CN118426493B - Unmanned aerial vehicle inspection system and method based on cloud platform - Google Patents
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Abstract
The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle inspection system and method based on a cloud platform, wherein the unmanned aerial vehicle inspection system comprises a growth analysis module, an environment analysis module, an unmanned aerial vehicle inspection arrangement module, an unmanned aerial vehicle inspection acquisition module, an execution analysis module, an execution processing module and a display terminal; according to the invention, the growth state of crops in a target area is judged and analyzed, once abnormal growth trend is found, the evaluation of the external environment state is triggered, under the condition that the normal external environment condition is ensured, the proper unmanned aerial vehicle is intelligently selected and dispatched to carry out the inspection task based on the state parameter of the inspection unmanned aerial vehicle, so that the image set of the crops in the target area is obtained, the health state of the crops in the target area is evaluated based on the image set, and the corresponding execution instruction is generated and executed accordingly, so that the purposes of monitoring the growth condition of the crops in real time and automatically controlling and calling the unmanned aerial vehicle to operate according to the growth condition are realized.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle inspection, in particular to an unmanned aerial vehicle inspection system and method based on a cloud platform.
Background
With the rapid development of agricultural technology and the continuous promotion of large-scale production, the traditional manual inspection mode is worry about the high-efficiency and accurate management requirement of modern agriculture, and the mode not only consumes a great deal of manpower and time, but also is difficult to timely and comprehensively inspect a wide farmland due to the limitation of inspection coverage and frequency, so that the farmland management efficiency is low, and potential problems cannot be timely perceived and treated;
Furthermore, the traditional manual inspection is highly dependent on personal experience and subjective judgment of inspection personnel, so that the evaluation of the growth condition and the pest and disease damage condition of crops is difficult to accurately and quantitatively evaluate, and moreover, the uncertainty of the artificial factors often leads to errors of inspection results, thereby influencing the quality of farmland management;
However, when the crop is damaged, such delayed manual inspection methods tend to be difficult to handle quickly, as the crop may already be damaged to a greater extent when found, and further measures are often difficult to recover the damage.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides an unmanned aerial vehicle inspection system and method based on a cloud platform, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme:
unmanned aerial vehicle inspection system based on cloud platform's unmanned on duty includes:
The growth analysis module is used for judging and analyzing the growth state of crops in the target area to obtain a growth trend abnormal signal, feeding the growth trend abnormal signal back to the cloud platform, and executing the environment analysis module by the cloud platform according to the growth trend abnormal signal;
The environment analysis module is used for analyzing and processing the external environment state of crops in the target area to obtain an external environment normal signal, feeding the external environment normal signal back to the cloud platform, and executing the unmanned aerial vehicle inspection and arrangement module by the cloud platform according to the external environment normal signal;
The unmanned aerial vehicle routing arrangement module is used for monitoring and analyzing the state parameters of each routing inspection unmanned aerial vehicle of the unmanned aerial vehicle base station to obtain the optimal dispatch value of each routing inspection unmanned aerial vehicle, comparing and analyzing the optimal dispatch value of each routing inspection unmanned aerial vehicle with a preset optimal dispatch threshold value, and judging the routing inspection unmanned aerial vehicle of the unmanned aerial vehicle base station as a routing inspection unmanned aerial vehicle which can be dispatched according to a set routing inspection path if the optimal dispatch value of each routing inspection unmanned aerial vehicle is larger than the preset optimal dispatch threshold value;
the unmanned aerial vehicle inspection acquisition module is used for acquiring image data of crops in a target area through the dispatchable inspection unmanned aerial vehicle, constructing an image set, feeding back to the cloud platform, and sending the image set to the execution analysis module by the cloud platform;
The execution analysis module is used for judging and analyzing the health state of crops in the target area based on the image set to obtain an execution type signal, feeding the execution type signal back to the cloud platform, and sending the execution type signal to the execution processing module by the cloud platform;
the execution processing module is used for receiving the execution type signal, so as to perform operation analysis on crops in the target area, obtain corresponding execution instructions according to the execution type signal, and simultaneously execute corresponding operation processing.
Further, the judging and analyzing the growth state of crops in the target area comprises the following specific analysis processes:
The growth cycle of crops in a target area is obtained, and the growth cycle of the crops in the target area is marked as M;
The method comprises the steps of obtaining the wet culture value, the plant height value, the temperature value and the light intensity value in the growth state parameters of crops in a target area within a period of time, and calibrating the values as I represents each monitoring time point in a period of time, i=1, 2,3 … n, n is a positive integer greater than zero, and the values of the four are extracted for normalization processing according to the formula: Obtaining a growth state evaluation coefficient SZP of crops in a target area, wherein, Respectively representing a reference wet culture value, a reference plant height value, a reference temperature value and a reference light intensity value, wherein eta 1, eta 2, eta 3 and eta 4 respectively represent set weight coefficients;
comparing and analyzing the growth state evaluation coefficient of the crops in the target area with a preset growth state evaluation threshold, and generating a growth trend abnormal signal when the growth state evaluation coefficient of the crops in the target area is larger than the preset growth state evaluation threshold.
Further, the analysis processing is performed on the external environment state of the crops in the target area, and the specific analysis process is as follows:
The method comprises the steps of obtaining wind speed, rainfall and visibility of external environment state parameters of crops in a target area in a current monitoring period, extracting a maximum wind speed value, a minimum wind speed value, an average wind speed value, a maximum rainfall value, a minimum rainfall value, an average rainfall value, a maximum visibility value, a minimum visibility value and an average visibility value from the wind speed, the rainfall and the visibility values, and calibrating the wind speed, the rainfall and the visibility values as follows And simultaneously taking the numerical value to perform normalization processing according to the formula: Obtaining an external environment state evaluation coefficient HJP of crops in a target area, wherein, Respectively representing a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index, wherein a1, a2 and a3 respectively represent correction coefficients, and beta 1, beta 2 and beta 3 respectively represent a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index weight coefficient;
Comparing and analyzing the external environment state evaluation coefficient of the crops in the target area with a preset reference comparison interval, and generating an external environment normal signal if the external environment state evaluation coefficient of the crops in the target area is within the preset reference comparison interval.
Further, the monitoring analysis is performed on the state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station, and the specific analysis process is as follows:
The method comprises the steps of obtaining the residual electric quantity, the running times and the interval time length in the state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station, and calibrating the residual electric quantity, the running times and the interval time length as Extracting the three numerical values for normalization processing according to the formula: Obtaining the optimal dispatch value XJP of each inspection unmanned aerial vehicle, wherein λ1, λ2 and λ3 respectively represent the weight coefficients of the residual electric quantity, the running times and the interval duration.
Further, the judging and analyzing the health status of the crops in the target area comprises the following specific analysis processes:
Arranging the image sets of the crops in the target area according to the shooting time sequence to obtain an image sequence of the crops in the target area, and calibrating each image as a detection point to obtain each detection point of the crops in the target area;
Obtaining the number of the pests in each detection point of the crops in the target area, calculating the difference value of the number of the pests in the adjacent detection points to obtain a pest number wave value of the crops in the target area, comparing and analyzing the pest number wave value of the crops in the target area with a preset pest number wave threshold, judging the crops to be in an abnormal state if the pest number wave value of the crops in the target area is larger than the preset pest number wave threshold, counting the number of times of judging the crops to be in the abnormal state and the total number of times of judging the crops to be in the abnormal state, and performing the proportion analysis on the number of times of judging the crops to be in the abnormal state and the total number of times of judging the crops to obtain a pest number dip value;
obtaining a color variation surface average value by acquiring color anomaly areas in each detection point of crops in a target area and carrying out average analysis on the color anomaly areas in each detection point of crops in the target area;
analyzing by acquiring the number of cracks and the numerical value of the area of the cracks of the soil in each detection point of crops in a target area to obtain a crack evaluation value;
Extracting the values of the pest number inclination value, the face mean value of the color change and the crack evaluation value, and carrying out normalization processing to obtain a health state evaluation coefficient of crops in a target area;
Comparing and analyzing the health state evaluation coefficient of the crops in the target area with a health state evaluation threshold value, and generating a health abnormal signal when the health state evaluation coefficient of the crops in the target area is greater than or equal to the health state evaluation threshold value;
According to the generated health abnormal signal, the health state evaluation coefficient of the crops in the target area is called, and the difference value calculation is carried out between the health state evaluation coefficient and the health state evaluation threshold value, so that the health deviation value of the crops in the target area is obtained;
Setting three health deviation gradient comparison intervals of the health deviation value of crops in a target area, wherein the three health deviation gradient comparison intervals are a first gradient health deviation interval, a second gradient health deviation interval and a third gradient health deviation interval respectively;
Generating a first-level execution signal when the health deviation value of the crops in the target area is in a preset first gradient health deviation interval, generating a second-level execution signal when the health deviation value of the crops in the target area is in a preset second gradient health deviation interval, and generating a third-level execution signal when the health deviation value of the crops in the target area is in a preset third gradient health deviation interval;
The execution type signal is constituted by a primary execution signal, a secondary execution signal, and a tertiary execution signal.
Further, the operation analysis is performed on crops in a target area, and the specific analysis process is as follows:
If the first-level execution signal in the execution type signal is captured, triggering an deinsectization execution instruction, and according to the triggered deinsectization execution instruction, scheduling the deinsectization unmanned aerial vehicle to go to a target area for spraying the deinsectization medicine in a set L1 time period;
Triggering a fertilization execution instruction if a secondary execution signal in the execution type signal is captured, and scheduling the fertilization unmanned aerial vehicle to go to a target area for precise fertilization operation in a set L2 time period according to the triggered fertilization execution instruction;
And triggering an irrigation execution instruction if the three-level execution signal in the execution type signal is captured, and scheduling the unmanned irrigation plane to go to a target area for irrigation and spraying operation in a set L3 time period according to the triggered irrigation execution instruction.
Further, an unmanned aerial vehicle inspection method based on a cloud platform comprises the following steps:
Step one: judging and analyzing the growth state of crops in a target area to obtain a growth trend abnormal signal, and executing the second step according to the growth trend abnormal signal;
Step two: analyzing and processing the external environment state of crops in the target area to obtain an external environment normal signal, and executing the third step according to the external environment normal signal;
step three: monitoring and analyzing state parameters of each inspection unmanned aerial vehicle of an unmanned aerial vehicle base station to obtain a priority value of each inspection unmanned aerial vehicle, comparing and analyzing the priority value of each inspection unmanned aerial vehicle with a preset priority threshold, judging the inspection unmanned aerial vehicle of the unmanned aerial vehicle base station as an assignable inspection unmanned aerial vehicle if the priority value of each inspection unmanned aerial vehicle is larger than the preset priority threshold, and forwarding the inspection unmanned aerial vehicle judged as the assignable inspection unmanned aerial vehicle to an inspection target area crop according to a set inspection path;
step four: acquiring image data of crops in a target area through a routing inspection unmanned aerial vehicle, constructing an image set, and executing a fifth step according to the image set;
Step five: based on the image set, judging and analyzing the health state of crops in the target area to obtain an execution type signal, and executing the step six according to the execution type signal;
step six: based on the execution type signal, the crop in the target area is subjected to execution operation analysis, so that a corresponding execution instruction is obtained, and corresponding operation processing is executed.
Compared with the prior art, the technical scheme provided by the invention has the following beneficial effects:
1. According to the invention, the growth state parameters of crops in a target area are monitored in real time, and the parameters are subjected to accurate normalization processing, so that a growth state evaluation coefficient is obtained, when the evaluation coefficient exceeds a preset growth state evaluation threshold, an abnormal signal is immediately sent out to warn that the growth trend of the crops is abnormal, meanwhile, the external environment state parameters of the crops in the target area are closely monitored, and the external environment state evaluation coefficient is obtained through analysis and calculation, if the evaluation coefficient is in a preset reference comparison interval, the current external environment condition is indicated to be suitable for unmanned aerial vehicle inspection, under the condition, the cloud platform automatically starts unmanned aerial vehicle inspection arrangement, and rapidly dispatches unmanned aerial vehicles to carry out detailed on-site investigation on the target area, and the automatic inspection mode not only remarkably improves inspection efficiency, but also effectively reduces labor cost, so that agricultural management is more efficient and accurate;
2. according to the invention, through monitoring the residual electric quantity, the running times and the interval time in the state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station and carrying out normalization processing by combining with the weight coefficient, the optimal dispatch value is obtained, the larger the optimal dispatch value is, the larger the probability that the inspection unmanned aerial vehicle is arranged to go to the crops in the inspection target area is indicated, thereby realizing intelligent dispatching of the unmanned aerial vehicle inspection task, and the decision mode can ensure efficient execution of the inspection task and prolong the service life of the unmanned aerial vehicle;
3. according to the invention, through carrying out accurate health state evaluation on crops in a target area, the growth condition and potential problems of the crops can be reflected in real time, once the abnormal health condition of the crops is found, execution signals of different levels can be generated immediately according to the degree of the health state, so that unmanned aerial vehicles of corresponding types are scheduled to go to the target area for corresponding operation treatment, the accurate evaluation on the health state of the crops is realized, the quick response and effective treatment on different health problems are ensured, and through reasonably utilizing the unmanned aerial vehicles of different types, the continuous optimization on the growth environment of the crops can be realized, thereby promoting the healthy growth of the crops and improving the yield and quality.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is an overall block diagram of the module of the present invention;
Fig. 2 is an overall flow diagram of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, an unmanned aerial vehicle inspection system based on a cloud platform includes: the system comprises a cloud platform, wherein a growth analysis module, an environment analysis module, an unmanned aerial vehicle inspection arrangement module, an unmanned aerial vehicle inspection acquisition module, an execution analysis module, an execution processing module and a display terminal are arranged in the cloud platform;
The cloud platform is responsible for receiving, processing, storing and transmitting data, providing a communication interface with other modules, and ensuring real-time interaction of the data;
The growth analysis module is used for monitoring growth state parameters of crops in a target area, so that the growth state of the crops in the target area is judged and analyzed, and the specific analysis process is as follows:
The method comprises the steps of obtaining a growth period of crops in a target area, dividing the growth period of the crops in the target area into four growth stages, namely a first-order growth period, a second-order growth period, a third-order growth period and a fourth-order growth period, calibrating the growth period of the crops in the target area to be M, wherein M=q1, q2, q3 and q4, q1 represents the number of the first-order growth period, q2 represents the number of the second-order growth period, q3 represents the number of the third-order growth period, and q4 represents the number of the fourth-order growth period;
It should be noted that, the first-order growth period refers to a seed germination period, the set growth duration is [ FS1, FS2 ], the second-order growth period refers to a seedling stage, the set growth duration is [ FS2, FS 3], the third-order growth period refers to a growth period, the set growth duration is [ FS3, FS 4), the fourth-order growth period refers to a maturation period, the set growth duration is [ FS4, FS 5), wherein 0.ltoreq.fs1 < FS2 < FS3 < FS 4.ltoreq.fs5; wherein the setting of the growth time length of each growth cycle is based on the limit value of the growth cycle to ensure the accuracy and practicality of division;
The method comprises the steps of obtaining the wet culture value, the plant height value, the temperature value and the light intensity value in the growth state parameters of crops in a target area within a period of time, and calibrating the values as I represents each monitoring time point in a period of time, i=1, 2,3 … n, n is a positive integer greater than zero, and the values of the four are extracted for normalization processing according to the formula: Obtaining a growth state evaluation coefficient SZP of crops in a target area, wherein, Respectively representing a reference wet culture value, a reference plant height value, a reference temperature value and a reference light intensity value, wherein eta 1, eta 2, eta 3 and eta 4 respectively represent set weight coefficients, and eta 1 is more than eta 2 is more than eta 3 is more than eta 4;
The wet-raising value refers to an evaluation value of the comprehensive state of moisture and nutrients in the crop growth soil in the target area, reflects the suitability of the soil for the crop growth in the target area, and is specifically solved as follows: acquiring the moisture content, the nitrogen content, the phosphorus content and the potassium content in soil through a soil sensor, extracting the values of the moisture content, the nitrogen content, the phosphorus content and the potassium content, multiplying the values by corresponding weight coefficients respectively, and adding the values to obtain a wet culture value; the plant height value refers to the height of the crop growth in the target area, which is specifically the vertical distance of the crop from the ground to its top (typically the tip of the highest leaf or the top of the spike) in the target area; the temperature value refers to the air temperature of the space where crops grow in the target area; the light intensity value refers to the illumination intensity irradiated onto the surface of crops in the target area in unit time;
comparing and analyzing the growth state evaluation coefficient SZP of the crops in the target area with a preset growth state evaluation threshold SY, when the growth state evaluation coefficient SZP of the crops in the target area is larger than the preset growth state evaluation threshold SY, generating a growth trend abnormal signal, otherwise, generating a growth trend normal signal;
The generated growth trend abnormal signals are fed back to the cloud platform, and the cloud platform executes an environment analysis module according to the growth trend abnormal signals;
The environment analysis module is used for monitoring external environment state parameters of crops in a target area, so that the external environment state of the crops in the target area is analyzed and processed, and the specific analysis process is as follows:
The method comprises the steps of obtaining wind speed, rainfall and visibility of external environment state parameters of crops in a target area in a current monitoring period, extracting a maximum wind speed value, a minimum wind speed value, an average wind speed value, a maximum rainfall value, a minimum rainfall value, an average rainfall value, a maximum visibility value, a minimum visibility value and an average visibility value from the wind speed, the rainfall and the visibility values, and calibrating the wind speed, the rainfall and the visibility values as follows And simultaneously taking the numerical value to perform normalization processing according to the formula: Obtaining an external environment state evaluation coefficient HJP of crops in a target area, wherein, Respectively representing a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index, wherein a1, a2 and a3 respectively represent correction coefficients, and beta 1, beta 2 and beta 3 respectively represent wind speed evaluation index, rainfall evaluation index and visibility evaluation index weight coefficients, and beta 1 > beta 2 > beta 3;
Comparing and analyzing the external environment state evaluation coefficient of the crops in the target area with a preset reference comparison interval, if the external environment state evaluation coefficient of the crops in the target area is within the preset reference comparison interval, generating an external environment normal signal, otherwise, generating an external environment abnormal signal;
the generated external environment normal signal is fed back to the cloud platform, and the cloud platform executes the unmanned aerial vehicle inspection arrangement module according to the external environment normal signal;
the unmanned aerial vehicle inspection scheduling module is used for monitoring state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station, so that each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station is subjected to scheduling analysis, and the specific analysis process is as follows:
The method comprises the steps of obtaining the residual electric quantity, the running times and the interval time length in the state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station, and calibrating the residual electric quantity, the running times and the interval time length as Extracting the three numerical values for normalization processing according to the formula: Obtaining a priority value XJP of each inspection unmanned aerial vehicle, wherein λ1, λ2 and λ3 respectively represent the weight coefficients of the residual electric quantity, the running times and the interval duration, and λ1 is more than λ2 and more than λ3; the larger the priority value is, the larger the probability that the unmanned aerial vehicle is arranged to go to the crop in the inspection target area is, the higher the priority value of each unmanned aerial vehicle is compared with a preset priority threshold value, and if the priority value of each unmanned aerial vehicle is larger than the preset priority threshold value, the unmanned aerial vehicle is judged to be the unmanned aerial vehicle capable of being dispatched;
the unmanned aerial vehicle which is judged to be capable of being dispatched to carry out patrol and examine is sent to crops in a target area according to a set patrol and examine path;
The unmanned aerial vehicle inspection acquisition module is used for shooting crops in a target area through equipment carried by the inspection unmanned aerial vehicle, so that an image set of the crops in the target area is obtained, the image set of the crops in the target area is fed back to the cloud platform, and the cloud platform sends the image set of the crops in the target area to the execution analysis module;
the equipment carried by the inspection unmanned aerial vehicle comprises an infrared thermal imager and a high-definition camera, so that crops in a target area can be accurately shot;
The execution analysis module is based on the image set of the crops in the target area, so that the health state of the crops in the target area is judged and analyzed, and the specific analysis process is as follows:
Arranging the image sets of the crops in the target area according to the shooting time sequence to obtain an image sequence of the crops in the target area, and calibrating each image as a detection point to obtain each detection point of the crops in the target area;
Obtaining the number of the pests in each detection point of the crops in the target area, calculating the difference value of the number of the pests in the adjacent detection points to obtain the number of the pests of the crops in the target area, comparing and analyzing the number of the pests of the crops in the target area with a preset number of pests threshold, judging the crops to be in an abnormal state if the number of the pests of the crops in the target area is larger than the preset number of the pests threshold, counting the number of the times of judging the crops to be in the abnormal state and the total number of the times of judging, and calibrating the crops to be in the abnormal state and the total number of the times of judging respectively The number of times of the abnormal state and the total number of times of the judgment are subjected to duty ratio analysis according to the formula: Obtaining the value of the number of the pests Wherein k1 and k2 represent correction coefficients of the number of times of determination as the abnormal state and the total number of times of determination, respectively;
by acquiring the abnormal color area of each detection point of crops in a target area, the target area is calibrated as And carrying out average analysis on the abnormal areas of the colors in each detection point of the crops in the target area according to the formula: Obtaining the face-changing average value Wherein j represents the number of each detection point, and j=1, 2,3 … w, w represents the total number of each detection point number;
Wherein, the abnormal color area refers to the area of the area with obvious difference from the color of normal and healthy crops due to insufficient nutrition, unbalanced water or other factors in each detection point of crops in a target area, and the abnormal color is usually expressed as obvious symptoms such as yellow leaf, red leaf, brown leaf and the like;
By acquiring the number of cracks and the area of cracks of the soil in each detection point of crops in a target area and calibrating the number of cracks and the area of cracks as According to the formula: Obtaining crack evaluation value Wherein δ1 and δ2 represent the number of cracks and the weight coefficient of the area of the crack, respectively;
Extracting the value of the number of the pests Mean value of face changeCrack evaluation valueThe values of (2) are normalized according to the formula: Obtaining a health state evaluation coefficient ZPH of crops in a target area, wherein u represents a natural constant, gamma 1, gamma 2 and gamma 3 respectively represent a pest number inclination value, a color change surface mean value and a weight coefficient of a crack evaluation value, and gamma 1 is more than gamma 2 is more than gamma 3;
setting a health state evaluation threshold value of crops in a target area as ZY, comparing and analyzing the health state evaluation coefficient of the crops in the target area with the health state evaluation threshold value, and generating a health abnormal signal when the health state evaluation coefficient of the crops in the target area is greater than or equal to the health state evaluation threshold value, otherwise, generating a health normal signal;
According to the generated health abnormal signal, the health state evaluation coefficient of the crops in the target area is called, and the difference value calculation is carried out between the health state evaluation coefficient and the health state evaluation threshold value, so that the health deviation value of the crops in the target area is obtained;
Setting three health deviation gradient comparison intervals of the health deviation value of crops in a target area, namely a first gradient health deviation interval qngh, a second gradient health deviation interval qngh and a third gradient health deviation interval qngh3, wherein qngh1 =Φ qngh 2=2Φ qngh3, wherein qngh > qngh2 > qngh3, Φ represents the multiple of the gradient, and the setting of specific numerical values of Φ is specifically set by a person skilled in the art in a specific unmanned aerial vehicle inspection example;
When the health deviation value of the crops in the target area is in a preset first gradient health deviation interval qngh, a first-level execution signal is generated, when the health deviation value of the crops in the target area is in a preset second gradient health deviation interval qngh, a second-level execution signal is generated, and when the health deviation value of the crops in the target area is in a preset third gradient health deviation interval qngh3, a third-level execution signal is generated;
Forming an execution type signal by the first-level execution signal, the second-level execution signal and the third-level execution signal;
feeding the generated execution type signal back to the cloud platform, and sending the execution type signal to the execution processing module by the cloud platform;
The execution processing module is used for receiving the execution type signal, so as to execute operation analysis on crops in a target area, and the specific analysis process is as follows:
if the first-level execution signal in the execution type signal is captured, triggering an insect disinfestation execution instruction, and according to the triggered insect disinfestation execution instruction, scheduling the insect disinfestation unmanned aerial vehicle to go to a target area for spraying the insect disinfestation medicine in a set L1 time period, so that crops are ensured to be prevented and controlled by insect pests in time, and displaying notification is carried out on a display terminal;
Triggering a fertilization execution instruction if a secondary execution signal in the execution type signal is captured, and scheduling the fertilization unmanned aerial vehicle to go to a target area for precise fertilization operation in a set L2 time period according to the triggered fertilization execution instruction, so that crops are helped to obtain necessary nutrition support in time, healthy growth of the crops is promoted, and display notification is carried out on a display terminal;
If the three-level execution signals in the execution type signals are captured, an irrigation execution instruction is triggered, and according to the triggered irrigation execution instruction, the irrigation unmanned aerial vehicle is scheduled to go to a target area for irrigation and spraying operation in a set L3 time period, so that timely irrigation is realized, the requirements of crops on moisture are met, the normal growth of the crops is ensured, and a display terminal is used for displaying and notifying.
As shown in fig. 2, an unmanned aerial vehicle inspection method based on a cloud platform comprises the following steps:
Step one: judging and analyzing the growth state of crops in a target area, wherein the specific analysis process is as follows:
The growth cycle of crops in a target area is obtained, and the growth cycle of the crops in the target area is marked as M;
The method comprises the steps of obtaining the wet culture value, the plant height value, the temperature value and the light intensity value in the growth state parameters of crops in a target area within a period of time, and calibrating the values as I represents each monitoring time point in a period of time, i=1, 2,3 … n, n is a positive integer greater than zero, and the values of the four are extracted for normalization processing according to the formula: Obtaining a growth state evaluation coefficient SZP of crops in a target area, wherein, Respectively representing a reference wet culture value, a reference plant height value, a reference temperature value and a reference light intensity value, wherein eta 1, eta 2, eta 3 and eta 4 respectively represent set weight coefficients;
Comparing and analyzing the growth state evaluation coefficient SZP of the crops in the target area with a preset growth state evaluation threshold SY, and generating a growth trend abnormal signal when the growth state evaluation coefficient SZP of the crops in the target area is larger than the preset growth state evaluation threshold SY;
Step two: the external environment state of crops in the target area is analyzed and treated, and the specific analysis process is as follows:
The method comprises the steps of obtaining wind speed, rainfall and visibility of external environment state parameters of crops in a target area in a current monitoring period, extracting a maximum wind speed value, a minimum wind speed value, an average wind speed value, a maximum rainfall value, a minimum rainfall value, an average rainfall value, a maximum visibility value, a minimum visibility value and an average visibility value from the wind speed, the rainfall and the visibility values, and calibrating the wind speed, the rainfall and the visibility values as follows And simultaneously taking the numerical value to perform normalization processing according to the formula: Obtaining an external environment state evaluation coefficient HJP of crops in a target area, wherein, Respectively representing a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index, wherein a1, a2 and a3 respectively represent correction coefficients, and beta 1, beta 2 and beta 3 respectively represent a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index weight coefficient;
Comparing and analyzing the external environment state evaluation coefficient of the crops in the target area with a preset reference comparison interval, if the external environment state evaluation coefficient of the crops in the target area is within the preset reference comparison interval, generating an external environment normal signal, and executing the third step according to the external environment normal signal;
Step three: each inspection unmanned aerial vehicle of unmanned aerial vehicle base station is arranged and analyzed, and the specific analysis process is as follows:
The method comprises the steps of obtaining the residual electric quantity, the running times and the interval time length in the state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station, and calibrating the residual electric quantity, the running times and the interval time length as Extracting the three numerical values for normalization processing according to the formula: Obtaining a priority value XJP of each inspection unmanned aerial vehicle, wherein λ1, λ2 and λ3 respectively represent the weight coefficients of the residual electric quantity, the running times and the interval duration, and λ1 is more than λ2 and more than λ3; the larger the priority value is, the larger the probability that the unmanned aerial vehicle is arranged to go to the crop in the inspection target area is, the higher the priority value of each unmanned aerial vehicle is compared with a preset priority threshold value, and if the priority value of each unmanned aerial vehicle is larger than the preset priority threshold value, the unmanned aerial vehicle is judged to be the unmanned aerial vehicle capable of being dispatched;
the unmanned aerial vehicle which is judged to be capable of being dispatched to carry out patrol and examine is sent to crops in a target area according to a set patrol and examine path;
Step four: shooting crops in a target area through equipment carried by a routing inspection unmanned aerial vehicle, so as to obtain an image set of the crops in the target area, and executing a step five according to the image set of the crops in the target area;
Step five: based on the image set of the crops in the target area, judging and analyzing the health state of the crops in the target area, wherein the specific analysis process is as follows:
Arranging the image sets of the crops in the target area according to the shooting time sequence to obtain an image sequence of the crops in the target area, and calibrating each image as a detection point to obtain each detection point of the crops in the target area;
Obtaining the number of the pests in each detection point of the crops in the target area, calculating the difference value of the number of the pests in the adjacent detection points to obtain a pest number wave value of the crops in the target area, comparing and analyzing the pest number wave value of the crops in the target area with a preset pest number wave threshold, judging the crops to be in an abnormal state if the pest number wave value of the crops in the target area is larger than the preset pest number wave threshold, counting the number of times of judging the crops to be in the abnormal state and the total number of times of judging the crops to be in the abnormal state, and performing the proportion analysis on the number of times of judging the crops to be in the abnormal state and the total number of times of judging the crops to obtain a pest number dip value;
obtaining a color variation surface average value by acquiring color anomaly areas in each detection point of crops in a target area and carrying out average analysis on the color anomaly areas in each detection point of crops in the target area;
analyzing by acquiring the number of cracks and the numerical value of the area of the cracks of the soil in each detection point of crops in a target area to obtain a crack evaluation value;
Extracting the values of the pest number inclination value, the face mean value of the color change and the crack evaluation value, and carrying out normalization processing to obtain a health state evaluation coefficient of crops in a target area;
Comparing and analyzing the health state evaluation coefficient of the crops in the target area with a health state evaluation threshold value, and generating a health abnormal signal when the health state evaluation coefficient of the crops in the target area is greater than or equal to the health state evaluation threshold value;
According to the generated health abnormal signal, the health state evaluation coefficient of the crops in the target area is called, and the difference value calculation is carried out between the health state evaluation coefficient and the health state evaluation threshold value, so that the health deviation value of the crops in the target area is obtained;
Setting three health deviation gradient comparison intervals of the health deviation value of crops in a target area, wherein the three health deviation gradient comparison intervals are a first gradient health deviation interval, a second gradient health deviation interval and a third gradient health deviation interval respectively;
Generating a first-level execution signal when the health deviation value of the crops in the target area is in a preset first gradient health deviation interval, generating a second-level execution signal when the health deviation value of the crops in the target area is in a preset second gradient health deviation interval, and generating a third-level execution signal when the health deviation value of the crops in the target area is in a preset third gradient health deviation interval;
Forming an execution type signal by the first-stage execution signal, the second-stage execution signal and the third-stage execution signal, and executing the step six according to the execution type signal;
step six: based on the execution type signal, the crop in the target area is subjected to operation analysis, and the specific analysis process is as follows:
If the first-level execution signal in the execution type signal is captured, triggering an deinsectization execution instruction, and according to the triggered deinsectization execution instruction, scheduling the deinsectization unmanned plane to go to a target area for spraying the deinsectization medicine in a set L1 time period, and displaying a notice on a display terminal;
Triggering a fertilization execution instruction if a secondary execution signal in the execution type signal is captured, scheduling the fertilization unmanned aerial vehicle to go to a target area for precise fertilization operation in a set L2 time period according to the triggered fertilization execution instruction, and displaying a notice on a display terminal;
If the three-level execution signals in the execution type signals are captured, triggering an irrigation execution instruction, and according to the triggered irrigation execution instruction, scheduling the unmanned irrigation plane to go to a target area for irrigation and spraying operation in a set L3 time period, and displaying a notice on a display terminal.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (4)
1. Unmanned aerial vehicle inspection system based on cloud platform's unmanned on duty, its characterized in that includes:
The growth analysis module is used for judging and analyzing the growth state of crops in a target area, and the specific analysis is as follows: the growth cycle of crops in a target area is obtained, and the growth cycle of the crops in the target area is marked as M;
Obtaining a wet culture value, a plant height value, a temperature value and a light intensity value in growth state parameters of crops in a target area within a period of time, calibrating the wet culture value, the plant height value, the temperature value and the light intensity value as tsnz M i、zhnzM i、wdnzM i and gqnz M i respectively, wherein i represents each monitoring time point within the period of time, i=1, 2,3 … n, n is a positive integer greater than zero, extracting values of the four values, and carrying out normalization processing according to a formula: Obtaining a growth state evaluation coefficient SZP of crops in a target area, wherein tsnz M *、zhnzM *、wdnzM * and gqnz M * respectively represent a reference wet-growing value, a reference plant height value, a reference temperature value and a reference light intensity value, and eta 1, eta 2, eta 3 and eta 4 respectively represent set weight coefficients;
comparing and analyzing the growth state evaluation coefficient of the crops in the target area with a preset growth state evaluation threshold, generating a growth trend abnormal signal when the growth state evaluation coefficient of the crops in the target area is larger than the preset growth state evaluation threshold, feeding back the growth trend abnormal signal to the cloud platform, and executing an environment analysis module by the cloud platform according to the growth trend abnormal signal;
The environment analysis module is used for analyzing and processing the external environment state of crops in a target area, and the specific analysis is as follows: the method comprises the steps of obtaining wind speed, rainfall and visibility of external environment state parameters of crops in a target area in a current monitoring period, extracting a maximum wind speed value, a minimum wind speed value, an average wind speed value, a maximum rainfall value, a minimum rainfall value, an average rainfall value, a maximum visibility value, a minimum visibility value and an average visibility value from the external environment state parameters, and respectively calibrating the values as FSmax M、FSmin M、FSmpj M、JYmax M、JYmin M、JYmpj M、TGmax M、TGmin M and TG mpj M, and simultaneously taking the values for normalization processing according to a formula: Obtaining an external environment state evaluation coefficient HJP of crops in a target area, wherein FSZ M、JYZM and TGZ M respectively represent a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index, a1, a2 and a3 respectively represent correction coefficients, and beta 1, beta 2 and beta 3 respectively represent a wind speed evaluation index, a rainfall evaluation index and a visibility evaluation index weight coefficient;
Comparing and analyzing the external environment state evaluation coefficient of the crops in the target area with a preset reference comparison interval, if the external environment state evaluation coefficient of the crops in the target area is within the preset reference comparison interval, generating an external environment normal signal, feeding back the external environment normal signal to the cloud platform, and executing the unmanned aerial vehicle inspection and arrangement module by the cloud platform according to the external environment normal signal;
The unmanned aerial vehicle inspection arrangement module is used for monitoring and analyzing the state parameters of each inspection unmanned aerial vehicle of an unmanned aerial vehicle base station, and the specific analysis is as follows: residual electric quantity, running times and interval duration in each inspection unmanned aerial vehicle state parameter of the unmanned aerial vehicle base station are obtained, calibrated to be syz M、yxcM and sjz M respectively, and the values of the three are extracted for normalization processing according to the formula: Obtaining a priority value XJP of each unmanned aerial vehicle, wherein lambda 1, lambda 2 and lambda 3 respectively represent the weight coefficients of the residual electric quantity, the running times and the interval time, comparing and analyzing the priority value of each unmanned aerial vehicle with a preset priority threshold, judging the unmanned aerial vehicle of an unmanned aerial vehicle base station as a routing inspection unmanned aerial vehicle capable of being dispatched, and judging the unmanned aerial vehicle capable of being dispatched to carry out routing inspection according to a set routing inspection path if the priority value of each unmanned aerial vehicle is larger than the preset priority threshold;
the unmanned aerial vehicle inspection acquisition module is used for acquiring image data of crops in a target area through the dispatchable inspection unmanned aerial vehicle, constructing an image set, feeding back to the cloud platform, and sending the image set to the execution analysis module by the cloud platform;
The execution analysis module is used for judging and analyzing the health state of crops in the target area based on the image set to obtain an execution type signal, feeding the execution type signal back to the cloud platform, and sending the execution type signal to the execution processing module by the cloud platform;
the execution processing module is used for receiving the execution type signal, so as to perform operation analysis on crops in the target area, obtain corresponding execution instructions according to the execution type signal, and simultaneously execute corresponding operation processing.
2. The unmanned aerial vehicle inspection system based on the cloud platform according to claim 1, wherein the judgment analysis is performed on the health status of crops in a target area, and the specific analysis process is as follows:
Arranging the image sets of the crops in the target area according to the shooting time sequence to obtain an image sequence of the crops in the target area, and calibrating each image as a detection point to obtain each detection point of the crops in the target area;
Obtaining the number of the pests in each detection point of the crops in the target area, calculating the difference value of the number of the pests in the adjacent detection points to obtain a pest number wave value of the crops in the target area, comparing and analyzing the pest number wave value of the crops in the target area with a preset pest number wave threshold, judging the crops to be in an abnormal state if the pest number wave value of the crops in the target area is larger than the preset pest number wave threshold, counting the number of times of judging the crops to be in the abnormal state and the total number of times of judging the crops to be in the abnormal state, and performing the proportion analysis on the number of times of judging the crops to be in the abnormal state and the total number of times of judging the crops to obtain a pest number dip value;
obtaining a color variation surface average value by acquiring color anomaly areas in each detection point of crops in a target area and carrying out average analysis on the color anomaly areas in each detection point of crops in the target area;
analyzing by acquiring the number of cracks and the numerical value of the area of the cracks of the soil in each detection point of crops in a target area to obtain a crack evaluation value;
Extracting the values of the pest number inclination value, the face mean value of the color change and the crack evaluation value, and carrying out normalization processing to obtain a health state evaluation coefficient of crops in a target area;
Comparing and analyzing the health state evaluation coefficient of the crops in the target area with a health state evaluation threshold value, and generating a health abnormal signal when the health state evaluation coefficient of the crops in the target area is greater than or equal to the health state evaluation threshold value;
According to the generated health abnormal signal, the health state evaluation coefficient of the crops in the target area is called, and the difference value calculation is carried out between the health state evaluation coefficient and the health state evaluation threshold value, so that the health deviation value of the crops in the target area is obtained;
Setting three health deviation gradient comparison intervals of the health deviation value of crops in a target area, wherein the three health deviation gradient comparison intervals are a first gradient health deviation interval, a second gradient health deviation interval and a third gradient health deviation interval respectively;
Generating a first-level execution signal when the health deviation value of the crops in the target area is in a preset first gradient health deviation interval, generating a second-level execution signal when the health deviation value of the crops in the target area is in a preset second gradient health deviation interval, and generating a third-level execution signal when the health deviation value of the crops in the target area is in a preset third gradient health deviation interval;
The execution type signal is constituted by a primary execution signal, a secondary execution signal, and a tertiary execution signal.
3. The unmanned aerial vehicle inspection system based on the cloud platform as claimed in claim 1, wherein the operation analysis is performed on crops in a target area, and the specific analysis process is as follows:
If the first-level execution signal in the execution type signal is captured, triggering an deinsectization execution instruction, and according to the triggered deinsectization execution instruction, scheduling the deinsectization unmanned aerial vehicle to go to a target area for spraying the deinsectization medicine in a set L1 time period;
Triggering a fertilization execution instruction if a secondary execution signal in the execution type signal is captured, and scheduling the fertilization unmanned aerial vehicle to go to a target area for precise fertilization operation in a set L2 time period according to the triggered fertilization execution instruction;
And triggering an irrigation execution instruction if the three-level execution signal in the execution type signal is captured, and scheduling the unmanned irrigation plane to go to a target area for irrigation and spraying operation in a set L3 time period according to the triggered irrigation execution instruction.
4. An unmanned aerial vehicle inspection method based on a cloud platform is characterized by comprising the following steps:
Step one: judging and analyzing the growth state of crops in a target area, wherein the specific analysis is as follows: the method comprises the steps of obtaining wet-growing values, plant height values, temperature values and light intensity values in growth state parameters of crops in a target area within a period of time, extracting values of the wet-growing values, the plant height values, the temperature values and the light intensity values, and carrying out normalization treatment to obtain growth state evaluation coefficients of the crops in the target area;
Comparing and analyzing the growth state evaluation coefficient of the crops in the target area with a preset growth state evaluation threshold, generating a growth trend abnormal signal when the growth state evaluation coefficient of the crops in the target area is larger than the preset growth state evaluation threshold, and executing the second step according to the growth trend abnormal signal;
Step two: the external environment state of crops in a target area is analyzed and treated, and the specific analysis is as follows: the method comprises the steps of obtaining wind speed, rainfall and visibility of external environment state parameters of crops in a target area in a current monitoring period, extracting a maximum wind speed value, a minimum wind speed value, an average wind speed value, a maximum rainfall value, a minimum rainfall value, an average rainfall value, a maximum visibility value, a minimum visibility value and an average visibility value from the wind speed, the rainfall and the visibility parameters, and carrying out normalization processing on the values to obtain an external environment state evaluation coefficient of the crops in the target area;
Comparing and analyzing the external environment state evaluation coefficient of the crops in the target area with a preset reference comparison interval, if the external environment state evaluation coefficient of the crops in the target area is within the preset reference comparison interval, generating an external environment normal signal, and executing the third step according to the external environment normal signal;
Step three: monitoring and analyzing state parameters of each inspection unmanned aerial vehicle of an unmanned aerial vehicle base station, wherein the specific analysis is as follows: extracting the values of the residual electric quantity, the running times and the interval time in the state parameters of each inspection unmanned aerial vehicle of the unmanned aerial vehicle base station, carrying out normalization processing to obtain the optimal dispatch value of each inspection unmanned aerial vehicle, comparing and analyzing the optimal dispatch value of each inspection unmanned aerial vehicle with a preset optimal dispatch threshold value, judging the inspection unmanned aerial vehicle of the unmanned aerial vehicle base station as an assignable inspection unmanned aerial vehicle if the optimal dispatch value of each inspection unmanned aerial vehicle is larger than the preset optimal dispatch threshold value, and carrying out crop inspection on a target area according to a set inspection path by the determined assignable inspection unmanned aerial vehicle;
step four: acquiring image data of crops in a target area through a routing inspection unmanned aerial vehicle, constructing an image set, and executing a fifth step according to the image set;
Step five: based on the image set, judging and analyzing the health state of crops in the target area to obtain an execution type signal, and executing the step six according to the execution type signal;
step six: based on the execution type signal, the crop in the target area is subjected to execution operation analysis, so that a corresponding execution instruction is obtained, and corresponding operation processing is executed.
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