Unmanned aerial vehicle ejection system for emergency response

Abstract

The invention discloses an unmanned aerial vehicle ejection system for emergency response, and relates to the technical field of unmanned aerial vehicle ejection control, and the system comprises a vibration baseline collection module which is used for installing an accelerometer array and a vibration sensor on an ejection tackle and an unmanned aerial vehicle body, collecting static and no-load tackle vibration baseline data, and building a vibration spectrum model; the signal quality monitoring module is used for collecting a link quality index and calculating a vibration-link influence signal score in combination with the vibration spectrum model; the dynamic priority queue generation module is used for calculating priority scores of all instruction tasks and generating priority queues according to a descending order of the priority scores; the hierarchical transmission module is used for performing hierarchical scheduling on the instruction tasks according to the priority queues; and the increment triggering module is used for adjusting the priority of the instruction task according to the real-time data of the sensor. The reliability, the real-time performance and the intelligent level of the unmanned aerial vehicle ejection emergency response system under the complex vibration and communication interference environment are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) ejection control, and in particular to a UAV ejection system for emergency response. Background Art

[0002] With the widespread adoption of unmanned aerial vehicles (UAVs) in emergency response applications such as firefighting and rescue, earthquake search and rescue, and chemical leak monitoring, rapid deployment and reliable communications have become key technical bottlenecks. Currently, mainstream UAV launch methods rely on vertical takeoff and landing (VTOL) or fixed runways, while catapult launch has attracted significant attention due to its runway-free and rapid deployment. However, the catapult launch process involves the coupling effect of severe mechanical vibration and communication link jitter, which poses a serious threat to the timely transmission of critical data streams such as flight control commands and emergency alerts.

[0003] The high acceleration shock and wobbling at the moment of ejection can cause a broadband vibration spectrum in the drone body and sled, disrupting the directional performance of the communication antenna and exacerbating the Doppler spread of the channel. This can seriously inaccurate traditional link quality assessments based on static or slowly changing environment assumptions. Furthermore, the minute attitude jitter caused by mechanical flutter can significantly reduce the coherence time of the wireless channel, making it impossible for the receiver to maintain stable carrier phase tracking, leading to a sharp increase in the bit error rate. Existing communication quality models often ignore this dynamic impact and are unable to promptly reflect changes in link reliability.

[0004] Existing solutions usually separate and optimize mechanical design and communication systems, lack compensation models that can conduct cross-domain coupling analysis of vibration characteristics and link quality, making it difficult to achieve real-time compensation for vibration-communication interaction effects at the system level, resulting in insufficient reliability of emergency response.

[0005] To this end, the present invention provides a UAV ejection system for emergency response. Summary of the Invention

[0006] The object of the present invention is to provide a drone ejection system for emergency response to solve the existing problems raised in the above background technology.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a drone ejection system for emergency response, comprising:

[0008] The vibration baseline acquisition module is used to install accelerometer arrays and vibration sensors on the catapult sled and UAV body to collect static and unloaded sled vibration baseline data and establish a vibration spectrum model;

[0009] A signal quality monitoring module, configured to collect link quality indicators and calculate a vibration-link impact signal score in combination with the vibration spectrum model;

[0010] Dynamic priority queue generation module, used to calculate the priority scores of all instruction tasks and generate a priority queue in descending order of priority scores;

[0011] Hierarchical transmission module, used to schedule instruction tasks hierarchically according to priority queues;

[0012] The incremental trigger module adjusts the instruction task priority according to the real-time sensor data.

[0013] A further improvement of the present invention is that the vibration baseline acquisition module specifically includes, through sensors, sequentially collecting platform benchmark vibration data under static working conditions and vibration time domain signals during the movement of the ejection sled in an unloaded state; based on the collected data, a spectrum analysis algorithm is used to establish a vibration spectrum model; the real-time collected vibration signal is compared and analyzed with the pre-established vibration spectrum model in the time-frequency domain, and the vibration coefficient Vic is obtained by the ratio of the mean of the square of the acceleration sampling value to the reference root mean square value in the unloaded state; at the same time, a dual redundant communication architecture of the millimeter wave main communication link and the LTE backup link is established, the antenna directional parameters are initialized, and the RTK / GNSS combined positioning system is used to complete the absolute positioning and fixation of the platform and the UAV, and synchronize the timestamps of the flight control system and the ground station.

[0014] A further improvement of the present invention is that the signal quality monitoring module includes a link quality indicator calculation unit and a vibration-link coupling impact assessment unit; the link quality indicator calculation unit includes a communication module deployed on the catapult platform and the drone, which collects link quality parameters in real time at a fixed period, including the received signal strength indicator RSSI, the signal-to-noise ratio SNR, the packet loss rate L and the round-trip delay D; based on the link parameters collected in real time, the quality parameters are normalized and then weighted summed to calculate the signal quality score SQ.

[0015] The present invention is further improved in that the vibration-link coupling impact assessment unit includes a channel coherence time correction model based on the vibration coefficient for the time-varying impact of drone flutter on the wireless channel. where k v represents the vibration coupling coefficient, f D Indicates Doppler frequency shift; incorporates vibration impact into the signal quality evaluation system and establishes a revised vibration-link impact signal score Where T C,ref represents the standard coherence time in reference static state.

[0016] A further improvement of the present invention is that the dynamic priority queue generation module includes a task importance calculation unit and a priority scoring unit;

[0017] The task importance calculation unit includes the definition of typical emergency event types, including {fire investigation E1, personnel search and rescue E2, hazardous chemical leakage monitoring E3, earthquake disaster area assessment E4}. Each emergency event type is represented by a set of semantic feature vectors including hazard level, time sensitivity, and regional complexity; a judgment matrix is constructed for event features, and the importance weight W of each task category relative to the event type is automatically calculated based on the information entropy of the event features. ij , represents the jth instruction task category of the i-th event type, and then the final judgment matrix W is obtained; for the j-th instruction task category, the "event classifier" is constructed through the preliminary sensor data to determine the event type Ek, and the weight of each category at the time of output by the XGBoost model is the event importance score of the j-th instruction task category under the event type Ek.

[0018] A further improvement of the present invention is that the priority scoring unit includes combining the vibration-link impact signal score OSQ and the vibration coefficient Vic to calculate the priority score: Where α1 represents the current event importance score weight, α2 represents the vibration-link impact signal score weight, and α3 represents the vibration coefficient weight.

[0019] A further improvement of the present invention is that the layered transmission module dynamically allocates link resources to high-priority signals based on the real-time task load, specifically including dividing the task instructions ranked before m1 in the priority queue into a high-priority layer, dividing the task instructions ranked from m1+1 to m2 into a medium-priority layer, and dividing the task instructions ranked from m2+1 to mn into a low-priority layer; enabling a short transmission cycle for the high-priority layer and reserving dedicated bandwidth; enabling a normal transmission cycle for the medium-priority layer, and enabling suspended or reduced-frequency transmission in the ejection phase for the low-priority layer.

[0020] A further improvement of the present invention is that after each round of data transmission, the incremental trigger module calculates the signal quality score SQ' by the ground station based on the actual backhaul link, normalizes the sensor data change rate, and performs weighted summation to obtain a backhaul evaluation value ΔX, sets a backhaul quality threshold, and when the backhaul evaluation value is greater than the backhaul quality threshold, the relevant data stream is immediately upgraded by one level of priority; and is equipped with a vibration trigger strategy, setting a vibration coefficient threshold TVic, when the vibration coefficient is greater than the vibration coefficient threshold, the priority score of all data in the high priority layer and the medium priority layer is Q'=Q+λ(Vic-TVic), where λ represents the adjustment coefficient.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] 1. The present invention first uses an accelerometer array in a vibration baseline acquisition module to collect static and unloaded pulley vibration time domain signals and a spectrum analysis algorithm to establish a vibration spectrum model to provide an accurate vibration coefficient Vic for subsequent compensation to avoid communication and flight control failures caused by vibration;

[0023] 2. In the dynamic priority queue generation module, task importance weights are generated based on event types. End-to-end priority assessment integrates event-driven and physical state integration to ensure that critical tasks are transmitted first.

[0024] 3. Through the end-to-end coupling compensation mechanism, the three dimensions of vibration, link, and event are integrated and scored, combined with dynamic layering and incremental triggering closed loop, to achieve cross-domain compensation machinery-communication-task integrated scheduling, ensuring reliable coordinated control of the entire ejection process. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a framework diagram of a UAV ejection system for emergency response according to the present invention. DETAILED DESCRIPTION

[0026] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0027] The term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can mean: A exists alone, A and B exist simultaneously, or B exists alone. Additionally, the character " / " generally indicates an "or" relationship between the related objects.

[0028] Example 1

[0029] Figure 1 The framework diagram of a drone ejection system for emergency response disclosed in this embodiment is shown, including:

[0030] The vibration baseline acquisition module is used to install accelerometer arrays and vibration sensors on the catapult sled and UAV body to collect static and unloaded sled vibration baseline data and establish a vibration spectrum model;

[0031] The vibration baseline acquisition module specifically includes, through sensors, sequentially collecting platform benchmark vibration data under static working conditions and vibration time domain signals during the movement of the ejection sled in an unloaded state; based on the collected data, a spectrum analysis algorithm is used to establish a vibration spectrum model; the real-time collected vibration signal is compared with the pre-established vibration spectrum model in the time-frequency domain, and the vibration coefficient Vic is obtained by the ratio of the mean of the square of the acceleration sampling value to the reference root mean square value in the unloaded state; at the same time, a dual redundant communication architecture of the millimeter wave main communication link and the LTE backup link is established, the antenna directional parameters are initialized, and the transmission power is dynamically adjusted according to the environmental electromagnetic parameters to ensure the reliability and anti-interference capability of the communication link; the RTK / GNSS combined positioning system is used to complete the absolute positioning and fixation of the platform and the UAV, and synchronize the timestamps of the flight control system and the ground station; the time synchronization accuracy is guaranteed to be better than 1 microsecond through precise timing technology, providing a high-precision time reference for multi-system collaborative control.

[0032] A signal quality monitoring module, configured to collect link quality indicators and calculate a vibration-link impact signal score in combination with the vibration spectrum model;

[0033] The signal quality monitoring module includes a link quality indicator calculation unit and a vibration-link coupling impact assessment unit. The link quality indicator calculation unit collects link quality parameters in real time at a fixed period through communication modules deployed on the catapult platform and the drone, including received signal strength indicator RSSI, signal-to-noise ratio SNR, packet loss rate L, and round-trip delay D. Based on the real-time collected link parameters, the quality parameters are normalized and then weighted and summed to calculate the signal quality score SQ.

[0034] The vibration-link coupling impact assessment unit includes a channel coherence time correction model based on the vibration coefficient for the time-varying impact of drone vibration on the wireless channel. where k v represents the vibration coupling coefficient, f D Indicates Doppler shift, reflecting the frequency offset caused by platform motion; incorporates vibration impact into the signal quality evaluation system and establishes a revised vibration-link impact signal score Where T C,ref represents the standard coherence time in reference static state.

[0035] This compensation mechanism realizes cross-domain coupling analysis of vibration state and communication quality, providing double protection for the reliable transmission of control instructions during the ejection process.

[0036] The present invention realizes real-time quantitative evaluation of the communication link status through high-frequency link quality indicator collection and adaptive scoring model; the innovatively constructed vibration-link coupling influence model dynamically associates mechanical vibration characteristics with wireless channel parameters, effectively compensating for the interference of vibration on communication quality in motion scenarios, and significantly improving the coordinated control accuracy and anti-interference capability of the ejection system under complex working conditions.

[0037] Dynamic priority queue generation module, used to calculate the priority scores of all instruction tasks and generate a priority queue in descending order of priority scores;

[0038] The dynamic priority queue generation module includes a task importance calculation unit and a priority scoring unit;

[0039] The task importance calculation unit includes the definition of typical emergency event types, including {fire investigation E1, personnel search and rescue E2, hazardous chemical leakage monitoring E3, earthquake disaster area assessment E4}. Each emergency event type is represented by a set of semantic feature vectors including hazard level, time sensitivity, and regional complexity; a judgment matrix is constructed for event features, and the importance weight W of each task category relative to the event type is automatically calculated based on the information entropy of the event features. ij , represents the jth instruction task category of the i-th event type, and then the final judgment matrix W is obtained; for the j-th instruction task category, the "event classifier" is constructed through the preliminary sensor data to determine the event type Ek, and the weight of each category at the time of output by the XGBoost model is the event importance score of the j-th instruction task category under the event type Ek.

[0040] The specific process of model training includes: collecting scenario emergency task logs, including event type labels, actual packet loss feedback for each data stream, and manual priority assessment; using AHP to initially generate a training set of "event → importance weight" pairs; and training a regression model to predict event importance scores.

[0041] The priority scoring unit includes combining the vibration-link impact signal score OSQ and the vibration coefficient Vic to calculate the priority score: Where α1 represents the current event importance score weight, α2 represents the vibration-link impact signal score weight, and α3 represents the vibration coefficient weight.

[0042] Hierarchical transmission module, used to schedule instruction tasks hierarchically according to priority queues;

[0043] The layered transmission module dynamically allocates link resources to high-priority signals based on the real-time task load. Specifically, the task instructions ranked first m1 in the priority queue are classified into a high-priority layer, the task instructions ranked m1+1 to m2 are classified into a medium-priority layer, and the task instructions ranked m2+1 to mn are classified into a low-priority layer. For the high-priority layer, a short transmission cycle (10ms) is enabled, and dedicated bandwidth is reserved. For the medium-priority layer, a normal transmission cycle (50-100ms) is enabled, and for the low-priority layer, a suspended or reduced-frequency transmission during the ejection phase is enabled.

[0044] Instructions include but are not limited to: flight attitude control instructions, such as angular velocity control and emergency braking; emergency fault alarms, such as battery failure and communication interruption warnings; flight control status data, such as GPS and IMU data; mission data, such as images and sensor feedback; auxiliary information, such as environmental monitoring and general logs.

[0045] The incremental trigger module adjusts the instruction task priority according to the real-time sensor data.

[0046] After each round of data transmission, the incremental trigger module calculates the signal quality score SQ' based on the actual backhaul link by the ground station, normalizes the sensor data change rate, and performs weighted summation to obtain a backhaul evaluation value ΔX. A backhaul quality threshold is set. When the backhaul evaluation value is greater than the backhaul quality threshold, the relevant data stream is immediately upgraded by one level of priority. A vibration trigger strategy is also provided to set a vibration coefficient threshold TVic. When the vibration coefficient is greater than the vibration coefficient threshold, the priority score of all data in the high priority layer and the medium priority layer is Q'=Q+λ(Vic-TVic), where λ represents the adjustment coefficient.

[0047] The thresholds, weights and other setting values may be set by default according to the present invention, or may be set by those skilled in the art.

[0048] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0051] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0052] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which are all protected by the present invention.

Claims

1. A drone ejection system for emergency response, characterized by: include: The vibration baseline acquisition module is used to install accelerometer arrays and vibration sensors on the catapult sled and UAV body to collect static and unloaded sled vibration baseline data and establish a vibration spectrum model; A signal quality monitoring module, configured to collect link quality indicators and calculate a vibration-link impact signal score in combination with the vibration spectrum model; Dynamic priority queue generation module, used to calculate the priority scores of all instruction tasks and generate a priority queue in descending order of priority scores; Hierarchical transmission module, used to schedule instruction tasks hierarchically according to priority queues; The incremental trigger module adjusts the instruction task priority according to the real-time sensor data.

2. The UAV ejection system for emergency response according to claim 1, characterized in that: The vibration baseline acquisition module specifically includes, through sensors, sequentially collecting platform benchmark vibration data under static working conditions and vibration time domain signals during the motion of the ejection sled in an unloaded state; based on the collected data, a spectrum analysis algorithm is used to establish a vibration spectrum model; the real-time collected vibration signal is compared and analyzed with the pre-established vibration spectrum model in the time and frequency domains, and the vibration coefficient Vic is obtained by the ratio of the mean of the square of the acceleration sampling value to the reference root mean square value in the unloaded state; at the same time, a dual redundant communication architecture of the millimeter wave main communication link and the LTE backup link is established, the antenna directional parameters are initialized, and the RTK / GNSS combined positioning system is used to complete the absolute positioning and fixation of the platform and the UAV, and synchronize the timestamps of the flight control system and the ground station.

3. The UAV ejection system for emergency response according to claim 1, characterized in that: The signal quality monitoring module includes a link quality indicator calculation unit and a vibration-link coupling impact assessment unit. The link quality indicator calculation unit collects link quality parameters in real time at a fixed period through communication modules deployed on the catapult platform and the drone, including received signal strength indicator RSSI, signal-to-noise ratio SNR, packet loss rate L, and round-trip delay D. Based on the real-time collected link parameters, the quality parameters are normalized and then weighted and summed to calculate the signal quality score SQ.

4. The UAV ejection system for emergency response according to claim 3, characterized in that: The vibration-link coupling impact assessment unit includes a channel coherence time correction model based on the vibration coefficient for the time-varying impact of drone vibration on the wireless channel. where k v represents the vibration coupling coefficient, f D Indicates Doppler frequency shift; incorporates vibration impact into the signal quality evaluation system and establishes a revised vibration-link impact signal score Where T C,ref represents the standard coherence time in reference static state.

5. The UAV ejection system for emergency response according to claim 1, characterized in that: The dynamic priority queue generation module includes a task importance calculation unit and a priority scoring unit; The task importance calculation unit includes the definition of typical emergency event types, including {fire investigation E1, personnel search and rescue E2, hazardous chemical leakage monitoring E3, earthquake disaster area assessment E4}. Each emergency event type is represented by a set of semantic feature vectors including hazard level, time sensitivity, and regional complexity; a judgment matrix is constructed for event features, and the importance weight W of each task category relative to the event type is automatically calculated based on the information entropy of the event features. ij , represents the jth instruction task category of the i-th event type, and then the final judgment matrix W is obtained; for the j-th instruction task category, the "event classifier" is constructed through the preliminary sensor data to determine the event type Ek. The weight of each category at the time of output by the XGBoost model is the event importance score of the j-th instruction task category under the event type Ek.

6. The UAV ejection system for emergency response according to claim 5, characterized in that: The priority scoring unit includes combining the vibration-link impact signal score OSQ and the vibration coefficient Vic to calculate the priority score: Where α1 represents the current event importance score weight, α2 represents the vibration-link impact signal score weight, and α3 represents the vibration coefficient weight.

7. The UAV ejection system for emergency response according to claim 1, characterized in that: The layered transmission module dynamically allocates link resources for high-priority signals based on the real-time task load, specifically by classifying the task instructions ranked before m1 in the priority queue into a high-priority layer, the task instructions ranked from m1+1 to m2 into a medium-priority layer, and the task instructions ranked from m2+1 to mn into a low-priority layer; enabling a short transmission cycle for the high-priority layer and reserving dedicated bandwidth; enabling a normal transmission cycle for the medium-priority layer, and enabling suspended or reduced-frequency transmission during the ejection phase for the low-priority layer.

8. The UAV ejection system for emergency response according to claim 1, characterized in that: After each round of data transmission, the incremental trigger module calculates the signal quality score SQ' based on the actual backhaul link by the ground station, normalizes the sensor data change rate, and performs weighted summation to obtain a backhaul evaluation value ΔX. A backhaul quality threshold is set. When the backhaul evaluation value is greater than the backhaul quality threshold, the relevant data stream is immediately upgraded by one level of priority. A vibration trigger strategy is also provided to set a vibration coefficient threshold TVic. When the vibration coefficient is greater than the vibration coefficient threshold, the priority score of all data in the high priority layer and the medium priority layer is Q'=Q+λ(Vic-TVic), where λ represents the adjustment coefficient.

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