Low-air anti-collision intelligent sensor based on big data

Abstract

The invention discloses a low-altitude anti-collision intelligent sensor based on big data. The low-altitude anti-collision intelligent sensor comprises a data acquisition module, a big data processing module, a collision risk assessment module, an alarm and obstacle avoidance module and a communication module, the data acquisition module is used for capturing flight parameters and surrounding environment data of the low-altitude aircraft in real time; the big data processing module is used for preprocessing, storing and deeply analyzing the collected data and identifying potential collision risks; the collision risk assessment module calculates a collision risk value by using a predetermined collision risk assessment algorithm according to the big data analysis result; when the collision risk value exceeds a preset threshold value, the alarm and obstacle avoidance module automatically triggers alarm and generates an obstacle avoidance instruction; the communication module sends alarm information and obstacle avoidance instructions to a low-altitude aircraft control system or related operators. The low-altitude aircraft anti-collision system is intelligent, real-time and high in reliability, the safety of low-altitude flight is effectively improved, and the low-altitude aircraft anti-collision system has remarkable application value and market prospect.

Description

Technical Field

[0001] The present invention relates to the technical field, and in particular to a low-altitude anti-collision intelligent sensor based on big data. Background Art

[0002] With the widespread application of drones, helicopters and other low-altitude aircraft in various fields, the safety of low-altitude flight has become an important concern. When performing missions, low-altitude aircraft often face complex environments and changing flight conditions, such as the presence of obstacles such as buildings, trees, mountains, and the activities of other aircraft, which threaten the safety of low-altitude flight. Therefore, it is particularly important to develop a low-altitude collision avoidance system that can perceive the environment in real time and respond quickly.

[0003] Traditional low-altitude collision avoidance technologies mainly rely on sensors such as radar, lidar or sonar. Although these sensors can detect obstacles, they are often unable to cope with dynamic environmental changes and complex flight conditions. In addition, these systems usually operate independently and lack the ability to integrate and coordinate with other systems, making it difficult to fully utilize the advantages of big data analysis to improve the accuracy of detection and response.

[0004] In recent years, the rapid development of big data technology has provided new opportunities for the improvement of low-altitude collision avoidance technology. By collecting and analyzing massive amounts of flight data and environmental data, potential collision risks can be predicted more accurately and more intelligent obstacle avoidance strategies can be generated. However, the effective application of big data technology in low-altitude collision avoidance systems still requires solving key technical challenges such as real-time data processing, risk assessment algorithm optimization, and system integration. Summary of the invention

[0005] The purpose of the present invention is to provide a low-altitude collision avoidance intelligent sensor based on big data to solve the problems raised in the above-mentioned background technology, and to realize real-time monitoring and intelligent obstacle avoidance of low-altitude aircraft through advanced data acquisition, processing and analysis technologies combined with machine learning algorithms. The system can not only evaluate flight risks in real time, but also generate optimized obstacle avoidance paths to ensure the safe flight of aircraft in complex environments. By introducing redundant communication protocols and dynamic performance limitations, the system also has high reliability and adaptability, and can maintain stable performance under changing flight conditions.

[0006] To achieve the above object, the present invention provides the following technical solution: a low-altitude collision avoidance intelligent sensor based on big data, comprising: Data acquisition module, used to capture the flight parameters and surrounding environment data of low-altitude aircraft in real time; Big data processing module, used to pre-process, store and deeply analyze the collected data to identify potential collision risks; A collision risk assessment module calculates a collision risk value using a predetermined collision risk assessment algorithm based on the big data analysis results; The alarm and obstacle avoidance module automatically triggers an alarm and generates obstacle avoidance instructions when the collision risk value exceeds the preset threshold; The communication module sends alarm information and obstacle avoidance instructions to the low-altitude aircraft control system or relevant operators.

[0007] Preferably, the collision risk assessment algorithm performs the following steps: Determine the flight trajectory and speed of the target aircraft; Analyze the obstacle distribution and dynamic changes in the surrounding environment; Calculate the minimum safe distance between the target aircraft and obstacles; Apply the formula , comprehensive minimum safety distance , Flight speed , obstacle density , obstacle type coefficient And the collision risk assessment function To calculate the collision risk value .

[0008] Preferably, the collision risk assessment function Obtained through machine learning algorithm training, the training data set covers historical collision records, safe flight records and simulated flight data.

[0009] Preferably, the alarm and obstacle avoidance module is additionally provided with an obstacle avoidance strategy generation submodule to formulate an optimal obstacle avoidance strategy according to the collision risk value and the obstacle type.

[0010] Preferably, the obstacle avoidance strategy generation submodule applies the formula ; Considering the path cost and path risk value Based on the weight coefficient Optimize obstacle avoidance paths .

[0011] Preferably, the obstacle avoidance strategy is generated by taking into account the dynamic performance constraints of the aircraft, the maximum turning angle , Maximum climb / descent rate and maximum flight speed .

[0012] Preferably, the big data processing module is equipped with a data cleaning submodule and an anomaly detection submodule, using the formula Identify and remove abnormal data, including is the data mean, is the data standard deviation, is the threshold coefficient, is the abnormal data detection threshold, which is calculated by the mean of the data and standard deviation , and set a reasonable threshold coefficient , get the abnormal data detection threshold ; Any data points exceeding this threshold will be considered abnormal and eliminated, thereby improving the accuracy of subsequent data analysis.

[0013] Preferably, the communication module adopts a redundant communication protocol, using the formula Evaluate the transmission success rate and use the formula Calculate the fault tolerance rate under redundant communication; Transmission success rate : By counting the number of messages successfully transmitted Percentage of total transmitted messages ratio to evaluate the transmission success rate of the communication system ; Fault tolerance rate :When redundant communication protocols are used, the number of redundant communication channels that actually work is calculated Subtract the number of channels that failed , and then divided by the total number of redundant channels to get the fault tolerance rate , to quantify the fault tolerance of communication systems.

[0014] The invention proposes a low-altitude collision avoidance intelligent sensor based on big data, which has the following beneficial effects: 1. Real-time and accuracy: The data acquisition module captures the flight parameters and surrounding environment data of low-altitude aircraft in real time, and uses the big data processing module to pre-process and deeply analyze the data, which can identify potential collision risks in real time; compared with traditional collision avoidance systems, the detection and response capabilities of the present invention in dynamic environments are significantly improved, ensuring that the aircraft can avoid obstacles in time.

[0015] 2. Intelligent risk assessment: The collision risk assessment module calculates the collision risk value by applying a predetermined collision risk assessment algorithm, combining multiple factors such as minimum safe distance, flight speed, obstacle density and type. The assessment algorithm has been trained through machine learning and can continuously optimize and improve the assessment accuracy, significantly reducing the probability of false alarms and missed alarms.

[0016] 3. Optimized obstacle avoidance strategy: When the collision risk value exceeds the preset threshold, the alarm and obstacle avoidance module automatically triggers an alarm and generates obstacle avoidance instructions. Through the obstacle avoidance strategy generation submodule, the optimal obstacle avoidance path is generated based on the optimized calculation formula. This path takes into account the trade-off between path cost and risk value, ensuring that the obstacle avoidance strategy achieves the best balance between safety and efficiency.

[0017] 4. High reliability and fault tolerance: The communication module adopts redundant communication protocols and evaluates the transmission success rate and fault tolerance rate to ensure that alarm information and obstacle avoidance instructions can be transmitted reliably under changing flight conditions. Even when a communication part fails, the system can still maintain a high working performance, enhancing the reliability of the overall system.

[0018] 5. Data preprocessing and anomaly detection: The data cleaning submodule and anomaly detection submodule in the big data processing module clean the data through reasonable threshold coefficients and remove abnormal data, which improves the accuracy and reliability of data analysis and provides a high-quality data foundation for subsequent risk assessment and obstacle avoidance strategy generation.

[0019] 6. Adaptable dynamic performance limitations: During the obstacle avoidance strategy generation process, the dynamic performance limitations of the aircraft are fully considered, such as the maximum turning angle, maximum climb / descent rate, and maximum flight speed, to ensure that the generated obstacle avoidance strategy is not only safe and feasible in actual flight, but also in line with the aircraft's operational performance, thereby improving the system's adaptability and practicality.

[0020] In summary, the present invention realizes the intelligence, real-time and high reliability of the low-altitude aircraft collision avoidance system through innovative technical solutions and optimized calculation methods, effectively improves the safety of low-altitude flight, and has significant application value and market prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a principle block diagram of the present invention; Figure 2 A diagram showing the execution steps of the collision risk assessment algorithm of the present invention; Figure 3 Schematic diagram of a big data processing module of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] For examples, see Figure 1-3 ,The present invention provides a technical solution: a low-altitude collision avoidance intelligent sensor based on big data, comprising: a data acquisition module, a big data processing module, a collision risk assessment module, an alarm and obstacle avoidance module and a communication module; The data acquisition module is used to capture the flight parameters and surrounding environment data of low-altitude aircraft in real time, including the flight parameters of the aircraft itself (such as speed, altitude, direction, etc.) and information about the surrounding environment (such as obstacle location, weather conditions, etc.); The big data processing module is used to pre-process, store and deeply analyze the collected data to identify potential collision risks; pre-processing includes data cleaning, outlier detection, etc. to ensure the accuracy and consistency of the data. The purpose of the analysis is to identify potential collision risks. Through big data technology, valuable information can be mined from massive data to identify risk factors that may be overlooked in traditional methods, thereby improving the overall risk identification ability of the system; The collision risk assessment module calculates the collision risk value based on the big data analysis results using a predetermined collision risk assessment algorithm; this risk value is quantified to facilitate subsequent decision-making and processing; The alarm and obstacle avoidance module automatically triggers an alarm and generates obstacle avoidance instructions when the collision risk value exceeds the preset threshold; these instructions can guide the aircraft to take appropriate maneuvers to avoid collisions; The communication module sends alarm information and obstacle avoidance instructions to the low-altitude aircraft control system or relevant operators, including wireless communication, data link and other methods to ensure that the information can be transmitted quickly.

[0024] The collision risk assessment algorithm performs the following steps: Determine the flight trajectory and speed of the target aircraft; Analyze the obstacle distribution and dynamic changes in the surrounding environment, including determining the dynamic information such as the location, movement direction and speed of the obstacles; Calculate the minimum safe distance between the target aircraft and obstacles; Apply the formula , comprehensive minimum safety distance , Flight speed , obstacle density , obstacle type coefficient And the collision risk assessment function To calculate the collision risk value ,This formula provides a quantitative risk assessment result by integrating multiple factors, enabling the system to determine whether ,avoidance measures need to be taken based on the size of the risk value.

[0025] The collision risk assessment function Obtained through machine learning algorithm training, the training data set covers historical collision records, safe flight records and simulated flight data.

[0026] The alarm and obstacle avoidance module adds an obstacle avoidance strategy generation submodule, formulates the optimal obstacle avoidance strategy according to the collision risk value and obstacle type, and uses machine learning algorithms to train a large amount of historical data and simulation data, which can continuously optimize the evaluation function. , improving the accuracy of its risk assessment under different flight conditions. This data-driven approach enables the system to adapt to changing flight environments and continuously improve performance as data increases and algorithms are optimized.

[0027] Obstacle avoidance strategy generation submodule application formula ; Considering the path cost and path risk value Based on the weight coefficient Optimize obstacle avoidance paths , this formula is used to select an optimal path Popt from all possible path sets P; "optimal" here means that the comprehensive evaluation index of the path (composed of path cost and path risk value) is minimized; the optimization target Popt is the optimal obstacle avoidance path calculated by the above formula; this path provides a balanced solution while considering cost and risk; finding the optimal path is the ultimate goal of obstacle avoidance strategy generation, ensuring that the aircraft can avoid obstacles safely and efficiently.

[0028] The obstacle avoidance strategy is generated by taking into account the dynamic performance limitations of the aircraft and the maximum turning angle. , Maximum climb / descent rate and maximum flight speed ; Limiting the turning angle is to ensure that the aircraft maintains a stable flight state during the obstacle avoidance process, avoiding loss of control or structural damage due to excessive maneuvers; controlling the climb and descent rates helps maintain the aircraft's maneuverability and ride comfort, and avoids safety risks caused by rapid altitude changes; limiting the flight speed is to prevent slow response or other safety hazards caused by excessive speed during the obstacle avoidance process, and also to ensure that the aircraft can effectively execute obstacle avoidance commands in an emergency.

[0029] The big data processing module is additionally provided with a data cleaning submodule and an anomaly detection submodule, which are responsible for identifying and removing abnormal data points in the data set; these abnormal data may be generated due to sensor errors, data transmission problems or other reasons, and if not processed, may affect subsequent data analysis results; by cleaning and removing abnormal data, it is ensured that the data set on which the analysis is based is accurate and reliable, thereby improving the analysis accuracy and decision-making quality of the entire system.

[0030] Using formula Identify and remove abnormal data, including is the data mean, is the data standard deviation, is the threshold coefficient, is the abnormal data detection threshold, which is calculated by the mean of the data and standard deviation , and set a reasonable threshold coefficient , get the abnormal data detection threshold Any data point exceeding this threshold will be considered abnormal and removed, thereby improving the accuracy of subsequent data analysis. Through this anomaly detection method, the data cleaning submodule and the anomaly detection submodule can effectively filter out abnormal data that may affect the accuracy of analysis, thereby improving the efficiency and effectiveness of the entire big data processing module. This process ensures that the data used by the system for collision risk assessment and other analysis is verified and accurate, thereby enhancing the decision-making ability and reliability of the system.

[0031] The communication module adopts a redundant communication protocol, using the formula Evaluate the transmission success rate and use the formula Calculate the fault tolerance rate under redundant communication; Transmission success rate : By counting the number of messages successfully transmitted Percentage of total transmitted messages ratio to evaluate the transmission success rate of the communication system ; A high transmission success rate indicates that the communication system can effectively transmit information and is an important indicator of system reliability; Fault tolerance rate :When redundant communication protocols are used, the number of redundant communication channels that actually work is calculated Subtract the number of channels that failed , and then divided by the total number of redundant channels to get the fault tolerance rate , to quantify the fault tolerance of the communication system. A high fault tolerance rate means that even when some channels fail, the system can still maintain normal communication functions. It is a key indicator for evaluating the effectiveness of system redundancy design.

[0032] Redundant communication protocols use multiple communication channels to transmit the same information to ensure that the information can still be successfully transmitted when some channels fail. This design improves the reliability and stability of the communication system. In critical missions or high-risk environments, communication reliability is crucial. Redundant design can effectively reduce the risk of information transmission interruption caused by failure of a single communication channel.

[0033] Through these two indicators, the communication module can comprehensively evaluate its performance and reliability; the transmission success rate provides an evaluation of the performance of the system under normal operating conditions, while the fault tolerance rate provides an evaluation of the robustness of the system under abnormal or fault conditions; the analysis of these indicators helps to identify potential communication bottlenecks and failure risks, guide the optimization and improvement of the system, and ensure that efficient communication capabilities are maintained under various circumstances.

[0034] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-altitude collision avoidance intelligent sensor based on big data, characterized in that: include: Data acquisition module, used to capture the flight parameters and surrounding environment data of low-altitude aircraft in real time; Big data processing module, used to pre-process, store and deeply analyze the collected data to identify potential collision risks; A collision risk assessment module calculates a collision risk value using a predetermined collision risk assessment algorithm based on the big data analysis results; The alarm and obstacle avoidance module automatically triggers an alarm and generates obstacle avoidance instructions when the collision risk value exceeds the preset threshold; The communication module sends alarm information and obstacle avoidance instructions to the low-altitude aircraft control system or relevant operators.

2. According to the big data based low-altitude collision avoidance intelligent sensor according to claim 1, it is characterized in that: The collision risk assessment algorithm performs the following steps: Determine the flight trajectory and speed of the target aircraft; Analyze the obstacle distribution and dynamic changes in the surrounding environment; Calculate the minimum safe distance between the target aircraft and obstacles; Apply the formula , comprehensive minimum safety distance , Flight speed , obstacle density , obstacle type coefficient And the collision risk assessment function To calculate the collision risk value .

3. The low-altitude collision avoidance intelligent sensor based on big data according to claim 2 is characterized in that: The collision risk assessment function Obtained through machine learning algorithm training, the training data set covers historical collision records, safe flight records and simulated flight data.

4. The low-altitude collision avoidance intelligent sensor based on big data according to claim 3 is characterized in that: The alarm and obstacle avoidance module is additionally provided with an obstacle avoidance strategy generation submodule, which formulates an optimal obstacle avoidance strategy according to the collision risk value and the obstacle type.

5. The low-altitude collision avoidance intelligent sensor based on big data according to claim 4 is characterized in that: Obstacle avoidance strategy generation submodule application formula ; Considering the path cost and path risk value Based on the weight coefficient Optimize obstacle avoidance paths .

6. The low-altitude collision avoidance intelligent sensor based on big data according to claim 5 is characterized in that: The obstacle avoidance strategy is generated by taking into account the dynamic performance limitations of the aircraft and the maximum turning angle. , Maximum climb / descent rate and maximum flight speed .

7. The low-altitude collision avoidance intelligent sensor based on big data according to claim 6 is characterized in that: The big data processing module adds a data cleaning submodule and an anomaly detection submodule, using the formula Identify and remove abnormal data, including is the data mean, is the data standard deviation, is the threshold coefficient, is the abnormal data detection threshold, which is calculated by the mean of the data and standard deviation , and set a reasonable threshold coefficient , get the abnormal data detection threshold ; Any data points exceeding this threshold will be considered as anomalies and removed, thereby improving the accuracy of subsequent data analysis.

8. The low-altitude collision avoidance intelligent sensor based on big data according to claim 7 is characterized in that: The communication module adopts a redundant communication protocol, using the formula Evaluate the transmission success rate and use the formula Calculate the fault tolerance rate under redundant communication; Transmission success rate : By counting the number of messages successfully transmitted Percentage of total transmitted messages ratio to evaluate the transmission success rate of the communication system ; Fault tolerance rate :When redundant communication protocols are used, the number of redundant communication channels that actually work is calculated Subtract the number of channels that failed , and then divided by the total number of redundant channels to get the fault tolerance rate , to quantify the fault tolerance of communication systems.

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