Intelligent precise bird repelling system and method based on voiceprint recognition and bionic natural enemies

Abstract

The invention relates to the technical field of bird repelling, and discloses an intelligent precise bird repelling system and method based on voiceprint recognition and bionic natural enemies. A bird repelling strategy making module; a bird repelling strategy execution feedback module; the strategy optimization module is used for acquiring bird stress response data and environmental parameters in the implementation of the bird repelling strategy when the bird repelling strategy is judged to be optimized; and optimizing a bird repelling strategy based on the real-time bird sound signal, the bird stress response data and the environmental parameters to obtain an optimized bird repelling strategy. According to the invention, the voiceprint acquisition and identification module is used to accurately identify the types and behavior states of the birds in the target area, so that a scientific basis is provided for formulating differentiated bird repelling strategies, and the blindness of'one-step 'of all birds in a traditional bird repelling method is avoided.

Description

Technical Field

[0001] This invention relates to the field of bird control technology, and more specifically, to an intelligent and precise bird control system and method based on voiceprint recognition and bionic predators. Background Technology

[0002] Traditional bird control methods, relying on manual deterrence, physical barriers, or single-sound repellency, have limitations. Manual deterrence is labor-intensive and its effectiveness is not lasting, as birds easily adapt. Physical barriers, such as bird nets, can block birds, but their installation and maintenance costs are high, they can injure birds, and they affect the aesthetics and functionality of the area. Single-sound repellency, due to its fixed pattern, is quickly adapted to by birds, and its effectiveness declines rapidly, making it difficult to maintain long-term bird control. Furthermore, existing bird control technologies lack precise identification of bird species and behavioral states, making it impossible to adopt differentiated strategies. Bird control is indiscriminate, wasteful of resources, and may also disturb non-target birds, which is detrimental to ecological protection.

[0003] Therefore, it is necessary to design an intelligent and precise bird-repelling system and method based on voiceprint recognition and bionic predators to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes an intelligent and precise bird deterrence system and method based on voiceprint recognition and bionic predators, aiming to solve the problems of the current bird deterrence methods, such as the bird deterrence effect not lasting, the birds being highly adaptable, and the lack of precise identification and differentiated strategies.

[0005] This invention proposes an intelligent and precise bird-repelling system based on voiceprint recognition and biomimetic predator technology, comprising: The voiceprint acquisition and recognition module is used to acquire bird sound signals in the target area before the implementation of bird deterrence strategies, and to extract features from the bird sound signals to obtain bird voiceprint features; it is also used to match and recognize the bird voiceprint features with a preset bird voiceprint feature database to determine the species and behavioral status of the birds. The bird deterrence strategy formulation module is used to obtain a comprehensive risk assessment value of the target bird for the target area based on the species and behavioral status of the bird, and to determine the bird deterrence strategy based on the comprehensive risk assessment value. The bird deterrence strategy execution feedback module is used to drive away target birds in the target area using the bird deterrence strategy, and to collect real-time bird sound signals in the target area after a preset time interval. The bird deterrence effect is evaluated based on the changes in the real-time bird sound signals to determine whether the bird deterrence strategy needs to be optimized. The optimization strategy module is used to collect bird stress response data and environmental parameters during the implementation of the bird repelling strategy when determining whether to optimize the bird repelling strategy; and to optimize the bird repelling strategy based on the real-time bird sound signals, bird stress response data and environmental parameters to obtain an optimized bird repelling strategy.

[0006] Furthermore, the bird species include birds of prey, medium to large gregarious birds, small songbirds, waterfowl, and nocturnal birds; the behavioral states include foraging, nesting, migratory, territorial defense, and flocking.

[0007] Furthermore, when obtaining the comprehensive risk assessment value of the target bird for the target area based on the species and behavioral status of the bird, it includes: Based on the bird species, corresponding basic risk parameters are determined, including size risk coefficient, social behavior risk coefficient, and activity time risk coefficient. Extract behavioral feature parameters corresponding to the behavioral state, including dwell time, activity frequency, and behavioral clustering degree; The basic risk parameters and the behavioral characteristic parameters are weighted and fused to obtain the behavioral risk assessment value of the target bird. Obtain the region attribute parameters of the target region; The behavioral risk assessment value is correlated with the regional attribute parameters to obtain the correlation impact value, and a comprehensive risk assessment value of the impact of the target bird on the target area is obtained based on the correlation impact value.

[0008] Furthermore, when performing correlation analysis between the behavioral risk assessment value and the regional attribute parameters to obtain the correlation impact value, the following steps are included: The behavioral risk assessment values ​​are standardized over time to obtain a behavioral risk time series. The regional attribute parameters are decoupled and divided into static regional parameters and dynamic regional parameters. The static regional parameters include regional function type and environmental sensitivity level, while the dynamic regional parameters include real-time environmental change parameters and historical bird activity frequency parameters. Based on the behavioral risk time series and the dynamic regional parameters, a temporal correlation analysis was performed to obtain the correlation index between the behavioral changes of the target birds and the regional environmental changes. Calculate the risk amplification factor based on the behavioral risk assessment value and the static area parameters; The correlation index and the risk amplification factor are weighted and fused to obtain the correlation impact value of the target bird behavior on the target area.

[0009] Furthermore, when obtaining the comprehensive risk assessment value of the impact of the target bird on the target area based on the correlation impact value, it includes: The associated impact value is compared with the first associated impact value and the second associated impact value, and the comprehensive risk assessment value is determined based on the comparison result; wherein, the first associated impact value is less than the second associated impact value; When the associated impact value is less than or equal to the first associated impact value, the comprehensive risk assessment value is determined to be the first comprehensive risk assessment value; When the associated impact value is greater than the first associated impact value and less than or equal to the second associated impact value, the comprehensive risk assessment value is determined to be the second comprehensive risk assessment value; When the associated impact value is greater than the second associated impact value, the comprehensive risk assessment value is determined to be the third comprehensive risk assessment value.

[0010] Furthermore, when determining the bird deterrence strategy based on the comprehensive risk assessment value, the following steps are included: The comprehensive risk assessment value is compared with a preset bird deterrence strategy mapping set, and the bird deterrence strategy is determined based on the comparison result.

[0011] Furthermore, when evaluating the bird-repelling effect based on changes in the real-time bird sound signals to determine whether the bird-repelling strategy needs optimization, the process includes: The bird sound signals before and after the implementation of the bird deterrence strategy are subjected to feature alignment processing to extract the voiceprint intensity features, voiceprint frequency features, and voiceprint duration features within the corresponding time period. Based on the aforementioned voiceprint intensity characteristics, voiceprint frequency characteristics, and voiceprint duration characteristics, an index of changes in bird activity before and after bird driving is constructed. The bird activity change index is compared with the bird activity change index threshold, and the comparison result is used to determine whether the bird deterrence strategy needs to be optimized. When the bird activity change index is less than or equal to the bird activity change index threshold, it is determined that the bird deterrence effect has reached the expected level, and there is no need to optimize the bird deterrence strategy. When the bird activity change index exceeds the bird activity change index threshold, it is determined that the bird deterrence effect has not met expectations and the bird deterrence strategy needs to be optimized.

[0012] Furthermore, when optimizing the bird-repelling strategy based on the real-time bird sound signals, bird stress response data, and environmental parameters to obtain an optimized bird-repelling strategy, the following steps are included: Stress features are extracted from the real-time bird sound signals to obtain voiceprint stress feature parameters, which include alarm sound ratio changes, voiceprint spectrum change amplitude, and voiceprint continuity change features. An optimized feature set was constructed based on the bird activity change indicators, vocalization stress characteristic parameters, and environmental parameter levels. The optimized feature group is compared with the historical bird deterrence optimization group, and the optimized parameters of the bird deterrence strategy are determined based on the comparison results. If the historical bird deterrence optimization group has a historical optimization feature group that matches the optimization feature group, the historical optimization parameter corresponding to the historical optimization feature group is used as the optimization parameter; If the historical bird deterrence optimization group does not have a historical optimization feature group that matches the optimization feature group, the optimization parameters are determined based on the optimization feature group.

[0013] Further, when determining the optimization parameters based on the optimization feature set, the process includes: The bird activity change indicators, vocalization stress characteristic parameters, and environmental parameters were normalized. Based on the normalized bird activity change index, voiceprint stress characteristic parameters and environmental parameters, behavioral change sub-state values, voiceprint stress sub-state values ​​and environmental correction sub-state values ​​are constructed respectively, and the comprehensive stress-adaptation state value of the target birds to the current bird deterrence strategy is obtained by weighted fusion. The stress response stage of the target bird is determined by matching the comprehensive stress-adaptation state value with a preset stress-adaptation state threshold range; wherein, the stress response stage includes a low stress adaptation stage, a moderate stress resistance stage, and a high stress avoidance stage. The optimized parameters are determined based on the stress response phase.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: Achieving precise bird control: By using a voiceprint collection and recognition module to accurately identify bird species and behavioral states, this method changes the traditional "one-size-fits-all" approach to bird control. It enables the development of differentiated bird control strategies for different species and behavioral states, avoiding interference with non-target birds and improving the accuracy and targeting of bird control.

[0015] Enhancing the durability of bird control effectiveness: The bird control strategy formulation module determines bird control strategies based on comprehensive risk assessment values, and combines this with the bird control strategy execution feedback module to conduct real-time evaluation and optimization of bird control effectiveness. By continuously collecting data on changes in bird sound signals, stress responses, and environmental parameters, the bird control strategy is dynamically adjusted to effectively address bird adaptability issues, avoiding the attenuation of bird control effectiveness due to long-term use of a single strategy, and extending the durability of bird control effectiveness.

[0016] The scientific and intelligent optimization of bird control strategies: The optimization strategy module utilizes real-time bird sound signals, stress response data, and environmental parameters for multi-dimensional analysis to construct optimized feature groups. These are then combined with historical data or determined through comprehensive stress-adaptation state values ​​to identify optimal parameters. This data-driven optimization approach makes adjustments to bird control strategies more scientific and intelligent, enabling flexible responses to various complex bird activities and environmental changes based on actual conditions, further enhancing the overall performance of the bird control system.

[0017] In another aspect, this invention also proposes an intelligent and precise bird-repelling method based on voiceprint recognition and bionic predators, comprising the following steps: The system collects bird sound signals within the target area before implementing bird deterrence strategies, extracts features from the bird sound signals to obtain bird voiceprint features, and matches and identifies the bird voiceprint features with a preset bird voiceprint feature database to determine the species and behavioral status of the birds. Based on the species and behavioral status of the birds, obtain a comprehensive risk assessment value of the target area for the target birds, and determine the bird repelling strategy based on the comprehensive risk assessment value; The bird-repelling strategy is used to drive away target birds in the target area, and real-time bird sound signals in the target area are collected after a preset time interval. The bird-repelling effect is evaluated based on the changes in the real-time bird sound signals to determine whether the bird-repelling strategy needs to be optimized. When determining whether to optimize the bird deterrence strategy, data on bird stress response and environmental parameters are collected during the implementation of the bird deterrence strategy. The bird deterrence strategy is then optimized based on the real-time bird sound signals, bird stress response data, and environmental parameters to obtain an optimized bird deterrence strategy.

[0018] It is understandable that the aforementioned intelligent and precise bird deterrence system and method based on voiceprint recognition and bionic predators have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a structural block diagram of an intelligent precision bird-repelling system based on voiceprint recognition and bionic predators, provided in an embodiment of the present invention. Figure 2 The flowchart illustrates the intelligent and precise bird-repelling method based on voiceprint recognition and bionic predators provided in this embodiment of the invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] See Figure 1 As shown in some embodiments of this application, this embodiment provides an intelligent and precise bird-repelling system based on voiceprint recognition and bionic predators, including: The voiceprint acquisition and recognition module is used to collect bird sound signals in the target area before the implementation of bird deterrence strategies, extract features from the bird sound signals, and obtain bird voiceprint features; it is also used to match and recognize bird voiceprint features with a preset bird voiceprint feature database to determine the species and behavioral status of birds. The bird deterrence strategy formulation module is used to obtain the comprehensive risk assessment value of the target bird to the target area based on the bird species and behavioral status, and to determine the bird deterrence strategy based on the comprehensive risk assessment value. The bird deterrence strategy execution feedback module is used to drive away target birds in the target area using the bird deterrence strategy, and to collect real-time bird sound signals in the target area after a preset time interval. The bird deterrence effect is evaluated based on the changes in the real-time bird sound signals to determine whether the bird deterrence strategy needs to be optimized. The optimization strategy module is used to collect bird stress response data and environmental parameters during the implementation of bird repelling strategies when determining whether to optimize bird repelling strategies; and to optimize bird repelling strategies based on real-time bird sound signals, bird stress response data and environmental parameters to obtain optimized bird repelling strategies.

[0022] Understandably, the intelligent precision bird control system based on voiceprint recognition and bionic predator technology provided in this embodiment achieves accurate identification of bird species and behavioral states in the target area through the voiceprint acquisition and recognition module. This provides a scientific basis for formulating differentiated bird control strategies and avoids the blind "one-size-fits-all" approach of traditional bird control methods. The bird control strategy formulation module calculates a comprehensive risk assessment value based on bird species and behavioral states, enabling more targeted bird control measures for birds with different risk levels, thus improving the accuracy and effectiveness of bird control. The bird control strategy execution feedback module forms a closed-loop feedback mechanism by collecting bird sound signals in real time and evaluating the bird control effect, ensuring that the bird control strategy can be dynamically adjusted according to the actual situation. The strategy optimization module further optimizes the bird control strategy by combining bird stress response data and environmental parameters, effectively solving the problem of birds easily developing adaptability and significantly improving the persistence of the bird control effect. The system's modules work together to achieve intelligent management of the entire process, from accurate bird identification and intelligent strategy formulation to real-time effect evaluation and dynamic strategy optimization. This greatly improves the efficiency and scientific nature of bird control work and reduces the potential harm caused by bird activity to various related fields.

[0023] Specifically, the bird species include birds of prey, medium to large gregarious birds, small songbirds, waterfowl, and nocturnal birds; their behavioral states include foraging, nesting, migratory, territorial defense, and flocking.

[0024] Understandably, different bird species pose varying types and degrees of risk to target areas due to differences in size, diet, and activity patterns. For example, birds of prey threaten airport aircraft safety; medium to large gregarious birds such as corvids impact agriculture, power supply, and public health; large-scale gatherings of small songbirds disrupt order in specific areas; waterbirds damage aquaculture; and nocturnal birds interfere with communication base stations. Bird behavior is also crucial for risk assessment: foraging birds continuously damage farmland and orchards; nesting birds' prolonged activity harms buildings and power lines; temporary flocks of migratory birds threaten aviation and crop safety; birds in territorial defense are highly aggressive, harassing personnel and equipment; and flocks of circling birds interfere with airport runways and high-voltage lines. Therefore, accurately classifying and identifying bird species and their behavioral states is fundamental to comprehensive risk assessment and the development of bird control strategies, effectively mitigating risks and reducing the impact on non-target birds and the ecological environment.

[0025] Specifically, when obtaining the comprehensive risk assessment value of the target bird species to the target area based on the bird species and behavioral status, it includes: Based on the bird species, the corresponding basic risk parameters are determined. The basic risk parameters include the size risk coefficient, the social risk coefficient, and the activity time risk coefficient. Extract behavioral feature parameters corresponding to the behavioral state. These behavioral feature parameters include dwell time, activity frequency, and behavioral clustering degree. The basic risk parameters and behavioral characteristic parameters are weighted and fused to obtain the behavioral risk assessment value of the target bird. Retrieve the region attribute parameters of the target region; The behavioral risk assessment value is correlated with the regional attribute parameters to obtain the correlation impact value, and the comprehensive risk assessment value of the impact of the target bird on the target area is obtained based on the correlation impact value.

[0026] In this embodiment, the size risk coefficient is set according to the size of the bird. The larger the bird (such as large birds of prey or swans), the higher the potential physical hazard (such as impact or nesting weight load) to the target area (such as airport runways or high-voltage lines), and the larger the coefficient value. The social risk coefficient reflects the degree of risk of bird flocking behavior. The larger the flock size (such as crow flocks or starling flocks), the higher the risk of concentrated damage to agriculture and public facilities caused by their foraging and excretion activities, and the coefficient value increases with the increase of flock density. The activity time risk coefficient is determined based on the degree of overlap between the bird's activity time and the key operating time of the target area. For example, birds that are active during the peak flight hours at the airport or foraging during the critical period of crop ripening in farmland have a higher activity time risk coefficient, thereby quantifying the degree of interference of bird activities to the target area at different times.

[0027] In this embodiment, dwell time, activity frequency, and behavioral aggregation degree refer to the length of time a target bird stays continuously in the target area, the number of times it appears in the target area per unit time, and the density of individual birds per unit space, respectively.

[0028] In this embodiment, a weighted fusion of basic risk parameters and behavioral characteristic parameters is performed to obtain the behavioral risk assessment value of the target bird. This includes: First, assigning preset weight values ​​to the basic risk parameters (size, social behavior, and activity time risk coefficients) and behavioral characteristic parameters (staying duration, activity frequency, and behavioral aggregation). These weight values ​​are set based on historical data and expert experience regarding the impact of different parameters on bird behavioral risk. For example, the weights for size and activity time risk coefficients may be higher in airport areas because large birds pose a greater threat to flight safety during peak flight times; the weights for social behavior risk coefficients and staying duration may be more prominent in agricultural areas because large flocks of birds that stay for extended periods can severely damage crops. Next, the specific values ​​of each parameter are multiplied by their corresponding weights to obtain a weighted value. Then, the weighted values ​​of all parameters are summed, and the result is the behavioral risk assessment value of the target bird. This value comprehensively considers the bird species characteristics and current behavioral performance, and can more comprehensively reflect the behavioral risk level of the target bird in its current state.

[0029] Specifically, when performing correlation analysis between behavioral risk assessment values ​​and regional attribute parameters to obtain correlation impact values, the following are included: The behavioral risk assessment values ​​are standardized over time to obtain a behavioral risk time series. The regional attribute parameters are decoupled and divided into static regional parameters and dynamic regional parameters. The static regional parameters include regional function type and environmental sensitivity level, while the dynamic regional parameters include real-time environmental change parameters and historical bird activity frequency parameters. Time-series correlation analysis was conducted based on behavioral risk time series and dynamic regional parameters to obtain correlation indicators between changes in target bird behavior and changes in the regional environment. Calculate the risk amplification factor based on behavioral risk assessment values ​​and static regional parameters; By weighting and fusing correlation indicators with risk amplification factors, the correlation impact value of target bird behavior on the target area is obtained.

[0030] In this embodiment, the regional functional types cover airports (including runways, etc.), farmland (including various crop areas), power facilities (including power lines, power stations, etc.), aquaculture areas (including fish ponds, etc.), communication base stations (including transmission towers, etc.), public activity areas (including parks, etc.), and warehousing and logistics areas (including warehouses, etc.). Environmental sensitivity levels are classified according to regional ecological protection value into extremely high-sensitivity areas (such as the core area of ​​nature reserves), high-sensitivity areas (such as areas surrounding residential areas), medium-sensitivity areas (such as general farmland), and low-sensitivity areas (such as power transmission lines in remote deserts).

[0031] In this embodiment, the sliding window correlation coefficient method is used for time-series correlation analysis based on behavioral risk time series and dynamic regional parameters. The specific process is as follows: First, a fixed time window length (e.g., 30 minutes) is set, and behavioral risk time series and dynamic regional parameters (e.g., real-time wind speed) are simultaneously extracted to obtain subsequences within multiple time windows. Next, Pearson correlation coefficients are calculated for each behavioral risk subsequence and each dynamic regional parameter subsequence within each time window. The entire time series is traversed using a sliding window to obtain correlation coefficient change curves at different times. Finally, correlation indices are extracted based on curve characteristics, including the average correlation coefficient, the maximum positive correlation coefficient and its corresponding lag time, and the correlation coefficient variance, comprehensively quantifying the temporal correlation between changes in target bird behavior and dynamic changes in the regional environment.

[0032] In this embodiment, the risk amplification factor is calculated based on the behavioral risk assessment value and static regional parameters, including: First, determining the basic amplification coefficient according to the regional functional type. Different functional types of regions have different sensitivities to bird behavior risks. For example, the basic amplification coefficient for airport runway areas is significantly higher than that for ordinary farmland areas because the consequences of bird strikes are severe. Second, determining the level correction coefficient based on the environmental sensitivity level. The higher the environmental sensitivity level, the lower the tolerance for bird disturbance and the larger the level correction coefficient. For example, the level correction coefficient for the core area of ​​a nature reserve is higher than that for remote desert power transmission line areas. Finally, the basic amplification coefficient and the level correction coefficient are multiplied together, and then multiplied by the behavioral risk assessment value to obtain the risk amplification factor.

[0033] Understandably, when weighting and fusing the correlation index and the risk amplification factor, the preferred weight coefficients for the correlation index and the risk amplification factor are 0.3 and 0.7, respectively. In this case, the preferred correlation index is the average correlation coefficient.

[0034] Specifically, when obtaining the comprehensive risk assessment value of the impact of the target bird species on the target area based on the correlation impact value, it includes: The associated impact value is compared with the first associated impact value and the second associated impact value, and the comprehensive risk assessment value is determined based on the comparison results; wherein, the first associated impact value is less than the second associated impact value; When the associated impact value is less than or equal to the first associated impact value, the comprehensive risk assessment value is determined to be the first comprehensive risk assessment value. When the associated impact value is greater than the first associated impact value and less than or equal to the second associated impact value, the comprehensive risk assessment value is determined to be the second comprehensive risk assessment value. When the associated impact value is greater than the second associated impact value, the comprehensive risk assessment value is determined to be the third comprehensive risk assessment value.

[0035] In this embodiment, the first and second associated impact values ​​refer to the associated impact thresholds preset based on historical bird deterrence data of the target area, statistical results of bird hazard events, and industry safety standards. For example, in an airport scenario, the first associated impact value can be set as the associated impact value corresponding to a single low-altitude flight of birds over the runway without causing flight delays, while the second associated impact value corresponds to the associated impact value corresponding to a flock of birds circling above the runway for more than 10 minutes. In an agricultural scenario, the first associated impact value can correspond to the associated impact value corresponding to a single instance of birds foraging resulting in a crop loss rate of less than 5%, while the second associated impact value corresponds to the associated impact value corresponding to a continuous foraging by birds for several days resulting in a crop loss rate of more than 20%.

[0036] In this embodiment, the preferred value for the first comprehensive risk assessment value is 0.2; the preferred value for the second comprehensive risk assessment value is 0.5; and the preferred value for the third comprehensive risk assessment value is 0.8.

[0037] Specifically, when determining bird deterrence strategies based on comprehensive risk assessment values, the following should be included: The comprehensive risk assessment value is compared with the preset bird deterrence strategy mapping set, and the bird deterrence strategy is determined based on the comparison results.

[0038] Understandably, the preset bird deterrence strategy mapping set is a pre-established set of mapping relationships between comprehensive risk assessment values ​​and corresponding bird deterrence strategies, covering elements such as bird deterrence methods, intensity, frequency, and mode of action under different risk levels. For example, when the comprehensive risk assessment value is the first comprehensive risk assessment value (0.2), the target birds pose a low risk to the target area, so a low-intensity, low-frequency bird deterrence method is adopted, such as intermittently playing bionic predator sounds once per hour for 1-2 minutes each time, with an intensity that does not disturb the surrounding area, gently deterring birds; when the comprehensive risk assessment value is the second comprehensive risk assessment value (0.5), the risk is medium, so the bird deterrence intensity and frequency are increased, and a "sound + vision" synergy method is adopted, playing predator soundprints once every 30 minutes for 3-5 minutes each time, and simultaneously activating the bionic predator dynamic model to prevent birds from adapting; when the comprehensive risk assessment value is the third comprehensive risk assessment value (0.8), the risk is high, so a high-intensity, multi-method combined bird deterrence measure is adopted, strengthening sound and visual bird deterrence, introducing directional sound wave equipment for precise emission, combined with strong light flashing, once every 15 minutes for 6-8 minutes each time.

[0039] Specifically, when evaluating the effectiveness of bird deterrence based on changes in real-time bird sound signals to determine whether the bird deterrence strategy needs optimization, this includes: Feature alignment processing is performed on bird sound signals before and after the implementation of bird deterrence strategy, and voiceprint intensity features, voiceprint frequency features, and voiceprint duration features are extracted within the corresponding time period. Based on the characteristics of voiceprint intensity, frequency of voiceprint occurrence, and duration of voiceprint, an index of changes in bird activity before and after bird driving is constructed. Compare the bird activity change index with the bird activity change index threshold, and determine whether the bird deterrence strategy needs to be optimized based on the comparison results; When the bird activity change index is less than or equal to the bird activity change index threshold, the bird deterrence effect is considered to have met expectations, and there is no need to optimize the bird deterrence strategy. When the bird activity change index exceeds the bird activity change index threshold, it is determined that the bird deterrence effect has not met expectations and the bird deterrence strategy needs to be optimized.

[0040] In this embodiment, an index of changes in bird activity before and after bird driving is constructed based on the characteristics of voiceprint intensity, frequency of occurrence, and duration. This is achieved by calculating the changes in the three characteristics (voiceprint intensity change rate, frequency of occurrence decrease, and duration of duration reduction) before and after bird driving and then summing them by weight to obtain a comprehensive index.

[0041] In this embodiment, the threshold for bird activity change index refers to a critical value pre-set based on the ecological protection requirements of the target area, the cost-effectiveness of bird control, and industry standards.

[0042] Specifically, optimizing bird control strategies based on real-time bird sound signals, bird stress response data, and environmental parameters to obtain an optimized bird control strategy includes: Stress features were extracted from real-time bird sound signals to obtain voiceprint stress feature parameters, including alarm sound ratio changes, voiceprint spectrum variation amplitude, and voiceprint continuity variation characteristics. An optimized feature set was constructed based on bird activity change indicators, vocalization stress characteristic parameters, and environmental parameter levels. The optimized feature group is compared with the historical bird deterrence optimization group, and the optimization parameters of the bird deterrence strategy are determined based on the comparison results. If a historical bird deterrence optimization group exists that matches the optimization feature group, the historical optimization parameters corresponding to the historical optimization feature group will be used as optimization parameters. If there is no historical optimization feature group that matches the optimization feature group, the optimization parameters are determined based on the optimization feature group.

[0043] Understandably, acoustic stress characteristic parameters can accurately capture the real-time physiological and behavioral responses of birds under bird-repelling measures. For example, when birds are deterred by the acoustic signatures of predators, the proportion of alarm sounds increases, the high-frequency components of the acoustic spectrum increase, and continuity is interrupted, reflecting the interference effect of bird-repelling measures. Environmental parameters, including temperature, humidity, wind force, and light intensity, affect sound propagation, bird activity, and the effectiveness of visual bird-repelling equipment. For example, sound propagation attenuates quickly on windy days, requiring increased power of acoustic devices; on rainy days, birds tend to concentrate in specific areas, necessitating adjustments to the effective range of bird-repelling equipment. After constructing an optimized feature group, it is compared with historical bird-repelling optimization groups, using past experience to guide adjustments to the current strategy. Historical bird-repelling optimization groups contain data on different environments, bird species, initial strategies, and optimization effects. If a matching group is found, it indicates that the current scenario is similar to historical successful cases, and optimization parameters can be reused, such as adjusting the sound wave frequency and increasing the switching frequency of the visual dynamic model to quickly optimize the strategy. If no matching group exists, parameters are calculated based on the current optimized feature group.

[0044] Specifically, when determining optimization parameters based on the optimization feature set, the following are included: Normalize bird activity change indicators, vocalization stress characteristic parameters, and environmental parameters; Based on the normalized bird activity change index, voiceprint stress characteristic parameters and environmental parameters, behavioral change sub-state values, voiceprint stress sub-state values ​​and environmental correction sub-state values ​​are constructed respectively, and the comprehensive stress-adaptation state value of the target birds to the current bird deterrence strategy is obtained by weighted fusion. The stress response stage of the target bird is determined by matching the comprehensive stress-adaptation state value with the preset stress-adaptation state threshold range; the stress response stage includes low stress adaptation stage, moderate stress resistance stage and high stress avoidance stage. Optimal parameters are determined based on the stress response phase.

[0045] In this embodiment, based on normalized bird activity change indicators, voiceprint stress characteristic parameters, and environmental parameters, sub-state values ​​are constructed: the bird activity change indicator is used as the behavior change sub-state value to reflect the overall change in bird activity after bird deterrence; the smaller the value, the more obvious the reduction in activity and the better the effect of behavior inhibition; the voiceprint stress characteristic parameters (alarm sound ratio change, voiceprint spectrum change amplitude, voiceprint continuity change characteristics) are summed with equal weights to obtain the voiceprint stress sub-state value; the higher the value, the stronger the bird stress response; the environmental parameters (temperature, humidity, wind force, light intensity, etc.) are multiplied by preset weights and summed to obtain the environmental correction sub-state value, which quantifies the corrective effect of the environment on bird stress response and bird deterrence effect. For example, when the wind force is strong, this value may be negative, indicating that the bird deterrence intensity should be increased to offset the weakening effect of the environment on sound propagation.

[0046] In this embodiment, the preset stress-adaptation state threshold range includes three stages: low stress adaptation, moderate stress resistance, and high stress avoidance. The low stress adaptation stage threshold corresponds to a low range of overall stress-adaptation state values, meaning that the target birds have adapted to the current bird-repelling strategy, have weak stress, and their activity changes are not significant. The moderate stress resistance stage threshold corresponds to a moderate range of overall stress-adaptation state values, indicating that the target birds are in a state of both resistance and adaptation, with fluctuating stress and unstable activity changes. The high stress avoidance stage threshold corresponds to a high range of overall stress-adaptation state values, indicating that the target birds are under strong stress, are actively avoiding the area, and their activity has significantly decreased or moved away from the target area. For example, in an airport scenario, the range [0, 0.3) represents the low stress adaptation stage, [0.3, 0.7) represents the moderate stress resistance stage, and [0.7, 1.0] represents the high stress avoidance stage. The threshold range can be adjusted according to the actual situation for different target areas to accurately reflect the stress-adaptation state of the birds.

[0047] In this embodiment, the optimized parameters are based on the original bird deterrence strategy, with improvements made to the intensity and frequency.

[0048] In this embodiment, when the stress response stage is low stress adaptation, the optimized parameters are preferably increased by 30% and 50% respectively based on the original intensity and frequency; when the stress response stage is moderate stress resistance, the optimized parameters are preferably increased by 20% and 30% respectively based on the original intensity and frequency; when the stress response stage is high stress avoidance, the optimized parameters are preferably maintained at the current intensity and frequency, or only the frequency is slightly adjusted to decrease by 10%, to avoid excessive bird deterrence from disturbing the surrounding ecological environment, while continuously monitoring bird activity changes to ensure that the impact on non-target birds and other organisms is reduced while effectively deterring birds.

[0049] Understandably, by dynamically adjusting and optimizing parameters in stages, bird control strategies can be made more precise and intelligent. For example, when the system determines that the target birds are in a low-stress adaptation stage, and the current bird control measures are insufficient in deterrence, increasing the bird control intensity by 30% and the frequency by 50% can enhance the soundprint propagation effect and stimulus intensity, increase the frequency of interference, break their adaptation mechanism, and stimulate a stress response. For birds in a moderate-stress resistance stage, since they are still adapting to or resisting bird control measures, moderately increasing the intensity by 20% and the frequency by 30% can promote their transition to a high-stress avoidance stage, avoiding excessive stimulation that could lead to extreme behaviors or new adaptations. When birds are in a high-stress avoidance stage, it indicates that the strategy is effective. Maintaining the existing intensity and frequency can consolidate the results, and fine-tuning the frequency by 10% can reduce energy consumption and environmental impact while ensuring bird control effectiveness, reflecting a balance between efficient bird control and ecological protection. This parameter optimization method based on the real-time stress-adaptation state of birds allows bird control strategies to be dynamically and flexibly adjusted, avoiding the problem of diminishing effectiveness in traditional bird control methods, and improving the long-term effectiveness and eco-friendliness of bird control systems.

[0050] See Figure 2 As shown in some embodiments of this application, this embodiment provides an intelligent and precise bird-repelling method based on voiceprint recognition and bionic predators, including the following steps: S100: Collects bird sound signals in the target area before implementing bird deterrence strategies, extracts features from the bird sound signals to obtain bird voiceprint features; also used to match and identify bird voiceprint features with a preset bird voiceprint feature database to determine the species and behavioral status of the birds. S200: Based on the species and behavioral status of the birds, obtain a comprehensive risk assessment value of the target birds for the target area, and determine the bird repelling strategy based on the comprehensive risk assessment value; S300: Drive away target birds in the target area using the bird-repelling strategy, and collect real-time bird sound signals in the target area after a preset time interval. Evaluate the bird-repelling effect based on the changes in the real-time bird sound signals to determine whether the bird-repelling strategy needs to be optimized. S400: When determining to optimize the bird deterrence strategy, collect bird stress response data and environmental parameters during the implementation of the bird deterrence strategy; optimize the bird deterrence strategy based on the real-time bird sound signals, bird stress response data and environmental parameters to obtain an optimized bird deterrence strategy.

[0051] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0055] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An intelligent and precise bird-repelling system based on voiceprint recognition and bionic predator design, characterized in that, include: The voiceprint acquisition and recognition module is used to collect bird sound signals in the target area before the implementation of bird deterrence strategies, and to extract features from the bird sound signals to obtain bird voiceprint features. It is also used to match and identify bird voiceprint features with a preset bird voiceprint feature database in order to determine the species and behavioral status of the bird. The bird deterrence strategy formulation module is used to obtain a comprehensive risk assessment value of the target bird for the target area based on the species and behavioral status of the bird, and to determine the bird deterrence strategy based on the comprehensive risk assessment value. The bird deterrence strategy execution feedback module is used to drive away target birds in the target area using the bird deterrence strategy, and to collect real-time bird sound signals in the target area after a preset time interval. The bird deterrence effect is evaluated based on the changes in the real-time bird sound signals to determine whether the bird deterrence strategy needs to be optimized. The optimization strategy module is used to collect bird stress response data and environmental parameters during the implementation of the bird repelling strategy when determining whether to optimize the bird repelling strategy; and to optimize the bird repelling strategy based on the real-time bird sound signals, bird stress response data and environmental parameters to obtain an optimized bird repelling strategy.

2. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 1, characterized in that, The bird species include birds of prey, medium to large gregarious birds, small songbirds, waterfowl, and nocturnal birds; the behavioral states include foraging, nesting, migratory, territorial defense, and flocking.

3. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 2, characterized in that, When obtaining the comprehensive risk assessment value of the target bird for the target area based on the bird species and behavioral status, the following are included: Based on the bird species, corresponding basic risk parameters are determined, including size risk coefficient, social behavior risk coefficient, and activity time risk coefficient. Extract behavioral feature parameters corresponding to the behavioral state, including dwell time, activity frequency, and behavioral clustering degree; The basic risk parameters and the behavioral characteristic parameters are weighted and fused to obtain the behavioral risk assessment value of the target bird. Obtain the region attribute parameters of the target region; The behavioral risk assessment value is correlated with the regional attribute parameters to obtain the correlation impact value, and a comprehensive risk assessment value of the impact of the target bird on the target area is obtained based on the correlation impact value.

4. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 3, characterized in that, When performing correlation analysis between the behavioral risk assessment value and the regional attribute parameters to obtain the correlation impact value, the following steps are included: The behavioral risk assessment values ​​are standardized over time to obtain a behavioral risk time series. The regional attribute parameters are decoupled and divided into static regional parameters and dynamic regional parameters. The static regional parameters include regional function type and environmental sensitivity level, while the dynamic regional parameters include real-time environmental change parameters and historical bird activity frequency parameters. Based on the behavioral risk time series and the dynamic regional parameters, a temporal correlation analysis was performed to obtain the correlation index between the behavioral changes of the target birds and the regional environmental changes. Calculate the risk amplification factor based on the behavioral risk assessment value and the static area parameters; The correlation index and the risk amplification factor are weighted and fused to obtain the correlation impact value of the target bird behavior on the target area.

5. The intelligent precision bird deterrence system based on voiceprint recognition and bionic predator as described in claim 4, characterized in that, When obtaining the comprehensive risk assessment value of the impact of the target bird on the target area based on the aforementioned correlation impact value, it includes: The associated impact value is compared with the first associated impact value and the second associated impact value, and the comprehensive risk assessment value is determined based on the comparison result; wherein, the first associated impact value is less than the second associated impact value; When the associated impact value is less than or equal to the first associated impact value, the comprehensive risk assessment value is determined to be the first comprehensive risk assessment value; When the associated impact value is greater than the first associated impact value and less than or equal to the second associated impact value, the comprehensive risk assessment value is determined to be the second comprehensive risk assessment value; When the associated impact value is greater than the second associated impact value, the comprehensive risk assessment value is determined to be the third comprehensive risk assessment value.

6. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 5, characterized in that, When determining the bird deterrence strategy based on the comprehensive risk assessment value, the following are included: The comprehensive risk assessment value is compared with a preset bird deterrence strategy mapping set, and the bird deterrence strategy is determined based on the comparison result.

7. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 6, characterized in that, When evaluating the bird-repelling effect based on changes in the real-time bird sound signals to determine whether the bird-repelling strategy needs optimization, the following steps are included: The bird sound signals before and after the implementation of the bird deterrence strategy are subjected to feature alignment processing to extract the voiceprint intensity features, voiceprint frequency features, and voiceprint duration features within the corresponding time period. Based on the aforementioned voiceprint intensity characteristics, voiceprint frequency characteristics, and voiceprint duration characteristics, an index of changes in bird activity before and after bird driving is constructed. The bird activity change index is compared with the bird activity change index threshold, and the comparison result is used to determine whether the bird deterrence strategy needs to be optimized. When the bird activity change index is less than or equal to the bird activity change index threshold, it is determined that the bird deterrence effect has reached the expected level, and there is no need to optimize the bird deterrence strategy. When the bird activity change index exceeds the bird activity change index threshold, it is determined that the bird deterrence effect has not met expectations and the bird deterrence strategy needs to be optimized.

8. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 7, characterized in that, When optimizing the bird-repelling strategy based on the real-time bird sound signals, bird stress response data, and environmental parameters to obtain an optimized bird-repelling strategy, the following steps are included: Stress features are extracted from the real-time bird sound signals to obtain voiceprint stress feature parameters, which include alarm sound ratio changes, voiceprint spectrum change amplitude, and voiceprint continuity change features. An optimized feature set was constructed based on the bird activity change indicators, vocalization stress characteristic parameters, and environmental parameter levels. The optimized feature group is compared with the historical bird deterrence optimization group, and the optimized parameters of the bird deterrence strategy are determined based on the comparison results. If the historical bird deterrence optimization group has a historical optimization feature group that matches the optimization feature group, the historical optimization parameter corresponding to the historical optimization feature group is used as the optimization parameter; If the historical bird deterrence optimization group does not have a historical optimization feature group that matches the optimization feature group, the optimization parameters are determined based on the optimization feature group.

9. The intelligent precision bird-repelling system based on voiceprint recognition and bionic predator as described in claim 8, characterized in that, Determining the optimization parameters based on the optimization feature set includes: The bird activity change indicators, vocalization stress characteristic parameters, and environmental parameters were normalized. Based on the normalized bird activity change index, voiceprint stress characteristic parameters and environmental parameters, behavioral change sub-state values, voiceprint stress sub-state values ​​and environmental correction sub-state values ​​are constructed respectively, and the comprehensive stress-adaptation state value of the target birds to the current bird deterrence strategy is obtained by weighted fusion. The stress response stage of the target bird is determined by matching the comprehensive stress-adaptation state value with a preset stress-adaptation state threshold range; wherein, the stress response stage includes a low stress adaptation stage, a moderate stress resistance stage, and a high stress avoidance stage. The optimized parameters are determined based on the stress response phase.

10. A smart and precise bird-repelling method based on voiceprint recognition and bionic predator, applied to the smart and precise bird-repelling system based on voiceprint recognition and bionic predator as described in any one of claims 1-9, characterized in that, include: Collect bird sound signals in the target area before implementing bird deterrence strategies, and extract features from the bird sound signals to obtain bird voiceprint features; It is also used to match and identify bird voiceprint features with a preset bird voiceprint feature database in order to determine the species and behavioral status of the bird. Based on the species and behavioral status of the birds, obtain a comprehensive risk assessment value of the target area for the target birds, and determine the bird repelling strategy based on the comprehensive risk assessment value; The bird-repelling strategy is used to drive away target birds in the target area, and real-time bird sound signals in the target area are collected after a preset time interval. The bird-repelling effect is evaluated based on the changes in the real-time bird sound signals to determine whether the bird-repelling strategy needs to be optimized. When determining whether to optimize the bird deterrence strategy, data on bird stress response and environmental parameters are collected during the implementation of the bird deterrence strategy. The bird deterrence strategy is then optimized based on the real-time bird sound signals, bird stress response data, and environmental parameters to obtain an optimized bird deterrence strategy.

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