High-yield and high-efficiency rape cultivation integrated treatment method and system

Abstract

The invention discloses a high-yield and high-efficiency rape cultivation integrated treatment method and system, and belongs to the technical field of vegetable cultivation. Obtaining an unmanned aerial vehicle monitoring performance influence factor and an unmanned aerial vehicle adjustment parameter set, carrying out unmanned aerial vehicle adjustment, obtaining aerial thermal imaging parameters of the rape field, and carrying out analysis to obtain a thermal imaging complex index; the deviation difference value is compared with a preset difference value interval in a database, and an unmanned aerial vehicle monitoring judgment result is obtained; if the unmanned aerial vehicle monitoring judgment result shows that monitoring is qualified, unmanned aerial vehicle monitoring adjustment is not executed, and a rape abnormity judgment result is output, and if the unmanned aerial vehicle monitoring judgment result shows that monitoring is unqualified, matching is carried out based on a deviation difference value to obtain a deviation adjustment parameter set, and therefore a final rape correction output result is obtained and displayed. The problem that the rape yield and the rape quality are reduced due to the fact that rape supervision is not timely in the prior art is solved.

Description

Technical Field

[0001] The invention relates to the technical field of vegetable cultivation, and in particular to a high-yield and high-efficiency rapeseed cultivation integrated processing method and system. Background Art

[0002] With the rapid development of agricultural modernization, rapeseed has been highly valued as an important economic crop. Since the growth of rapeseed is easily affected by many factors, the rapeseed cultivation process needs to be strictly controlled. The existing rapeseed cultivation system achieves rapeseed cultivation management through integrated water and fertilizer and temperature control or through shading cover, seedling management, etc.

[0003] For example, the invention patent with publication number: CN112005784B discloses a three-dimensional cultivation system for leafy vegetables and its implementation method, which includes: a three-dimensional cultivation tray, a cultivation shed, a water-fertilizer integrated system, a temperature system and an Internet of Things control system. A cultivation shed is installed on the upper end of the three-dimensional cultivation tray. The cultivation shed is an installation carrier for the water-fertilizer integrated system and the temperature system, and covers the top of the three-dimensional cultivation tray to form a closed cultivation space. The cultivation shed includes a shed body and a drive assembly. The shed body is installed on the drive assembly. The drive assembly, the water-fertilizer integrated system and the temperature system are all connected to the Internet of Things control system.

[0004] For example, the invention patent with publication number: CN104855170B discloses a method for raising seedlings in a frame-type blanket of rapeseed, which includes: preparing a breeding site; making a breeding frame; laying soil; sowing; watering; shading and covering; managing seedlings; transplanting; and removing the frame. The method for raising seedlings in a frame-type blanket of rapeseed of the present invention breaks through the traditional idea of ​​raising seedlings in seedling trays. The seedling frame is made of low-cost and reusable materials, which solves the problems of high cost, low efficiency, and short seedlings in raising blanket of rapeseed using standard rice seedling trays, thereby reducing the cost of raising seedlings and raising blanket of rapeseed with adjustable length.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] In the prior art, the cultivation and management of rapeseed usually relies on manual on-site analysis and judgment. However, it is difficult to supervise a large area of ​​rapeseed in a timely manner when manually cultivating and managing rapeseed, and a large amount of manpower is required. When rapeseed is infected with diseases and insect pests, the quality and yield of rapeseed will be affected due to untimely supervision of rapeseed. Therefore, there is a problem of reduced rapeseed yield and rapeseed quality due to untimely supervision of rapeseed. Summary of the invention

[0007] The embodiment of the present application solves the problem of reduced rapeseed yield and quality due to untimely rapeseed supervision in the prior art by providing a high-yield and efficient integrated processing method and system for rapeseed cultivation, and achieves accurate and efficient monitoring of abnormal conditions in rapeseed fields.

[0008] The embodiment of the present application provides a high-yield and high-efficiency integrated processing method for rapeseed cultivation, comprising the following steps: using an intelligent plant protection robot to conduct patrol monitoring of rapeseed fields, obtain surface abnormality parameters of the rapeseed fields, and analyze them to obtain surface abnormality threat indicators; using drone technology to conduct aerial patrol monitoring of rapeseed fields, obtain drone monitoring performance influencing parameters, analyze to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby performing drone adjustments, and obtaining aerial thermal imaging parameters of rapeseed fields, and analyzing to obtain thermal imaging complex indicators; analyzing the surface abnormality threat indicators and thermal imaging complex indicators to obtain deviation differences, and comparing them with the difference intervals preset in the database to obtain drone monitoring judgment results; if the drone monitoring judgment result is that the monitoring is qualified, the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the drone monitoring judgment result is that the monitoring is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, thereby adjusting the drone equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed, which is displayed.

[0009] Furthermore, the method of obtaining surface anomaly parameters of a rapeseed field and analyzing them to obtain surface anomaly threat indicators comprises the following specific steps: sampling the rapeseed in the rapeseed field to obtain surface anomaly parameters of the rapeseed field, wherein the surface anomaly parameters of the rapeseed field include the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height of each sampled rapeseed; obtaining the current planting cycle of the rapeseed, and obtaining a surface anomaly reference set corresponding to each planting cycle interval of the rapeseed preset in the database, obtaining a surface anomaly reference set corresponding to the current planting cycle of the rapeseed by matching, recorded as a designated surface anomaly reference set, and analyzing it with the surface anomaly parameters of the rapeseed field to obtain a surface anomaly threat indicator; the designated surface anomaly reference set includes a reference value for the number of leaf spots, a reference value for the spot diameter, a reference value for the leaf area and a reference value for the plant height; the surface anomaly threat indicator is used to characterize the degree of abnormality in the surface monitoring growth of the rapeseed.

[0010] Furthermore, the method of obtaining the parameters affecting the monitoring performance of the UAV and analyzing the factors affecting the monitoring performance of the UAV and a set of adjustment parameters of the UAV comprises the following specific steps: obtaining the parameters affecting the monitoring performance of the UAV, wherein the parameters affecting the monitoring performance of the UAV include light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization and electromagnetic interference intensity; obtaining the UAV monitoring reference set preset in the database, and analyzing it with the parameters affecting the monitoring performance of the UAV to obtain the UAV monitoring influence index; obtaining the factors affecting the monitoring performance of the UAV and a set of adjustment parameters of the UAV based on the UAV monitoring influence index and matching it with the database; the UAV monitoring reference set includes a reference value for light intensity, a reference value for wind speed, a reference value for humidity, a reference value for wireless signal strength, a reference value for data transmission speed, a reference value for spectrum utilization and a reference value for electromagnetic interference intensity; the UAV monitoring influence index is used to characterize the degree to which the UAV is affected during the detection of rapeseed.

[0011] Furthermore, the specific method for obtaining the drone monitoring impact index is as follows:

[0012]

[0013] Wherein, WJ represents the impact index of UAV monitoring, WQ represents light intensity, ΔWQ represents the reference value of light intensity, WF represents wind speed, ΔWF represents the reference value of wind speed, WS represents humidity, ΔWS represents the reference value of humidity, WX represents wireless signal strength, ΔWX represents the reference value of wireless signal strength, WC represents data transmission speed, ΔWC represents the reference value of data transmission speed, WP represents spectrum utilization, ΔWP represents the reference value of spectrum utilization, WR represents electromagnetic interference intensity, ΔWR represents the reference value of electromagnetic interference intensity, and e represents a natural constant.

[0014] Furthermore, based on the UAV monitoring impact index and matching with the database, the UAV monitoring performance impact factor and the UAV adjustment parameter set are obtained. The specific steps include: obtaining each UAV monitoring impact index interval preset in the database and the UAV monitoring performance impact reference factor and the UAV pre-adjustment parameter set corresponding to each UAV monitoring impact index interval, and matching with the UAV monitoring impact index; if the UAV monitoring impact index is within a certain UAV monitoring impact index interval, then obtaining the UAV monitoring performance impact reference factor and the UAV pre-adjustment parameter set corresponding to the interval, and marking them as UAV monitoring performance impact factors and UAV adjustment parameter sets; the UAV adjustment parameter set includes the camera resolution of the UAV and the network transmission signal strength.

[0015] Furthermore, the aerial thermal imaging parameters of the rapeseed field are obtained and analyzed to obtain the thermal imaging complexity index, and the specific steps include: obtaining the aerial thermal imaging parameters of the rapeseed field, the aerial thermal imaging parameters of the rapeseed field include the imaging temperature of each rapeseed sampling point, the area of ​​abnormal temperature region and the density of abnormal temperature distribution blocks; obtaining an aerial thermal imaging reference set preset in a database, and analyzing it with the aerial thermal imaging parameters of the rapeseed field to obtain the thermal imaging complexity index; the aerial thermal imaging reference set includes an imaging temperature reference value, an abnormal temperature region area reference value and an abnormal temperature distribution block density reference value; the thermal imaging complexity index is used to characterize the degree of thermal imaging abnormality of the rapeseed field.

[0016] Furthermore, the specific steps of obtaining the drone monitoring judgment result include: obtaining each surface abnormal threat index interval preset in the database and the surface abnormal threat reference value corresponding to each surface abnormal threat index interval, and matching them with the surface abnormal threat index; if the surface abnormal threat index is within a certain surface abnormal threat index interval, then obtaining the surface abnormal threat reference value corresponding to the surface abnormal threat index interval as the surface abnormal threat value; obtaining each thermal imaging complex index interval preset in the database and the thermal imaging complex reference value corresponding to each thermal imaging complex index interval, and matching them with the thermal imaging complex index; if the thermal imaging complex index is within a certain thermal imaging complex index interval, then obtaining the thermal imaging complex reference value corresponding to the thermal imaging complex index interval as the thermal imaging complex value; performing difference processing on the surface abnormal threat value and the thermal imaging complex value. The method further comprises the following steps: first, obtaining a preset deviation difference interval in the database and comparing it with the deviation difference; second, obtaining a preset deviation difference interval in the database and comparing it with the deviation difference; third, if the deviation difference is within the deviation difference interval, the UAV monitoring judgment result is qualified detection, and the UAV monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; fourth, if the deviation difference exceeds the deviation difference interval, the UAV monitoring judgment result is unqualified detection; fifth, obtaining a preset surface abnormality threat threshold and a thermal imaging complexity threshold in the database and comparing them with the surface abnormality threat index and the thermal imaging complexity index respectively; fourth, if the surface abnormality threat index is less than the surface abnormality threat threshold and the thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed abnormality judgment result is normal rapeseed; fifth, if the surface abnormality threat index is above the surface abnormality threat threshold or the thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed abnormality judgment result is rapeseed abnormality.

[0017] Furthermore, if the UAV monitoring judgment result is that the monitoring is unqualified, a deviation adjustment parameter set is obtained by matching based on the deviation difference, and the specific steps include: obtaining each deviation difference interval preset in the database and a deviation adjustment reference set corresponding to each deviation difference interval; if the UAV monitoring judgment result is that the monitoring is unqualified, the deviation difference is compared with each deviation difference interval, and if the deviation difference is within a preset deviation difference interval, the deviation adjustment reference set corresponding to the interval is obtained as the deviation adjustment parameter set.

[0018] Furthermore, the drone equipment is adjusted and a secondary aerial patrol monitoring is performed to obtain the final corrected output result of the rapeseed, which is displayed. The specific steps include: a secondary adjustment of the drone equipment based on the deviation adjustment parameter set, a secondary aerial patrol monitoring is performed through the drone equipment after the secondary adjustment, a secondary thermal imaging parameter is obtained, and a thermal imaging complexity index is analyzed again based on the secondary thermal imaging parameters, and recorded as a secondary execution thermal imaging complexity index; an analysis is performed based on the surface abnormality threat index, the secondary execution thermal imaging complexity index, the surface abnormality threat threshold and the thermal imaging complexity threshold to obtain the final corrected output result, and the final corrected output result is displayed; the secondary execution thermal imaging complexity index is used to characterize the degree of thermal imaging anomaly of the rapeseed detected after the secondary adjustment.

[0019] The embodiment of the present application provides a high-yield and high-efficiency rapeseed cultivation integrated processing system, including: a surface assessment module, a thermal imaging assessment module, a drone monitoring and judgment module, and a secondary adjustment module; wherein the surface assessment module is used to use an intelligent plant protection robot to conduct patrol monitoring of rapeseed fields, obtain surface abnormality parameters of rapeseed fields, and analyze to obtain surface abnormality threat indicators; the thermal imaging assessment module is used to use drone technology to conduct aerial patrol monitoring of rapeseed fields, obtain drone monitoring performance influencing parameters, analyze to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby adjusting drones, and obtaining aerial thermal parameters of rapeseed fields. Image parameters are analyzed to obtain complex thermal imaging indicators; the UAV monitoring and judgment module is used to analyze the surface abnormal threat indicators and the thermal imaging complex indicators to obtain the deviation difference, and compare it with the difference interval preset in the database to obtain the UAV monitoring judgment result; the secondary adjustment module is used to not perform the UAV monitoring adjustment if the UAV monitoring judgment result is that the monitoring is qualified, and output the rapeseed abnormal judgment result; if the UAV monitoring judgment result is that the monitoring is unqualified, the deviation adjustment parameter set is obtained based on the deviation difference, thereby adjusting the UAV equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed and display it.

[0020] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0021] 1. The present invention provides a high-yield and high-efficiency integrated processing method for rapeseed cultivation. By performing difference processing on the abnormal surface threat value and the complex thermal imaging value, and comparing them with the difference interval preset in the database, the unmanned aerial vehicle monitoring and judgment result is obtained, and the unmanned aerial vehicle equipment is adjusted according to the monitoring and judgment result and a secondary aerial patrol monitoring is performed, thereby realizing accurate and efficient monitoring of abnormal conditions in rapeseed fields, and effectively solving the problem of reduced rapeseed yield and quality caused by untimely rapeseed supervision in the prior art.

[0022] 2. The present invention obtains each deviation difference interval preset in the database and the deviation adjustment reference set corresponding to each deviation difference interval, and compares them with the deviation difference to obtain a deviation adjustment parameter set, and performs a secondary adjustment on the UAV, and then performs a secondary aerial patrol monitoring to obtain the final corrected output result, thereby avoiding the problem of inaccurate rapeseed status monitoring caused by unreasonable network or equipment configuration during the monitoring process of the UAV.

[0023] 3. By obtaining the parameters affecting the UAV monitoring performance and matching them with the preset UAV monitoring reference set in the database, the UAV monitoring performance influencing factors and the UAV adjustment parameter set are obtained, so that the UAV equipment can be adjusted according to the UAV adjustment parameter set, thereby ensuring the stability and adaptability of the UAV equipment in different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A flow chart of a high-yield and high-efficiency rapeseed cultivation integrated processing method provided in an embodiment of the present application;

[0025] Figure 2 A schematic diagram of the structure of a high-yield and high-efficiency rapeseed cultivation integrated processing system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application solves the problem of reduced rapeseed yield and quality due to untimely rapeseed supervision in the prior art by providing a high-yield and efficient integrated processing method and system for rapeseed cultivation, thereby achieving accurate and efficient monitoring of abnormal conditions in rapeseed fields.

[0027] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0028] like Figure 1As shown, it is a flow chart of a high-yield and high-efficiency rapeseed cultivation integrated processing method provided by an embodiment of the present application, the method comprising the following steps: using an intelligent plant protection robot to conduct patrol monitoring of rapeseed fields, obtain surface abnormality parameters of the rapeseed fields, and analyze to obtain surface abnormality threat indicators; using drone technology to conduct aerial patrol monitoring of rapeseed fields, obtain drone monitoring performance influencing parameters, analyze to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby performing drone adjustments, and obtaining aerial thermal imaging parameters of rapeseed fields, and analyzing to obtain thermal imaging complex indicators; analyzing the surface abnormality threat indicators and thermal imaging complex indicators to obtain deviation differences, and comparing them with the difference intervals preset in the database to obtain drone monitoring judgment results; if the drone monitoring judgment result is that the monitoring is qualified, the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output; if the drone monitoring judgment result is that the monitoring is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, thereby adjusting the drone equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed, which is displayed.

[0029] In this embodiment, by introducing a combination of intelligent plant protection robots and drone technology, rapeseed fields are efficiently monitored and data analyzed, thereby achieving real-time monitoring and accurate evaluation of the growth status of rapeseed. By obtaining abnormal surface parameters of rapeseed fields through intelligent plant protection robots, and combining drone aerial thermal imaging technology to obtain aerial thermal imaging parameters of rapeseed fields, the growth status and potential risks of rapeseed can be comprehensively evaluated. The drone equipment is adjusted according to the parameters affecting the drone monitoring performance to ensure the accuracy and stability of the monitoring results, which not only improves the supervision efficiency of rapeseed, reduces labor input, and reduces the impact of pests and diseases on rapeseed yield and quality, but also enables intelligent management of the rapeseed cultivation process, significantly improving the production efficiency and quality of rapeseed.

[0030] By obtaining the abnormal surface parameters of rapeseed (such as the number of leaf spots, spot diameter, and the proportion of leaf missing area, etc.), it can directly reflect the growth status of rapeseed plants, timely discover abnormal problems caused by factors such as pests and diseases, and achieve high-precision ground monitoring, helping farmers to adjust their planting strategies in a timely manner. It can more accurately display the growth status of rapeseed and is easier for agricultural managers to understand and operate. Thermal imaging technology can reveal information such as plant moisture conditions, photosynthesis efficiency, and vegetation coverage by capturing the temperature changes of rapeseed leaves in rapeseed fields. It has significant advantages in monitoring rapeseed under extreme weather conditions such as low temperature and drought. Complex thermal imaging indicators can provide comprehensive performance of rapeseed in terms of heat distribution and temperature anomalies. Thermal imaging can quickly scan a large area of ​​rapeseed fields without contacting rapeseed plants, greatly improving monitoring efficiency. Compared with traditional manual inspections, thermal imaging can not only cover a wider area, but also monitor under any weather conditions, providing all-weather data support.

[0031] Furthermore, surface anomaly parameters of the rapeseed field are obtained, and the surface anomaly threat index is obtained by analysis. The specific steps include: sampling the rapeseed in the rapeseed field to obtain the surface anomaly parameters of the rapeseed field, the surface anomaly parameters of the rapeseed field include the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height of each sampled rapeseed; obtaining the current planting cycle of the rapeseed surface anomaly, and obtaining the constant reference set corresponding to each planting cycle interval of the rapeseed preset in the database, and obtaining the surface anomaly reference set corresponding to the current planting cycle of the rapeseed by matching, recorded as the designated surface anomaly reference set, and analyzed with the surface anomaly parameters of the rapeseed field to obtain the surface anomaly threat index; the designated surface anomaly reference set includes a reference value for the number of leaf spots, a reference value for the spot diameter, a reference value for the leaf area and a reference value for the plant height; the surface anomaly threat index is used to characterize the degree of abnormality in the surface monitoring growth of rapeseed.

[0032] In this embodiment, by sampling the rapeseed field and obtaining multiple surface abnormality parameters (such as the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height, etc.), the growth condition of the rapeseed can be evaluated in many aspects, which helps to identify problems in the growth, such as pests and diseases, nutrient deficiencies and other problems.

[0033] By obtaining the current planting cycle of rapeseed and matching it with the preset surface anomaly reference set in the database, it is possible to automatically determine the deviation between the current growth status of rapeseed and historical data, thus achieving standardization and quantification of the growth status of rapeseed fields, reducing the subjectivity and errors of manual evaluation, and improving the efficiency and accuracy of monitoring. Through timely analysis of surface anomaly threat indicators, abnormal phenomena in rapeseed growth can be detected early, especially in the early growth stage of rapeseed. For example, if abnormal leaf spots appear or the plant height is lower than the reference value, potential pests and diseases or poor growth factors can be quickly identified through data comparison and matching, thereby achieving early warning.

[0034] The number of leaf spots can be obtained by using an intelligent plant protection robot to take rapeseed pictures with a camera, and then automatically identifying them using image processing software (such as OpenCV). The maximum diameter of the spots and the average leaf area can be automatically extracted by using image analysis software (such as Photoshop) after the intelligent plant protection robot takes rapeseed pictures with a camera. The plant height can be monitored and counted by the intelligent plant protection robot emitting radar waves.

[0035] The surface abnormality threat index is obtained by analyzing the surface abnormality parameters of rapeseed fields, taking into account the mutual influence relationship between these parameters. For example, the more spots there are and the larger the maximum diameter of the spots, the more serious the rapeseed has suffered from pests and diseases or abnormal growth. The leaf area reflects the photosynthesis and overall growth of rapeseed. Rapeseed will be controlled at different growth stages of the plant. The height of the plant is closely related to its overall nutritional status and growth environment. If the leaf area is small, there are more spots, and the plant height is low, it means that the growth of rapeseed has been affected by adverse factors (such as pests and diseases), resulting in abnormal growth. If the rapeseed plants are too tall, they will easily fall over.

[0036] Obtaining surface anomaly threat indicators, the specific methods include:

[0037]

[0038] Where DY represents the surface anomaly threat index, i represents the number of the sampled rapeseed, i = 1, 2, 3, ..., i max ,i max Indicates the total number of sampled rapeseed, YB i represents the number of leaf spots of the i-th sampled rapeseed, ΔYB represents the reference value of leaf spot number, and YZ i represents the maximum diameter of the spot of the i-th sampled rapeseed, ΔYZ represents the reference value of the spot diameter, and YS i represents the average leaf area of ​​the i-th sampled rapeseed, ΔYS represents the reference value of leaf area, and YH i represents the plant height of the i-th sampled rapeseed, ΔYH represents the reference value of plant height, and e represents the natural constant.

[0039] Furthermore, the parameters affecting the monitoring performance of the UAV are obtained, and the factors affecting the monitoring performance of the UAV and the set of adjustment parameters of the UAV are obtained through analysis. The specific steps include: obtaining the parameters affecting the monitoring performance of the UAV, which include light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization and electromagnetic interference intensity; obtaining the preset UAV monitoring reference set in the database, and analyzing it with the parameters affecting the monitoring performance of the UAV to obtain the UAV monitoring impact index; obtaining the factors affecting the monitoring performance of the UAV and the set of adjustment parameters of the UAV based on the UAV monitoring impact index and matching it with the database; the UAV monitoring reference set includes light intensity reference value, wind speed reference value, humidity reference value, wireless signal strength reference value, data transmission speed reference value, spectrum utilization reference value and electromagnetic interference intensity reference value; the UAV monitoring impact index is used to characterize the degree to which the UAV is affected during the detection of rapeseed.

[0040] In this embodiment, the light intensity can be measured by a light sensor, the wind speed can be measured by an anemometer, the humidity can be measured by a humidity sensor, the wireless signal strength and the data transmission speed can be measured by a network speed meter (such as Fluke Networks OptiView XG Network Analysis Tablet, Chinese name: Fujitsu Network OptiView XG Network Analyzer), the spectrum utilization can be analyzed by a spectrum analyzer (such as Keysight Technologies N9010A X-Series Signal Analyzers, Chinese name: Jabil Technology N9010A Series Signal Analyzer), and the electromagnetic interference intensity can be measured by using an electromagnetic interference intensity measuring instrument. The spectrum utilization refers to the proportion of the allocated frequency resources (such as radio spectrum) in a certain frequency band that is actually used to transmit information. It measures the efficiency of spectrum use, that is, the ratio between the amount of data actually transmitted and the total amount of available frequency in the frequency band within a certain frequency band.

[0041] Furthermore, the impact index of drone monitoring is obtained. The specific method is as follows:

[0042]

[0043] Wherein, WJ represents the impact index of UAV monitoring, WQ represents light intensity, ΔWQ represents the reference value of light intensity, WF represents wind speed, ΔWF represents the reference value of wind speed, WS represents humidity, ΔWS represents the reference value of humidity, WX represents wireless signal strength, ΔWX represents the reference value of wireless signal strength, WC represents data transmission speed, ΔWC represents the reference value of data transmission speed, WP represents spectrum utilization, ΔWP represents the reference value of spectrum utilization, WR represents electromagnetic interference intensity, ΔWR represents the reference value of electromagnetic interference intensity, and e represents a natural constant.

[0044] In this embodiment, the UAV monitoring influencing index is obtained by analyzing the parameters affecting the UAV monitoring performance (light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization and electromagnetic interference intensity), taking into account the mutual influence relationship between these parameters, for example: light intensity directly affects the image acquisition quality, and too strong or too weak light will reduce the image quality, thereby affecting the detection accuracy of rapeseed. Wind speed will affect the stability of the UAV. When the wind speed is high, the UAV flight will be unstable, thereby affecting the accuracy of the monitoring results. In a high humidity environment, the flight control difficulty increases, and it will affect the clarity of the camera shooting and thus cause monitoring errors. When the signal strength is weak, the data transmission speed will be reduced, resulting in transmission delay or loss of real-time monitoring information, affecting the monitoring efficiency. Excessive spectrum resources will cause signal interference, thereby affecting the stability of data transmission. The electromagnetic interference intensity affects the wireless communication and sensor operation of the UAV. Strong electromagnetic interference will affect the control and data collection accuracy of the UAV.

[0045] By acquiring and analyzing the parameters that affect the monitoring performance of UAVs (including light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization and electromagnetic interference intensity), the potential impact of environmental factors on the monitoring performance of UAVs is evaluated, which facilitates the analysis of the extent to which the UAVs are affected. When environmental factors change, UAV equipment or network equipment can be adjusted in time, enabling UAV equipment to monitor the status of rapeseed more efficiently and with higher quality.

[0046] Furthermore, based on the UAV monitoring impact index and matching with the database, the UAV monitoring performance impact factor and the UAV adjustment parameter set are obtained. The specific steps include: obtaining each UAV monitoring impact index interval preset in the database and the UAV monitoring performance impact reference factor and the UAV pre-adjustment parameter set corresponding to each UAV monitoring impact index interval, and matching them with the UAV monitoring impact index. If the UAV monitoring impact index is within a certain UAV monitoring impact index interval, the UAV monitoring performance impact reference factor and the UAV pre-adjustment parameter set corresponding to the interval are obtained, and marked as the UAV monitoring performance impact factor and the UAV adjustment parameter set; the UAV adjustment parameter set includes the UAV's camera resolution and network transmission signal strength.

[0047] In this embodiment, by matching the drone monitoring impact index with each monitoring impact index interval preset in the database, the most suitable drone monitoring performance impact reference factor and adjustment parameter set in the current environment can be automatically identified, ensuring that the drone is always in the best working state under different environmental conditions, avoiding performance degradation caused by environmental changes or unsatisfactory monitoring conditions. According to the changes in the monitoring impact index, the drone monitoring performance impact factor and adjustment parameter are automatically matched, so that the drone can quickly adapt and adjust the operation strategy under different climate, weather and geographical conditions, ensure the smooth execution of the monitoring task, improve the collection accuracy and transmission stability of the monitoring data, and reduce the energy consumption and time waste during the operation of the drone.

[0048] Furthermore, the aerial thermal imaging parameters of the rapeseed field are obtained, and the thermal imaging complexity index is obtained by analysis. The specific steps include: obtaining the aerial thermal imaging parameters of the rapeseed field, the aerial thermal imaging parameters of the rapeseed field include the imaging temperature of each rapeseed sampling point, the area of ​​abnormal temperature region and the density of abnormal temperature distribution blocks; obtaining the aerial thermal imaging reference set preset in the database, and analyzing it with the aerial thermal imaging parameters of the rapeseed field to obtain the thermal imaging complexity index; the aerial thermal imaging reference set includes imaging temperature reference values, abnormal temperature region area reference values ​​and abnormal temperature distribution block density reference values; the thermal imaging complexity index is used to characterize the degree of thermal imaging abnormality of the rapeseed field.

[0049] In this embodiment, it should be noted that the imaging temperature of each rapeseed sampling point represents the temperature of most leaf surfaces, i.e., the temperature of normal areas of the leaves, which can be measured by an infrared thermal imager, and the area of ​​the abnormal temperature area can be obtained by image processing software (such as ThermoVision, whose Chinese name is "thermal imaging software"). The density of the abnormal temperature distribution block can be obtained by using images obtained by drones, and using a thermal imager to obtain and identify abnormal temperature areas, and calculate the distribution of these areas, that is, to obtain the distribution density of the abnormal temperature blocks. The density of the abnormal temperature distribution block refers to the distribution density of the abnormal temperature area.

[0050] The surface of rapeseed leaves will change slightly in different seasons, so the imaging temperature of each rapeseed sampling point will also change accordingly. Therefore, the imaging temperature reference value refers to the reference temperature corresponding to rapeseed in different growth cycles and different growing seasons. For example, the temperature of rapeseed in summer is higher than the surface temperature of rapeseed leaves in winter. Therefore, the closer the imaging temperature of rapeseed is to the temperature reference value, the better the condition of rapeseed. Abnormal temperature areas appear at rapeseed sampling points due to insects or mutual occlusion between plants. Therefore, the larger the area of ​​abnormal temperature area, the more serious the abnormality of rapeseed. The greater the density of abnormal temperature distribution blocks, the greater the degree of rapeseed pests and diseases or the more unreasonable the rapeseed planting interval.

[0051] The complex thermal imaging indicators obtained by analyzing the aerial thermal imaging parameters of rapeseed fields take into account the mutual influence relationship between these parameters. For example, changes in imaging temperature directly affect the identification of abnormal temperature areas. When rapeseed is infected with pests and diseases or there is occlusion between leaves, resulting in abnormal imaging temperature, an abnormal temperature area will be formed, which in turn affects the area of ​​the abnormal temperature area. The larger the area of ​​the abnormal temperature area, the higher the density of the abnormal temperature distribution blocks will be with a certain probability.

[0052] The complex indicators of thermal imaging are obtained by:

[0053]

[0054] In the formula, RF represents the thermal imaging complexity index, RW j represents the imaging temperature of the jth rapeseed sampling point, j represents the number of the rapeseed sampling point, j=1,2,3,...,j max , j max represents the total number of rapeseed sampling points, ΔRW represents the imaging temperature reference value, RS j represents the abnormal temperature area of ​​the jth rapeseed sampling point, ΔRS represents the reference value of the abnormal temperature area, and ρ j represents the abnormal temperature distribution block density of the j-th rapeseed sampling point, Δρ represents the reference value of the abnormal temperature distribution block density, and e represents the natural constant.

[0055] By analyzing the aerial thermal imaging parameters of rapeseed fields and obtaining complex thermal imaging indicators, the degree of thermal imaging abnormality of rapeseed fields can be effectively characterized, thereby providing a scientific and accurate assessment of the growth status and potential pests and diseases of rapeseed. By analyzing the complex thermal imaging indicators of rapeseed fields, areas with abnormal surface temperature of rapeseed leaves can be discovered early. For example, the appearance of abnormal temperature areas may indicate that some areas of rapeseed are invaded by pests and diseases or their growth is blocked. By identifying these abnormal areas early, corresponding prevention and control measures can be taken before the problem spreads on a large scale, thereby avoiding yield losses. By analyzing the complex thermal imaging indicators, the growth status and potential problems of rapeseed can be judged more scientifically, and targeted measures can be taken to optimize management. Keeping rapeseed in the best growth environment can effectively promote its healthy growth and maximize the yield and quality of rapeseed. Complex thermal imaging indicators make farmland monitoring more timely. With seasonal changes, climate changes, and changes in the growth status of rapeseed, users can dynamically adjust management strategies based on real-time monitoring thermal imaging data.

[0056] Furthermore, the UAV monitoring judgment result is obtained, and the specific steps include: obtaining each surface abnormal threat index interval preset in the database and the surface abnormal threat reference value corresponding to each surface abnormal threat index interval, and matching them with the surface abnormal threat index. If the surface abnormal threat index is within a certain surface abnormal threat index interval, then the surface abnormal threat reference value corresponding to the surface abnormal threat index interval is obtained as the surface abnormal threat value; obtaining each thermal imaging complex index interval preset in the database and the thermal imaging complex reference value corresponding to each thermal imaging complex index interval, and matching them with the thermal imaging complex index. If the thermal imaging complex index is within a certain thermal imaging complex index interval, then the thermal imaging complex reference value corresponding to the thermal imaging complex index interval is obtained as the thermal imaging complex value; performing difference processing on the surface abnormal threat value and the thermal imaging complex value. , and mark the difference as a deviation difference; obtain the deviation difference interval preset in the database, and compare it with the deviation difference. If the deviation difference is within the deviation difference interval, the UAV monitoring judgment result is qualified detection, and the UAV monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output. If the deviation difference exceeds the deviation difference interval, the UAV monitoring judgment result is unqualified detection; obtain the surface anomaly threat threshold and thermal imaging complexity threshold preset in the database, and compare them with the surface anomaly threat index and thermal imaging complexity index respectively. If the surface anomaly threat index is less than the surface anomaly threat threshold and the thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed abnormality judgment result is normal rapeseed. If the surface anomaly threat index is above the surface anomaly threat threshold or the thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed abnormality judgment result is rapeseed abnormality.

[0057] In this embodiment, it should be noted that by matching the surface abnormal threat index and the thermal imaging complexity index with the reference values ​​preset in the database to obtain the surface abnormal threat value and the thermal imaging complexity value, and performing difference processing to obtain the deviation difference, the drone monitoring judgment result is obtained by analysis, which can more accurately judge the monitoring results of the rapeseed field, avoid the subjectivity and inconsistency of manual judgment, improve the accuracy and reliability of the drone monitoring results, and provide a scientific basis for the timely detection of rapeseed abnormalities. Automatically determine the health status of rapeseed based on monitoring data, reduce the need for manual inspections and frequent interventions, thereby avoiding unnecessary waste and excessive management, and achieving the effect of saving costs and improving resource utilization efficiency.

[0058] It should be noted that by matching the surface anomaly threat index and the thermal imaging complexity index with the surface anomaly threat index intervals and thermal imaging complexity index intervals preset in the database respectively, the corresponding surface anomaly threat reference values ​​and thermal imaging complexity reference values ​​are obtained, thereby converting the surface anomaly threat index and the thermal imaging complexity index into indicators of the same dimension.

[0059] The surface abnormality threat index and thermal imaging complexity index represent different characteristics of rapeseed fields (such as surface growth abnormality and thermal imaging temperature abnormality). By converting the surface abnormality threat index and thermal imaging complexity index into evaluation results of the same dimension, the growth status of rapeseed can be judged more clearly, which helps managers make quick and accurate decisions, thereby improving the production efficiency and health management level of rapeseed.

[0060] Furthermore, if the UAV monitoring judgment result is that the monitoring is unqualified, a deviation adjustment parameter set is obtained by matching based on the deviation difference, and the specific steps include: obtaining each deviation difference interval preset in the database and a deviation adjustment reference set corresponding to each deviation difference interval; if the UAV monitoring judgment result is that the monitoring is unqualified, the deviation difference is compared with each deviation difference interval. If the deviation difference is within a preset deviation difference interval, the deviation adjustment reference set corresponding to the interval is obtained as the deviation adjustment parameter set.

[0061] In this embodiment, by obtaining a deviation adjustment parameter set based on deviation difference matching, the relevant parameters of the drone monitoring can be accurately adjusted in response to unqualified monitoring results, ensuring that the drone better meets the preset standards during the monitoring process, thereby improving the monitoring pass rate and data accuracy, and providing higher flexibility and adaptability for monitoring tasks under changing environments and conditions.

[0062] Furthermore, the UAV equipment is adjusted and a secondary aerial patrol monitoring is carried out to obtain the final corrected output result of the rapeseed, which is displayed. The specific steps include: a secondary adjustment of the UAV equipment based on the deviation adjustment parameter set, a secondary aerial patrol monitoring is carried out through the UAV equipment after the secondary adjustment, a secondary thermal imaging parameter is obtained, and a thermal imaging complexity index is analyzed again based on the secondary thermal imaging parameters, and recorded as a secondary execution thermal imaging complexity index; an analysis is carried out based on the surface abnormality threat index, the secondary execution thermal imaging complexity index, the surface abnormality threat threshold and the thermal imaging complexity threshold to obtain the final corrected output result, and the final corrected output result is displayed; the secondary execution thermal imaging complexity index is used to characterize the degree of thermal imaging anomaly of the rapeseed detected after the secondary adjustment.

[0063] In this embodiment, it should be noted that by making secondary adjustments to the drone equipment based on the deviation adjustment parameter set (image resolution, imaging angle of view, flight altitude), the performance of the drone can be further optimized when the initial monitoring results are unqualified, eliminating errors in equipment and parameter settings, thereby ensuring the accuracy of the drone in subsequent monitoring, improving the reliability of the monitoring results, and ensuring the accuracy and consistency of the rapeseed field monitoring results. After the secondary adjustment, the drone equipment can conduct aerial patrol monitoring again and collect new thermal imaging parameters, effectively reducing the possible misjudgment problems in the initial monitoring, especially when the rapeseed field faces complex environmental changes. Secondary monitoring helps to avoid erroneous thermal imaging complex indicators, making the final corrected output results more accurate and reducing potential risks in the decision-making process.

[0064] The display of the final corrected output results not only provides more accurate monitoring results, but also improves the visualization of the data. By displaying the corrected results in real time, users can more intuitively understand the monitoring status of the rapeseed field, and then take more effective management measures based on the actual monitoring results, which helps users better understand the monitoring data.

[0065] Based on the surface anomaly threat index, the secondary execution thermal imaging complexity index, the surface anomaly threat threshold and the thermal imaging complexity threshold, analysis is performed to obtain the final corrected output result. The specific steps include: obtaining the surface anomaly threat threshold and the thermal imaging complexity threshold preset in the database, and comparing them with the surface anomaly threat index and the secondary execution thermal imaging complexity index respectively; if the surface anomaly threat index is less than the surface anomaly threat threshold and the secondary execution thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is normal; if the surface anomaly threat index is above the surface anomaly threat threshold or the secondary execution thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is abnormal.

[0066] It should be noted that the secondary thermal imaging parameters include: the imaging temperature review value of each rapeseed sampling point, the abnormal temperature area review value and the abnormal temperature distribution block density review value.

[0067] The complex index of secondary thermal imaging is obtained by:

[0068]

[0069] Where XF represents the complex index of secondary thermal imaging, Y represents the influencing factor of UAV monitoring performance, and XW j represents the imaging temperature review value of the j-th rapeseed sampling point, j represents the number of the rapeseed sampling point, j=1,2,3,...,j max , j maxrepresents the total number of rapeseed sampling points, ΔXW represents the imaging temperature reference value, XS represents the abnormal temperature area review value of the j-th rapeseed sampling point, ΔXS represents the abnormal temperature area reference value, Xρ j represents the review value of the abnormal temperature distribution block density of the j-th rapeseed sampling point, ΔXρ represents the reference value of the abnormal temperature distribution block density, and e represents a natural constant.

[0070] It should be noted that the secondary thermal imaging parameters are the data obtained when the UAV equipment conducts secondary aerial patrol monitoring. For example, the imaging temperature review value refers to the temperature detected when the UAV equipment conducts secondary aerial patrol monitoring. The abnormal temperature area review value refers to the abnormal temperature area detected when the UAV equipment conducts secondary aerial patrol monitoring. The abnormal temperature distribution block density review value refers to the abnormal temperature distribution block density detected when the UAV equipment conducts secondary aerial patrol monitoring.

[0071] like Figure 2 As shown, it is a structural schematic diagram of a high-yield and efficient rapeseed cultivation integrated processing system provided by an embodiment of the present application. The high-yield and efficient rapeseed cultivation integrated processing system provided by an embodiment of the present application includes: a surface evaluation module, a thermal imaging evaluation module, an unmanned aerial vehicle monitoring and judgment module and a secondary adjustment module; wherein the surface evaluation module is used to use an intelligent plant protection robot to conduct patrol monitoring of rapeseed fields, obtain surface abnormality parameters of rapeseed fields, and analyze to obtain surface abnormality threat indicators; the thermal imaging evaluation module is used to use unmanned aerial vehicle technology to conduct aerial patrol monitoring of rapeseed fields, obtain unmanned aerial vehicle monitoring performance influencing parameters, analyze to obtain unmanned aerial vehicle monitoring performance influencing factors and unmanned aerial vehicle adjustment parameter sets, thereby performing The drone is adjusted and the aerial thermal imaging parameters of the rapeseed field are obtained, and the complex thermal imaging indicators are analyzed; the drone monitoring and judgment module is used to analyze the surface abnormal threat indicators and the complex thermal imaging indicators to obtain the deviation difference, and compare it with the preset difference interval in the database to obtain the drone monitoring judgment result; the secondary adjustment module is used to not perform the drone monitoring adjustment if the drone monitoring judgment result is qualified, and output the rapeseed abnormal judgment result; if the drone monitoring judgment result is unqualified, the deviation adjustment parameter set is obtained based on the deviation difference, thereby adjusting the drone equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed and display it.

[0072] To summarize, this embodiment obtains the UAV monitoring and judgment result by performing difference processing on the surface abnormal threat index and the thermal imaging complex index, and compares them with the preset difference interval in the database, so as to adjust the UAV equipment according to the monitoring and judgment result and conduct secondary aerial patrol monitoring, thereby realizing accurate and efficient monitoring of abnormal conditions in rapeseed fields, and effectively solving the problem of reduced rapeseed yield and quality caused by untimely rapeseed supervision in the prior art.

[0073] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0077] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0078] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A high-yield and high-efficiency rapeseed cultivation integrated processing method, characterized in that: The following steps are involved: Use intelligent plant protection robots to patrol rapeseed fields, obtain abnormal surface parameters of rapeseed fields, and analyze them to obtain abnormal surface threat indicators; Use drone technology to conduct aerial patrol monitoring of rapeseed fields, obtain the parameters affecting drone monitoring performance, analyze and obtain the factors affecting drone monitoring performance and the drone adjustment parameter set, adjust the drone accordingly, obtain aerial thermal imaging parameters of rapeseed fields, and analyze and obtain complex thermal imaging indicators; Analyze the surface abnormal threat index and thermal imaging complexity index to obtain the deviation difference, and compare it with the preset difference interval in the database to obtain the UAV monitoring judgment result; If the UAV monitoring result is qualified, the UAV monitoring adjustment will not be performed, and the rapeseed abnormality judgment result will be output. If the UAV monitoring result is unqualified, the deviation adjustment parameter set is obtained based on the deviation difference, and the UAV equipment is adjusted accordingly and a second aerial patrol monitoring is carried out to obtain the final corrected output result of the rapeseed and display it.

2. A high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 1, characterized in that: The method of obtaining the surface anomaly parameters of the rapeseed field and analyzing them to obtain the surface anomaly threat index comprises the following specific steps: Sampling rapeseed in the rapeseed field to obtain abnormal surface parameters of the rapeseed field, wherein the abnormal surface parameters of the rapeseed field include the number of leaf spots, the maximum diameter of the spots, the average leaf area and the plant height of each sampled rapeseed; Obtain the current planting cycle of rapeseed, and obtain the surface anomaly reference set corresponding to each planting cycle interval of rapeseed preset in the database, obtain the surface anomaly reference set corresponding to the current planting cycle of rapeseed by matching, record it as the designated surface anomaly reference set, and analyze it with the surface anomaly parameters of the rapeseed field to obtain the surface anomaly threat index; The designated surface anomaly reference set includes a reference value for the number of leaf spots, a reference value for the diameter of a spot, a reference value for the leaf area, and a reference value for the height of a plant; The surface abnormality threat index is used to characterize the abnormal degree of surface monitoring growth of rapeseed.

3. The high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 1, characterized in that: The specific steps of obtaining the parameters affecting the monitoring performance of the UAV and analyzing the factors affecting the monitoring performance of the UAV and the set of adjustment parameters of the UAV include: Obtaining parameters affecting the monitoring performance of the drone, wherein the parameters affecting the monitoring performance of the drone include light intensity, wind speed, humidity, wireless signal strength, data transmission speed, spectrum utilization, and electromagnetic interference intensity; Obtain the preset UAV monitoring reference set in the database, and analyze it with the UAV monitoring performance influencing parameters to obtain the UAV monitoring impact index; Based on the UAV monitoring impact indicators and matching with the database, the UAV monitoring performance impact factors and the UAV adjustment parameter set are obtained; The drone monitoring reference set includes a light intensity reference value, a wind speed reference value, a humidity reference value, a wireless signal strength reference value, a data transmission speed reference value, a spectrum utilization reference value, and an electromagnetic interference intensity reference value; The UAV monitoring impact index is used to characterize the degree to which the UAV is affected during the process of detecting rapeseed.

4. A high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 3, characterized in that: The specific method for obtaining the UAV monitoring impact index is as follows: Wherein, WJ represents the impact index of UAV monitoring, WQ represents light intensity, ΔWQ represents the reference value of light intensity, WF represents wind speed, ΔWF represents the reference value of wind speed, WS represents humidity, ΔWS represents the reference value of humidity, WX represents wireless signal strength, ΔWX represents the reference value of wireless signal strength, WC represents data transmission speed, ΔWC represents the reference value of data transmission speed, WP represents spectrum utilization, ΔWP represents the reference value of spectrum utilization, WR represents electromagnetic interference intensity, ΔWR represents the reference value of electromagnetic interference intensity, and e represents a natural constant.

5. The high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 3, characterized in that: The method of obtaining the UAV monitoring performance influencing factor and the UAV adjustment parameter set based on the UAV monitoring influencing index and matching with the database includes the following specific steps: Obtain each UAV monitoring impact index interval preset in the database and the UAV monitoring performance impact reference factor and UAV pre-adjustment parameter set corresponding to each UAV monitoring impact index interval, and match them with the UAV monitoring impact index. If the UAV monitoring impact index is within a certain UAV monitoring impact index interval, obtain the UAV monitoring performance impact reference factor and UAV pre-adjustment parameter set corresponding to the interval, and mark them as UAV monitoring performance impact factor and UAV adjustment parameter set; The drone adjustment parameter set includes the drone's camera resolution and network transmission signal strength.

6. The high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 1, characterized in that: The steps of obtaining aerial thermal imaging parameters of the rapeseed field and analyzing the thermal imaging complex indicators include: Acquire aerial thermal imaging parameters of the rapeseed field, wherein the aerial thermal imaging parameters of the rapeseed field include imaging temperature of each rapeseed sampling point, area of ​​abnormal temperature region, and density of abnormal temperature distribution blocks; Obtain the preset aerial thermal imaging reference set in the database and analyze it with the aerial thermal imaging parameters of the rapeseed field to obtain the thermal imaging complex index; The aerial thermal imaging reference set includes an imaging temperature reference value, an abnormal temperature region area reference value, and an abnormal temperature distribution block density reference value; The thermal imaging complexity index is used to characterize the abnormality degree of thermal imaging of the rapeseed field.

7. The high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 1, characterized in that: The specific steps of obtaining the drone monitoring and determination result include: Obtain each surface anomaly threat index interval preset in the database and the surface anomaly threat reference value corresponding to each surface anomaly threat index interval, and match them with the surface anomaly threat index. If the surface anomaly threat index is within a certain surface anomaly threat index interval, obtain the surface anomaly threat reference value corresponding to the surface anomaly threat index interval as the surface anomaly threat value; Obtain each thermal imaging complex index interval preset in the database and the thermal imaging complex index reference value corresponding to each thermal imaging complex index interval, and match them with the thermal imaging complex index. If the thermal imaging complex index is within a certain thermal imaging complex index interval, obtain the thermal imaging complex reference value corresponding to the thermal imaging complex index interval as the thermal imaging complex value; Perform difference processing on the surface abnormal threat value and the thermal imaging complex value, and mark the difference as a deviation difference; Obtain the deviation difference interval preset in the database and compare it with the deviation difference. If the deviation difference is within the deviation difference interval, the drone monitoring judgment result is qualified, and the drone monitoring adjustment is not performed, and the rapeseed abnormality judgment result is output. If the deviation difference exceeds the deviation difference interval, the drone monitoring judgment result is unqualified. The preset surface anomaly threat threshold and thermal imaging complexity threshold in the database are obtained, and compared with the surface anomaly threat index and thermal imaging complexity index respectively. If the surface anomaly threat index is less than the surface anomaly threat threshold and the thermal imaging complexity index is less than the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is normal; if the surface anomaly threat index is above the surface anomaly threat threshold or the thermal imaging complexity index is above the thermal imaging complexity threshold, the rapeseed anomaly judgment result is that the rapeseed is abnormal.

8. The high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 1, characterized in that: If the drone monitoring result is unqualified, matching is performed based on the deviation difference to obtain a deviation adjustment parameter set, and the specific steps include: Obtaining each deviation difference interval preset in the database and a deviation adjustment reference set corresponding to each deviation difference interval; If the UAV monitoring judgment result is that the monitoring is unqualified, the deviation difference is compared with each deviation difference interval. If the deviation difference is within a preset deviation difference interval, the deviation adjustment reference set corresponding to the interval is obtained as the deviation adjustment parameter set.

9. The high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in claim 1, characterized in that: The above steps include adjusting the drone equipment and performing a second aerial patrol monitoring to obtain the final corrected output result of rapeseed and displaying it. The specific steps include: Based on the deviation adjustment parameter set, the UAV equipment is adjusted for the second time, and the UAV equipment after the second adjustment is used for the second aerial patrol monitoring to obtain the second thermal imaging parameters, and the thermal imaging complex index is analyzed again based on the second thermal imaging parameters, and recorded as the second execution thermal imaging complex index; Based on the surface anomaly threat index, the secondary execution thermal imaging complexity index, the surface anomaly threat threshold and the thermal imaging complexity threshold, the final correction output result is obtained and displayed; The secondary execution thermal imaging complex index is used to characterize the degree of abnormality of the rapeseed field thermal imaging detected after the secondary adjustment.

10. A system using a high-yield and high-efficiency rapeseed cultivation integrated processing method as claimed in any one of claims 1 to 9, characterized in that: include: Surface assessment module, thermal imaging assessment module, drone monitoring and determination module, and secondary adjustment module; The surface assessment module is used to use the intelligent plant protection robot to patrol and monitor the rapeseed field, obtain the abnormal surface parameters of the rapeseed field, and analyze and obtain the abnormal surface threat index; The thermal imaging evaluation module is used to use drone technology to conduct aerial patrol monitoring of rapeseed fields, obtain parameters affecting drone monitoring performance, analyze to obtain drone monitoring performance influencing factors and drone adjustment parameter sets, thereby adjusting drones, and obtain aerial thermal imaging parameters of rapeseed fields, and analyze to obtain thermal imaging complex indicators; The UAV monitoring and determination module is used to analyze the surface abnormal threat index and the thermal imaging complex index to obtain the deviation difference, and compare it with the difference interval preset in the database to obtain the UAV monitoring and determination result; The secondary adjustment module is used for not executing the drone monitoring adjustment and outputting the rapeseed abnormality determination result if the drone monitoring determination result is qualified monitoring; if the drone monitoring determination result is unqualified monitoring, matching is performed based on the deviation difference to obtain the deviation adjustment parameter set, thereby adjusting the drone equipment and conducting secondary aerial patrol monitoring to obtain the final corrected output result of the rapeseed and display it.

Images