A rodent monitoring method and system based on drone radar trajectory detection

Abstract

The present invention provides a rodent monitoring method and system based on drone radar trajectory detection, and relates to the technical field of rodent monitoring. The method comprises: capturing images of rodents through a high-definition camera of a drone to obtain rodent information within the current monitoring period; determining a rodent presence score based on the rodent information; obtaining drone location information; obtaining trajectory information of rodents through a high-frequency radar carried by the drone; determining a rodent activity trend score based on the drone location information and trajectory information; obtaining amplitude information of radar echo signals within the current monitoring period; determining a rodent activity intensity score based on the amplitude information; generating a rodent monitoring report based on the rodent presence score, rodent activity trend score, and rodent activity intensity score. According to the present invention, rodent pests can be comprehensively monitored based on three aspects: rodent presence status, rodent activity trend status, and rodent activity intensity status, thereby improving the accuracy and comprehensiveness of rodent pest monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of rodent monitoring, and in particular to a rodent monitoring method and system based on unmanned aerial vehicle radar trajectory detection. Background Art

[0002] In the related art, CN111123269A discloses a ground clutter suppression method, module and device for unmanned aerial vehicle obstacle avoidance radar, the method steps include: S1. Obtain the flight status information of the unmanned aerial vehicle and the information of the target trajectory detected by the obstacle avoidance radar, and preliminarily determine whether the detected target trajectory is ground clutter based on the flight status information and the target trajectory information of the unmanned aerial vehicle, and if it is, proceed to step S2; S2. Obtain the braking distance of the unmanned aerial vehicle and the trajectory distance of the target, and determine whether to perform clutter suppression processing based on the size relationship between the braking distance of the unmanned aerial vehicle and the trajectory distance of the target. This solution can be applied to unmanned aerial vehicle obstacle avoidance radar to achieve moving ground clutter suppression, and the operation is simple, the cost is low, and the suppression effect is good.

[0003] CN114966659A discloses a radar target detection system and method based on machine learning, which relates to the field of radar target detection technology, including a reconnaissance drone detection module, an antenna design module, a detection target analysis module and a monitoring alarm module; the reconnaissance drone detection module is used to detect targets outward through a radar installed on the reconnaissance drone and verify the authenticity of the detected targets; the antenna design module is used to optimize the antenna directional diagram to obtain an antenna model; the detection target analysis module is used to analyze the distance result between the second reconnaissance drone and the target according to the state of the target and the flight trajectory of the second reconnaissance drone; the ability of the antenna to send and receive signals in a specific direction is enhanced, and an antenna that meets specific requirements can be flexibly designed, thereby improving the ability of the first reconnaissance drone to detect targets; at the same time, according to the movement state of the target, the first reconnaissance drone sends an interference signal to prevent the second reconnaissance drone from detecting the real target.

[0004] Therefore, although the relevant technology can detect the target trajectory, it does not take into account the impact of the presence of rats, the trend of rat activity and the intensity of rat activity on rodent infestation, that is, it is impossible to conduct comprehensive monitoring of rodent infestation based on the three aspects of the presence of rats, the trend of rat activity and the intensity of rat activity.

[0005] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a rodent pest monitoring method and system based on UAV radar trajectory detection, which can solve the technical problem that the related technology cannot comprehensively monitor rodent pests according to three aspects: rodent existence status, rodent activity trend status and rodent activity intensity status.

[0007] According to a first aspect of the present invention, there is provided a rodent monitoring method based on drone radar trajectory detection, comprising: capturing images of rodents by a high-definition camera of a drone to obtain rodent information within a current monitoring period, wherein the rodent information includes the number of rodents and the type of rodents; determining a rodent presence score based on the rodent information; obtaining drone position information at multiple moments in the current monitoring period; obtaining rodent trajectory information by a high-frequency radar carried by the drone at multiple moments in the current monitoring period, wherein the trajectory information includes relative position information and speed data, and the relative position information is the relative position of the drone and the rodents; determining a rodent activity trend score based on the drone position information and the trajectory information; obtaining amplitude information of a radar echo signal within the current monitoring period, wherein the amplitude information includes a peak amplitude and an average amplitude; determining a rodent activity intensity score based on the amplitude information; and generating a rodent monitoring report based on the rodent presence score, the rodent activity trend score and the rodent activity intensity score.

[0008] Further, determining a rodent presence score based on the rodent information includes: obtaining historical rodent information of multiple historical monitoring periods, wherein the historical rodent information includes historical rodent numbers and historical rodent species; determining a rodent presence score based on the historical rodent information and the rodent information.

[0009] Further, determining a rodent presence score based on the historical rodent information and the rodent information includes: according to the formula Determine rodent presence score ,in, is the number of rodents in the current monitoring period, is the historical rodent population in the first historical monitoring period, is the historical rodent population in the hth historical monitoring period, is the historical number of rodents in the h+1th historical monitoring period, is the rodent species in the current monitoring period, The historical rodent species in the first historical monitoring cycle are: and is the preset weight, H is the number of historical monitoring periods, h≤H, and both h and H are positive integers, and max is the maximum value function.

[0010] Furthermore, according to the drone position information and the trajectory information, a rodent activity trend score is determined, including: obtaining rodent position information according to the drone position information and the relative position information; performing a connected domain analysis on the multiple rodent position information in the current monitoring cycle to obtain a first rodent activity area, and determining the area of ​​the first rodent activity area; obtaining historical rodent position information at multiple moments in the first historical monitoring cycle; performing a connected domain analysis on the multiple historical rodent position information in the first historical monitoring cycle to obtain a second rodent activity area, and determining the area of ​​the second rodent activity area; determining a speed distribution function according to the rodent position information and the speed data; determining, according to the speed distribution function, comfortable position information of rodents whose speed data is greater than a preset speed data; and Performing a connected domain analysis on the multiple rodent comfort position information within the period to obtain a first rodent comfort area, and determining the area of ​​the first rodent comfort area; obtaining historical speed data at multiple moments in the first historical monitoring period; determining a historical speed distribution function based on the historical rodent position information and the historical speed data; determining historical rodent comfort position information whose historical speed data is greater than a preset speed data based on the historical speed distribution function; performing a connected domain analysis on the multiple historical rodent comfort position information within the first historical monitoring period to obtain a second rodent comfort area, and determining the area of ​​the second rodent comfort area; determining a rodent activity trend score based on the area of ​​the first rodent activity area, the area of ​​the second rodent activity area, the area of ​​the first rodent comfort area, and the area of ​​the second rodent comfort area.

[0011] Further, determining a rodent activity trend score according to the first rodent activity area area, the second rodent activity area area, the first rodent comfort area area, and the second rodent comfort area area includes: according to the formula Determine rodent activity trend score ,in, is the area of ​​the first rodent activity area, is the area of ​​the second rodent activity area, is the area of ​​the first rodent comfort zone, is the area of ​​the second rodent comfort zone, and The preset weights.

[0012] Furthermore, based on the amplitude information, a rodent activity intensity score is determined, including: obtaining the peak time interval and peak frequency of the peak amplitude in the current monitoring cycle; obtaining the historical average amplitude of multiple historical monitoring cycles; and determining the rodent activity intensity score based on the peak time interval, the peak frequency, the average amplitude and the historical average amplitude.

[0013] Further, determining the rodent activity intensity score according to the peak time interval, the peak frequency, the average amplitude and the historical average amplitude includes: according to the formula Determining rodent activity intensity scores ,in, is the peak time interval, is the preset peak time interval threshold, is the peak frequency, is the preset peak frequency threshold, is the average amplitude of the current monitoring period, is the historical average amplitude of the first historical monitoring period, is the historical average amplitude of the hth historical monitoring period, is the historical average amplitude of the h+1th historical monitoring period, is the preset average amplitude threshold, , and is the preset weight, H is the number of historical monitoring periods, h≤H, and both h and H are positive integers, and if is the conditional function.

[0014] According to a second aspect of the present invention, a rodent monitoring system based on drone radar trajectory detection is provided, comprising: a rodent information module, used to capture images of rodents through a high-definition camera of a drone, and obtain rodent information in a current monitoring period, wherein the rodent information includes the number of rodents and the type of rodents; a rodent presence scoring module, used to determine a rodent presence score based on the rodent information; a drone position information module, used to obtain drone position information at multiple moments in the current monitoring period; a trajectory information module, used to obtain trajectory information of rodents through a high-frequency radar carried by the drone at multiple moments in the current monitoring period, wherein the trajectory The track information includes relative position information and speed data, wherein the relative position information is the relative position of the UAV and the rodents; a rodent activity trend scoring module is used to determine the rodent activity trend score according to the UAV position information and the track information; an amplitude information module is used to obtain the amplitude information of the radar echo signal within the current monitoring period, wherein the amplitude information includes the peak amplitude and the average amplitude; a rodent activity intensity scoring module is used to determine the rodent activity intensity score according to the amplitude information; a rodent pest monitoring report module is used to generate a rodent pest monitoring report according to the rodent presence score, the rodent activity trend score and the rodent activity intensity score.

[0015] Technical effect: According to the present invention, by using drones equipped with high-definition cameras and high-frequency radars, efficient real-time monitoring of rodents can be achieved with a wide coverage, reducing the time and labor intensity of manual monitoring. The changing trend of rodent activities can be monitored in real time through the drone location information and the trajectory information of rodents. Comprehensive monitoring of rodent infestation in three aspects, namely, the presence of rodents, the trend of rodent activity, and the intensity of rodent activity, can comprehensively evaluate the severity of rodent infestation and improve the accuracy and comprehensiveness of rodent infestation monitoring. When determining the rodent presence score, the rodent presence score can be determined through historical rodent information and rodent information, and the number and type of rodents can be judged from the increase or decrease in the number of rodents and the number of rodent species, so as to understand the severity of the rodent infestation and improve the comprehensiveness and reliability of the rodent presence score. When determining the rodent activity trend score, the rodent activity trend score can be determined by the area of ​​the first rodent activity area, the area of ​​the second rodent activity area, the area of ​​the first rodent comfort area, and the area of ​​the second rodent comfort area. By comparing the areas of rodent activity and comfort areas in the current monitoring period with those in the historical monitoring period, the changing trend of rodent behavior can be understood, which helps to identify whether the rodent population is spreading or adapting to environmental changes. When determining the rodent activity intensity score, the rodent activity intensity score can be determined by the peak time interval, peak frequency, average amplitude, and historical average amplitude. From the perspective of rodent activity frequency and rodent distribution density, the change in rodent activity intensity can be monitored in real time, and the increase in rodent activity intensity can be discovered in time, so that corresponding measures can be taken to reduce the losses caused by rodent damage.

[0016] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work.

[0018] Figure 1 A schematic diagram of a process flow of a rodent monitoring method based on drone radar trajectory detection according to an embodiment of the present invention is exemplarily shown;

[0019] Figure 2 A block diagram of a rodent monitoring system based on drone radar trajectory detection according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0022] Figure 1 A flow chart of a rodent monitoring method based on drone radar trajectory detection according to an embodiment of the present invention is exemplarily shown, the method comprising: step S101, capturing images of rodents by a high-definition camera of a drone to obtain rodent information in a current monitoring period, wherein the rodent information includes the number of rodents and the type of rodents; step S102, determining a rodent presence score based on the rodent information; step S103, obtaining drone location information at multiple moments in the current monitoring period; step S104, obtaining rodent trajectory information at multiple moments in the current monitoring period by using a high-frequency radar carried by the drone , wherein the trajectory information includes relative position information and speed data, and the relative position information is the relative position of the UAV and the rodents; step S105, determining the rodent activity trend score according to the UAV position information and the trajectory information; step S106, obtaining the amplitude information of the radar echo signal in the current monitoring period, wherein the amplitude information includes the peak amplitude and the average amplitude; step S107, determining the rodent activity intensity score according to the amplitude information; step S108, generating a rodent monitoring report according to the rodent presence score, the rodent activity trend score and the rodent activity intensity score.

[0023] According to the rodent monitoring method based on drone radar trajectory detection in the embodiment of the present invention, by using a drone equipped with a high-definition camera and a high-frequency radar, efficient real-time monitoring of rodents can be achieved, with a wide coverage, reducing the time and labor intensity of manual monitoring. Through the drone location information and the trajectory information of the rodents, the changing trend of rodent activities can be monitored in real time. Comprehensive monitoring of rodent infestation in three aspects, namely, the presence of rodents, the trend of rodent activity, and the intensity of rodent activity, can comprehensively evaluate the severity of rodent infestation, thereby improving the accuracy and comprehensiveness of rodent infestation monitoring.

[0024] According to one embodiment of the present invention, in step S101, each monitoring cycle can be set to 3 hours, 6 hours, etc., and the present invention is not limited to this. When the drone flies in a predetermined monitoring area, the high-definition camera will continuously capture images of the surrounding environment. In the captured images, relevant information about rodents can be identified and extracted. The total number of rodents captured in the current monitoring cycle is counted, and at the same time, different types of rodents are identified and their type information is recorded.

[0025] According to an embodiment of the present invention, in step S102, a rodent presence score is determined based on the rodent information.

[0026] According to one embodiment of the present invention, step S102 includes: obtaining historical rodent information of multiple historical monitoring periods, wherein the historical rodent information includes historical rodent numbers and historical rodent species; and determining a rodent presence score based on the historical rodent information and the rodent information.

[0027] According to one embodiment of the present invention, the rodent information (ie, the number of rodents and the types of rodents) acquired during the current monitoring period is compared with the historical rodent information collected previously to analyze the presence of rodents.

[0028] According to an embodiment of the present invention, determining a rodent presence score based on the historical rodent information and the rodent information includes: determining the rodent presence score according to formula (1): ,

[0029] (1),

[0030] in, is the number of rodents in the current monitoring period, is the historical rodent population in the first historical monitoring period, is the historical rodent population in the hth historical monitoring period, is the historical number of rodents in the h+1th historical monitoring period, is the rodent species in the current monitoring period, The historical rodent species in the first historical monitoring cycle are: and is the preset weight, H is the number of historical monitoring periods, h≤H, and both h and H are positive integers, and max is the maximum value function.

[0031] According to one embodiment of the present invention, in formula (1), It is the difference between the number of rodents in the current monitoring cycle and the historical number of rodents in the first historical monitoring cycle, indicating the increase or decrease in the number of rodents in the current monitoring cycle. The larger the difference, the greater the increase in the number of rodents in the current monitoring cycle. The maximum value of the difference between the historical rodent numbers in multiple adjacent historical monitoring periods is taken, which represents the maximum value of the increase or decrease in the historical rodent numbers in adjacent historical monitoring periods. It is the relative difference between the increase or decrease in the number of rodents in the current monitoring period and the maximum increase or decrease in the number of rodents in the adjacent historical monitoring periods. When the relative difference is a positive number, the larger the relative difference is, the more obvious the increase in the number of rodents in the current monitoring period, indicating that the rodent infestation is more serious. When the relative difference is a negative number, the smaller the relative difference is, the more obvious the decrease in the number of rodents in the current monitoring period, indicating that the rodent infestation is weakening. It is the relative difference between the rodent species in the current monitoring cycle and the historical rodent species in the first historical monitoring cycle. When the relative difference is a positive number, the larger the relative difference is, the more the rodent species in the current monitoring cycle increase relative to the historical rodent species in the first historical monitoring cycle, indicating that the rodent infestation is more serious. When the relative difference is a negative number, the smaller the relative difference is, the more the rodent species in the current monitoring cycle decreases relative to the historical rodent species in the first historical monitoring cycle, indicating that the rodent infestation is weakening. The relative difference between the increase or decrease in the number of rodents in the current monitoring cycle and the maximum increase or decrease in the number of rodents in the adjacent historical monitoring cycles, and the relative difference between the rodent species in the current monitoring cycle and the historical rodent species in the first historical monitoring cycle are weighted and summed to obtain the rodent presence score. The larger the rodent presence score is, the more the number and types of rodents there are, and the more serious the rodent infestation is.

[0032] In this way, the rodent presence score can be determined through historical rodent information and rodent information, and the number and type of rodents present can be judged from the increase or decrease in both the number of rodents and the number of rodent species, so as to understand the severity of the rodent infestation and improve the comprehensiveness and reliability of the rodent presence score.

[0033] According to an embodiment of the present invention, in step S103, the interval between adjacent moments can be set to one minute, two minutes, etc., which is not limited by the present invention. The position information of the drone can be obtained through the GPS positioning module of the drone.

[0034] According to one embodiment of the present invention, in step S104, the high-frequency radar of the drone can detect the position and movement trajectory of the target object by transmitting and receiving electromagnetic waves. The high-frequency radar optimizes parameters to transmit specific electromagnetic waves, which will be reflected when encountering the target. The reflected waves are received by the radar antenna and converted into electrical signals. Through signal processing and machine learning algorithms, the characteristics of the electrical signals, such as amplitude, phase, frequency, etc., can be analyzed to identify different types of animals and capture the activity trajectory of rodents. By combining the image of rodents captured by high-definition cameras with radar detection, a more accurate activity trajectory of rodents can be obtained. For example, the relative position information between the rodents and the drone at multiple times can be determined, so as to determine the activity trajectory of the rodents based on the relative position information, and the speed data of the rodents can also be determined based on the change in the relative position information and the time interval between the moments. The data processing system processes and analyzes the data detected by the radar, and extracts the trajectory information of the rodents, including relative position information and speed data. The relative position information is the relative position of the drone and the rodents.

[0035] According to an embodiment of the present invention, in step S105, a rodent activity trend score is determined based on the drone location information and the trajectory information.

[0036] According to one embodiment of the present invention, step S105 includes: obtaining rat position information based on the drone position information and the relative position information; performing a connected domain analysis on the multiple rat position information in the current monitoring period to obtain a first rat activity area, and determining the area of ​​the first rat activity area; obtaining historical rat position information at multiple moments in the first historical monitoring period; performing a connected domain analysis on the multiple historical rat position information in the first historical monitoring period to obtain a second rat activity area, and determining the area of ​​the second rat activity area; determining a speed distribution function based on the rat position information and the speed data; determining the comfortable position information of the rat whose speed data is greater than the preset speed data based on the speed distribution function; and performing a connected domain analysis on the multiple rat position information in the current monitoring period to obtain a second rat activity area, and determining the area of ​​the second rat activity area. Perform a connected domain analysis on the comfort position information to obtain a first rodent comfort zone, and determine the area of ​​the first rodent comfort zone; obtain historical speed data at multiple moments in the first historical monitoring cycle; determine a historical speed distribution function based on the historical rodent position information and the historical speed data; determine historical rodent comfort position information whose historical speed data is greater than a preset speed data based on the historical speed distribution function; perform a connected domain analysis on the multiple historical rodent comfort position information in the first historical monitoring cycle to obtain a second rodent comfort zone, and determine the area of ​​the second rodent comfort zone; determine a rodent activity trend score based on the area of ​​the first rodent activity area, the area of ​​the second rodent activity area, the area of ​​the first rodent comfort zone, and the area of ​​the second rodent comfort zone.

[0037] According to one embodiment of the present invention, the rodent position information can be calculated based on the drone position information and the relative position information detected by the radar at the drone position information. The connectivity relationship between multiple rodent position information can be determined to form a connected domain, wherein the connected domain refers to a collection of rodent position information with mutual connection or adjacent relationship. For example, if the straight-line distance between two rodent position information is less than a preset distance, it can be determined that the two rodent position information belong to the same connected domain. The connected domain analysis can determine which rodent position information is interconnected and belongs to a large area (connected domain). Based on this, the area composed of multiple large areas (connected domains) can be used as the area of ​​the first rodent activity area. Similarly, the area of ​​the second rodent activity area can be obtained through historical rodent position information. The rodent position information and speed data at any time in the current monitoring cycle are fitted to obtain a speed distribution function for describing the relationship between the rodent position information and speed data at that time in the monitoring cycle. That is, by inputting any rodent position information into the speed distribution function, the speed data at the rodent position information can be obtained. Similarly, the historical rat position information and historical speed data at any time in the first historical monitoring cycle are fitted to obtain the historical speed distribution function. According to the speed distribution function, the rat comfortable position information with speed data greater than the preset speed data is identified. The speed data greater than the preset speed data indicates that the rat moves faster at this position. Therefore, the rat comfortable position information is the rat comfort area. The connectivity relationship between multiple rat comfort position information can be determined to form a connected domain. The boundary and range of the connected domain can be used as the boundary and range of the first rat comfort area, thereby determining the area of ​​the first rat comfort area. Similarly, the area of ​​the second rat comfort area can be obtained through the historical rat comfort position information.

[0038] According to one embodiment of the present invention, determining a rodent activity trend score according to the first rodent activity area, the second rodent activity area, the first rodent comfort area, and the second rodent comfort area includes: determining the rodent activity trend score according to formula (2): ,

[0039] (2),

[0040] in, is the area of ​​the first rodent activity area, is the area of ​​the second rodent activity area, is the area of ​​the first rodent comfort zone, is the area of ​​the second rodent comfort zone, and The preset weights.

[0041] According to one embodiment of the present invention, in formula (2), It is the relative difference between the area of ​​the first rodent activity area and the area of ​​the second rodent activity area. The larger the relative difference is, the greater the area of ​​the first rodent activity area is compared to the area of ​​the second rodent activity area, indicating that the activity range of rodents is larger. is the relative difference between the first rodent comfort zone and the second rodent comfort zone. The larger the relative difference, the larger the first rodent comfort zone is compared to the second rodent comfort zone, indicating that the rodent's comfort zone is larger and the rodent's ability to adapt to the environment is stronger. and By weighted summation, we can get the rodent activity trend score. The larger the rodent activity trend score is, the more serious the rodent infestation is.

[0042] In this way, the rat activity trend score can be determined by the area of ​​the first rat activity area, the area of ​​the second rat activity area, the area of ​​the first rat comfort area, and the area of ​​the second rat comfort area. By comparing the areas of rat activity and comfort areas in the current monitoring period with those in the historical monitoring period, the changing trend of rat behavior can be understood, which helps to identify whether the rat population is spreading or adapting to environmental changes.

[0043] According to one embodiment of the present invention, in step S106, the received radar echo signal needs to be processed to extract its amplitude information. The peak amplitude is the highest amplitude value detected in the radar echo signal in the current monitoring cycle, indicating the strongest part of the echo signal. When the peak amplitude is high, it means that a large and fast-moving rodent is approaching the radar, or multiple rodents appear in the detection range of the radar at the same time. The average amplitude is the average value of the amplitude values ​​detected in the radar echo signal in the current monitoring cycle, indicating the overall strength level of the radar echo signal. When the average amplitude is high, it means that the rodent distribution density is high.

[0044] According to an embodiment of the present invention, in step S107, a rodent activity intensity score is determined based on the amplitude information.

[0045] According to one embodiment of the present invention, step S107 includes: obtaining the peak time interval and peak frequency of the peak amplitude in the current monitoring cycle; obtaining the historical average amplitude of multiple historical monitoring cycles; and determining the rodent activity intensity score based on the peak time interval, the peak frequency, the average amplitude and the historical average amplitude.

[0046] According to one embodiment of the present invention, the radar echo signal is processed by signal processing technology, the peak amplitude in the radar echo signal is identified, and all peak amplitudes occurring in the current monitoring cycle are determined. The peak time interval is the time difference between the occurrence of two adjacent peak amplitudes in the current monitoring cycle, which can reflect the frequency and regularity of rodent activities. The peak frequency refers to the number of peak amplitudes occurring in a unit time (for example, per minute or per hour), which can reflect the activity level of rodent activities.

[0047] According to one embodiment of the present invention, the rodent activity intensity score is determined according to the peak time interval, the peak frequency, the average amplitude and the historical average amplitude, including: determining the rodent activity intensity score according to formula (3): ,

[0048] (3),

[0049] in, is the peak time interval, is the preset peak time interval threshold, is the peak frequency, is the preset peak frequency threshold, is the average amplitude of the current monitoring period, is the historical average amplitude of the first historical monitoring period, is the historical average amplitude of the hth historical monitoring period, is the historical average amplitude of the h+1th historical monitoring period, is the preset average amplitude threshold, , and is the preset weight, H is the number of historical monitoring periods, h≤H, and both h and H are positive integers, and if is the conditional function.

[0050] According to one embodiment of the present invention, in formula (3), It means that when the peak time interval is less than or equal to the preset peak time interval threshold, the conditional function value is 1, otherwise, the conditional function value is 0. The smaller the peak time interval, the more frequent the rodent activity. It means that when the peak frequency is greater than or equal to the preset peak frequency threshold, the conditional function value is 1, otherwise, the conditional function value is 0. The greater the peak frequency, the more frequent the rodent activity. It is the degree of change between the relative difference between the average amplitude of the current monitoring period and the preset average amplitude threshold, and the relative difference between the historical average amplitude of the first historical monitoring period and the preset average amplitude threshold, that is, the degree of change between the average amplitude between the current monitoring period and the first historical monitoring period and the preset average amplitude threshold. It is the average value of the relative difference between the historical average amplitude of the h+1th historical monitoring period and the preset average amplitude threshold, and the relative difference between the historical average amplitude of the hth historical monitoring period and the preset average amplitude threshold, that is, the average degree of change between the average amplitude and the preset average amplitude threshold between adjacent historical monitoring periods. is the relative difference between the two change amplitudes mentioned above. The larger the relative difference, the greater the change between the average amplitude of the current monitoring cycle and the first historical monitoring cycle and the preset average amplitude threshold relative to the historical monitoring cycle. Therefore, the greater the rodent distribution density, the smaller the relative difference, indicating that the rodent distribution density is smaller. , and The weighted sum of the three items can give the rodent activity intensity score. The larger the rodent activity intensity score, the greater the rodent activity intensity and the more serious the rodent infestation.

[0051] In this way, the rodent activity intensity score can be determined by the peak time interval, peak frequency, average amplitude and historical average amplitude. The changes in rodent activity intensity can be monitored in real time from the perspective of rodent activity frequency and rodent distribution density. Increases in rodent activity intensity can be discovered in time, so that corresponding measures can be taken to reduce losses caused by rodent damage.

[0052] According to one embodiment of the present invention, in step S108, the rodent presence score, the rodent activity trend score and the rodent activity intensity score are compared with their respective scoring thresholds. If all three scores are greater than their respective scoring thresholds, the rodent damage monitoring result is high hazard. If any two of the three scores are greater than their respective scoring thresholds, the rodent damage monitoring result is medium hazard. If any one of the three scores is greater than their respective scoring thresholds, the rodent damage monitoring result is low hazard. If all three scores are less than their respective scoring thresholds, the rodent damage monitoring result is no hazard.

[0053] According to the rodent monitoring method based on drone radar trajectory detection of the embodiment of the present invention, by using a drone equipped with a high-definition camera and a high-frequency radar, efficient real-time monitoring of rodents can be achieved, with a wide coverage range, reducing the time and labor intensity of manual monitoring. Through the drone location information and the trajectory information of the rodents, the changing trend of rodent activities can be monitored in real time. Comprehensive monitoring of rodent infestation in three aspects, namely, the presence of rodents, the trend of rodent activity, and the intensity of rodent activity, can comprehensively evaluate the severity of rodent infestation, thereby improving the accuracy and comprehensiveness of rodent infestation monitoring. When determining the rodent presence score, the rodent presence score can be determined through historical rodent information and rodent information, and the number and type of rodents can be judged from the increase or decrease in the number of rodents and the number of rodents, so as to understand the severity of the rodent infestation and improve the comprehensiveness and reliability of the rodent presence score. When determining the rodent activity trend score, the rodent activity trend score can be determined by the area of ​​the first rodent activity area, the area of ​​the second rodent activity area, the area of ​​the first rodent comfort area, and the area of ​​the second rodent comfort area. By comparing the areas of rodent activity and comfort areas in the current monitoring period with those in the historical monitoring period, the changing trend of rodent behavior can be understood, which helps to identify whether the rodent population is spreading or adapting to environmental changes. When determining the rodent activity intensity score, the rodent activity intensity score can be determined by the peak time interval, peak frequency, average amplitude, and historical average amplitude. From the perspective of rodent activity frequency and rodent distribution density, the change in rodent activity intensity can be monitored in real time, and the increase in rodent activity intensity can be discovered in time, so that corresponding measures can be taken to reduce the losses caused by rodent damage.

[0054] Figure 2The block diagram of a rodent monitoring system based on drone radar trajectory detection according to an embodiment of the present invention is exemplarily shown, wherein the system comprises: a rodent information module, which is used to capture images of rodents through a high-definition camera of a drone to obtain rodent information in a current monitoring period, wherein the rodent information includes the number of rodents and the type of rodents; a rodent presence scoring module, which is used to determine a rodent presence score based on the rodent information; a drone position information module, which is used to obtain drone position information at multiple moments in the current monitoring period; and a trajectory information module, which is used to obtain rodent trajectory information through a high-frequency radar carried by a drone at multiple moments in the current monitoring period, wherein The trajectory information includes relative position information and speed data, the relative position information is the relative position of the drone and the rodents; a rodent activity trend scoring module is used to determine the rodent activity trend score according to the drone position information and the trajectory information; an amplitude information module is used to obtain the amplitude information of the radar echo signal in the current monitoring period, wherein the amplitude information includes the peak amplitude and the average amplitude; a rodent activity intensity scoring module is used to determine the rodent activity intensity score according to the amplitude information; a rodent pest monitoring report module is used to generate a rodent pest monitoring report according to the rodent presence score, the rodent activity trend score and the rodent activity intensity score.

[0055] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

Claims

1. A rodent monitoring method based on drone radar trajectory detection, characterized in that: include: The image of rats is captured by the high-definition camera of the drone to obtain the rat information in the current monitoring period, wherein the rat information includes the number of rats and the type of rats; the rat presence score is determined based on the rat information; the drone position information is obtained at multiple times in the current monitoring period; the trajectory information of the rats is obtained by the high-frequency radar carried by the drone at multiple times in the current monitoring period, wherein the trajectory information includes relative position information and speed data, and the relative position information is the relative position of the drone and the rats; the rat activity trend score is determined based on the drone position information and the trajectory information; the amplitude information of the radar echo signal in the current monitoring period is obtained, wherein the amplitude information includes the peak amplitude and the average amplitude; the rat activity intensity score is determined based on the amplitude information; and a rodent monitoring report is generated based on the rat presence score, the rat activity trend score and the rat activity intensity score.

2. The rodent monitoring method based on drone radar trajectory detection according to claim 1 is characterized in that: Determining a rodent presence score according to the rodent information includes: acquiring historical rodent information of multiple historical monitoring periods, wherein the historical rodent information includes historical rodent numbers and historical rodent species; determining a rodent presence score according to the historical rodent information and the rodent information.

3. The rodent monitoring method based on drone radar trajectory detection according to claim 2 is characterized in that: Determining a rodent presence score based on the historical rodent information and the rodent information includes: determining a rodent presence score based on a formula Determine rodent presence score ,in, is the number of rodents in the current monitoring period, is the historical rodent population in the first historical monitoring period, is the historical rodent population in the hth historical monitoring period, is the historical number of rodents in the h+1th historical monitoring period, is the rodent species in the current monitoring period, The historical rodent species in the first historical monitoring cycle are: and is the preset weight, H is the number of historical monitoring periods, h≤H, and both h and H are positive integers, and max is the maximum value function.

4. The rodent monitoring method based on drone radar trajectory detection according to claim 1 is characterized in that: Determine the rodent activity trend score according to the drone position information and the trajectory information, including: obtain the rodent position information according to the drone position information and the relative position information; perform a connected domain analysis on the multiple rodent position information in the current monitoring cycle to obtain a first rodent activity area, and determine the area of ​​the first rodent activity area; obtain historical rodent position information at multiple moments in the first historical monitoring cycle; perform a connected domain analysis on the multiple historical rodent position information in the first historical monitoring cycle to obtain a second rodent activity area, and determine the area of ​​the second rodent activity area; determine a speed distribution function according to the rodent position information and the speed data; determine the comfortable position information of the rodent whose speed data is greater than the preset speed data according to the speed distribution function; and perform a connected domain analysis on the multiple historical rodent position information in the first historical monitoring cycle to obtain a second rodent activity area, and determine the area of ​​the second rodent activity area. Performing a connected domain analysis on the multiple rodent comfort position information to obtain a first rodent comfort area, and determining the area of ​​the first rodent comfort area; acquiring historical speed data at multiple moments in the first historical monitoring cycle; determining a historical speed distribution function based on the historical rodent position information and the historical speed data; determining historical rodent comfort position information whose historical speed data is greater than a preset speed data based on the historical speed distribution function; performing a connected domain analysis on the multiple historical rodent comfort position information in the first historical monitoring cycle to obtain a second rodent comfort area, and determining the area of ​​the second rodent comfort area; determining a rodent activity trend score based on the area of ​​the first rodent activity area, the area of ​​the second rodent activity area, the area of ​​the first rodent comfort area, and the area of ​​the second rodent comfort area.

5. The rodent monitoring method based on drone radar trajectory detection according to claim 4 is characterized in that: Determining a rodent activity trend score according to the first rodent activity area, the second rodent activity area, the first rodent comfort area, and the second rodent comfort area, including: determining a rodent activity trend score according to the formula Determine rodent activity trend score ,in, is the area of ​​the first rodent activity area, is the area of ​​the second rodent activity area, is the area of ​​the first rodent comfort zone, is the area of ​​the second rodent comfort zone, and The preset weights.

6. The rodent monitoring method based on drone radar trajectory detection according to claim 1 is characterized in that: Determine the rodent activity intensity score based on the amplitude information, including: obtaining the peak time interval and peak frequency of the peak amplitude in the current monitoring cycle; obtaining the historical average amplitude of multiple historical monitoring cycles; determine the rodent activity intensity score based on the peak time interval, the peak frequency, the average amplitude and the historical average amplitude.

7. The rodent monitoring method based on drone radar trajectory detection according to claim 6 is characterized in that: Determining a rodent activity intensity score according to the peak time interval, the peak frequency, the average amplitude and the historical average amplitude includes: according to the formula Determining rodent activity intensity scores ,in, is the peak time interval, is the preset peak time interval threshold, is the peak frequency, is the preset peak frequency threshold, is the average amplitude of the current monitoring period, is the historical average amplitude of the first historical monitoring period, is the historical average amplitude of the hth historical monitoring period, is the historical average amplitude of the h+1th historical monitoring period, is the preset average amplitude threshold, , and is the preset weight, H is the number of historical monitoring periods, h≤H, and both h and H are positive integers, and if is the conditional function.

8. A rodent monitoring system based on drone radar trajectory detection for executing the rodent monitoring method based on drone radar trajectory detection as described in any one of claims 1 to 7, characterized in that: include: A rodent information module is used to capture images of rodents through a high-definition camera of a drone to obtain rodent information within a current monitoring period, wherein the rodent information includes the number of rodents and the type of rodents; a rodent presence scoring module is used to determine a rodent presence score based on the rodent information; a drone position information module is used to obtain drone position information at multiple moments in the current monitoring period; a trajectory information module is used to obtain the trajectory information of rodents through a high-frequency radar carried by the drone at multiple moments in the current monitoring period, wherein the trajectory information includes relative position information and speed data, and the relative position information is the relative position of the drone and the rodents; a rodent activity trend scoring module is used to determine a rodent activity trend score based on the drone position information and the trajectory information; an amplitude information module is used to obtain the amplitude information of the radar echo signal within the current monitoring period, wherein the amplitude information includes a peak amplitude and an average amplitude; a rodent activity intensity scoring module is used to determine a rodent activity intensity score based on the amplitude information; and a rodent pest monitoring report module is used to generate a rodent pest monitoring report based on the rodent presence score, the rodent activity trend score, and the rodent activity intensity score.

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