Bird detection radar device

Abstract

The present application discloses a bird detection radar device, which relates to the field of radio ranging technology. The bird detection radar device includes: a wind speed sensor for obtaining wind speed time series data; a bird detection radar for obtaining the echo time series data of the bird; a server for obtaining the distance time series data between the bird detection radar and the bird based on the echo time series data; updating the flight prediction data; and a bird repellent for driving away the bird based on the updated flight prediction data. In the process of predicting and analyzing the flight trajectory of the bird, the present application takes into account the influence of wind speed and wind direction on the flight trajectory of the bird, so that the flight prediction data of the bird can be predicted more accurately. And subsequently, the bird is accurately driven away based on the flight prediction data, that is, if the bird will not enter the warning area in the future, the bird will not be driven away, and if the bird may enter the warning area in the future, the bird will be driven away.

Description

Technical Field

[0001] The present application relates to the technical field of radio ranging, and in particular to a bird detection radar device. Background Art

[0002] Bird detection radar is a radar system used to monitor bird activities. It is mainly used in places such as airports, wind farms, flight test sites, etc. where bird collisions need to be prevented. In order to effectively detect birds, bird detection radar devices usually use a variety of technical means. For example: Doppler radar technology and pulse radar technology. Among them, pulse radar technology calculates the distance of the target by emitting short-term high-energy radar pulse waves and measuring the reflection time of the signal. Its advantage is that it is suitable for long-distance detection of birds and can provide high distance resolution.

[0003] In the application scenario of wind power generation, flying birds have a great impact on the safety of equipment such as wind turbines. In order to avoid damage to equipment such as wind turbines caused by bird collisions, it is necessary to detect birds around equipment such as wind turbines in real time. The existing technology generally uses pulse radar technology to analyze the real-time quantity of flying birds and the threat level of flying birds to the equipment, and then chooses to drive away the birds according to the threat level. For example: the closer the bird is to equipment such as wind turbines, the greater the threat of the bird, and the more it needs to be driven away. In other words, the existing technology uses pulse radar technology to detect the presence of birds within the effective range, and then drives the birds away. This method not only has high energy consumption, but also has low efficiency and generates large noise pollution. Summary of the invention

[0004] The purpose of the present application is to provide a bird detection radar device to solve the technical problems of high energy consumption, low efficiency and large noise pollution in the prior art of driving away birds.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] A bird detection radar device, comprising:

[0007] Wind speed sensor, used to obtain wind speed time series data;

[0008] Bird detection radar, used to obtain the echo time series data of flying birds;

[0009] A server, configured to obtain time series data of the distance between the bird detection radar and the bird based on the echo time series data;

[0010] and, based on the distance time series data, obtaining flight prediction data of the flying bird;

[0011] And, based on the wind speed time series data and the flight prediction data, a pseudo-random parameter is obtained; the pseudo-random parameter is at least used to evaluate the credibility of the flight prediction data;

[0012] and, based on the pseudo-random parameters, updating the flight prediction data;

[0013] The bird repeller repels flying birds based on the updated flight prediction data.

[0014] As a specific solution in the technical solution of the present application, the server is further used to obtain first data and second data based on the distance time series data; the first data and the second data are two adjacent data in the distance time series data; and the second data is the current distance time series data;

[0015] and, based on the first data and the second data, obtaining a first time interval;

[0016] And, based on the first data and the second data, a first straight-line distance is obtained; the first straight-line distance is the straight-line distance that the flying bird moves within the first time interval;

[0017] And, based on the first time interval and the first straight-line distance, flight prediction data of the flying bird is obtained; the flight prediction data includes a predicted speed value and a predicted direction value of the flying bird.

[0018] As a specific solution in the technical solution of the present application, the server is further used to obtain a first time based on the echo timing data; the first time is the time when the bird detection radar emits a radar beam;

[0019] and, based on the echo timing data, obtaining a second time; the second time is the time when the bird detection radar receives the radar beam;

[0020] And, based on the first time and the second time, obtaining time series data of the distance between the bird detection radar and the bird.

[0021] As a specific solution in the technical solution of the present application, the server is also used to obtain the current time series wind direction value based on the wind speed time series data;

[0022] and, based on the flight prediction data, obtaining a predicted direction value of the flying bird;

[0023] And, based on the current time series wind direction value and the predicted direction value, obtaining a first parameter;

[0024] And, based on the first parameter, a pseudo-random parameter is obtained.

[0025] As a specific solution in the technical solution of the present application, the server obtains the calculation formula of the first parameter based on the current time series wind direction value and the predicted direction value as follows:

[0026]

[0027] Among them, R a represents the first parameter; π represents the degree, i.e. 180°; c represents the number of radar beams received by the bird detection radar in the current time period; θ n Indicates the predicted direction value of the nth flying bird in the current time period; w c It indicates the current time series wind direction value; exp() indicates the exponential function with the natural constant e as the base; || indicates the absolute value.

[0028] As a specific solution in the technical solution of the present application, the server is also used to obtain the current time series wind speed value based on the wind speed time series data;

[0029] and, based on the flight prediction data, obtaining a predicted speed value of the flying bird;

[0030] And, based on the current time series wind speed value and the predicted speed value, obtaining a second parameter;

[0031] And, based on the first parameter and the second parameter, a pseudo-random parameter is obtained.

[0032] As a specific solution in the technical solution of the present application, the server obtains the calculation formula of the second parameter based on the current time series wind speed value and the predicted speed value as follows:

[0033]

[0034] Among them, R b represents the second parameter; c represents the number of radar beams received by the bird detection radar in the current time period; v n Indicates the predicted speed value of the nth flying bird in the current time period; k c Indicates the current time series wind speed value; || indicates seeking the absolute value.

[0035] As a specific solution in the technical solution of the present application, the server obtains a calculation formula for a pseudo-random parameter based on the first parameter and the second parameter as follows:

[0036]

[0037] Where R represents a pseudo-random parameter; R a Represents the first parameter; R b Indicates the second parameter.

[0038] As a specific solution in the technical solution of the present application, the server is further used to obtain a first weight and a second weight based on the pseudo-random parameter; the first weight is a weight related to the predicted direction value of the bird in the prediction model; the second weight is a weight related to the predicted speed value of the bird in the prediction model;

[0039] And, updating the flight prediction data through a prediction model based on the first weight and the second weight.

[0040] As a specific solution in the technical solution of the present application, the server updates the calculation formula of the flight prediction data based on the pseudo-random parameters as follows:

[0041] Pre2=Pre1*(1+sigmoid(R))

[0042] Wherein, Pre2 represents the updated flight prediction data; Pre1 represents the flight prediction data before the update; R represents a pseudo-random parameter; sigmoid() represents an arbitrary function that maps a numerical value to the interval (-1, 1).

[0043] Compared with the prior art, the beneficial effects of this application are:

[0044] In the process of predicting and analyzing the bird's trajectory, the present application takes into account the influence of wind speed and wind direction on the bird's flight trajectory, so that the flight prediction data of the bird can be predicted more accurately. And the bird is subsequently accurately driven away based on the flight prediction data, that is, if the bird will not enter the warning area in the future, the bird will not be driven away. If the bird may enter the warning area in the future, the bird will be driven away. In other words, compared with the prior art, as long as the bird is detected, the bird will be driven away. This application can effectively reduce the frequency of bird driving, that is, reduce the energy consumption of driving away the bird, improve the efficiency of driving away the bird, and reduce the noise pollution formed. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A schematic diagram of a flow chart of a bird detection radar method proposed in an embodiment of the present application;

[0046] Figure 2 A schematic diagram of the structure of a bird detection radar device proposed in an embodiment of the present application;

[0047] Figure 3 A schematic diagram of the structure of the bird detection radar detecting the distance of flying birds proposed in the embodiment of the present application;

[0048] Figure 4 A schematic diagram of the flight trend of birds and wind direction proposed in an embodiment of the present application.

[0049] In the figure: 12, bird detection radar; 2, flying birds; 3, early warning area. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] The terms "first", "second", etc. in the specification of the embodiment of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence, such as the first time and the second time proposed below, which belong to different times. It should be understood that the time used in this way can be interchanged in appropriate circumstances, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device including a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of the modules that appear in the embodiment of the present application is only a logical division. There may be other division methods when implemented in practical applications, such as multiple modules can be combined into or integrated in another system, or some features can be ignored, or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiment of the present application. Furthermore, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0052] In order to solve the technical problems of the prior art proposed in the background technology that the energy consumption of the existing bird-repelling technology is high, the efficiency is low, and the noise pollution is large, the present application proposes a bird detection radar method, the bird detection radar method is applied to a bird detection radar device, and the bird detection radar device includes a bird detection radar and a wind speed sensor, such as Figure 1 As shown, the method includes steps S100 to S700.

[0053] Step S100: Based on the wind speed sensor, obtain wind speed time series data.

[0054] It should be clear that in the embodiments of the present application, the wind speed time series data includes wind speed and wind direction. In the embodiments of the present application, the wind speed sensor can be any sensor that can measure wind speed and wind direction. For example, an ultrasonic wind speed and wind direction sensor, a cup wind speed sensor, a hot wire wind speed sensor, a photoelectric wind direction sensor, a magnetoresistive wind direction sensor, etc.

[0055] Step S200: Based on the bird detection radar, acquire the echo time series data of the bird.

[0056] It should be clear that the principle of bird detection radar mainly depends on the emission, reflection and reception of radar beams, as well as the analysis and processing of reflected signals. General bird detection radars transmit radar beams through antennas. When these beams encounter obstacles (such as birds), they will be reflected back and received by radar receivers. In modern radar systems, the judgment of targets is mainly completed by computers. By calculating the area of ​​the reflected radar beam, the volume of the target can be determined; by continuously tracking and calculating the position of the target when the radar beam is reflected, the movement speed and trajectory of the target can be calculated. This technology can effectively detect low-altitude moving targets, such as flocks of birds or drones, and locate and identify them. Since bird detection radar is a mature technology, it will not be described here. In an embodiment of the present application, the echo timing data includes at least the time when the radar beam is emitted and the return time of the radar beam (that is, the time when the radar beam is received).

[0057] Step S300: Based on the echo time series data, obtain the distance time series data between the bird detection radar and the bird.

[0058] It should be clear that in the present application, any method can be used to obtain the distance time series data between the bird detection radar and the bird based on the echo time series data. For example, the method disclosed in the patent application with application number CN202410219672.5 and the name of a radar detection distance calculation method based on a statistical model can be used. It should be clear that the speed of the bird is almost negligible relative to the speed of the radar beam (that is, the speed of light). In order to reduce the calculation complexity of the distance time series data between the bird detection radar and the bird, in one embodiment of the present application, step S300, based on the echo time series data, obtains the distance time series data between the bird detection radar and the bird, including steps S310 to S330.

[0059] Step S310: Acquire a first time based on the echo timing data.

[0060] In this embodiment, the first time is the time when the bird detection radar sends out a radar beam.

[0061] Step S320: Acquire a second time based on the echo timing data.

[0062] In this embodiment, the second time is the time when the bird detection radar receives the radar beam.

[0063] Step S330: Based on the first time and the second time, obtaining time series data of the distance between the bird detection radar and the bird.

[0064] In a specific embodiment of the present application, Figure 3 As shown, assuming that at time t1, the bird detection radar 12 receives the radar beam emitted at t0, the distance time series data between the bird detection radar 12 and the bird 2 at time t1 is calculated as follows:

[0065]

[0066] Wherein, L1 represents the distance between the flying bird 2 and the flying bird detection radar 12 at time t1; c represents the speed of light; t1 represents the second time; and t0 represents the first time. It should be noted that the flying speed of the flying bird 2 is almost negligible relative to the speed of light, so calculating the distance between the flying bird 2 and the flying bird detection radar 12 by the above formula can effectively reduce the complexity of the calculation, that is, reduce the amount of calculation.

[0067] Step S400: Acquire flight prediction data of the flying bird based on the distance time series data.

[0068] It should be clear that in an embodiment of the present application, any suitable method can be used to obtain the flight prediction data of the flying bird based on the distance time series data. For example, in an embodiment of the present application, the method disclosed in the patent document with application number CN202210079975.2 and the name of a method for simulating radar trajectories of bird flight can be used. In order to further reduce the computational complexity of obtaining the flight prediction data of the flying bird based on the distance time series data, in one embodiment of the present application, step S400, based on the distance time series data, obtains the flight prediction data of the flying bird, including steps S410 to S440.

[0069] Step S410: Acquire first data and second data based on the distance time series data.

[0070] In this embodiment, the first data and the second data are two adjacent data in the distance time series data; and the second data is the current distance time series data. Figure 3 As shown, if the current time is t2 and the previous time series is t1, then the current distance time series data is as follows Figure 3 L2 shown; the previous distance time series data is as follows Figure 3 L1 shown.

[0071] Step S420: Acquire a first time interval based on the first data and the second data.

[0072] It should be noted that the first time interval is equal to t2-t1.

[0073] Step S430: Acquire a first straight-line distance based on the first data and the second data.

[0074] It should be clear that the first straight-line distance is the straight-line distance that the bird moves within the first time interval. Figure 3 As shown, the first straight-line distance is S1. Since the position of the bird 2 at time t1 and time t2 is determined, S1 can be directly obtained.

[0075] Step S440: Acquire flight prediction data of the flying bird based on the first time interval and the first straight-line distance.

[0076] It should be clear that in this embodiment, the flight prediction data includes the predicted speed value and predicted direction value of the flying bird. It is easy to understand that since the first time interval is known and the first straight-line distance is also known, the average speed of the flying bird from time t1 to time t2 can be calculated based on the first straight-line distance and the first time interval. Specifically, the calculation formula of the average speed is as follows:

[0077] V=S1 / (t2-t1)

[0078] Wherein, V represents the average speed; S1 represents the first straight-line distance; and t2-t1 represents the first time interval.

[0079] In the embodiment of the present application, the average speed can be directly used as the predicted speed value of the flying bird; the direction of the flying bird's position at time t1 pointing to the position at time t2 is used as the predicted direction value, that is, Figure 3 As shown, angle A1 is used as the predicted direction value of the flying bird after time t2. Specifically, if the lengths of the sides L1, L2 and S1 of the triangle are all known, the size of angle A1 can be calculated based on trigonometric functions, which is a mature technology and will not be described in detail here.

[0080] It is easy to understand that Figure 3 As shown, the distance S2 that the bird moves from time t2 to time t3 can also be calculated based on the same method, and the direction angle A2 that the bird flies from time t2 to time t3 can also be calculated, which will not be repeated here.

[0081] Of course, in the embodiment of the present application, in order to make the predicted speed value and predicted direction value of the bird more reasonable, in the embodiment of the present application, the autoregressive integrated moving average (ARIMA) prediction model can be used to predict the trajectory (i.e., flight direction) and speed of the bird's flight. The ARIMA prediction model is a powerful tool commonly used in time series data analysis and prediction. It models and predicts time series data by combining three techniques: autoregression (AR), difference (I) and moving average (MA). It can better handle the characteristics of many time series data, such as seasonality, trend, periodicity, etc., and can fit the data with fewer parameters.

[0082] It should be noted that the existing technology only predicts the future trajectory of the bird based on its original trajectory, and does not consider the impact of natural factors on the trajectory of the bird. In nature, although the flight trajectory of a bird is randomly affected by environmental factors (such as wind direction, buildings, etc.), this randomness is not completely without rules. On the contrary, the behavior of birds often follows certain natural laws (for example, reducing physical exertion, avoiding collisions, etc.), which make the trajectory of birds present a certain pattern under specific conditions, namely "pseudo-randomness". For example, when the wind direction is opposite to the flight direction of the bird, the bird will tend to change direction to reduce physical exertion; when the bird approaches a building, it will change its flight path to avoid obstacles. Although these behaviors appear random, they are actually the response of the bird to the environment, and the results are predictable.

[0083] These pseudo-random phenomena are not considered in the prior art. For example, the prior art uses pulse radar to detect birds in real time, and drives them away based on the presence or absence of birds in the detection results. However, the flight trajectories of some birds do not actually affect large facilities, that is, their trajectories do not enter the early warning area, that is, they will not affect the facilities in the early warning area. In order to be able to identify the trajectory of birds more accurately, in the embodiments of the present application, it is necessary to predict the flight trajectory (that is, flight direction) and speed of birds based on the randomness of their flight trajectories. Among them, the randomness of bird flight is expressed as pseudo-randomness under the influence analysis of environmental parameters, that is, the change of bird's orbit is related to dimensional parameters such as wind direction and equipment building distance in environmental parameters. The more opposite the wind direction is to the real-time moving direction of the bird, the greater the impact on the orbit direction weight in the predicted orbit, because the real-time consumption of the bird is higher. Correspondingly, the higher the pseudo-randomness, that is, the more obvious the difference between the parameters such as the bird's flight trajectory and speed and the environment, the higher the adjustment weight in the prediction process, the more effectively the bird's trajectory prediction error caused by randomness can be avoided, and the more accurately the bird's flight trajectory can be predicted. This is conducive to laying the foundation for solving the technical problems raised in the background technology of this application.

[0084] Step S500: Acquire pseudo-random parameters based on the wind speed time series data and the flight prediction data.

[0085] In an embodiment of the present application, the pseudo-random parameter is at least used to evaluate the credibility of the flight prediction data. It is easy to understand that if the future flight direction of the bird in the flight prediction data is more opposite, the credibility of the obtained bird's flight trajectory is lower, that is, the flight trajectory needs to be adjusted. If the future flight direction of the bird in the flight prediction data is more opposite and the wind speed is greater, the credibility of the obtained bird's flight trajectory is also lower, that is, the flight trajectory needs to be adjusted. That is to say, in an embodiment of the present application, the pseudo-random parameter can be any data that can evaluate the credibility of the flight prediction data.

[0086] In one embodiment of the present application, step S500, obtaining pseudo-random parameters based on the wind speed time series data and the flight prediction data, includes steps S510 to S540.

[0087] Step S510: Based on the wind speed time series data, obtain the current time series wind direction value.

[0088] As can be seen from the foregoing, the current time series wind direction value can be directly obtained by the wind speed sensor, which is a mature technology and will not be described in detail here.

[0089] Step S520: Based on the flight prediction data, obtain the predicted direction value of the flying bird.

[0090] As can be seen from the foregoing, the current flight direction value of the bird can be used as the predicted direction value of the bird, and the predicted direction value of the bird can also be obtained through a prediction model.

[0091] Step S530: acquiring a first parameter based on the current time series wind direction value and the predicted direction value;

[0092] It should be clear that in this embodiment, the first parameter is mainly a parameter used to evaluate the difference between the current time series wind direction value and the predicted direction value. As can be seen from the foregoing, the greater the difference between the current time series wind direction value and the predicted direction value, the more it is necessary to adjust the flight prediction data to make the flight prediction data more accurate.

[0093] In an embodiment of the present application, any method can be used to obtain the first parameter based on the current time series wind direction value and the predicted direction value. For example, the first parameter can be the difference between the current time series wind direction value and the predicted direction value, or the absolute value of the difference between the current time series wind direction value and the predicted direction value. In order to facilitate the subsequent use of the first parameter, in an embodiment of the present application, step S530, based on the current time series wind direction value and the predicted direction value, obtains the calculation formula of the first parameter as follows:

[0094]

[0095] Among them, R a represents the first parameter; π represents the degree, i.e. 180°; c represents the number of radar beams received by the bird detection radar in the current time period; θ n Indicates the predicted direction value of the nth flying bird in the current time period; w c It indicates the current time series wind direction value; exp() indicates the exponential function with the natural constant e as the base; || indicates the absolute value.

[0096] In the embodiment of the present application, the current time period can be any time period, for example, the current time period can be 1 second, 2 seconds or 3 seconds. If the current time period is 1 second, and the number of radar beams received by the bird detection radar per second is 60 times, then c=60. It is easy to understand that in this embodiment, the greater the difference between the current time series wind direction value and the predicted direction value, the greater the first parameter (i.e., R a ) is closer to 1; if the difference between the current time series wind direction value and the predicted direction value is smaller, the first parameter (i.e. R a ) is closer to 0.

[0097] Step S540: Based on the first parameter, obtain a pseudo-random parameter.

[0098] In an embodiment of the present application, the first parameter can be directly used as a pseudo-random parameter. That is, as mentioned above, the closer the first parameter (i.e., the pseudo-random parameter) is to 1, the more it is necessary to adjust the flight prediction data. It should be noted that sometimes, although the flight direction of the bird is opposite to the wind direction, if the wind speed is small, the bird may also fly against the wind. In order to obtain more accurate pseudo-random parameters, the influence of wind speed on the flight direction of the bird should also be considered, that is, the greater the wind speed, the more likely the flight direction of the bird is the same as the wind direction.

[0099] In a specific embodiment of the present application, step S540, based on the first parameter, obtains a pseudo-random parameter, including steps S541 to S544.

[0100] Step S541: Based on the wind speed time series data, obtain the current time series wind speed value.

[0101] As can be seen from the foregoing, when the current time series wind speed value can be directly obtained by the wind speed sensor, it is a mature technology and will not be described in detail here.

[0102] Step S542: Based on the flight prediction data, obtain the predicted speed value of the flying bird.

[0103] As can be seen from the foregoing, the current flying speed value of the bird can be used as the predicted speed value of the bird, and the predicted speed value of the bird can also be obtained through a prediction model.

[0104] Step S543: Acquire a second parameter based on the current time series wind speed value and the predicted speed value.

[0105] In the embodiment of the present application, the second parameter is mainly used to evaluate the current wind speed. As can be seen from the foregoing, the greater the current wind speed, the less likely it is that the flight direction of the bird is opposite to the wind speed direction, that is, the greater the weight of the current wind speed on the bird flight prediction data, that is, the more it is necessary to adjust the bird flight prediction data. In the embodiment of the present application, the current time series wind speed value can be directly used as the second parameter.

[0106] It should be noted that, when there is a large difference between the wind direction and the flight direction of the bird, the greater the wind speed, the higher the pseudo-random relationship between it and the flight speed of the bird. That is, headwind will increase the energy consumption of the bird, so the bird is more likely to adjust its flight direction to follow the wind direction or reduce the time and distance of flying against the wind. In other words, the greater the difference between the flight speed of the bird and the wind speed, the more likely the bird is to change its flight direction. Therefore, in another embodiment of the present application, step S543, based on the current time series wind speed value and the predicted speed value, the calculation formula for obtaining the second parameter is as follows:

[0107]

[0108] Among them, R b represents the second parameter; c represents the number of radar beams received by the bird detection radar in the current time period; v n Indicates the predicted speed value of the nth flying bird in the current time period; k c Indicates the current time series wind speed value; || indicates seeking the absolute value.

[0109] In this embodiment, the above formula can reflect the difference between the wind speed and the average speed of the bird in the current time period, which is beneficial for subsequent accurate judgment of whether the bird has changed its flight direction.

[0110] Step S544: Acquire a pseudo-random parameter based on the first parameter and the second parameter.

[0111] As can be seen from the foregoing, the pseudo-random parameters are at least used to evaluate the credibility of the flight prediction data. That is to say, in this embodiment, the pseudo-random parameters can be obtained based on the first parameter and the second parameter in any manner, as long as the pseudo-random parameters can reflect the credibility of the flight prediction data. For example, in one embodiment of the present application, the sum or product of the first parameter and the second parameter can be used as a pseudo-random parameter. It is easy to understand that in this embodiment, the larger the pseudo-random parameter, the smaller the credibility of the obtained flight prediction data, that is, the more it is necessary to update and adjust the flight prediction data. In another embodiment of the present application, step S544, based on the first parameter and the second parameter, the calculation formula for obtaining the pseudo-random parameters is as follows:

[0112]

[0113] Where R represents a pseudo-random parameter; R a Represents the first parameter; R b Indicates the second parameter.

[0114] It should be noted that, in the embodiment of the present application, the direction basis is used as the coefficient in the pseudo-random calculation (ie, 1 / (1-R a )) to obtain pseudo-random parameters. When the direction basis satisfies the large difference, that is, the difference result is small, the coefficient becomes larger, and the pseudo-random relationship at this time is greater, because the direction of the basis is opposite; further, when the direction is opposite, the higher the difference between the flying speed of the bird and the wind direction, the higher the energy consumption of the bird, and the higher the pseudo-randomness at this time.

[0115] Step S600: updating the flight prediction data based on the pseudo-random parameters.

[0116] As can be seen from the foregoing, if the pseudo-random parameter is larger, the flight prediction data needs to be updated more. In an embodiment of the present application, the flight prediction data can be updated based on the pseudo-random parameter in any manner. For example: In a specific embodiment of the present application, step S600, based on the pseudo-random parameter, updates the flight prediction data, including step S610 and step S620.

[0117] In an embodiment of the present application, an autoregressive differential moving average prediction model is used to obtain the predicted time series value of the flight prediction data.

[0118] Step S610: Based on the pseudo-random parameters, obtain a first weight and a second weight.

[0119] In this embodiment, the first weight is the weight associated with the predicted bird direction value in the prediction model, that is, the first weight is used to adjust the weight of the predicted bird direction value in the autoregressive difference moving average prediction model. The second weight is the weight associated with the predicted bird speed value in the prediction model, that is, the second weight is used to adjust the weight of the predicted bird speed value in the autoregressive difference moving average prediction model.

[0120] Step S620: updating the flight prediction data through a prediction model based on the first weight and the second weight.

[0121] In the embodiment of the present application, the pseudo-random parameter may be normalized and used as the first weight and the second weight, or the first coefficient may be normalized and used as the first weight, and the second coefficient may be normalized and used as the second weight.

[0122] In another embodiment of the present application, the acquired flight prediction data can be directly updated by pseudo-random parameters. Specifically, in step S600, based on the pseudo-random parameters, the calculation formula for updating the flight prediction data is as follows:

[0123] Pre2=Pre1*(1+sigmoid(R))

[0124] Wherein, Pre2 represents the updated flight prediction data; Pre1 represents the flight prediction data before the update; R represents a pseudo-random parameter; sigmoid() represents an arbitrary function that maps a numerical value to the interval (-1, 1).

[0125] Step S700: driving away flying birds based on the updated flight prediction data.

[0126] It is easy to understand that in the embodiment of the present application, the flight prediction data includes the future flight direction, speed and distance from the warning area of ​​the bird. Figure 4As shown, if it is detected that the future flight trajectory of the bird may enter the warning area 3, the bird is driven away; if the future flight trajectory of the bird will not enter the warning area 3, there is no need to drive the bird away even if the bird is within the effective detection range of the bird detection radar device. Figure 4 As shown, the effective detection range of the bird detection radar 12 must be larger than the early warning area 3. The early warning area 3 is an artificially divided area, which means that if a bird enters the area, it may cause damage to the wind power generation equipment.

[0127] Finally, in order to enable those skilled in the art to clearly understand the technical principles of the present application, it is necessary to explain the technical principles of the present application. Figure 4 As shown in FIG. 1 , it is assumed that the bird 2 is outside the warning area 3 at the current moment, that is, the bird 2 will not cause any damage to the relevant equipment within the warning area 3. However, if the wind direction is Figure 4 As shown in the direction A in the figure, bird 2 is likely to enter warning area 3 in the future with the help of wind direction A. And, the greater the wind speed, the greater the possibility that bird 2 will enter warning area 3 with the help of wind direction A in the future. Figure 4 If the wind speed is opposite to the direction of A in FIG, the bird 2 is very likely not to enter the warning area 3 in the future time period. Moreover, if the wind speed is greater, even if the bird 2 is relatively close to the warning area 3 at present, the possibility of the bird 2 entering the warning area 3 in the future is not great.

[0128] It should be clear that the bird detection radar method proposed in this application takes into account the influence of wind speed and wind direction on the flight trajectory of birds during the bird trajectory prediction and analysis process, so that the flight prediction data of birds can be predicted more accurately. And the birds are subsequently accurately driven away based on the flight prediction data, that is, if the birds will not enter the early warning area in the future, the birds will not be driven away. If the birds may enter the early warning area in the future, the birds will be driven away. In other words, compared with the prior art, as long as the birds are detected, the birds will be driven away. This application can effectively reduce the frequency of bird driving, that is, reduce the energy consumption of driving away birds, improve the efficiency of driving away birds, and reduce the noise pollution formed.

[0129] After introducing the bird detection radar method proposed in the embodiment of the present application, the following introduces a bird detection radar device proposed in the present application. Figure 2 As shown, the bird detection radar device 10 includes:

[0130] Wind speed sensor 11, used to obtain wind speed time series data;

[0131] A bird detection radar 12, used to obtain the echo time series data of the bird;

[0132] The server 13 is used to obtain the distance time series data between the bird detection radar and the bird based on the echo time series data;

[0133] and, based on the distance time series data, obtaining flight prediction data of the flying bird;

[0134] And, based on the wind speed time series data and the flight prediction data, a pseudo-random parameter is obtained; the pseudo-random parameter is at least used to evaluate the credibility of the flight prediction data;

[0135] and, based on the pseudo-random parameters, updating the flight prediction data;

[0136] The bird repeller 14 repels flying birds based on the updated flight prediction data.

[0137] As a specific embodiment of the present application, the server 13 is further used to obtain first data and second data based on the distance time series data; the first data and the second data are two adjacent data in the distance time series data; and the second data is the current distance time series data;

[0138] and, based on the first data and the second data, obtaining a first time interval;

[0139] And, based on the first data and the second data, a first straight-line distance is obtained; the first straight-line distance is the straight-line distance that the flying bird moves within the first time interval;

[0140] And, based on the first time interval and the first straight-line distance, flight prediction data of the flying bird is obtained; the flight prediction data includes a predicted speed value and a predicted direction value of the flying bird.

[0141] As a specific embodiment of the present application, the server 13 is further used to obtain a first time based on the echo timing data; the first time is the time when the bird detection radar emits a radar beam;

[0142] and, based on the echo timing data, obtaining a second time; the second time is the time when the bird detection radar receives the radar beam;

[0143] And, based on the first time and the second time, obtaining time series data of the distance between the bird detection radar and the bird.

[0144] As a specific embodiment of the present application, the server 13 is further used to obtain the current time series wind direction value based on the wind speed time series data;

[0145] and, based on the flight prediction data, obtaining a predicted direction value of the flying bird;

[0146] And, based on the current time series wind direction value and the predicted direction value, obtaining a first parameter;

[0147] And, based on the first parameter, a pseudo-random parameter is obtained.

[0148] As a specific embodiment of the present application, the server 13 obtains the calculation formula of the first parameter based on the current time series wind direction value and the predicted direction value as follows:

[0149]

[0150] Among them, R a represents the first parameter; π represents the degree, i.e. 180°; c represents the number of radar beams received by the bird detection radar in the current time period; θ n Indicates the predicted direction value of the nth flying bird in the current time period; w c It indicates the current time series wind direction value; exp() indicates the exponential function with the natural constant e as the base; || indicates the absolute value.

[0151] As a specific embodiment of the present application, the server 13 is further used to obtain the current time series wind speed value based on the wind speed time series data;

[0152] and, based on the flight prediction data, obtaining a predicted speed value of the flying bird;

[0153] And, based on the current time series wind speed value and the predicted speed value, obtaining a second parameter;

[0154] And, based on the first parameter and the second parameter, a pseudo-random parameter is obtained.

[0155] As a specific embodiment of the present application, the server 13 obtains the calculation formula of the second parameter based on the current time series wind speed value and the predicted speed value as follows:

[0156]

[0157] Among them, R b represents the second parameter; c represents the number of radar beams received by the bird detection radar in the current time period; v n Indicates the predicted speed value of the nth flying bird in the current time period; k c Indicates the current time series wind speed value; || indicates seeking the absolute value.

[0158] As a specific embodiment of the present application, the server 13 obtains a calculation formula for a pseudo-random parameter based on the first parameter and the second parameter as follows:

[0159]

[0160] Where R represents a pseudo-random parameter; R a Represents the first parameter; R b Indicates the second parameter.

[0161] As a specific embodiment of the present application, the server 13 is further used to obtain a first weight and a second weight based on the pseudo-random parameter; the first weight is a weight related to the predicted direction value of the bird in the prediction model; the second weight is a weight related to the predicted speed value of the bird in the prediction model;

[0162] And, updating the flight prediction data through a prediction model based on the first weight and the second weight.

[0163] As a specific embodiment of the present application, the server 13 updates the calculation formula of the flight prediction data based on the pseudo-random parameters as follows:

[0164] Pre2=Pre1*(1+sigmoid(R))

[0165] Wherein, Pre2 represents the updated flight prediction data; Pre1 represents the flight prediction data before the update; R represents a pseudo-random parameter; sigmoid() represents an arbitrary function that maps a numerical value to the interval (-1, 1).

[0166] It should be clear that the bird detection radar device proposed in this application takes into account the influence of wind speed and wind direction on the flight trajectory of birds during the bird trajectory prediction and analysis process, so that the flight prediction data of birds can be predicted more accurately. And the birds are subsequently accurately driven away based on the flight prediction data, that is, if the birds will not enter the early warning area in the future, the birds will not be driven away. If the birds may enter the early warning area in the future, the birds will be driven away. In other words, compared with the prior art, as long as the birds are detected, the birds will be driven away. This application can effectively reduce the frequency of bird driving, that is, reduce the energy consumption of driving away birds, improve the efficiency of driving away birds, and reduce the noise pollution formed.

[0167] After introducing the bird detection radar device proposed in the embodiment of the present application, the following introduces a computer-readable storage medium proposed in the present application, which stores a computer program. When the computer program is executed by a processor, a bird detection radar method as described in any of the above embodiments is implemented.

[0168] It should be clear that the computer-readable storage media in this application include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technology, read-only compact disc read-only memory, digital versatile disc or other optical storage, magnetic cassettes, disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include temporary computer-readable media, such as modulated data signals and carrier waves.

[0169] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0170] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the methods, devices and equipment described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0171] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0172] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0173] In addition, each functional module in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0174] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0175] The computer program product includes one or more computer instructions. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means to another website site, computer, server, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disk), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0176] Although the embodiments of the present application have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles of the present application.

Claims

1. A bird detection radar device, characterized in that: include: Wind speed sensor, used to obtain wind speed time series data; Bird detection radar, used to obtain the echo time series data of flying birds; A server, configured to obtain time series data of the distance between the bird detection radar and the bird based on the echo time series data; and, based on the distance time series data, obtaining flight prediction data of the flying bird; and, based on the wind speed time series data and the flight prediction data, obtaining pseudo-random parameters; The pseudo-random parameter is at least used to evaluate the credibility of the flight prediction data; and, based on the pseudo-random parameters, updating the flight prediction data; A bird repellent device, which repels flying birds based on the updated flight prediction data; The server is further used to obtain a current time series wind direction value based on the wind speed time series data; Based on the flight prediction data, obtaining a predicted direction value of the flying bird; Based on the current time series wind direction value and the predicted direction value, obtaining a first parameter; And, based on the wind speed time series data, obtaining the current time series wind speed value; Based on the flight prediction data, obtaining a predicted speed value of the flying bird; Based on the current time series wind speed value and the predicted speed value, obtain a second parameter; based on the first parameter and the second parameter, obtain a pseudo-random parameter; The server obtains a calculation formula for the first parameter based on the current time series wind direction value and the predicted direction value as follows: Among them, R a represents the first parameter; π represents the degree, i.e. 180°; c represents the number of radar beams received by the bird detection radar in the current time period; θ n Indicates the predicted direction value of the nth flying bird in the current time period; w c Indicates the current time series wind direction value; exp() indicates an exponential function with the natural constant e as the base; || indicates finding the absolute value; The server obtains the calculation formula of the second parameter based on the current time series wind speed value and the predicted speed value as follows: Among them, R b represents the second parameter; c represents the number of radar beams received by the bird detection radar in the current time period; v n Indicates the predicted speed value of the nth flying bird in the current time period; k c Indicates the current time series wind speed value; || indicates the absolute value; The server obtains a calculation formula for a pseudo-random parameter based on the first parameter and the second parameter as follows: Where R represents a pseudo-random parameter; R a Represents the first parameter; R b Indicates the second parameter.

2. A bird detection radar device according to claim 1, characterized in that: The server is further used to obtain first data and second data based on the distance time series data; the first data and the second data are two adjacent data in the distance time series data; and the second data is current distance time series data; and, based on the first data and the second data, obtaining a first time interval; And, based on the first data and the second data, a first straight-line distance is obtained; the first straight-line distance is the straight-line distance that the flying bird moves within the first time interval; And, based on the first time interval and the first straight-line distance, flight prediction data of the flying bird is obtained; the flight prediction data includes a predicted speed value and a predicted direction value of the flying bird.

3. A bird detection radar device according to claim 1, characterized in that: The server is further used to obtain a first time based on the echo timing data; the first time is the time when the bird detection radar emits a radar beam; and, based on the echo timing data, acquiring a second time; The second time is the time when the bird detection radar receives the radar beam; And, based on the first time and the second time, obtaining time series data of the distance between the bird detection radar and the bird.

4. A flying bird detection radar device according to any one of claims 1 to 3, characterized in that: The server is further used to obtain a first weight and a second weight based on the pseudo-random parameter; the first weight is a weight in the prediction model related to the predicted direction value of the flying bird; The second weight is a weight in the prediction model related to the predicted speed value of the flying bird; And, updating the flight prediction data through a prediction model based on the first weight and the second weight.

5. A flying bird detection radar device according to any one of claims 1 to 3, characterized in that: The server updates the flight prediction data based on the pseudo-random parameters using the following calculation formula: Pre2=Pre1*(1+sigmoid(R)) Wherein, Pre2 represents the updated flight prediction data; Pre1 represents the flight prediction data before the update; R represents a pseudo-random parameter; sigmoid() represents an arbitrary function that maps a numerical value to the interval (-1, 1).

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