CN111223263A - Full-automatic comprehensive fire early warning response system - Google Patents
Full-automatic comprehensive fire early warning response system Download PDFInfo
- Publication number
- CN111223263A CN111223263A CN202010166270.5A CN202010166270A CN111223263A CN 111223263 A CN111223263 A CN 111223263A CN 202010166270 A CN202010166270 A CN 202010166270A CN 111223263 A CN111223263 A CN 111223263A
- Authority
- CN
- China
- Prior art keywords
- data
- early warning
- analysis platform
- comprehensive analysis
- response system
- 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.)
- Pending
Links
- 230000004044 response Effects 0.000 title claims abstract description 24
- 238000004458 analytical method Methods 0.000 claims abstract description 56
- 238000012544 monitoring process Methods 0.000 claims abstract description 28
- 230000007613 environmental effect Effects 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000013473 artificial intelligence Methods 0.000 claims description 20
- 238000007781 pre-processing Methods 0.000 claims description 14
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 239000000779 smoke Substances 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 abstract description 3
- 230000000875 corresponding effect Effects 0.000 description 13
- 230000008859 change Effects 0.000 description 11
- 238000000034 method Methods 0.000 description 11
- 230000009471 action Effects 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 5
- 238000001914 filtration Methods 0.000 description 5
- 230000009466 transformation Effects 0.000 description 5
- 238000012351 Integrated analysis Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 238000009499 grossing Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000005507 spraying Methods 0.000 description 2
- 238000012706 support-vector machine Methods 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 238000012886 linear function Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- 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
-
- 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
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/12—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions
- G08B17/125—Actuation by presence of radiation or particles, e.g. of infrared radiation or of ions by using a video camera to detect fire or smoke
Abstract
The invention relates to a full-automatic comprehensive fire early warning response system which comprises a sensor subsystem, a camera device, a data concentrator, a central data comprehensive analysis platform and terminal equipment, wherein the sensor subsystem is arranged in a disaster monitoring area and used for collecting environmental data in the area, the camera device is used for collecting video stream data in the monitoring area, the data concentrator is used for collecting the environmental data and the video stream data and uploading the environmental data and the video stream data to the central data comprehensive analysis platform, the central data comprehensive analysis platform is used for storing data and performing data processing and resource scheduling, and the terminal equipment is used for receiving messages sent by the central data comprehensive analysis platform and performing corresponding response. The data collected by the sensor is analyzed, and then artificial intelligent identification and prediction are carried out by combining with the image information collected by the camera device so as to finally confirm the fire and send a message to the terminal equipment, so that the invention has the effects of improving early warning accuracy and responding in time.
Description
Technical Field
The invention relates to the technical field of fire monitoring, in particular to a full-automatic comprehensive fire early warning response system.
Background
At present, sensors are mostly adopted for monitoring fire and then data are judged, but at the initial stage of fire occurrence, the position and the area of the fire are uncertain, various sensors cannot necessarily acquire relevant data in time and inform relevant personnel, the development situation of the fire can not be monitored, and powerful information support can not be provided for managers to grasp the field situation in real time and make management decisions, so that the best time for extinguishing the fire is missed, or the situation of mistakenly reporting the fire due to the abnormal sensors and the like can occur.
For example, the existing chinese patent application with publication number CN110148288A discloses an intelligent fire-fighting early-warning system and an early-warning method, which collect field data through a sensor cluster and push early-warning messages to a terminal after the field data is processed by a central processing platform.
The above prior art solutions have the following drawbacks: the acquired data is not fully utilized to carry out comprehensive analysis, particularly the analysis of image data containing rich information, and the early warning accuracy of the method has a further improved space.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a full-automatic comprehensive fire early warning response system which has the effects of improving early warning accuracy and responding in time.
The above object of the present invention is achieved by the following technical solutions:
the utility model provides a full-automatic generalized type fire early warning response system, includes sensor subsystem, camera device, data concentrator, central data integrated analysis platform, terminal equipment, sensor subsystem lays in disaster monitoring area and is used for gathering the environmental data in this region, camera device is used for gathering the video stream data in the monitoring area, the data concentrator is used for collecting environmental data and video stream data upload the central data integrated analysis platform, central data integrated analysis platform is used for storing data and data processing and resource scheduling, terminal equipment is used for receiving the message that central data integrated analysis platform sent and carries out corresponding response.
By adopting the technical scheme, the environmental data of the disaster monitoring area is collected by the sensor subsystem and the camera device and is uniformly transmitted to the central data comprehensive analysis platform for storage and processing through the data concentrator, then the central data comprehensive analysis platform performs preliminary analysis in real time from various data collected by the sensor and obtains preliminary fire early warning information, and then the results of artificial intelligence classification recognition and prediction are performed by combining the image data collected by the camera device, so that the early warning information of fire occurrence is finally confirmed, and the early warning information is sent to the terminal equipment so that the terminal equipment and related personnel can take corresponding actions in time, thereby improving the accuracy of system early warning and the timeliness of response.
The present invention in a preferred example may be further configured to: the sensor subsystem includes one or more of a temperature sensor, a smoke sensor, a humidity sensor, and a flame detection sensor.
By adopting the technical scheme, a plurality of sensors of different types are scattered and arranged at each position of the monitoring area, and the environmental monitoring data of the monitoring area with multiple dimensions can be obtained through the sensors, so that the data can be comprehensively analyzed to more comprehensively know the field condition, and the false alarm rate is reduced.
The present invention in a preferred example may be further configured to: the central data comprehensive analysis platform comprises a preliminary judgment module, wherein threshold values corresponding to all sensors of the sensor subsystem are preset in the preliminary judgment module, and disaster preliminary judgment is carried out according to the threshold values.
By adopting the technical scheme, the threshold corresponding to the sensor subsystem is preset in the preliminary judgment module of the central data comprehensive analysis platform, and the data collected in real time is compared with the threshold, so that preliminary judgment of disaster can be comprehensively carried out in real time.
The present invention in a preferred example may be further configured to: the central data comprehensive analysis platform further comprises an artificial intelligence prediction module, and the artificial intelligence prediction module carries out artificial intelligence prediction on disaster situations on the basis of the video stream data.
By adopting the technical scheme, the artificial intelligence prediction module is used for preprocessing the video stream data in the monitoring area collected by the camera device, then carrying out artificial intelligence classification prediction and outputting fire early warning information, so that the accuracy of fire prediction can be further improved.
The present invention in a preferred example may be further configured to: the artificial intelligence prediction module comprises:
the preprocessing unit is used for preprocessing the video stream data;
the extraction unit is used for extracting flame characteristics from the preprocessed video stream data;
and the prediction unit is used for carrying out classification recognition on the flame characteristics through a pre-trained neural network and making a prediction.
By adopting the technical scheme, the processing amount and the storage amount of data information can be reduced by preprocessing the video data stream, and the calculation time cost is reduced; the flame characteristics are extracted and the characteristics are fused, so that the information of the image can be further mined, and the identification efficiency is improved.
The present invention in a preferred example may be further configured to: and the central data comprehensive analysis platform performs early warning according to the outputs of the preliminary judgment module and the artificial intelligence prediction module and outputs early warning information to the terminal equipment.
By adopting the technical scheme, the central data comprehensive analysis platform combines the fire early warning information of the artificial intelligent prediction module and the preliminary early warning information output by the preliminary judgment module to perform comprehensive analysis so as to finally confirm whether a fire disaster occurs, so that the accuracy of fire early warning is improved, and the condition of fire false alarm is reduced.
The present invention in a preferred example may be further configured to: and the central data comprehensive analysis platform plans and schedules resources according to the early warning information to manage and control the disaster.
By adopting the technical scheme, after the central data comprehensive analysis platform confirms that a fire happens, the sensor and the camera device continue to monitor the fire, the subsequent fire early warning information including the position, the area, the trend and the like of the fire is analyzed from the data uploaded by the data concentrator, and then resources (such as managers, execution equipment and the like) in the corresponding area are scheduled to send messages so as to respond to the fire in time, so that the spreading of the fire is controlled.
The present invention in a preferred example may be further configured to: the terminal equipment comprises a manager terminal and an executive terminal.
By adopting the technical scheme, the manager terminal is convenient for managers to receive early warning information sent by the central data comprehensive analysis platform and make decisions and actions in time; and the execution terminal is used for responding in time after receiving the execution instruction sent by the central data comprehensive analysis platform.
The present invention in a preferred example may be further configured to: the manager terminal receives fire early warning information of the central data comprehensive analysis platform, wherein the fire early warning information comprises a fire area and a fire level; and the execution terminal receives the execution instruction of the central data comprehensive analysis platform and responds.
By adopting the technical scheme, the manager can receive fire early warning information such as the area, the level and the like of a fire through the manager terminal, so that the manager can make correct rescue decisions and actions; the central data comprehensive analysis platform sends an execution instruction to schedule an execution terminal in the fire area to automatically execute corresponding actions and timely respond to the fire, so that the action of extinguishing the fire can be carried out at the first time when a manager does not arrive, the spread of the fire is initially prevented, and the loss is reduced as much as possible.
In summary, the invention includes at least one of the following beneficial technical effects:
1. the method comprises the steps of carrying out preliminary analysis and judgment on environmental data of a monitoring area acquired by a sensor, carrying out artificial intelligent identification and prediction by combining image information of the area acquired by a camera device so as to finally confirm fire early warning information, and sending the information to terminal equipment so that the terminal equipment and personnel can take corresponding actions in time, thereby improving the accuracy of system early warning and the timeliness of response;
2. the sensors of different types are distributed and arranged at each position of the monitoring area, and the sensors can obtain environment monitoring data of the monitoring area with multiple dimensions, so that the data can be comprehensively analyzed to more comprehensively know the field condition, and the false alarm rate is reduced;
3. the preprocessing of the video data stream can reduce the processing amount and the memory space of data information and reduce the cost of calculation time; the flame characteristics are extracted and the characteristics are fused, so that the information of the image can be further mined, and the identification efficiency is improved.
Drawings
FIG. 1 is a schematic structural diagram of a fully automatic and comprehensive fire early warning response system disclosed by the invention;
FIG. 2 is a schematic diagram of the artificial intelligence prediction module 1042 in FIG. 1.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Referring to fig. 1, a fully automatic comprehensive fire early warning response system 100 disclosed by the present invention includes a sensor subsystem 101, a camera 102, a data concentrator 103, a central data comprehensive analysis platform 104, and a terminal device 105, wherein the sensor subsystem 101 is disposed in a disaster monitoring area and is used for collecting environmental data in the area, the camera 102 is used for collecting video stream data in the monitoring area, the data concentrator 103 is used for collecting the environmental data and the video stream data and uploading the environmental data and the video stream data to the central data comprehensive analysis platform 104, the central data comprehensive analysis platform 104 is used for storing data and performing data processing and resource scheduling, and the terminal device 105 is used for receiving a message sent by the central data comprehensive analysis platform 104 and performing a corresponding response. The sensor subsystem 101 and the camera device 102 collect environmental data of a disaster monitoring area and upload the environmental data to the central data comprehensive analysis platform 104 for storage and processing through the data concentrator 103 in a unified manner, then the central data comprehensive analysis platform 104 performs preliminary analysis in real time from data collected by the sensor and obtains preliminary fire early warning information, and then prediction results of artificial intelligent prediction are performed by combining image data collected by the camera device, so that the fire early warning information is finally confirmed, and the information is sent to the terminal device so that the terminal device and personnel can take corresponding actions in time, thereby improving the accuracy of system early warning and the timeliness of response.
Specifically, the sensor subsystem 101 includes one or more of a temperature sensor, a smoke sensor, a humidity sensor, and a flame detection sensor, and may employ a wireless sensor to communicate with the data concentrator 103 in a wireless manner to transmit data, and a plurality of sensors of different types may be distributed at various locations of a monitoring area, so that environmental monitoring data of the monitoring area with multiple dimensions, such as temperature and humidity data, smoke data, flame infrared data, etc., may be obtained by the sensors, and then the monitoring data may be uploaded to the central data comprehensive analysis platform 104 through the data concentrator 103 in a wired or wireless manner to be stored, or real-time and historical data processing and analysis may be performed, so that the data may be comprehensively analyzed to more comprehensively understand the field conditions and reduce the false alarm rate.
Further, the central data comprehensive analysis platform 104 includes a preliminary judgment module 1041 and an artificial intelligence prediction module 1042, the preliminary judgment module 1041 is preset with threshold values corresponding to the sensors of the sensor subsystem 101 and performs preliminary disaster judgment according to the threshold values, for example, a threshold value table (such as a database table or a configuration file, etc.) may be set in the preliminary judgment module 1041, including temperature threshold values, humidity threshold values, smoke concentration threshold values, etc., each threshold value of the threshold value table may be modified according to actual conditions, the threshold values in the table are read when the preliminary judgment module 1041 operates, and then compared with each real-time data uploaded by the data concentrator 103 to see whether each set threshold value is exceeded, if yes, preliminary fire warning information is generated.
As shown in fig. 2, fig. 2 is a schematic structural diagram of the artificial intelligence prediction module 1042, and the artificial intelligence prediction module 1042 includes:
a preprocessing unit 10421, configured to perform preprocessing on the video stream data, where the preprocessing includes graying and smoothing. Graying is the process of converting a color image into a grayscale image, and the image graying processing can reduce the processing amount and the storage amount of data information, reduce the calculation time cost and increase the identification efficiency. In the process of graying the flame image, a linear function may be used for transformation to obtain a grayscale image with more distinct features, and in addition to the linear transformation, there are methods such as logarithmic transformation, gamma transformation, and threshold transformation, among which: by setting a gray threshold T and then comparing each pixel in the original image with the gray threshold T, the output pixel is set to 0 if the comparison result is less than the gray threshold T, and is set to 255 if the comparison result is greater than the gray threshold T, the processing procedure is simple and practical. During the process of forming, transmitting, receiving and processing images, due to the practical performance of a transmission medium passing through and the limit of the performance of a receiving device, external interference and internal interference inevitably exist, so various noises can be generated, and flames are also influenced by noises such as weather, illumination and the like during the process of forming, so smooth filtering processing should be carried out on the flame images before flame identification is carried out; smoothing is performed by filtering, such as mean filtering, gaussian filtering, median filtering, and the like. The video stream data in the monitoring area acquired by the camera device 102 is subjected to graying and smoothing preprocessing by the preprocessing unit 10421, so that the noise and data volume of the image can be reduced, and the subsequent calculation speed can be increased.
An extracting unit 10422, configured to extract flame features from the preprocessed video stream data, where the flame features include flame color features, flame shape features, flame texture features, flame area features, and the like; firstly, segmenting the preprocessed video stream data by an interframe difference method to obtain the flame foreground of each frame of gray level image, and supposing the images of the kth frame and the k +1 frame
(x，y)、
The change between (x, y) is represented by a binary differential image D (x, y) as follows:
wherein, T is a threshold value selected during binarization of the difference image, D (x, y) = 1 represents foreground, and D (x, y) = 0 represents background. All pixels with the value of 1 in the binary image are considered as the result of the object motion, and the flame motion foreground is composed of the motion pixels including the suspicious flame pixels.
Then calculating the flame area change rate based on the flame foreground pixel change of two adjacent frames of gray images as the flame area characteristic; when the flame occurs, the shape and the size of the flame continuously change, and the flame shows a continuous growth trend in the initial stage, the area of a common object does not frequently change, and the area of the common object can be kept relatively stable even if an interference source exists, so that an area change rate threshold value can be set to judge whether a certain area is possibly a flame area, and the flame area change rate can be used as an important standard for identifying the expansion change of the flame. Rate of change of flame area
According to the two adjacent frames in the video stream data after the preprocessing
(x，y)、
(x, y) flame pixel variation is calculated as follows:
wherein the content of the first and second substances,
、
representing the number of flame pixels of the k frame and the k +1 frame; then can be combined with
And judging whether the flame area is a flame area or not according to a preset area change rate threshold value.
Further, the flame texture features can be obtained through statistical features such as first-order statistics (e.g. mean, histogram of variance), second-order statistics (e.g. entropy, contrast); the flame shape characteristics are obtained by calculating the circularity, and the calculation formula of the circularity is as follows:
wherein
The area of the region is shown as,
the perimeter of the area is represented as,
indicating the circularity of the region. A circle is the largest area geometry at the same circumference. The closer the object shape is to a circle, the more nearly the object shape is to a circle
The larger and conversely the more complex the shape
The smaller the size of the tube is,
is between 0 and 1. Also, due to the irregular shape of the flame, a threshold C (e.g., 1/2.56) may be set when
<C, it is considered that this region may be a flame region.
Further, extracting flame color characteristics from the tunnel video stream data by a spatial difference analysis method; the flame is also composed of several different colors in a small area, and the spatial color change of the pixels can be analyzed by a spatial difference analysis method, so that whether the flame is a car lamp or a fire can be distinguished, and specifically, the rule can be analyzed by the standard deviation of the green component histogram and is used as the flame color feature to preliminarily judge whether the flame is the flame.
The prediction unit 10423 is used for carrying out classification recognition on the flame characteristics through a pre-trained neural network and making a prediction; firstly, in order to further mine the information of the image, the obtained flame color feature, flame shape feature, flame texture feature and flame area feature can be fused by weighted splicing, and then the fused flame color feature, flame shape feature, flame texture feature and flame area feature are input into a pre-trained neural network classifier for classification recognition and prediction, such as secondary classification, so that the probability of flame can be obtained, and then fire early warning information is output; the classifier can use a Support Vector Machine (SVM) classifier, a Deep Convolutional Neural Network (DCNN), an extreme learning machine or the like; the pre-training of the neural network comprises the steps of constructing the neural network, obtaining and preprocessing training data, and training the neural network by using a dropout method to prevent overfitting so as to improve the prediction accuracy of the model.
The units of the artificial intelligence prediction module 1042 preprocess video stream data in a monitoring area acquired by the camera device 102, then perform deep learning classification prediction, and then output fire early warning information, so that the accuracy of fire prediction can be further improved; then, the central data comprehensive analysis platform 104 performs comprehensive analysis to finally determine whether a fire occurs by combining the fire early warning information of the artificial intelligence prediction module 1042 and the preliminary early warning information output by the preliminary judgment module 1041, so that the accuracy of fire early warning is improved, and the situation of fire misinformation is reduced.
Further, the central data comprehensive analysis platform 104 plans and schedules resources according to the early warning information, and manages and controls the disaster; the terminal device 105 includes a manager terminal and an execution terminal, and the manager terminal receives fire early warning information of the central data comprehensive analysis platform 104, including a fire area and a fire level; and the execution terminal receives the execution instruction of the central data comprehensive analysis platform and responds. For example, after the central data comprehensive analysis platform 104 determines that a fire occurs, the central data comprehensive analysis platform 104 may continue to monitor the fire, analyze subsequent fire warning information from the data uploaded by the data concentrator, analyze and determine the location, area, trend, etc. of the fire by using the location, temperature, smoke concentration, etc. of the sensor and the camera, and then send the information to the manager terminal (such as a mobile phone, an application program on a computer, etc.) in the corresponding area to schedule resources in the area so as to timely notify the corresponding responsible person to make a correct rescue decision and action, further send an execution instruction to the area where the fire occurs by the central data comprehensive analysis platform 104 to schedule an execution terminal (such as an automatic spraying device) in the area where the fire occurs to perform an action, such as opening a valve of a pipeline and making a nozzle perform an execution instruction of automatic spraying, the fire disaster detector can respond to the fire disaster in time, so that fire extinguishing action can be carried out in the first time when managers do not arrive, the spread of fire is initially prevented, and loss is reduced as much as possible.
The implementation principle of the embodiment is as follows: the environmental data of the disaster monitoring area is collected through the sensor subsystem 101 and the camera device 102 and is uniformly transmitted to the central data comprehensive analysis platform 104 through the data concentrator 103 to be stored and processed, then the central data comprehensive analysis platform 104 performs preliminary analysis in real time from various data collected by the sensor and obtains preliminary fire early warning information, and then artificial intelligence classification recognition and prediction results are performed by combining with image data collected by the camera device, so that the early warning information of fire occurrence is finally confirmed, and the early warning information is sent to the terminal device so that the terminal device and related personnel can take corresponding actions in time, and therefore the accuracy of system early warning and the timeliness of response are improved.
The embodiments of the present invention are preferred embodiments of the present invention, and the scope of the present invention is not limited by these embodiments, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.
Claims (9)
1. The utility model provides a full-automatic integrated fire early warning response system which characterized in that: the disaster situation monitoring system comprises a sensor subsystem, a camera device, a data concentrator, a central data comprehensive analysis platform and terminal equipment, wherein the sensor subsystem is arranged in a disaster situation monitoring area and used for collecting environmental data in the area, the camera device is used for collecting video stream data in the monitoring area, the data concentrator is used for collecting the environmental data and the video stream data and uploading the environmental data and the video stream data to the central data comprehensive analysis platform, the central data comprehensive analysis platform is used for storing data, processing the data and scheduling resources, and the terminal equipment is used for receiving messages sent by the central data comprehensive analysis platform and responding correspondingly.
2. The fully automatic integrated fire early warning response system according to claim 1, wherein: the sensor subsystem includes one or more of a temperature sensor, a smoke sensor, a humidity sensor, and a flame detection sensor.
3. The fully automatic integrated fire early warning response system according to claim 2, wherein: the central data comprehensive analysis platform comprises a preliminary judgment module, wherein threshold values corresponding to all sensors of the sensor subsystem are preset in the preliminary judgment module, and disaster preliminary judgment is carried out according to the threshold values.
4. The fully automatic integrated fire early warning response system according to claim 3, wherein: the central data comprehensive analysis platform further comprises an artificial intelligence prediction module, and the artificial intelligence prediction module carries out artificial intelligence prediction on disaster situations on the basis of the video stream data.
5. The fully automatic integrated fire early warning response system according to claim 4, wherein: the artificial intelligence prediction module comprises:
the preprocessing unit is used for preprocessing the video stream data;
the extraction unit is used for extracting flame characteristics from the preprocessed video stream data;
and the prediction unit is used for carrying out classification recognition on the flame characteristics through a pre-trained neural network and making a prediction.
6. The fully automatic integrated fire early warning response system according to claim 5, wherein: and the central data comprehensive analysis platform performs early warning according to the outputs of the preliminary judgment module and the artificial intelligence prediction module and outputs early warning information to the terminal equipment.
7. The fully automatic integrated fire early warning response system according to claim 6, wherein: and the central data comprehensive analysis platform plans and schedules resources according to the early warning information to manage and control the disaster.
8. The fully automatic integrated fire early warning response system according to claim 7, wherein: the terminal equipment comprises a manager terminal and an executive terminal.
9. The fully automatic integrated fire early warning response system according to claim 8, wherein: the manager terminal receives fire early warning information of the central data comprehensive analysis platform, wherein the fire early warning information comprises a fire area and a fire level; and the execution terminal receives the execution instruction of the central data comprehensive analysis platform and responds.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010166270.5A CN111223263A (en) | 2020-03-11 | 2020-03-11 | Full-automatic comprehensive fire early warning response system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010166270.5A CN111223263A (en) | 2020-03-11 | 2020-03-11 | Full-automatic comprehensive fire early warning response system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111223263A true CN111223263A (en) | 2020-06-02 |
Family
ID=70826165
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010166270.5A Pending CN111223263A (en) | 2020-03-11 | 2020-03-11 | Full-automatic comprehensive fire early warning response system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111223263A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111882803A (en) * | 2020-08-25 | 2020-11-03 | 北京华正明天信息技术股份有限公司 | Intelligent fire hazard identification early warning method based on temperature sensing and video image information |
| CN112637453A (en) * | 2020-12-07 | 2021-04-09 | 国网山东省电力公司应急管理中心 | Ubiquitous Internet of things-based following type intelligent sensing interaction equipment and method for electric power emergency disposal |
| CN115884005A (en) * | 2023-02-22 | 2023-03-31 | 成都维迦科技有限公司 | Enterprise production and operation management system and method based on intelligent data analysis |
| CN117275165A (en) * | 2023-09-07 | 2023-12-22 | 广州云创数据科技有限公司 | Fire-fighting smoke treatment device based on artificial intelligence |
| CN117373196A (en) * | 2023-11-17 | 2024-01-09 | 北京北元电器有限公司 | Electrical fire alarm method and system |
| EP4177592A4 (en) * | 2020-07-06 | 2024-01-17 | Panasonic Ip Man Co Ltd | Sign determination system, integrated system, sign determination method, and program |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101770644A (en) * | 2010-01-19 | 2010-07-07 | 浙江林学院 | Forest-fire remote video monitoring firework identification method |
| CN102682565A (en) * | 2012-03-23 | 2012-09-19 | 合肥博构元丰信息技术有限公司 | Fire protection and security integrated intelligent video monitoring system suitable for open space |
| CN103116746A (en) * | 2013-03-08 | 2013-05-22 | 中国科学技术大学 | Video flame detecting method based on multi-feature fusion technology |
| CN103150856A (en) * | 2013-02-28 | 2013-06-12 | 江苏润仪仪表有限公司 | Fire flame video monitoring and early warning system and fire flame detection method |
| CN205845067U (en) * | 2016-04-27 | 2016-12-28 | 云南昆钢集团电子信息工程有限公司 | The pneumatic steelmaking blowing state recognition system of static nature is moved based on image |
| CN108038510A (en) * | 2017-12-22 | 2018-05-15 | 湖南源信光电科技股份有限公司 | A kind of detection method based on doubtful flame region feature |
| CN109886227A (en) * | 2019-02-27 | 2019-06-14 | 哈尔滨工业大学 | Inside fire video frequency identifying method based on multichannel convolutive neural network |
| CN110047241A (en) * | 2019-04-27 | 2019-07-23 | 刘秀萍 | A kind of forest fire unmanned plane cruise monitoring system |
| CN110148288A (en) * | 2019-04-04 | 2019-08-20 | 深圳市保国特卫机器人科技有限公司 | Intelligent fire early warning system and method for early warning |
| CN110652685A (en) * | 2019-10-17 | 2020-01-07 | 广东德臻消防机电工程有限公司 | Indoor fire extinguishing method and system |
-
2020
- 2020-03-11 CN CN202010166270.5A patent/CN111223263A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101770644A (en) * | 2010-01-19 | 2010-07-07 | 浙江林学院 | Forest-fire remote video monitoring firework identification method |
| CN102682565A (en) * | 2012-03-23 | 2012-09-19 | 合肥博构元丰信息技术有限公司 | Fire protection and security integrated intelligent video monitoring system suitable for open space |
| CN103150856A (en) * | 2013-02-28 | 2013-06-12 | 江苏润仪仪表有限公司 | Fire flame video monitoring and early warning system and fire flame detection method |
| CN103116746A (en) * | 2013-03-08 | 2013-05-22 | 中国科学技术大学 | Video flame detecting method based on multi-feature fusion technology |
| CN205845067U (en) * | 2016-04-27 | 2016-12-28 | 云南昆钢集团电子信息工程有限公司 | The pneumatic steelmaking blowing state recognition system of static nature is moved based on image |
| CN108038510A (en) * | 2017-12-22 | 2018-05-15 | 湖南源信光电科技股份有限公司 | A kind of detection method based on doubtful flame region feature |
| CN109886227A (en) * | 2019-02-27 | 2019-06-14 | 哈尔滨工业大学 | Inside fire video frequency identifying method based on multichannel convolutive neural network |
| CN110148288A (en) * | 2019-04-04 | 2019-08-20 | 深圳市保国特卫机器人科技有限公司 | Intelligent fire early warning system and method for early warning |
| CN110047241A (en) * | 2019-04-27 | 2019-07-23 | 刘秀萍 | A kind of forest fire unmanned plane cruise monitoring system |
| CN110652685A (en) * | 2019-10-17 | 2020-01-07 | 广东德臻消防机电工程有限公司 | Indoor fire extinguishing method and system |
Non-Patent Citations (3)
| Title |
|---|
| 吴龙标: "《火灾探测与控制工程》", 30 September 2013 * |
| 王志喜: "《计算机图形图像技术》", 31 January 2018 * |
| 蒋先刚: "《基于稀疏表达的火焰与烟雾探测方法研究》", 31 August 2017 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP4177592A4 (en) * | 2020-07-06 | 2024-01-17 | Panasonic Ip Man Co Ltd | Sign determination system, integrated system, sign determination method, and program |
| CN111882803A (en) * | 2020-08-25 | 2020-11-03 | 北京华正明天信息技术股份有限公司 | Intelligent fire hazard identification early warning method based on temperature sensing and video image information |
| CN112637453A (en) * | 2020-12-07 | 2021-04-09 | 国网山东省电力公司应急管理中心 | Ubiquitous Internet of things-based following type intelligent sensing interaction equipment and method for electric power emergency disposal |
| CN112637453B (en) * | 2020-12-07 | 2022-06-21 | 国网山东省电力公司应急管理中心 | Ubiquitous Internet of things-based following type intelligent sensing interaction equipment and method for electric power emergency disposal |
| CN115884005A (en) * | 2023-02-22 | 2023-03-31 | 成都维迦科技有限公司 | Enterprise production and operation management system and method based on intelligent data analysis |
| CN115884005B (en) * | 2023-02-22 | 2023-08-29 | 中交西北投资发展有限公司 | Enterprise production operation management system and method based on intelligent data analysis |
| CN117275165A (en) * | 2023-09-07 | 2023-12-22 | 广州云创数据科技有限公司 | Fire-fighting smoke treatment device based on artificial intelligence |
| CN117373196A (en) * | 2023-11-17 | 2024-01-09 | 北京北元电器有限公司 | Electrical fire alarm method and system |
| CN117373196B (en) * | 2023-11-17 | 2024-04-05 | 北京北元电器有限公司 | Electrical fire alarm method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111223263A (en) | Full-automatic comprehensive fire early warning response system | |
| CN106600888B (en) | Automatic forest fire detection method and system | |
| CN111047818A (en) | Forest fire early warning system based on video image | |
| CN105100724A (en) | Remote and safe intelligent household monitoring method and device based on visual analysis | |
| CN107679471A (en) | Indoor occupant sky hilllock detection method based on video monitoring platform | |
| CN111222478A (en) | Construction site safety protection detection method and system | |
| CN109377713A (en) | A kind of fire alarm method and system | |
| CN107330414A (en) | Act of violence monitoring method | |
| KR20200052418A (en) | Automated Violence Detecting System based on Deep Learning | |
| CN116629465B (en) | Smart power grids video monitoring and risk prediction response system | |
| CN116664359A (en) | Intelligent fire early warning decision system and method based on multi-sensor fusion | |
| CN111862065A (en) | Power transmission line diagnosis method and system based on multitask deep convolutional neural network | |
| CN107688793A (en) | A kind of outside transformer substation fire automatic monitoring method for early warning | |
| CN115691034A (en) | Intelligent household abnormal condition warning method, system and storage medium | |
| CN114898261A (en) | Sleep quality assessment method and system based on fusion of video and physiological data | |
| CN110930632B (en) | Early warning system based on artificial intelligence | |
| CN113269039A (en) | On-duty personnel behavior identification method and system | |
| CN116416281A (en) | Grain depot AI video supervision and analysis method and system | |
| CN117197713A (en) | Extraction method based on digital video monitoring system | |
| CN112883755A (en) | Smoking and calling detection method based on deep learning and behavior prior | |
| CN116310922A (en) | Petrochemical plant area monitoring video risk identification method, system, electronic equipment and storage medium | |
| CN116761049B (en) | Household intelligent security monitoring method and system | |
| CN105095891A (en) | Human face capturing method, device and system | |
| CN111652128B (en) | High-altitude power operation safety monitoring method, system and storage device | |
| CN112232235A (en) | Intelligent factory remote monitoring method and system based on 5G |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200602 |