CN114157836A - Forest fire prevention scheduling system based on candidate frame fusion - Google Patents
Forest fire prevention scheduling system based on candidate frame fusion Download PDFInfo
- Publication number
- CN114157836A CN114157836A CN202111400714.8A CN202111400714A CN114157836A CN 114157836 A CN114157836 A CN 114157836A CN 202111400714 A CN202111400714 A CN 202111400714A CN 114157836 A CN114157836 A CN 114157836A
- Authority
- CN
- China
- Prior art keywords
- video
- unit
- forest fire
- monitoring
- video images
- 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
- 230000004927 fusion Effects 0.000 title claims abstract description 41
- 230000002265 prevention Effects 0.000 title claims abstract description 18
- 238000012544 monitoring process Methods 0.000 claims abstract description 110
- 238000012937 correction Methods 0.000 claims abstract description 11
- 238000000605 extraction Methods 0.000 claims description 6
- 230000004048 modification Effects 0.000 claims description 6
- 238000012986 modification Methods 0.000 claims description 6
- 238000001514 detection method Methods 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 2
- 238000011161 development Methods 0.000 abstract description 11
- 238000000034 method Methods 0.000 abstract description 8
- 230000008569 process Effects 0.000 abstract description 7
- 230000004044 response Effects 0.000 abstract description 5
- 230000007547 defect Effects 0.000 abstract description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 241000282414 Homo sapiens Species 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 241001465754 Metazoa Species 0.000 description 1
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 229910052760 oxygen Inorganic materials 0.000 description 1
- 239000001301 oxygen Substances 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 230000007480 spreading Effects 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/253—Fusion techniques of extracted features
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B17/00—Fire alarms; Alarms responsive to explosion
- G08B17/005—Fire alarms; Alarms responsive to explosion for forest fires, e.g. detecting fires spread over a large or outdoors area
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/222—Studio circuitry; Studio devices; Studio equipment
- H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
- H04N5/265—Mixing
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Life Sciences & Earth Sciences (AREA)
- Emergency Management (AREA)
- Business, Economics & Management (AREA)
- Signal Processing (AREA)
- Theoretical Computer Science (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biodiversity & Conservation Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Alarm Systems (AREA)
Abstract
A forest fire prevention scheduling system based on candidate frame fusion relates to the field of forest fire prevention monitoring and scheduling. The defects that a forest fire fighting system cannot truly reflect the development situation of the fire source, accurate dynamic response cannot be made according to the development situation of the fire source, and accurate judgment can be made for real-time and effective guiding and scheduling of the rear are overcome. The method combines the modes of candidate frame selection and feature fusion to process decoded images to obtain high-precision video images after preliminary fusion, further corrects the video images after preliminary fusion through a video correction unit to further improve the precision of the video images, simulates video images in a target monitoring area in a future time period according to the corrected video images, accurately pre-judges the development situation of forest fires in the future, judges the category corresponding to the video images in the target monitoring area in the future time period, and carries out corresponding scheduling according to the judged category. The invention is mainly used for forest fire prevention.
Description
Technical Field
The invention relates to the field of forest fire prevention monitoring and scheduling.
Background
The oxygen and forest products provided by forests cannot be kept for the survival and development of human beings at any moment. Although our country is in the world with large land, the total amount of forest resources in China is still insufficient, and the forest coverage rate is only 60% of the average level in the world and is 130 th in the world.
Because the total quantity of forest resources is insufficient and the quality is not high, the problems of land desertification, water and soil loss, drought and water shortage, flood disasters and the like in China are very prominent, and the national ecological safety faces serious threats. Among the threats affecting forest resources, forest fires are one of the most prominent threats, which can cause a forest to turn into ash between hectares.
Forest fires are the most dangerous enemies of forests and the most feared disasters of forestry, and can bring the most harmful and devastating consequences to forests. Forest fires not only burn out a large number of forests to damage animals in the forests, but also reduce the forest renewal capacity, cause soil impoverishment and destroy the effect of forest conservation water sources, and further cause the ecological environment to lose balance. Although the science of the world is gradually advancing day by day, human beings still have not made long-term progress in the aspect of overcoming forest fires. According to the burning part, the spreading speed, the damaged part and the degree of the forest fire, the forest fire can be roughly divided into three categories: the forest fire is divided into four types by taking the area of the damaged forest as a standard, namely surface fire, crown fire and underground fire:
1. forest fire alarm: the area of the damaged forest is less than 1 hectare or other forest lands are on fire (including wildfires);
2. general forest fires: the area of the damaged forest is more than 1 hectare and less than 100 hectare;
3. major forest fires: the area of the damaged forest is more than 100 hectares and less than 1000 hectares;
4. fire disaster in super forest: the area of the damaged forest is more than 1000 hectares;
forest fire's emergence is often far away from the residential area, can't in time discover the conflagration emergence, the emergence and the development of forest fire are monitored through remote video monitoring to current comparatively advanced forest fire extinguishing system, survey the monitoring area whole, the command dispatch of making a response, but in the time measuring of observing the surveillance video part in the monitoring process, the picture quality becomes virtual, can't really reflect the fire source development situation, still exist and can't make accurate dynamic response according to the fire source development situation, make the defect of accurate judgement for the real-time effectual instruction dispatch in rear, consequently above problem needs to be solved urgently.
Disclosure of Invention
The invention aims to solve the defects that a forest fire fighting system cannot truly reflect the development situation of a fire source, cannot make accurate dynamic response according to the development situation of the fire source and can make accurate judgment for real-time and effective guiding and scheduling at the rear, and therefore the invention provides a forest fire fighting scheduling system based on candidate frame fusion.
The forest fire prevention dispatching system based on the fusion of the candidate frames comprises N video monitoring terminals, a central dispatching platform, a temperature and humidity monitoring module, a wind direction monitoring module and a wind speed monitoring module;
the N video monitoring terminals are respectively arranged in the corresponding target monitoring areas and used for acquiring video images in the areas and sending the video images to the central scheduling platform;
the temperature and humidity monitoring module is used for collecting the temperature and the humidity in the target monitoring area and sending the collected result to the central dispatching platform;
the wind direction monitoring module is used for monitoring the wind direction in the target monitoring area and sending the wind direction monitoring result to the central dispatching platform;
the wind speed monitoring module is used for monitoring the wind speed in the target monitoring area and sending the wind speed monitoring result to the central dispatching platform;
the central dispatching platform comprises N decoding units, a candidate frame feature extraction unit, a candidate frame feature fusion unit, a video correction unit, a data loading unit, a forest fire category judgment unit, an emergency plan dispatching unit, a storage unit and a display unit;
the N decoding units correspond to the N video monitoring terminals one by one, and decode video images in areas collected by the N video monitoring terminals respectively to obtain a video image of each frame;
the candidate frame feature extraction unit is used for selecting a corresponding decoding unit according to the video monitoring candidate instruction, selecting a corresponding position of a video image of a corresponding frame decoded by the selected decoding unit according to the candidate frame selection instruction, and sending the obtained candidate frame to the candidate frame feature fusion unit;
the candidate frame feature fusion unit is used for performing feature fusion on features in all selected candidate frames to obtain a preliminarily fused video image;
the video correction unit is used for correcting the preliminarily fused video image to obtain a corrected video image;
the data loading unit is used for loading the acquired temperature, humidity, wind direction monitoring result and wind speed monitoring result into the corrected video image so as to simulate the video image in the target monitoring area in the future time period;
the forest fire category distinguishing unit is internally stored with 4 types of forest fire danger video images, and the categories of the 4 types of forest fire danger video images are respectively defined as forest fire alarms, general forest fires, major forest fires and extra-large forest fires;
the forest fire category distinguishing unit is used for calculating the correlation between the video images in the target monitoring area in the future time period and the 4 types of forest fire video images respectively to obtain 4 correlation values, and taking the category of the corresponding forest fire video image with the maximum correlation value as the category of the video images in the target monitoring area in the future time period;
the emergency plan scheduling unit is used for generating a corresponding scheduling instruction according to the category of the video image in the target monitoring area in the future time period to schedule the fire service terminal;
the storage unit is used for storing the video images decoded by the N decoding units, storing the modified video images output by the video modification unit and storing the video images in the target monitoring area in the future time period simulated by the data loading unit;
the display unit is used for displaying the video image in the target monitoring area in the future time period simulated by the data loading unit, the video image decoded by the decoding unit in the storage unit and the modified video image output by the video modifying unit by the data loading unit in a split screen mode.
The invention has the following beneficial effects: the invention provides a forest fire prevention dispatching system based on candidate frame fusion, which combines the modes of candidate frame selection and feature fusion to process decoded images to obtain high-precision preliminarily fused video images which clearly and truly reflect the current damaged forest conditions in a target monitoring area, further corrects the preliminarily fused video images through a video correction unit to further improve the precision of the video images and ensure the definition of the video images, simulates the video images in the target monitoring area in a future time period according to the corrected video images, accurately pre-judges the development situation of forest fire in the future, judges the corresponding category of the video images in the target monitoring area in the future time period, automatically makes corresponding dispatching instructions according to the judged category, improves the precision of command dispatching, and does not need to manually participate in command dispatching in the dispatching instruction generating process, the central dispatching platform automatically and timely makes dispatching reflection.
When the device is used, 3 video images can be contrasted and displayed through the display unit, so that observation is facilitated.
Drawings
FIG. 1 is a schematic diagram of a forest fire prevention scheduling system based on candidate frame fusion according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
Example 1:
referring to fig. 1 to illustrate the present embodiment, the forest fire prevention scheduling system based on candidate frame fusion in the present embodiment includes N video monitoring terminals 100, a central scheduling platform 200, a temperature and humidity monitoring module 300, a wind direction monitoring module 400, and a wind speed monitoring module 500;
the N video monitoring terminals 100 are respectively arranged in corresponding target monitoring areas and used for acquiring video images in the areas and sending the video images to the central scheduling platform 200;
the temperature and humidity monitoring module 300 is used for collecting the temperature and the humidity in the target monitoring area and sending the collection result to the central dispatching platform 200;
the wind direction monitoring module 400 is used for monitoring the wind direction in the target monitoring area and sending the wind direction monitoring result to the central dispatching platform 200;
the wind speed monitoring module 500 is used for monitoring the wind speed in the target monitoring area and sending the wind speed monitoring result to the central dispatching platform 200;
the central scheduling platform 200 comprises N decoding units 201, a candidate frame feature extraction unit 202, a candidate frame feature fusion unit 203, a video correction unit 204, a data loading unit 205, a forest fire category discrimination unit 206, an emergency plan scheduling unit 207, a storage unit 208 and a display unit 209;
the N decoding units 201 correspond to the N video monitoring terminals 100 one to one, and the N decoding units 201 respectively decode video images in the region acquired by the N video monitoring terminals 100 to obtain a video image of each frame;
the candidate frame feature extraction unit 202 selects the corresponding decoding unit 201 according to the video monitoring candidate instruction, selects the corresponding position of the video image of the corresponding frame decoded by the selected decoding unit 201 according to the candidate frame selection instruction, and sends the obtained candidate frame to the candidate frame feature fusion unit 203;
a candidate frame feature fusion unit 203, configured to perform feature fusion on features in all selected candidate frames to obtain a preliminarily fused video image;
the video correction unit 204 is configured to correct the preliminarily fused video image to obtain a corrected video image;
the data loading unit 205 is configured to load the acquired temperature, humidity, wind direction monitoring result and wind speed monitoring result into the corrected video image to simulate a video image in a target monitoring area in a future time period;
the forest fire category discrimination unit 206 stores 4 types of forest fire video images, and the categories of the 4 types of forest fire video images are respectively defined as forest fire alarms, general forest fires, major forest fires and extra-large forest fires;
a forest fire category discrimination unit 206, configured to calculate correlation degrees between video images in a target monitoring area in a future time period and 4 types of forest fire video images, respectively, to obtain 4 correlation values, and take a category of a corresponding forest fire video image when the correlation value is the largest as a category of the video images in the target monitoring area in the future time period;
the emergency plan scheduling unit 207 is configured to generate a corresponding scheduling instruction according to the category of the video image in the target monitoring area in the future time period to schedule the fire service terminal 600;
a storage unit 208, configured to store the video images decoded by the N decoding units 201, store the modified video images output by the video modification unit 204, and store the video images in the target monitoring area in the future time period simulated by the data loading unit 205;
the display unit 209 is configured to invoke, for the video image in the target monitoring area in the future time period simulated by the data loading unit 205, the video image decoded by the decoding unit 201 in the storage unit 208 and the modified video image output by the video modifying unit 204 through the data loading unit 205, and perform split-screen display.
The forest fire prevention dispatching system based on candidate frame fusion in the embodiment can decode the collected video images in the target monitoring area to obtain the video images of each frame, further process the decoded images by combining the selection of the candidate frames and the characteristic fusion mode to obtain high-precision preliminarily fused video images, clearly and truly reflect the current forest damage condition in the target monitoring area, further correct the preliminarily fused video images by the video correction unit 204 to further improve the precision of the video images and ensure the definition of the video images, simulate the video images in the target monitoring area in the future time period according to the corrected video images, accurately predict the development situation of the future forest fire, and finally lay a tamping foundation for the next forest fire category discrimination according to the video images in the target monitoring area in the future time period, the category judgment is accurately made, corresponding scheduling instructions are automatically made according to the judged category, the command scheduling precision is improved, manual participation in command scheduling is not needed in the scheduling instruction generation process, and the central scheduling platform 200 can make scheduling reflection in time.
In specific application, the N video monitoring terminals 100 may be fixedly disposed in corresponding target monitoring areas, and the display unit 209 may compare and display the 3 video images for observation.
Further, the video correction unit 204 corrects the preliminarily fused video image, and the implementation manner of obtaining the corrected video image is as follows:
firstly, extracting the background of each frame of image in the preliminarily fused video image, and separating the background area and the motion area in each frame of image in the preliminarily fused video image;
and denoising the motion region in each frame of image, and performing characteristic fusion on the denoised motion region in each frame of image and the corresponding background region to obtain each modified frame of image, thereby obtaining a modified video image.
In the preferred embodiment, an implementation manner for obtaining the corrected video image is provided, and the correction manner obtains a high-precision operation result by using a small operation amount, so that the whole operation process is simple and convenient to implement.
Further, the scheduling system further includes a detection module 700, configured to perform real-time detection on the N video monitoring terminals 100, and when detecting that no signal is output from the corresponding video monitoring terminal 100, report the signal to the central scheduling platform 200 for warning in a wireless transmission manner.
In the preferred embodiment, the detection module 700 can detect the video monitoring terminal 100 in real time to determine whether the current video monitoring terminal 100 fails and make a dynamic response in time.
Furthermore, the N video monitoring terminals 100 can be loaded on the corresponding unmanned aerial vehicle, and the unmanned aerial vehicle can be used for periodic inspection monitoring, and the cruise track of the unmanned aerial vehicle can be commanded and scheduled by the central scheduling platform 200 during application.
Furthermore, the N video monitoring terminals 100 may be loaded on the corresponding pan/tilt control systems, and when the system is applied, the central scheduling platform 200 may control the corresponding pan/tilt control systems, so as to control the angles of the video monitoring terminals 100.
Further, the candidate frame feature fusion unit 203 performs feature fusion on features in all selected candidate frames to obtain a preliminarily fused video image in the following implementation manner:
and denoising all the selected candidate frames, then extracting high-dimensional features to obtain the high-dimensional image features of all the selected candidate frames, and then performing feature fusion on the high-dimensional image features of all the selected candidate frames to obtain a preliminarily fused video image.
In the preferred embodiment, an implementation mode for obtaining the preliminarily fused video image is provided, and the whole process is simple and convenient to implement.
Furthermore, 4 types of scheduling instructions are stored in the emergency plan scheduling unit 207, and the 4 types of scheduling instructions are respectively in one-to-one correspondence with the types of the 4 types of forest fire danger video images.
In the preferred embodiment, the correspondence between the class 4 scheduling instructions and the classes of the class 4 forest fire risk video images is given in advance, and the emergency plan scheduling unit 207 automatically generates corresponding scheduling instructions according to the class of the video images in the target monitoring area in the future time period determined by the forest fire class determination unit 206 to schedule the fire service terminal 600.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (7)
1. The forest fire prevention dispatching system based on the fusion of the candidate frames is characterized by comprising N video monitoring terminals (100), a central dispatching platform (200), a temperature and humidity monitoring module (300), a wind direction monitoring module (400) and a wind speed monitoring module (500);
the N video monitoring terminals (100) are respectively arranged in the corresponding target monitoring areas and used for acquiring video images in the areas and sending the video images to the central scheduling platform (200);
the temperature and humidity monitoring module (300) is used for collecting the temperature and the humidity in the target monitoring area and sending the collection result to the central dispatching platform (200);
the wind direction monitoring module (400) is used for monitoring the wind direction in the target monitoring area and sending the wind direction monitoring result to the central dispatching platform (200);
the wind speed monitoring module (500) is used for monitoring the wind speed in the target monitoring area and sending the wind speed monitoring result to the central dispatching platform (200);
the central scheduling platform (200) comprises N decoding units (201), a candidate frame feature extraction unit (202), a candidate frame feature fusion unit (203), a video correction unit (204), a data loading unit (205), a forest fire category judgment unit (206), an emergency plan scheduling unit (207), a storage unit (208) and a display unit (209);
the N decoding units (201) correspond to the N video monitoring terminals (100) one by one, and the N decoding units (201) respectively decode video images in areas acquired by the N video monitoring terminals (100) to obtain a video image of each frame;
a candidate frame feature extraction unit (202) which selects a corresponding decoding unit (201) according to the video monitoring candidate instruction, selects a corresponding position of the video image of the corresponding frame decoded by the selected decoding unit (201) according to the candidate frame selection instruction, and sends the obtained candidate frame to a candidate frame feature fusion unit (203);
a candidate frame feature fusion unit (203) for performing feature fusion on features in all selected candidate frames to obtain a preliminarily fused video image;
the video correction unit (204) is used for correcting the preliminarily fused video image to obtain a corrected video image;
the data loading unit (205) is used for loading the acquired temperature, humidity, wind direction monitoring result and wind speed monitoring result into the corrected video image so as to simulate the video image in the target monitoring area in the future time period;
the forest fire category distinguishing unit (206) stores 4 types of forest fire video images, and the categories of the 4 types of forest fire video images are respectively defined as forest fire alarms, general forest fires, major forest fires and extra-large forest fires;
the forest fire category distinguishing unit (206) is used for calculating the correlation between the video images in the target monitoring area in the future time period and the 4 types of forest fire video images respectively to obtain 4 correlation values, and the category of the corresponding forest fire video image with the maximum correlation value is taken as the category of the video images in the target monitoring area in the future time period;
the emergency plan scheduling unit (207) is used for generating a corresponding scheduling instruction according to the type of the video image in the target monitoring area in the future time period to schedule the fire service terminal (600);
the storage unit (208) is used for storing the video images decoded by the N decoding units (201), storing the modified video images output by the video modification unit (204), and storing the video images in the target monitoring area in the future time period simulated by the data loading unit (205);
the display unit (209) is used for calling the video image in the target monitoring area in the future time period simulated by the data loading unit (205), calling the video image decoded by the decoding unit (201) in the storage unit (208) and the modified video image output by the video modification unit (204) through the data loading unit (205), and performing split-screen display.
2. The forest fire prevention dispatching system based on candidate frame fusion of claim 1, wherein the video modification unit (204) modifies the preliminarily fused video image to obtain a modified video image by:
firstly, extracting the background of each frame of image in the preliminarily fused video image, and separating the background area and the motion area in each frame of image in the preliminarily fused video image;
and denoising the motion region in each frame of image, and performing characteristic fusion on the denoised motion region in each frame of image and the corresponding background region to obtain each modified frame of image, thereby obtaining a modified video image.
3. The forest fire prevention dispatching system based on candidate frame fusion as claimed in claim 1, wherein the dispatching system further comprises a detection module (700) for detecting the N video monitoring terminals (100) in real time, and reporting to the central dispatching platform (200) for warning in a wireless transmission mode when detecting that the corresponding video monitoring terminal (100) has no signal output.
4. The forest fire prevention scheduling system based on candidate frame fusion of claim 1, wherein N video monitoring terminals (100) are loadable on respective drones.
5. The forest fire prevention dispatching system based on candidate frame fusion of claim 1, wherein N video monitoring terminals (100) are loadable on a corresponding pan-tilt-control system.
6. The forest fire prevention dispatching system based on candidate frame fusion as claimed in claim 1, wherein the candidate frame feature fusion unit (203) performs feature fusion on features in all selected candidate frames, and the implementation manner of obtaining the preliminarily fused video image is as follows:
and denoising all the selected candidate frames, then extracting high-dimensional features to obtain the high-dimensional image features of all the selected candidate frames, and then performing feature fusion on the high-dimensional image features of all the selected candidate frames to obtain a preliminarily fused video image.
7. The forest fire prevention scheduling system based on candidate frame fusion as claimed in claim 1, wherein 4 types of scheduling instructions are stored in the emergency plan scheduling unit (207), and the 4 types of scheduling instructions are respectively in one-to-one correspondence with the types of the 4 types of forest fire danger video images.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111400714.8A CN114157836A (en) | 2021-11-19 | 2021-11-19 | Forest fire prevention scheduling system based on candidate frame fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111400714.8A CN114157836A (en) | 2021-11-19 | 2021-11-19 | Forest fire prevention scheduling system based on candidate frame fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114157836A true CN114157836A (en) | 2022-03-08 |
Family
ID=80457266
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111400714.8A Pending CN114157836A (en) | 2021-11-19 | 2021-11-19 | Forest fire prevention scheduling system based on candidate frame fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114157836A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114697469A (en) * | 2022-03-15 | 2022-07-01 | 华能大理风力发电有限公司洱源分公司 | Video processing method and device suitable for photovoltaic power station and electronic equipment |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5237308A (en) * | 1991-02-18 | 1993-08-17 | Fujitsu Limited | Supervisory system using visible ray or infrared ray |
| CN103617691A (en) * | 2013-11-11 | 2014-03-05 | 成都市晶林电子技术有限公司 | Forest fire early warning monitoring center |
| CN105096508A (en) * | 2015-07-27 | 2015-11-25 | 中国电子科技集团公司第三十八研究所 | Forest-fire-prevention digital informatization integration command system |
| CN105719421A (en) * | 2016-04-27 | 2016-06-29 | 丛静华 | Big data mining based integrated forest fire prevention informatization system |
| CN108416963A (en) * | 2018-05-04 | 2018-08-17 | 湖北民族学院 | Forest Fire Alarm method and system based on deep learning |
| CN108470424A (en) * | 2018-03-06 | 2018-08-31 | 深圳森阳环保材料科技有限公司 | A kind of forest safety monitoring system based on characteristics of image |
| CN112836608A (en) * | 2021-01-25 | 2021-05-25 | 南京恩博科技有限公司 | Forest fire source estimation model training method, estimation method and system |
-
2021
- 2021-11-19 CN CN202111400714.8A patent/CN114157836A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5237308A (en) * | 1991-02-18 | 1993-08-17 | Fujitsu Limited | Supervisory system using visible ray or infrared ray |
| CN103617691A (en) * | 2013-11-11 | 2014-03-05 | 成都市晶林电子技术有限公司 | Forest fire early warning monitoring center |
| CN105096508A (en) * | 2015-07-27 | 2015-11-25 | 中国电子科技集团公司第三十八研究所 | Forest-fire-prevention digital informatization integration command system |
| CN105719421A (en) * | 2016-04-27 | 2016-06-29 | 丛静华 | Big data mining based integrated forest fire prevention informatization system |
| CN108470424A (en) * | 2018-03-06 | 2018-08-31 | 深圳森阳环保材料科技有限公司 | A kind of forest safety monitoring system based on characteristics of image |
| CN108416963A (en) * | 2018-05-04 | 2018-08-17 | 湖北民族学院 | Forest Fire Alarm method and system based on deep learning |
| CN112836608A (en) * | 2021-01-25 | 2021-05-25 | 南京恩博科技有限公司 | Forest fire source estimation model training method, estimation method and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114697469A (en) * | 2022-03-15 | 2022-07-01 | 华能大理风力发电有限公司洱源分公司 | Video processing method and device suitable for photovoltaic power station and electronic equipment |
| CN114697469B (en) * | 2022-03-15 | 2024-02-06 | 华能大理风力发电有限公司洱源分公司 | Video processing method and device suitable for photovoltaic power station and electronic equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110599727B (en) | Emergency command management system for forest fire | |
| CN101334924B (en) | Fire hazard probe system and its fire hazard detection method | |
| CN111508181A (en) | Forest fire prevention system based on multiple unmanned aerial vehicles and method thereof | |
| CN114792194B (en) | Whole fire control safety intelligent system of building | |
| CN111569346B (en) | Intelligent fire extinguisher remote monitoring and management system | |
| KR20070028813A (en) | Method and system for monitoring forest fire | |
| CN111401581A (en) | Building operation and maintenance management method, system, equipment and storage medium | |
| CN110517441A (en) | Based on the frame-embedded smog of deep learning and flame video alarming system and method | |
| CN114330024A (en) | Digital twin-based fire-fighting drilling method and system | |
| CN110766685A (en) | Power transmission line forest fire monitoring method and system based on remote sensing data cloud detection | |
| CN115063942B (en) | Fire-fighting fire re-ignition monitoring and early warning method and device, electronic equipment and storage medium | |
| CN111598443B (en) | Task allocation method and device for intelligent fire extinguishing system | |
| CN114272548B (en) | Intelligent fire extinguishing equipment for buildings and fire extinguishing method thereof | |
| CN114157836A (en) | Forest fire prevention scheduling system based on candidate frame fusion | |
| Guan et al. | Fire risk assessment and daily maintenance management of cultural relic buildings based on ZigBee technology | |
| CN115662098A (en) | Intelligent building fire-fighting early warning method and system, electronic equipment and storage medium | |
| CN114863489B (en) | Virtual reality-based movable intelligent auxiliary inspection method and system for construction site | |
| CN110853287A (en) | Flame real-time monitoring system and method based on Internet of things distributed architecture | |
| CN114418932A (en) | Historical building repair method and system based on digital twinning technology | |
| CN111539264A (en) | Ship flame detection positioning system and detection positioning method | |
| CN113593170A (en) | Intelligent early warning system based on remote smoke detection | |
| KR20070028812A (en) | Method and apparatus for monitoring forest fire using images photographed by camera device | |
| CN209708270U (en) | A kind of forest fire prevention and control monitoring system convenient for safeguarding | |
| CN111330185A (en) | Ancient building decoration fire prevention method and system | |
| CN113536030B (en) | Intelligent forest fire prevention and extinguishing information management system |
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 | ||
| CB03 | Change of inventor or designer information |
Inventor after: Chen Zhijian Inventor after: Wang Lipeng Inventor after: Su Shenggang Inventor before: Chen Zhijian Inventor before: Wang Lipeng |
|
| CB03 | Change of inventor or designer information | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220308 |
|
| RJ01 | Rejection of invention patent application after publication |