Low-altitude airspace three-dimensional traffic light management method, system and device and computer readable storage medium

Abstract

The invention provides a low-altitude airspace three-dimensional traffic light management method, system and device and a computer readable storage medium. The method comprises the steps of obtaining genome sequence data to be annotated; setting annotation parameters, and carrying out structure annotation on the genome sequence data; and performing function annotation on the genome subjected to structure annotation. In the scheme provided by the embodiment of the invention, by dividing the airspace into the grids, calculating the airspace complexity entropy of each grid in real time, mapping the complexity entropy value into the traffic light state, and controlling the flight state of the unmanned aerial vehicle in the grids, the traffic flow of the three-dimensional airspace is effectively managed and controlled, the required parameters are simple, and the implementation is easy. The method can adapt to complex scenes, has the advantages of real-time performance, three-dimensional performance, wide application range and the like, and can remarkably improve the efficiency and safety of low-altitude traffic organization.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent airspace management, and in particular to a method, system, device and computer-readable storage medium for managing three-dimensional traffic lights in low-altitude airspace. Background Art

[0002] With the increasing application of unmanned aerial vehicles in logistics, inspection and other fields, the traffic organization problem in low-altitude airspace is becoming more and more serious. The existing unmanned aerial vehicle conflict resolution method mainly relies on flight path planning, lacking dynamic and three-dimensional real-time traffic management methods.

[0003] The ground traffic traffic light system in the prior art sets traffic lights at traffic intersections and controls the state and flow of traffic in different directions by changing the color of the traffic lights. This method requires setting up dedicated traffic lights at traffic intersections and is not suitable for virtual routes with ever-changing airspace.

[0004] There is also a method for implementing air traffic flow management based on flight transfer altitude. By establishing an altitude correction model, the objectives and constraints of the altitude correction model are determined, and a flight queue is established according to the altitude flow control type and flight priority. An altitude correction method is proposed to select the altitude of the flight processing route restricted points in sequence. Finally, the route trajectory is calculated while satisfying all altitude flow controls. This method is suitable for traffic flow management of a small number of fixed flights, but is not applicable to unmanned aerial vehicles with large traffic and strong temporary nature.

[0005] Therefore, there is an urgent need for an innovative technology to optimize the low-altitude traffic organization of unmanned aerial vehicles. Summary of the invention

[0006] Multiple aspects of the present application provide a method, system, device and computer-readable storage medium for managing three-dimensional traffic lights in low-altitude airspace, which optimize the low-altitude traffic organization of unmanned aerial vehicles through systematic guidance similar to traffic lights.

[0007] In order to achieve the above technical effects, one aspect of the present application provides a low-altitude airspace three-dimensional traffic traffic light management method, comprising:

[0008] Obtain UAV information in the target area in real time;

[0009] Divide the target area into grids and calculate the spatial complexity entropy of each grid;

[0010] The airspace complexity entropy is mapped to a traffic light state, and the airspace traffic in the target area is managed according to the traffic light state.

[0011] According to a preferred embodiment of the present invention, the real-time acquisition of UAV information of the target area further comprises:

[0012] Obtain the number, position, speed direction, and velocity information of UAVs in the target area.

[0013] According to a preferred embodiment of the present invention, the gridding of the target area and the calculation of the spatial complexity entropy of each grid further include:

[0014] According to a preset spatial grid coding rule, the target area is gridded to generate a grid set of flyable airspace;

[0015] Count the number of UAVs in each grid set and encode each UAV;

[0016] Each grid set is weighted according to the number of UAVs in each grid set;

[0017] Calculate the weighted spatial complexity entropy of each grid set.

[0018] According to a preferred embodiment of the present invention, the gridding of the target area according to a preset spatial grid coding rule to generate a grid set of flyable airspace further comprises:

[0019] According to the geospatial grid coding rule, grid coding is performed on the target area to obtain a first grid coding set;

[0020] Dividing each grid in the first grid code set into a plurality of sub-grids, and performing grid coding on each sub-grid to obtain a second grid code set;

[0021] Match the location of the unmanned aerial vehicle with the sub-grid code of the second grid code set.

[0022] According to a preferred embodiment of the present invention, the calculation of the weighted spatial complexity entropy of each grid set further includes:

[0023] Calculating the probability distribution density entropy value E1 of the sub-grid according to the density weighting function;

[0024] Calculate the direction field consistency entropy value E2 of the sub-grid;

[0025] Calculate the probability distribution speed entropy value E3 of the sub-grid according to the speed weighting function;

[0026] The weighted sum of E1, E2 and E3 is performed to obtain the spatial complexity entropy value of the sub-grid.

[0027] According to a preferred embodiment of the present invention, mapping the airspace complexity entropy to a traffic light state, and managing the airspace traffic in the target area according to the traffic light state further comprises:

[0028] Set the spatial complexity entropy threshold and associate it with the traffic light status;

[0029] According to the spatial complexity entropy threshold of each grid in the target area, a signal of corresponding color is generated to control the passage status of the UAV in the grid area.

[0030] According to a preferred embodiment of the present invention, setting the spatial complexity entropy threshold and correspondingly associating the traffic light status further includes:

[0031] When the complexity entropy value within the grid is lower than 70% of the airspace complexity entropy threshold, a green light signal is generated and the UAV can enter and exit freely;

[0032] When the complexity entropy value within the grid is between 70% of the airspace complexity entropy threshold and the threshold, a yellow light signal is generated to warn the unmanned aerial vehicle to enter;

[0033] When the complexity entropy value within the grid is higher than the airspace complexity entropy threshold, a red light signal is generated and unmanned aerial vehicles are strictly prohibited from entering.

[0034] Another aspect of the present application provides a low-altitude airspace three-dimensional traffic traffic light management system, comprising:

[0035] An information acquisition module is used to obtain the UAV information in the target area in real time;

[0036] A complexity entropy calculation module is used to divide the target area into grids and calculate the spatial complexity entropy of each grid;

[0037] The airspace traffic management module is used to map the airspace complexity entropy to the traffic light status and manage the airspace traffic in the target area according to the traffic light status.

[0038] Another aspect of the present application provides a low-altitude airspace three-dimensional traffic traffic light management electronic device, the device comprising:

[0039] at least one processor; and

[0040] a memory communicatively connected to the at least one processor; wherein,

[0041] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described above.

[0042] In another aspect of the present application, a computer-readable storage medium is provided, on which computer program instructions are stored. The computer program instructions can be executed by a processor to implement the above method.

[0043] In the solution provided in the embodiment of the present application, the airspace is divided into grids, the airspace complexity entropy of each grid is calculated in real time, and the complexity entropy value is mapped to the traffic light status, so as to control the flight status of the unmanned aerial vehicle in the grid, and effectively manage and control the traffic flow in the three-dimensional airspace. The required parameters are simple, can adapt to complex scenes, and have the advantages of real-time, three-dimensional, and wide applicability, which can significantly improve the efficiency and safety of low-altitude traffic organization. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, a brief introduction will be given below to 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0046] Figure 1 A flowchart of a low-altitude airspace three-dimensional traffic traffic light management method provided in one embodiment of the present application;

[0047] Figure 2 A schematic diagram of a low-altitude airspace three-dimensional traffic traffic light management system provided in one embodiment of the present application;

[0048] Figure 3 The present invention is a schematic diagram of the structure of a device suitable for implementing the solution in the embodiment of the present application.

[0049] The same or similar reference numerals in the drawings represent the same or similar components. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0051] In a typical configuration of the present application, the terminal and the equipment of the service network each include one or more processors (CPU), input / output interface, network interface and memory.

[0052] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0053] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer program instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0054] In actual scenarios, the execution subject of the method can be a user device, or a device formed by integrating a user device and a network device through a network, or an application running on the above device. The user device includes but is not limited to various terminal devices such as computers, mobile phones, tablet computers, smart watches, and bracelets. The network device includes but is not limited to network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets, which can be used to implement some processing functions when setting an alarm. Here, the cloud is composed of a large number of hosts or network servers based on cloud computing, where cloud computing is a type of distributed computing, a virtual computer composed of a group of loosely coupled computer sets.

[0055] In order to implement the solution of the present invention, the present invention provides a low-altitude airspace three-dimensional traffic traffic light management method, system, equipment and computer-readable storage medium, which realizes the optimization of low-altitude traffic organization of unmanned aerial vehicles through systematic guidance similar to traffic lights.

[0056] Figure 1 A flowchart of a low-altitude airspace three-dimensional traffic traffic light management method provided in an embodiment of the present application is as follows: Figure 1 As shown, the low-altitude airspace three-dimensional traffic traffic light management method at least includes the following steps:

[0057] S101. Acquire the unmanned aerial vehicle information in the target area in real time.

[0058] Specifically, first obtain the number, position, speed direction, and velocity information of the UAVs in the target area. Obtain the size information of the target area, for example, the target area is 4096 meters long, 4096 meters wide, and 1024 meters high. Then obtain the number, position, speed direction, speed size, and other information of the UAVs in the target area. For example, 16,384 UAVs are detected in the target area, and the position and flight direction of each UAV are obtained, and the speed is between 0 meters / second and 35 meters / second.

[0059] S102, dividing the target area into grids, and calculating the spatial complexity entropy of each grid.

[0060] Specifically, the following steps are included: S1021, gridding the target area according to a preset spatial grid coding rule to generate a grid set of flyable airspace.

[0061] First, the target area is grid-coded according to the size of the target area and the earth space grid coding rules to obtain a first grid code set, then each grid in the first grid code set is divided into multiple sub-grids, and each sub-grid is grid-coded to obtain a second grid code set, and finally the sub-grid code of the second grid code set is matched according to the location of the unmanned aerial vehicle.

[0062] For example, the target area is 4096 meters long, 4096 meters wide, and 1024 meters high. According to the Earth Space Grid Coding Rules, a 19-level grid is used to divide the target area, and a 19-level first grid code set C19 is generated; for each grid in the grid code set C19, the sub-grid of the sub-grid of its sub-grid is obtained to generate a 22-level second grid code set C22, and finally the codes of the second grid code set C22 are grouped according to the first 19 bits to obtain the codes of the sub-grids in each grid space in the first grid code set C19.

[0063] When the target area is 16,384 meters long, 16,384 meters wide, and 4,096 meters high, according to the Earth Space Grid Coding Rules, a 17-level grid is used to divide the target area, and a 17-level first grid code set C17 is generated; for each grid in the grid code set C17, the sub-grid of the sub-grid of its sub-grid is obtained to generate a 20-level second grid code set C20, and finally the codes of the second grid code set C20 are grouped according to the first 17 bits, to obtain the codes of the sub-grids in each grid space in the first grid code set C17.

[0064] S1022. Count the number of unmanned aerial vehicles in each grid set and encode each unmanned aerial vehicle.

[0065] According to the "Earth Space Grid Coding Rules", each unmanned aerial vehicle is coded with a 26-level grid (meter level) to obtain the 26-level grid code set C26 of the unmanned aerial vehicle, and then the 26-level grid code set C26 of the unmanned aerial vehicle is grouped according to the regional grid code set divided in step S1021.

[0066] If the second grid code set is C22, grouping is performed according to the first 22 bits to obtain the code of the unmanned aerial vehicle in each grid space in the second grid code set C22, and the number of unmanned aerial vehicles in each grid space can be counted according to the unmanned aerial vehicle code. Since the number of second grid code sets C22 in the first grid code set C19 is fixed, the number of unmanned aerial vehicles in each grid space in the first grid code set C19 can be calculated.

[0067] If the second grid code set is C20, grouping is performed according to the first 20 bits to obtain the code of the unmanned aerial vehicle in each grid space in the second grid code set C20, and the number of unmanned aerial vehicles in each grid space can be counted according to the unmanned aerial vehicle code. Since the number of second grid code sets C20 in the first grid code set C17 is fixed, the number of unmanned aerial vehicles in each grid space in the first grid code set C17 can be calculated.

[0068] S1023. Weight each grid set according to the number of UAVs in each grid set.

[0069] If the first grid code set is C19, for the 19-level grid, set a multi-segment quantity weighting function (the number of UAVs in the grid is represented by n) to obtain the weight weight_pos:

[0070] 0≤n<64:w(n)=1

[0071]

[0072] Set the multi-stage speed size (represented by v) weighting function to get the weight weight_speed:

[0073] 0≤v<16:w(v)=1+0.5×v

[0074] 16≤v<30:w(v)=v-7

[0075] 30≤v<36:w(v)=2×(v-18.5)

[0076] 36≤v:w(v)=70.

[0077] If the first grid code set is C17, for the 17-level grid, set a multi-segment quantity weighting function (the number of UAVs in the grid is represented by n) to obtain the weight weight_pos:

[0078] 0≤n<64:w(n)=1 / 10

[0079]

[0080] Set the multi-stage speed size (represented by v) weighting function to get the weight weight_speed:

[0081] w(v)=2×v / 60.

[0082] S1024. Calculate the weighted spatial complexity entropy of each grid set.

[0083] If the first grid code set is C19, for each grid in C19: there are M 22-level grids, count the number of UAVs in each 22-level grid, calculate the total number of UAVs in all 22-level grids, and calculate the ratio P of the number of UAVs in each 22-level grid to the total number i , calculate the average speed of the UAV in all 22-level grids.

[0084] Then the probability distribution density entropy value E1 of the sub-grid of the 19-level grid is calculated according to the density weighting function:

[0085]

[0086] Convert the velocity direction of the UAV to a unit vector:

[0087] v i = {v i,1 ,v i,2 ,v i,3}

[0088] Calculate the direction field consistency entropy value E2 of the 19-level grid:

[0089]

[0090] Finally, the probability distribution speed entropy value E3 of the sub-grid of the 19-level grid is calculated according to the speed weighting function:

[0091]

[0092] After obtaining E1, E2 and E3, weighted sum of E1, E2 and E3 is performed to obtain the spatial complexity entropy value E of the sub-grid of the set C19:

[0093] E=α×E1+β×E2+γ×E3

[0094] Here, α, β, and γ are weights of three entropies, and values ​​are determined according to actual needs, for example, α=0.47, β=0.06, and γ=0.47.

[0095] If the first grid code set is C17, for each grid in C17: there are M 20-level grids, count the number of UAVs in each 20-level grid, calculate the total number of UAVs in all 20-level grids, and calculate the ratio P of the number of UAVs in each 20-level grid to the total number i , calculate the average speed of the UAV in all 20-level grids.

[0096] Then, the probability distribution density entropy value E1 of the sub-grid of the 17-level grid is calculated according to the density weighted function, the direction field consistency entropy value E2 of the 17-level grid is calculated, and the probability distribution speed size entropy value E3 of the sub-grid of the 17-level grid is calculated according to the speed weighted function. The calculation process is the same as the above-mentioned set C19 and will not be repeated here.

[0097] Finally, the spatial complexity entropy value E of the sub-grid of the set C17 is obtained, where α=0.60, β=0.15, and γ=0.25.

[0098] S103: Map the airspace complexity entropy to a traffic light status, and manage airspace traffic in the target area according to the traffic light status.

[0099] Specifically, the steps include: setting an airspace complexity entropy threshold and correspondingly associating the traffic light status; generating a signal of a corresponding color according to the airspace complexity entropy threshold of each grid in the target area, and controlling the traffic status of the unmanned aerial vehicle in the grid area.

[0100] Establish a mapping between entropy value range and traffic light signals, and use traffic light signals to dynamically indicate the trafficability of airspace grids in real time, for example:

[0101] When the complexity entropy value in the grid is lower than 70% of the airspace complexity entropy threshold, that is, when the current entropy value is within the threshold range of 0-0.7, a green light signal is generated, and the unmanned aerial vehicle can enter and exit freely;

[0102] When the complexity entropy value within the grid is between 70% of the airspace complexity entropy threshold and the threshold, that is, when the current entropy value is within the 0.7-1 threshold interval, a yellow light signal is generated to warn the unmanned aerial vehicle to enter;

[0103] When the complexity entropy value within the grid is higher than the airspace complexity entropy threshold, that is, when the current entropy value is greater than the threshold interval, a red light signal is generated and unmanned aerial vehicles are strictly prohibited from entering.

[0104] Preferably, the system can display the current sub-grid complexity entropy state, represented by red, green and yellow, and subdivide the difference in numerical value by the depth of the color.

[0105] When the flight paths of multiple UAVs may intersect, the system generates a red light signal in advance through an entropy prediction algorithm, and instructs the conflicting UAVs to slow down or wait; when the UAVs are roaming autonomously, they choose the most open area to enter based on the traffic light signals in the current area.

[0106] For example, five UAVs are flying at a speed of 30m / s and are about to enter a yellow light grid. According to system calculations, the entropy value will increase after the UAVs enter and the grid will turn red. In order to ensure the normal use of the airspace, the UAVs are required to wait or slow down before entering.

[0107] In the solution provided by the embodiment of the method, the airspace is divided into grids, the airspace complexity entropy of each grid is calculated in real time, and the complexity entropy value is mapped to the traffic light status, so as to control the flight status of the unmanned aerial vehicle in the grid, and effectively manage and control the traffic flow in the three-dimensional airspace. The required parameters are simple, and it can adapt to complex scenes. It has the advantages of real-time, three-dimensional, and wide applicability, and can significantly improve the efficiency and safety of low-altitude traffic organization.

[0108] Figure 2 A schematic diagram of a low-altitude airspace three-dimensional traffic traffic light management system provided in an embodiment of the present application is as follows: Figure 2 As shown, the system comprises:

[0109] The information acquisition module 11 is used to obtain the UAV information in the target area in real time;

[0110] The complexity entropy calculation module 22 is used to divide the target area into grids and calculate the spatial complexity entropy of each grid;

[0111] The airspace traffic management module 33 is used to map the airspace complexity entropy to the traffic light status, and manage the airspace traffic in the target area according to the traffic light status.

[0112] The above system can execute the low-altitude airspace three-dimensional traffic traffic light management method in the above-mentioned embodiment, wherein the information acquisition module 11 executes step S101 to obtain the number, position, speed direction, and rate information of unmanned aerial vehicles in the target area.

[0113] The complexity entropy calculation module 22 executes step S102, grids the target area according to a preset spatial grid coding rule, generates a grid set of flyable airspace, counts the number of UAVs in each grid set, and encodes each UAV, weights each grid set according to the number of UAVs in each grid set, and calculates the airspace complexity entropy of each weighted grid set.

[0114] The airspace traffic management module 33 executes step S103, sets the airspace complexity entropy threshold, and corresponds to the associated traffic light status. According to the airspace complexity entropy threshold of each grid in the target area, a signal of corresponding color is generated to control the traffic status of the unmanned aerial vehicle in the grid area.

[0115] Based on the same inventive concept, an electronic device is also provided in an embodiment of the present application, and the method corresponding to the electronic device may be the low-altitude airspace three-dimensional traffic traffic light management method in the aforementioned embodiment, and its principle of solving the problem is similar to that of the method. The electronic device provided in an embodiment of the present application includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the methods and / or technical solutions of the aforementioned multiple embodiments of the present application.

[0116] The electronic device may be a user device, or a device formed by integrating a user device and a network device through a network, or may be an application running on the above device. The user device includes but is not limited to various terminal devices such as computers, mobile phones, tablet computers, smart watches, and bracelets. The network device includes but is not limited to network hosts, single network servers, multiple network server sets, or cloud computing-based computer sets, which can be used to implement some processing functions when setting an alarm. Here, the cloud is composed of a large number of hosts or network servers based on cloud computing, where cloud computing is a type of distributed computing, a virtual computer composed of a group of loosely coupled computer sets.

[0117] Figure 3The structure of a device suitable for implementing the method and / or technical solution in the embodiment of the present application is shown, and the device 1200 includes a central processing unit (CPU, Central Processing Unit) 1201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM, Read Only Memory) 1202 or the program loaded from the storage part 1208 to the random access memory (RAM, Random Access Memory) 1203. In RAM1203, various programs and data required for system operation are also stored. CPU 1201, ROM 1202 and RAM 1203 are connected to each other through bus 1204. Input / output (I / O, Input / Output) interface 1205 is also connected to bus 1204.

[0118] The following components are connected to the I / O interface 1205: an input section 1206 including a keyboard, a mouse, a touch screen, a microphone, an infrared sensor, etc.; an output section 1207 including a cathode ray tube (CRT), a liquid crystal display (LCD), an LED display, an OLED display, etc., and a speaker, etc.; a storage section 1208 including one or more computer-readable media such as a hard disk, an optical disk, a magnetic disk, a semiconductor memory, etc.; and a communication section 1209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 1209 performs communication processing via a network such as the Internet.

[0119] In particular, the methods and / or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. When the computer program is executed by the central processing unit (CPU) 1201, the above functions defined in the method of the present application are executed.

[0120] Another embodiment of the present application further provides a computer-readable storage medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the methods and / or technical solutions of any one or more embodiments of the present application described above.

[0121] Specifically, the present embodiment may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device, or device.

[0122] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer readable signal media may also be any computer readable medium other than a computer readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0123] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0124] Computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., using an Internet service provider to connect through the Internet).

[0125] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0126] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0127] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or page components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0130] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit 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.) or a processor to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0131] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application 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 application.

[0132] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a device claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

Claims

1. A low-altitude airspace three-dimensional traffic traffic light management method, characterized in that: include: Obtain UAV information in the target area in real time; Divide the target area into grids and calculate the spatial complexity entropy of each grid; The airspace complexity entropy is mapped to a traffic light state, and the airspace traffic in the target area is managed according to the traffic light state.

2. The low-altitude airspace three-dimensional traffic traffic light management method according to claim 1 is characterized in that: The real-time acquisition of the UAV information of the target area further comprises: Obtain the number, position, speed direction, and velocity information of UAVs in the target area.

3. The low-altitude airspace three-dimensional traffic traffic light management method according to claim 1 is characterized in that: The step of dividing the target area into grids and calculating the spatial complexity entropy of each grid further includes: According to a preset spatial grid coding rule, the target area is gridded to generate a grid set of flyable airspace; Count the number of UAVs in each grid set and encode each UAV; Each grid set is weighted according to the number of UAVs in each grid set; Calculate the weighted spatial complexity entropy of each grid set.

4. The low-altitude airspace three-dimensional traffic traffic light management method according to claim 3 is characterized in that: The gridding of the target area according to the preset spatial grid coding rule to generate a grid set of flyable airspace further includes: According to the geospatial grid coding rule, grid coding is performed on the target area to obtain a first grid coding set; Dividing each grid in the first grid code set into a plurality of sub-grids, and performing grid coding on each sub-grid to obtain a second grid code set; Match the location of the unmanned aerial vehicle with the sub-grid code of the second grid code set.

5. The low-altitude airspace three-dimensional traffic traffic light management method according to claim 4 is characterized in that: The weighted calculation of the spatial complexity entropy of each grid set further includes: Calculating the probability distribution density entropy value E1 of the sub-grid according to the density weighting function; Calculate the direction field consistency entropy value E2 of the sub-grid; Calculate the probability distribution speed entropy value E3 of the sub-grid according to the speed weighting function; The weighted sum of E1, E2 and E3 is performed to obtain the spatial complexity entropy value of the sub-grid.

6. The low-altitude airspace three-dimensional traffic traffic light management method according to claim 1 is characterized in that: Mapping the airspace complexity entropy to a traffic light state, and managing airspace traffic in the target area according to the traffic light state further comprises: Set the spatial complexity entropy threshold and associate it with the traffic light status; According to the spatial complexity entropy threshold of each grid in the target area, a signal of corresponding color is generated to control the passage status of the UAV in the grid area.

7. The low-altitude airspace three-dimensional traffic light management method according to claim 6 is characterized in that: Setting the spatial complexity entropy threshold and correspondingly associating the traffic light status further includes: When the complexity entropy value within the grid is lower than 70% of the airspace complexity entropy threshold, a green light signal is generated and the UAV can enter and exit freely; When the complexity entropy value within the grid is between 70% of the airspace complexity entropy threshold and the threshold, a yellow light signal is generated to warn the unmanned aerial vehicle to enter; When the complexity entropy value within the grid is higher than the airspace complexity entropy threshold, a red light signal is generated and unmanned aerial vehicles are strictly prohibited from entering.

8. A low-altitude airspace three-dimensional traffic traffic light management system, characterized in that: include: An information acquisition module is used to obtain the UAV information in the target area in real time; A complexity entropy calculation module is used to divide the target area into grids and calculate the spatial complexity entropy of each grid; The airspace traffic management module is used to map the airspace complexity entropy to the traffic light status and manage the airspace traffic in the target area according to the traffic light status.

9. An electronic device for managing low-altitude three-dimensional traffic lights, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer readable medium having computer program instructions stored thereon, characterized in that: The computer program instructions can be executed by a processor to implement the method according to any one of claims 1-7.

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