Low-altitude activity signboard generation and airspace management method based on low-altitude activity signboard

Abstract

The application provides a low-altitude activity marker generation and airspace management method based on the low-altitude activity marker. The method comprises the following steps: collecting flight activity data of a UAV in a current airspace, and fitting to obtain real-time track data of the UAV; acquiring basic data and historical flight activity data of the UAV; based on the basic data and the real-time track data, identifying a UAV illegal flight feature of the UAV, and obtaining an illegal flight identification result and alarm information of the UAV in the current airspace; using the real-time track data and the historical track data, verifying the similarity with a preset related route, and determining a flight task nature of the UAV; performing weighted processing on historical illegal data, and obtaining a safety index of the UAV; and combining to obtain a low-altitude activity marker of the UAV. The method of the application realizes activity tracking and dynamic information label generation of the UAV, improves the monitoring effect of the UAV, and provides a situational and personalized UAV monitoring means for the jurisdiction airspace.

Description

TECHNICAL FIELD

[0001] The present application relates to the communication technology, and particularly to a low-altitude activity marker generation method and a low-altitude activity marker-based airspace management method. BACKGROUND

[0002] In recent years, the unmanned aerial vehicle technology has made significant progress, and the unmanned aerial vehicle has a wide range of application scenarios and broad market space. With the continuous progress of technology and the expansion of application scenarios, the application of unmanned aerial vehicles in emergency rescue, environmental monitoring, urban planning and other aspects will be further developed, and the monitoring demand for unmanned aerial vehicles will also be further expanded with the wide application of unmanned aerial vehicles.

[0003] In the related art, after identifying the unmanned aerial vehicle in the airspace, the flight activity data of the unmanned aerial vehicle is mostly used for trajectory fitting to obtain the flight trajectory of the unmanned aerial vehicle in the current period. The flight trajectory data of each unmanned aerial vehicle in the airspace needs to be calculated to realize the low-altitude activity monitoring of the unmanned aerial vehicle. The calculation amount is large, and it is difficult to quickly determine the unmanned aerial vehicle that needs to be monitored. At the same time, since the monitoring basis only includes the flight trajectory data of the unmanned aerial vehicle, it is impossible to comprehensively and individually represent the low-altitude flight activity of the unmanned aerial vehicle, resulting in poor monitoring effect of the unmanned aerial vehicle.

[0004] Therefore, in order to improve the monitoring effect of the unmanned aerial vehicle, it is necessary to provide an unmanned aerial vehicle activity identification method. SUMMARY

[0005] The low-altitude activity marker generation method and the low-altitude activity marker-based airspace management method provided by the embodiments of the present application solve the problem of poor monitoring effect of the unmanned aerial vehicle.

[0006] In a first aspect, the embodiments of the present application provide a low-altitude activity marker generation method, comprising:

[0007] Collecting flight activity data of an unmanned aerial vehicle in a current airspace, and fitting real-time trajectory data of the unmanned aerial vehicle based on the flight activity data;

[0008] Obtaining basic data and historical flight activity data of the unmanned aerial vehicle, wherein the historical flight activity data includes historical trajectory data and historical violation data;

[0009] Performing unmanned aerial vehicle violation flight feature identification on the unmanned aerial vehicle based on the basic data and the real-time trajectory data to obtain a violation flight identification result of the unmanned aerial vehicle in the current airspace and alarm information corresponding to the violation flight identification result;

[0010] Verifying the similarity of the real-time trajectory data and the historical trajectory data with a preset related route to determine the flight task nature of the unmanned aerial vehicle;

[0011] Performing weighted processing on the historical violation data to obtain a safety index of the unmanned aerial vehicle;

[0012] The low-altitude activity label of the UAV is obtained by combining the illegal flight identification result, the flight task nature, the safety index, the alarm information and the real-time track data, and the low-altitude activity label is used for hierarchical monitoring and risk early warning of each UAV in the airspace.

[0013] In a possible implementation, based on the basic data and the real-time track data, the UAV is subjected to UAV illegal flight feature identification to obtain an illegal flight identification result of the UAV in the current airspace and alarm information corresponding to the illegal flight identification result, including:

[0014] Based on the basic data, the real-time track data is compared and identified with the flight height limit in the airspace to obtain an ultrahigh identification result of the UAV;

[0015] Based on the basic data, it is judged whether the real-time track data exists illegal intrusion and / or enters the operation area / air route risk, and an illegal intrusion identification result and an entering operation area / air route risk identification result are identified;

[0016] The pre-trained collision prediction model is used for UAV operation situation awareness of the real-time track data, and according to the identified real-time operation situation of the UAV, a UAV obstacle collision risk identification result and a UAV other UAV collision risk identification result are determined;

[0017] The ultrahigh identification result, the illegal intrusion identification result, the entering operation area / air route risk identification result, the UAV obstacle collision risk identification result and the UAV other UAV collision risk identification result are combined to generate the alarm information corresponding to the illegal flight identification result.

[0018] In a possible implementation, the real-time track data and the historical track data are used for similarity verification with a preset related air route to determine the flight task nature of the UAV, including:

[0019] The real-time track data and the historical track data are respectively subjected to similarity calculation with a first type of preset related air route to obtain a similarity calculation result;

[0020] When the similarity calculation result is greater than a preset threshold, it is determined that the flight task nature of the UAV is a fixed air route type, and the first type of preset related air route is used to represent the related air route of the fixed air route type UAV;

[0021] When the similarity calculation result is less than or equal to the preset threshold, the real-time track data and the historical track data are subjected to operation identification, and the flight task nature of the UAV is determined according to the operation identification result.

[0022] In a possible implementation, the real-time flight path data and the historical flight path data are subjected to operation identification, and flight task properties of the UAV are determined according to the operation identification result, including:

[0023] According to the operation flight route deviation angle, the coincidence coverage rate and the turning angle in the real-time flight path data, a preset real-time dynamic positioning operation identification model is used to identify and determine whether the flight task properties of the UAV are geographic mapping type;

[0024] If the flight task properties of the UAV are not the geographic mapping type, it is determined that the flight task properties of the UAV are the consumer type.

[0025] In a possible implementation, the method further includes:

[0026] Obtaining meteorological data and radio frequency band data in a current airspace;

[0027] Based on the meteorological data and the radio frequency band data, the dynamic airworthiness information of the UAV is determined in combination with the real-time flight path data;

[0028] Correspondingly, the low-altitude activity label of the UAV is obtained by combining the illegal flight identification result, the flight task properties, the safety index, the alarm information and the real-time flight path data, including:

[0029] The low-altitude activity label of the UAV is obtained by combining the illegal flight identification result, the flight task properties, the safety index, the alarm information, the real-time flight path data and the dynamic airworthiness information.

[0030] In a possible implementation, the method further includes:

[0031] The flight preference identification model trained is used to identify the flight preference of the UAV from the historical flight path data and the real-time flight path data, including: flight smoothness, flight path deviation, flight area and flight time period;

[0032] Correspondingly, the low-altitude activity label of the UAV is obtained by combining the illegal flight identification result, the flight task properties, the safety index, the alarm information and the real-time flight path data, including:

[0033] The low-altitude activity label of the UAV is obtained by combining the illegal flight identification result, the flight task properties, the safety index, the alarm information, the real-time flight path data and the flight preference.

[0034] In a second aspect, the embodiments of the present application provide an airspace management method based on the low-altitude activity label, including:

[0035] Obtaining the low-altitude activity label of all UAVs in the airspace, wherein the low-altitude activity label is generated by using the low-altitude activity label generation method of any one of the first aspect;

[0036] screening dimensions corresponding to the airspace, screening all low-altitude activity tags in the airspace to determine the target unmanned aerial vehicle to be monitored; wherein the screening dimensions are used to indicate part of the information in the low-altitude activity tag;

[0037] The low-altitude activity tag of the target unmanned aerial vehicle is used to perform risk warning on the target unmanned aerial vehicle.

[0038] In a possible implementation, the low-altitude activity tag includes a safety index and a flight preference;

[0039] Screening all low-altitude activity tags in the airspace based on the screening dimensions corresponding to the airspace to determine the target unmanned aerial vehicle to be monitored, comprising:

[0040] determining whether the safety index of the current low-altitude activity tag is less than a safety index threshold;

[0041] When the safety index of the current low-altitude activity tag is less than the safety index threshold, determining that the unmanned aerial vehicle corresponding to the current low-altitude activity tag is the target unmanned aerial vehicle to be monitored;

[0042] and / or determining whether the flight smoothness in the flight preference of the current low-altitude activity tag is less than a flight smoothness threshold;

[0043] When the flight smoothness in the flight preference of the current low-altitude activity tag is less than the flight smoothness threshold, determining that the unmanned aerial vehicle corresponding to the current low-altitude activity tag is the target unmanned aerial vehicle to be monitored.

[0044] In a possible implementation, the low-altitude activity tag of the target unmanned aerial vehicle is used to perform risk warning on the target unmanned aerial vehicle, comprising:

[0045] Using the pre-trained risk warning model to perform flight path risk warning identification on the real-time flight path data, flight task properties and flight preference of the target unmanned aerial vehicle to obtain a flight path risk warning result of the target unmanned aerial vehicle;

[0046] Performing weighted calculation on the number of illegal flight identification results, the safety index and the number of alarm information of the target unmanned aerial vehicle, and fitting to obtain a violation risk warning result;

[0047] Based on the flight path risk warning result and the violation risk warning result, performing risk warning on the target unmanned aerial vehicle.

[0048] In a third aspect, an embodiment of the present application provides a low-altitude activity tag generation device, comprising:

[0049] a flight path data fitting module, configured to collect flight activity data of unmanned aerial vehicles in a current airspace, and based on the flight activity data, fit to obtain real-time flight path data of the unmanned aerial vehicles;

[0050] The data acquisition module is configured to acquire basic data and historical flight activity data of the UAV, and the historical flight activity data comprises historical flight path data and historical violation data.

[0051] The violation feature identification module is configured to identify a UAV violation flight feature of the UAV based on the basic data and the real-time flight path data, to obtain a violation flight identification result of the UAV in the current airspace and alarm information corresponding to the violation flight identification result.

[0052] The flight task nature determination module is configured to determine a flight task nature of the UAV by performing similarity verification on the real-time flight path data and the historical flight path data with respect to a preset relevant flight path.

[0053] The safety index acquisition module is configured to obtain a safety index of the UAV by performing weighted processing on the historical violation data.

[0054] The UAV label module is configured to combine the violation flight identification result, the flight task nature, the safety index, the alarm information and the real-time flight path data to obtain a low-altitude activity label of the UAV, and the low-altitude activity label is used for hierarchical monitoring and risk early warning of each UAV in the airspace.

[0055] In a possible implementation, the violation feature identification module is specifically configured to:

[0056] The real-time flight path data is compared and identified with a flight height limit in the airspace based on the basic data to obtain an ultrahigh identification result of the UAV.

[0057] Based on the basic data, it is determined whether the real-time flight path data exists a violation intrusion and / or an entering operation area / flight path risk, and an intrusion violation identification result and an entering operation area / flight path risk identification result are identified.

[0058] The real-time flight path data is subjected to UAV operation situation awareness by using a pre-trained collision prediction model, and a UAV collision risk identification result with an obstacle and a UAV collision risk identification result with other UAVs are determined according to the identified real-time operation situation of the UAV.

[0059] The alarm information corresponding to the violation flight identification result is generated in combination with the ultrahigh identification result, the intrusion violation identification result, the entering operation area / flight path risk identification result, the collision risk identification result with the obstacle and the collision risk identification result with other UAVs.

[0060] In a possible implementation, the flight task nature determination module is specifically configured to:

[0061] The real-time flight path data and the historical flight path data are subjected to similarity calculation with respect to a first type of preset relevant flight path respectively, and a similarity calculation result is fitted.

[0062] When the similarity calculation result is greater than a preset threshold, the flight task property of the UAV is determined as a fixed route type, and the first type of preset related route is used to represent the related route of the UAV of the fixed route type.

[0063] When the similarity calculation result is less than or equal to the preset threshold, operation recognition is performed on the real-time flight path data and the historical flight path data, and the flight task property of the UAV is determined according to the operation recognition result.

[0064] In a possible implementation, the flight task property determination module is specifically configured to:

[0065] According to the operation flight route deviation angle, the coincidence coverage rate and the turning angle in the real-time flight path data, a preset real-time dynamic positioning operation recognition model is used to recognize and determine whether the flight task property of the UAV is a geographic mapping type.

[0066] If the flight task property of the UAV is not the geographic mapping type, it is determined that the flight task property of the UAV is a consumer type.

[0067] In a possible implementation, the device further includes:

[0068] Obtaining meteorological data and radio frequency band data in a current airspace;

[0069] Based on the meteorological data and the radio frequency band data, the dynamic airworthiness information of the UAV is determined in combination with the real-time flight path data.

[0070] Correspondingly, the UAV label module is further specifically configured to:

[0071] The illegal flight recognition result, the flight task property, the safety index, the alarm information, the real-time flight path data and the dynamic airworthiness information are combined to obtain the low-altitude activity label of the UAV.

[0072] In a possible implementation, the device further includes:

[0073] The trained flight preference recognition model is used to perform flight preference recognition on the historical flight path data and the real-time flight path data to determine the flight preference of the UAV; wherein the flight preference includes: flight smoothness, flight path deviation, flight area and flight time period.

[0074] Correspondingly, the UAV label module is further specifically configured to:

[0075] The illegal flight recognition result, the flight task property, the safety index, the alarm information, the real-time flight path data and the flight preference are combined to obtain the low-altitude activity label of the UAV.

[0076] In a fourth aspect, the embodiments of the present application provide an airspace management device based on a low-altitude activity label, comprising:

[0077] The low-altitude activity tag acquisition module is configured to acquire low-altitude activity tags of all unmanned aerial vehicles in the airspace, wherein the low-altitude activity tags are generated by the low-altitude activity tag generation device of the third aspect;

[0078] The unmanned aerial vehicle preliminary screening module is configured to screen all low-altitude activity tags in the airspace based on a screening dimension corresponding to the airspace, and determine a target unmanned aerial vehicle to be monitored, wherein the screening dimension is used to indicate part of information in the low-altitude activity tag;

[0079] The risk early warning module is configured to perform risk early warning on the target unmanned aerial vehicle by using the low-altitude activity tag of the target unmanned aerial vehicle.

[0080] In a possible implementation, the low-altitude activity tag includes a safety index and a flight preference.

[0081] The unmanned aerial vehicle preliminary screening module is specifically configured to:

[0082] determine whether the safety index of the current low-altitude activity tag is less than a safety index threshold value;

[0083] when the safety index of the current low-altitude activity tag is less than the safety index threshold value, determine that the unmanned aerial vehicle corresponding to the current low-altitude activity tag is the target unmanned aerial vehicle to be monitored;

[0084] and / or determine whether the flight smoothness in the flight preference of the current low-altitude activity tag is less than a flight smoothness threshold value;

[0085] when the flight smoothness in the flight preference of the current low-altitude activity tag is less than the flight smoothness threshold value, determine that the unmanned aerial vehicle corresponding to the current low-altitude activity tag is the target unmanned aerial vehicle to be monitored.

[0086] In a possible implementation, the risk early warning module is specifically configured to:

[0087] perform flight path risk early warning identification on real-time flight path data, flight task properties, and the flight preference of the target unmanned aerial vehicle by using the pre-trained risk early warning model, to obtain a flight path risk early warning result of the target unmanned aerial vehicle;

[0088] perform weighted calculation on the number of the irregular flight identification result, the safety index, and the number of the alarm information of the target unmanned aerial vehicle, and fit to obtain an irregular risk early warning result;

[0089] perform risk early warning on the target unmanned aerial vehicle based on the flight path risk early warning result and the irregular risk early warning result.

[0090] In a fifth aspect, an electronic device is provided, including a memory and a processor.

[0091] The memory stores computer execution instructions.

[0092] The processor executes computer-executed instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementation manners of the first aspect as above, or executes the second aspect and / or various possible implementation manners of the second aspect as above.

[0093] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores computer-executed instructions. When the computer-executed instructions are executed by a processor, the computer-executed instructions are used to implement the first aspect and / or various possible implementation manners of the first aspect as above, or to implement the second aspect and / or various possible implementation manners of the second aspect as above.

[0094] In a seventh aspect, an embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the computer program implements the first aspect and / or various possible implementation manners of the first aspect as above, or implements the second aspect and / or various possible implementation manners of the second aspect as above.

[0095] The low-altitude activity tag generation method and the airspace management method based on the low-altitude activity tag provided in the embodiments of the present application improve the detection accuracy of the real-time flight path data of the unmanned aerial vehicle by fitting, facilitate the identification and warning of the illegal flight of the unmanned aerial vehicle in the current airspace, and further improve the accuracy of subsequent screening, monitoring and risk warning using the real-time flight path data. The low-altitude activity tag of the unmanned aerial vehicle is generated by using the real-time flight path data of the unmanned aerial vehicle and combining the historical flight activity data of the unmanned aerial vehicle, so as to realize the activity tracking and dynamic information tag generation of the unmanned aerial vehicle. The low-altitude activity tag includes the flight task nature and safety index of the unmanned aerial vehicle, facilitating the user to use these tags to perform hierarchical monitoring on each unmanned aerial vehicle in the airspace. The low-altitude activity tag also includes the real-time flight path of the unmanned aerial vehicle, facilitating the intuitive activity tracking of the unmanned aerial vehicle, providing a situational and personalized unmanned aerial vehicle monitoring means for the jurisdiction airspace, and facilitating the realization of the functions of unmanned aerial vehicle identity recognition, real-time sensing, intelligent portrait, individual identification, dynamic command, operation scheduling, automatic warning, handover disposal and the like by using the unmanned aerial vehicle activity tag, and realizing the technical effect of improving the monitoring effect of the unmanned aerial vehicle. BRIEF DESCRIPTION OF DRAWINGS

[0096] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0097] Figure 1 An application scenario diagram of the low-altitude activity tag generation method provided in the present application;

[0098] Figure 2 A flowchart of the low-altitude activity tag generation method provided in the embodiments of the present application;

[0099] Figure 3 A flowchart of a low-altitude activity marker-based airspace management method provided by an embodiment of the present application is shown in FIG. 1.

[0100] Figure 4A A large-screen display of real-time flight path data in a low-altitude activity marker provided by an embodiment of the present application is shown in FIG. 2.

[0101] Figure 4B A large-screen display of UAV basic data and illegal flight identification results in a low-altitude activity marker provided by an embodiment of the present application is shown in FIG. 3.

[0102] Figure 4C A large-screen display of flight task nature, safety index and historical illegal data in a low-altitude activity marker provided by an embodiment of the present application is shown in FIG. 4.

[0103] Figure 4D A large-screen display of alarm information in a low-altitude activity marker provided by an embodiment of the present application is shown in FIG. 5.

[0104] Figure 5 A structural diagram of a low-altitude activity marker generation device provided by an embodiment of the present application is shown in FIG. 6.

[0105] Figure 6 A structural diagram of a low-altitude activity marker-based airspace management device provided by an embodiment of the present application is shown in FIG. 7.

[0106] Figure 7 A block diagram of an electronic device according to an embodiment of the present application is shown in FIG. 8.

[0107] The above-described figures have shown the specific embodiments of the present application, which will be described in more detail hereinafter. These figures and the following description are not intended to limit the scope of the present application concept in any way, but to illustrate the present application concept to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0108] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, the same numbers are used to indicate the same elements in different drawings, unless otherwise specified. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0109] In the technical solution of the present application, the collection, storage, use, processing, transmission, provision and disclosure of the information such as UAV data involved in the technical solution comply with the relevant legal regulations and do not violate public order and good customs.

[0110] It should be noted that in the embodiments of the present application, some industry existing solutions of software, components, models, etc. may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility of the implementation of the technical solutions of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0111] In some optional embodiments, after identifying the unmanned aerial vehicle in the airspace, the flight activity data of the unmanned aerial vehicle is mostly used for flight path fitting to obtain the flight trajectory of the unmanned aerial vehicle in the current period. In order to realize the low-altitude activity monitoring of the unmanned aerial vehicle, the flight trajectory data of each unmanned aerial vehicle in the airspace needs to be calculated. The calculation amount is large, and it is difficult to quickly determine the unmanned aerial vehicle that needs to be monitored. At the same time, since the monitoring basis only contains the flight trajectory data of the unmanned aerial vehicle, it is impossible to comprehensively and individually represent the low-altitude flight activity of the unmanned aerial vehicle, resulting in poor monitoring effect of the unmanned aerial vehicle.

[0112] The low-altitude activity tag generation provided by the present application aims to solve the above technical problems of the prior art. Specifically, the low-altitude activity tag generation method provided by the present application improves the detection accuracy of the flight path of the unmanned aerial vehicle by fitting to obtain real-time flight path data of the unmanned aerial vehicle, which facilitates the identification and warning of the illegal flight of the unmanned aerial vehicle in the current airspace, and further improves the accuracy of subsequent screening, monitoring and risk warning using real-time flight path data. Real-time flight path data of the unmanned aerial vehicle is used in combination with historical flight activity data of the unmanned aerial vehicle to generate a low-altitude activity tag of the unmanned aerial vehicle, which realizes activity tracking and dynamic information tag generation of the unmanned aerial vehicle. The low-altitude activity tag contains flight task properties, safety index and other tags of the unmanned aerial vehicle, which facilitates the user to use these tags to monitor the unmanned aerial vehicles in the airspace in different levels. The low-altitude activity tag also includes the real-time flight path of the unmanned aerial vehicle, which facilitates the intuitive tracking of the unmanned aerial vehicle, and provides situational and individualized unmanned aerial vehicle monitoring means for the jurisdiction airspace, which facilitates the use of the unmanned aerial vehicle activity tag to realize the functions of unmanned aerial vehicle identity recognition, real-time sensing, intelligent portrait, individual identification, dynamic command, operation scheduling, automatic warning, handover disposal, etc., and realizes the technical effect of improving the monitoring effect of the unmanned aerial vehicle.

[0113] Figure 1 An application scenario of the low-altitude activity tag generation method provided by the present application. The technical solution provided by the present application can be applied to a low-altitude flight service center to assign a low-altitude activity tag to each unmanned aerial vehicle in the jurisdiction airspace. Referring to Figure 1 In the above application scenario, the low-altitude flight service center assigns a unique low-altitude activity tag to each unmanned aerial vehicle, and performs hierarchical screening, monitoring and risk warning on the unmanned aerial vehicle through the low-altitude activity tag of each unmanned aerial vehicle.

[0114] In the above scheme, the real-time flight path data of the unmanned aerial vehicle is obtained by fitting, which improves the detection accuracy of the flight path of the unmanned aerial vehicle, facilitates the identification and warning of the illegal flight of the unmanned aerial vehicle in the current airspace, and further improves the accuracy of subsequent screening monitoring and risk warning using real-time flight path data. The real-time flight path data of the unmanned aerial vehicle is used in combination with the historical flight activity data of the unmanned aerial vehicle to jointly generate a low-altitude activity tag of the unmanned aerial vehicle, realizing the activity tracking and dynamic information tag generation of the unmanned aerial vehicle. The low-altitude activity tag includes flight task properties, safety index and other tags of the unmanned aerial vehicle, facilitating users to use these tags to monitor the unmanned aerial vehicles in the airspace in different levels. The low-altitude activity tag also includes the real-time flight path of the unmanned aerial vehicle, facilitating the intuitive activity tracking of the unmanned aerial vehicle, providing situational and personalized unmanned aerial vehicle monitoring means for the jurisdiction airspace, and facilitating the realization of unmanned aerial vehicle identity recognition, real-time sensing, intelligent portrait, individual identification, dynamic command, operation scheduling, automatic warning, handover disposal and other functions by using the unmanned aerial vehicle activity tag, and realizing the technical effect of improving the monitoring effect of the unmanned aerial vehicle.

[0115] In combination with the above scenarios, in the prior art, after identifying the unmanned aerial vehicle in the airspace, the flight activity data of the unmanned aerial vehicle is mostly used for flight path fitting to obtain the flight trajectory of the unmanned aerial vehicle in the current period. The flight trajectory data of each unmanned aerial vehicle in the airspace needs to be calculated to realize the low-altitude activity monitoring of the unmanned aerial vehicle, which has a large amount of calculation and is difficult to quickly determine the unmanned aerial vehicle that needs to be monitored. At the same time, since the monitoring basis only includes the flight trajectory data of the unmanned aerial vehicle, it is impossible to comprehensively and individually represent the low-altitude flight activity of the unmanned aerial vehicle, and there is a technical problem that the monitoring effect of the unmanned aerial vehicle is poor.

[0116] The low-altitude activity tag generation and airspace management method based on the low-altitude activity tag provided in the present application improve the detection accuracy of the flight path of the unmanned aerial vehicle by fitting the real-time flight path data of the unmanned aerial vehicle, facilitate the identification and warning of the illegal flight of the unmanned aerial vehicle in the current airspace, and further improve the accuracy of subsequent screening monitoring and risk warning using real-time flight path data. The real-time flight path data of the unmanned aerial vehicle is used in combination with the historical flight activity data of the unmanned aerial vehicle to jointly generate a low-altitude activity tag of the unmanned aerial vehicle, realizing the activity tracking and dynamic information tag generation of the unmanned aerial vehicle. The low-altitude activity tag includes flight task properties, safety index and other tags of the unmanned aerial vehicle, facilitating users to use these tags to monitor the unmanned aerial vehicles in the airspace in different levels. The low-altitude activity tag also includes the real-time flight path of the unmanned aerial vehicle, facilitating the intuitive activity tracking of the unmanned aerial vehicle, providing situational and personalized unmanned aerial vehicle monitoring means for the jurisdiction airspace, and facilitating the realization of unmanned aerial vehicle identity recognition, real-time sensing, intelligent portrait, individual identification, dynamic command, operation scheduling, automatic warning, handover disposal and other functions by using the unmanned aerial vehicle activity tag, and realizing the technical effect of improving the monitoring effect of the unmanned aerial vehicle.

[0117] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes can not be described again in some examples. The embodiments of the present application will be described below with reference to the drawings.

[0118] Figure 2 A flowchart of a low-altitude activity signboard generation method provided by an embodiment of the present application is shown. The method can be executed by a low-altitude activity signboard generation device, which can be a server or an electronic device. The following will be described by taking an electronic device as an example. The method in the embodiment can be implemented by software, hardware, or a combination of software and hardware. As shown in the figure, the method comprises the following steps: Figure 2

[0119] S201, collect flight activity data of a UAV in a current airspace, and fit to obtain real-time track data of the UAV based on the flight activity data.

[0120] In an example, the sensing information of the flight body in the airspace collected on the ground can be used. The input sensing information of the flight body in the airspace is filtered to filter out data that does not actually belong to the UAV. The filtered data is aligned in time and space to fit to obtain real-time track data of the UAV.

[0121] S202, obtain basic data and historical flight activity data of the UAV.

[0122] In the embodiment, the historical flight activity data comprises historical track data and historical violation data.

[0123] In an example, the basic data of the UAV can comprise an ID of the UAV, model information of the UAV, and other data for representing the identity of the UAV. The historical flight track data can comprise records of historical flight of the UAV, such as historical track data of a historical flight start time, an end time, a maximum flight height, and a flight range of the UAV. The historical violation data can comprise historical violation times, time of each violation flight behavior, violation content, violation geographical position, and region, and other violation information.

[0124] S203, identify a UAV violation flight feature of the UAV based on the basic data and the real-time track data, to obtain a UAV violation flight identification result of the UAV in the current airspace and alarm information corresponding to the UAV violation flight identification result.

[0125] ​In an optional embodiment, based on the basic data and the real-time track data, the UAV is identified for the UAV illegal flight feature, and the illegal flight identification result of the UAV in the current airspace and the alarm information corresponding to the illegal flight identification result are obtained, which can include:

[0126] Based on the basic data, the real-time track data is compared and identified with the flight height limit in the airspace to obtain the over-height identification result of the UAV;

[0127] Based on the basic data, it is judged whether the real-time track data exists illegal intrusion and / or enters the operation area / air route risk, and the illegal intrusion identification result and the entering operation area / air route risk identification result are identified;

[0128] The pre-trained collision prediction model is used for UAV operation situation awareness of the real-time track data, and according to the identified real-time operation situation of the UAV, the UAV collision risk identification result with obstacles and the UAV collision risk identification result with other UAVs are determined;

[0129] The over-height identification result, the illegal intrusion identification result, the entering operation area / air route risk identification result, the UAV collision risk identification result with obstacles and the UAV collision risk identification result with other UAVs are combined to generate the alarm information corresponding to the illegal flight identification result.

[0130] Optionally, according to the UAV model, flight task nature and current airspace attribute information, the flight height threshold of the UAV in the current airspace is determined; when the current flight height of the UAV in the real-time track data of the UAV is greater than the flight height threshold of the current airspace, it is determined that the UAV has over-height; when the current flight height of the UAV in the real-time track data of the UAV is not greater than the flight height threshold of the current airspace, it is determined that the UAV has no over-height.

[0131] According to the UAV model, flight task nature and other information, the prohibited entry area of the UAV is determined, when the geographic location of the UAV is in the prohibited entry area of the UAV, it is determined that the UAV has illegal intrusion; when the geographic location of the UAV is not in the prohibited entry area of the UAV, it is determined that the UAV has no illegal intrusion.

[0132] According to the UAV model, flight task nature, operation area information and air route area information and other information, it is judged whether the geographic location in the real-time track data of the UAV is in the operation area or the air route area; when the geographic location of the UAV is in the operation area or the air route area, it is determined that the UAV has the entering operation area / air route risk; when the geographic location of the UAV is not in the operation area or the air route area, it is determined that the UAV has no entering operation area / air route risk.

[0133] The real-time flight path data is input into a pre-trained collision prediction model by using the operation situation awareness technology and the collision prediction method, and the unmanned aerial vehicle and obstacle collision risk of the unmanned aerial vehicle is output. When the unmanned aerial vehicle and obstacle collision risk of the unmanned aerial vehicle is greater than a preset unmanned aerial vehicle and obstacle collision risk threshold, it is determined that the unmanned aerial vehicle has a collision risk with the obstacle. When the unmanned aerial vehicle and obstacle collision risk of the unmanned aerial vehicle is not greater than the preset unmanned aerial vehicle and obstacle collision risk threshold, it is determined that the unmanned aerial vehicle does not have a collision risk with the obstacle. The real-time flight path data is input into the pre-trained collision prediction model, and the unmanned aerial vehicle and other unmanned aerial vehicle collision risk of the unmanned aerial vehicle is output. When the unmanned aerial vehicle and other unmanned aerial vehicle collision risk of the unmanned aerial vehicle is greater than a preset unmanned aerial vehicle and other unmanned aerial vehicle collision risk threshold, it is determined that the unmanned aerial vehicle has a collision risk with other unmanned aerial vehicles. When the unmanned aerial vehicle and other unmanned aerial vehicle collision risk of the unmanned aerial vehicle is not greater than the preset unmanned aerial vehicle and other unmanned aerial vehicle collision risk threshold, it is determined that the unmanned aerial vehicle does not have a collision risk with other unmanned aerial vehicles. The specific manner of the operation situation awareness technology and the collision prediction model in the present application is not limited.

[0134] When the unmanned aerial vehicle has any one or more of the illegal flight characteristics such as ultra-high, illegal intrusion, close to the operation area / air route risk, collision risk with obstacles, collision risk with unmanned aerial vehicles, etc., the real-time flight path data, basic data and specific illegal flight characteristics of the unmanned aerial vehicle are recorded, and corresponding alarm information is generated. The alarm information can include: the specific illegal flight characteristics of the unmanned aerial vehicle at a certain time, a certain place, etc. The specific content of the alarm information in the present application is not limited.

[0135] In the present embodiment, by using the basic data and real-time flight path data of the unmanned aerial vehicle, the multi-dimensional unmanned aerial vehicle illegal flight characteristic identification is performed on the unmanned aerial vehicle from the ultra-high, illegal intrusion, close to the operation area / air route risk, collision risk with obstacles, collision risk with unmanned aerial vehicles, etc. It is convenient to comprehensively master the specific illegal flight behavior of the unmanned aerial vehicle. By recording the illegal flight identification result and generating corresponding alarm information, it is convenient to subsequently perform risk warning on the unmanned aerial vehicle.

[0136] In S204, the real-time flight path data and the historical flight path data are used to perform similarity verification with the preset relevant air route, and the flight task nature of the unmanned aerial vehicle is determined.

[0137] In an optional implementation, the real-time flight path data and the historical flight path data are used to perform similarity verification with the preset relevant air route, and the flight task nature of the unmanned aerial vehicle is determined, including:

[0138] The real-time flight path data and the historical flight path data are used to perform similarity calculation with the first type of preset relevant air route respectively, and the similarity calculation result is fitted;

[0139] When the similarity calculation result is greater than a preset threshold value, the flight task property of the unmanned aerial vehicle is determined as a fixed route type, and the first type of preset related route is used to represent the related route of the unmanned aerial vehicle of the fixed route type.

[0140] When the similarity calculation result is less than or equal to the preset threshold value, operation recognition is performed on the real-time flight path data and the historical flight path data, and the flight task property of the unmanned aerial vehicle is determined according to the operation recognition result.

[0141] Optionally, a first similarity can be obtained by performing similarity calculation on the real-time flight path data and the first type of preset related route. When the first similarity is greater than a preset threshold value of the first type of preset related route, the flight task property of the unmanned aerial vehicle is determined as the fixed route type. When the first similarity is not greater than the preset threshold value of the first type of preset related route, the flight task property of the unmanned aerial vehicle is determined as the fixed route type, and similarity calculation is performed on the historical flight path data and the first type of preset related route to obtain a second similarity. When the second similarity is greater than the preset threshold value of the first type of preset related route, the flight task property of the unmanned aerial vehicle is determined as the fixed route type. The fixed route type can be a flight task property of an unmanned aerial vehicle whose route is fixed, such as a government, logistics, tourism, and the like.

[0142] When the second similarity is not greater than the preset threshold value of the first type of preset related route, the flight task property of the unmanned aerial vehicle is determined as not being the fixed route type, the real-time flight path data and the historical flight path data are input into a pre-trained operation recognition model to perform operation recognition, and the specific flight task property of the unmanned aerial vehicle is further determined.

[0143] In this embodiment, the flight task property of the unmanned aerial vehicle is determined by performing similarity verification on the real-time flight path data and the historical flight path data and a specific related route. The flight task property of the unmanned aerial vehicle is used as part of the unmanned aerial vehicle activity sign, which facilitates subsequent determination of whether to focus on monitoring the unmanned aerial vehicle by using the low-altitude activity sign, and further improves the unmanned aerial vehicle monitoring effect.

[0144] For example, according to the operation flight route deviation angle, the coincidence coverage rate, and the turning angle in the real-time flight path data, a preset real-time dynamic positioning operation recognition model is used to recognize and determine whether the flight task property of the unmanned aerial vehicle is the geographic mapping type.

[0145] If the flight task property of the unmanned aerial vehicle is not the geographic mapping type, the flight task property of the unmanned aerial vehicle is determined as the consumer type.

[0146] Specifically, the flight route deviation angle is used to represent the deviation degree of the real-time flight path data of the unmanned aerial vehicle from the direction of the first preset related route, and is usually expressed by an angle value; the overlap coverage rate is used to represent the overlap coverage proportion of the area where the real-time flight path data of the unmanned aerial vehicle is located and the area of the first preset related route; and the turning angle is used to represent the turning angle between adjacent flight segments when the heading of the real-time flight path data of the unmanned aerial vehicle is changed.

[0147] Optionally, the real-time flight path data and the historical flight path data are fused to obtain fused flight path data; the fused flight path data is input into a pre-trained RTK (Real-Time Kinematic) operation recognition model, and a similarity between the fused flight path data and the geographic surveying and mapping type flight path data is output by comprehensively recognizing the flight route deviation angle, the overlap coverage rate, the turning angle and the like between the fused flight path data and the geographic surveying and mapping type flight path data; when the similarity between the fused flight path data and the geographic surveying and mapping type flight path data is greater than a similarity threshold, it is determined that the flight task property of the unmanned aerial vehicle is the geographic surveying and mapping type; and when the similarity between the fused flight path data and the geographic surveying and mapping type flight path data is not greater than the similarity threshold, it is determined that the flight task property of the unmanned aerial vehicle is the consumer type.

[0148] In the embodiment, the flight task property of the unmanned aerial vehicle is determined to be the geographic surveying and mapping type or the consumer type by the preset RTK operation recognition model, the flight task property of the unmanned aerial vehicle is further subdivided, all possible flight task scenarios of the unmanned aerial vehicle are more comprehensively included, and the unmanned aerial vehicle is more accurately and comprehensively profiled.

[0149] Optionally, after the flight task property of the unmanned aerial vehicle is determined, the following can also be included: the flight progress of the unmanned aerial vehicle is calculated according to the real-time flight path data and the preset related route corresponding to the flight task property of the unmanned aerial vehicle. Correspondingly, the low-altitude activity signboard of the unmanned aerial vehicle is obtained by combining the illegal flight recognition result, the flight task property, the safety index, the alarm information and the real-time flight path data, including: the low-altitude activity signboard of the unmanned aerial vehicle is obtained by combining the illegal flight recognition result, the flight task property, the safety index, the alarm information, the real-time flight path data and the flight progress.

[0150] Specifically, the flight progress can be flight plan completion x%. The flight progress of the unmanned aerial vehicle can be calculated by any of the following methods.

[0151] In one implementation manner, the total length of the preset related route corresponding to the flight task property of the unmanned aerial vehicle can be calculated by accumulating the distance between adjacent points; the actual flight length corresponding to the real-time flight path data can be calculated according to the real-time flight path data; and the ratio between the actual flight length and the total length is calculated to obtain the flight progress.

[0152] In an implementation manner, the segmented task points corresponding to the preset relevant flight path according to the flight task nature of the UAV can be determined, such as an inspection area, a delivery point, etc.; the number of the segmented task points passed by the real-time flight path data is calculated according to the implementation flight path data; and the ratio between the number of the segmented task points passed by the real-time flight path data and the total number of the segmented task points of the preset relevant flight path is calculated to obtain the flight progress.

[0153] In the embodiment, after the flight task nature of the UAV is determined, the flight progress of the UAV is calculated according to the determined flight task nature of the UAV, and the flight progress of the UAV is taken as part of the low-altitude activity signboard, so that the user can control the UAV accurately according to the flight progress of the UAV.

[0154] S205, the historical violation data is weighted to obtain the safety index of the UAV.

[0155] Optionally, different weights can be given to the historical violation data of different airspaces, for example, higher weights are given to the historical violation data in the airport airspace, and lower weights are given to the historical violation data in the commercial airspace, the historical violation data of different airspaces is weighted to obtain the safety index of the UAV. Different weights can also be given to the historical violation data of different time periods, for example, higher weights are given to the historical violation data in the current week, and lower weights are given to the historical violation data before the current week, the historical violation data of different time periods is weighted to obtain the safety index of the UAV, and the basis and method of the weighting processing are not limited in the application.

[0156] In an optional implementation manner, the safety index of the UAV further includes dynamic airworthiness information of the UAV; the dynamic airworthiness information is obtained by the following steps: obtaining meteorological data and radio frequency band data in the current airspace; and determining the dynamic airworthiness information of the UAV based on the meteorological data and the radio frequency band data in combination with the real-time flight path data.

[0157] For example, the meteorological data can be obtained by a meteorological API, meteorological radar data, etc., including the weather conditions of the current position of the UAV and the area in front of the flight path, and the signal attenuation data corresponding to different weather conditions. The radio frequency band data can be obtained by a spectrum monitoring device or a database, including the occupation of each frequency band in the current airspace, the position and intensity of the interference source, etc.

[0158] According to the weather condition corresponding to each position in the meteorological data and the signal attenuation data corresponding to different weather conditions, the signal attenuation data corresponding to each position is determined, the signal attenuation data is interpolated into the real-time position of the unmanned aerial vehicle in the real-time flight path data, the occupation of each frequency band in the current airspace, the position and intensity of the interference source are interpolated into the real-time position of the unmanned aerial vehicle in the real-time flight path data, so as to determine the frequency band interference received by the real-time position of the unmanned aerial vehicle, wherein the frequency band interference includes two parts, the signal attenuation data and the radio frequency band data, and the frequency band interference is calculated according to the signal attenuation data and the radio frequency band data. It is judged whether the frequency band interference is greater than the interference threshold. When the frequency band interference is greater than the interference threshold, the first type of dynamic airworthiness information is generated; when the frequency band interference is not greater than the interference threshold, the second type of dynamic airworthiness information is generated according to the meteorological condition. The dynamic airworthiness information can include the risk level of the current unmanned aerial vehicle and the navigation suggestion corresponding to the risk level, such as the first type of dynamic airworthiness information, which can include: the current unmanned aerial vehicle is in a dangerous state, and it is recommended to avoid obstacles immediately; the second type of dynamic airworthiness information can include: if the current weather is sunny, it prompts "current safe state, recommend to continue navigation"; if the current weather is light rain, it prompts "current warning state, recommend to slow down and observe".

[0159] In the embodiment, the corresponding frequency band interference data is calculated through the meteorological data and the radio frequency band data in the current airspace, and the dynamic airworthiness information corresponding to the real-time flight path data of the unmanned aerial vehicle is dynamically determined according to the frequency band interference data, so as to facilitate the user to determine the airworthiness state of the unmanned aerial vehicle under the current environment, and to realize the precise control of the unmanned aerial vehicle, thereby avoiding the loss of the unmanned aerial vehicle.

[0160] S206, the low-altitude activity label of the unmanned aerial vehicle is obtained by combining the illegal flight identification result, the flight task nature, the safety index, the alarm information and the real-time flight path data.

[0161] In the embodiment, the low-altitude activity label is used for hierarchical monitoring and risk warning of each unmanned aerial vehicle in the airspace.

[0162] In addition, the flight preference identification model is trained to identify the flight preference of the unmanned aerial vehicle from the historical flight path data and the real-time flight path data, wherein the flight preference includes: flight smoothness, flight path deviation, flight area and flight time period.

[0163] Correspondingly, the low-altitude activity label of the unmanned aerial vehicle is obtained by combining the illegal flight identification result, the flight task nature, the safety index, the alarm information and the real-time flight path data, including:

[0164] The low-altitude activity label of the unmanned aerial vehicle is obtained by combining the illegal flight identification result, the flight task nature, the safety index, the alarm information, the real-time flight path data and the flight preference.

[0165] Optionally, the historical activity data of the unmanned aerial vehicles belonging to different flight preference groups can be obtained as the training sample data set, and flight preference features such as flight smoothness, track deviation, flight area and flight period belonging to different flight preference groups can be extracted, and the flight preference recognition model can be trained by using the training sample data set of different flight preference groups, so that the flight preference recognition model has the ability to identify the flight preference category corresponding to the flight route data. The historical flight track data and the real-time flight track data of the unmanned aerial vehicle are fused to obtain fused flight track data, the fused flight track data is input into the trained flight preference recognition model, and the similarity between the fused flight track data and the historical activity data of the unmanned aerial vehicle of different flight preference groups is output, so as to determine the flight preference of the unmanned aerial vehicle as the flight preference corresponding to the flight preference group with the highest similarity.

[0166] Optionally, the low-altitude activity sign can also include whether the unmanned aerial vehicle is a cooperative target. According to the official unmanned aerial vehicle registration database, combined with the flight activity declaration data of the unmanned aerial vehicle, it is determined whether the unmanned aerial vehicle is in the official unmanned aerial vehicle registration database and whether the unmanned aerial vehicle has made flight activity declaration. When the unmanned aerial vehicle is not in the official unmanned aerial vehicle registration database or the unmanned aerial vehicle has not made flight activity declaration, it is determined that the unmanned aerial vehicle is not a cooperative target. When the unmanned aerial vehicle is in the official unmanned aerial vehicle registration database and the unmanned aerial vehicle has made flight activity declaration, it is determined that the unmanned aerial vehicle is a cooperative target.

[0167] In this embodiment, the flight preference of the unmanned aerial vehicle is identified and determined from multiple dimensions such as flight smoothness, track deviation, flight area and flight period by the flight preference recognition model, and the flight preference is added to the low-altitude activity sign, so as to facilitate subsequent use of the low-altitude activity sign flight preference to quickly determine whether to perform key monitoring on the unmanned aerial vehicle, and further improve the unmanned aerial vehicle monitoring effect.

[0168] The low-altitude activity tag generation method provided in the application improves the detection accuracy of the flight track of the unmanned aerial vehicle, facilitates the identification and warning of the illegal flight of the unmanned aerial vehicle in the current airspace, and further improves the accuracy of subsequent screening, monitoring and risk warning using real-time flight track data. The low-altitude activity tag of the unmanned aerial vehicle is generated by using the real-time flight track data of the unmanned aerial vehicle and combining the historical flight activity data of the unmanned aerial vehicle, so as to realize the activity tracking and dynamic information tag generation of the unmanned aerial vehicle. The low-altitude activity tag includes the flight task nature and safety index of the unmanned aerial vehicle, facilitating the user to monitor the unmanned aerial vehicles in the airspace in different levels by using these tags. The low-altitude activity tag also includes the real-time flight track of the unmanned aerial vehicle, facilitating the intuitive activity tracking of the unmanned aerial vehicle, providing situational and personalized unmanned aerial vehicle monitoring means for the jurisdiction airspace, and facilitating the realization of the functions of unmanned aerial vehicle identity recognition, real-time sensing, intelligent portrait, individual identification, dynamic command, operation scheduling, automatic warning, handover disposal and the like by using the unmanned aerial vehicle activity tag, and realizing the technical effect of improving the monitoring effect of the unmanned aerial vehicle.

[0169] Figure 3 A flowchart of an airspace management method based on a low-altitude activity tag is provided for the embodiments of the application. The method can be executed by an airspace management device based on a low-altitude activity tag, which can be a server or an electronic device. The method in the embodiments can be realized by software, hardware or a combination of software and hardware, as shown in the figure, and the method includes the following steps: Figure 3

[0170] S301, obtaining low-altitude activity tags of all unmanned aerial vehicles in the airspace.

[0171] In the embodiments, the low-altitude activity tags are generated by the low-altitude activity tag generation method described above.

[0172] S302, screening all low-altitude activity tags in the airspace based on the screening dimensions corresponding to the airspace, and determining a target unmanned aerial vehicle to be monitored.

[0173] In an optional embodiment, screening all low-altitude activity tags in the airspace based on the screening dimensions corresponding to the airspace, and determining a target unmanned aerial vehicle to be monitored, includes:

[0174] determining whether the safety index of the current low-altitude activity tag is less than a safety index threshold;

[0175] when the safety index of the current low-altitude activity tag is less than the safety index threshold, determining that the unmanned aerial vehicle corresponding to the current low-altitude activity tag is the target unmanned aerial vehicle to be monitored;

[0176] ​And / or, determine whether the flight smoothness in the flight preference of the current low-altitude activity marker is less than a flight smoothness threshold value;

[0177] When the flight smoothness in the flight preference of the current low-altitude activity marker is less than the flight smoothness threshold value, determine that the unmanned aerial vehicle corresponding to the current low-altitude activity marker is the target unmanned aerial vehicle to be monitored.

[0178] In the embodiment, based on the screening dimension corresponding to the airspace, all low-altitude activity markers in the airspace are screened to determine the target unmanned aerial vehicle to be monitored, including: using the flight task property and the flight preference to perform weighted calculation on the real-time track data to obtain the flight smoothness of the unmanned aerial vehicle; when the flight smoothness of the unmanned aerial vehicle is less than the smoothness threshold value, the unmanned aerial vehicle is determined as the target unmanned aerial vehicle to be monitored.

[0179] Optionally, the flight preference portrait of the flight smoothness dimension of the unmanned aerial vehicle can be used to screen the unmanned aerial vehicle with the flight smoothness lower than the flight smoothness threshold value, and the unmanned aerial vehicle with the flight smoothness lower than the flight smoothness threshold value can be monitored. In the real-time monitoring process, the flight preference portrait of the safety index dimension of the unmanned aerial vehicle can be used to screen the unmanned aerial vehicle with the safety index lower than the safety index threshold value, and the unmanned aerial vehicle with the safety index lower than the safety index threshold value can be determined as the unmanned aerial vehicle with more illegal flights, and the unmanned aerial vehicle with the safety index lower than the safety index threshold value can be monitored.

[0180] S303, using the remaining information of the low-altitude activity marker of the target unmanned aerial vehicle, performing risk warning on the target unmanned aerial vehicle.

[0181] In an optional implementation manner, using the low-altitude activity marker of the target unmanned aerial vehicle to perform risk warning on the target unmanned aerial vehicle includes: using the pre-trained risk warning model to perform track risk warning identification on the real-time track data, the flight task property and the flight preference of the target unmanned aerial vehicle to obtain the track risk warning result of the target unmanned aerial vehicle; performing weighted calculation on the number of illegal flight identification results, the safety index and the number of alarm information of the target unmanned aerial vehicle, and fitting to obtain the illegal risk warning result; based on the track risk warning result and the illegal risk warning result, performing risk warning on the target unmanned aerial vehicle.

[0182] Optionally, after the preliminary screening, the remaining content in the low-altitude activity marker of the unmanned aerial vehicle can be used to perform risk warning on the unmanned aerial vehicle, and the risk of the unmanned aerial vehicle can be further identified in detail through the risk warning model, the collision identification model and the like.

[0183] In an implementation scenario, Figure 4A A large-screen display of real-time track data in a low-altitude activity marker provided by the embodiment of the application; Figure 4BA large screen display of the UAV basic data and the illegal flight identification result in the low-altitude activity marker is provided for the embodiments of the present application. Figure 4C A large screen display of the flight task nature, the safety index and the historical illegal flight data in the low-altitude activity marker is provided for the embodiments of the present application. Figure 4D A large screen display of the alarm information in the low-altitude activity marker is provided for the embodiments of the present application. The low-altitude flight service center sends the low-altitude activity marker to the visual large screen, referring to Figures 4A to 4D The visual large screen displays the real-time flight path data, the UAV basic data, the illegal flight identification result, the flight task nature, the safety index, the historical illegal flight data and the alarm information in the low-altitude activity marker. In response to the input instruction of the user, the visual large screen determines the low-altitude activity marker of the UAV selected by the user according to the input instruction of the user, and displays the low-altitude activity marker of the UAV selected by the user. The specific content displayed on the large screen is not limited in the present application.

[0184] In another implementation scenario, the low-altitude flight service center sends the low-altitude activity marker to the device of the business upstream and downstream corresponding to the application in response to the application of the business upstream and downstream, so that the device of the business upstream and downstream performs corresponding scheduling and command on the UAV according to the low-altitude activity marker.

[0185] Figure 5 A structural schematic diagram of a low-altitude activity marker generation device is provided for the embodiments of the present application. Referring to Figure 5 The device comprises:

[0186] The flight path data fitting module 501 is configured to collect flight activity data of the UAV in the current airspace, and fit to obtain real-time flight path data of the UAV based on the flight activity data.

[0187] The data acquisition module 502 is configured to acquire basic data and historical flight activity data of the UAV, wherein the historical flight activity data comprises historical flight path data and historical illegal flight data.

[0188] The illegal flight feature identification module 503 is configured to identify illegal flight features of the UAV based on the basic data and the real-time flight path data, to obtain illegal flight identification result of the UAV in the current airspace and alarm information corresponding to the illegal flight identification result.

[0189] The flight task nature determination module 504 is configured to perform similarity verification on the real-time flight path data and the historical flight path data with a preset related route, to determine the flight task nature of the UAV.

[0190] The safety index acquisition module 505 is configured to perform weighted processing on the historical illegal flight data, to obtain the safety index of the UAV.

[0191] The UAV signboard module 506 is configured to combine the illegal flight identification result, the flight task nature, the safety index, the warning information and the real-time flight path data to obtain a low-altitude activity signboard of the UAV, and the low-altitude activity signboard is used for hierarchical monitoring and risk early warning of each UAV in the airspace.

[0192] In a possible implementation, the illegal feature identification module 503 is specifically configured to:

[0193] Based on the basic data, the real-time flight path data is compared and identified with the flight height limit in the airspace to obtain an over-height identification result of the UAV;

[0194] Based on the basic data, it is judged whether the real-time flight path data exists illegal intrusion and / or enters the operation area / air route risk, and an illegal intrusion identification result and an entering operation area / air route risk identification result are identified;

[0195] The pre-trained collision prediction model is used for UAV operation situation awareness on the real-time flight path data, and according to the identified real-time operation situation of the UAV, a UAV obstacle collision risk identification result and a UAV other UAV collision risk identification result are determined;

[0196] The over-height identification result, the illegal intrusion identification result, the entering operation area / air route risk identification result, the UAV obstacle collision risk identification result and the UAV other UAV collision risk identification result are combined to generate the warning information corresponding to the illegal flight identification result.

[0197] In a possible implementation, the flight task nature determination module 504 is specifically configured to:

[0198] The real-time flight path data and the historical flight path data are respectively used for similarity calculation with the first type of preset related air route, and a similarity calculation result is fitted;

[0199] When the similarity calculation result is greater than a preset threshold, it is determined that the flight task nature of the UAV is a fixed air route type, and the first type of preset related air route is used to represent the related air route of the fixed air route type UAV;

[0200] When the similarity calculation result is less than or equal to the preset threshold, the real-time flight path data and the historical flight path data are identified, and the flight task nature of the UAV is determined according to the operation identification result.

[0201] In a possible implementation, the flight task nature determination module 504 is specifically configured to:

[0202] According to the operation flight route deviation angle, the coincidence coverage rate and the turning angle in the real-time flight path data, a preset real-time dynamic positioning operation identification model is used to identify and determine whether the flight task nature of the UAV is a geographic mapping type;

[0203] If the flight task nature of the UAV is not a geographic mapping type, it is determined that the flight task nature of the UAV is a consumer type.

[0204] In a possible implementation, the apparatus further includes:

[0205] acquiring meteorological data and radio frequency band data in the current airspace;

[0206] based on the meteorological data and the radio frequency band data, in combination with real-time flight path data, determining dynamic airworthiness information of the UAV;

[0207] Correspondingly, the UAV label module 506 is further specifically configured to:

[0208] combining the illegal flight identification result, the flight task nature, the safety index, the alarm information, the real-time flight path data and the dynamic airworthiness information to obtain a low-altitude activity label of the UAV.

[0209] In a possible implementation, the apparatus further includes:

[0210] using the trained flight preference identification model to perform flight preference identification on the historical flight path data and the real-time flight path data to determine flight preference of the UAV; wherein the flight preference includes flight smoothness, flight path deviation, flight region and flight time period;

[0211] Correspondingly, the UAV label module 506 is further specifically configured to:

[0212] combining the illegal flight identification result, the flight task nature, the safety index, the alarm information, the real-time flight path data and the flight preference to obtain a low-altitude activity label of the UAV.

[0213] The low-altitude activity label generation apparatus provided in this embodiment can perform the method provided in the method embodiment, and has similar implementation principles and technical effects, which will not be described here in detail.

[0214] Figure 6 A structural schematic diagram of an airspace management apparatus based on a low-altitude activity label is provided in this embodiment. Referring to Figure 6 The apparatus includes:

[0215] The low-altitude activity label acquisition module 601 is configured to, when it is identified that the flight body is a UAV, acquire a low-altitude activity label of the UAV, wherein the low-altitude activity label is generated by the low-altitude activity label generation apparatus described above;

[0216] The UAV preliminary screening module 602 is configured to screen all low-altitude activity labels in the airspace based on a screening dimension corresponding to the airspace to determine a target UAV to be monitored; wherein the screening dimension is used to indicate part of the information in the low-altitude activity label.

[0217] The risk warning module 603 is configured to perform risk warning on the target unmanned aerial vehicle by using the remaining information of the low-altitude activity marker of the target unmanned aerial vehicle.

[0218] In a possible implementation, the low-altitude activity marker comprises a safety index and a flight preference.

[0219] The unmanned aerial vehicle preliminary screening module 602 is specifically configured to:

[0220] determine whether the safety index of the current low-altitude activity marker is less than a safety index threshold value;

[0221] when the safety index of the current low-altitude activity marker is less than the safety index threshold value, determine that the unmanned aerial vehicle corresponding to the current low-altitude activity marker is the target unmanned aerial vehicle to be monitored;

[0222] and / or determine whether the flight smoothness in the flight preference of the current low-altitude activity marker is less than a flight smoothness threshold value;

[0223] when the flight smoothness in the flight preference of the current low-altitude activity marker is less than the flight smoothness threshold value, determine that the unmanned aerial vehicle corresponding to the current low-altitude activity marker is the target unmanned aerial vehicle to be monitored.

[0224] In a possible implementation, the risk warning module 603 is specifically configured to:

[0225] perform real-time track risk warning identification on real-time track data, flight task properties and flight preferences of the target unmanned aerial vehicle by using the pre-trained risk warning model, to obtain a track risk warning result of the target unmanned aerial vehicle;

[0226] perform weighted calculation on the number of the violation flight identification result, the safety index and the number of the alarm information of the target unmanned aerial vehicle, and fit to obtain a violation risk warning result;

[0227] perform risk warning on the target unmanned aerial vehicle based on the track risk warning result and the violation risk warning result.

[0228] The airspace management apparatus based on the low-altitude activity marker provided in this embodiment can perform the method provided in the method embodiments, and has similar implementation principles and technical effects, which will not be described here in detail.

[0229] Figure 7 The structural schematic diagram of the electronic device provided in this application is shown in FIG. 7. Figure 7 As shown in FIG. 7, the electronic device 70 provided in this embodiment comprises at least one processor 701 and a memory 702. Optionally, the device 70 further comprises a communication component 703. The processor 701, the memory 702 and the communication component 703 are connected through a bus.

[0230] In the implementation process, the at least one processor 701 executes the computer-executable instructions stored in the memory 702, so that the at least one processor 701 executes the method described above.

[0231] The specific implementation process of the processor 701 can refer to the method embodiments described above, which have similar implementation principles and technical effects, and details are not described here again in the embodiment.

[0232] In the above embodiments, it should be understood that the processor can be a central processing unit (English: Central Processing Unit, CPU for short), and can also be other general-purpose processors, digital signal processors (English: Digital Signal Processor, DSP for short), application specific integrated circuits (English: Application Specific Integrated Circuit, ASIC for short), etc. The general-purpose processor can be a microprocessor, or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0233] The memory can include a random access memory (RAM), and can also include a non-volatile memory (NVM), for example at least one disk memory.

[0234] The bus can be an industry standard architecture (ISA) bus, a peripheral component (PCI) bus, or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, the bus in the drawings of the present application does not limit to only one bus or one type of bus.

[0235] The present application also provides a computer program product, comprising a computer program, which is executed by a processor to implement the method described above.

[0236] The present application also provides a computer-readable storage medium, which stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method described above is implemented.

[0237] The above-mentioned readable storage medium can be realized by any type of volatile or nonvolatile storage devices or their combinations, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0238] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist as discrete components in the device.

[0239] The division of units is only a logical functional division, and in actual implementation, there can be another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0240] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment scheme.

[0241] In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit.

[0242] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the embodiments of the method of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0243] It can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The aforementioned program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, and various media that can store program codes.

[0244] Finally, it should be noted that: those skilled in the art will easily think of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application that follow the general principles of the present application and include common knowledge or conventional technical means in the art that are not disclosed in the present application, and is not limited to the precise structure described above and shown in the drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is only limited by the appended claims.

Claims

1. A low altitude activity sign generation method, characterized by, The method comprises: Collecting flight activity data of unmanned aerial vehicles in a current airspace, and fitting real-time flight path data of the unmanned aerial vehicles based on the flight activity data; Obtaining basic data and historical flight activity data of the unmanned aerial vehicles, wherein the historical flight activity data comprises historical flight path data and historical violation data; Based on the basic data and the real-time flight path data, identifying the unmanned aerial vehicle violation flight characteristics of the unmanned aerial vehicles to obtain the unmanned aerial vehicle violation flight identification result in the current airspace and the alarm information corresponding to the violation flight identification result; Using the real-time flight path data and the historical flight path data, similarity verification is performed with a preset relevant flight path to determine the flight task nature of the unmanned aerial vehicles; The historical violation data is weighted to obtain a safety index of the unmanned aerial vehicles; The violation flight identification result, the flight task nature, the safety index, the alarm information and the real-time flight path data are combined to obtain a low-altitude activity label of the unmanned aerial vehicles, and the low-altitude activity label is used for hierarchical monitoring and risk warning of each unmanned aerial vehicle in the airspace.

2. The method of claim 1, wherein, Based on the basic data and the real-time flight path data, the unmanned aerial vehicle violation flight characteristics of the unmanned aerial vehicles are identified to obtain the unmanned aerial vehicle violation flight identification result in the current airspace and the alarm information corresponding to the violation flight identification result, comprising: Based on the basic data, the real-time flight path data is compared and identified with the flight height limit in the airspace to obtain an ultra-high identification result of the unmanned aerial vehicles; Based on the basic data, it is judged whether the real-time flight path data exists violation intrusion and / or enters the operation area / flight path risk to identify the violation intrusion identification result and the entering operation area / flight path risk identification result; Using a pre-trained collision prediction model, unmanned aerial vehicle operation situation awareness is performed on the real-time flight path data, and according to the identified real-time operation situation of the unmanned aerial vehicles, the unmanned aerial vehicle collision risk identification result with obstacles and the unmanned aerial vehicle collision risk identification result with other unmanned aerial vehicles are determined; The ultra-high identification result, the violation intrusion identification result, the entering operation area / flight path risk identification result, the unmanned aerial vehicle collision risk identification result with obstacles and the unmanned aerial vehicle collision risk identification result with other unmanned aerial vehicles are combined to generate the alarm information corresponding to the violation flight identification result.

3. The method of claim 1, wherein, Using the real-time flight path data and the historical flight path data, similarity verification is performed with a preset relevant flight path to determine the flight task nature of the unmanned aerial vehicles, comprising: Similarity calculation is performed on the real-time flight path data and the historical flight path data with a first type of preset relevant flight path respectively to obtain a similarity calculation result; When the similarity calculation result is greater than a preset threshold, it is determined that the flight task nature of the unmanned aerial vehicles is a fixed flight path type, and the first type of preset relevant flight path is used to represent the relevant flight path of the fixed flight path type unmanned aerial vehicles; When the similarity calculation result is less than or equal to a preset threshold, operation identification is performed on the real-time flight path data and the historical flight path data, and the flight task nature of the unmanned aerial vehicles is determined according to the operation identification result.

4. The method of claim 3, wherein, The operation recognition on the real-time flight path data and the historical flight path data determines the flight task property of the UAV according to the operation recognition result, and the flight task property of the UAV is determined as a geographic surveying and mapping type according to the operation recognition result. The flight task property of the UAV is determined as a consumer type if the flight task property of the UAV is not the geographic surveying and mapping type. The safety index of the UAV further includes UAV dynamic airworthiness information, and the method further includes:

5. The method of claim 1, wherein, acquiring meteorological data and radio frequency band data in a current airspace; determining the dynamic airworthiness information of the UAV based on the meteorological data and the radio frequency band data and in combination with the real-time flight path data. The method further includes:

6. The method of claim 1, wherein, performing flight preference recognition on the historical flight path data and the real-time flight path data by using a trained flight preference recognition model to determine the flight preference of the UAV, wherein the flight preference includes flight smoothness, flight path deviation, flight area, and flight time period. Correspondingly, the combination of the illegal flight recognition result, the flight task property, the safety index, the warning information, and the real-time flight path data to obtain the low-altitude activity label of the UAV includes: The combination of the illegal flight recognition result, the flight task property, the safety index, the warning information, the real-time flight path data, and the flight preference to obtain the low-altitude activity label of the UAV. The method includes:

7. A method for airspace management based on low altitude activity signs, characterized in that, acquiring low-altitude activity labels of all UAVs in an airspace, wherein the low-altitude activity labels are generated by using the low-altitude activity label generation method in any one of claims 1-6; screening all low-altitude activity labels in the airspace based on a screening dimension corresponding to the airspace to determine a target UAV to be monitored, wherein the screening dimension is used to indicate part of information in the low-altitude activity label; performing risk warning on the target UAV by using the low-altitude activity label of the target UAV. The low-altitude activity label includes a safety index and a flight preference.

8. The method of claim 7, wherein, The screening of all low-altitude activity labels in the airspace based on a screening dimension corresponding to the airspace to determine a target UAV to be monitored includes: determining whether the safety index of a current low-altitude activity label is less than a safety index threshold value; determining that the UAV corresponding to the current low-altitude activity label is the target UAV to be monitored when the safety index of the current low-altitude activity label is less than the safety index threshold value; and / or, determining whether the flight smoothness in the flight preference of the current low-altitude activity label is less than a flight smoothness threshold value; determining that the UAV corresponding to the current low-altitude activity label is the target UAV to be monitored when the flight smoothness in the flight preference of the current low-altitude activity label is less than the flight smoothness threshold value. The risk warning on the target UAV by using the low-altitude activity label of the target UAV includes:

9. The method of claim 8, wherein, ​ The pre-trained risk early warning model is used for flight path risk early warning identification on real-time flight path data, flight task properties and flight preferences of the target UAV, to obtain flight path risk early warning results of the target UAV; The number of the target UAV's illegal flight identification results, the safety index and the number of the alarm information are weighted and calculated to obtain the illegal risk early warning results through fitting; The target UAV is given a risk early warning based on the flight path risk early warning results and the illegal risk early warning results.

10. A low altitude activity sign generating device characterized by, It comprises: a flight path data fitting module for collecting flight activity data of UAVs in the current airspace, and fitting real-time flight path data of the UAVs based on the flight activity data; a data acquisition module for acquiring basic data and historical flight activity data of the UAVs, the historical flight activity data including historical flight path data and historical illegal data; an illegal feature identification module for identifying illegal flight features of the UAVs based on the basic data and the real-time flight path data, to obtain illegal flight identification results of the UAVs in the current airspace and alarm information corresponding to the illegal flight identification results; a flight task property determination module for verifying the similarity of the real-time flight path data and the historical flight path data with a preset relevant route, to determine the flight task properties of the UAVs; a safety index acquisition module for weighting the historical illegal data to obtain a safety index of the UAVs; a UAV label module for combining the illegal flight identification results, the flight task properties, the safety index, the alarm information and the real-time flight path data to obtain a low-altitude activity label of the UAVs, which is used for hierarchical monitoring and risk early warning of UAVs in the airspace.

11. An airspace management device based on low-altitude activity signage, characterized by, It comprises: a low-altitude activity label acquisition module for acquiring low-altitude activity labels of all UAVs in the airspace, wherein the low-altitude activity labels are generated by the low-altitude activity label generation device of claim 10; a UAV preliminary screening module for screening all low-altitude activity labels in the airspace based on a corresponding screening dimension of the airspace to determine a target UAV to be monitored, wherein the screening dimension is used to indicate part of the information in the low-altitude activity labels; a risk early warning module for giving a risk early warning to the target UAV by using the low-altitude activity label of the target UAV.

12. An electronic device, comprising: It comprises: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, so that the at least one processor executes the low-altitude activity label generation method of any one of claims 1-6 or executes the airspace management method based on the low-altitude activity label of any one of claims 7-9.

13. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer execution instructions, which are executed by the processor to implement the low-altitude activity label generation method of any one of claims 1-6 or the airspace management method based on the low-altitude activity label of any one of claims 7-9.

14. A computer program product, characterised in that, A computer program product comprising a computer program which, when executed by a processor, implements the low-altitude activity marker generation method according to any one of claims 1-6 or the low-altitude activity marker based airspace management method according to any one of claims 7-9. A computer program product comprising a computer program which, when executed by a processor, implements the low-altitude activity marker generation method according to any one of claims 1-6 or the low-altitude activity marker based airspace management method according to any one of claims 7-9.

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