Airport bird flock data acquisition system based on Internet of Things and intelligent sensing technology

Abstract

The invention discloses an airport bird flock data acquisition system based on the Internet of Things and an intelligent sensing technology, and relates to the technical field of bird flock data acquisition. The multi-element sensing unit comprises a high-definition camera, a thermal imager and the like, is distributed at each part of an airport, monitors bird flocks with the radius of 5 kilometers, has the bird species recognition accuracy of more than 90%, and is self-cleaning; the data transmission network adopts 5G and satellite communication, the bandwidth exceeds 100Mbps, the delay is low within 2 seconds, and the low packet loss rate of 0.03% transmits data to the center; the intelligent preprocessing module is de-noised and standardized by using an algorithm within 0.5 second, and the data accuracy is improved by more than 30%; the storage management module adopts SSD and distributed storage, the energy supply assembly depends on a solar panel and a lithium battery, and the remote monitoring terminal assists the personnel mobile terminal and the PC terminal to check bird flock and equipment states in real time, update is carried out for 3 seconds, and regulation and control are convenient. According to the invention, accurate, real-time and omnibearing bird flock data acquisition is realized; the airport bird flock monitoring system has the advantages of fast transmission, strong processing, stable storage, sufficient energy, self-adaption, linkage early warning, and improvement of airport bird flock monitoring efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of bird flock collection systems, and particularly to an airport bird flock data collection system based on Internet of Things and intelligent sensing technologies. Background Art

[0002] With the rapid development of the air transportation industry, the number of flight takeoffs and landings at airports is increasing day by day, and the safety of the airport area has received more and more attention. Among them, bird activities pose a major threat to the safe operation of airports. Bird strikes may cause serious consequences such as aircraft engine failures and wing damage, endangering flight safety, causing huge economic losses and even casualties.

[0003] Traditional airport bird flock monitoring methods have many limitations. On the one hand, relying on manual observation, staff regularly patrol around the airport and observe the dynamics of bird flocks with the naked eye. However, airports cover a vast area, including runways, aprons, large areas of lawns, and complex surrounding ecological environments. Manual monitoring is difficult to achieve full coverage without dead ends and is extremely inefficient. For example, large hub airports cover an area of thousands of hectares. It takes a long time for staff to conduct a single patrol, and the activities of bird flocks during the patrol interval cannot be grasped in real time. It is very easy to miss key information and it is difficult to give early warnings about the risk of bird flocks approaching key flight areas in a timely manner.

[0004] On the other hand, some early simple monitoring devices, such as single acoustic detectors or ordinary cameras, have single functions and limited accuracy. Ordinary cameras are greatly affected by light and distance. Images are blurred when shooting at night or from a long distance, making it difficult to accurately identify bird species and track flight trajectories; acoustic detectors can only capture sound information, cannot perceive the visual distribution of bird flocks, flight postures, etc., and are easily interfered by environmental noise, resulting in frequent false alarms and missed alarms.

[0005] In addition, the past data collection lacked systematicness and coherence. The data of each monitoring point was stored in isolation, without a unified and efficient data transmission and integration mechanism, making it difficult to form a large dataset that comprehensively and real-time reflects the activity patterns of airport bird flocks. This has made it lack a solid data foundation for in-depth research on bird flock habits, migration patterns, etc., and cannot provide strong support for the formulation of precise bird repelling strategies.

[0006] Moreover, information sharing among different departments is not smooth. There are delays and deviations in the acquisition of bird flock-related information by airport operation management departments, bird repelling departments, air traffic control departments, etc., greatly reducing the ability to jointly respond to bird flock threats. When bird flocks suddenly gather in large numbers and approach the runway, if information cannot be shared in a timely manner and unified actions cannot be taken, the consequences will be unimaginable.

[0007] Nowadays, with the booming development of Internet of Things technology and the continuous innovation of intelligent sensing devices, new opportunities have been brought for the data collection of airport bird flocks. Building an airport bird flock data collection system based on Internet of Things and intelligent sensing technology to achieve accurate, real-time, and all-round data collection, break departmental barriers, integrate and analyze data, has become an urgent need to ensure the safe operation of airports and improve the ability to prevent bird strikes. Summary of the Invention

[0008] The present invention proposes an airport bird flock data collection system and method based on Internet of Things and intelligent sensing technology to solve the problems mentioned in the above prior art.

[0009] To achieve the above object, the present invention adopts the following technical solutions: An airport bird flock data collection system based on Internet of Things and intelligent sensing technology, comprising:

[0010] Multi-sensor unit: Composed of high-definition cameras, thermal imagers, acoustic wave detectors, and micro radars distributed in areas such as airport runways, aprons, lawns, and surrounding woods, which can capture visual images, thermal signals, sound characteristics, and flight trajectories of bird flocks in all directions. Set the bird species recognition accuracy formula: Where I acc is the image recognition accuracy, F id is the feature recognition accuracy, and it is required that P BS ≥90, the monitoring range covers a radius of 5 kilometers around the airport, the spatial resolution reaches 0.1 meters, it can accurately identify the types of birds, the recognition accuracy is above 90%, the equipment has a self-cleaning function and is automatically cleaned once a week to ensure the clarity of data collection.

[0011] Data transmission network: Adopts a combination of 5G and satellite communication to transmit the collected data to the airport data center in real time. 5G ensures high-speed transmission at close range, and satellite communication ensures stable data transmission in remote areas or when the signal is blocked. The transmission bandwidth is not less than 100 Mbps, the transmission delay is less than 2 seconds, and the data packet loss rate is controlled within 0.03%. Set the transmission reliability index: Where D r is the data packet loss rate, T d is the transmission delay (unit: second), and it is required that R TT ≥0.97 to ensure stable data transmission.

[0012] Intelligent preprocessing module: Performs preliminary processing on the received data, uses algorithms such as image recognition and signal filtering to remove interference information such as noise and blur, standardizes the data, improves the efficiency of subsequent analysis, the preprocessing time does not exceed 0.5 seconds, and the accuracy of the processed data is improved by more than 30%. Set the preprocessing efficiency enhancement coefficient: Where A n is the available information volume of the processed data, Ao is the available information volume of the data before processing, T p is the preprocessing time (unit: second), and it is required that E PP ≥0.3 to improve the data processing effect.

[0013] Storage management module: Equipped with a large-capacity solid-state drive (SSD) and a distributed storage system, it stores the original and processed data at a speed of 1TB per day, with a storage period of 1 year for convenient retrospective query. It has a data redundancy backup function with a redundancy of 15% to ensure data security and prevent data loss. Let the data storage safety factor be: where R d is the redundancy, T s is the storage period (unit: year), and it is required that S DS ≥0.9 to ensure reliable data storage.

[0014] Energy supply component: Utilizes a combination of solar panels and rechargeable lithium battery packs for power supply. The conversion efficiency of the solar panels reaches 22%, and the capacity of the lithium battery pack meets the requirement that the system can operate continuously for more than 96 hours. It has an intelligent charge and discharge management function and can automatically adjust the charge and discharge according to the light intensity and the power consumption demand of the equipment, extending the battery life by 40%. Let the energy self-sufficiency rate formula be: where E g is the electric energy generated by the solar energy, E c is the electric energy consumed by the system, and it is required that S ES ≥80% to ensure stable energy supply.

[0015] Remote monitoring terminal: For use by airport management personnel and bird research experts, it can view the dynamics of the bird flock and the operating status of the equipment in real time, supports access from mobile devices and PC terminals, has a friendly interface and convenient operation, and the data update interval is 3 seconds, facilitating timely situation awareness and issuing control instructions. Let the information acquisition convenience index be: where F u is the number of times users effectively obtain information, T up is the information update timeliness rate, F t is the total number of access times, T std is the standard update cycle, and it is required that I GA ≥0.9 for convenient user use.

[0016] Furthermore, an airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology further includes the following modules:

[0017] Adaptive adjustment module: According to the changes in the activity rules of the bird flock in different seasons, time periods, and weather conditions, it automatically adjusts the monitoring parameters of the multi-sensor units, such as the sensitivity of the camera and the sensitivity of the acoustic wave detector, and the adjustment response time is within 8 minutes. Let the adaptive adjustment fit degree be: Among them A s is the number of adjustments to successfully adapt to environmental changes, C s A is the value of data collection accuracy improvement after adjustment. t is the total number of environmental changes, C t To adjust the accuracy of data collection before, A is required AC ≥0.9, ensuring the accuracy of collected data.

[0018] Model update module: Regularly (every 10 days) collect new bird species and bird flock behavior feature data, and perform incremental learning training on image recognition, signal processing and other algorithms. Each update improves the algorithm's recognition accuracy of new bird flock features by at least 3%. Set the model update growth coefficient: Among them A p is the recognition accuracy of the updated algorithm for the new bird flock features, A o is the recognition accuracy of the new bird flock features by the algorithm before updating, T u To update the time (unit: day), G is required MU ≥0.03, maintain the advancement of the system.

[0019] Linkage warning module: When it is detected that the flock of birds tends to gather in key areas such as airport runways and aprons, in addition to sending an early warning to the airport operation department, it also pushes information to the bird repellent system and air traffic control department at the same time. The early warning push time does not exceed 0.4 seconds. Set the linkage warning timeliness index: Where T p Time requirement for warning push I LW ≥0.96, ensuring timely and coordinated response by all departments.

[0020] Furthermore, the equipment in the multi-sensing unit adopts waterproof, dustproof, corrosion-resistant and impact-resistant design, with a protection level of IP68, which can adapt to the complex and harsh outdoor environment of the airport, ensure long-term stable operation and reduce maintenance frequency. Set the sensor environmental adaptability index: Where T tol The duration (unit: hours) that the sensor can withstand harsh environments. SA ≥0.9, ensuring stable operation of the equipment.

[0021] Furthermore, when processing data, the intelligent preprocessing module can automatically classify and archive it according to data type and source, which is convenient for subsequent retrieval and call, and the classification accuracy rate is over 95%. Assume that the data classification accuracy rate is: Among them C a is the amount of correctly classified data, C t is the total amount of data, T s is the classification time (unit: seconds), requiring A DC ≥0.95, improve data management efficiency.

[0022] A method for applying the airport bird flock data acquisition system based on Internet of Things and intelligent sensing technology as described above, including:

[0023] System initialization step: After power-on, each module performs self-check, the multi-sensing unit starts monitoring, the data transmission network establishes a connection, the self-check completion rate reaches 100%, and the startup time does not exceed 20 seconds.

[0024] Set the startup stability index: Where C s is the self-check completion ratio, T s is the startup time consumption (unit: second), and it is required that S SI ≥0.95 to ensure the normal operation of the system.

[0025] Real-time acquisition step: The multi-sensing unit continuously acquires bird flock data and transmits it to the intelligent preprocessing module in real time through the data transmission network. The acquisition frequency is 10 frames of images, 20 acoustic signals, and 10 thermal signals per second to ensure the timeliness of the data. Set the data acquisition timeliness index: Where F a is the actual acquired data volume, F t is the data volume to be acquired, C s is the acquisition frequency, C t is the standard acquisition frequency, and it is required that I DC ≥0.9 to ensure timely data acquisition.

[0026] Data analysis step: After the intelligent preprocessing module processes the data, it transmits it to the storage management module, and at the same time, uses data analysis algorithms to preliminarily analyze bird flock behaviors such as flight altitude, speed, and aggregation degree, and the analysis time does not exceed 1 second. Set the data analysis efficiency index: Where A a is the number of effective information analyzed, A t is the total data volume, T s is the analysis time (unit: second), and it is required that E DA ≥0.9 to provide reference for subsequent research.

[0027] Effect evaluation step: Regularly (every 24 hours) evaluate the acquisition effect of the system, compare the actually acquired bird flock data with the expected data. If the acquisition accuracy rate is lower than the standard, adjust the parameters of the multi-sensing unit or optimize the algorithm to continuously improve the acquisition effect. Set the acquisition effect improvement coefficient: Where C n is the acquisition accuracy rate after adjustment, C o is the acquisition accuracy rate before adjustment, T p is the evaluation period (unit: hour), and it is required that E CE ≥0.1 to ensure system performance optimization.

[0028] Compared with the existing technologies, the beneficial effects of the present invention are as follows:

[0029] In terms of data acquisition accuracy, the multi-sensing unit integrates multiple devices such as high-definition cameras and thermal imagers, with a monitoring radius of up to 5 kilometers, a spatial resolution of 0.1 meter, a formula score of over 90 for bird species recognition accuracy, accurately capturing all-round information of the bird flock, and the self-cleaning function and high protection level ensure stable data acquisition by the device.

[0030] The data transmission is efficient and reliable. The combination of 5G and satellite communication has a transmission reliability index of over 0.97, sufficient bandwidth, a delay of less than 2 seconds, and a packet loss rate within 0.03%. A large amount of data is delivered to the data center losslessly in real time. The intelligent preprocessing module can increase efficiency by over 30% within 0.5 second, remove interference, standardize data, and improve the subsequent analysis efficiency.

[0031] The storage management is safe and convenient. The large-capacity SSD and distributed storage can store 1TB of data per day for 1 year, and the redundant backup ensures that the data is not lost. The classification and filing accuracy rate is over 95%, which is convenient for retrospective query. The energy supply is stable. The combination of solar energy and lithium batteries has a self-sufficiency rate of over 80%, and the intelligent management can extend the battery life by 40%.

[0032] The system also has adaptability and growth. The adaptability adjustment fit degree is over 0.9, and the acquisition parameters can be optimized within 8 minutes according to environmental changes; the model update growth coefficient is over 0.03, and the algorithm recognition accuracy is improved regularly. The linkage warning timeliness index is over 0.96, and multiple departments cooperate to respond, greatly improving the overall airport bird flock monitoring and prevention level. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a schematic block diagram of an airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology proposed by the present invention;

[0034] Figure 2 It is a schematic block diagram of an airport bird flock data acquisition method based on the Internet of Things and intelligent sensing technology proposed by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus cannot be construed as a limitation on the present invention.

[0037] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the present invention, the meaning of "a plurality" is two or more unless otherwise specifically defined. In addition, the terms "installed", "connected", "connected to" should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances. The present invention will be further described in detail below with reference to the drawings.

[0038] Refer to Figure 1-2 : An airport bird flock data acquisition system based on Internet of Things and intelligent sensing technology, comprising:

[0039] Multi-sensor unit: Composed of high-definition cameras, thermal imagers, acoustic detectors, and micro radars distributed in areas such as airport runways, aprons, lawns, and surrounding woods, it can capture the visual images, thermal signals, sound characteristics, and flight trajectories of bird flocks in all directions. Set the bird species recognition accuracy formula: where I acc is the image recognition accuracy rate, F id is the feature recognition accuracy rate, and it is required that P BS ≥90, the monitoring range covers a radius of 5 kilometers around the airport, the spatial resolution reaches 0.1 meters, it can accurately identify the types of birds, the recognition accuracy rate is above 90%, the device has a self-cleaning function and automatically cleans once a week to ensure the clarity of data acquisition.

[0040] Data Transmission Network: Adopting a combination of 5G and satellite communication, it transmits the collected data to the airport data center in real time. 5G ensures high-speed transmission in the short range, and satellite communication guarantees stable data transmission in remote areas or when signals are blocked. The transmission bandwidth is not less than 100 Mbps, the transmission delay is less than 2 seconds, and the data packet loss rate is controlled within 0.03%. Let the transmission reliability index be: where D r is the data packet loss rate, and T d is the transmission delay (unit: second). It is required that R TT ≥0.97 to ensure stable data transmission. The data transmission network adopts an innovative combination of 5G and satellite communication, aiming to transmit the collected data to the airport data center in real time, efficiently, and stably. Among them, 5G technology, with its characteristics of high speed and low latency, is mainly responsible for ensuring high-speed data transmission in the short range.

[0041] Intelligent Preprocessing Module: It preliminarily processes the received data, uses algorithms such as image recognition and signal filtering to remove interference information such as noise and blur, standardizes the data, and improves the efficiency of subsequent analysis. The preprocessing time does not exceed 0.5 seconds, and the accuracy of the processed data is increased by more than 30%. Let the preprocessing efficiency coefficient be: where A n is the available information volume of the processed data, A o is the available information volume of the data before processing, and T p is the preprocessing time (unit: second). It is required that E PP ≥0.3. By removing noise and interference information, it significantly improves the accuracy and reliability of the data, enabling subsequent analysis to be based on higher-quality data, avoiding analysis errors and decision-making mistakes caused by data quality problems, and enhancing the data processing effect.

[0042] Storage Management Module: It is equipped with a large-capacity solid-state drive (SSD) and a distributed storage system, storing the original and processed data at a speed of 1 TB per day. The storage period is 1 year, facilitating retrospective query. It has a data redundancy backup function with a redundancy of 15% to ensure data security and prevent data loss. Let the data storage security coefficient be: where R d is the redundancy, and T s is the storage period (unit: year). It is required that S DS ≥0.9 to ensure reliable data storage.

[0043] Energy supply component: It uses a combination of solar panels and rechargeable lithium battery packs for power supply. The conversion efficiency of the solar panels reaches 22%, and the capacity of the lithium battery pack can meet the continuous operation of the system for more than 96 hours. It has an intelligent charge and discharge management function, which can automatically adjust the charge and discharge according to the light intensity and the power consumption requirements of the equipment, and extend the battery life by 40%. Set the energy self-sufficiency rate formula: Where E g is the electric energy generated by solar energy, and E c is the electric energy consumed by the system. It is required that S ES ≥80% to ensure stable energy supply.

[0044] Remote monitoring terminal: It is used by airport management personnel and bird research experts. It can view the dynamics of bird flocks and the operating status of equipment in real time, support access from mobile devices and PC terminals, has a user-friendly interface, convenient operation, and a data update interval of 3 seconds, facilitating timely understanding of the situation and issuing control instructions. Set the information acquisition convenience index: Where F u is the number of times users effectively obtain information, T up is the information update timeliness rate, F t is the total number of accesses, T std is the standard update cycle. It is required that I GA ≥0.9 for the convenience of users.

[0045] In the present invention, an airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology further includes the following modules:

[0046] Adaptive adjustment module: According to the changes in the activity rules of bird flocks in different seasons, time periods, and weather conditions, it automatically adjusts the monitoring parameters of the multi-sensor unit, such as the sensitivity of the camera and the sensitivity of the acoustic wave detector, and the adjustment response time is within 8 minutes. Set the adaptive adjustment fitness: Where A s is the number of adjustments that successfully adapt to environmental changes, C s is the improvement value of data acquisition accuracy after adjustment, A t is the total number of environmental changes, C t is the data acquisition accuracy before adjustment. It is required that A AC ≥0.9 to ensure the accuracy of the collected data.

[0047] Model update module: Regularly (every 10 days) collect data on newly emerging bird species and bird flock behavior characteristics, and perform incremental learning training on algorithms such as image recognition and signal processing. Each update can increase the recognition accuracy of the algorithm for new bird flock characteristics by at least 3%. Set the model update growth coefficient: Where A p is the recognition accuracy of the algorithm for new bird flock characteristics after update, and A o is the recognition accuracy of the algorithm for new bird flock characteristics before update, Tu For the update time consumption (unit: days), it is required that G MU ≥0.03 to maintain the advancement of the system.

[0048] Linkage warning module: When it is detected that the bird flock has a tendency to gather in key areas such as the airport runway and apron, in addition to sending a warning to the airport operation department, information is simultaneously pushed to the bird repelling system and the air traffic control department, and the warning push time does not exceed 0.4 seconds. Set the linkage warning timeliness index: Where T p Is the warning push time requirement I LW ≥0.96 to ensure that all departments can cooperate in a timely manner.

[0049] In the present invention, the devices in the multi-sensor unit are designed with waterproof, dustproof, corrosion-resistant, and impact-resistant features, and the protection level reaches IP68, adapting to the complex and harsh outdoor environment of the airport, ensuring long-term stable operation and reducing the maintenance frequency. Set the sensor environmental adaptability index: Where T tol Is the duration (unit: hours) for the sensor to withstand the harsh environment, and it is required that E SA ≥0.9 to ensure the stable operation of the device.

[0050] In the present invention, when the intelligent preprocessing module processes data, it can automatically classify and file according to the data type and source, facilitating subsequent retrieval and call, and the classification accuracy rate reaches more than 95%. Set the data classification accuracy rate: Where C a Is the amount of correctly classified data, C t Is the total amount of data, T s Is the classification time (unit: seconds), and it is required that A DC ≥0.95. The automatic classification and filing function makes data management more efficient and convenient. Whether it is for the storage of a large amount of data or the rapid retrieval of data, it can significantly save time and effort. The orderly classification provides a good foundation for subsequent data analysis and mining, improving the data management efficiency.

[0051] The present invention also discloses a method for an airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology, including:

[0052] System initialization step: After power-on, each module performs self-check, the multi-sensor unit starts monitoring, the data transmission network establishes a connection, the self-check completion rate reaches 100%, and the startup time does not exceed 20 seconds. Set the startup stability index: Where C s Is the self-check completion ratio, T s Is the startup time consumption (unit: seconds), and it is required that S SI≥0.95, a fast and stable system initialization process provides users with a good user experience. Users do not need to wait a long time for the system to start up and can use the system with confidence because the system has undergone strict self-check and stability assessment during the initialization phase.

[0053] In the present invention, a method for collecting airport bird flock data based on Internet of Things and intelligent sensing technology further includes the following steps:

[0054] Real-time collection step: The multi-sensing unit continuously collects bird flock data and transmits it to the intelligent preprocessing module in real time through the data transmission network. The collection frequency is 10 frames of images, 20 acoustic signals, and 10 thermal signals per second to ensure the timeliness of the data. Let the data collection timeliness index be: where F a is the actual amount of collected data, F t is the amount of data that should be collected, C s is the collection frequency, C t is the standard collection frequency, and it is required that I DC ≥0.9. Collecting various types of data (images, acoustic waves, thermal signals) per second provides a comprehensive perception of the bird flock, enabling subsequent analysis and processing to be based on rich and diverse information. A high collection frequency (such as 10 frames of images per second, etc.) ensures the real-time update of the data and can timely reflect the dynamic changes of the bird flock.

[0055] Data analysis step: After the intelligent preprocessing module processes the data, it transmits it to the storage management module. At the same time, it uses data analysis algorithms to preliminarily analyze the behavior of the bird flock, such as flight altitude, speed, and aggregation degree, and the analysis time does not exceed 1 second. Let the data analysis efficiency index be: where A a is the number of effective information analyzed, A t is the total amount of data, T s is the analysis time (unit: second), and it is required that E DA ≥0.9 to provide a reference for subsequent research.

[0056] Effect evaluation step: Regularly (every 24 hours) evaluate the collection effect of the system, compare the actually collected bird flock data with the expected data. If the collection accuracy rate is lower than the standard, adjust the parameters of the multi-sensing unit or optimize the algorithm to continuously improve the collection effect. Let the collection effect improvement coefficient be: where C n is the collection accuracy rate after adjustment, C o is the collection accuracy rate before adjustment, T p is the evaluation period (unit: hour), and it is required that E CEThe effect is evaluated once every 24 hours with a frequency of ≥0.1, which can timely detect the changing trend of the system's acquisition effect, avoid the accumulation of problems such as system performance degradation or inaccurate data acquisition caused by long-term undetected, and through comparing the actual data with the expected data and quantitatively evaluating according to the acquisition effect improvement coefficient, the system can precisely adjust the parameters of the multi-sensor unit or optimize the algorithm based on the data.

[0057] The above is only the preferred specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, making equivalent substitutions or changes, should be covered by the protection scope of the present invention.

Claims

1. An airport bird flock data acquisition system based on Internet of Things and intelligent sensing technology, characterized in that, Including: Information perception unit: It consists of cameras, thermal imagers, acoustic wave detectors, and micro radars distributed in the airport runway, apron, lawn, and surrounding forest areas, capturing visual images, thermal signals, sound characteristics, and flight trajectories of bird flocks in all directions; The bird species recognition accuracy formula is set as follows: Where I acc is the image recognition accuracy rate, and F id is the feature recognition accuracy rate. It is required that P BS ≥90. The monitoring range covers a radius of 5 kilometers around the airport, with a spatial resolution of 0.1 meters. The equipment has a self-cleaning function and is automatically cleaned once a week to ensure the clarity of data collection; Data transmission network: Adopting a combination of 5G and satellite communication, the collected data is transmitted to the airport data center in real time. 5G ensures the transmission, and satellite communication ensures the stable transmission of data in surrounding areas or when the signal is blocked; Set the transmission reliability index: where D r is the data packet loss rate, and T d is the transmission delay. It is required that R TT ≥0.97 to ensure stable data transmission; Preprocessing module: It performs preliminary processing on the received data. Through image recognition and signal filtering algorithms, it removes noise, image noise, and interference information to improve the efficiency of subsequent analysis. Set the preprocessing efficiency enhancement coefficient: Where A n is the information volume of the processed data, and A o is the information volume of the data before processing, and T p is the preprocessing time. It is required that E PP ≥0.3 to improve the data processing effect; Storage management module: Equipped with a solid-state drive (SSD) and a distributed storage system, it stores and processes the data at a speed of 1 TB per day, with a storage period of 1 year, and has a data redundancy backup function; Set the data storage security factor: where R d is the redundancy, T s is the storage period, and it is required that S DS ≥ 0.9 to ensure reliable data storage; Energy supply component: It is jointly powered by solar panels and a rechargeable lithium battery pack, has charge and discharge management functions, and automatically adjusts charging and discharging according to light intensity and equipment power demand; Set the energy self-sufficiency rate formula: where E g is the electric energy generated by solar energy, and E c is the electric energy consumed by the system. It is required that S ES ≥80%, to ensure stable energy supply; Remote monitoring terminal: It is used by airport management personnel and bird research experts to view the dynamics of bird flocks and the operating status of equipment in real time, and supports mobile and PC access; set the information acquisition convenience index: Among them, F u is the number of times users effectively obtain information, and T up is the information update timeliness rate, F t is the total number of accesses, and T std is the standard update cycle, and it is required that I GA ≥0.9 for the convenience of users.

2. The airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology according to claim 1, characterized in that, Also including: Adaptive adjustment module: Automatically adjusts the monitoring parameters of the sensing unit according to the changes in the activity patterns of bird flocks in different seasons, time periods, and weather conditions, including the sensitivity of the camera and the sensitivity of the acoustic detector; Set the adaptive adjustment fit: Where A s is the number of adjustments that successfully adapt to environmental changes, and C s is the improvement value of data acquisition accuracy after adjustment, A t is the total number of environmental changes, and C t is the data acquisition accuracy before adjustment, and it is required that A AC ≥0.

9.

3. The airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology according to claim 1, characterized in that, Also including: Model update module: Regularly collect data on newly emerging bird species and bird flock behavior characteristics, and train image recognition and signal processing algorithms; Set Model update growth coefficient: Where A p is the recognition accuracy of the updated algorithm for the characteristics of the new bird flock, and A o is the recognition accuracy of the algorithm before update for the characteristics of the new bird flock, T u is the update time consumption, and it is required that G MU ≥0.

03.

4. The airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology according to claim 1, characterized in that, Also including: Linkage warning module: When it is detected that the bird flock is gathering towards the airport runway and apron areas, in addition to sending a warning to the airport operation department, information is simultaneously pushed to the bird repellent system and the air traffic control department; Set the linkage warning timeliness index: where T p is the warning push time requirement I LW ≥0.

96.

5. The airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology according to claim 1, characterized in that: The devices in the sensing unit are designed to be waterproof, dustproof, corrosion-resistant and shock-resistant to adapt to the outdoor environment of the airport. The sensor environmental adaptability index is set as follows: where T tol is the duration for the sensor to withstand harsh environments, and it is required that E SA ≥ 0.9 to ensure the stable operation of the device.

6. The airport bird flock data acquisition system based on the Internet of Things and intelligent sensing technology according to claim 1, characterized in that: When processing data, the preprocessing module automatically classifies and archives it according to the data type and source, facilitating subsequent retrieval and invocation; set the data classification accuracy rate: where C a is the amount of correctly classified data, C t is the total amount of data, T s is the classification time, and it is required that A DC ≥0.95 to improve the data management efficiency.

7. A method for applying the airport bird flock data acquisition system based on Internet of Things and intelligent sensing technology according to any one of claims 1-6, characterized in that, Including: System initialization step: After power-on, each module performs self-check, the sensing unit starts monitoring, and the data transmission network establishes a connection; Set Startup stability index: where C s is the self-check completion ratio, T s is the startup time, and it is required that S SI ≥0.95 to ensure the normal operation of the system.

8. A method for collecting airport bird flock data based on Internet of Things and intelligent sensing technology according to claim 7, characterized in that, Also including: Real-time acquisition step: The sensing unit continuously acquires bird flock data and transmits it to the preprocessing module in real time through the data transmission network; Set the data collection timeliness index: where F a is the actual amount of data collected, and F t is the amount of data that should be collected, C s is the collection frequency, and C t is the standard collection frequency, and it is required that I DC ≥ 0.

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9. A method for collecting airport bird flock data based on Internet of Things and intelligent sensing technology according to claim 7, characterized in that, Also including: Data analysis steps: After the preprocessing module processes the data, it is transmitted to the storage management module. At the same time, the bird flock behavior is initially analyzed through data analysis algorithms, including flight altitude, speed, and aggregation degree. Let the data analysis efficiency index be: where A a is the number of valid information analyzed, A t is the total data volume, T s is the analysis time, and it is required that E DA ≥0.

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10. The method for collecting airport bird flock data based on the Internet of Things and intelligent sensing technology according to claim 7, wherein Also including: Effect evaluation steps: Regularly evaluate the acquisition effect of the system, compare the actually acquired bird flock data with the expected data. If the acquisition accuracy rate is lower than the standard, adjust the parameters of the sensing unit or optimize the algorithm to continuously improve the acquisition effect; Set the acquisition effect improvement coefficient: where C n is the acquisition accuracy rate after adjustment, C o is the acquisition accuracy rate before adjustment, and T p is the evaluation period.

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