Bird repelling method for power distribution iron tower and related equipment

Abstract

The invention discloses a bird repelling method and related equipment for a power distribution iron tower, and the method employs a multi-dimensional sensor for cooperative operation, and collects infrared, sound, images and radar reflection signals in real time. Through multi-dimensional data fusion, the appearance, sound wave and behavior characteristics of the bird flock are deeply mined, the bird flock type is accurately identified, and the problem of insufficient detection of a single sensor is solved. Based on the sensitivity difference of different bird flocks to vision, hearing and ultrasonic waves, bird repelling modes such as strong light stroboflash, sound or ultrasonic waves are dynamically matched from the preset bird repelling strategy library, the adaptability of birds to a single bird repelling means is avoided, and the bird repelling success rate is remarkably increased. And a closed-loop optimization mechanism is formed by iteratively optimizing a strategy library matching rule by using bird repelling effect feedback data, so that the system can adapt to the environment and bird behavior changes, and the long-term bird repelling effect is enhanced. Compared with the prior art, line fault risks caused by bird activities are effectively reduced, and safe and reliable operation of a power system is powerfully guaranteed.

Description

Technical Field

[0001] This application relates to the technical field of bird repelling, and more specifically, to a method for repelling birds from distribution towers and related equipment. Background Art

[0002] As a key facility for power transmission, distribution towers play a crucial supporting role in the power transmission network. However, birds are frequently active around them, and distribution towers have long faced problems such as short circuits caused by bird nesting and perching, and equipment pollution.

[0003] Currently, common methods for repelling birds from distribution towers have many drawbacks. Mechanical bird repelling devices, such as windmills and reflectors, are vulnerable to adverse weather conditions such as strong winds, rain, and snow. They not only require frequent maintenance, but also, over time, birds are likely to adapt, resulting in a significant reduction in the bird repelling effect. Single-signal bird repellers, such as ultrasonic generators or recording devices, do not fully consider the differences in the sensitivity of different birds to various signals. Some birds are insensitive to specific signals, and when using a fixed-frequency signal for a long time, birds will gradually adapt, weakening the bird repelling effect. The method of manual inspection and bird repelling is inefficient, relies on a large amount of manpower, cannot monitor the dynamics of bird flocks in real time, and there are relatively high safety risks in high-altitude operations. Most traditional intelligent bird repelling systems only use a single sensor, such as a camera, which is difficult to comprehensively capture the behavioral characteristics of bird flocks, and their bird repelling strategies are also fixed and difficult to adapt to the complex and changing activities of bird flocks.

[0004] Based on this, in view of the above-mentioned defects in the prior art, this application proposes a bird repelling solution for distribution towers, which can accurately identify the types of bird flocks, flexibly adjust the bird repelling strategy, and has an adaptive optimization function. Summary of the Invention

[0005] This application provides a method for repelling birds from distribution towers and related equipment, which can accurately identify the types of bird flocks, flexibly adjust the bird repelling strategy, and has an adaptive optimization function.

[0006] A method for repelling birds from distribution towers includes:

[0007] Using a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time to generate sensor data;

[0008] Performing fusion recognition on the sensor data, extracting the shape characteristics, acoustic wave characteristics, and behavioral characteristics of the current bird flock, and determining the type of the current bird flock based on the shape characteristics, the acoustic wave characteristics, and the behavior;

[0009] Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, determine a target bird repelling strategy by matching in a preset bird repelling strategy library, where the target bird repelling strategy includes at least one bird repelling method among strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling;

[0010] Execute the target bird repelling strategy, and iteratively optimize the matching rules of the bird repelling strategy library according to the bird repelling effect feedback data.

[0011] Optionally, perform fusion recognition on the sensor data, extract the shape features, acoustic wave features, and behavior features of the current bird flock, and determine the current bird flock species based on the shape features, the acoustic wave features, and the behavior, including:

[0012] Perform image enhancement, edge detection, and feature extraction processing on the image data in the sensor data to obtain shape features;

[0013] Perform noise reduction, spectrum analysis, and feature extraction processing on the sound signal in the sensor data to obtain acoustic wave features;

[0014] Perform data fusion and trajectory tracking processing on the infrared radiation signal and the radar reflection signal in the sensor data to determine the current flight trajectory, and determine the behavior features based on the current flight trajectory;

[0015] Determine the current bird flock species based on the shape features, the acoustic wave features, and the behavior features.

[0016] Optionally, it further includes:

[0017] Perform data fusion and target detection processing on the infrared radiation signal and the radar reflection signal in the sensor data to determine the number of individual birds in the flock;

[0018] Predict the predicted flight trajectory within a preset future time period according to the historical flight features of the current bird flock species and the current flight trajectory;

[0019] Optimize and adjust the target bird repelling strategy in combination with the number of individual birds in the flock and the predicted flight trajectory.

[0020] Optionally, it further includes:

[0021] Obtain the current date and the current position of the distribution tower;

[0022] Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, determine a target bird repelling strategy by matching in a preset bird repelling strategy library, including:

[0023] Determine the seasonal period of the current bird flock species on the current date and the time-domain species habits corresponding to the geographical region where the current location is located;

[0024] Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, and in combination with the time-domain species habits, match and determine the target bird repelling strategy in a preset bird repelling strategy library.

[0025] Optionally, it further includes:

[0026] Obtain real-time environmental data, where the real-time environmental data at least includes wind speed, temperature, and light intensity;

[0027] Optimize and adjust the target bird repelling strategy according to the real-time environmental data.

[0028] A bird repelling device for a distribution tower, comprising:

[0029] A multi-dimensional sensor module, configured to use multi-dimensional sensors to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generate sensor data;

[0030] A data processing module, configured to perform fusion recognition on the sensor data, extract the shape characteristics, acoustic characteristics, and behavior characteristics of the current bird flock, and determine the current bird flock species based on the shape characteristics, the acoustic characteristics, and the behavior;

[0031] A strategy selection module, configured to match and determine a target bird repelling strategy in a preset bird repelling strategy library according to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, where the target bird repelling strategy includes at least one bird repelling method such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling;

[0032] A bird repelling execution module, configured to execute the target bird repelling strategy, and iteratively optimize the matching rules of the bird repelling strategy library according to the bird repelling effect feedback data.

[0033] Optionally, it further includes an electric field interference module;

[0034] Both the electric field interference module and the bird repelling execution module are arranged at the junction of the tower cross arm and the insulator string;

[0035] The pulsed electric field generated by the electric field interference module is coupled with the ultrasonic wave emitted by the bird repelling execution module to form a composite bird repelling field.

[0036] A bird repelling device for a distribution tower, comprising a memory and a processor;

[0037] The memory is used for storing programs;

[0038] The processor is configured to execute the program to implement each step of the bird expulsion method for distribution towers as described in any one of the above.

[0039] A readable storage medium stores a computer program, wherein when the computer program is executed by a processor, each step of the bird expulsion method for distribution towers as described in any one of the above is implemented.

[0040] A computer program product includes a computer program, wherein when the computer program is run by a processor, each step of the bird expulsion method for distribution towers as described in any one of the above is executed.

[0041] As can be seen from the above technical solutions, a bird expulsion method for distribution towers and related devices provided by the embodiments of the present application collect infrared radiation signals, sound signals, image data, and radar reflection signals in real time and in all directions with the help of multi-dimensional sensors, and deeply mine the shape characteristics, acoustic wave characteristics, and behavior characteristics of bird flocks through data fusion technology, so as to accurately determine the types of bird flocks. Based on the sensitivities of different bird flocks to vision, hearing, and ultrasonic waves, strategies such as strong light stroboscopic bird expulsion, sound bird expulsion, or ultrasonic bird expulsion are dynamically matched, and the bird expulsion strategy library is continuously optimized according to the feedback data of the bird expulsion effect.

[0042] Through the collaborative work of infrared sensors, sound sensors, image recognition sensors, and radar sensors, the present application collects multi-dimensional data such as the shape characteristics, acoustic wave characteristics, and flight trajectories of birds in real time, solves the problems of narrow detection range and low accuracy of single sensors, and realizes the accurate identification of bird species and behaviors. Secondly, based on the preset bird expulsion strategy library, combined with the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the target birds, the dynamically matched bird expulsion methods avoid the adaptive failure caused by the single bird expulsion means, and significantly improve the bird expulsion success rate. The non-contact bird expulsion means are adopted to greatly reduce the operation and maintenance costs, extend the service life of the equipment, and also avoid physical harm to birds. In addition, through the iterative analysis of the feedback data of the bird expulsion effect, the closed-loop optimization mechanism for continuously optimizing the matching rules of the strategy library enables the system to adapt to environmental changes and the evolution of bird behavior patterns, further enhancing the long-term bird expulsion effect. Compared with the prior art, the present application breaks through the limitations of traditional bird expulsion methods, greatly reduces the risk of line failures caused by bird activities, and provides a solid guarantee for the safe and reliable operation of the power system. Description of the Drawings

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0044] Figure 1 Flow chart of a bird repelling method for a distribution tower disclosed in an embodiment of the present application;

[0045] Figure 2 Schematic diagram of a bird repelling device for a distribution tower disclosed in an embodiment of the present application;

[0046] Figure 3 Hardware structure block diagram of a bird repelling device for a distribution tower disclosed in an embodiment of the present application. Detailed implementation manners

[0047] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0048] The present application can be used in many general or specific computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or equipment, and so on.

[0049] Next, the solutions of the present application will be introduced. The present application proposes the following technical solutions. For details, please refer to the following text.

[0050] Figure 1 Flow chart of a bird repelling method for a distribution tower disclosed in an embodiment of the present application.

[0051] As Figure 1 shown, the method may include:

[0052] Step S1: Use a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generate sensor data.

[0053] Specifically, use a multi-dimensional sensor to collect data around the distribution tower in real time. The collection of infrared radiation signals is realized through an infrared sensor, which can accurately capture the infrared radiation intensity and distribution of different objects in the surrounding environment of the distribution tower. When a flock of birds approaches, the infrared radiation emitted by their body temperature will be keenly sensed by the sensor. The sensor will convert the received infrared radiation into an electrical signal and perform digital processing according to a specific coding format, so as to generate an infrared radiation signal that can be analyzed by the subsequent system.

[0054] The collection of sound signals depends on sound sensors. Sound sensors can be distributed at different locations on the distribution tower to collect surrounding sounds in all directions. When a flock of birds chirps, the sound sensor samples and quantifies the frequency, amplitude, duration and other parameters of the sound to form a sound signal.

[0055] The image data is collected with the help of a high-definition camera, which has autofocus and wide dynamic range functions, and can clearly capture the scenes around the distribution tower under different lighting conditions. The camera continuously captures images at a set frame rate to form image data, which contains intuitive information such as the number, location, and flight posture of the bird flock.

[0056] The radar reflection signal is collected by the radar sensor. The radar emits electromagnetic waves into the surrounding space and receives the reflected signals. When the flock of birds enters the radar detection range, their bodies will reflect the radar waves. The radar equipment calculates the distance between the flock of birds and the distribution tower, flight speed and direction based on the intensity, frequency change and return time of the reflected waves, and then generates radar reflection signals. These different types of sensor data together constitute a multi-dimensional description of the environment around the distribution tower and the status of the flock of birds.

[0057] Step S2: fusing and identifying the sensor data, extracting the appearance characteristics, acoustic characteristics and behavioral characteristics of the current bird flock, and determining the type of the current bird flock based on the appearance characteristics, acoustic characteristics and behavior.

[0058] Specifically, the fusion and identification of the collected sensor data is the key link in determining the species of the flock. First, for the extraction of appearance features, the image recognition algorithm is used to process the image data. The algorithm uses edge detection, contour extraction and other technologies to identify the outline shape of each bird in the flock, and then calculates the key external dimensions of the bird, such as body length and wingspan. At the same time, the characteristic information of the bird's body proportions, head shape, feather color distribution, etc. is analyzed, and these appearance feature data are extracted and integrated.

[0059] The extraction of sound wave features is based on sound data analysis. Through signal processing methods such as Fourier transform, the sound signal is converted from the time domain to the frequency domain, the frequency composition of the bird flock sound is analyzed, and the main frequency range of its chirping and whether there are specific harmonic components are determined. In addition, the characteristics of the sound amplitude change, rhythm pattern, etc. are extracted. These sound wave features can reflect the uniqueness of the bird flock sound.

[0060] The extraction of behavioral features is mainly based on radar reflection signals and image data. The flight trajectory, flight altitude changes, and flight speed fluctuations of the flock of birds are obtained from radar data. Combined with the flight posture and group aggregation patterns of the flock of birds in the image data, it is possible to determine whether the flock of birds is in a migratory flight, foraging flight, or resting state.

[0061] Based on the extracted appearance features, acoustic features, and behavioral features, classification algorithms in machine learning are used to determine the current bird flock species. First, a training set containing feature data of various known bird flocks is constructed. The training set covers appearance, acoustic, and behavioral feature samples of different bird species and their corresponding bird species labels. Then, classification algorithms such as support vector machines and decision trees are used to learn and train the training set to generate a bird flock species classification model. When the currently collected bird flock feature data is input, the classification model calculates the most likely bird flock species according to the feature pattern matching rules learned during training, thus achieving accurate identification of the bird flock species around the distribution tower.

[0062] Step S3: According to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, match and determine the target bird repelling strategy in the preset bird repelling strategy library. The target bird repelling strategy includes at least one bird repelling method such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling.

[0063] Specifically, based on the currently identified bird flock species, deeply analyze its visual sensitivity, auditory sensitivity, and ultrasonic sensitivity, and on this basis, accurately match the target bird repelling strategy in the preset bird repelling strategy library.

[0064] For visual sensitivity, it is necessary to comprehensively consider the adaptation degree of different bird species to light stimuli of different intensities, frequencies, and colors, that is, visual sensitivity. For example, some diurnal birds react strongly to strong light stimuli. Their retinas are rich in cone cells and can keenly perceive color and strong light changes. While nocturnal birds have better vision in low light environments, they will have a frightened and evasive reaction to sudden strong light strobing.

[0065] For auditory sensitivity, due to differences in the auditory system structures of different bird flocks, their perception ranges of sound frequencies, amplitudes, and timbres are different. Some bird species are more sensitive to high-frequency sounds. For example, eagles can capture subtle high-frequency sounds with their auditory systems to locate prey; while some water birds are more alert to low-frequency sounds. This application needs to clarify the sensitivity degrees of different bird flocks to sounds in different frequency bands, that is, auditory sensitivity.

[0066] For ultrasonic sensitivity, although most birds cannot use ultrasonic waves for navigation like animals such as bats, some birds have a certain perception ability for ultrasonic waves in a specific frequency range. This application needs to determine the sensitive range of different bird flocks to ultrasonic waves, that is, ultrasonic sensitivity.

[0067] In the preset bird-repelling strategy library, there are detailed parameter settings and applicable scenario descriptions for each bird-repelling method. The strong light stroboscopic bird-repelling strategy covers strong light settings of different frequencies, brightness and color combinations to adapt to bird flocks with different visual sensitivities. The sound bird-repelling strategy includes a variety of bird natural enemy sounds, warning sounds, and simulated bird distress calls. The frequency, volume and playback duration of each sound are precisely controlled to match bird flocks with different auditory sensitivities. The ultrasonic bird-repelling strategy sets ultrasonic emission schemes of different frequencies and intensities based on the ultrasonic sensitivity data of the bird flock. During the matching process, the system will search for the most suitable target bird-repelling strategy in the bird-repelling strategy library based on the comprehensive scores of the bird flock's vision, hearing and ultrasonic sensitivity. The strategy may be a single bird-repelling method or a combination of multiple bird-repelling methods to achieve the best bird-repelling effect.

[0068] Step S4: executing the target bird-repelling strategy, and iteratively optimizing the matching rules of the bird-repelling strategy library according to the bird-repelling effect feedback data.

[0069] Specifically, after executing the target bird repellent strategy, bird repellent effect feedback data is collected and the matching rules of the bird repellent strategy library are iteratively optimized, which is the key to improving the long-term effectiveness of the bird repellent system.

[0070] When implementing the targeted bird-repelling strategy, bird-repelling effect feedback data is collected in a variety of ways. Sensors deployed around the distribution towers, such as cameras, sound sensors, and radar equipment, are used to continuously monitor the behavior changes of the bird flock after the bird-repelling strategy is implemented. The camera records the flight trajectory of the bird flock, the length of stay, and whether it approaches the distribution tower again in real time; the sound sensor captures the sound made by the bird flock and analyzes whether its sound characteristics show emotions such as panic and avoidance; the radar equipment tracks the flight speed, direction, and distance changes of the bird flock to determine the impact of the bird-repelling strategy on the behavior of the bird flock.

[0071] At the same time, staff will also conduct regular on-site inspections of the environment around the distribution towers, recording the operating status of the bird-repellent equipment, whether there is any accumulation of bird droppings, and signs of bird nesting. These field observation data serve as supplementary information and can more comprehensively evaluate the effectiveness of the bird-repellent.

[0072] Based on the collected feedback data on bird repelling effects, the matching rules of the bird repelling strategy library are iteratively optimized. If the repelling effect is not good after a specific bird repelling strategy is executed for a certain bird flock, the system will analyze whether certain sensitivity factors have not been fully considered or whether the parameter settings of the bird repelling strategy are unreasonable. For example, if the strong light stroboscopic bird repelling is ineffective for a certain bird flock, it may be that the frequency and brightness of the light do not reach the sensitive threshold of the bird flock, or the bird flock has adapted to the current light stimulation mode. In response to these problems, the system will adjust the matching rules corresponding to the bird flock in the bird repelling strategy library, such as increasing the intensity of the light, changing the stroboscopic frequency, or combining other bird repelling methods to form a new strategy combination. By continuously incorporating new bird repelling effect feedback data into the analysis and continuously optimizing the matching rules of the bird repelling strategy library, the bird repelling system can better cope with different bird flocks and complex environments, and improve the efficiency and reliability of bird repelling.

[0073] As can be seen from the above technical solutions, a bird repelling method and related equipment provided by an embodiment of the present application use multi-dimensional sensors to collect infrared radiation signals, sound signals, image data, and radar reflection signals in real time and in all directions, and deeply mine the shape characteristics, acoustic wave characteristics, and behavior characteristics of bird flocks through data fusion technology, so as to accurately determine the types of bird flocks. Based on the sensitivities of different bird flocks to vision, hearing, and ultrasonic waves, strategies such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling are dynamically matched, and the bird repelling strategy library is continuously optimized according to the bird repelling effect feedback data.

[0074] Through the collaborative work of infrared sensors, sound sensors, image recognition sensors, and radar sensors in the present application, multi-dimensional data such as the shape characteristics, acoustic wave characteristics, and flight trajectories of birds are collected in real time, solving the problems of narrow detection range and low accuracy of a single sensor, and realizing the accurate identification of bird species and behaviors. Secondly, based on the preset bird repelling strategy library, combined with the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the target birds, dynamically matching the bird repelling methods avoids the adaptive failure caused by the single bird repelling means, and significantly improves the bird repelling success rate. The non-contact bird repelling means are adopted, greatly reducing the operation and maintenance costs, extending the service life of the equipment, and also avoiding physical harm to birds. In addition, through the iterative analysis of the bird repelling effect feedback data and the closed-loop optimization mechanism of continuously optimizing the matching rules of the strategy library, the system can adapt to environmental changes and the evolution of bird behavior patterns, further enhancing the long-term bird repelling effect. Compared with the prior art, the present application breaks through the limitations of traditional bird repelling methods, greatly reducing the risk of line failures caused by bird activities, and providing a solid and powerful guarantee for the safe and reliable operation of the power system.

[0075] In some embodiments of the present application, the process of step S2, fusing and identifying the sensor data, extracting the shape features, acoustic features, and behavioral features of the current bird flock, and determining the type of the current bird flock based on the shape features, the acoustic features, and the behavior will be introduced. Specifically, it may include:

[0076] Step S21: Perform image enhancement, edge detection, and feature extraction on the image data in the sensor data to obtain shape features.

[0077] Specifically, in the image enhancement step, in order to improve the image quality and highlight the details of the bird flock, techniques such as histogram equalization can be used. This technique redistributes the gray values of the image pixels, expands the gray range of the image, makes the contour of the bird flock clearer in the image, and makes details such as feather textures easier to identify. At the same time, an adaptive contrast enhancement algorithm is used to dynamically adjust the contrast according to the characteristics of local regions of the image, avoiding the loss of details in some areas of the bird flock due to uneven illumination.

[0078] Canny edge detection algorithm can be used for edge detection. This algorithm smooths the image through Gaussian filtering to reduce noise interference, then calculates the image gradient to determine the intensity and direction of the edge. During the double-threshold processing, two thresholds, high and low, are set. Edges above the high threshold are determined as strong edges, edges below the low threshold are ignored, and edges between the two are judged whether to be retained according to their connectivity with the strong edges, thus accurately outlining the edge contour of the bird flock.

[0079] After edge detection is completed, feature extraction is performed, and shape descriptors are used to quantify the shape features of the bird flock. For example, basic shape parameters such as contour perimeter, area, and aspect ratio of the circumscribed rectangle are calculated. These parameters can intuitively reflect the overall shape characteristics of the bird flock. At the same time, invariant moment features are used. It is invariant to translation, rotation, and scaling of the image and can be used to describe the geometric shape characteristics of the bird flock contour. Even if the bird flock has pose changes in the image, its unique shape features can be accurately extracted.

[0080] Step S22: Perform noise reduction, spectral analysis, and feature extraction on the sound signal in the sensor data to obtain acoustic features.

[0081] Specifically, wavelet denoising algorithm can be used for noise reduction. This algorithm decomposes the sound signal into wavelet subbands of different frequencies. Noise is usually concentrated in some high-frequency subbands. By setting appropriate thresholds to process the coefficients of the high-frequency subbands, the noise components are removed, and then the signal is reconstructed, thereby reducing background noise interference while retaining the useful sound information to the greatest extent.

[0082] Spectrum analysis can use the Fast Fourier Transform (FFT) to convert the sound signal in the time domain to the frequency domain. Through the FFT, the frequency composition of the sound signal can be clearly seen, and the fundamental frequency and the frequencies of each harmonic of the bird flock's calls can be analyzed. At the same time, the power spectral density is calculated, which reflects the energy distribution of the sound signal at different frequencies and helps to identify the unique frequency characteristics of the bird flock's sound.

[0083] In the feature extraction stage, in addition to frequency features, amplitude features of the sound are also extracted, such as the average amplitude, the maximum amplitude, etc. These amplitude information can reflect the intensity changes of the bird flock's calls. In addition, time features such as the duration and interval of the sound are analyzed, as well as the rhythm pattern of the sound, such as whether there are periodic calls. By integrating these multi-dimensional features such as frequency, amplitude, and time, the acoustic wave characteristics of the bird flock's sound are comprehensively described.

[0084] Step S23: Perform data fusion and trajectory tracking processing on the infrared radiation signal and the radar reflection signal in the sensor data, determine the current flight trajectory, and determine the behavior characteristics based on the current flight trajectory.

[0085] Specifically, the infrared radiation signal can provide the temperature information of the bird flock, reflecting its position and general outline; the radar reflection signal accurately measures the distance, speed, and direction of the bird flock. In this application, the behavior characteristics are determined by fusing the infrared radiation signal and the radar reflection signal. In terms of data fusion, the Kalman filtering algorithm can be used. The Kalman filtering algorithm utilizes the complementarity of these two signals to perform an optimal estimation of the state of the bird flock. It continuously fuses new measurement data through two steps of prediction and update, eliminates noise and errors, and obtains more accurate position and motion parameters of the bird flock.

[0086] The trajectory tracking is based on the fused data for multi-target tracking. By correlating the state information of the bird flock targets at different times, calculating the similarity matrix between the targets, and finding the optimal match, the flight trajectory of each individual bird flock can be accurately tracked. According to the obtained current flight trajectory, analyze whether the flight direction of the bird flock is consistent, determine whether it is a migratory flight or a random flight; observe the change in flight speed to determine whether the bird flock is in a cruising state or an accelerating or decelerating state; check the fluctuation of the flight altitude to determine whether the bird flock is foraging or preparing to roost, etc., and then determine the behavior characteristics of the bird flock.

[0087] Step S24: Determine the current bird flock species based on the shape characteristics, the acoustic wave characteristics, and the behavior characteristics.

[0088] Specifically, the current bird flock species is determined based on the extracted shape features, acoustic wave features, and behavior features. Classification models in machine learning, such as the random forest classifier, are used. First, a training set is constructed by collecting a large amount of sample data of known bird flock species. Each sample includes its corresponding shape feature vector, acoustic wave feature vector, and behavior feature vector, as well as the labeled bird flock species label. During the training process, the random forest classifier randomly selects multiple sample subsets and feature subsets from the training set to construct multiple decision trees. Each decision tree is trained based on its respective samples and features to classify and predict the input bird flock feature data. Finally, by integrating the prediction results of multiple decision trees, a voting mechanism is adopted to determine the current bird flock species. For example, if the majority of decision trees predict a certain bird species, it is determined that the current bird flock belongs to that species, thereby achieving accurate identification of the bird flock species around the distribution tower.

[0089] In some embodiments of the present application, on the basis of the previous embodiment, it is also possible to add the process of identifying the number of individual birds in the flock and incorporating it into the optimization and adjustment of the target bird repelling strategy.

[0090] Step S5: Perform data fusion and target detection processing on the infrared radiation signal and the radar reflection signal in the sensor data to determine the number of individual birds in the flock;

[0091] Step S6: Predict the predicted flight trajectory within a predetermined future period based on the historical flight characteristics of the current bird flock species and the current flight trajectory;

[0092] Step S7: Optimize and adjust the target bird repelling strategy by combining the number of individual birds in the flock and the predicted flight trajectory.

[0093] Specifically, the bird flock area information segmented from the infrared image is fused with the target position information detected by the radar target. For each possible individual bird in the flock, based on the confidence levels given by the infrared and radar data, its final existence probability is calculated. For example, when the infrared image and the radar both detect a target at a similar position with high confidence, the probability of the existence of an individual bird at this position will increase significantly. Through this fusion, by integrating the advantages of the two signals, the number of individual birds in the flock is accurately determined.

[0094] Based on the historical flight characteristics of the current bird flock species (i.e., the flight rules and patterns of this species of birds in previous similar environments or conditions) and the current flight trajectory (real-time information such as the current flight route of the bird flock), the predicted flight trajectory of the bird flock within a predetermined future period is calculated.

[0095] The two key factors of the number of individual birds in the flock determined previously and the predicted flight trajectory are combined to optimize and adjust the target bird repelling strategy.

[0096] Adjustment based on the number of individual birds in the flock: A large number of individual birds in the flock means a large scale, and at this time, more powerful bird repelling means are required. For strong light stroboscopic bird repelling, the number and power of stroboscopic lights can be increased to expand the light coverage area, so that more individual birds are stimulated; for sound bird repelling, the volume can be increased, and the types and combinations of sounds can be increased to cover a larger sound propagation area and attract the attention of more individual birds in the flock.

[0097] Adjustment based on the predicted flight trajectory: If the flock of birds is expected to approach the power distribution tower, ultrasonic stimulation can be used in advance in the direction of its flight path to change the flight direction of the flock of birds and intercept the flock of birds in advance; or if the flock of birds is expected to stay or circle in a certain area, bird repelling can be concentrated in that area to enhance the bird repelling effect. This is to layout the defense in advance according to the action route of the flock of birds.

[0098] Dynamically adjust the execution time and frequency: According to the number of individual birds in the flock and the predicted flight trajectory, dynamically adjust the execution time and frequency of the bird repelling strategy. If the number of individual birds in the flock is large and it is expected to continue to move near the power distribution tower, increase the working duration of the bird repelling equipment and improve the execution frequency, such as shortening the interval time of strong light stroboscopy and increasing the number of sound playback loops, etc., to continuously deter the flock of birds, achieve the optimization and adjustment of the target bird repelling strategy, and respond to the threat of the flock of birds in a more flexible and effective way.

[0099] In some embodiments of the present application, it is also possible to add the determination of the temporal species habits corresponding to the current date and the current position of the power distribution tower for the current species of the flock of birds, and incorporate it into the process of optimizing and adjusting the target bird repelling strategy.

[0100] Step S8, obtain the current date and the current position of the power distribution tower.

[0101] On this basis, step S3, according to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current species of the flock of birds, match and determine the target bird repelling strategy in the preset bird repelling strategy library, including:

[0102] Step S31, determine the temporal species habits corresponding to the season period where the current species of the flock of birds is located on the current date and the geographical region where it is located at the current position;

[0103] Step S32, based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current species of the flock of birds, combined with the temporal species habits, match and determine the target bird repelling strategy in the preset bird repelling strategy library.

[0104] Specifically, it is first necessary to obtain the current date and the current position of the power distribution tower. The current date is very important for determining the season period, and the activity rules and habits of birds may be different in different seasons. The current position of the power distribution tower determines the geographical region where it is located, and there are also differences in the ecological environment and bird distribution characteristics in different geographical regions.

[0105] Determine the temporal species habits corresponding to the season period of the current bird flock species on the current date and the geographical region at the current location. This step combines the bird flock species with the current time (season period) and space (geographical region) information to deeply analyze the habit characteristics of the bird flock at a specific time and location. For example, some birds may be in the breeding period in spring and are more active; in certain mountainous geographical regions, birds may have specific foraging or roosting habits, etc.

[0106] Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, combined with the temporal species habits, match and determine the target bird repelling strategy in the preset bird repelling strategy library. Here, the physiological perception characteristics of the bird flock (visual, auditory, ultrasonic sensitivity) and the previously determined temporal species habits are comprehensively considered, and the most suitable bird repelling strategy is selected from the preset bird repelling strategy library. For example, if a certain bird likes to forage at a specific time in the current season period and location, has a high visual sensitivity to a certain color, and is sensitive to a specific frequency of sound, then a strategy of using the corresponding color and sound to repel the bird flock can be selected in the bird repelling strategy library.

[0107] By introducing the factor of temporal species habits, the process of determining the target bird repelling strategy is further improved, enabling the bird repelling strategy to better adapt to different time and space conditions, more effectively repel the bird flock, and protect facilities such as distribution towers from the possible impacts of bird activities.

[0108] In some embodiments of the present application, it is also possible to add the process of obtaining real-time environmental data and incorporating it into the optimization and adjustment of the target bird repelling strategy.

[0109] Step S9: Obtain real-time environmental data, where the real-time environmental data at least includes wind speed, temperature, and light intensity;

[0110] Step S10: Optimize and adjust the target bird repelling strategy according to the real-time environmental data.

[0111] Specifically, environmental factors have a significant impact on the activities of birds. Considering real-time environmental data, such as key environmental parameters like light intensity, temperature, and wind speed, can make the bird repelling strategy more in line with the actual situation and improve the effectiveness and accuracy of bird repelling.

[0112] Specifically, light intensity has a significant impact on bird activities. When the light intensity is low, such as at nightfall, the visual sensitivity of birds decreases, and some activity patterns change. At this time, according to the light intensity data feedback by the sensor, the usage frequency of strong light strobe lights can be appropriately increased. By increasing the flashing frequency, it can more effectively attract the attention of birds, making them stay away from the target area due to discomfort. At the same time, the brightness of the strong light strobe lights can also be adjusted according to the actual situation. On the premise of ensuring sufficient deterrence, it can avoid energy waste caused by excessive strong light or adverse effects on the surrounding environment. In addition, changing the flashing mode, for example, switching from regular uniform flashing to random flashing, can increase the unpredictability of the strong light stimulation for birds and further enhance the bird repelling effect.

[0113] Wind speed is also an important environmental factor affecting bird repelling strategies. In strong wind weather, the transmission of sound will be greatly affected. When strong wind weather is detected, bird repelling methods less affected by wind speed, such as ultrasonic generators, should be preferred. The ultrasonic waves emitted by ultrasonic generators have a high frequency and strong directivity, and are relatively stable when propagating in the air and are not easily interfered by strong winds. It can still transmit effective bird repelling signals to birds in the harsh strong wind environment, ensuring the continuity and effectiveness of bird repelling work.

[0114] Different birds have different activity habits in different temperature ranges. Some birds have reduced activity levels in low-temperature environments and tend to seek warm habitats; while some birds may reduce their flights during high-temperature periods and choose shady places to avoid the heat. Through continuous monitoring of temperature by multi-dimensional sensors and in-depth research on the habits of local birds, the bird repelling strategy can be adjusted accordingly when the temperature changes.

[0115] By collecting environmental parameters such as light intensity, temperature, and wind speed in real time, and integrating and analyzing these data with the known bird habit information. An association model between environmental factors and bird behavior is established, and the bird repelling strategy is dynamically adjusted according to the model feedback. This optimization mechanism can fully consider the behavior changes of birds under different environmental conditions, greatly improve the stability and adaptability of the bird repelling effect, better meet the bird repelling needs in various scenarios, and effectively protect the distribution tower from bird intrusion.

[0116] Next, a distribution tower bird repelling device provided by an embodiment of the present application will be described. The distribution tower bird repelling device described below can be mutually referred to with the distribution tower bird repelling method described above.

[0117] See Figure 2 , Figure 2 which is a schematic diagram of a distribution tower bird repelling device disclosed in an embodiment of the present application.

[0118] As Figure 2 shown, the distribution tower bird repelling device may include:

[0119] A multi-dimensional sensor module 110, which is used to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time by using multi-dimensional sensors, and generate sensor data;

[0120] A data processing module 120, which is used to perform fusion recognition on the sensor data, extract the shape features, acoustic wave features, and behavior features of the current bird flock, and determine the current bird flock type based on the shape features, the acoustic wave features, and the behavior;

[0121] A strategy selection module 130, which is used to match and determine a target bird repelling strategy in a preset bird repelling strategy library according to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock type, and the target bird repelling strategy includes at least one bird repelling method such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling;

[0122] A bird repelling execution module 140, which is used to execute the target bird repelling strategy and iteratively optimize the matching rules of the bird repelling strategy library according to the bird repelling effect feedback data.

[0123] As can be seen from the above technical solutions, a distribution tower bird repelling method and related equipment provided by the embodiments of the present application collect infrared radiation signals, sound signals, image data, and radar reflection signals in real time and all-round by means of multi-dimensional sensors, and deeply excavate the shape features, acoustic wave features, and behavior features of the bird flock through data fusion technology, so as to accurately determine the bird flock type. Based on the sensitivities of different bird flocks to vision, hearing, and ultrasonic waves, strategies such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling are dynamically matched, and the bird repelling strategy library is continuously optimized according to the bird repelling effect feedback data.

[0124] The present application solves the problems of narrow detection range and low accuracy of a single sensor by the collaborative work of an infrared sensor, a sound sensor, an image recognition sensor, and a radar sensor, and realizes the accurate recognition of bird species and behaviors. Secondly, based on a preset bird repelling strategy library, combined with the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the target birds, the dynamic matching of bird repelling methods avoids the adaptive failure caused by the singleization of bird repelling means, and significantly improves the bird repelling success rate. The non-contact bird repelling means are adopted, which greatly reduces the operation and maintenance costs, prolongs the service life of the equipment, and also avoids physical harm to birds. In addition, through the iterative analysis of the bird repelling effect feedback data, a closed-loop optimization mechanism for continuously optimizing the matching rules of the strategy library enables the system to adapt to environmental changes and the evolution of bird behavior patterns, and further enhances the long-term bird repelling effect. Compared with the prior art, the present application breaks through the limitations of traditional bird repelling methods, greatly reduces the risk of line faults caused by bird activities, and provides a solid and powerful guarantee for the safe and reliable operation of the power system.

[0125] Optionally, the bird repelling device for the distribution tower may further include an electric field interference module;

[0126] Both the electric field interference module and the bird repelling execution module are arranged at the junction of the tower cross arm and the insulator string;

[0127] The pulsed electric field generated by the electric field interference module and the ultrasonic wave emitted by the bird repelling execution module are coupled to form a composite bird repelling field.

[0128] Specifically, the electric field interference module and the bird repelling execution module are arranged together at the junction of the tower cross arm and the insulator string, which is a key area where birds are likely to stay and pose potential hazards to power facilities.

[0129] After the electric field interference module is turned on, it generates a pulsed electric field with a specific frequency and intensity. This pulsed electric field does not act independently, but cooperates with the ultrasonic wave emitted by the bird repelling execution module. The ultrasonic wave emitted by the bird repelling execution module itself can utilize the characteristic that birds are sensitive to high-frequency sounds to stimulate their auditory systems and prompt birds to stay away. When the pulsed electric field is coupled with the ultrasonic wave, a composite bird repelling field is formed. This composite bird repelling field has multiple advantages. On the one hand, from the physical action mechanism, the dual stimulation of the electric field and the ultrasonic wave increases the influence range and intensity on the sensory system of birds. Compared with a single bird repelling method, it is more difficult for birds to adapt to this composite stimulation, thus more effectively driving away birds. On the other hand, in practical applications, the composite bird repelling field can be optimized according to the characteristics of different species of birds. For example, for birds that are more sensitive to the perception of the electric field, the electric field intensity of the electric field interference module can be appropriately increased; for birds that are highly sensitive to sound, the frequency and power of the ultrasonic wave can be adjusted.

[0130] At the same time, since the electric field interference module and the bird repelling execution module are arranged at the junction of the tower cross arm and the insulator string, which is close to the position where birds often perch and move, the composite bird repelling field can play a role at the key node where birds approach power facilities, minimizing the intrusion of birds on the distribution tower and related power facilities to ensure the stable operation of the power system.

[0131] The bird repelling device for the distribution tower provided by the embodiment of the present application can be applied to the bird repelling equipment for the distribution tower. Figure 3 The hardware structure block diagram of the bird repelling equipment for the distribution tower is shown. Referring to Figure 3 , the hardware structure of the bird repelling equipment for the distribution tower may include: at least one processor 1, at least one communication interface 2, at least one memory 3, and at least one communication bus 4;

[0132] In the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete communication with each other through the communication bus 4;

[0133] The processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present invention, etc.;

[0134] The memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, etc., such as at least one disk memory;

[0135] Wherein, the memory stores a program, and the processor can call the program stored in the memory, and the program is used for:

[0136] Using a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generating sensor data;

[0137] Performing fusion recognition on the sensor data, extracting the shape features, sound wave features, and behavior features of the current bird flock, and determining the current bird flock type based on the shape features, the sound wave features, and the behavior;

[0138] According to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock type, matching and determining a target bird repelling strategy in a preset bird repelling strategy library, where the target bird repelling strategy includes at least one bird repelling method such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling;

[0139] Executing the target bird repelling strategy, and iteratively optimizing the matching rules of the bird repelling strategy library according to the bird repelling effect feedback data.

[0140] Optionally, the refined functions and extended functions of the program can be referred to the above description.

[0141] The embodiments of the present application further provide a readable storage medium, which can store a program suitable for being executed by a processor, and the program is used for:

[0142] Using a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generating sensor data;

[0143] Performing fusion recognition on the sensor data, extracting the shape features, sound wave features, and behavior features of the current bird flock, and determining the current bird flock type based on the shape features, the sound wave features, and the behavior;

[0144] Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, determine a target bird repelling strategy by matching in a preset bird repelling strategy library. The target bird repelling strategy includes at least one bird repelling method among strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling;

[0145] Execute the target bird repelling strategy, and iteratively optimize the matching rules of the bird repelling strategy library according to the feedback data of the bird repelling effect.

[0146] Optionally, the refinement function and expansion function of the program can be referred to the above description.

[0147] The embodiment of the present application further provides a computer program product, including a computer program. When the computer program is run by a processor, the method executed is:

[0148] Use a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generate sensor data;

[0149] Fuse and identify the sensor data, extract the shape features, acoustic wave features, and behavior features of the current bird flock, and determine the current bird flock species based on the shape features, the acoustic wave features, and the behavior;

[0150] Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, determine a target bird repelling strategy by matching in a preset bird repelling strategy library. The target bird repelling strategy includes at least one bird repelling method among strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling;

[0151] Execute the target bird repelling strategy, and iteratively optimize the matching rules of the bird repelling strategy library according to the feedback data of the bird repelling effect.

[0152] Optionally, the refinement function and expansion function of the program can be referred to the above description.

[0153] Finally, it should also be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element.

[0154] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference can be made to each other.

[0155] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for driving birds away from distribution towers, characterized in that, Including: Using a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generating sensor data; Performing fusion recognition on the sensor data, extracting the shape features, acoustic wave features, and behavior features of the current bird flock, and determining the current bird flock species based on the shape features, the acoustic wave features, and the behavior; According to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, matching and determining a target bird repelling strategy in a preset bird repelling strategy library, where the target bird repelling strategy includes at least one bird repelling method such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling; Executing the target bird repelling strategy, and iteratively optimizing the matching rules of the bird repelling strategy library according to the bird repelling effect feedback data.

2. The method according to claim 1, characterized in that, Performing fusion recognition on the sensor data, extracting the shape features, acoustic wave features, and behavior features of the current bird flock, and determining the current bird flock species based on the shape features, the acoustic wave features, and the behavior, including: Performing image enhancement, edge detection, and feature extraction processing on the image data in the sensor data to obtain shape features; Performing noise reduction, spectrum analysis, and feature extraction processing on the sound signal in the sensor data to obtain acoustic wave features; Performing data fusion and trajectory tracking processing on the infrared radiation signal and the radar reflection signal in the sensor data, determining the current flight trajectory, and determining the behavior features based on the current flight trajectory; Determining the current bird flock species based on the shape features, the acoustic wave features, and the behavior features.

3. The method according to claim 2, wherein Also including: Performing data fusion and target detection processing on the infrared radiation signal and the radar reflection signal in the sensor data to determine the number of individual birds in the flock; Predicting the predicted flight trajectory within a future predetermined time period according to the historical flight features of the current bird flock species and the current flight trajectory; Optimizing and adjusting the target bird repelling strategy in combination with the number of individual birds in the flock and the predicted flight trajectory.

4. The method according to claim 1, wherein Also including: Obtaining the current date and the current position of the distribution tower; According to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, matching and determining a target bird repelling strategy in a preset bird repelling strategy library, including: Determining the time-domain species habits corresponding to the season period where the current bird flock species is located on the current date and the geographical area where it is located at the current position; Based on the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock species, and in combination with the time-domain species habits, matching and determining a target bird repelling strategy in a preset bird repelling strategy library.

5. The method according to claim 1, wherein Also including: Obtaining real-time environmental data, where the real-time environmental data at least includes wind speed, temperature, and light intensity; Optimizing and adjusting the target bird repelling strategy according to the real-time environmental data.

6. A bird repelling device for a distribution tower, characterized in that, Including: A multi-dimensional sensor module for using a multi-dimensional sensor to collect infrared radiation signals, sound signals, image data, and radar reflection signals around the distribution tower in real time, and generating sensor data; A data processing module, configured to fuse and identify the sensor data, extract the shape features, acoustic features, and behavior features of the current bird flock, and determine the type of the current bird flock based on the shape features, the acoustic features, and the behavior; A strategy selection module, configured to match and determine a target bird repelling strategy in a preset bird repelling strategy library according to the visual sensitivity, auditory sensitivity, and ultrasonic sensitivity of the current bird flock type, where the target bird repelling strategy includes at least one bird repelling method such as strong light stroboscopic bird repelling, sound bird repelling, or ultrasonic bird repelling; A bird repelling execution module, configured to execute the target bird repelling strategy and iteratively optimize the matching rules of the bird repelling strategy library according to the bird repelling effect feedback data.

7. The device according to claim 6, characterized in that, It further includes an electric field interference module; Both the electric field interference module and the bird repelling execution module are arranged at the junction of the tower cross arm and the insulator string; The pulsed electric field generated by the electric field interference module is coupled with the ultrasonic wave emitted by the bird repelling execution module to form a composite bird repelling field.

8. A bird repelling device for a distribution tower, characterized in that, It includes a memory and a processor; The memory is used to store programs; The processor is used to execute the program to implement each step of the distribution tower bird repelling method according to any one of claims 1-5.

9. A readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it implements each step of the distribution tower bird repelling method according to any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, When the computer program runs on the processor, it executes each step of the distribution tower bird repelling method according to any one of claims 1-5.

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