Unmanned aerial vehicle traffic management method and device, electronic equipment and storage medium

Abstract

The invention provides an unmanned aerial vehicle traffic management method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-source weather forecast time series data of a target region in a future time period, and determining the models of a plurality of unmanned aerial vehicles corresponding to the target region; fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; on the basis of the models of the multiple unmanned aerial vehicles and a pre-constructed database, abnormal recognition is carried out on the fused weather forecast time sequence data, and an abnormal weather recognition result is obtained; performing risk assessment on the target unmanned aerial vehicle according to an abnormal weather identification result under the condition of determining that the abnormal weather exists, and generating a no-fly zone under the condition of determining that the medium risk and / or the high risk exists; wherein the database comprises abnormal meteorological detection rules corresponding to the unmanned aerial vehicles of various models. According to the invention, the abnormal weather identification efficiency and the traffic management efficiency are improved, the rapid response to the abnormal weather is facilitated, and the flight safety of the unmanned aerial vehicle can be ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of drone management, and in particular to a drone traffic management method, device, electronic equipment and storage medium. Background Art

[0002] The use of drones is becoming more and more popular, involving logistics, monitoring, agriculture and other fields. With the surge in the number of drones, traffic management has become particularly important, especially in the face of sudden abnormal weather. At present, in the face of sudden abnormal weather, manual methods are usually used to identify abnormal weather and manually define no-fly zones, which is inefficient and slow to respond, resulting in drones facing safety risks. Summary of the invention

[0003] The present invention provides a method, device, electronic device and storage medium for managing unmanned aerial vehicle traffic, which are used to solve the defects of the prior art that abnormal weather identification is performed manually and no-fly zones are manually demarcated, which is inefficient and slow to respond, and may cause safety risks to unmanned aerial vehicles.

[0004] In a first aspect, the present invention provides a method for managing drone traffic, comprising: Acquire multi-source weather forecast time series data for a target area in a future time period, and determine the models of multiple drones corresponding to the target area; fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; Based on the models of the multiple drones and a pre-built database, anomaly identification is performed on the fused weather forecast time series data to obtain an abnormal weather identification result; When it is determined that abnormal weather exists, a risk assessment is performed on the target UAV based on the abnormal weather identification result, and when it is determined that there is a medium risk and / or a high risk, a no-fly zone is generated; Among them, the database includes abnormal weather detection rules corresponding to each type of drone, and the abnormal weather detection rules corresponding to each type of drone include: if a specific value of the weather forecast data is greater than a preset threshold corresponding to each type of drone, it is determined that abnormal weather exists.

[0005] In some embodiments, based on the models of the multiple drones and a pre-built database, the fused weather forecast time series data is subjected to anomaly identification to obtain an abnormal weather identification result, including: According to the models of the multiple drones, query the database to obtain abnormal weather detection rules corresponding to the multiple drones; According to the abnormal weather detection rules corresponding to the multiple UAVs, abnormalities are identified on the fused weather forecast time series data to obtain abnormal weather identification results.

[0006] In some embodiments, when it is determined that abnormal weather exists, performing risk assessment on the target UAV according to the abnormal weather identification result includes: According to the abnormal weather identification result, determining a target drone affected by abnormal weather in a future time period from the multiple drones; The potential risk of the target UAV flying in the future time period is evaluated to obtain a risk level.

[0007] In some embodiments, generating a no-fly zone when determining that there is a medium risk and / or a high risk includes: In the case where it is determined that there is a medium risk and / or a high risk, determining an affected area of ​​the abnormal weather from the target area according to the abnormal weather identification result; According to a preset buffer threshold, the periphery of the affected area is expanded, and the coordinates of the expanded area are transformed to obtain polyhedron information; A no-fly zone is generated based on the polyhedron information.

[0008] In some embodiments, fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data includes: Performing data cleaning on the multi-source weather forecast time series data to obtain cleaned multi-source weather forecast time series data; The cleaned multi-source weather forecast time series data are spatially aligned to obtain the fused weather forecast time series data.

[0009] In some embodiments, after generating the no-fly zone, the method further includes: Encapsulating the abnormal weather identification result and the location information of the no-fly zone to obtain warning information; Broadcast the warning information to the target UAV.

[0010] In some embodiments, the models of the multiple drones include: models of drones currently flying in the target area, and models of drones planned to fly in the target area in a future time period.

[0011] In a second aspect, the present invention further provides a drone traffic management device, comprising: An acquisition unit, used to acquire multi-source weather forecast time series data for a target area in a future time period, and acquire models of multiple UAVs corresponding to the target area; A fusion unit, used for fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; an identification unit, configured to identify abnormalities of the fused weather forecast time series data based on the models of the plurality of UAVs and a pre-built database, and obtain abnormal weather identification results; A generating unit is used to, when it is determined that abnormal weather exists, perform a risk assessment on the target UAV according to the abnormal weather identification result, and generate a no-fly zone when it is determined that medium risk and / or high risk exists; Among them, the database includes abnormal weather detection rules corresponding to each type of drone, and the abnormal weather detection rules corresponding to each type of drone include: if a specific value of the weather forecast data is greater than a preset threshold corresponding to each type of drone, it is determined that abnormal weather exists.

[0012] In a third aspect, the present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements any of the above-mentioned drone traffic management methods.

[0013] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described drone traffic management methods.

[0014] The drone traffic management method, device, electronic device and storage medium provided by the present invention obtain multi-source weather forecast time series data of a target area in a future time period, determine the models of multiple drones corresponding to the target area, fuse the multi-source weather forecast time series data to obtain fused weather forecast time series data, perform abnormality identification on the fused weather forecast time series data based on the models of multiple drones and abnormal weather detection rules in a pre-constructed database to obtain abnormal weather identification results, and when it is determined that abnormal weather exists, perform risk assessment on the target drone based on the abnormal weather identification results, and when it is determined that medium risk and / or high risk exist, generate a no-fly zone, with a high degree of automation, improving the efficiency of abnormal weather identification and traffic management efficiency, facilitating a rapid response to abnormal weather, and ensuring the safety of drone flight. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a schematic diagram of the structure of a drone traffic management system provided by an embodiment of the present invention; Figure 2 This is one of the flow charts of the UAV traffic management method provided by an embodiment of the present invention; Figure 3 This is a second flow chart of the UAV traffic management method provided by an embodiment of the present invention; Figure 4 is a schematic diagram of the structure of a drone traffic management device provided by an embodiment of the present invention; Figure 5 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] At present, the response process of traditional UAV traffic management system to various low-altitude abnormal weather is mainly manual, and its defects are mainly reflected in the following aspects: 1) Decision-making response is delayed. The process of manually identifying meteorological anomalies and making decisions is slow, and it is impossible to respond quickly to the ever-changing meteorological conditions; 2) Incomplete monitoring coverage: manual monitoring is difficult to achieve all-weather, all-round meteorological monitoring, and local sudden meteorological events are easily missed; 3) Low efficiency of information dissemination. The traditional point-to-point communication method of "controller-drone operator" limits the timely dissemination of emergency information, resulting in some drones continuing to fly under dangerous weather conditions; 4) The degree of automation is low. The existing system is difficult to automatically adjust the flight plan to avoid obstacles, which increases the risk of drone flight.

[0019] To this end, the embodiment of the present invention provides a method, device, electronic device and storage medium for drone traffic management, which obtains multi-source weather forecast time series data for a future time period of a target area, determines the models of multiple drones corresponding to the target area, fuses the multi-source weather forecast time series data, and performs abnormal identification on the fused weather forecast time series data based on the models of multiple drones and abnormal weather detection rules in a pre-built database. When it is determined that abnormal weather exists, a risk assessment is performed on the target drone based on the abnormal weather identification results, and a no-fly zone is generated when it is determined that medium risk and / or high risk exist. The present invention has a high degree of automation, can improve the efficiency of abnormal weather identification and traffic management efficiency, is helpful for rapid response to abnormal weather, and can ensure the safety of drone flight.

[0020] Figure 1 A schematic diagram of the structure of the UAV traffic management system provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, a drone traffic management system is provided, which includes a low-altitude air traffic management system, an external weather forecast system, various types of drones in the air and drone operators; the low-altitude air traffic management system is connected to the external weather forecast system and drone operators through Ethernet communication, and the low-altitude air traffic management system is connected to various types of drones in the air through 4G / 5G / satellite and other communication methods.

[0021] Figure 2 This is a flow chart of a method for managing drone traffic provided by an embodiment of the present invention. Figure 2 As shown, a method for managing unmanned aerial vehicle traffic is provided, and the method for managing unmanned aerial vehicle traffic is applied to Figure 1 The low altitude air traffic management system terminal in FIG. 1 is used as an example to illustrate the method, which includes the following steps: step 210, step 220, step 230 and step 240. The method flow steps are only used as a possible implementation mode of the present invention.

[0022] Step 210: Obtain multi-source weather forecast time series data for the target area in the future time period, and determine the models of multiple UAVs corresponding to the target area.

[0023] Optionally, multi-source weather forecast time series data is periodically acquired through the Meteorological Bureau, civil aviation weather forecast stations or low-altitude weather forecast stations.

[0024] Among them, the multi-source meteorological forecast time series data includes at least: wind speed, wind direction, rainfall intensity, thunderstorm activity, visibility, temperature and humidity.

[0025] In some embodiments, the models of the multiple drones include: models of drones currently flying in the target area, and models of drones planned to fly in the target area in a future time period.

[0026] It should be noted that different models of drones have different performances and different tolerances to abnormal weather conditions. By determining the models of drones currently flying in the target area and the models of drones planned to fly in the target area in the future, it is helpful to quickly lock on to the affected target drones.

[0027] Step 220: Fuse the multi-source weather forecast time series data to obtain fused weather forecast time series data.

[0028] It can be understood that by fusing multi-source weather forecast time series data, the fused weather forecast time series data can provide a more comprehensive and accurate data basis for abnormal weather identification.

[0029] In some embodiments, the multi-source weather forecast time series data is fused to obtain the fused weather forecast time series data, including: Performing data cleaning on multi-source weather forecast time series data to obtain cleaned multi-source weather forecast time series data; The cleaned multi-source weather forecast time series data are spatially aligned to obtain the fused weather forecast time series data.

[0030] Optionally, invalid data and erroneous data in the multi-source weather forecast time series data are eliminated, and missing values ​​in the multi-source weather forecast time series data are filled.

[0031] Optionally, the cleaned multi-source weather forecast time series data are spatially aligned, a compensation algorithm is used to fill in the blank areas, and geographic coordinate conversion is performed to ensure that all data are based on the same coordinate system, thereby forming a continuous and smooth weather forecast grid.

[0032] Optionally, missing values ​​are filled with specific values ​​(such as using the mean, median, or value of the previous moment), or interpolation methods (such as linear interpolation and spline interpolation) are used to estimate missing values.

[0033] Step 230: Based on the models of multiple UAVs and a pre-built database, anomaly identification is performed on the fused weather forecast time series data to obtain an abnormal weather identification result.

[0034] Among them, the database includes abnormal weather detection rules corresponding to each type of drone. The abnormal weather detection rules corresponding to each type of drone include: if the specific value of the weather forecast data is greater than the preset threshold corresponding to each type of drone, it is determined that abnormal weather exists.

[0035] Optionally, key performance indicators of each type of drone are obtained in advance, such as the maximum allowable wind speed of the drone, the maximum allowable rainfall intensity of the drone, the maximum endurance time and the minimum remaining endurance time, the maximum return distance, the maximum turning rate, the minimum hovering radius, the maximum ceiling, the longest hovering time, etc.

[0036] Optionally, a preset threshold corresponding to each model of UAV is determined based on key performance indicators of each model of UAV.

[0037] For example, the preset wind speed threshold corresponding to each type of drone is determined based on the maximum allowable wind speed of each type of drone; for another example, the preset rainfall intensity threshold corresponding to each type of drone is determined based on the maximum allowable rainfall intensity of each type of drone.

[0038] Optionally, for multi-rotor models, the abnormal weather detection rules corresponding to each model of drone include: If the wind speed in the weather forecast data is greater than the preset wind speed threshold corresponding to each type of drone, it is determined that abnormal weather exists; If the rainfall intensity of the weather forecast data is greater than the preset rainfall intensity threshold corresponding to each type of drone, it is determined that abnormal weather exists; If the distance between the return / alternate landing point and the abnormal cloud cluster is greater than the preset return distance threshold corresponding to each type of drone, it is determined that abnormal weather exists.

[0039] In some embodiments, based on the models of multiple drones and a pre-built database, abnormality recognition is performed on the fused weather forecast time series data to obtain abnormal weather recognition results, including: According to the models of multiple drones, query in the database to obtain abnormal weather detection rules corresponding to multiple drones; According to the abnormal weather detection rules corresponding to multiple UAVs, the fused weather forecast time series data is used to identify anomalies and obtain abnormal weather identification results.

[0040] Optionally, based on the fused weather forecast time series data, the forecast wind speed, forecast rainfall intensity, and the distance between the return / alternate landing point and the abnormal cloud cluster are obtained.

[0041] Optionally, it is determined whether the forecast wind speed is greater than a preset wind speed threshold corresponding to a plurality of drones. If so, it is determined that abnormal weather exists, and an affected target drone is determined from the plurality of drones.

[0042] Optionally, it is determined whether the forecast rainfall intensity is greater than a preset rainfall intensity threshold corresponding to a plurality of drones. If so, it is determined that abnormal weather exists, and an affected target drone is determined from the plurality of drones.

[0043] Optionally, it is determined whether the distance between the return / alternate landing point and the abnormal cloud cluster is greater than a preset return distance threshold corresponding to multiple drones. If so, it is determined that abnormal weather exists, and the affected target drone is determined from the multiple drones.

[0044] Among them, the abnormal weather identification results include at least: abnormal weather type, abnormal weather location, impact range and expected duration, affected drone models and corresponding flight plans, and the location of flying drones.

[0045] Step 240: When it is determined that abnormal weather exists, a risk assessment is performed on the target UAV based on the abnormal weather identification result, and when it is determined that medium risk and / or high risk exists, a no-fly zone is generated.

[0046] Optionally, the no-fly zone is updated in real time, and the flight path of the target drone is automatically adjusted to avoid entering the no-fly zone.

[0047] It is understandable that under abnormal meteorological conditions, risk assessment and no-fly zone generation can ensure the safe flight of drones and help reduce flight accidents caused by bad weather.

[0048] In some embodiments, after generating the no-fly zone, the method further includes: The abnormal weather identification results and the location information of the no-fly zone are packaged to obtain warning information; Broadcast warning information to the target drone.

[0049] Optionally, for the drones that are planned to fly, an Ethernet interface is used to communicate with the drone traffic management center and send the above-mentioned warning information.

[0050] Optionally, for drones in flight, Ethernet interfaces, drone communication systems (such as satellite communication systems, 4G / 5G networks, etc.) and backup communication links are used for communication to ensure that warning information can be quickly and reliably transmitted to each affected drone.

[0051] Optionally, a feedback mechanism is established to ensure that each drone can send a confirmation receipt after receiving the warning information, and the receipt status is monitored. For drones that have not confirmed the receipt, the drone operator is notified by communication.

[0052] Figure 3 The second flow chart of the UAV traffic management method provided by the embodiment of the present invention is as follows: Figure 3 As shown, a method for managing drone traffic is provided, the method comprising the following steps: Establish a flight performance database of UAVs under various climatic conditions; Multi-source weather forecast data fusion; Rapid identification of abnormal weather forecast information; Dynamic no-fly zones are quickly generated; Automatic broadcast of no-fly zones.

[0053] Among them, the flight performance database includes flight performance data of various types of UAVs.

[0054] Optionally, the models of multiple drones corresponding to the target area are determined, the flight performance data is queried, and preset thresholds of each model of drone corresponding to the target area are determined, such as a preset wind speed threshold and a preset rainfall intensity threshold.

[0055] Optionally, abnormal weather is identified based on preset thresholds of various types of UAVs corresponding to the target area and fused multi-source weather forecast data to obtain abnormal weather identification results.

[0056] Optionally, based on the abnormal weather identification results, a no-fly zone is generated, and the no-fly zone information is automatically broadcast to the target UAV.

[0057] In an embodiment of the present invention, by acquiring multi-source weather forecast time series data for a future time period of a target area, the models of multiple UAVs corresponding to the target area are determined, the multi-source weather forecast time series data are fused, and based on the models of multiple UAVs and abnormal weather detection rules in a pre-constructed database, anomalies are identified for the fused weather forecast time series data. When it is determined that abnormal weather exists, a risk assessment is performed on the target UAV based on the abnormal weather identification results. When it is determined that medium risk and / or high risk exists, a no-fly zone is generated. The system has a high degree of automation, can improve the efficiency of abnormal weather identification and traffic management efficiency, help to respond quickly to abnormal weather, and can ensure the safety of UAV flight.

[0058] In some embodiments, when it is determined that abnormal weather exists, a risk assessment is performed on the target UAV based on the abnormal weather identification result, including: According to the abnormal weather identification result, a target UAV that will be affected by abnormal weather in the future time period is determined from multiple UAVs; The potential risk of the target UAV flying in the future time period is evaluated to obtain the risk level.

[0059] Optionally, the risk level of the drone can be low risk, medium risk, or high risk.

[0060] Optionally, the potential risk of the target UAV flying in a future time period is evaluated according to a preset risk assessment rule, and the risk assessment rule is set based on the experience data of artificial experts.

[0061] In some embodiments, when it is determined that there is a medium risk and / or a high risk, generating a no-fly zone includes: In the case where it is determined that there is a medium risk and / or a high risk, the affected area of ​​the abnormal weather is determined from the target area based on the abnormal weather identification result; According to the preset buffer threshold, the periphery of the affected area is expanded, and the coordinates of the expanded area are transformed to obtain polyhedron information; Generate a no-fly zone based on the polyhedron information.

[0062] It can be understood that by identifying the impact area of ​​abnormal weather, expanding the impact area, coordinate conversion, and generating a no-fly zone, the safety of drones in the face of severe weather conditions is ensured. The generated no-fly zone can effectively reduce drone flight accidents under dangerous weather conditions and protect the safety of drones and the surrounding environment.

[0063] The following is a description of the drone traffic management device provided in an embodiment of the present invention. The drone traffic management device described below and the drone traffic management method described above can be referenced to each other.

[0064] Figure 4 A schematic diagram of the structure of a drone traffic management device provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, the drone traffic management device 400 includes: An acquisition unit 410 is used to acquire multi-source weather forecast time series data for a future time period of a target area, and acquire models of multiple UAVs corresponding to the target area; A fusion unit 420 is used to fuse the multi-source weather forecast time series data to obtain fused weather forecast time series data; The identification unit 430 is used to identify abnormalities in the fused weather forecast time series data based on the models of multiple UAVs and a pre-built database to obtain abnormal weather identification results; A generating unit 440 is used to, when it is determined that abnormal weather exists, perform a risk assessment on the target UAV according to the abnormal weather identification result, and generate a no-fly zone when it is determined that medium risk and / or high risk exists; Among them, the database includes abnormal weather detection rules corresponding to each type of drone. The abnormal weather detection rules corresponding to each type of drone include: if the specific value of the weather forecast data is greater than the preset threshold corresponding to each type of drone, it is determined that abnormal weather exists.

[0065] Optionally, based on the models of multiple drones and a pre-built database, abnormality recognition is performed on the fused weather forecast time series data to obtain abnormal weather recognition results, including: According to the models of multiple drones, query in the database to obtain abnormal weather detection rules corresponding to multiple drones; According to the abnormal weather detection rules corresponding to multiple UAVs, the fused weather forecast time series data is used to identify anomalies and obtain abnormal weather identification results.

[0066] Optionally, when it is determined that abnormal weather exists, a risk assessment is performed on the target UAV based on the abnormal weather identification result, including: According to the abnormal weather identification result, a target UAV that will be affected by abnormal weather in the future time period is determined from multiple UAVs; The potential risk of the target UAV flying in the future time period is evaluated to obtain the risk level.

[0067] Optionally, in the event that a medium risk and / or a high risk is determined to exist, a no-fly zone is generated, including: In the case where it is determined that there is a medium risk and / or a high risk, the affected area of ​​the abnormal weather is determined from the target area based on the abnormal weather identification result; According to the preset buffer threshold, the periphery of the affected area is expanded, and the coordinates of the expanded area are transformed to obtain polyhedron information; Generate a no-fly zone based on the polyhedron information.

[0068] Optionally, the multi-source weather forecast time series data are fused to obtain fused weather forecast time series data, including: Performing data cleaning on multi-source weather forecast time series data to obtain cleaned multi-source weather forecast time series data; The cleaned multi-source weather forecast time series data are spatially aligned to obtain the fused weather forecast time series data.

[0069] Optionally, the drone traffic management device 400 further includes: An information encapsulation unit is used to encapsulate abnormal weather identification results and location information of no-fly zones to obtain warning information; Broadcast unit, used to broadcast warning information to the target UAV.

[0070] Optionally, the multiple drone models include: models of drones currently flying in the target area, and models of drones planned to fly in the target area in a future time period.

[0071] It should be noted here that the drone traffic management device provided in the embodiment of the present invention can implement all the method steps implemented in the above-mentioned drone traffic management method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as the method embodiment will not be described in detail here.

[0072] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 5As shown, the electronic device may include: a processor (processor) 510, a communication interface (Communications Interface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the drone traffic management method, which includes: obtaining multi-source weather forecast time series data for a future time period of the target area, and determining the models of multiple drones corresponding to the target area; fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; based on the models of multiple drones and a pre-built database, performing anomaly identification on the fused weather forecast time series data to obtain an abnormal weather identification result; when it is determined that abnormal weather exists, performing a risk assessment on the target drone based on the abnormal weather identification result, and generating a no-fly zone when it is determined that medium risk and / or high risk exists; wherein the database includes abnormal weather detection rules corresponding to each model of drone, and the abnormal weather detection rules corresponding to each model of drone include: if a specific value of the weather forecast data is greater than a preset threshold corresponding to each model of drone, it is determined that abnormal weather exists.

[0073] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0074] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the drone traffic management method provided by the above-mentioned methods, the method comprising: obtaining multi-source weather forecast time series data for a future time period of a target area, and determining the models of multiple drones corresponding to the target area; fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; based on the models of multiple drones and a pre-constructed database, performing anomaly identification on the fused weather forecast time series data to obtain an abnormal weather identification result; when it is determined that abnormal weather exists, performing a risk assessment on the target drone based on the abnormal weather identification result, and generating a no-fly zone when it is determined that medium risk and / or high risk exists; wherein the database includes abnormal weather detection rules corresponding to each model of drone, and the abnormal weather detection rules corresponding to each model of drone include: if a specific value of the weather forecast data is greater than a preset threshold corresponding to each model of drone, it is determined that abnormal weather exists.

[0075] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0076] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing drone traffic, characterized in that: include: Acquire multi-source weather forecast time series data for a target area in a future time period, and determine the models of multiple drones corresponding to the target area; fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; Based on the models of the multiple drones and a pre-built database, anomaly identification is performed on the fused weather forecast time series data to obtain an abnormal weather identification result; When it is determined that abnormal weather exists, a risk assessment is performed on the target UAV based on the abnormal weather identification result, and when it is determined that there is a medium risk and / or a high risk, a no-fly zone is generated; Among them, the database includes abnormal weather detection rules corresponding to each type of drone, and the abnormal weather detection rules corresponding to each type of drone include: if a specific value of the weather forecast data is greater than a preset threshold corresponding to each type of drone, it is determined that abnormal weather exists.

2. The method for managing drone traffic according to claim 1, characterized in that: Based on the models of the multiple drones and the pre-built database, the fused weather forecast time series data is subjected to anomaly identification to obtain an abnormal weather identification result, including: According to the models of the multiple drones, query the database to obtain abnormal weather detection rules corresponding to the multiple drones; According to the abnormal weather detection rules corresponding to the multiple UAVs, abnormalities are identified on the fused weather forecast time series data to obtain abnormal weather identification results.

3. The method for managing drone traffic according to claim 1, characterized in that: When it is determined that abnormal weather exists, a risk assessment is performed on the target UAV according to the abnormal weather identification result, including: According to the abnormal weather identification result, determining a target drone affected by abnormal weather in a future time period from the multiple drones; The potential risk of the target UAV flying in the future time period is evaluated to obtain a risk level.

4. The method for managing drone traffic according to claim 1, characterized in that: When determining that there is a medium risk and / or a high risk, generating a no-fly zone includes: In the case where it is determined that there is a medium risk and / or a high risk, determining an affected area of ​​the abnormal weather from the target area according to the abnormal weather identification result; According to a preset buffer threshold, the periphery of the affected area is expanded, and the coordinates of the expanded area are transformed to obtain polyhedron information; A no-fly zone is generated based on the polyhedron information.

5. The method for managing drone traffic according to claim 1, characterized in that: The step of fusing the multi-source weather forecast time series data to obtain the fused weather forecast time series data includes: Performing data cleaning on the multi-source weather forecast time series data to obtain cleaned multi-source weather forecast time series data; The cleaned multi-source weather forecast time series data are spatially aligned to obtain the fused weather forecast time series data.

6. The method for managing drone traffic according to claim 1, characterized in that: After generating the no-fly zone, the method further includes: Encapsulating the abnormal weather identification result and the location information of the no-fly zone to obtain warning information; Broadcast the warning information to the target UAV.

7. The method for managing drone traffic according to any one of claims 1 to 6, characterized in that: The models of the multiple drones include: models of drones currently flying in the target area, and models of drones that are planned to fly in the target area in a future time period.

8. A drone traffic management device, characterized in that: include: An acquisition unit, used to acquire multi-source weather forecast time series data for a target area in a future time period, and acquire models of multiple UAVs corresponding to the target area; A fusion unit, used for fusing the multi-source weather forecast time series data to obtain fused weather forecast time series data; an identification unit, configured to identify abnormalities of the fused weather forecast time series data based on the models of the plurality of UAVs and a pre-built database, and obtain abnormal weather identification results; A generating unit is used to, when it is determined that abnormal weather exists, perform a risk assessment on the target UAV according to the abnormal weather identification result, and generate a no-fly zone when it is determined that medium risk and / or high risk exists; Among them, the database includes abnormal weather detection rules corresponding to each type of drone, and the abnormal weather detection rules corresponding to each type of drone include: if a specific value of the weather forecast data is greater than a preset threshold corresponding to each type of drone, it is determined that abnormal weather exists.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the drone traffic management method as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the drone traffic management method as described in any one of claims 1 to 7 is implemented.

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