CN111526209A - Forestry big data artificial intelligence analysis system and method - Google Patents
Forestry big data artificial intelligence analysis system and method Download PDFInfo
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Abstract
A forestry big data artificial intelligence analysis system and a method relate to the technical field of forestry data monitoring and comprise the following steps: the system comprises a data acquisition module and a monitoring center, wherein the data acquisition module acquires video data and environmental data of a forest area and sends the acquired video data and environmental data to the monitoring center, the monitoring center is used for receiving the video data and environmental data transmitted by the data acquisition module and analyzing the video data and the environmental data, the data analysis module arranged in the monitoring center is used for analyzing and comparing with a preset threshold value, whether the felling is stolen or not, whether the fire or the disease and pest disasters exist or not is judged according to the comparison result, when the situations of cutting by theft, fire and disease and pest disasters occur, alarm information is sent, the development trend of the disaster is analyzed, the monitoring center distributes the working personnel nearest to the disaster point to go to the site for processing, meanwhile, an optimal route is planned to enable workers to arrive at a disaster point in time, so that the expansion of the disaster is avoided, and the loss caused by the disaster is reduced to the minimum.
Description
Technical Field
The invention relates to the technical field of forestry data monitoring, in particular to an artificial intelligence analysis system and method for forestry big data.
Background
Forest resources are the most basic natural resources, which are the practical conditions of green development and cyclic utilization of social resources, forest resources are damaged by fire, pest and disease disasters and illegal felling, forest fires not only burn a piece of forest and damage animals in the forest, but also reduce the reproductive capacity of the forest, cause poor soil and destroy forest conservation water sources, even cause the ecological environment to lose balance, the forest fires have strong burst property and large destructiveness and cause great loss to the ecological environment of the forest area once the forest fires occur, meanwhile, part of the fires are caused by underground fallen leaves and accumulated temperature rise and are difficult to find and prevent in time, meanwhile, the fire is difficult to predict the development trend of the fire due to the wind direction and the distribution condition of the forest, the fire can not be accurately put out, and the pest and disease disasters mainly refer to the problems of various pests, pathogenic bacteria and the like in the forest, on one hand, diseases and insect pests are mainly caused by pathogen infection in soil, such as withered trees, rotten roots and the like are caused by pathogen infection in soil, on the other hand, periodic disease problems exist during the growth period of various trees, such as locusts easily occurring in spring and summer are representatives of periodic disasters of trees, if the diseases and insect pests are not found in time, a large number of forests are easily killed, illegal cutting can also cause damage to forest regions, the valve stealing by illegal cutting personnel is not easily found due to overlarge forest region range, so that the loss of forestry resources is caused, and the forest regions are too large, so that the forest resources cannot be found in time after the illegal cutting behaviors and the disaster occur, and working personnel cannot arrive at the site in time, so that the forest resources can be found after great loss occurs, and the damage to the forestry resources is caused.
Disclosure of Invention
The embodiment of the invention provides a forestry big data artificial intelligence analysis system and a method, which are used for collecting environmental data and video data of a forest area by using a sensor and video collection equipment, analyzing and monitoring the environmental data and the video data in real time, and arranging personnel to process the environmental data and the video data in time when illegal cutting, fire and disease and pest disasters are found so as to avoid disaster enlargement and reduce loss caused by illegal cutting.
In view of the above problems, the present invention is proposed to provide a forestry big data artificial intelligence analysis system and method.
Forestry big data artificial intelligence analytic system includes: the system comprises a data acquisition module and a monitoring center;
the data acquisition module is used for acquiring video data and environmental data of a forest area and sending the acquired video data and environmental data to the monitoring center;
the monitoring center is used for receiving the video data and the environmental data transmitted by the data acquisition module and analyzing the video data and the environmental data, and comprises a data analysis module, a scheduling module, a storage module and an early warning monitoring module; the data analysis module is used for analyzing and predicting development situation and loss situation of disaster according to the video data and the environmental data; the scheduling module is used for planning a path for the staff to reach a disaster point and scheduling the staff; the storage module is used for storing the video data and the environmental data transmitted by the data acquisition module; and the early warning monitoring module compares the stored preset threshold values and sends out early warning information according to the comparison result.
Further, the data acquisition module comprises a video acquisition device, a temperature sensor, a humidity sensor, a wind speed and direction sensor, a soil temperature and humidity sensor, a smoke sensor and a GPS device, the video acquisition device comprises a high-definition camera and an unmanned aerial vehicle, and the GPS device is carried by a worker and is installed on the unmanned aerial vehicle and used for acquiring the position of the worker and the position of the unmanned aerial vehicle;
further, the data analysis module comprises an image analysis unit, a loss estimation unit, an area division unit and an environmental data analysis unit; the image analysis unit is used for analyzing the collected video data and comparing the video data with the stored historical image to obtain a parameter comparison result; the loss estimation unit is used for estimating the loss caused by the disaster according to the disaster area and the development trend; the area dividing unit is used for dividing the forest area into a plurality of small areas, and the data acquisition module is used for respectively acquiring video data and environmental data of the small areas; the environmental data analysis unit is used for receiving the environmental data acquired by the data acquisition module, comparing the environmental data with a stored preset threshold value to obtain a parameter comparison result, and transmitting the comparison result to the early warning monitoring module.
Further, the scheduling module comprises a path planning unit and a staff planning module; the path planning unit is used for acquiring GIS map information and planning a route for a worker to reach a disaster point and a route withdrawn from the disaster point; the personnel planning module is used for acquiring positioning information of the GPS device and preferentially distributing the workers nearest to the disaster point to go to the site for processing according to the positions of the workers.
Furthermore, the data analysis module analyzes the video data and the environmental data collected by the data acquisition module and then transmits the analyzed data to the early warning monitoring module, and the early warning monitoring module compares the video data and the environmental data according to a stored preset threshold value and sends out early warning information according to a comparison result.
In a second aspect, an embodiment of the present invention provides an artificial intelligence analysis method for forestry big data, including the following steps:
s1, acquiring data, wherein the data acquisition module acquires the environment video data and the environment parameter data of the forest area; transmitting the environmental video data, the environmental parameter data and the environmental video data to a monitoring center;
and S2, analyzing the data, comparing the received environment video data and the environment parameter data according to a stored preset threshold value by the monitoring center, sending out early warning information according to the comparison result, and scheduling the working personnel to go to the disaster point for processing.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
according to the real-time monitoring system, various sensors, video monitoring equipment and the like are applied to a forest area through an internet of things big data technology to form a real-time monitoring system for the forest area, video, temperature, humidity, wind speed and direction, soil temperature and humidity and smoke data of the forest area are collected in real time through a data collection module, the data are analyzed through a data analysis module arranged in a monitoring center and compared with a preset threshold value, whether the situations of pilfering, fire and disease and insect disasters exist or not is judged according to the comparison result, when the situations of pilfering, fire and disease and insect disasters occur, alarm information is sent, the development trend of the disaster is analyzed, the monitoring center distributes workers nearest to the disaster point to go to the site for processing, meanwhile, an optimal route is planned to enable the workers to timely move to the disaster point, expansion of the disaster is avoided, and accordingly loss caused by the disaster is reduced to the minimum.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic view of example 1 of the present invention;
fig. 2 is a schematic view of embodiment 2 of the present invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example one
As shown in fig. 1, an embodiment of the present invention provides an artificial intelligence analysis system for forestry big data, including: the system comprises a data acquisition module and a monitoring center;
the data acquisition module is used for acquiring video data and environmental data of a forest area and sending the acquired video data and environmental data to the monitoring center;
specifically, data acquisition module includes video acquisition device, temperature sensor, humidity transducer, wind speed and direction sensor, soil temperature and humidity sensor, smoke transducer and GPS device, video acquisition device includes high definition digtal camera and unmanned aerial vehicle, the GPS device is hand-carried by the staff for acquire staff real-time position, data acquisition module transmits the scene whole and local real-time video image information and forest area air temperature, air humidity, wind speed, wind direction, soil temperature, soil humidity and the smoke information of the forest zone of gathering for the surveillance center after gathering, and the staff can also real time monitoring forest zone environmental data except can remote video monitoring forest zone real-time video image information, guarantees the safety of forest zone.
The monitoring center is used for receiving the video data and the environmental data transmitted by the data acquisition module and analyzing the video data and the environmental data, and comprises a data analysis module, a scheduling module, a storage module and an early warning monitoring module; the data analysis module is used for analyzing and predicting development situation and loss situation of disaster according to the video data and the environmental data; the scheduling module is used for planning a path for the staff to reach a disaster point and scheduling the staff; the storage module is used for storing the video data and the environmental data transmitted by the data acquisition module; the early warning monitoring module compares the stored preset threshold value and sends out early warning information according to the comparison result;
the data analysis module comprises an image analysis unit, a loss estimation unit, an area division unit and an environmental data analysis unit; the image analysis unit is used for analyzing the acquired video data and comparing the acquired video data with a stored preset threshold value to obtain a parameter comparison result, for example, the video acquisition device sends the acquired video and image information to the image analysis unit, the image analysis unit analyzes the content in the received video and image and compares the content with the stored historical image, the image analysis unit finds specific information of different areas in different areas after comparison, for example, the phenomenon that leaves fall and wither and yellow after the tree has suffered from diseases and insect pests, the possible disasters in the current forest area can be obtained according to specific analysis on the different areas, for example, an empty space can appear after the tree is fell illegally, the image analysis unit analyzes the content in the received video and image and compares the content with the stored historical image to specifically analyze the place where the empty space appears to obtain the comparison result that the tree is fell illegally, the image analysis unit sends the analyzed result to the early warning monitoring module, the early warning monitoring module sends alarm information, and forest region workers can timely arrive at the location for processing after receiving the alarm information; the loss estimation unit is used for estimating the loss caused by the disaster according to the disaster area and the development trend; the loss estimation unit calculates the trend of the fire and the insect disaster according to the forest air temperature, the air humidity, the wind speed, the wind direction, the soil temperature, the soil humidity and the video image information which are acquired by the data acquisition module, estimates the extinguishing time and the loss caused by the fire and the insect disaster according to the acquired environmental data information, for example, when the fire occurs, the loss estimation unit calculates in real time according to the forest air temperature, the air humidity, the wind speed, the wind direction, the soil temperature, the soil humidity and the video image information which are acquired by the data acquisition module and corrects in real time, calculates the proper fire extinguishing point and the time required for fire extinguishing according to the real-time environmental data of the forest, and the worker carries out fire extinguishing work according to the calculated fire extinguishing point, and meanwhile, the loss estimation unit can calculate the loss caused by the fire according to the data, thereby avoiding the trouble of later estimation, the area dividing unit is used for dividing the forest area into a plurality of small areas, and the data acquisition module is used for acquiring video data and environmental data of the small areas respectively, so that the data acquired by the data acquisition module is more accurate; the environment data analysis unit is used for receiving the environment data acquired by the data acquisition module, comparing the environment data with a stored preset threshold value to obtain a parameter comparison result, transmitting the comparison result to the early warning monitoring module, for example, receiving the data of air temperature and air humidity, and transmitting an air temperature and air humidity normal signal to the early warning monitoring module when the received air temperature and air humidity accord with the preset threshold value; when the received air temperature exceeds a preset threshold and the air humidity is lower than the preset threshold, transmitting signals of overhigh air temperature and overlow air humidity to the early warning monitoring module; the early warning monitoring module is compared and analyzed with the threshold value stored in the threshold value library, and alarm information which is easy to catch fire is sent according to the comparison and analysis result, so that forest area real-time detection and certain disaster prevention effects are achieved;
the scheduling module comprises a path planning unit and a personnel planning module; the path planning unit is used for acquiring GIS map information and planning a route for a worker to reach a disaster point and a route withdrawn from the disaster point; the personnel planning module is used for acquiring positioning information of the GPS device, preferentially distributing the workers nearest to the disaster point to the site according to the positions of the workers for processing, for example, an unmanned aerial vehicle at a certain part of a forest area finds a fire, a specific position of the fire is obtained by analyzing GPS device positioning information on the unmanned aerial vehicle, a path planning unit analyzes and calculates and plans an optimal path for a worker to reach a fire point according to the GPS device positioning information on the unmanned aerial vehicle and the GPS device positioning information carried by the worker, a worker planning module arranges the worker nearest to the fire point to rapidly go to the fire point for processing according to the GPS device positioning information on the unmanned aerial vehicle and the GPS device positioning information carried by the worker, meanwhile, other personnel are arranged to go to the fire point according to the fire situation, so that the occurring disaster situation or the robbery situation is treated at the fastest speed, and the loss caused is reduced;
the invention overcomes the defects that the prior forestry management can not find out in time when disasters and artificial robbing phenomena occur, so that larger loss occurs, applies various sensors, video monitoring equipment and the like to a forest area through an Internet of things big data technology to form a real-time forest area monitoring system, acquires video, temperature, humidity, wind speed and wind direction, soil temperature and humidity and smoke data of the forest area in real time through a data acquisition module, analyzes and compares the data with a preset threshold value through a data analysis module arranged in a monitoring center, judges whether the robbing, the fire and the pest disasters exist according to the comparison result, sends alarm information and analyzes the development trend of the disaster when the robbing, the fire and the pest disasters occur, distributes working personnel nearest to the disaster point to carry out processing on site through the monitoring center, and plans an optimal route to lead the working personnel to arrive at the disaster point in time, avoiding the expansion of the disaster situation, thereby reducing the loss caused by the disaster situation to the minimum.
Example two
The embodiment of the invention also discloses an artificial intelligence analysis method for forest industry big data, which comprises the following steps as shown in figure 2:
s1, acquiring data, wherein the data acquisition module acquires the environment video data and the environment parameter data of the forest area; transmitting the environmental video data, the environmental parameter data and the environmental video data to a monitoring center;
specifically, the data acquisition module acquires the whole site and local real-time video image information of the forest area and the air temperature, air humidity, wind speed, wind direction, soil temperature, soil humidity and smoke information of the forest area in real time through the video acquisition device, the temperature sensor, the humidity sensor, the wind speed and the wind direction sensor, the soil temperature and the smoke sensor and the GPS device and then transmits the information to the monitoring center.
And S2, analyzing the data, comparing the received environment video data and the environment parameter data according to a stored preset threshold value by the monitoring center, sending out early warning information according to the comparison result, and scheduling the working personnel to go to the disaster point for processing.
Specifically, a data analysis module, a scheduling module, a storage module and an early warning monitoring module are arranged in the monitoring center, wherein an image analysis unit, a loss estimation unit, a region division unit and an environmental data analysis unit which are arranged in the data analysis module respectively send analysis results to the early warning monitoring module according to the on-site whole and local real-time video image information of the forest region and forest region air temperature, air humidity, wind speed, wind direction, soil temperature, soil humidity and smoke information, the early warning monitoring module compares the received information with a preset threshold value for analysis, sends alarm information or does not process according to the comparison result, when a disaster or a cutting condition occurs, the scheduling module preferentially distributes workers nearest to the disaster point to the on-site for processing according to the positions of the workers, so as to achieve the fastest processing of the disaster or the cutting condition, the loss caused is reduced and the working efficiency is improved.
The forestry big data artificial intelligence analysis method disclosed by the embodiment can monitor environmental data and video data of a forest area in real time, overcomes the defects that the occurrence of disasters and artificial robbery phenomena in the existing forestry management can not be found in time and great loss is caused, applies various sensors, video monitoring and other equipment to the forest area through the Internet of things big data technology to form a forest area real-time monitoring system, acquires video, temperature, humidity, wind speed and wind direction, soil temperature and humidity and smoke data of the forest area in real time through a data acquisition module, analyzes and compares the data with a preset threshold value through a data analysis module arranged in a monitoring center, judges whether the robbery, the fire and the pest disasters exist according to a comparison result, sends alarm information when the robbery, the fire and the pest disasters occur, and analyzes the development trend of the fire and the pest disasters, the monitoring center distributes the working personnel nearest to the disaster point to go to the site for processing, and simultaneously plans an optimal route to enable the working personnel to arrive at the disaster point in time, so that the expansion of the disaster is avoided, and the loss caused by the disaster is reduced to the minimum.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not intended to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (6)
1. Forestry big data artificial intelligence analytic system, its characterized in that includes: the system comprises a data acquisition module and a monitoring center;
the data acquisition module is used for acquiring video data and environmental data of a forest area and sending the acquired video data and environmental data to the monitoring center;
the monitoring center is used for receiving the video data and the environmental data transmitted by the data acquisition module and analyzing the video data and the environmental data, and comprises a data analysis module, a scheduling module, a storage module and an early warning monitoring module; the data analysis module is used for analyzing and predicting development situation and loss situation of disaster according to the video data and the environmental data; the scheduling module is used for planning a path for the staff to reach a disaster point and scheduling the staff; the storage module is used for storing the video data and the environmental data transmitted by the data acquisition module to form historical data; and the early warning monitoring module compares the stored preset threshold values and sends out early warning information according to the comparison result.
2. The forestry big data artificial intelligence analysis system of claim 1, wherein the data acquisition module comprises a video acquisition device, a temperature sensor, a humidity sensor, a wind speed and direction sensor, a soil temperature and humidity sensor, a smoke sensor and a GPS device, the video acquisition device comprises a high definition camera and an unmanned aerial vehicle, and the GPS device is carried by a worker and is installed on the unmanned aerial vehicle for acquiring the position of the worker and the position of the unmanned aerial vehicle.
3. The forestry big data artificial intelligence analysis system of claim 1, wherein the data analysis module comprises an image analysis unit, a loss estimation unit, a region division unit and an environmental data analysis unit; the image analysis unit is used for analyzing the collected video data and comparing the video data with the stored historical image to obtain a parameter comparison result; the loss estimation unit is used for estimating the loss caused by the disaster according to the disaster area and the development trend; the area dividing unit is used for dividing the forest area into a plurality of small areas, and the data acquisition module is used for respectively acquiring video data and environmental data of the small areas; the environmental data analysis unit is used for receiving the environmental data acquired by the data acquisition module, comparing the environmental data with a stored preset threshold value to obtain a parameter comparison result, and transmitting the comparison result to the early warning monitoring module.
4. The forestry big data artificial intelligence analysis system of claim 3, wherein the scheduling module comprises a path planning unit and an personnel planning module; the path planning unit is used for acquiring GIS map information and planning a route for a worker to reach a disaster point and a route withdrawn from the disaster point; the personnel planning module is used for acquiring positioning information of the GPS device and preferentially distributing the workers nearest to the disaster point to go to the site for processing according to the positions of the workers.
5. The forestry big data artificial intelligence analysis system of claim 1, wherein the data analysis module analyzes the video data and the environmental data collected by the data acquisition module and then transmits the analyzed data to the early warning monitoring module, the early warning monitoring module compares the video data and the environmental data according to a stored preset threshold value, and sends out early warning information according to the comparison result.
6. An analysis method applied to the forestry big data artificial intelligence analysis system as claimed in claim 1, characterized by comprising the following steps:
s1, acquiring data, wherein the data acquisition module acquires the environment video data and the environment parameter data of the forest area; transmitting the environmental video data, the environmental parameter data and the environmental video data to a monitoring center;
and S2, analyzing the data, comparing the received environment video data and the environment parameter data according to a stored preset threshold value by the monitoring center, sending out early warning information according to the comparison result, and scheduling the working personnel to go to the disaster point for processing.
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