CN117315492B - Planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology - Google Patents

Planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology Download PDF

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CN117315492B
CN117315492B CN202311604692.6A CN202311604692A CN117315492B CN 117315492 B CN117315492 B CN 117315492B CN 202311604692 A CN202311604692 A CN 202311604692A CN 117315492 B CN117315492 B CN 117315492B
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early warning
crop
data
unmanned aerial
aerial vehicle
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CN117315492A (en
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刘泽平
陈当阳
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Ping An Property and Casualty Insurance Company of China Ltd
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Ping An Property and Casualty Insurance Company of China Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/188—Vegetation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
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    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/467—Encoded features or binary features, e.g. local binary patterns [LBP]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/54—Extraction of image or video features relating to texture
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/62—Extraction of image or video features relating to a temporal dimension, e.g. time-based feature extraction; Pattern tracking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/17—Terrestrial scenes taken from planes or by drones
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/68—Food, e.g. fruit or vegetables
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
    • Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture

Abstract

The invention relates to the technical field of unmanned aerial vehicles, and provides a planting risk early warning method, a system, equipment and a medium based on unmanned aerial vehicle technology, wherein the method comprises the following steps: acquiring planting risk information of crops, and generating an early warning tracking task according to the planting risk information; monitoring crops by using an unmanned aerial vehicle technology based on an early warning tracking task to obtain unmanned aerial vehicle aerial photographing data; performing early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data, and judging whether the early warning data reach a preset early warning threshold value or not; when the early warning data reach an early warning threshold value, analyzing the growth condition of crops according to the early warning data, and judging whether the growth condition reaches a preset risk threshold value; and when the growth condition reaches the risk threshold, damage assessment and early warning are carried out on the crops. The invention can be applied to the field of financial insurance, and timely early warning is carried out on damaged crops, so that measures for protecting the crops are timely taken, the economic loss can be reduced, and the sustainability of financial planting industry insurance can be improved.

Description

Planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a planting risk early warning method, system, equipment and medium based on unmanned aerial vehicle technology.
Background
With the continuous innovative development of agricultural financial insurance and service systems, the application of the planting industry insurance is more and more widespread, but the current planting industry insurance generally has no special early warning mechanism, and financial insurance companies mainly evaluate and determine insurance claim amounts through historical data and statistical models. Therefore, when crops are affected by natural disasters, insect pests and the like, farmers need to make claim requests to the financial insurance companies, provide corresponding evidence and proof materials, and then check and evaluate the products by the financial insurance companies to finally determine whether to pay the claim payment.
However, this way of claim settlement suffers from three problems and disadvantages: firstly, the timeliness is lacking: farmers generally apply for claims to financial insurance companies after crops are lost, which causes time delay, and farmers cannot pay in time; secondly, subjectivity is stronger: farmers need to provide certain evidence and evidence materials, which often need certain subjective judgment and easily cause disputes and disputes; thirdly, it is difficult to accurately evaluate the loss: traditional insurance claim payment methods mainly depend on historical data and statistical models, and certain errors and inaccuracy often exist for actual loss conditions of crops.
In summary, the current agricultural financial insurance lacks an effective early warning mechanism, and cannot discover and predict the risk of planting risk in advance, which also causes that farmers cannot take measures in time when facing planting risk, so as to protect crops and reduce economic losses.
Disclosure of Invention
The invention provides a planting risk early warning method, a planting risk early warning system, planting risk early warning equipment and planting risk early warning media based on unmanned aerial vehicle technology, and aims to solve the problem of how to early warn timely, so that measures for protecting crops can be taken timely, and economic losses can be reduced.
In order to achieve the above purpose, the invention provides a planting risk early warning method based on unmanned aerial vehicle technology, comprising the following steps:
acquiring planting risk information of crops, and generating an early warning tracking task according to the planting risk information;
monitoring the crops by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data;
performing early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data, and judging whether the early warning data reach a preset early warning threshold value or not;
When the early warning data reach the early warning threshold value, analyzing the growth condition of the crops according to the early warning data, and judging whether the growth condition reaches a preset risk threshold value or not;
and when the growth condition reaches the risk threshold, damage assessment and early warning are carried out on the crops.
Optionally, the generating an early warning tracking task according to the planting risk information includes:
determining the name, planting position and growth period of crops according to the planting risk information;
extracting a planting length and a planting width according to the planting position, and calculating a planting area according to the planting length and the planting width;
and formulating tracking time according to the growth period, and creating an early warning tracking task according to the name of the crop, the planting area and the tracking time.
Optionally, the monitoring of the crops by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data includes:
setting an aerial photographing time interval according to the tracking duration in the early warning and tracking task;
according to the tracking range of the early warning tracking task and the aerial photography time interval, carrying out high-resolution aerial photography on the crops by utilizing the unmanned aerial vehicle technology to obtain aerial photography crop images;
Extracting crop features from the aerial crop images to obtain crop features;
and carrying out data analysis on the crop characteristics to obtain unmanned aerial vehicle aerial photographing data.
Optionally, the extracting crop features from the aerial crop image to obtain crop features includes:
performing rotation and scaling transformation on the aerial crop image to obtain a transformed crop image;
performing texture recognition on the transformed crop image to obtain crop texture features;
performing edge detection on the transformed crop image to obtain crop edge characteristics;
and integrating the crop texture features and the crop edge features to obtain crop features.
Optionally, the performing early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data includes:
generating an early warning analysis graph according to the aerial photographing time interval and the unmanned aerial vehicle aerial photographing data;
setting a trend line and a warning line, and extracting abnormal unmanned aerial vehicle aerial photographing data corresponding to unmanned aerial vehicle aerial photographing data in the early warning analysis graph according to the trend line and the warning line;
taking the abnormal unmanned aerial vehicle aerial photographing data as early warning data.
Optionally, the analyzing the growth condition of the crop according to the early warning data includes:
Performing difference calculation according to the early warning data and preset standard data to obtain a data difference;
identifying a growth stage and a development trend corresponding to the crops according to the data difference value;
and analyzing the crop growth vigor corresponding to the crops according to the growth stage and the development trend, and evaluating the growth condition of the crops according to the crop growth vigor.
Optionally, the damage assessment and early warning for the crops includes:
positioning the crops to obtain damaged positions, and carrying out loss evaluation on the crops based on the damaged positions to obtain crop losses;
and extracting a target crop name corresponding to the crop, generating an early warning notice according to the target crop name, the damaged position and the crop loss, and carrying out early warning according to the early warning notice.
In order to solve the above problems, the present invention further provides a planting risk early warning system based on unmanned aerial vehicle technology, the system comprising:
the task generating module is used for acquiring planting risk information of crops and generating an early warning tracking task according to the planting risk information;
the data monitoring module is used for monitoring the crops by utilizing a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data;
The early warning analysis module is used for carrying out early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data and judging whether the early warning data reach a preset early warning threshold value or not;
the growth condition analysis module is used for analyzing the growth condition of the crops according to the early warning data when the early warning data reach the early warning threshold value and judging whether the growth condition reaches a preset risk threshold value or not;
and the crop early warning module is used for carrying out damage assessment and early warning on the crops when the growth condition reaches the risk threshold.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the above-described unmanned aerial vehicle technology-based planting risk early warning method.
In order to solve the above-mentioned problems, the present invention further provides a computer readable storage medium, in which at least one computer program is stored, the at least one computer program being executed by a processor in an electronic device to implement the planting risk early warning method based on unmanned aerial vehicle technology.
According to the embodiment of the invention, the early warning and tracking task can be accurately generated through the planting risk information; the unmanned aerial vehicle technology is utilized to monitor crops based on the early warning tracking task, so that unmanned aerial vehicle aerial photographing data can be accurately obtained, and monitoring efficiency and accuracy are improved; early warning analysis is carried out on unmanned aerial vehicle aerial photographing data, so that early warning data can be accurately obtained, economic loss is reduced, and early warning timeliness is improved; the growth condition of crops can be accurately analyzed through the early warning data, and treatment measures can be timely taken; through damage assessment and early warning to crops, can improve the management efficiency of crops, improve accuracy and the efficiency of settlement claim to improve the sustainability of financial planting industry insurance. Therefore, the planting risk early warning method, system, equipment and medium based on the unmanned aerial vehicle technology can solve the problem of how to early warn in time, so that measures for protecting crops can be taken in time, and further economic loss is reduced.
Drawings
Fig. 1 is a schematic flow chart of a planting risk early warning method based on unmanned aerial vehicle technology according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of generating an early warning and tracking task according to planting risk information according to an embodiment of the present invention;
Fig. 3 is a schematic flow chart of monitoring crops by using a preset unmanned aerial vehicle technology based on an early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a planting risk early warning system based on unmanned aerial vehicle technology according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device for implementing the planting risk early warning method based on the unmanned aerial vehicle technology according to an embodiment of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides a planting risk early warning method based on unmanned aerial vehicle technology. The execution main body of the planting risk early warning method based on unmanned aerial vehicle technology comprises at least one of electronic equipment, such as a server side and a terminal, which can be configured to execute the method provided by the embodiment of the application. In other words, the planting risk early warning method based on the unmanned aerial vehicle technology can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a planting risk early warning method based on unmanned aerial vehicle technology according to an embodiment of the present invention is shown. In this embodiment, the planting risk early warning method based on unmanned aerial vehicle technology includes:
s1, acquiring planting risk information of crops, and generating an early warning tracking task according to the planting risk information.
In the embodiment of the invention, the crops comprise wheat, corn, rice, oil crops, sorghum and the like; the planting insurance refers to financial planting industry insurance, and is one of financial products; the planting risk information refers to information related to the materialization cost occurring in the growing period of crops as an insurance target, wherein the materialization cost comprises the cost of seeds, fertilizers, pesticides, irrigation, machine tillage, mulching films and the like; the planting risk information comprises a crop name, an insurance label category, planting risk duration, a crop period, insurer information and the like.
Referring to fig. 2, in the embodiment of the present invention, the generating an early warning tracking task according to the planting risk information includes:
s21, determining the name, the planting position and the growth period of crops according to the planting risk information;
s22, extracting a planting length and a planting width according to the planting position, and calculating a planting area according to the planting length and the planting width;
S23, formulating tracking time according to the growth period, and creating an early warning tracking task according to the name of the crop, the planting area and the tracking time.
In the embodiment of the invention, the name of the participating crops, the geographical position where the crops are planted, namely the planting position, and the corresponding growth period of the crops are extracted from the planting risk information, wherein the growth period refers to the time required from sowing to harvesting of the crops.
Further, measuring by using a preset measuring instrument according to the planting position to obtain planting length and planting width; and calculating the product according to the planting length and the planting width to obtain a planting area, and specifically calculating the planting area by using the following formula:
wherein,representing the planting area,/->Representing the planting length,/->Representing the planting width.
In the embodiment of the invention, the growth period corresponding to the crop is set as the tracking duration corresponding to the early warning tracking task, for example, the growth period is 1 month, and the tracking duration corresponding to the early warning tracking task is set as 1 month.
In the embodiment of the invention, the name of the early warning tracking task, such as a wheat tracking task, is formulated according to the name of the crop; setting a tracking range according to the planting area; and generating an early warning tracking task according to the name of the early warning tracking task, the tracking range and the tracking time length, and recording real-time data of the early warning tracking task.
In the embodiment of the invention, the early warning and tracking task can be accurately generated according to the planting risk information, so that the tracking efficiency is improved, and early warning is timely carried out.
S2, monitoring the crops by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data.
In the embodiment of the invention, the unmanned aerial vehicle technology refers to an unmanned aerial vehicle system, an unmanned aerial vehicle working process and an unmanned aerial vehicle related application technology, and is a technology for performing task execution by controlling an aircraft remotely or automatically.
Referring to fig. 3, in the embodiment of the present invention, the monitoring of the crop by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data includes:
s31, setting an aerial photography time interval according to the tracking duration in the early warning and tracking task;
s32, carrying out high-resolution aerial photography on the crops by utilizing the unmanned aerial vehicle technology according to the tracking range of the early warning tracking task and the aerial photography time interval to obtain aerial photography crop images;
s33, extracting crop features of the aerial crop image to obtain crop features;
s34, carrying out data analysis on the crop characteristics to obtain unmanned aerial vehicle aerial photographing data.
In the embodiment of the invention, the tracking duration is segmented according to the preset segmentation duration to obtain a plurality of segmentation time points, and the time interval between two adjacent segmentation time points is used as an aerial photographing time interval, for example, the segmentation duration is set to be one hour, and the tracking duration is divided into a plurality of segmentation time periods according to one hour, namely, the aerial photographing time interval is one hour.
In the embodiment of the invention, the unmanned aerial vehicle technology refers to a sensor carried by an unmanned aerial vehicle, wherein the sensor comprises sensors such as multispectral sensor, infrared sensor and the like; shooting the crops in the tracking range by utilizing a sensor corresponding to the unmanned aerial vehicle according to the aerial shooting time interval to obtain aerial shooting crop images, wherein the aerial shooting crop images comprise characteristic images such as vegetation states, soil humidity, pest and disease conditions and the like; the multispectral sensor can collect spectral data of a plurality of wave bands, and indexes such as chlorophyll content, moisture content and physiological conditions of crops can be analyzed through the spectral data.
In the embodiment of the present invention, the crop feature extraction is performed on the aerial crop image to obtain a crop feature, including:
Performing rotation and scaling transformation on the aerial crop image to obtain a transformed crop image;
performing texture recognition on the transformed crop image to obtain crop texture features;
performing edge detection on the transformed crop image to obtain crop edge characteristics;
and integrating the crop texture features and the crop edge features to obtain crop features.
In the embodiment of the invention, the aerial crop image can be horizontally or vertically rotated, and the rotated aerial crop image is scaled according to the preset scaling proportion, so that the obtained transformed crop image is more accurate; and performing texture recognition on the transformed crop image by using an LBP method or a gray level co-occurrence matrix method to obtain crop texture features.
Further, a Canny operator edge detection method or an edge detector can be adopted to carry out edge detection on the transformed crop image, so as to obtain crop edge characteristics; and summarizing the crop texture characteristics and the crop edge characteristics to obtain crop characteristics.
In the embodiment of the invention, the index data corresponding to the crops are analyzed according to the characteristics of the crops, wherein the index data comprise index data such as chlorophyll content, moisture content, physiological conditions and the like; and analyzing whether relevant data such as plant diseases and insect pests are contained or not according to the characteristics of the crops, such as the number of the plant diseases and insect pests, the hazard level and the like; finally, analyzing soil data corresponding to the crops according to the characteristics of the crops, wherein the soil data comprise soil humidity and the like; and summarizing the index data, the insect pest related data and the soil data to obtain unmanned aerial vehicle aerial photography data.
In the embodiment of the invention, the crop is monitored by using the unmanned aerial vehicle technology based on the early warning and tracking task, and unmanned aerial vehicle aerial photographing data can be accurately obtained, so that the possible planting risk is early warned more accurately, and the monitoring efficiency and the monitoring precision are higher.
S3, carrying out early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data, and judging whether the early warning data reach a preset early warning threshold value or not.
In the embodiment of the present invention, the performing early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data includes:
generating an early warning analysis graph according to the aerial photographing time interval and the unmanned aerial vehicle aerial photographing data;
setting a trend line and a warning line, and extracting abnormal unmanned aerial vehicle aerial photographing data corresponding to unmanned aerial vehicle aerial photographing data in the early warning analysis graph according to the trend line and the warning line;
taking the abnormal unmanned aerial vehicle aerial photographing data as early warning data.
In the embodiment of the invention, the aerial photography time interval is taken as an abscissa, and the unmanned aerial vehicle aerial photography data is taken as an ordinate to establish an early warning analysis curve graph; the trend line refers to a normal curve corresponding to the unmanned aerial vehicle aerial photographing data; the warning line refers to an early warning curve corresponding to the unmanned aerial vehicle aerial photographing data; when the unmanned aerial vehicle aerial photographing data float in a left normal range and a right normal range preset by the trend line and do not reach the warning line, the unmanned aerial vehicle aerial photographing data are indicated to be normal data; when the unmanned aerial vehicle aerial photographing data is on the warning line or exceeds the warning line, the unmanned aerial vehicle aerial photographing data is represented as abnormal data, namely abnormal unmanned aerial vehicle aerial photographing data, and the abnormal unmanned aerial vehicle aerial photographing data is used as early warning data.
In the embodiment of the invention, the early warning threshold value refers to a preset minimum value which needs early warning; further, judging whether the early warning data reach the early warning threshold value refers to judging whether the early warning data reach the minimum value, when the early warning data reach the early warning threshold value, sending an early warning signal through the unmanned aerial vehicle technology, notifying farmers and claimants according to the early warning signal, and executing S4; and when the early warning data does not reach the early warning threshold value, returning to S2.
In the embodiment of the invention, the early warning analysis is carried out on the unmanned aerial vehicle aerial photographing data, the early warning data can be accurately obtained, whether the early warning data reach the early warning threshold value is judged, the early warning timeliness can be improved, and therefore farmers can take measures in time, and the loss is reduced.
And when the early warning data does not reach the early warning threshold value, returning to S2.
In the embodiment of the invention, when the early warning data does not reach the early warning threshold, returning to S2 refers to continuously executing the step of monitoring the crops by using a preset unmanned aerial vehicle technology based on the early warning tracking task to obtain unmanned aerial vehicle aerial photographing data, and then performing early warning analysis on the unmanned aerial vehicle aerial photographing data.
And when the early warning data reach the early warning threshold, executing S4, analyzing the growth condition of the crops according to the early warning data, and judging whether the growth condition reaches a preset risk threshold or not.
In the embodiment of the invention, when the early warning data reaches the early warning threshold value, the early warning data is abnormal, and the growth condition of the crops needs to be further analyzed, so that the accuracy and timeliness of early warning are ensured.
In an embodiment of the present invention, the analyzing the growth condition of the crop according to the early warning data includes:
performing difference calculation according to the early warning data and preset standard data to obtain a data difference;
identifying a growth stage and a development trend corresponding to the crops according to the data difference value;
and analyzing the crop growth vigor corresponding to the crops according to the growth stage and the development trend, and evaluating the growth condition of the crops according to the crop growth vigor.
In the embodiment of the invention, the standard data refers to normal data corresponding to the growth of the crops; and analyzing the growth stage of the crop according to the data difference, comparing the growth stage with a standard growth stage corresponding to the standard data, and judging whether the crop is abnormal in development, for example, in the case of wheat, the early warning data show that the growth stage is in the trefoil stage, but the standard growth stage is in the overwintering stage, so that the crop is abnormal in development, namely the development trend may be delayed development or decay.
Further, extracting crop data of a current growth stage corresponding to the crop, wherein the crop data comprises plant height, plant density, leaf number, dry matter accumulation amount and the like, comparing the crop data and the development trend with preset standard crop data to obtain crop growth vigor corresponding to the crop, performing hazard grade division on the crop growth vigor to obtain crop hazard grade, and taking the crop hazard grade as the growth condition of the crop.
In the embodiment of the invention, the risk threshold refers to the corresponding hazard degree of the crops, namely judging whether the growth condition reaches the hazard degree, and returning to the step S2 when the growth condition does not reach the hazard degree; and when the growth condition reaches the hazard degree, a danger is required, the early warning and tracking task is ended, and S5 is executed.
According to the embodiment of the invention, the growth condition of the crops can be accurately analyzed according to the early warning data, the influence of subjective factors is avoided, and the accuracy and the efficiency of claim settlement are improved.
And returning to S2 when the growth condition does not reach the risk threshold.
In the embodiment of the invention, when the early warning data does not reach the early warning threshold, returning to S2 refers to continuously executing the step of monitoring the crops by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data.
And when the growth condition reaches the risk threshold, executing S5 to damage the crops and early warning.
In the embodiment of the invention, when the growth condition reaches the risk threshold, damage assessment and early warning are required to be carried out on the crops, so that the accuracy of claim settlement is improved, the cost is reduced, and the data management efficiency is improved; meanwhile, timely early warning and control measures can reduce economic loss of farmers and improve the sustainability of financial planting industry insurance.
In the embodiment of the present invention, the damage assessment and early warning for the crops includes:
positioning the crops to obtain damaged positions, and carrying out loss evaluation on the crops based on the damaged positions to obtain crop losses;
and extracting a target crop name corresponding to the crop, generating an early warning notice according to the target crop name, the damaged position and the crop loss, and carrying out early warning according to the early warning notice.
In the embodiment of the invention, as the unmanned aerial vehicle is provided with the high-precision positioning system, the high-precision positioning system can be adopted to position the crops to obtain the damaged positions corresponding to the crops; and extracting the growth data of crops corresponding to the damaged positions, and calculating the current crop loss corresponding to the crops according to the growth data and the pre-acquired historical profit.
Further, an early warning table is generated according to the name of the target crop, the damaged position and the crop loss, the early warning table is used as an early warning notice, and the early warning notice is visualized to realize early warning of the crop.
In the embodiment of the invention, damage assessment and early warning are carried out on the crops, evidence and proof materials can be provided, and the accuracy and efficiency of claim settlement are improved.
According to the embodiment of the invention, the early warning and tracking task can be accurately generated through the planting risk information; the unmanned aerial vehicle technology is utilized to monitor crops based on the early warning tracking task, so that unmanned aerial vehicle aerial photographing data can be accurately obtained, and monitoring efficiency and accuracy are improved; early warning analysis is carried out on unmanned aerial vehicle aerial photographing data, so that early warning data can be accurately obtained, economic loss is reduced, and early warning timeliness is improved; the growth condition of crops can be accurately analyzed through the early warning data, and treatment measures can be timely taken; through damage assessment and early warning to crops, can improve the management efficiency of crops, improve accuracy and the efficiency of settlement claim to improve the sustainability of financial planting industry insurance. Therefore, the planting risk early warning method based on the unmanned aerial vehicle technology can solve the problem of how to early warn in time, so that measures for protecting crops can be taken in time, and further economic loss is reduced.
Fig. 4 is a functional block diagram of a planting risk early warning system based on unmanned aerial vehicle technology according to an embodiment of the present invention.
The planting risk early warning system 400 based on the unmanned aerial vehicle technology can be installed in electronic equipment. Depending on the functions implemented, the planting risk early warning system 400 based on unmanned aerial vehicle technology may include a task generating module 401, a data monitoring module 402, an early warning analysis module 403, a growth status analysis module 404, and a crop early warning module 405. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
the task generating module 401 is configured to obtain planting risk information of crops, and generate an early warning tracking task according to the planting risk information;
the data monitoring module 402 is configured to monitor the crop by using a preset unmanned aerial vehicle technology based on the early warning and tracking task, so as to obtain unmanned aerial vehicle aerial photography data;
the early warning analysis module 403 is configured to perform early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data, and determine whether the early warning data reaches a preset early warning threshold;
The growth condition analysis module 404 is configured to analyze the growth condition of the crop according to the early warning data when the early warning data reaches the early warning threshold, and determine whether the growth condition reaches a preset risk threshold;
the crop early warning module 405 is configured to perform damage assessment and early warning on the crop when the growth status reaches the risk threshold.
In detail, each module in the planting risk early warning system 400 based on the unmanned aerial vehicle technology in the embodiment of the present invention adopts the same technical means as the planting risk early warning method based on the unmanned aerial vehicle technology in the drawings when in use, and can produce the same technical effects, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device for implementing a planting risk early warning method based on unmanned aerial vehicle technology according to an embodiment of the present invention.
The electronic device 500 may comprise a processor 501, a memory 502, a communication bus 503 and a communication interface 504, and may further comprise a computer program stored in the memory 502 and executable on the processor 501, such as a planting risk early warning program based on unmanned aerial vehicle technology.
The processor 501 may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), a microprocessor, a digital processing chip, a graphics processor, a combination of various control chips, and so on. The processor 501 is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, executes or executes programs or modules stored in the memory 502 (e.g., executes a planting risk early warning program based on unmanned aerial vehicle technology, etc.), and invokes data stored in the memory 502 to perform various functions of the electronic device and process data.
The memory 502 includes at least one type of readable storage medium including flash memory, a removable hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 502 may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory 502 may also be an external storage device of the electronic device in other embodiments, for example, a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like. Further, the memory 502 may also include both internal storage units and external storage devices of the electronic device. The memory 502 may be used to store not only application software installed in an electronic device and various data, such as codes of a planting risk early warning program based on unmanned aerial vehicle technology, but also temporarily store data that has been output or is to be output.
The communication bus 503 may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable connected communication between the memory 502 and the at least one processor 501 etc.
The communication interface 504 is used for communication between the electronic device and other devices, including network interfaces and user interfaces. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
Fig. 5 illustrates only an electronic device having components, and it will be appreciated by those skilled in the art that the configuration illustrated in fig. 5 is not limiting of the electronic device 500 and may include fewer or more components than illustrated, or may combine certain components, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and the power source may be logically connected to the at least one processor 501 through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The planting risk early warning program based on unmanned aerial vehicle technology stored in the memory 502 of the electronic device 500 is a combination of a plurality of instructions, which when executed in the processor 501, can implement:
acquiring planting risk information of crops, and generating an early warning tracking task according to the planting risk information;
monitoring the crops by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data;
Performing early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data, and judging whether the early warning data reach a preset early warning threshold value or not;
when the early warning data reach the early warning threshold value, analyzing the growth condition of the crops according to the early warning data, and judging whether the growth condition reaches a preset risk threshold value or not;
and when the growth condition reaches the risk threshold, damage assessment and early warning are carried out on the crops.
In particular, the specific implementation method of the above instruction by the processor 501 may refer to the description of the relevant steps in the corresponding embodiment of the drawings, which is not repeated herein.
Further, the modules/units integrated with the electronic device 500 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
acquiring planting risk information of crops, and generating an early warning tracking task according to the planting risk information;
monitoring the crops by using a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data;
performing early warning analysis on the unmanned aerial vehicle aerial photographing data to obtain early warning data, and judging whether the early warning data reach a preset early warning threshold value or not;
when the early warning data reach the early warning threshold value, analyzing the growth condition of the crops according to the early warning data, and judging whether the growth condition reaches a preset risk threshold value or not;
and when the growth condition reaches the risk threshold, damage assessment and early warning are carried out on the crops.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.

Claims (6)

1. The planting risk early warning method based on the unmanned aerial vehicle technology is characterized by comprising the following steps of:
Acquiring planting risk information of crops, and generating an early warning tracking task according to the planting risk information, wherein the early warning tracking task comprises the following steps: determining a crop name, a planting position and a growth period according to the planting risk information, extracting a planting length and a planting width according to the planting position, calculating a planting area according to the planting length and the planting width, formulating a tracking time length according to the growth period, and creating the early warning tracking task according to the crop name, the planting area and the tracking time length;
based on the early warning tracking task, the crop is monitored by using a preset unmanned aerial vehicle technology to obtain unmanned aerial vehicle aerial photographing data, and the method comprises the following steps: setting an aerial photography time interval according to the tracking duration in the early warning and tracking task, carrying out high-resolution aerial photography on the crops according to the tracking range of the early warning and tracking task and the aerial photography time interval by using the unmanned aerial vehicle technology to obtain aerial photography crop images, carrying out crop feature extraction on the aerial photography crop images to obtain crop features, and carrying out data analysis on the crop features to obtain unmanned aerial vehicle aerial photography data;
taking an aerial photography time interval of the unmanned aerial vehicle aerial photography data as an abscissa, taking the unmanned aerial vehicle aerial photography data as an ordinate to construct an early warning analysis graph, analyzing the early warning analysis graph to obtain early warning data, and judging whether the early warning data reach a preset early warning threshold value, wherein analyzing the early warning analysis graph to obtain the early warning data comprises the following steps: setting a trend line and a warning line, extracting abnormal unmanned aerial vehicle aerial photographing data corresponding to unmanned aerial vehicle aerial photographing data in the early warning analysis graph according to the trend line and the warning line, and taking the abnormal unmanned aerial vehicle aerial photographing data as the early warning data;
When the early warning data reach the early warning threshold value, analyzing the growth condition of the crops according to the early warning data, and judging whether the growth condition reaches a preset risk threshold value or not;
when the growth condition reaches the risk threshold, a positioning system of the unmanned aerial vehicle is utilized to position the damaged position of the crops to obtain the damaged position, loss evaluation is carried out on the crops based on the damaged position to obtain crop loss, a target crop name corresponding to the crops is extracted, an early warning notice is generated according to the target crop name, the damaged position and the crop loss, and early warning is carried out according to the early warning notice.
2. The planting risk early warning method based on unmanned aerial vehicle technology as set forth in claim 1, wherein the performing crop feature extraction on the aerial crop image to obtain crop features includes:
performing rotation and scaling transformation on the aerial crop image to obtain a transformed crop image;
performing texture recognition on the transformed crop image to obtain crop texture features;
performing edge detection on the transformed crop image to obtain crop edge characteristics;
and integrating the crop texture features and the crop edge features to obtain crop features.
3. The unmanned aerial vehicle technology-based planting risk early warning method according to claim 1, wherein the analyzing the growth condition of the crop according to the early warning data comprises:
performing difference calculation according to the early warning data and preset standard data to obtain a data difference;
identifying a growth stage and a development trend corresponding to the crops according to the data difference value;
and analyzing the crop growth vigor corresponding to the crops according to the growth stage and the development trend, and evaluating the growth condition of the crops according to the crop growth vigor.
4. Planting danger early warning system based on unmanned aerial vehicle technique, characterized in that, the system includes:
the task generating module is used for acquiring planting risk information of crops and generating an early warning tracking task according to the planting risk information, and comprises the following steps: determining a crop name, a planting position and a growth period according to the planting risk information, extracting a planting length and a planting width according to the planting position, calculating a planting area according to the planting length and the planting width, formulating a tracking time length according to the growth period, and creating the early warning tracking task according to the crop name, the planting area and the tracking time length;
The data monitoring module is used for monitoring crops by utilizing a preset unmanned aerial vehicle technology based on the early warning and tracking task to obtain unmanned aerial vehicle aerial photographing data, and comprises the following components: setting an aerial photography time interval according to the tracking duration in the early warning and tracking task, carrying out high-resolution aerial photography on the crops according to the tracking range of the early warning and tracking task and the aerial photography time interval by using the unmanned aerial vehicle technology to obtain aerial photography crop images, carrying out crop feature extraction on the aerial photography crop images to obtain crop features, and carrying out data analysis on the crop features to obtain unmanned aerial vehicle aerial photography data;
the early warning analysis module is used for taking the aerial photography time interval of the unmanned aerial vehicle aerial photography data as an abscissa and taking the unmanned aerial vehicle aerial photography data as an ordinate to construct an early warning analysis graph, analyzing the early warning analysis graph to obtain early warning data and judging whether the early warning data reaches a preset early warning threshold value, wherein the analyzing the early warning analysis graph to obtain the early warning data comprises the following steps: setting a trend line and a warning line, extracting abnormal unmanned aerial vehicle aerial photographing data corresponding to unmanned aerial vehicle aerial photographing data in the early warning analysis graph according to the trend line and the warning line, and taking the abnormal unmanned aerial vehicle aerial photographing data as the early warning data;
The growth condition analysis module is used for analyzing the growth condition of the crops according to the early warning data when the early warning data reach the early warning threshold value and judging whether the growth condition reaches a preset risk threshold value or not;
and the crop early warning module is used for positioning the damaged position of the crop by using a positioning system of the unmanned aerial vehicle to obtain the damaged position when the growth condition reaches the risk threshold, carrying out loss evaluation on the crop based on the damaged position to obtain crop loss, extracting a target crop name corresponding to the crop, generating an early warning notice according to the target crop name, the damaged position and the crop loss, and carrying out early warning according to the early warning notice.
5. An electronic device, the electronic device comprising:
at least one processor; the method comprises the steps of,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the unmanned aerial vehicle technology-based planting risk early warning method of any one of claims 1 to 3.
6. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the unmanned aerial vehicle technology-based planting risk early warning method according to any one of claims 1 to 3.
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