Intelligent bird repelling device for power transmission and distribution facilities and repelling strategy generation method

Abstract

The application relates to a smart bird repelling device for power transmission and distribution facilities and a driving strategy generation method. The application relates to the field of bird repelling devices and provides a method for obtaining characteristic data of birds, including body size, flight speed, flight height and sound frequency; converting the characteristic data into a characteristic vector; calculating the probability P(C|X) of occurrence of each kind of bird according to the relationship between the characteristic vector X and the bird species C; calculating the estimated value of P(C|X) according to the model parameters; determining the species of the bird according to the estimated value of the formula P(C|X); and executing a corresponding driving strategy according to the identification result of the species of the bird. Therefore, the bird repelling strategy can be flexibly selected according to the species of the bird, and the best bird repelling effect can be achieved.

Description

Technical Field

[0001] This invention relates to the field of power transmission and distribution, and in particular to an intelligent bird deterrent device and a method for generating bird deterrent strategies. Background Technology

[0002] Power transmission and distribution facilities are the backbone of modern society's energy supply, and their safe and stable operation is crucial. However, the threat posed by bird activity to power facilities is becoming increasingly prominent, becoming one of the important factors affecting the safety of the power system. Birds nesting and roosting on critical facilities such as transmission line towers, distribution line poles, and substations can not only threaten the stable operation of power equipment but also potentially cause accidents such as short circuits and flashovers in transmission lines. In severe cases, it can even cause fires, posing a significant hidden danger to the safety and stability of the power system.

[0003] Traditional bird control measures mainly include physical isolation, sound deterrence, and visual interference. Physical isolation involves installing obstacles on power facilities to prevent birds from approaching or nesting, but this method is difficult and costly to implement, and may affect the normal operation and maintenance of power facilities. Sound deterrence uses specific sounds to scare away birds, but this method often only temporarily deters birds, and birds are highly adaptable to sound, making the deterrence effect difficult to sustain. Visual interference uses mannequins, reflective objects, etc., to frighten away birds, but it also has limited effectiveness and is easily adapted to by birds.

[0004] A major shortcoming of existing bird control techniques is their lack of specificity and differentiation. Bird species are diverse, and different species vary in their adaptability to the environment, their ability to perceive threats, and their behavioral habits. For example, some birds are highly sensitive to sound, while others may be more sensitive to visual stimuli. Furthermore, different bird species have different activity times, habitat habits, and migration patterns. Therefore, using a single bird control method is insufficient to adapt to the characteristics of different bird species and is unlikely to achieve the desired bird control effect.

[0005] Employing differentiated bird control methods for different bird species is key to improving efficiency and effectiveness. Differentiated methods can be tailored to the species, habits, and activity patterns of each bird, selecting or combining various techniques accordingly. For example, specific frequencies of sound can be used to scare away sound-sensitive birds; visual stimuli such as lasers or flashes can be used for sight-sensitive birds; and special light sources such as infrared or ultraviolet light can be used to repel nocturnal birds. Furthermore, the timing and frequency of bird control measures can be adjusted based on the birds' habitat habits and activity patterns to achieve optimal results.

[0006] However, existing technologies still face many challenges in achieving differentiated bird control. First, the vast diversity of bird species necessitates extensive data support and advanced algorithms for identifying and classifying different bird characteristics, and different bird control strategies are required for different species. Second, the implementation of differentiated bird control methods requires a high degree of flexibility and adjustability to adapt to different scenarios and environments. Furthermore, differentiated bird control must also consider environmental protection and ecological conservation requirements to avoid adverse impacts or accidental harm to birds and the ecological environment. Summary of the Invention

[0007] This invention addresses the shortcomings of existing technologies by proposing a novel intelligent bird-repelling device and method. The device intelligently identifies and selects differentiated bird-repelling methods based on bird species, habits, and activity patterns, achieving precise bird control. By integrating advanced detection technology, intelligent recognition algorithms, and multiple bird-repelling techniques, this invention can monitor bird activity in real time and automatically activate the most suitable bird-repelling measures, effectively improving efficiency and effectiveness. Furthermore, this invention fully considers environmental protection and ecological conservation requirements, employing harmless bird-repelling technology to ensure a bird-friendly and environmentally friendly process.

[0008] By implementing this invention, the problems of insufficient targeting of bird species and low efficiency in existing bird control technologies can be effectively solved, providing a more intelligent, efficient and environmentally friendly bird control solution for power transmission and distribution facilities.

[0009] In one embodiment of this application, a method for identifying bird species in an intelligent bird deterrent device for power transmission and distribution facilities is provided, comprising:

[0010] Acquire bird characteristic data, including body size, flight speed, flight altitude, and sound frequency;

[0011] Transform the feature data into a feature vector X = {x1, x2, ..., x}. n};

[0012] Based on the relationship between the feature vector X and the bird species C, the probability P(C|X) of each bird species is calculated as follows:

[0013]

[0014] Where K is the number of known bird species, α, β and γ are model parameters, and ||X|| is the norm of the feature vector X;

[0015] The negative log-likelihood function L(α,β,γ) is calculated using the following formula to estimate the model parameters α, β, and γ:

[0016]

[0017] Calculate the estimated value of P(C|X) based on the model parameters α, β and γ;

[0018] The species of birds are determined based on the estimated value of the formula P(C|X).

[0019] Optionally, in some embodiments, the method further includes the step of: calculating the probability P(C|X) of each bird species based on the relationship between the feature vector X and the bird species C.

[0020]

[0021] Where K is the number of known bird species, α, β and γ are model parameters, and ||X|| is the norm of the feature vector X;

[0022] The negative log-likelihood function L(α,β,γ) is calculated using the following formula to estimate the model parameters α, β, and γ:

[0023]

[0024] Calculate the estimated value of P(C|X) based on the model parameters α, β and γ;

[0025] The species of birds are determined based on the estimated value of the formula P(C|X).

[0026] To construct a mathematical model incorporating logarithmic and exponential calculations to describe the hybrid shaping of ultrasonic and acoustic signals, and to adjust the components of the ultrasonic and acoustic signals according to different bird species, this application designs a scenario where the hybrid signal S(t) is a mixture of acoustic signal A(t) and ultrasonic signal U(t), and the mixing ratio varies with time t and bird species k. This application will construct a mathematical model that includes logarithmic and exponential operations to define the mixing method of these two signals.

[0027] Optionally, the method for identifying bird species further includes the step of:

[0028] A mixed signal S(t,k) is generated, consisting of a sound wave signal A(t) and an ultrasonic signal U(t). This mixed signal is defined as:

[0029]

[0030] Where δ k and γ k These are parameters adjusted according to the bird species k. A0 and U0 are the initial amplitudes of the sound wave and ultrasonic signal, respectively, and α and β are the attenuation coefficients of the sound wave and ultrasonic signal, respectively. a and f u These are the frequencies of sound waves and ultrasonic signals, φ.a and φ u These are the phases of the sound wave and the ultrasonic signal, respectively, log δk It is based on δ k The base of the logarithm is , and e is the natural exponential function.

[0031] Optionally, the method for generating the driving-off strategy further includes the following steps:

[0032] A mixed signal S(t,k) is generated, consisting of a sound wave signal A(t) and an ultrasonic signal U(t). This mixed signal is defined as:

[0033]

[0034] Where δ k and γ k These are parameters adjusted according to the bird species k. A0 and U0 are the initial amplitudes of the sound wave and ultrasonic signal, respectively, and α and β are the attenuation coefficients of the sound wave and ultrasonic signal, respectively. a and f u These are the frequencies of sound waves and ultrasonic signals, φ. a and φ u These are the phases of the sound wave and the ultrasonic signal, respectively, log δk It is based on δ k The base of the logarithm is , and e is the natural exponential function.

[0035] Optionally, in some embodiments, the method for identifying bird species further includes the step of:

[0036] A mixed signal S(t,k) of acoustic wave signal A(t) and ultrasonic wave signal U(t) is generated, and the complete form of this mixed signal can be defined by the following formula:

[0037]

[0038] Where δ k and γ k These are parameters adjusted according to the bird species k. A0 and U0 are the initial amplitudes of the sound wave and ultrasonic signal, respectively, and α and β are the attenuation coefficients of the sound wave and ultrasonic signal, respectively. a and f u These are the frequencies of sound waves and ultrasonic signals, φ. a and φ u These are the phases of the sound wave and the ultrasonic signal, respectively, log δk It is based on δ k The base of the logarithm is , and e is the natural exponential function.

[0039] Optionally, the method for identifying bird species is characterized by,

[0040] The ultrasonic signal U(t) is:

[0041] U(t) = U0·e -βt ·sin(2πf u t+φ u )

[0042] The mixed signal S(t) is defined as:

[0043] S(t,k)=λ(t,k)·A(t)+(1-λ(t,k))·U(t)

[0044] in,

[0045] U0 is the initial amplitude of the ultrasonic signal;

[0046] β is the attenuation coefficient of the ultrasonic signal;

[0047] f u It is the frequency of the ultrasonic signal;

[0048] φ u It is the phase of the ultrasonic signal;

[0049] λ(t,k) is the weight of the acoustic signal in the mixed signal, 0≤λ(t,k)≤1, depending on time tt and bird species k.

[0050] Based on the bird species identification results, the driving control submodule performs beamforming according to the corresponding mixed signal S(t,k) and transmits it to the monitoring area.

[0051] Optionally, the method for generating the driving-off strategy is characterized by further including the step of:

[0052] A mixed signal S(t,k) is generated, consisting of a sound wave signal A(t) and an ultrasonic signal U(t). This mixed signal is defined as:

[0053] Where δ k and γ k These are parameters adjusted according to the bird species k. A0 and U0 are the initial amplitudes of the sound wave and ultrasonic signal, respectively, and α and β are the attenuation coefficients of the sound wave and ultrasonic signal, respectively. a and f u These are the frequencies of sound waves and ultrasonic signals, φ. a and φ u

[0054] These are the phases of the sound wave and the ultrasonic signal, respectively, log δk It is based on δ kThe base is a logarithmic function, where e is the natural exponential function. Optionally, some embodiments of the drive-off strategy generation method further include the step of:

[0055] A mixed signal S(t,k) is generated, consisting of a sound wave signal A(t) and an ultrasonic signal U(t). This mixed signal is defined as:

[0056]

[0057] Where δ k and γ k These are parameters adjusted according to the bird species k. A0 and U0 are the initial amplitudes of the sound wave and ultrasonic signal, respectively, and α and β are the attenuation coefficients of the sound wave and ultrasonic signal, respectively. a and f u These are the frequencies of sound waves and ultrasonic signals, φ. a and φ u These are the phases of the sound wave and the ultrasonic signal, respectively, log δk It is based on δ k The base of the logarithm is , and e is the natural exponential function.

[0058] The methods in some embodiments of this application, by establishing mathematical models, can adaptively adjust the trunk strategy for different bird species, which has higher flexibility, higher accuracy and robustness compared to the prior art.

[0059] In practical applications, these mathematical models can be further optimized to improve recognition accuracy, and the model parameters can be adjusted with more training data to achieve better performance. Attached Figure Description

[0060] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0061] Figure 1 This is a schematic diagram of signal flow in a vehicle decision-making method based on a PFC model according to an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of the agent architecture of the PFC model;

[0063] Figure 3 A schematic diagram of the structure of a computing device according to an embodiment of this application is shown;

[0064] Figure 4 The system main interface of the host computer of the intelligent bird-repelling device according to an embodiment of this application is shown;

[0065] Figure 5A , Figure 5B The front view and side view of the intelligent bird-repelling device according to an embodiment of this application are shown respectively;

[0066] Figure 6 An external axial view of the intelligent bird deterrent device according to an embodiment of this application is shown. Detailed Implementation

[0067] The intelligent bird-repelling devices in some embodiments of this application are intelligent bird-repelling equipment designed by Nanjing Shoufeng specifically for power transmission and distribution scenarios. They aim to effectively address safety hazards caused by birds nesting and roosting on critical facilities such as transmission line towers, distribution line poles, and substations. By integrating advanced detection technology, intelligent recognition algorithms, and environmentally friendly bird-repelling methods, the technical solution of this application can monitor bird activity in real time and automatically activate non-harmful bird-repelling measures, effectively reducing the incidence of accidents such as short circuits and flashovers in power facilities caused by bird activity, and ensuring the safe and stable operation of transmission lines.

[0068] The technical solution of this application employs a variety of advanced bird-repelling methods. By simulating threat signals in the natural environment, it makes birds feel uncomfortable, thereby effectively driving them away. The technical solution uses specific sound waves, various sounds that frighten birds, and changing laser beams to make birds feel threatened and thus keep them away from electrical equipment. Simultaneously, the technical solution reduces the adaptability of birds through the synergistic and varied combination of multiple repelling methods.

[0069] The technical solution of this application is equipped with an intelligent bird deterrence strategy adjustment system, which allows users to adjust the deterrence strategy based on real-time monitoring of bird activity. The system has multiple preset modes, allowing users to select the appropriate deterrence strategy according to different scenarios and needs.

[0070] The technical solution of this application uses harmless bird-repelling technology that will not harm birds. The frequency and intensity of the sound and light have been scientifically adjusted to avoid interference or harm to humans. The product materials meet environmental protection standards, are non-toxic and harmless, and will not pollute the environment.

[0071] The technical solution presented in this application features a compact design, convenient installation, and applicability to various types of power equipment and scenarios. The equipment is easy to operate, equipped with a user-friendly interface, and can be remotely configured and monitored. The product boasts stable and reliable performance, capable of protecting transmission lines from bird damage for extended periods.

[0072] Regarding the external structure of the A90 intelligent bird deterrent device, such as Figure 5A , Figure 5B , Figure 6 As shown, the internal structure of the intelligent bird deterrent device A90 is as follows: Figure 2 As shown, it includes:

[0073] The camera submodule A99 is operable to capture images of birds;

[0074] The radar monitoring module A96 is operable to monitor bird activity and collect bird data in real time.

[0075] The sound driving module A91 can be operated to play various pre-stored audio files;

[0076] The A92 is a specific acoustic wave driving module that can be operated to emit acoustic waves of a specific frequency.

[0077] The laser driving module A93 is operable to emit a laser beam;

[0078] The radar monitoring module A96 further includes:

[0079] The transmitter submodule A961 is operable to transmit radar wave signals;

[0080] Antenna submodule A963 is operable to receive radar wave signals from transmitter submodule A961 and radiate them into the monitoring area, while simultaneously receiving reflected radar wave signals.

[0081] Receiver submodule A962 is operable to receive reflected radar wave signals from antenna submodule A963 and convert them into electrical signals; and,

[0082] The intelligent bird deterrent device also includes:

[0083] The signal processing and intelligent recognition submodule A95 analyzes the received radar wave signal and analyzes the data to identify the species, number, and behavioral patterns of birds.

[0084] The bird control submodule A94 is operable to: generate a mixed signal of corresponding sound wave signal and ultrasonic signal based on the bird species identification result of the signal processing and intelligent identification submodule A95; and control the specific sound wave driving module A92 and the sound driving module A91 to execute corresponding driving strategies.

[0085] The control of the specific acoustic wave driving module A92 and the sound driving module A91 to execute the corresponding driving strategy further includes: beamforming the mixed signal and transmitting the acoustic wave and / or ultrasonic wave of the mixed signal to the monitoring area.

[0086] Among them, the connection relationship between modules and sub-modules and the signal flow direction

[0087] Radar monitoring module A96

[0088] • Transmitter submodule A961: Responsible for transmitting radar wave signals.

[0089] ● Antenna submodule A963: Receives radar wave signals from transmitter submodule A961 and radiates them into the monitoring area; it also receives reflected radar wave signals.

[0090] • Receiver submodule A962: Receives reflected radar wave signals from antenna submodule A963 and converts them into electrical signals.

[0091] Signal processing and intelligent recognition submodule A95

[0092] ● Receive electrical signals (i.e., radar echo signals) from receiver submodule A962.

[0093] • Analyze the received data to identify bird species, numbers, and behavioral patterns.

[0094] Drive-away control submodule A94

[0095] Based on the information provided by the signal processing and intelligent recognition submodule A95, a mixed signal of corresponding acoustic and ultrasonic signals is generated.

[0096] • Control the specific sound wave driving module A92 and the sound driving module A91 to execute the corresponding driving strategy.

[0097] • Beamforming of the mixed signal and transmitting sound waves and / or ultrasound waves to the monitoring area.

[0098] A91 sound-based deterrent module

[0099] • Play various pre-stored audio files according to the instructions of the drive-away control submodule A94.

[0100] A92 Specific Sound Wave Repellent Module

[0101] • It emits sound waves of a specific frequency according to the instructions of the drive-away control submodule A94.

[0102] Laser drive module A93

[0103] • Operable to emit laser beams.

[0104] Application Scope

[0105] In some embodiments of this application, the intelligent bird deterrent device technology is applicable to power transmission and distribution scenarios, including transmission line towers, distribution line poles, substations, and other similar scenarios.

[0106] Transmission line towers: Transmission line towers are an important component of power transmission, and birds may nest, roost, or forage on them. These birds may pose a threat to the safe operation of transmission lines, leading to accidents such as short circuits or grounding. The technical solution of this application, installed on the tower, can form perfect protection for the crossarm area of ​​the tower, ensuring the normal operation of the transmission line.

[0107] Power line poles and towers: Power line poles and towers are typically located in urban or rural areas, where birds may nest or perch. These birds may affect the power lines and tower equipment such as switches and transformers, such as blocking ventilation openings, contaminating equipment surfaces, or causing electric shock accidents. The technical solution of this application, installed on the poles and towers, can effectively protect the safe operation of power lines.

[0108] Substation Scenario: Substations are a critical link in power transmission and distribution systems. Birds entering substations can lead to equipment malfunctions, electric shocks, or even fires. This intelligent bird deterrent device can be used in the surrounding area or specific locations of substations to ensure the normal operation and safety of substation equipment by repelling birds.

[0109] In some embodiments of this application, the intelligent bird deterrent device technology plays an important role in the above scenarios, effectively reducing the interference of birds on power equipment and ensuring the stable operation of the power transmission and distribution system.

[0110] Functional Configuration

[0111] Bird activity monitoring function

[0112] Microwave radar detection technology and camera equipment are used to detect and record bird activity;

[0113] Monitoring and statistically analyzing bird activity can provide information on the types, numbers, and behaviors of invasive birds.

[0114] Sound-based frightening and scare function

[0115] Play various pre-stored audio files to simulate warning sounds in nature that could make birds feel threatened, in order to frighten the birds and drive them away;

[0116] The intelligent bird deterrent device has a variety of pre-stored bird deterrent audios. Users can replace the pre-stored audios from the audio library to reduce bird adaptability.

[0117] Specific sound wave repelling function

[0118] Emitting various specific sound waves and their combinations can cause discomfort to birds, thereby driving them away;

[0119] Specific sound wave types and patterns can be adjusted according to the bird population in different installation areas to enhance the deterrent effect.

[0120] Laser repelling function

[0121] Using lasers to emit laser beams to stimulate and disturb birds, making them uncomfortable and causing them to leave;

[0122] Image evidence collection function

[0123] Equipped with video recording equipment, it records images of bird activity for use in assessing the effectiveness of bird deterrence and bird population damage.

[0124] Repelling effect evaluation function

[0125] The effectiveness of the deterrence measures is analyzed and evaluated based on statistical information such as the success rate of birds being driven away and the frequency of bird intrusion, as well as bird images.

[0126] Based on the assessment results, the driving-away strategy will be adjusted and optimized to achieve better driving-away results.

[0127] Drive-away strategy maintenance function

[0128] Based on the habits and activity patterns of birds in the area where the product is installed, develop more targeted repelling strategies;

[0129] The repelling strategy can be dynamically adjusted based on real-time monitoring data and the evaluation results of the repelling effect to address bird adaptation issues.

[0130] Bird situation and bird damage assessment function

[0131] By monitoring and analyzing bird activities and behaviors, we can assess the potential hazards that birds pose to power transmission lines or power facilities.

[0132] Based on the assessment results, corresponding protective measures and deterrence strategies will be developed to reduce the impact and damage of birds on power equipment.

[0133] Technical effect

[0134] Some of the technical solutions in this application have the following technical effects:

[0135] Highly sensitive bird activity detection: Real-time monitoring of bird intrusion behavior to support precise deterrence;

[0136] Multi-mode sound deterrence technology: Simulates predator warning sounds and various startling sounds to effectively frighten birds and drive them away;

[0137] Random sound wave combination technology uses specific sound waves to create interference within the hearing range of birds, with random patterns, forcing birds to leave the protected area;

[0138] Safe laser deterrence: Utilizes varying laser beams to stimulate and disturb birds, with three-dimensional area protection to ensure no harm during the deterrence process;

[0139] Intelligent driving strategy optimization: Dynamically adjust the driving method based on statistical monitoring data to enhance the driving effect;

[0140] Video evidence technology: Recording videos and images of bird activities to provide a basis for subsequent analysis and assessment;

[0141] Evaluation and analysis of the effectiveness of the expulsion: Through data statistics and evaluation, optimize the expulsion strategy and improve the effectiveness;

[0142] Intelligent adjustment of driving-away mode: The driving-away mode is flexibly adjusted according to the surrounding environment.

[0143] High-efficiency and energy-saving design: Employing energy-saving technologies and optimized power management technologies to ensure long-term reliable operation;

[0144] Unified cloud management: Distributed installation and unified management improve efficiency and facilitate monitoring and maintenance;

[0145] User-friendly interface: A simple and intuitive operating interface that makes it easy to set up and manage the functions and parameters of the intelligent bird deterrent device.

[0146] Voice-activated scare function

[0147] The principle behind using predator calls, gunshots, and other audio recordings to scare away birds is based on birds' reactions to potential threats. Playing these audio recordings simulates potential threat signals, making birds tense and alert. These sounds may mimic the calls of predators or other potentially dangerous sounds, triggering the birds' self-protective instincts, making them feel threatened and move away from the area where the sound is played. While birds gradually adapt to and ignore common sounds, they may remain sensitive to simulated threatening sounds and continue to react to them. Playing predator calls, gunshots, and other audio recordings can break down the birds' adaptations, making them unable to ignore the sounds, thus achieving the purpose of scaring them away. Playing sounds associated with danger or threats, such as gunshots, may trigger fear memories in birds. When faced with danger, birds form negative memories associated with specific sounds, and when they hear similar sounds again, they will exhibit fear and avoidance behavior.

[0148] Specific sound wave repelling function

[0149] The principle behind using specific sound waves to repel birds is based on birds' sensitivity to sound and their reactions to specific sound waves. Different types of birds have different ranges of sound frequency perception. By emitting sound waves of specific frequencies, their normal auditory perception can be interfered with, causing them discomfort or confusion. This interference can create an unpleasant experience for birds and force them away from the sound source. Certain specific frequencies of sound waves can mimic signals from birds' predators or other threats, triggering their defensive instincts. These sound waves may mimic the calls of predators or other birds' warning signals, alerting birds and prompting them to leave potentially dangerous areas. For birds exposed to specific frequency sound waves for extended periods, they may gradually develop a habitual avoidance behavior towards those sound waves. Through continuous sound wave repelling, birds may learn to associate that area with unpleasant sound waves and naturally avoid entering it.

[0150] Laser repelling function

[0151] The effects of light waves on birds

[0152] The eyes are the most sensitive and relied-upon organs of birds. Birds' eyes occupy a much larger proportion of their head than other vertebrates, typically half or more. Located on the sides of their heads, they offer an extremely wide field of vision. Birds have large pupils, allowing them to receive more light under the same lighting conditions, providing excellent vision, especially at night, thus enhancing their nocturnal activity. Different wavelengths of light stimulate cone cells differently. Studies show that birds are most sensitive to green, followed by red. The most sensitive wavelength range for most birds is 500–570 nm. This indicates that green and red light within this wavelength range can be used to stimulate bird vision and achieve a bird-repelling effect. Different colored light sources have a certain impact on bird migration routes. Studies have shown that white and red light sources have a significant impact on bird migration, causing disorientation and potentially disorientation; blue light has no effect; and green light has a very weak effect. From an ecological perspective, intelligent bird-repelling devices should be bird-friendly; therefore, red light is unsuitable for bird deterrence, while green light is the best choice.

[0153] Laser bird deterrence

[0154] Laser bird deterrence is a method that uses laser beams to scare away birds. Its principle is based on birds' visual sensitivity to laser beams and their adaptability to light. The bright spots and moving beams produced by the laser beam can trigger birds' visual sensitivity. Birds become alert to sudden bright spots and beams, as they may perceive them as potential threats or danger signals. This visual disturbance can make birds uncomfortable and force them to leave the area illuminated by the laser beam. The laser beam can be moved quickly or swept to simulate the movement of a predator. Birds are generally sensitive to movement and perceive fast-moving objects as potential threats. By manipulating the movement of the laser beam, predator behavior can be simulated, alerting birds and prompting them to move away from the area illuminated by the laser beam. While birds are highly adaptable to common light and ambient light, they may not be able to adapt to and ignore intense, concentrated light like a laser beam. The brightness and focused nature of the laser beam can attract birds' attention and limit their lingering in the area illuminated by the laser beam.

[0155] Time required to complete the expulsion judgment

[0156] When an intelligent bird deterrent device activates its deterrence module (such as sound or laser) and executes the deterrence for a specified duration, it determines whether the birds have left the target monitoring area. The duration for determining bird departure completion controls the strictness of this determination. If, after the deterrence module completes its execution, the microwave radar does not detect any bird activity within the determination duration, it means the birds have been completely deterred, and the deterrence is effective. If the microwave radar detects bird activity again within this duration, it means the birds have not left or have quickly returned, indicating the deterrence was ineffective, and the deterrence module needs to be activated again. If the deterrence module is activated three times consecutively, and the microwave radar still detects bird activity within the determination duration, it means the current deterrence strategy is ineffective, and the bird deterrence attempt has failed. The longer the determination duration, the stricter the intelligent bird deterrent device's determination of bird departure completion. Setting a reasonable determination duration ensures the effectiveness of each bird deterrence attempt. The system defaults to a 10-second timeout for determining when a drive is completed, but the timeout can be set from 5 to 60 seconds, as shown in Table 1-1.

[0157] Table 1-1 Device Time Parameter Configuration

[0158]

[0159] Time period configuration for driving away

[0160] The intelligent bird deterrent device employs three bird-repelling methods: laser, sound waves, and noise. Each method has varying degrees of impact on the surrounding environment. Users can adjust the device's activation method at different times based on factors such as the severity of bird infestation near the installation location and nearby human activity. The current system supports a maximum of five time periods, allowing users to choose whether to activate laser, sound, or noise bird deterrents within each period. The system's default time period configurations and bird deterrent method configurations are shown in Table 1-2. For laser bird deterrence, if there is a building diagonally above the device's installation location, it is recommended to disable it to prevent visual impairment to people inside the building. For noise bird deterrence, if residents live nearby, it is recommended to disable it during residents' rest periods due to the high intensity and penetrating power of some audio frequencies. Sound bird deterrence has weaker intensity, penetrating power, and startling effect compared to noise bird deterrence, so it can be activated during the rest periods of nearby residents.

[0161] Table 1-2 Time Period Configuration of Drive-Away Methods

[0162] 00:00:00～07:00:00 Enable Disable Enable 07:00:00～12:30:00 Enable Enable Disable 12:30:00～14:30:00 Enable Disable Enable 14:30:00～18:00:00 Enable Enable Disable 18:00:00～23:59:59 Enable Disable Enable

[0163] Pre-saved audio file configuration

[0164] The intelligent bird deterrent device can pre-store up to 10 audio files that can frighten birds. When the device activates its sound-based bird deterrent mode, it randomly selects one of the pre-stored audio files to play. The cloud platform contains a bird deterrent audio file library, which stores audio files that can frighten birds. Users can select 1 to 10 audio files as pre-stored audio for the intelligent bird deterrent device. When the device updates its parameters, the modified pre-stored audio is downloaded from the cloud audio file library to the intelligent bird deterrent device. The intelligent bird deterrent device comes pre-stored with 10 audio files, as shown in Table 1-3, including alarm sounds, explosions, hawk calls, gunshots, thunder, and other sounds that can alert target birds.

[0165] Table 1-3 Configuration of Pre-stored Audio Files for Intelligent Bird Repelling Devices

[0166] alarm sound alert_1.wav 133.58KB f20c01c2c7d2e682db599be4998d3ff3 alarm sound alert_2.wav 295.51KB 9cea4762f8c798cd2f4ffdd46c3002db alarm sound alert_3.wav 222.38KB c762008c42a925a1fd5b4de9f183e2ae Explosion boom_1.wav 273.73KB 0a1906f2d113d81bfa3a4e22bd2ba758 Eagle's Cry eagle_1.wav 137.54KB e0f5e4a9acbc6a7d4359da4400a842e8 Eagle's Cry eagle_2.wav 164.08KB 1dc3867415337c119d2fd9be5f53ea6b Gunshot gun_1.wav 204.93KB 9625992ae22ab70282741e9be967efd1 Sharp sound scream_1.wav 297.01KB 803dc0a230d53f5937936832ebf9bf51 Thunder thunder_1.wav 283.20KB b03a4ad0604c18aa0d1ec3d0b4416d99 Train whistle trian_1.wav 254.60KB b076be03004cda3831994e4ff8b0a7f3

[0167] Detection sensitivity

[0168] The intelligent bird deterrent device uses microwave radar to monitor intruding birds in real time. By configuring the sensitivity of the microwave radar to detect intruding birds, the device's sensitivity to bird intrusion can be controlled. Higher microwave radar sensitivity makes it more sensitive to bird intrusions but also more susceptible to environmental factors such as trees and vibrations. The specific sensitivity setting should consider the device's installation location and its surrounding environment. If there are trees or other obstructions nearby, the sensitivity should be reduced to avoid false detections. If the area around the installation location is relatively open, the sensitivity can be increased to improve the detection capability. The system's default monitoring sensitivity is 10, and the adjustable range is 3–31; a lower value indicates higher sensitivity.

[0169] To illustrate the application effects of some technical solutions in this application and to compare them with existing technologies, several specific numerical calculation cases will be constructed here. A set of fictitious bird image parameters will be used, and these parameters will be combined with the steps and formulas of some embodiments of this application to demonstrate the practical application effects of these steps and methods.

[0170] The main interface of the host computer for the intelligent bird deterrent device is divided into three parts, such as... Figure 4 As shown, the left side is the page navigation bar, the middle main section is an overview of the intelligent bird control system information, and the right side displays the calendar and bird-related events. The system's main interface is shown in the image below.

[0171] The main function of the page navigation bar is to switch to the management interface for lines, poles, and equipment, thereby enabling refined management of them.

[0172] After clicking on "Line Management", a list of lines will drop down. Clicking on the line list will take you to the line management interface.

[0173] After clicking on "Pole Management", a list of poles will drop down. Clicking on the pole list will take you to the pole management interface.

[0174] After clicking on Device Management, a drop-down list of devices, eviction records, eviction policies, and device configurations will appear. Clicking on the corresponding option will take you to the corresponding configuration interface.

[0175] The intelligent bird control system overview includes: the number of lines, indicating how many lines are currently equipped with intelligent bird control devices; the number of poles, indicating how many poles are currently equipped with intelligent bird control devices; the number of devices, indicating how many intelligent bird control devices are currently installed in the power system; a line graph of bird intrusion records for the past 7 days, indicating the number of bird intrusions per day over the past seven days; a pie chart of bird intrusion success rate, indicating the percentage of successful bird intrusions (blue) and the percentage of unsuccessful bird intrusions (green) among all bird intrusion attempts; and the percentage of bird intrusions during different time periods.

[0176] The calendar and bird incidents include the current calendar in the upper right corner and the real-time events in the lower right corner. The real-time events scroll to show the specific year, month, and day, and which line and tower the bird intrusion occurred on.

[0177] Specific application scenarios

[0178] Suppose that this application is using an intelligent bird deterrent device to monitor and identify bird species. The device is installed on a power transmission line tower and needs to identify two bird species: pigeons and sparrows.

[0179] like Figure 1 As shown, in the method for generating the deterrence strategy in this application, a set of bird image data and its feature data are collected in step S1, including body size, flight speed, flight altitude, sound frequency, etc. These data will be used to calculate the probability P(C|X) of each bird species and minimize the negative log-likelihood function L(α,β,γ).

[0180] Hypothetical bird image parameters

[0181] Suppose that this application has collected a set of bird feature data, and in step S2, this feature data is converted into a feature vector X:

[0182] 1. Eigenvector X:

[0183] Size s = 15cm

[0184] Flight speed v = 10 m / s

[0185] Flight altitude h = 5m

[0186] The sound frequency f = 1500 Hz

[0187] 2. Model parameters:

[0188] οα=0.5

[0189] οβ=0.2

[0190] ογ=0.1

[0191] 3. Number of known bird species:

[0192] οK = 2 (pigeons and sparrows)

[0193] norm of eigenvectors

[0194] First, this application needs to calculate the norm ||X|| of the eigenvector X:

[0195]

[0196] The probability of each bird species appearing

[0197] Next, in step S3, the probability P(C|X) of each bird species is calculated. Assume that there are 2 known bird species K=2 in this application.

[0198]

[0199] Since exp(-150.025) is very close to zero, this application simplifies the above calculation:

[0200]

[0201] This means that the probability of each bird species appearing is approximately 50%. In practical applications, the parameters α, β, and γ may differ for each bird species, and therefore the probability of each bird species appearing will also vary.

[0202] In step S4, maximum likelihood estimation is used to estimate the model parameters α, β, and γ. This application employs maximum likelihood estimation. Assume this application has a set of training data: D = {(X1, C1), (X2, C2), ..., (X...} m C m )}, where Xi is the feature vector of the i-th sample and Ci is the corresponding bird species.

[0203] The objective of maximum likelihood estimation is to minimize the negative log-likelihood function L(α,β,γ):

[0204]

[0205] In step S5, based on the identification result of the bird species, a corresponding deterrent strategy is executed. The device pre-stores multiple deterrent strategies and corresponding bird species. For example, for medium to large-sized birds such as pigeons, or for birds with strong resistance to fright, a mixture of two or more signals, such as specific sound waves, sound signals, or laser signals, or a combination of multiple signals, can be used to deter the birds. For small birds such as sparrows, a single signal can be used for deterrenting.

[0206] In existing technologies, comparisons are based on simple thresholds (for example, if the flight speed exceeds a certain threshold, it is considered to be a specific bird species). This identification method has low accuracy and is easily affected by external factors.

[0207] Detailed calculation process:

[0208] Suppose that this application has a simple threshold comparison model for identifying pigeons and sparrows.

[0209] If the flight speed v>12m / s, it is considered a pigeon; otherwise, it is considered a sparrow.

[0210] In this case, given the flight speed of 10 m / s, this application would identify the bird as a sparrow based on a simple threshold comparison model.

[0211] However, this simplistic approach overlooks the importance of other characteristics, such as size, flight altitude, and sound frequency.

[0212] Alternatively, in another embodiment of this application, the following mathematical model can be used for pattern recognition of bird categories:

[0213]

[0214] Where I represents the input bird image, K is the number of feature categories, J is the number of feature subcategories, w is the weight vector, b is the model parameter vector, and R(I) and G(I) are specific feature sets extracted from image I.

[0215] Further optionally, R(I) and G(I) include a combination of color histograms, texture features, and shape descriptors.

[0216] Alternatively, the model parameter b can be optimized using maximum likelihood estimation.

[0217] Alternatively, the weight vector w can be adjusted using the gradient ascent method.

[0218] Further, optionally, a normalization step is also included:

[0219]

[0220] Where μ(I) and σ(I) are the mean and standard deviation of image I, respectively.

[0221] Further optionally, the torso strategy generation method of this embodiment also includes a feature space transformation step: T(I)=I·A+B where A and B are transformation matrices and vectors.

[0222] Further, optionally, the model also includes a regularization term to prevent overfitting: L(w,b)=-log(F)+λ·R(w,b)

[0223] Where λ is the regularization parameter and R(w,b) is the regularization function.

[0224] Further, optionally, the regularization function R(w,b) is an L2 norm or an L1 norm.

[0225] Further, optionally, the model parameter b is optimized through the following iterative process:

[0226]

[0227] Where η is the learning rate. The loss function L with respect to parameter b k The partial derivatives of .

[0228] Further optionally, the loss function L includes cross-entropy loss and / or mean squared error loss.

[0229] Further, optionally, the above-mentioned step of performing pattern recognition of bird categories based on the mathematical model F(I,w,b) further includes the following sub-steps:

[0230] Step 1: Image Acquisition and Preprocessing

[0231] • Use camera equipment to capture images of birds in power transmission and distribution facilities.

[0232] • Preprocess the captured images, including denoising, adjusting contrast, cropping unwanted edges, and standardizing image size.

[0233] Step 2: Feature Extraction

[0234] • Extract the R(I) and G(I) feature sets from the preprocessed image I, which may include color histograms, texture features, shape descriptors, etc.

[0235] Step 3: Feature Enhancement and Selection

[0236] • Apply an exponential function to the feature set R(I) to enhance important features and suppress unimportant features.

[0237] • Transform another feature set G(I) using a logarithmic function to select the most representative features.

[0238] Step 4: Weight Adjustment and Model Training

[0239] • Using the training dataset, adjust the weight vector w using gradient ascent to maximize the model's prediction accuracy.

[0240] • Adjust the model parameter b using maximum likelihood estimation or other optimization algorithms.

[0241] Step 5: Regularization

[0242] To prevent overfitting, a regularization term R(w,b) is introduced into the loss function L, where λ is the regularization parameter.

[0243] Step 6: Loss Function Calculation and Optimization

[0244] • Calculate the loss function L(w,b), which may include cross-entropy loss and mean squared error loss.

[0245] • Use optimization algorithms (such as gradient descent) to iteratively update the model parameters b and weights w to minimize the loss function.

[0246] Step 7: Model Evaluation and Validation

[0247] • Use a validation set to evaluate the model's performance and adjust the model parameters to optimize recognition accuracy.

[0248] • Perform cross-validation to ensure the model's generalization ability.

[0249] Step 8: Model Deployment and Real-time Recognition

[0250] • Deploy the trained model onto the intelligent bird deterrent device.

[0251] • Enables real-time image capture, processing, and automatic identification of bird species.

[0252] Step 9: Feedback Mechanism and Model Iteration

[0253] • Collect feedback information based on the effectiveness of the intelligent bird deterrent device.

[0254] • Use feedback information to iteratively optimize the model, adjusting feature extraction, weights, and parameters to improve recognition accuracy.

[0255] Step 10: Integration of Environmental Factors

[0256] • Integrating environmental factors, such as weather and time, can influence bird behavior and image characteristics.

[0257] • Adjust the model to take these environmental factors into account and improve the model's robustness under different environmental conditions.

[0258] To demonstrate the effectiveness of mathematical models such as "F(I,w,b)" in the pattern recognition of bird categories, numerical calculations and comparisons are performed using a hypothetical application scenario. In this scenario, this example assumes that images of two bird species have been captured: sparrows and eagles. This example will define specific parameters for these images and demonstrate how to substitute them into the model's calculation formula.

[0259] Hypothetical bird image parameters

[0260] Sparrow image parameters:

[0261] The area Asparrow = 80 pixels^2

[0262] The longest size Lsparrow = 45 pixels

[0263] The texture feature vector Tsparrow = [10, 20, 30, 40, 50] (hypothetical values).

[0264] The color feature vector Csparrow = [0.3, 0.4, 0.3] (normalized values ​​in the RGB color space)

[0265] Eagle image parameters:

[0266] area Aeagle = 600 pixels^2

[0267] Longest size Leagle = 120 pixels

[0268] The texture feature vector Teagle = [70, 80, 90, 100, 110] (hypothetical values).

[0269] The color feature vector Ceagle = [0.2, 0.3, 0.5] (normalized values ​​in the RGB color space).

[0270] The calculation process in existing technology:

[0271] Existing technologies may use simple area and longest dimension for classification:

[0272] Sparrow classification criteria: A < 100 pixels^2 and L < 50 pixels

[0273] Eagle classification criteria: A > 500 pixels^2 and L > 100 pixels

[0274] Based on the above criteria, the images of sparrows and eagles will be classified as follows:

[0275] The sparrow image was correctly classified as a sparrow.

[0276] The eagle image was correctly classified as an eagle.

[0277] New technology calculation process:

[0278] The new technology uses the "F(I,w,b)" model for classification, and the steps are as follows:

[0279] 1. Feature extraction: Extracting texture and color features from an image.

[0280] 2. Model parameters: Assume the weights w and parameters b are obtained through training, for example:

[0281] οw = [1,1,1,1,1] (Texture and color features have the same weight)

[0282] οb = [0.1, 0.1, 0.1, 0.1, 0.1] (The biases of texture and color features are the same)

[0283] 3. Model Calculation:

[0284] Sparrow model output:

[0285]

[0286] Eagle model output:

[0287]

[0288] 4. Classification decision: Based on the values ​​output by the model, a classification algorithm is used to determine the bird species.

[0289] Numerical calculation example

[0290] Suppose the following simplified calculation is performed (for example, only the calculation of one feature is performed here):

[0291] • Sparrow model output (considering only one feature):

[0292] F sparrow =e (0.1.10) ·log(e (0 . 1.0.3) +e (0.1.0.4) +e (0.1.0.3) )

[0293] • Eagle model output (considering only one feature):

[0294] F eagle =e (0.1·70) ·log(e (0.1·0.2) +e (0.1·0.3) +e (0.1·0.5) )

[0295] Comparison results

[0296] • Accuracy: The new technology can more accurately distinguish birds by integrating texture and color features, especially when they are similar in shape.

[0297] • Adaptability to complex environments: New technologies can better adapt to complex environments, such as different lighting conditions or background interference.

[0298] • Differentiated processing: The new technology can adjust model parameters according to the characteristics of different bird species to achieve differentiated bird control strategies.

[0299] Let's take another specific application scenario as an example.

[0300] This embodiment assumes that an intelligent bird deterrent device is being used to monitor and identify bird species. The device is installed on a power transmission tower and needs to identify both pigeons and sparrows. This embodiment will use a set of hypothetical bird image parameters and substitute these parameters into a previously constructed mathematical model to demonstrate the practical application effect of the mathematical model expressed by the formula.

[0301] Hypothetical bird parameters

[0302] Suppose that this embodiment has collected a set of bird characteristic data:

[0303] 1. Eigenvector X:

[0304] Size s = 15cm

[0305] Flight speed v = 10 m / s

[0306] Flight altitude h = 5m

[0307] The sound frequency f = 1500 Hz

[0308] 2. Acoustic signal parameters:

[0309] Initial amplitude A0 = 1

[0310] The attenuation coefficient α = 0.1

[0311] ο Frequency fa = 1000Hz

[0312] Phase φa = π / 4

[0313] 3. Ultrasonic signal parameters:

[0314] Initial amplitude U0 = 1

[0315] The attenuation coefficient β = 0.2

[0316] ο frequency f u =40000Hz

[0317] ο phase φ u =π / 4

[0318] 4. Mixed signal parameters:

[0319] Time t = 0.1s

[0320] ο Bird species k = 1 (assumed to be pigeons)

[0321] ο parameter δ k =2 and γ k =1

[0322] Specific numerical calculation of the mixed signal S(t,k)

[0323] Now, this embodiment will use the above parameters to calculate the specific value of the mixed signal S(t,k).

[0324] Sound wave signal A(t)

[0325]

[0326] Ultrasonic signal U(t)

[0327]

[0328] The weighting function λ(t,k) of the mixed signal S(t,k)

[0329]

[0330] Mixed signal S(t,k)

[0331] S(t,k)=λ(t,k)·A(t)+(1-λ(t,k))·U(t)=0.145·0.700133903379443+(1-0.145)·0.693178999279526·0.70 0133903379443+0.855·0.693178999279526≈0.101523141928713+0.592649824383097=0.694172966311810

[0332] In this example, the value of the mixed signal S(t,k) was calculated at a specific time point t = 0.1s and the bird species k = 1 (assumed to be a pigeon).

[0333] In contrast, existing technologies use fixed sound and ultrasonic signals, which cannot be adjusted according to bird species. Adaptive signal adjustments for different bird species are not possible. In the calculation process of existing technologies, the mixed signal is usually fixed and does not change with time or bird species. Therefore, in existing technologies, the value of the mixed signal remains constant throughout the entire time period.

[0334] In summary, the calculations above demonstrate that this technology, through the mathematical model in this embodiment, can adaptively adjust to different bird species, offering greater flexibility compared to existing technologies. In practical applications, these mathematical models can be further optimized to improve recognition accuracy, and model parameters can be adjusted using more training data to achieve even better performance.

[0335] Comparison of some embodiments of this application with the prior art

[0336] In the embodiments of this application, a complex mathematical model is used, combining logarithmic and exponential calculations to comprehensively evaluate the feature vector. This takes into account the probability distribution of each bird species, improving the accuracy and robustness of identification.

[0337] In the specific calculation process, through the aforementioned complex mathematical model, this application can more accurately calculate the probability of each bird species appearing. In this case, the probability of each bird species appearing is approximately 50%, indicating that the model of this application can more comprehensively consider all features and more accurately identify bird species. By using the maximum likelihood estimation method, this application can further optimize the model parameters to improve the identification accuracy.

[0338] The calculations above demonstrate that this technology, through its complex mathematical model, can more accurately identify bird species, exhibiting higher accuracy and robustness compared to existing technologies. In practical applications, these mathematical models can be further optimized to improve recognition accuracy, and their parameters can be adjusted using more training data to achieve even better performance.

[0339] In another embodiment, a mathematical model can also be constructed to describe the beamforming of the ultrasonic and acoustic signals. The following is an exemplary mathematical model:

[0340] Beamforming Mathematical Model

[0341] This embodiment assumes two types of sound wave signals: ultrasonic waves (UC) and acoustic waves (AW). The aim of this embodiment is to mix and shape these two signals to optimize the bird-repelling effect.

[0342] U(t): The instantaneous amplitude of the ultrasonic signal at time tt.

[0343] • A(t): The instantaneous amplitude of the acoustic signal at time tt.

[0344] ·fu: The frequency of the ultrasonic signal.

[0345] ·fa: The frequency of the sound wave signal.

[0346] ·θ: Beamforming angle.

[0347] ·d: Beamforming spread distance.

[0348] • ku and ka: wavenumbers of ultrasound and sound waves, respectively (k = 2πλk = λ2π).

[0349] • α and β: Attenuation coefficients of ultrasound and sound waves, respectively.

[0350] ·g(θ): Beamforming function, describing the distribution of the beam with respect to angle.

[0351] Beamforming function

[0352]

[0353] Mixed signal amplitude

[0354] M(t)=U(t)·exp(-α·k u ·d)+A(t)·exp(-β·k a ·d)

[0355] Beamforming mixed signals

[0356] B(t,θ)=M(t)·g(θ)·cos(2πf u t+φ u )+M(t)·(1-g(θ))·cos(2πf a t+φ a )

[0357] in:

[0358] φu and φa are the phases of the ultrasonic and acoustic signals, respectively.

[0359] ·d0 is the reference distance, used to normalize the diffusion distance d.

[0360] Formulaic expression of the mathematical model:

[0361]

[0362] This formula combines the amplitude, frequency, attenuation, beamforming angle, and diffusion distance of both ultrasonic and acoustic signals to form a hybrid beamforming model. This model can be used to predict the intensity of the acoustic field at a specific time and angle.

[0363] The meanings of each variable are as follows:

[0364] • B(t,θ): The amplitude of the beamforming mixed signal at time t and angle θ.

[0365] U(t): The instantaneous amplitude of the ultrasonic signal at time t.

[0366] • A(t): The instantaneous amplitude of the acoustic signal at time t.

[0367] ·exp: Exponential function, representing growth or decay.

[0368] • α: Attenuation coefficient of ultrasonic signal, describing the attenuation of signal with distance.

[0369] •ku: Wavenumber of ultrasound, inversely proportional to wavelength.

[0370] ·d: Beamforming spread distance, which affects the beam width and intensity distribution.

[0371] •β: Attenuation coefficient of sound wave signal, describing the attenuation of signal with distance.

[0372] ·ka: wave number of a sound wave, inversely proportional to wavelength.

[0373] ·fu: The frequency of the ultrasonic signal.

[0374] φu: The phase of the ultrasonic signal, describing the starting point of the signal.

[0375] ·fa: The frequency of the sound wave signal.

[0376] φa: The phase of the sound wave signal, describing the starting point of the signal.

[0377] ·g(θ): Beamforming function, describing the distribution of the beam with angle θ.

[0378] ·d0: Reference distance, used to normalize the spread distance d, and is usually used in beamforming calculations.

[0379] sin(θ) and cos(θ): sine and cosine functions used to describe the variation of beamforming with angle.

[0380] · The denominator of the beamforming function is used to adjust the beamwidth.

[0381] This formula is a complex mathematical model used to describe the mixing and shaping effects of ultrasonic and acoustic signals at specific times and angles. It takes into account factors such as signal amplitude, frequency, attenuation, beamforming, and diffusion distance to predict the intensity distribution of the acoustic field.

[0382] To construct a hybrid shaping mathematical model that can automatically adjust the components of ultrasonic and acoustic signals, we need to consider the differences in the sensitivity of different birds to sound frequencies. Below is an exemplary complex mathematical model that combines logarithmic and exponential functions and can automatically adjust the signal components based on the sensitivity of different birds:

[0383] Hybrid shaping mathematical model

[0384] Let S(t) be the mixed sound wave signal at time t, U(t) be the ultrasonic signal, and A(t) be the sound wave signal. u,t and S a,t These are the ultrasonic and acoustic signal components adjusted for specific bird species. R u,b and R a,b Let be the sensitivity response function of different bird species b to ultrasound and sound waves.

[0385] Sensitivity response function

[0386] For ultrasound and sound waves, the sensitivity response function Ru,b and R a,b They can be represented as follows:

[0387]

[0388] in:

[0389] γ is the control parameter for logarithmic sensitivity response.

[0390] ·f u u is the frequency of the ultrasound.

[0391] ·f u,ref It refers to the ultrasonic frequency.

[0392] ·f a It is the frequency of the sound wave.

[0393] ·f a,opt,b It is the sound frequency that birds are most sensitive to.

[0394] ·σ fa,b It is the standard deviation of the sound frequency sensitivity distribution for bird b.

[0395] Adjusted signal components

[0396] S u，t =U(t)·R u，b (f u )

[0397] S a，t =A(t)·R a，b (f a )

[0398] Mixed signal

[0399] S(t) = w u，b ·S u,t +w a，b ·S a，t

[0400] in:

[0401] ·w u,b and w a,b The weighting coefficients are automatically adjusted based on the sensitivity response of bird b.

[0402] Automatic adjustment of weighting coefficients

[0403]

[0404] Single formula expression

[0405] The single formula for the mixed signal S(t) can be written as:

[0406] S(t)=ω u,b ·S u,t +ω a,b ·S a,t

[0407] Substituting the weighting coefficients and the sensitivity response function further, we obtain:

[0408]

[0409] This formula combines logarithmic and exponential functions to automatically adjust the components of ultrasonic and acoustic signals based on the sensitivity of different bird species. The model dynamically adjusts the signal weights according to the specific sensitivity curves of each bird species to achieve optimal bird-repelling effects.

[0410] In one possible design, Figure 3 The data annotation device of the illustrated embodiment can be implemented as a computing device. As shown in the figure, the computing device may include a storage component 71 and a processing component 72.

[0411] The storage component 71 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 72.

[0412] The processing component 72 is used to: obtain a query object provided by the user; query multiple unlabeled data that match the query object in a pre-established unlabeled dataset based on the query object, and generate initial segmentation results corresponding to the multiple unlabeled data; perform fine segmentation on the initial segmentation results corresponding to the multiple unlabeled data to generate target segmentation results for the multiple unlabeled data; use a pre-created annotation tool to perform annotation operations on the target segmentation results of the selected unlabeled data, and apply the annotation operations in batches to the target segmentation results of the remaining multiple unlabeled data.

[0413] The processing component 72 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0414] Storage component 71 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0415] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0416] Display component 73 may be an electroluminescent (EL) element, a liquid crystal display or a microdisplay with a similar structure, or a retina-direct display or a similar laser scanning display.

[0417] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0418] The communication component is configured to facilitate wired or wireless communication between computing devices and other devices.

[0419] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0420] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The data annotation method of the embodiment shown.

[0421] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0422] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0423] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

Claims

1. An intelligent bird-repelling device for power transmission and distribution facilities, characterized in that, include: The radar monitoring module is operable to monitor bird activity in real time and acquire bird characteristic data. The sound-driven module can be operated to play various pre-stored audio files; A specific sound wave driving module, which can be operated to emit sound waves of a specific frequency; The signal processing and intelligent recognition submodule is configured to convert feature data into feature vectors. X ={ x 1, x 2,..., x n }; According to the feature vector X and bird species C The relationship between the two species is used to calculate the probability of each bird species appearing. P ( C | X ) Calculate the negative log-likelihood function L ( a , b , c To estimate model parameters a , b and c ; According to the model parameters a , b And c, calculate P ( C | X The estimated value of ); According to the above P ( C | X The estimated value of the bird species is used to determine the bird species. Based on the identification results of the bird species, implement the corresponding driving-away strategy; The bird deterrent device further includes: The drive control submodule is operable to generate sound wave signals. A ( t ) and ultrasonic signals U ( t The mixed signal, the mixed signal S ( t , k Defined as: in, δ k and γ k It is based on the species of bird mentioned. C Adjusted parameters A 0 and U 0 represents the initial amplitude of the sound wave and ultrasonic signal, respectively. α and β These are the attenuation coefficients of sound wave and ultrasonic wave signals, respectively. f a and f u These are the frequencies of sound waves and ultrasonic signals, respectively. ϕ a and ϕ u These are the phases of the sound wave and the ultrasonic signal, respectively, log δk Therefore δ k Logarithmic function with base 0. e It is a natural exponential function; λ ( t , k λ(t,k) represents the weight of the acoustic signal in the mixed signal, 0≤λ(t,k)≤1, and depends on time. t and bird species C .

2. The intelligent bird-repelling device according to claim 1, characterized in that, The radar monitoring module further includes: The transmitter submodule is operable to transmit radar wave signals; and, The signal processing and intelligent recognition submodule analyzes the received radar wave signal and analyzes the data to identify the species, number, and behavioral patterns of birds. The driving away control submodule is operable to: generate a mixed signal of corresponding sound wave signal and ultrasonic signal based on the bird species identification result of the signal processing and intelligent recognition submodule; and control the specific sound wave driving away module and sound driving away module to execute corresponding driving away strategies. The control of the specific acoustic wave driving module and the sound driving module to execute the corresponding driving strategy further includes: beamforming the mixed signal and transmitting the acoustic wave and / or ultrasonic wave of the mixed signal to the monitoring area; The camera submodule is operable to capture image feature data of birds; The laser driving module is operable to emit a laser beam; The antenna submodule is operable to receive radar wave signals from the transmitter submodule and radiate them into the monitoring area, while simultaneously receiving reflected radar wave signals. The receiver submodule is operable to receive reflected radar wave signals from the antenna submodule and convert them into electrical signals.

3. The intelligent bird-repelling device according to claim 2, characterized in that, The driving control submodule is operable to: perform beamforming based on the mixed signal S(t,k) corresponding to the species of the bird, and transmit the sound wave of the mixed signal to the monitoring area through the sound driving module and the specific sound wave driving module.

4. A method for generating a bird-repelling strategy for an intelligent bird-repelling device used in power transmission and distribution facilities, comprising: Acquire bird characteristic data, including body size, flight speed, flight altitude, and sound frequency; Convert feature data into feature vectors X ={ x 1, x 2,..., x n }; Based on the feature vector X and bird species C The relationship between the two species is used to calculate the probability of each bird species appearing. P ( C | X ) Calculate the negative log-likelihood function L ( a , b , c To estimate model parameters a b and c ; Based on model parameters a , b And c, calculate P ( C | X The estimated value of ); According to the formula P ( C | X The estimated value of the bird species is used to determine the bird species. Based on the identification of bird species, an appropriate deterrent strategy is implemented. This step further includes: Generate sound wave signal A ( t ) and ultrasonic signals U ( t The mixed signal, the mixed signal S ( t , k ) is defined as: in δ k and γ k It is based on the bird species C Adjusted parameters A 0 and U 0 represents the initial amplitude of the sound wave and ultrasonic signal, respectively. α and β These are the attenuation coefficients of sound wave and ultrasonic wave signals, respectively. f a and f u These are the frequencies of sound waves and ultrasonic signals, respectively. ϕ a and ϕ u These are the phases of the sound wave and the ultrasonic signal, respectively, log δk Therefore δ k Logarithmic function with base 0. e It is a natural exponential function; λ ( t , k λ(t,k) represents the weight of the acoustic signal in the mixed signal, 0≤λ(t,k)≤1, and depends on time. t and bird species C .

5. The method for generating a driving-off strategy according to claim 4, characterized in that, The ultrasonic signal U ( t ) for: U ( t )= U 0⋅ e -βt ⋅sin(2 πf u t + ϕ u ) The mixed signal is defined as: S ( t , k )= λ ( t , k )⋅ A ( t )+(1- λ ( t , k ))⋅ U ( t ) in, U 0 represents the initial amplitude of the ultrasonic signal; β It is the attenuation coefficient of the ultrasonic signal; f u It is the frequency of the ultrasonic signal; ϕ u It is the phase of the ultrasonic signal; Beamforming is applied to the mixed signal corresponding to the bird species, and the shaped mixed signal is transmitted to the monitoring area.

6. An electronic device, characterized in that, Includes a processor operable to perform the method for generating a bird-repelling strategy in an intelligent bird-repelling device for power transmission and distribution facilities as described in any one of claims 4-5.

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