Traffic safety management method and system combined with drone operation risk control

Abstract

The present invention provides a traffic safety management method and system combined with unmanned aerial vehicle (UAV) operation risk control, which relates to the technical field of traffic management. An airspace division map is determined based on attribute characteristics, and fence grids and free grids are identified. According to the traffic characteristics of unmanned aerial vehicles, motion uncertainty modeling is performed, soft constraints are determined, and dynamic flight management of routes under traffic safety is performed in combination with a safety database. The method and system are used to solve the technical problems that the existing airspace division system in the prior art cannot adapt to the high flexibility and diversity of unmanned aerial vehicles, and cannot respond to the dynamic changes of unmanned aerial vehicles in real time, resulting in insufficient risk control and dynamic adjustment. By optimizing the airspace division and performing targeted dynamic decision-making and management under gridding, the safety of unmanned aerial vehicle flight and the degree of mission completion can be effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of traffic management, and in particular to a traffic safety management method and system combined with unmanned aerial vehicle operation risk control. Background Art

[0002] With the rapid development of drone technology and the continuous expansion of its application scope, drones are becoming increasingly popular in transportation, logistics, agriculture, monitoring and other fields. Drone flights usually need to be carried out within a certain airspace. With the increase in the number of drones and the diversification of their application scenarios, airspace management faces huge challenges. The existing airspace management system cannot effectively deal with the risks brought by drone flights.

[0003] At present, the existing drone traffic safety management system mainly relies on static airspace division and rough risk assessment, and lacks an effective dynamic adjustment mechanism. The flight behavior of drones is highly uncertain, and there may be trajectory deviations, flight speed changes, etc. How to effectively avoid collisions and conflicts between drones in a dense airspace environment has become an urgent problem to be solved.

[0004] In summary, the existing airspace division system cannot adapt to the high flexibility and diversity of drones, and cannot respond to the dynamic changes of drones in real time, resulting in deficiencies in risk control and dynamic adjustment. How to conduct navigation safety control of drones in complex airspace is a key technical issue that urgently needs to be solved. Summary of the invention

[0005] The present application provides a traffic safety management method and system combined with drone operation risk control, which is used to solve the technical problems existing in the prior art that the existing airspace division system cannot adapt to the high flexibility and diversity of drones, and cannot respond to the dynamic changes of drones in real time, resulting in insufficient risk control and dynamic adjustment.

[0006] In view of the above problems, the present application provides a traffic safety management method and system combined with drone operation risk control.

[0007] In a first aspect, the present application provides a traffic safety management method combined with drone operation risk control, the method comprising:

[0008] The controlled airspace is divided into a primary level based on attribute characteristics and a secondary level based on a preset grid size to determine an airspace division map, wherein each divided airspace matches at least one edge control site, and the attribute characteristics include natural attributes, social attributes, and technical attributes;

[0009] According to the characteristics of UAV traffic, the motion uncertainty modeling is carried out to determine the soft constraints, among which the motion uncertainty is the superimposed factor of traffic safety management;

[0010] Traversing the airspace division map, identifying fence grids and free grids;

[0011] For the fence grid, combined with the security database, dynamic flight management of routes under entry and exit certification and traffic safety is carried out;

[0012] For the free grid, combined with the safety database, dynamic flight management of the route under traffic safety is carried out, wherein the flight management strategy is determined by the soft constraints of the dynamic programming results superimposed and matched, and the dynamic programming results are determined by fitting trajectory projection and lookup table methods, and the safety database is constructed based on the equivalent safety level under the differences in UAV specifications.

[0013] In a second aspect, the present application provides a traffic safety management system combined with drone operation risk control, the system comprising:

[0014] An airspace division unit, the airspace division unit is used to perform a primary division based on attribute characteristics and a secondary division based on a preset grid size for the controlled airspace, and determine an airspace division map, wherein each divided airspace matches at least one edge control site, and the attribute characteristics include natural attributes, social attributes, and technical attributes;

[0015] A condition determination unit, the condition determination unit is used to perform motion uncertainty modeling and determine soft constraint conditions according to the traffic characteristics of the UAV, wherein the motion uncertainty is an overlay element of traffic safety management;

[0016] A grid identification unit, the grid identification unit is used to traverse the airspace division diagram and identify fence grids and free grids;

[0017] A first flight management unit, the first flight management unit is used to perform entry and exit certification and route dynamic flight management under traffic safety for the fence grid in combination with a security database;

[0018] The second flight management unit is used to perform dynamic flight management of routes under traffic safety for the free grid in combination with a safety database, wherein the flight management strategy is determined by superimposing soft constraints matched by dynamic programming results, and the dynamic programming results are determined by fitting trajectory projection and lookup table methods, and the safety database is constructed based on equivalent safety levels under differences in UAV specifications.

[0019] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0020] The traffic safety management method combined with the risk control of UAV operation provided in the embodiment of the present application performs a primary division based on attribute characteristics for the controlled airspace, performs a secondary division based on a preset grid size, determines an airspace division map, performs motion uncertainty modeling for the UAV traffic characteristics, determines soft constraints, traverses the airspace division map, and identifies fence grids and free grids; for the fence grids, in combination with a security database, performs entry and exit approval and dynamic flight management of routes under traffic safety; for the free grids, in combination with a security database, performs dynamic flight management of routes under traffic safety, which is used to solve the technical problem that the existing airspace division system in the prior art cannot adapt to the high flexibility and diversity of UAVs, and cannot respond to the dynamic changes of UAVs in real time, resulting in insufficient risk control and dynamic adjustment. By optimizing the airspace division and performing targeted dynamic decision-making and management under gridding, the safety of UAV flight and the degree of mission completion can be effectively guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flow chart of a traffic safety management method combined with risk control of drone operations is provided for this application;

[0022] Figure 2 A flow chart of dynamic flight management of routes under traffic safety in a traffic safety management method combined with risk control of UAV operation is provided for this application;

[0023] Figure 3 A schematic diagram of the structure of a traffic safety management system combined with risk control of drone operations is provided for this application.

[0024] Explanation of the reference numerals: airspace division unit 11, condition determination unit 12, grid identification unit 13, first flight management unit 14, second flight management unit 15. DETAILED DESCRIPTION

[0025] The present application provides a traffic safety management method and system combined with risk control of drone operations, determines an airspace division map based on attribute characteristics, and identifies fence grids and free grids; performs motion uncertainty modeling based on drone traffic characteristics, determines soft constraints, and combines with a safety database to perform dynamic flight management of routes under traffic safety. This is used to solve the technical problems that the existing airspace division system in the prior art cannot adapt to the high flexibility and diversity of drones, and cannot respond to the dynamic changes of drones in real time, resulting in insufficient risk control and dynamic adjustment.

[0026] Embodiment 1: Figure 1 As shown, the present application provides a traffic safety management method combined with drone operation risk control, the method comprising:

[0027] S1: Performing a first-level division based on attribute characteristics for the controlled airspace, and performing a second-level division based on a preset grid size, and determining an airspace division map, wherein each divided airspace matches at least one edge control site, and the attribute characteristics include natural attributes, social attributes, and technical attributes;

[0028] In the traffic safety management method of drone operation risk control, firstly, for the controlled airspace, a first-level division method based on attribute characteristics is adopted. The controlled airspace is the flight space area of ​​the drone, and the attribute characteristics refer to the factors that affect the airspace control rules and the flight safety of the drone, including natural attributes, social attributes and technical attributes.

[0029] Specifically, the natural attributes refer to the geographical location, meteorological conditions, and terrain features of the airspace. For example, if an airspace is located in a mountainous area or an area with a changeable climate, its natural attributes will have a greater impact on flight safety, so special control measures are required. The social attributes involve social activities and population density around the airspace. For example, the airspace above a city may require stricter control, taking into account the flight risks in densely populated areas. The technical attributes refer to characteristics related to flight equipment and technical facilities, such as the coverage of communication equipment and the accuracy of navigation systems.

[0030] Based on the analysis of the attribute characteristics, the controlled airspace is divided into different areas according to the complexity and risk level of the airspace. For example, the airspace can be divided into "high-risk areas", "low-risk areas" and "conventional control areas", where high-risk areas may include drone no-fly zones or airspace near airports, and low-risk areas may be areas where drones can fly freely.

[0031] Furthermore, on the basis of the first-level division, a second-level division is performed based on the preset grid size. The preset grid size is set to manage the airspace more meticulously, and at the same time, as a basis for the management and control of navigation safety incidents, each first-level divided area is subdivided according to the preset grid scale. For example, a high-risk area may be divided into multiple smaller grids, each grid representing a specific management unit. Through such a division method, more precise management and control can be achieved, so that different flight needs and risks can be effectively monitored in a smaller area.

[0032] The airspace division map is determined by fusing the first-level division result and the second-level division result and marking each area based on the attribute characteristics.

[0033] Furthermore, each first-level divided airspace needs to be matched with at least one edge control site. An edge control site refers to a control site located at the boundary of an airspace, which is responsible for monitoring the flight conditions in the airspace and providing corresponding instructions. For example, an airspace division may be connected to a drone control center located at the edge of the area, which is responsible for real-time monitoring of flight data and providing intervention measures when the flight is abnormal. By matching edge control sites with divided airspaces, effective control of each airspace can be achieved to ensure that flight activities within the airspace comply with safety regulations.

[0034] In summary, by combining the multi-dimensional attribute characteristics of airspace, a comprehensive airspace control framework has been established. The control strategy can be flexibly adjusted according to the actual situation of the airspace, and precise grid division can be used to ensure efficient real-time management of drones during flight.

[0035] S2: Based on the characteristics of UAV traffic, motion uncertainty modeling is carried out to determine soft constraints, where motion uncertainty is an overlay factor of traffic safety management;

[0036] In the embodiment of the present application, in the drone traffic safety management method, it is necessary to model the motion uncertainty according to the traffic characteristics of the drone, so as to fully consider the dynamic changes and potential risks of the drone during flight. Since the flight behavior of the drone in the airspace is not completely predictable, there are many uncertain factors. For example, the speed, direction, flight altitude and other parameters of the drone may be affected by various factors such as meteorological changes, equipment failures, and manual operation errors, resulting in deviation or instability of the flight trajectory. Therefore, motion uncertainty refers to the changes in flight status caused by these factors that cannot be accurately predicted, which is one of the key factors affecting flight safety.

[0037] The following is a feasible modeling method. For example, a probability matrix model can be used to reflect the probability of flight state migration of the drone under different conditions to measure the fluctuation of the flight trajectory. For example, in strong winds, the flight trajectory of the drone may fluctuate greatly. At this time, the uncertainty of the flight trajectory can be predicted by establishing a probability distribution related to wind speed and wind direction. In addition, position errors in flight and the accuracy limitations of the navigation system may also cause uncertainty in movement.

[0038] By retrieving the preset flight records of the controlled airspace, the probability matrix mining under the scenario is carried out, and the preset flight records are used as samples for supervised training. The input of the uncertainty model is the flight status scenario, and the output is the soft constraint condition.

[0039] The soft constraints refer to the requirements for flight path, flight altitude, flight speed, etc. during the flight of the drone, which allow a certain degree of flexibility and adjustment under the premise of ensuring traffic safety. Soft constraints can play a balancing role in dynamic flight management, which can effectively avoid flight risks and give drones a certain degree of operational freedom in certain circumstances.

[0040] The determination of the soft constraints is based at least on the flight environment, flight path, and UAV performance factor limitations.

[0041] In summary, based on motion uncertainty as an overlying element of traffic safety management, the determination of auxiliary soft constraints ensures that the safety and flexibility of flight can be maintained when dealing with uncertainty. It can not only effectively reduce the potential risks in UAV flight, but also provide flexible adjustment space in actual operations, thereby optimizing flight paths and reducing the occurrence of conflict incidents.

[0042] S3: traverse the airspace division map to identify fence grids and free grids;

[0043] In an embodiment of the present application, the fence grid refers to those restricted areas where strict control of drone flight is required, that is, spaces that require flight approval. For example, airspace close to important infrastructure, such as airports, nuclear power plants, etc. Exemplarily, the fence grid is based on factors such as the risk level of the airspace, flight safety requirements, and social attributes. For example, certain high-risk areas, based on their social attributes, such as densely populated areas; natural attributes, such as poor weather conditions; or technical attributes, such as insufficient communication coverage, will be marked as fence grids, and strict flight restrictions will be set to prevent drones from flying illegally in this area. Drone flight activities within the fence grid need to be managed in accordance with special safety rules and procedures.

[0044] The free grid refers to an area where drones are allowed to fly relatively freely. These areas are usually located in places with low risk of drone operation, such as airspace away from dense traffic, areas with stable environment, or flight corridors dedicated to drones. Flight activities within the free grid usually do not require overly strict restrictions, but certain risk assessment and management are still required, that is, drones can make more flexible route adjustments and operations according to needs, but still need to follow some basic flight safety standards.

[0045] The airspace division map is traversed, and each grid is classified according to the attribute characteristics of the airspace, such as natural conditions, social activity density, technical support, etc., to ensure that different types of airspace are properly handled in actual management, thereby improving flight safety and efficiency, and providing accurate data support for subsequent route planning, flight path adjustment, risk assessment and dynamic management.

[0046] S4: For the fence grid, combined with the safety database, dynamic flight management of routes under entry and exit certification and traffic safety is performed;

[0047] Specifically, the management standards of the fenced grid and the free grid are different. The fenced grid requires entry and exit approval within the area, and its management rules are relatively fixed, so the management difficulty is relatively small. In the free grid, the types of drones and flight conditions are more diverse, and the flight freedom and control flexibility are higher, so the corresponding management risks and management difficulties are greater.

[0048] Specifically, for the fence grid, by real-time monitoring of the drone's entry and exit behavior and flight status, when the drone approaches the boundary of the fence grid, it is determined whether the drone has obtained approval to enter the area. For example, by verifying the drone's identity information, checking whether its flight plan complies with the flight regulations of the current airspace, and matching the flight parameters with safety standards, fence entry and exit certification management is performed.

[0049] For drones in the fenced grid space, dynamic flight management of routes is carried out under traffic safety, wherein the specific management method is consistent with the operation method of the free grid area, and the control complexity is relatively low.

[0050] S5: For the free grid, combined with the safety database, dynamic flight management of the route under traffic safety is carried out, wherein the flight management strategy is determined by the soft constraints superimposed on the dynamic programming results and matched, and the dynamic programming results are determined by fitting trajectory projection and lookup table methods, and the safety database is constructed based on the equivalent safety level under the differences in UAV specifications.

[0051] The free grid generally refers to those relatively open airspaces that allow UAVs to fly flexibly. However, although there are fewer flight restrictions in these areas, resulting in a higher degree of freedom in flight, the incidence of flight risk events is higher.

[0052] Specifically, the safety database is constructed based on the equivalent safety level under the difference in drone specifications. According to the specifications and characteristics of the drone, the safety database is traversed to match the corresponding safety control standards. On this basis, dynamic flight management of the route is carried out.

[0053] Among them, the soft constraints are elastic restrictions applied in the flight management process. The dynamic planning results are determined through dynamic decision-making, and the corresponding soft constraints are superimposed to improve the adaptability and freedom of planning. At the same time, the safety database provides tailored safety standards for each type of drone, which can ensure flight safety while flexibly adjusting the flight path.

[0054] Among them, the entry and exit approval management is performed, and step S4 also includes:

[0055] Determine the drone countermeasure rules, wherein the countermeasure methods include signal interference and physical interception, and the countermeasure strategy is constrained by the space situation; conduct entry and exit review on the fence grid, and if an unapproved drone is detected, perform countermeasure management on the unapproved drone based on the drone countermeasure rules, and generate a management data stream.

[0056] In the embodiment of the present application, the determination of the drone countermeasure rules is a measure to deal with illegal flights, illegal flights or potential threat drones. The rules are designed based on the airspace safety requirements, combined with the flight characteristics of the drone and potential risk factors. Specifically, the countermeasure methods include signal interference and physical interception, depending on the specific threat level of the drone, the airspace environment and the executability of the countermeasure strategy.

[0057] Signal jamming refers to disrupting the communication frequency of a drone to cut off the connection between the drone and its control station or navigation system. In actual operation, signal jamming will interfere with surrounding equipment, legal drones, aircraft, etc. to a certain extent. Physical interception refers to direct intervention in a drone through physical means. Physical interception is suitable for high-risk emergency situations and requires appropriate countermeasures to be configured for real-time flight scenarios.

[0058] Specifically, the countermeasure strategy must be subject to the constraints of the space situation, that is, in the countermeasure operation, the surrounding airspace environment and the safety of other aircraft or facilities in the airspace must be taken into account. Therefore, the formulation of the countermeasure strategy needs to be based on a comprehensive assessment of other aircraft activities in the airspace, the types of drones, the complexity of the airspace, and other factors to ensure the accuracy and safety of the measures.

[0059] Ensure that all drones entering restricted or controlled airspace have been approved and comply with relevant flight safety regulations. As a controlled area, the fence grid usually contains no-fly zones or limited flight zones. Drones entering these areas must be legally authorized. Dynamic review is performed by real-time monitoring of the drone's flight trajectory, flight identity information, etc. When a drone is detected entering the fence grid, check whether it has been approved. Otherwise, the countermeasure management procedure is immediately initiated. At the same time, a detailed management data stream is generated, including information such as the time, method, and result of the countermeasure operation, and stored in the corresponding edge control site.

[0060] In the embodiment of the present application, by introducing grid division and realizing concurrent management under the grid unit, management efficiency and accuracy can be effectively improved to ensure the safety and compliance of drone flight. Considering countermeasures through space situation constraints can prevent unnecessary risk spread while ensuring airspace safety.

[0061] Wherein, obtaining the security database, step S5 further includes:

[0062] Interact with drone specifications and determine baseline safety indicators; for the drone specifications, based on the baseline safety indicators, perform safety level equivalence under specification differentiation and determine a safety database; assist the safety database to perform drone traffic safety management.

[0063] Specifically, through communication and data exchange between the system and the drone, the basic performance, operating limitations, technical parameters and other information of the drone can be obtained. For example, the maximum take-off weight, flight speed, maximum flight altitude, operating radius and other technical indicators of the drone are all specification information.

[0064] The baseline safety index refers to the standardized safety requirements for a certain type of drone or a certain model of drone. For example, the requirements for the drone's flight stability, anti-interference capability, navigation accuracy, communication reliability, etc. That is, the baseline safety index is a flight safety standard for a drone of a reference specification.

[0065] The specification differentiation refers to the fact that due to the differences in design and performance parameters of different types of drones, different drones in the same airspace have different flight safety requirements. Therefore, the safety level equivalence process needs to adjust its safety management requirements according to the specification differences of the drones. Specifically, by identifying the specification differences between the drone specifications and the above-mentioned reference specifications, the adjustment standard of the safety index is determined. For example, based on the unit difference of the specifications, the amplitude modulation of the corresponding index can be determined, and a unit adjustment relationship can be established. Based on the unit value under the specification difference, the adjustment standard is determined.

[0066] For example, some high-performance drones may have strong anti-interference capabilities and high flight accuracy, and their safety level when performing tasks can be relatively relaxed; while low-performance drones may be more sensitive to changes in the external environment and have lower flight control accuracy, so more stringent safety measures need to be set. By implementing the equivalent safety level of differentiated specifications, the system can accurately adjust its flight safety requirements according to the characteristics of each drone, ensure that basic safety standards can be met in various situations, and achieve equivalent safety levels under differentiated specifications, so as to build the safety database, that is, a database that centrally stores drone specifications, safety standards and their equivalent safety levels, which can provide real-time data support for airspace management and provide a basis for the safety management of drone flights.

[0067] Furthermore, the auxiliary safety database is used to manage drone traffic safety, so as to achieve differentiated management of cluster drones in the airspace and improve the flexibility of management and the adaptability of drones.

[0068] Further, such as Figure 2 As shown, dynamic flight management of routes under traffic safety is performed, and step S5 also includes:

[0069] As the first UAV enters the first divided airspace, the first edge control station takes over the first UAV, including the flow of flight information between stations; as the flight data is transmitted back, the first edge control station updates the global distribution data under the first divided airspace in real time, and conducts traffic risk assessment and locates the risk event group, wherein the risk event group includes route deviation UAVs and affected UAVs; and dynamically adjusts the route of the risk event group.

[0070] In an embodiment of the present application, during the drone traffic safety management process, the first drone is any drone flying in the airspace, and the first divided airspace is the first-level divided area that the drone is to enter. As the first drone enters the first divided airspace, the edge control station corresponding to the first divided airspace, that is, the first edge control station will take over the flight management of the drone.

[0071] After the edge control station takes over the flight management of the drone, it will first circulate the drone's flight information. Flight information includes the drone's real-time flight data and its interaction data with the surrounding environment. Inter-station flow refers to the way that edge control stations share information and synchronize data to ensure that the drone's flight information can be transmitted between various control stations.

[0072] When the flight data is transmitted back to the first edge control station, the station will analyze the received data and promptly update the global distribution data under the first divided airspace, that is, the flight distribution of drones. Real-time data updates are carried out to comprehensively monitor the flight conditions of the entire airspace, so as to promptly discover potential risk factors.

[0073] As the data is updated, the flight data is evaluated for traffic risks in real time, specifically detecting possible flight conflicts or risk events in the airspace, and judging the probability of the event based on historical data and current conditions. For example, if a drone's flight deviation causes a trajectory intersection with other drones, the potential risk of flight collision is judged. For example, the actual trajectory is predicted based on the preset trajectory and deviation direction.

[0074] The route-deviating UAVs refer to those UAVs that deviate from the scheduled flight path due to flight control errors, equipment failure or other reasons; the affected UAVs refer to UAVs affected by the route-deviating UAVs, in order to determine the predicted risk event crew. Furthermore, for the risk event crew, based on the navigation trajectory and navigation speed, combined with the intervention of edge control nodes, risk event avoidance decisions are made. Through real-time monitoring, dynamic evaluation and timely adjustment of UAVs, the safe flight of UAVs and the maintenance of airspace order in a complex airspace environment are ensured.

[0075] At the same time, when multiple drones exist in the same airspace at the same time, the dynamic adjustment of the route not only considers the flight path of a single drone, but also needs to coordinate the flight behaviors between different drones. Preferably, collaborative control decisions are implemented based on global flight data and risk assessment results to ensure that there is no conflict between the flights of drones.

[0076] Furthermore, the route of the risk event crew is dynamically adjusted, and step S5 further includes:

[0077] The global distribution data is traversed to determine the deviation of the flight trajectory; if there is a trajectory deviation drone and the traffic collision event meets the preset probability of occurrence, the flight management strategy is determined by fitting the trajectory projection and the lookup table, wherein the trajectory projection analysis is performed by avoiding the wind control area under the free grid, and the lookup table analysis is performed by maneuver parameter control.

[0078] In the embodiment of the present application, the global distribution data refers to the real-time information of the flight position, trajectory, speed, heading, flight altitude, etc. of all drones in the current airspace. Based on the global distribution data, the flight status of each drone is determined, and then whether there is a trajectory deviation phenomenon is identified.

[0079] For example, by making a reference check with the preset route, the trajectory deviation is determined, specifically, whether the deviation between the current position of the drone and the target route exceeds the safety range. If the deviation is found to exceed the allowable deviation range, it is determined as trajectory deviation. Among them, there are two conditions for trajectory deviation: deviation within the controllable range, that is, there is a certain deviation but it will not cause a traffic collision event; there is a deviation and the probability of causing a traffic collision event is high. The trajectory deviation target that will not cause a traffic collision event is ignored.

[0080] Specifically, by analyzing the flight status of the drone and the relative position of other drones in the airspace, for example, if the distance between the deviated drone and other aircraft is close and meets certain collision conditions, the preset probability of traffic collision events is used to determine whether there is a potential risk of flight collision.

[0081] For example, the probability of a collision event is analyzed through statistics of historical data and real-time flight data. That is, the probability of a collision between two aircraft is determined based on the relative movement of the drones (such as relative speed, flight altitude difference, heading difference, etc.). If the probability exceeds a preset safety threshold, the collision risk will be considered high and the system will initiate a corresponding flight management strategy to handle it.

[0082] The trajectory projection method is to predict the future flight position and possible conflict points of the UAV based on the current flight trajectory and information of other aircraft in the airspace. For example, the future trajectory is determined based on the current speed, heading and other information of the UAV.

[0083] The control strategy is mined for the maneuvering parameter control of the UAV in traffic collision events, i.e., the historical maneuvering control records in different yaw scenarios, and the lookup table is generated. The collision affected area is taken as the risk area, and the trajectory adjustment line sections are determined grid by grid. Through the balanced trajectory projection method and the lookup table method, the strategy is iterated and optimized until the optimal control strategy is determined to ensure the safe flight of the UAV.

[0084] Furthermore, by fitting the trajectory projection and the lookup table, the flight management strategy is determined, and step S5 further includes:

[0085] Based on the free grid, a risk control area of ​​the traffic collision event is located, wherein the risk control area is determined based on a predetermined route and flight parameters; and for the risk control area, a flight management strategy is determined by balancing trajectory control and speed control.

[0086] Among them, the method of balancing trajectory control and speed control includes: determining a group of control strategies, wherein the group of control strategies includes a first control strategy for independent adjustment of trajectory projection, and a second control strategy for independent adjustment of the lookup table; setting unit indentation amplitude modulation, indenting the first control strategy, compensating based on the lookup table, and adding it into the first iterative strategy; indenting the second control strategy, compensating based on the trajectory projection, and adding it into the first iterative strategy; using the unit indentation amplitude modulation to perform strategy iterative adjustment and fitness calibration, and then taking the flight management strategy.

[0087] First, based on the spatial location of the traffic collision event in the free grid, the risk control area of ​​the traffic collision event is used. Since the flight freedom in the free grid is high and the drones are uneven, the uncertainty of traffic control is high and the control is difficult.

[0088] The predetermined route is a flight route planned in advance by the UAV, and the flight parameters include factors such as flight altitude, speed, and heading. Specifically, for a target UAV with deviation and neighboring UAVs within a preset range, a trajectory intersection prediction is performed, wherein the trajectory intersection is a three-dimensional trajectory intersection in space, and the wind control area is located.

[0089] Furthermore, the trajectory projection method is used as the first control method, and the first control strategy is determined by independent adjustment of the first control method. Exemplarily, the point where the UAV trajectory approaches the wind control area is set as the initial control position, and the first free grid under the initial position is determined. The second free grid is determined with the boundary of the wind control area as a constraint; further, the second free grid is used as the initial position, and the path grid of the subsequent step length is selected to determine the third free grid, and the grids are promoted in sequence to determine the first control strategy, which is the control for the path.

[0090] The second control method is to use a lookup table. Based on the lookup table, a search is performed for a traffic collision event based on the scheduled route and the flight parameters to determine a second control strategy, which is a control for maneuvering parameters.

[0091] The first control strategy and the second control strategy are used as initialization strategies, and the unit indentation amplitude modulation is set, that is, the single adjustment amplitude set for the adjustment parameters of the strategy. After the first control strategy is adjusted based on the unit indentation amplitude modulation, the compensation information for the maneuvering parameters is adjusted at the same time; similarly, after the second control strategy is adjusted based on the unit indentation amplitude modulation, compensation is performed for the path adjustment at the same time, and the determined two strategies are used as an iterative strategy.

[0092] Similarly, based on the unit indentation amplitude modulation, multiple iterative adjustments are performed to calibrate the determined strategy set, and the optimal strategy is selected as the flight management strategy to ensure that the flight management strategy can take into account both flight efficiency and mission completion.

[0093] Wherein, the set of control strategies includes a first control strategy for independent adjustment of trajectory projection, and step S5 further includes:

[0094] Based on the risk control area, determine the initial control position, locate the neighborhood free grids in turn, and combine to determine the first control strategy, wherein the neighborhood free grid is determined based on the upper grid neighborhood and the risk control boundary neighborhood; traverse the soft constraints to match and determine the uncertain constraints of the traffic collision event; based on the uncertain constraints, compensate for the first control strategy.

[0095] Specifically, the initial control position is set according to the risk control area. The risk control area is a known potential risk area. The system determines a specific position by analyzing the current flight trajectory of the drone and the surrounding environment, and starts subsequent control operations from this position. Exemplarily, the initial control position is the point where the drone trajectory approaches the risk control area, or the critical point where a collision is predicted to occur.

[0096] The neighborhood free grid is a free grid adjacent to the corresponding grid of the initial control position under the boundary definition of the wind control area. The best free grid is selected, and the first trajectory step length is first determined. The grid determined by the first trajectory step length is further used as the initial control position, and an iterative analysis is performed under the grid step length to determine the first control strategy.

[0097] Further considering the various constraints that may be encountered during the flight of the drone, all soft constraints are traversed and matched with the uncertain constraints of traffic collision events. Uncertain constraints refer to the unpredictable factors faced by drones during flight, such as the flight paths of other drones, airflow changes, etc. These factors will increase the risk of flight collisions. Therefore, the system determines the risk range of each constraint condition by analyzing these uncertain factors, and further modifies the control strategy based on these ranges.

[0098] Exemplary compensation methods include adjusting flight speed, changing heading or flight altitude (i.e., the position of the selected neighborhood free grid), etc. Adjusting and compensating based on the risk range of soft constraints can better adapt to the actual needs in complex flight environments, ensuring that the drone can flexibly respond to uncertainties in flight based on real-time data and prediction results during flight, thereby ensuring flight safety and mission completion.

[0099] The traffic safety management method combined with drone operation risk control provided by this application has the following technical effects:

[0100] 1. Carry out primary and secondary divisions of the jurisdictional airspace based on attribute characteristics. Use the primary division results for targeted channel management, and use the secondary division results for more fine-grained management and flight control, forming a detailed airspace division map, and matching them with edge control sites respectively to ensure accurate control of the airspace.

[0101] 2. Model the motion uncertainty based on the traffic characteristics of the UAV, identify and establish soft constraints, ensure the ability to flexibly respond to uncertainties, and ensure flight safety and mission completion.

[0102] 3. By identifying fence grids and free grids, targeted management under different control standards can be achieved. A security database is built based on the equivalent safety level under different drone specifications to achieve targeted management under the same security baseline under different drone specifications.

[0103] 4. Use trajectory projection and lookup table for collaborative analysis, perform balanced iterative optimization under two types of control methods, adjust navigation strategies for traffic collision events, and improve flight safety and flight mission completion under strategy execution.

[0104] In summary, it can provide effective risk control and dynamic flight management in complex flight environments.

[0105] Embodiment 2: Based on the same inventive concept as the traffic safety management method combined with drone operation risk control in the previous embodiment, Figure 3 As shown, the present application provides a traffic safety management system combined with drone operation risk control, the system comprising:

[0106] An airspace division unit 11, the airspace division unit 11 is used to perform a primary division based on attribute characteristics and a secondary division based on a preset grid size for the controlled airspace, and determine an airspace division map, wherein each divided airspace matches at least one edge control site, and the attribute characteristics include natural attributes, social attributes, and technical attributes;

[0107] A condition determination unit 12, wherein the condition determination unit 12 is used to perform motion uncertainty modeling and determine soft constraint conditions according to the traffic characteristics of the UAV, wherein the motion uncertainty is a superimposed element of traffic safety management;

[0108] A grid identification unit 13, the grid identification unit 13 is used to traverse the airspace division diagram and identify fence grids and free grids;

[0109] A first flight management unit 14, the first flight management unit 14 is used to perform entry and exit certification and route dynamic flight management under traffic safety for the fence grid in combination with a security database;

[0110] The second flight management unit 15 is used to perform dynamic flight management of the route under traffic safety for the free grid in combination with the safety database, wherein the flight management strategy is determined by the soft constraints of the dynamic programming results superimposed and matched, and the dynamic programming results are determined by fitting trajectory projection and lookup table methods, and the safety database is constructed based on the equivalent safety level under the differences in UAV specifications.

[0111] Among them, the second flight management unit 15 is also used to perform the following steps: as the first UAV enters the first divided airspace, the first edge control station takes over the first UAV, wherein the flight information is transferred between stations; as the flight data is transmitted back, the first edge control station updates the global distribution data under the first divided airspace in real time, and performs traffic risk assessment, locates the risk event group, wherein the risk event group includes route deviation UAVs and affected UAVs; and dynamically adjusts the route of the risk event group.

[0112] Among them, the second flight management unit 15 is also used to perform the following steps: traverse the global distribution data to determine the deviation of the flight trajectory; if there is a trajectory deviation drone and the traffic collision event meets the preset probability of occurrence, determine the flight management strategy by fitting the trajectory projection and the lookup table, wherein the trajectory projection analysis is performed by avoiding the wind control area under the free grid, and the lookup table analysis is performed by maneuvering parameter control.

[0113] The second flight management unit 15 is further used to perform the following steps: based on the free grid, locate the risk control area of ​​the traffic collision event, wherein the risk control area is determined based on the scheduled route and flight parameters; for the risk control area, determine the flight management strategy by balancing trajectory control and speed control;

[0114] Among them, the second flight management unit 15 is also used to perform the following steps: determine a group of control strategies, wherein the group of control strategies includes a first control strategy for independent adjustment of trajectory projection and a second control strategy for independent adjustment of the lookup table; set unit indentation modulation, indent the first control strategy, compensate based on the lookup table, and add it into the first iterative strategy; indent the second control strategy, compensate based on the trajectory projection, and add it into the first iterative strategy; use the unit indentation modulation to iteratively adjust the strategy and calibrate the fitness, and then take the flight management strategy.

[0115] Among them, the second flight management unit 15 is also used to perform the following steps: based on the wind control area, determine the initial control position, locate the neighborhood free grids in sequence, and combine to determine the first control strategy, wherein the neighborhood free grid is based on the upper grid neighborhood and the wind control boundary neighborhood to determine; traverse the soft constraints, match and determine the uncertain constraints of the traffic collision event; based on the uncertain constraints, compensate for the first control strategy.

[0116] Among them, the first flight management unit 14 also includes the following execution steps: determining drone countermeasure rules, wherein the countermeasure methods include signal interference and physical interception, and the countermeasure strategy is constrained by the space situation; performing entry and exit review on the fence grid, and if an unapproved drone is detected, based on the drone countermeasure rules, countermeasure management is performed on the unapproved drone, and a management data stream is generated.

[0117] Among them, the second flight management unit 15 also includes the following execution steps: interacting with drone specifications and determining baseline safety indicators; for the drone specifications, based on the baseline safety indicators, executing safety level equivalence under specification differentiation and determining a safety database; assisting the safety database to perform drone traffic safety management.

[0118] Through the above-mentioned detailed description of the traffic safety management method combined with drone operation risk control in this specification, those skilled in the art can clearly understand the traffic safety management method and system combined with drone operation risk control in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0119] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A traffic safety management method combined with risk control of drone operation, characterized in that: include: The controlled airspace is divided into a primary level based on attribute characteristics and a secondary level based on a preset grid size to determine an airspace division map, wherein each divided airspace matches at least one edge control site, and the attribute characteristics include natural attributes, social attributes, and technical attributes; According to the characteristics of UAV traffic, the motion uncertainty modeling is carried out to determine the soft constraints, among which the motion uncertainty is the superimposed factor of traffic safety management; Traversing the airspace division map, identifying fence grids and free grids; For the fence grid, combined with the security database, dynamic flight management of routes under entry and exit certification and traffic safety is carried out; For the free grid, combined with the safety database, dynamic flight management of the route under traffic safety is carried out, wherein the flight management strategy is determined by the soft constraints of the dynamic programming results superimposed and matched, the dynamic programming results are determined by fitting trajectory projection and lookup table, and the safety database is constructed based on the equivalent safety level under the difference of UAV specifications; Among them, dynamic flight management of routes under traffic safety includes: As the first UAV enters the first divided airspace, the first edge control station takes over the first UAV, including the flow of flight information between stations; As the flight data is transmitted back, the first edge control station updates the global distribution data under the first divided airspace in real time, and performs traffic risk assessment to locate the risk event crew, wherein the risk event crew includes the route deviation drone and the affected drone; Dynamically adjust the routes of the crews involved in the risk events; Among them, the dynamic adjustment of the route of the crew involved in the risk event includes: Traversing the global distribution data to determine the deviation of the flight trajectory; If there is a trajectory deviation of the drone, and the traffic collision event meets the preset probability of occurrence, the flight management strategy is determined by fitting the trajectory projection and the lookup table, where the trajectory projection analysis is performed by avoiding the wind control area under the free grid, and the lookup table analysis is performed by maneuver parameter control; Among them, the flight management strategy is determined by fitting trajectory projection and lookup table, including: Based on the free grid, locating a risk control area of ​​the traffic collision event, wherein the risk control area is determined based on a predetermined route and flight parameters; For the wind control area, determine the flight management strategy by balancing trajectory control and speed control; Among them, the methods of balancing trajectory control and speed control include: Determine a set of control strategies, wherein the set of control strategies includes a first control strategy for independent adjustment of trajectory projection and a second control strategy for independent adjustment of the lookup table; Set unit indentation amplitude modulation, indent the first control strategy, compensate based on the lookup table method, and add the first iterative strategy; The second control strategy is indented, compensated based on the trajectory projection method, and added into the first iteration strategy; The unit is indented and modulated to perform iterative strategy adjustment and fitness calibration to select the flight management strategy.

2. The traffic safety management method combined with unmanned aerial vehicle operation risk control as claimed in claim 1, characterized in that: The set of control strategies includes a first control strategy for independent adjustment of trajectory projections, including: Based on the risk control area, determine the initial control position, locate the neighborhood free grids in sequence, and combine to determine the first control strategy, wherein the neighborhood free grid is determined based on the upper grid neighborhood and the risk control boundary neighborhood; Traversing the soft constraint conditions, matching and determining the uncertain constraints of the traffic collision event; Based on the uncertain constraint, the first control strategy is compensated.

3. The traffic safety management method combined with unmanned aerial vehicle operation risk control as claimed in claim 1, characterized in that: Conduct entry and exit approval management, including: Determine the rules for countering drones, where the countermeasures include signal jamming and physical interception, and the countermeasure strategy is constrained by the space situation; The fence grid is reviewed for entry and exit. If an unapproved drone is detected, the unapproved drone is managed based on the drone countermeasure rules and a management data stream is generated.

4. The traffic safety management method combined with unmanned aerial vehicle operation risk control as claimed in claim 1, characterized in that: Build a secure database, including: Interact with drone specifications and determine baseline safety metrics; For the drone specifications, based on the baseline safety indicators, the safety level equivalence under specification differentiation is performed to determine a safety database.

5. The traffic safety management system combined with the risk control of drone operation is characterized by: A system for executing a traffic safety management method combined with risk control of drone operation as described in any one of claims 1 to 4, the system comprising: An airspace division unit, the airspace division unit is used to perform a primary division based on attribute characteristics and a secondary division based on a preset grid size for the controlled airspace, and determine an airspace division map, wherein each divided airspace matches at least one edge control site, and the attribute characteristics include natural attributes, social attributes, and technical attributes; A condition determination unit, the condition determination unit is used to perform motion uncertainty modeling and determine soft constraint conditions according to the traffic characteristics of the UAV, wherein the motion uncertainty is an overlay element of traffic safety management; A grid identification unit, the grid identification unit is used to traverse the airspace division diagram and identify fence grids and free grids; A first flight management unit, the first flight management unit is used to perform entry and exit certification and route dynamic flight management under traffic safety for the fence grid in combination with a security database; The second flight management unit is used to perform dynamic flight management of routes under traffic safety for the free grid in combination with a safety database, wherein the flight management strategy is determined by superimposing soft constraints matched by dynamic programming results, and the dynamic programming results are determined by fitting trajectory projection and lookup table methods, and the safety database is constructed based on equivalent safety levels under differences in UAV specifications.

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