the original alarm data set into a second detection and classification model to obtain a predicted alarm area and an alarm device; 第三计算模块，用于将所述原始告警数据集输入第三检测分类模型，获得告警的预测时间范围；a third calculation module, configured to input the original alarm data set into a third detection and classification model to obtain a predicted time range of the alarm; 输出模块，用于根据所述正常告警和误报的概率的预测值、预测的告警区域和告警设备和告警的预测时间范围，输出预测结果。The output module is configured to output the prediction result according to the predicted value of the probability of normal alarm and false alarm, the predicted alarm area, and the prediction time range of the alarm device and the alarm. 10.一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1至8任意一项所述的方法。10. An electronic device, comprising a memory, a processor and a computer program stored on the memory and running on the processor, wherein the processor implements any one of claims 1 to 8 when the processor executes the program method described in item.
CN202011440743.2A 2020-12-07 2020-12-07 Fire prediction method and device based on smoke temperature sensing and electronic equipment Active CN112634572B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011440743.2A CN112634572B (en) 2020-12-07 2020-12-07 Fire prediction method and device based on smoke temperature sensing and electronic equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011440743.2A CN112634572B (en) 2020-12-07 2020-12-07 Fire prediction method and device based on smoke temperature sensing and electronic equipment

Publications (2)

Publication Number Publication Date
CN112634572A true CN112634572A (en) 2021-04-09
CN112634572B CN112634572B (en) 2023-01-17

Family

ID=75309326

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011440743.2A Active CN112634572B (en) 2020-12-07 2020-12-07 Fire prediction method and device based on smoke temperature sensing and electronic equipment

Country Status (1)

Country Link
CN (1) CN112634572B (en)

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101656012A (en) * 2008-08-19 2010-02-24 侯荣琴 Intelligent image smoke and flame detector and detection method
JP5022292B2 (en) * 2008-04-22 2012-09-12 パナソニック株式会社 Fire alarm system
KR101532055B1 (en) * 2014-11-10 2015-07-17 진양공업주식회사 Forest fire early warning system
CN106485871A (en) * 2015-09-01 2017-03-08 霍尼韦尔国际公司 System and method of the model to provide the early prediction and forecast of false alarm are inferred by applied statistics
CN109978005A (en) * 2019-02-25 2019-07-05 深圳市中电数通智慧安全科技股份有限公司 A kind of fire alarm method, device, storage medium and terminal device
US20190303703A1 (en) * 2018-03-30 2019-10-03 Regents Of The University Of Minnesota Predicting land covers from satellite images using temporal and spatial contexts
CN110309961A (en) * 2019-06-20 2019-10-08 京东城市(北京)数字科技有限公司 Fire alarm method and apparatus
CN110910615A (en) * 2019-11-22 2020-03-24 华中科技大学 Building fire alarm classification method and system
CN110906381A (en) * 2019-11-29 2020-03-24 中原工学院 XGboost-based self-learning intelligent gas cooker system
CN111178828A (en) * 2019-12-18 2020-05-19 北京明略软件系统有限公司 Method and system for building fire safety early warning
CN111476970A (en) * 2020-03-30 2020-07-31 珠海格力电器股份有限公司 Fire monitoring method and device and storage medium
KR20200112123A (en) * 2019-03-21 2020-10-05 한국남동발전 주식회사 System and method for detecting spontaneous ignition of coal early in a power plant

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP5022292B2 (en) * 2008-04-22 2012-09-12 パナソニック株式会社 Fire alarm system
CN101656012A (en) * 2008-08-19 2010-02-24 侯荣琴 Intelligent image smoke and flame detector and detection method
KR101532055B1 (en) * 2014-11-10 2015-07-17 진양공업주식회사 Forest fire early warning system
CN106485871A (en) * 2015-09-01 2017-03-08 霍尼韦尔国际公司 System and method of the model to provide the early prediction and forecast of false alarm are inferred by applied statistics
US20190303703A1 (en) * 2018-03-30 2019-10-03 Regents Of The University Of Minnesota Predicting land covers from satellite images using temporal and spatial contexts
CN109978005A (en) * 2019-02-25 2019-07-05 深圳市中电数通智慧安全科技股份有限公司 A kind of fire alarm method, device, storage medium and terminal device
KR20200112123A (en) * 2019-03-21 2020-10-05 한국남동발전 주식회사 System and method for detecting spontaneous ignition of coal early in a power plant
CN110309961A (en) * 2019-06-20 2019-10-08 京东城市(北京)数字科技有限公司 Fire alarm method and apparatus
CN110910615A (en) * 2019-11-22 2020-03-24 华中科技大学 Building fire alarm classification method and system
CN110906381A (en) * 2019-11-29 2020-03-24 中原工学院 XGboost-based self-learning intelligent gas cooker system
CN111178828A (en) * 2019-12-18 2020-05-19 北京明略软件系统有限公司 Method and system for building fire safety early warning
CN111476970A (en) * 2020-03-30 2020-07-31 珠海格力电器股份有限公司 Fire monitoring method and device and storage medium

Also Published As

Publication number Publication date
CN112634572B (en) 2023-01-17

Similar Documents

Publication Publication Date Title
JP2021513170A (en) Unmonitored spoofing detection from traffic data on mobile networks
EP2722762A2 (en) Real time control system management
US10929765B2 (en) Content-level anomaly detection for heterogeneous logs
JP6456580B1 (en) Abnormality detection device, abnormality detection method and abnormality detection program
JP2017215157A (en) Information processing device, program, and fall prediction system
JP2013254372A5 (en)
CN110730164B (en) Safety early warning method, related equipment and computer readable storage medium
CN115665188A (en) Sensor data processing method and system applied to industrial internet manufacturing
WO2022115419A1 (en) Method of detecting an anomaly in a system
CN114205212A (en) Network security early warning method, device, equipment and readable storage medium
CN109409661A (en) Electronic device, the building Electrical Safety recognition methods based on cusum algorithm and storage medium
JPWO2018216258A1 (en) Processing device, processing method and program
CN108256626A (en) The Forecasting Methodology and device of time series
CN113670434A (en) Transformer substation equipment sound abnormality identification method and device and computer equipment
CN110838306B (en) Voice signal detection method, computer storage medium and related equipment
CN108564751A (en) The monitoring method of cable tunnel anti-intrusion, apparatus and system
CN112634572B (en) Fire prediction method and device based on smoke temperature sensing and electronic equipment
CN114167487B (en) Seismic magnitude estimation method and device based on characteristic waveform
CN118300280B (en) Magnetic levitation type safety electric device and state detection method
JP6253838B1 (en) Action log generation method, action log generation device, and action log generation program
KR20190075558A (en) Terminal apparatus and controlling method of the terminal apparatus
CN115357461B (en) Abnormality detection method, abnormality detection device, electronic device, and computer-readable storage medium
CN117421655A (en) Industrial Internet data stream anomaly detection method and system
CN116563944A (en) Abnormal behavior detection method, device, computer equipment and storage medium
JP5175881B2 (en) Monitoring device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20211104

Address after: 100089 south building, block a, Dongxu International Center, Fanyang Road, Fengtai District, Beijing

Applicant after: Beijing Zhongdian Feihua Communication Co.,Ltd.

Address before: 100070 south building, block a, Dongxu International Center, Fanyang Road, Fengtai District, Beijing

Applicant before: Beijing Zhongdian Feihua Communication Co.,Ltd.

Applicant before: STATE GRID INFORMATION & TELECOMMUNICATION GROUP Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant