Simulated UAV dispatching method, device, electronic device and storage medium

Abstract

The present application provides a scheduling method, device, electronic device, and storage medium for a simulated drone. The method includes: constructing a simulated virtual scene for a target area based on a simulation platform, the simulated virtual scene including a simulated geographical environment and at least one simulated drone, each simulated drone having simulated remaining power information; sending the simulated remaining power information of each simulated drone to a server; receiving a target flight instruction sent by the server, the target flight instruction carrying identification information of the target simulated drone and flight target location information; the target simulated drone is selected from at least one simulated drone by the server based on the simulated remaining power information of each simulated drone; and controlling the target simulated drone to execute a target flight mission according to the flight target location information. Because the simulated drone has simulated power information, the matching between the simulated scheduling of the simulated drone and the real scheduling can be improved, which is conducive to improving the verification effect.

Description

Technical Field

[0001] The present application relates to the field of drone control technology, and in particular to a scheduling method, device, electronic device, and storage medium for a simulated drone. Background Art

[0002] With the rapid adoption of drone technology in fields such as power inspection, disaster relief, and geographic mapping, simulation verification, a critical step before actual deployment, is increasingly facing technical limitations. For example, scheduling simulated drone swarms typically uses shortest path first or round-robin methods to determine which simulated drones are needed to perform a task. However, the scheduling of real drones requires consideration of many factors, resulting in discrepancies between the scheduling algorithms in simulated and real-world scenarios, affecting verification effectiveness. Summary of the Invention

[0003] In view of this, the present application provides a scheduling method, device, equipment and storage medium for simulated drones, which can improve the matching between simulated scheduling and real scheduling of simulated drones, and is conducive to improving the verification effect.

[0004] According to a first aspect of the present application, a scheduling method for a simulated drone is provided, which is applied to a terminal device, and the method includes:

[0005] Building a simulated virtual scene for the target area based on the simulation platform, the simulated virtual scene including a simulated geographical environment and at least one simulated drone, each of the simulated drones having simulated remaining power information;

[0006] Sending the simulated remaining power information of each simulated drone to the server;

[0007] receiving a target flight instruction sent by the server, the target flight instruction carrying identification information of a target simulated drone and flight target position information; the target simulated drone is selected by the server from the at least one simulated drone based on the user flight instruction and the simulated remaining power information of each simulated drone;

[0008] The target simulation UAV is controlled to execute the target flight mission according to the flight target position information.

[0009] According to a second aspect of the present application, a scheduling method for a simulated drone is provided, which is applied to a server, and the method includes:

[0010] Obtaining user flight instructions and receiving simulated remaining power information of at least one simulated drone and identification information of each simulated drone sent by the terminal device;

[0011] Determining a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction;

[0012] Generate a target flight instruction based on the identification information of the target simulated UAV and the user flight instruction;

[0013] The target flight instruction is sent to the terminal device; the target flight instruction is used to instruct the target simulated UAV to perform the target flight mission.

[0014] According to a third aspect of the present application, a scheduling device for a simulated drone is provided, the device comprising:

[0015] A scene construction module is used to construct a simulated virtual scene for a target area based on a simulation platform, wherein the simulated virtual scene includes a simulated geographical environment and at least one simulated drone, each of the simulated drones having simulated remaining power information;

[0016] An information sending module, configured to send the simulated remaining power information of each simulated drone to a server;

[0017] An instruction receiving module is configured to receive a target flight instruction sent by the server, the target flight instruction carrying identification information of a target simulated UAV and flight target position information; the target simulated UAV is obtained by the server from the at least one simulated UAV based on the simulated remaining power information of each simulated UAV and the user flight instruction;

[0018] The flight control module is used to control the target simulation UAV to perform the target flight mission according to the flight target position information.

[0019] According to a fourth aspect of the present application, a scheduling device for a simulated drone is provided, the device comprising:

[0020] An information receiving module, configured to obtain flight instructions from a user and receive simulated remaining power information of at least one simulated drone sent by a terminal device;

[0021] a drone screening module, configured to determine a target simulated drone from the at least one simulated drone based on the simulated remaining power information of each simulated drone and the user flight instruction;

[0022] An instruction generation module, configured to generate a target flight instruction based on the identification information of the target simulated UAV and the user flight instruction;

[0023] The instruction sending module is used to send the target flight instruction to the terminal device, and the target flight instruction is used to instruct the target simulated UAV to perform the target flight mission.

[0024] According to the fifth aspect of the present application, an electronic device is provided, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the scheduling method of the simulated drone described in the first or second aspect above is executed.

[0025] According to the sixth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the scheduling method of the simulated drone described in the first aspect or the second aspect is executed.

[0026] The scheduling method, device, electronic device, and storage medium for simulated drones provided in this application are such that, since each simulated drone has simulated remaining power information and the simulated remaining power information of each simulated drone is sent to a server, the server can make a task decision based on the user's flight instructions and the simulated remaining power information, thereby screening at least one simulated drone to obtain the optimal target simulated drone capable of executing the target flight mission. Thus, since the selection of target simulated drones is based not only on the user's flight instructions but also on the simulated remaining power information of each simulated drone, the simulation results can be made more closely aligned with the real scene, which is conducive to improving the simulation verification effect.

[0027] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the embodiments. The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and, together with the specification, are used to illustrate the technical solutions of the present disclosure. It should be understood that the following drawings only illustrate certain embodiments of the present disclosure and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without inventive effort.

[0029] Figure 1 This is a schematic diagram of the architecture of a scheduling system for simulating a drone, as shown in an exemplary embodiment of the present application;

[0030] Figure 2This is a flow chart of a method for simulating unmanned aircraft scheduling, shown in an exemplary embodiment of the present application;

[0031] Figure 3 This is a flow chart of a method for constructing a simulated virtual scene, shown in an exemplary embodiment of the present application;

[0032] Figure 4 This is a flow chart of another method for simulating unmanned aircraft scheduling according to an exemplary embodiment of the present application;

[0033] Figure 5 This is a schematic diagram of an interactive process of a simulated unmanned vehicle scheduling method according to an exemplary embodiment of the present application;

[0034] Figure 6 This is a functional module diagram of a simulated unmanned aircraft dispatching device shown in an exemplary embodiment of the present application;

[0035] Figure 7 This is a functional module diagram of another simulated unmanned aircraft dispatching device shown in an exemplary embodiment of the present application;

[0036] Figure 8 It is a structural diagram of an electronic device shown in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0037] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. 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 certain aspects of the present application, as detailed in the appended claims.

[0038] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0039] It should be understood that although the terms first, second, third, etc. may be used in this application to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0040] The term "and / or" herein simply describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, and the existence of B alone. In addition, the term "at least one" herein refers to any combination of at least two of any one or more of a plurality of items. For example, "at least one of A, B, and C" can represent any one or more elements selected from the set consisting of A, B, and C.

[0041] See also Figure 1 As shown in FIG, it is a schematic diagram of the architecture of a scheduling system for simulating drones provided in an embodiment of the present application. Figure 1 As shown, the simulated drone dispatching system may include a terminal device 100 and a server 200 capable of communicating with the terminal device 100. The terminal device 100 may include a mobile device, a user terminal, an in-vehicle device, a computing device, a wearable device, etc. For example, the terminal device 100 may include a tablet computer, a mobile phone, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, etc.

[0042] Server 200 can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud storage, big data and artificial intelligence platforms, which is not limited here.

[0043] For example, the terminal device 100 may be equipped with a simulation platform that uses computer simulation technology to simulate real-world behaviors and patterns based on mathematical models and algorithms. The server 200 may be equipped with a large language model, a deep learning-based artificial intelligence system that can understand and generate natural language and complete various complex tasks by learning from massive amounts of text data.

[0044] See also Figure 2FIG. 1 is a flowchart of a method for scheduling a simulated drone provided in an embodiment of the present application. The method is applied to a terminal device 100 and includes the following steps S101 to S104:

[0045] S101, constructing a simulated virtual scene for a target area based on a simulation platform, wherein the simulated virtual scene includes a simulated geographical environment and at least one simulated drone, and each of the simulated drones has simulated remaining power information.

[0046] Among them, the interface of the simulation platform is consistent with that of the real drone. The algorithm verified on the simulated drone can be deployed on the real drone to control the real drone accordingly.

[0047] In some embodiments, each of the simulated drones is integrated with a battery simulation model, and the method further includes: for each of the simulated drones, using the battery simulation model, according to preset discharge interval parameters, determining the simulated remaining power information of the simulated drone.

[0048] Exemplarily, the battery simulation model can be implemented using battery simulator code. The preset discharge interval parameters can be obtained based on multiple experiments and are not specifically limited thereto. Thus, by determining the simulated remaining battery capacity information of the simulated drone according to the preset discharge interval parameters, the simulated remaining battery capacity of the simulated drone can be caused to decay over time, thereby facilitating improved simulation accuracy of the simulated remaining battery capacity.

[0049] In some embodiments, the method may further include the following (a) to (b):

[0050] (a) for any simulated UAV, generating a battery failure simulation instruction using the battery simulation model;

[0051] (b) Responding to the battery failure simulation instruction, setting the simulated remaining power information of the simulated drone to zero.

[0052] For example, the voltage or current of the battery simulation model can be monitored. If an abnormality is detected in the simulated battery, an alarm message is generated and the simulated remaining battery power is reset to zero (cleared). In this way, simulated drones with zero remaining battery power will not participate in scheduling. The alarm message is not limited to any specific type and can be, for example, a buzzer alarm or a voice alarm.

[0053] In this embodiment, the battery simulation model can be used to simulate not only the remaining power but also battery failures, so that the simulation environment is more closely aligned with the real scenario.

[0054] In some embodiments, each simulated drone has a virtual port and an instance identifier, and different simulated drones have different virtual ports and different instance identifiers. Here, the simulation environment can avoid port conflicts by setting different ports. In addition, by configuring different instance identifiers for each simulated drone, it can be ensured that the corresponding simulated drone is started each time the system is started, and the posture of the restarted simulated drone matches the previous posture. For example, after the posture of each simulated drone is configured, it will maintain its original posture after the system is restarted. For another example, if the simulated power of simulated drone A is 80% before startup, then after the system is restarted, the simulated power of simulated drone A will still be 80%, and no reconfiguration is required.

[0055] S102: Send the simulated remaining power information of each simulated drone to the server.

[0056] Exemplarily, the terminal device 100 sends the simulated remaining power information of each simulated drone to the server 200 , and also carries the identification information of the simulated drone when sending the simulated remaining power information.

[0057] S103, receiving the target flight instruction sent by the server, the target flight instruction carries the identification information of the target simulated drone and the flight target position information; the target simulated drone is screened by the server from the at least one simulated drone based on the user flight instruction and the simulated remaining power information of each simulated drone.

[0058] It is understood that after receiving the simulated remaining power information of each simulated drone, the server 200 can determine the target simulated drone for performing the target task from the at least one simulated drone based on the user's flight instruction and the simulated remaining power information of each simulated drone. This specific determination process will be described in detail later. In addition, after determining the target simulated drone, the server 200 also generates a target flight instruction for instructing the target simulated drone to perform the target task, and sends the target flight instruction to the terminal device 100.

[0059] S104: Control the target simulation UAV to execute the target flight mission according to the flight target position information.

[0060] After receiving the target flight instruction sent by the server 200, the terminal device 100 can control the target simulated drone to perform the target flight mission according to the flight target location information. For example, the target flight mission may include flying from a first target location (such as the current location) to a second target location indicated by the target location information.

[0061] In the scheduling method for simulated drones provided in the embodiments of the present application, each simulated drone has simulated remaining power information, and the simulated remaining power information of each simulated drone is sent to the server 200. The server 200 can make a task decision based on the user's flight instructions and the simulated remaining power information to select the optimal target simulated drone that can perform the target flight mission from at least one simulated drone. In this way, since the selection of the target simulated drone is not only based on the user's flight instructions, but also combined with the simulated remaining power information of each simulated drone, the simulation results can be made to better match the real scene, which is conducive to improving the simulation verification effect.

[0062] See also Figure 3 As shown, in some embodiments, with respect to the above step S101, when constructing a simulated virtual scene for the target area based on the simulation platform, the following steps S1011 to S1013 may be specifically included:

[0063] S1011 , obtaining satellite image data, elevation data, and a vector map for the target area, and fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area.

[0064] Here, by fusing satellite imagery data (3D), elevation data, and a vector map (2D) of the target area to generate 3D fused map data, the accuracy of the 3D map data can be improved. Elevation data provides information about the height of a point on the Earth's surface relative to a specific vertical reference (typically sea level). Furthermore, semantic identification of key geographic features in the 3D fused map data can be performed to obtain semantic labels for these features. For example, key geographic features can be identified based on semantic labels from OpenStreetMap (OSM). For example, key geographic features may include mountains, rivers, and paths.

[0065] S1012: Utilize the simulation platform to simulate the three-dimensional fusion map data to obtain the simulated ground environment, and utilize the simulation platform to simulate at least one called drone model to obtain the at least one simulated drone.

[0066] Here, when generating a simulated drone, the designed drone model can be called for simulation processing, which can improve the simulation efficiency.

[0067] Specifically, when using the simulation platform to simulate the three-dimensional fused map data to obtain the simulated ground environment, it can include: adjusting the three-dimensional fused map data to obtain target map data, and using the simulation platform to simulate the target map data to obtain the simulated ground environment; wherein the adjustment includes at least one of element path adjustment, scaling adjustment and coordinate system adjustment.

[0068] For example, when importing 3D fused map data into a simulation platform, the element models within the 3D fused map data can be adjusted to ensure that the path lengths in the simulation platform are consistent with those in the real map. For example, the flight path and position coordinates of a simulated drone in a simulated geographic environment must be consistent with the flight path and position coordinates of a real drone on the real map. For another example, if the position paths between elements (models) in the 3D fused map data change, the model paths must be adjusted to ensure that the paths between the elements in the simulation environment are consistent with the paths between the corresponding elements in the real map.

[0069] By adjusting the scaling ratio, the simulated geographical environment can be made more consistent with the real one. Figure 1 :1 restoration. For example, if the 3D fused map data (captured map) is small, it is necessary to enlarge the scale in the simulation platform to make the simulated environment map and the real map Figure 1 To.

[0070] In addition, preset software or algorithms can be used to achieve automatic conversion between geographic coordinates and local coordinates of the simulation platform.

[0071] It should be noted that the above adjustment of the three-dimensional fusion map data can be achieved through manual adjustment or automatic adjustment, and there is no specific limitation.

[0072] S1013: Construct the simulated virtual scene based on the simulated ground environment and the at least one simulated drone.

[0073] The simulated virtual scene in the embodiment of the present application may include not only the simulated ground environment and the at least one simulated drone, but also other factors, such as simulated airflow, simulated lighting, and sensor noise, etc. This is conducive to improving the scene restoration degree and making the simulated environment more compatible with the real environment.

[0074] In some embodiments, to further improve the accuracy of 3D fused map data, after generating 3D fused map data for the target area, the method further includes: performing parametric modeling on target elements in the 3D fused map data to generate a 3D model corresponding to the target element, and embedding real physical attribute information into the 3D model to obtain a target 3D model; the target 3D model has a semantic label. The target element may include infrastructure that needs to be identified or avoided during cruising, such as utility poles. The infrastructure may vary depending on the cruising scenario.

[0075] For example, critical infrastructure (such as power transmission poles) can be parametrically modeled to generate millimeter-level accurate 3D models, embedding real-world physical properties. When the model is exported, semantic tags can be embedded to enhance the model's semantic recognition capabilities. For example, real-world physical properties might include a 5-cm redundant buffer layer added to the collision volume, material stiffness (elastic modulus of 200 GPa for steel), and friction coefficient (0.6 for concrete surface).

[0076] In the embodiments of this application, by parametrically modeling target elements in 3D fused map data and embedding real-world physical property information, the accuracy of simulation results can be improved. Taking power inspection as an example, in related technologies, because power poles are often simplified into cylindrical models in simulations, their collision detection parameters do not match their real-world physical properties, resulting in a misjudgment rate of up to 25% in real-world scenarios for drone obstacle avoidance algorithms. This solution can significantly reduce the misjudgment rate of drone obstacle avoidance algorithms in real-world scenarios.

[0077] In some embodiments, when a target area includes multiple sub-areas, fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area may include the following:

[0078] For any sub-region, the satellite image data, elevation data and vector map corresponding to the sub-region are fused to obtain sub-3D fused map data for the sub-region;

[0079] The sub-three-dimensional fused map data corresponding to each of the sub-areas are spliced ​​using the least squares method to generate three-dimensional fused map data for the target area.

[0080] In this way, when multiple sub-maps are spliced ​​together, combined with least squares fitting, the splicing errors of multi-source data can be eliminated, which is conducive to improving the accuracy of three-dimensional fused map data. For example, the root mean square average (RMS) of positioning accuracy can be achieved below 0.3 meters.

[0081] In some embodiments, while the target simulated drone is executing a target flight mission, an environmental image of the environment in which the target simulated drone is located during the mission can be obtained, and target detection can be performed on the environmental image to obtain a target detection result, which can be sent to the server. For example, a target detection model can be used to perform target detection on the environmental image to obtain a target detection result, which can include vehicles, birds, etc. In this way, the server can adjust flight instructions based on the target detection result, for example, changing the flight instruction to hover or descend.

[0082] In other embodiments, in order to provide more information to improve the decision-making accuracy of the server, a multimodal large language model can be called to perform screen analysis on the target detection result to generate screen description information for the target detection result. For example, if the target detection result is that a vehicle is detected, then based on the target detection result, corresponding screen description information "This is a white vehicle with damaged tires" can be generated. Optionally, the target detection result and the screen description information corresponding to the target detection result are also sent to the server. In this way, the server can determine whether the flight instructions for the target simulation drone need to be changed based on the target detection result and the screen description information.

[0083] Therefore, in some embodiments, the method further includes: calling a multimodal large language model to perform screen analysis on the target detection result to generate screen description information for the target detection result;

[0084] The sending the target detection result to the server includes:

[0085] The target detection result and the description information corresponding to the target detection result are sent to the server.

[0086] It should be noted that, in the process of the target simulation UAV performing the target flight mission, in addition to feeding back the target detection results and the image description information of the target detection results to the server, the simulation UAV can also perform three-dimensional trajectory replanning based on the target detection results to avoid obstacles.

[0087] See also Figure 4 FIG. 2 is a flowchart of a method for scheduling a simulated drone according to another embodiment of the present application. The method is applied to a server 200 and includes the following steps S201 to S204:

[0088] S201, obtaining a user flight instruction, and receiving simulated remaining power information of at least one simulated drone and identification information of each simulated drone sent by a terminal device.

[0089] The identification information of each simulated drone is the identity information of each simulated drone, which is used to distinguish different simulated drones.

[0090] For example, a user flight instruction can be obtained through a user interface. The user flight instruction is a flight instruction issued by the user. Of course, the user flight instruction can also be obtained through voice input. For example, the obtained user flight instruction can be "Dispatch a drone with a battery level greater than 60% near 31.2° north latitude" or "Dispatch a drone with a battery level greater than 70% near 31.2° north latitude to cruise to XX target point." In this embodiment of the present application, the user flight instruction is a natural language instruction.

[0091] S202: Determine a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction.

[0092] Exemplarily, a large language model is deployed on the server 200, and the large language model can be used to determine the target simulated drone from the at least one simulated drone based on the simulated remaining power information of each simulated drone and the user flight instruction.

[0093] In some embodiments, when determining a target simulated drone from the at least one simulated drone based on the simulated remaining power information of each simulated drone and the user flight instruction, the following steps (I) to (III) may be included:

[0094] (I) Based on the user flight instruction, determining a target mission range corresponding to the user flight instruction.

[0095] Specifically, after obtaining the user's flight instruction, the target mission range corresponding to the user's flight instruction can be learned. For example, the mission range may be cruising from 31.2° north latitude to 33.2° north latitude, which can be determined according to actual conditions.

[0096] (II) For each simulated UAV, based on the simulated remaining power information of the simulated UAV, determine the maximum flight mission range that the simulated UAV can perform.

[0097] For example, equation modeling can be used to determine the maximum mission range of the simulated UAV, for example, it can be achieved by the following formula (1):

[0098] R = R0×(1- ) (1)

[0099] Where R represents the maximum mission radius corresponding to the current simulated remaining power of the simulated drone, R0 represents the mission radius corresponding to the fully charged simulated drone, and ΔSOC represents the difference between the current simulated remaining power of the simulated drone and the fully charged state. For example, if the current simulated remaining power of the simulated drone is 80%, then ΔSOC is 20%. As can be seen from the above formula (1), when the SOC drops from 80% to 70%, the inspection distance is automatically reduced by 48%.

[0100] (III) Determine a simulated UAV whose maximum flight mission range is larger than the target mission range as a candidate simulated UAV, and determine the target simulated UAV from at least one of the candidate simulated UAVs.

[0101] For example, after determining the maximum mission range that each simulated drone can fly, a simulated drone whose maximum flight mission range is greater than the target mission range can be determined as a candidate simulated drone, and then the target simulated drone is determined from at least one of the candidate simulated drones.

[0102] In some embodiments, when determining the target simulated drone from at least one candidate simulated drone, the following may be included:

[0103] For each candidate simulated drone, determining evaluation index information of the candidate simulated drone based on the maximum flight mission range, power consumption information, urgency information, and weights corresponding to the maximum flight mission range, the power consumption information, and the urgency information, respectively; wherein the maximum flight mission range corresponds to a first weight, the power consumption information corresponds to a second weight, and the urgency information corresponds to a third weight;

[0104] Based on the evaluation index information of at least one candidate simulated drone, the target simulated drone is determined from the at least one candidate simulated drone.

[0105] The power consumption information refers to the power currently consumed by the candidate simulated drone. For example, if the candidate simulated drone currently has 80% remaining power, the power consumption information is 20%. The urgency information is used to represent the urgency of the flight mission.

[0106] Specifically, the candidate simulated drones with the best evaluation index information can be determined as the target simulated drone.

[0107] Here, the evaluation index information of each candidate simulated drone can be determined by the following formula (2):

[0108] M = α × maximum flight mission range + β × power consumption information + γ × urgency information (2)

[0109] Where M represents the evaluation index information of the candidate simulated drone, α represents the first weight, β represents the second weight, and γ represents the third weight. When the M value is minimum, it means the evaluation index information is optimal.

[0110] Optionally, the simulated virtual scene has a real-time simulated wind speed, and the first weight, the second weight and the third weight are determined based on the real-time simulated wind speed, and then each weight can be dynamically adjusted according to the simulation environment, which is conducive to improving the determination accuracy of the target simulated drone.

[0111] In other embodiments, the user flight instruction includes a delay time. When determining the maximum flight mission range that can be performed by each simulated drone based on the simulated remaining power information of the simulated drone, it may include: determining the maximum flight mission range that can be performed by the simulated drone based on the simulated remaining power information of the simulated drone after the delay time for each simulated drone.

[0112] For example, if a user's flight instruction is "Send a simulated drone near 31.2° North Latitude to cruise to target point XX in 3 hours," the behavior of each simulated drone within 3 hours needs to be considered. For example, if some simulated drones currently have low battery remaining but will recharge in 1 hour, they may be fully charged in 3 hours and therefore be listed as candidates for executing the target mission. In this way, delaying takeoff through the timing function can improve decision-making flexibility.

[0113] S203: Generate a target flight instruction based on the identification information of the target simulated UAV and the user flight instruction.

[0114] For example, a large language model is deployed on the server 200. When generating a target flight instruction based on the identification information of the target simulated drone and the user flight instruction, the server 200 may include the following:

[0115] Utilizing the large language model to convert the user's flight instructions into a format to obtain flight instructions in a target format;

[0116] The target flight instruction is generated based on the identification information of the target simulated UAV and the flight target position information indicated by the flight instruction in the target format.

[0117] Here, the large language model can be used to convert the user's flight instructions into a target format (e.g., a JSON object format) to parse the natural language instructions. Then, based on the identification information of the target simulated drone and the flight target location information indicated by the flight instructions in the target format, the target flight instructions are generated. In this way, the generated target flight instructions can carry the identification information of the target simulated drone and the flight target location information (also known as waypoint parameters).

[0118] For example, when a user sends a flight command for "dispatching a drone for inspection at 31.2° north latitude," the large language model can dynamically generate structured constraints (in JSON format) and select the optimal drone in real time, reducing response time. Specifically, when the large language model uses an attention mechanism to parse user flight commands, it can also implement a 1-kilometer radius expansion for ambiguous geographic descriptions, such as converting 31.2° latitude into a rectangular geofence between 31.195° and 31.205°, thereby improving the accuracy of target task determination.

[0119] S204, sending the target flight instruction to the terminal device; the target flight instruction is used to instruct the target simulated UAV to perform a target flight mission.

[0120] After the target flight instruction is generated, the target flight instruction can be sent to the terminal device to control the target simulation UAV to perform the target flight mission.

[0121] In some embodiments, the method further comprises the following:

[0122] Receiving a target detection result sent by a terminal device, wherein the target detection result is obtained by the target simulation UAV performing target detection based on the acquired environment image when performing the target flight mission;

[0123] Based on the target detection result, a flight adjustment instruction is generated, and the flight adjustment instruction is sent to the terminal device.

[0124] Here, when the simulated drone is performing a mission, it can acquire an environmental image and perform target detection on the environmental image to obtain a target detection result. After receiving the target detection result, server 200 can determine whether the target point indicated by the target flight position information has been found based on the target detection result. For example, the target point indicated by the target flight position information can be a fixed location or a non-fixed object (such as a moving white vehicle).

[0125] Optionally, to improve the accuracy of the judgment, the method further includes: receiving screen description information, the screen description information being obtained based on the target detection result. Here, when generating a flight adjustment instruction based on the target detection result, the method may include: generating a flight adjustment instruction based on the target detection result and the screen description information. For example, if the target flight instruction is to let the target simulated drone fly over a white car with a damaged tire, the server 200 can determine whether the target point is found based on the detection result and the screen description information, and send an adjustment instruction after determining that the target point is found, for example, to hover over the target point.

[0126] In addition, after the target simulated drone's remaining power is reset to zero due to a battery failure, another simulated drone can be replaced to continue the target flight mission. Specifically, the target simulated drone can be re-screened based on the above screening strategy, starting from the failure point of the simulated drone currently performing the mission.

[0127] The interactive process of the scheduling method for simulated drones provided in this application is described below.

[0128] See also Figure 5 As shown, the terminal device 100 first executes step S101 to construct a simulated virtual scene for a target area based on a simulation platform. The simulated virtual scene includes a simulated geographical environment and at least one simulated drone, each of which has simulated remaining battery information. Then, step S102 is executed to send the simulated remaining battery information of each simulated drone to a server. Next, the server 200 sequentially executes steps S201 to obtain a user flight instruction and receive the simulated remaining battery information of at least one simulated drone and identification information of each simulated drone from the terminal device. Step S202 determines a target simulated drone from the at least one simulated drone based on the simulated remaining battery information of each simulated drone and the user flight instruction. Step S203 generates a target flight instruction based on the identification information of the target simulated drone and the user flight instruction. Step S204 is then executed to send the target flight instruction to the terminal device. Finally, the terminal device 100 executes step S103 to receive the target flight instruction sent by the server. Step S104 controls the target simulated drone to execute the target flight mission based on the flight target location information.

[0129] Experiments have shown that the scheduling method of this application can achieve the following technical effects, as shown in Table 1.

[0130] Table 1

[0131]

[0132] Among them, the command response delay refers to the response delay time of the interactive command between the terminal device 100 and the server 200, and the edge inference energy efficiency ratio refers to the energy consumed in processing a preset number of frames of images.

[0133] In the scheduling method for simulated drones provided in the embodiments of the present application, the server 200 can receive information about the simulated remaining battery power of each simulated drone and, based on the user's flight instructions and the simulated remaining battery power information, make a task decision to select the optimal target simulated drone capable of executing the target flight mission from at least one simulated drone. In this way, since the selection of target simulated drones is based not only on the user's flight instructions but also on the simulated remaining battery power information of each simulated drone, the simulation results can be more closely aligned with the real-world scenario, which is conducive to improving the simulation verification effect.

[0134] Those skilled in the art will understand that in the above-mentioned method of the specific implementation method, the writing order of each step does not mean a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0135] Based on the same technical concept, the embodiment of the present disclosure also provides a scheduling device for a simulated drone corresponding to the scheduling method for a simulated drone. Since the principle of solving the problem by the device in the embodiment of the present disclosure is similar to the scheduling method for a simulated drone in the embodiment of the present disclosure, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0136] Reference Figure 6 FIG. 1 is a schematic diagram of a scheduling device for simulating a UAV provided by an embodiment of the present disclosure. The scheduling device 300 for simulating a UAV includes:

[0137] A scene construction module 301 is configured to construct a simulated virtual scene for a target area based on a simulation platform, wherein the simulated virtual scene includes a simulated geographical environment and at least one simulated drone, each of the simulated drones having simulated remaining battery information;

[0138] An information sending module 302 is used to send the simulated remaining power information of each simulated drone to a server;

[0139] The instruction receiving module 303 is configured to receive a target flight instruction sent by the server, wherein the target flight instruction carries identification information of a target simulated UAV and flight target position information; the target simulated UAV is selected by the server from the at least one simulated UAV based on the simulated remaining power information of each simulated UAV and the user flight instruction;

[0140] The flight control module 304 is used to control the target simulation UAV to perform the target flight mission according to the flight target position information.

[0141] In some embodiments, each of the simulated drones is integrated with a battery simulation model, and the scenario construction module 301 is further configured to:

[0142] For each of the simulated drones, the battery simulation model is used to determine the simulated remaining power information of the simulated drone according to preset discharge interval parameters.

[0143] In some embodiments, the scene construction module 301 is further configured to:

[0144] For any simulated UAV, using the battery simulation model, a battery failure simulation instruction is generated;

[0145] In response to the battery failure simulation instruction, the simulated remaining power information of the simulated drone is set to zero.

[0146] In some embodiments, each simulated drone has a virtual port and an instance identifier, and different simulated drones have different virtual ports and different instance identifiers.

[0147] In some embodiments, when constructing a simulated virtual scene for a target area based on a simulation platform, the scene construction module 301 is specifically configured to:

[0148] Acquiring satellite image data, elevation data, and a vector map for the target area, and fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area;

[0149] Using the simulation platform to simulate the three-dimensional fused map data to obtain the simulated ground environment, and using the simulation platform to simulate the at least one called drone model to obtain the at least one simulated drone;

[0150] The simulated virtual scene is constructed based on the simulated ground environment and the at least one simulated drone.

[0151] In some embodiments, the scene construction module 301 is further specifically configured to:

[0152] The target element in the three-dimensional fusion map data is parameterized and modeled to generate a three-dimensional model corresponding to the target element, and the three-dimensional model is embedded with real physical attribute information to obtain a target three-dimensional model; the target three-dimensional model has a semantic label.

[0153] In some embodiments, the scene construction module 301 is further specifically configured to:

[0154] Adjusting the three-dimensional fused map data to obtain target map data, wherein the adjustment includes at least one of element path adjustment, scaling adjustment, and coordinate system adjustment;

[0155] The target map data is simulated using the simulation platform to obtain the simulated ground environment.

[0156] In some embodiments, the flight control module 304 is further configured to:

[0157] Acquire an environmental image of the environment in which the target simulated UAV is located when performing the target flight mission;

[0158] Perform target detection on the environment image to obtain a target detection result, and send the target detection result to the server.

[0159] In some embodiments, the flight control module 304 is further configured to:

[0160] Calling a multimodal large language model to perform image analysis on the target detection result to generate image description information for the target detection result;

[0161] The target detection result and the screen description information corresponding to the target detection result are sent to the server.

[0162] Reference Figure 7 FIG. 1 is a schematic diagram of another simulated drone scheduling device provided by an embodiment of the present disclosure, wherein the simulated drone scheduling device 400 includes:

[0163] The information receiving module 401 is used to obtain the user's flight instructions and receive the simulated remaining power information of at least one simulated drone sent by the terminal device;

[0164] The drone screening module 402 is configured to determine a target simulated drone from the at least one simulated drone based on the simulated remaining power information of each simulated drone and the user flight instruction;

[0165] The instruction generation module 403 is used to generate a target flight instruction based on the identification information of the target simulated UAV and the user flight instruction;

[0166] The instruction sending module 404 is used to send the target flight instruction to the terminal device, and the target flight instruction is used to instruct the target simulated UAV to perform the target flight mission.

[0167] In some embodiments, the drone screening module 402 is specifically configured to:

[0168] Based on the user flight instruction, determining a target mission range corresponding to the user flight instruction;

[0169] For each simulated UAV, determining a maximum flight mission range that the simulated UAV can perform based on the simulated remaining power information of the simulated UAV;

[0170] A simulated UAV with a maximum flight mission range greater than the target mission range is determined as a candidate simulated UAV, and the target simulated UAV is determined from at least one of the candidate simulated UAVs.

[0171] In some embodiments, the drone screening module 402 is specifically configured to:

[0172] For each candidate simulated drone, determining evaluation index information of the candidate simulated drone based on the maximum flight mission range, power consumption information, urgency information, and weights corresponding to the maximum flight mission range, the power consumption information, and the urgency information, respectively; wherein the maximum flight mission range corresponds to a first weight, the power consumption information corresponds to a second weight, and the urgency information corresponds to a third weight;

[0173] Based on the evaluation index information of at least one candidate simulated drone, the target simulated drone is determined from the at least one candidate simulated drone.

[0174] In some embodiments, the simulated virtual scene has a real-time simulated wind speed; the first weight, the second weight, and the third weight are determined based on the real-time simulated wind speed.

[0175] In some embodiments, the user flight instruction includes a delay time; the drone screening module 402 is specifically configured to:

[0176] For each simulated UAV, based on the simulated remaining power information of the simulated UAV after the delay time, the maximum flight mission range that the simulated UAV can perform is determined.

[0177] In some embodiments, the server is deployed with a large language model, and the drone screening module 402 is specifically used to:

[0178] The large language model is used to determine a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction.

[0179] In some embodiments, the server is deployed with a large language model, and the drone screening module 402 is specifically used to:

[0180] Utilizing the large language model to convert the user's flight instructions into a format to obtain flight instructions in a target format;

[0181] The target flight instruction is generated based on the identification information of the target simulated UAV and the flight target position information indicated by the flight instruction in the target format.

[0182] In some embodiments, the information receiving module 401 is further configured to:

[0183] Receiving a target detection result sent by a terminal device, wherein the target detection result is obtained by the target simulation UAV performing target detection based on the acquired environment image when performing the target flight mission;

[0184] The instruction generation module 403 is further configured to: generate a flight adjustment instruction based on the target detection result;

[0185] The instruction sending module 404 is further configured to send the flight adjustment instruction to the terminal device.

[0186] In some embodiments, the information receiving module 401 is further configured to:

[0187] receiving picture description information, where the picture description information is obtained based on the target detection result;

[0188] The instruction generation module 403 is further configured to:

[0189] Based on the target detection result and the picture description information, the line adjustment instruction is generated.

[0190] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0191] Based on the same technical concept, the embodiment of the present disclosure also provides an electronic device. Figure 8, which is a schematic diagram of the structure of an electronic device 500 provided in an embodiment of the present disclosure, includes a processor 501, a memory 502, and a bus 503. The memory 502 is used to store execution instructions and includes a memory 5021 and an external memory 5022. The memory 5021 is also referred to as internal memory and is used to temporarily store operation data in the processor 501 and data exchanged with an external memory 5022 such as a hard disk. The processor 501 exchanges data with the external memory 5022 through the memory 5021.

[0192] In the embodiment of the present application, the memory 502 is specifically used to store application code for executing the solution of the present application, and the execution is controlled by the processor 501. That is, when the electronic device 500 is running, the processor 501 communicates with the memory 502 via the bus 503, so that the processor 501 executes the application code stored in the memory 502, thereby performing the method described in any of the aforementioned embodiments.

[0193] The memory 502 may be, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), etc.

[0194] Processor 501 may be an integrated circuit chip with signal processing capabilities. Such processors may be general-purpose processors, including central processing units (CPUs) and network processors (NPs). They may also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. These processors may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor.

[0195] It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device 500. In other embodiments of the present application, the electronic device 500 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0196] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, executes the steps of the scheduling method for a simulated robot in the above-mentioned method embodiment. The storage medium may be a volatile or non-volatile computer-readable storage medium.

[0197] The embodiments of the present disclosure also provide a computer program product, which carries program code. The instructions included in the program code can be used to execute the steps of the scheduling method of the simulation robot in the above method embodiment. For details, please refer to the above method embodiment and will not be repeated here.

[0198] The computer program product may be implemented in hardware, software, or a combination thereof. In one embodiment, the computer program product is implemented as a computer storage medium. In another embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).

[0199] In addition, embodiments of the subject matter and functional operations described in this specification may be implemented in the following: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more thereof. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver device for execution by the data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more thereof.

[0200] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special-purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0201] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to such mass storage devices to receive data from them or to transmit data to them, or both. However, a computer does not necessarily have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0202] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0203] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of the sub-combination.

[0204] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0205] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the particular order shown or sequential sequence to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0206] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A scheduling method for a simulated drone, characterized in that: Applied to a terminal device, the method includes: Building a simulated virtual scene for the target area based on the simulation platform, the simulated virtual scene including a simulated geographical environment and at least one simulated drone, each of the simulated drones having simulated remaining power information; Sending the simulated remaining power information of each simulated drone to the server; receiving a target flight instruction sent by the server, the target flight instruction carrying identification information of a target simulated drone and flight target position information; the target simulated drone is selected by the server from the at least one simulated drone based on the user flight instruction and the simulated remaining power information of each simulated drone; Controlling the target simulation UAV to perform the target flight mission according to the flight target position information; The construction of a simulated virtual scene for the target area based on the simulation platform includes: Acquiring satellite image data, elevation data, and a vector map for the target area, and fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area; Performing parametric modeling on a target element in the three-dimensional fused map data to generate a three-dimensional model corresponding to the target element, and embedding real physical attribute information into the three-dimensional model to obtain a target three-dimensional model; the target three-dimensional model has a semantic label; Using the simulation platform to simulate the three-dimensional fused map data to obtain the simulated geographical environment, and using the simulation platform to simulate the at least one called drone model to obtain the at least one simulated drone; The simulated virtual scene is constructed based on the simulated geographical environment and the at least one simulated drone.

2. The method according to claim 1, characterized in that Each of the simulated drones is integrated with a battery simulation model, and the method further comprises: For each of the simulated drones, the battery simulation model is used to determine the simulated remaining power information of the simulated drone according to preset discharge interval parameters.

3. The method according to claim 2, characterized in that The method further comprises: For any simulated UAV, using the battery simulation model, a battery failure simulation instruction is generated; In response to the battery failure simulation instruction, the simulated remaining power information of the simulated drone is set to zero.

4. The method according to claim 1, wherein Each simulated drone has a virtual port and an instance identifier. Different simulated drones have different virtual ports and instance identifiers.

5. The method according to claim 1, wherein The using the simulation platform to simulate the three-dimensional fusion map data to obtain the simulated geographical environment includes: Adjusting the three-dimensional fused map data to obtain target map data, wherein the adjustment includes at least one of element path adjustment, scaling adjustment, and coordinate system adjustment; The target map data is simulated using the simulation platform to obtain the simulated geographical environment.

6. The method according to claim 1, characterized in that The method further comprises: Acquire an environmental image of the environment in which the target simulated UAV is located when performing the target flight mission; Perform target detection on the environment image to obtain a target detection result, and send the target detection result to the server.

7. The method according to claim 6, characterized in that The method further comprises: Calling a multimodal large language model to perform image analysis on the target detection result to generate image description information for the target detection result; The sending the target detection result to the server includes: The target detection result and the screen description information corresponding to the target detection result are sent to the server.

8. A method for scheduling a simulated drone, characterized in that: Applied to a server, the method includes: Obtaining user flight instructions and receiving simulated remaining power information of at least one simulated drone and identification information of each simulated drone sent by the terminal device; Determining a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction; Generate a target flight instruction based on the identification information of the target simulated UAV and the user flight instruction; The target flight instruction is sent to the terminal device; the target flight instruction is used to instruct the target simulated UAV to perform the target flight mission in the simulated virtual scene; the simulated virtual scene is constructed by the following steps: Acquiring satellite image data, elevation data, and a vector map for the target area, and fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area; Performing parametric modeling on a target element in the three-dimensional fused map data to generate a three-dimensional model corresponding to the target element, and embedding real physical attribute information into the three-dimensional model to obtain a target three-dimensional model; the target three-dimensional model has a semantic label; Using a simulation platform to simulate the three-dimensional fused map data to obtain a simulated geographical environment, and using the simulation platform to simulate at least one called drone model to obtain the at least one simulated drone; The simulated virtual scene is constructed based on the simulated geographical environment and the at least one simulated drone.

9. The method according to claim 8, characterized in that The step of determining a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction includes: Based on the user flight instruction, determining a target mission range corresponding to the user flight instruction; For each simulated UAV, determining a maximum flight mission range that the simulated UAV can perform based on the simulated remaining power information of the simulated UAV; A simulated UAV with a maximum flight mission range greater than the target mission range is determined as a candidate simulated UAV, and the target simulated UAV is determined from at least one of the candidate simulated UAVs.

10. The method according to claim 9, characterized in that The determining the target simulated drone from at least one candidate simulated drone comprises: For each candidate simulated drone, determining evaluation index information of the candidate simulated drone based on the maximum flight mission range, power consumption information, urgency information, and weights corresponding to the maximum flight mission range, the power consumption information, and the urgency information, respectively; wherein the maximum flight mission range corresponds to a first weight, the power consumption information corresponds to a second weight, and the urgency information corresponds to a third weight; Based on the evaluation index information of at least one candidate simulated drone, the target simulated drone is determined from the at least one candidate simulated drone.

11. The method according to claim 10, characterized in that The simulated virtual scene has a real-time simulated wind speed; the first weight, the second weight, and the third weight are determined based on the real-time simulated wind speed.

12. The method according to claim 10, characterized in that The user flight instruction includes a delay time; The step of determining, for each simulated UAV, a maximum flight mission range that the simulated UAV can perform based on the simulated remaining power information of the simulated UAV includes: For each simulated UAV, based on the simulated remaining power information of the simulated UAV after the delay time, the maximum flight mission range that the simulated UAV can perform is determined.

13. The method according to claim 8, characterized in that The server is deployed with a large language model, and determining a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction includes: The large language model is used to determine a target simulated drone from the at least one simulated drone according to the simulated remaining power information of each simulated drone and the user flight instruction.

14. The method according to claim 8, characterized in that The server is deployed with a large language model, and generating a target flight instruction based on the identification information of the target simulated drone and the user flight instruction includes: Utilizing the large language model to convert the user's flight instructions into a format to obtain flight instructions in a target format; The target flight instruction is generated based on the identification information of the target simulated UAV and the flight target position information indicated by the flight instruction in the target format.

15. The method according to claim 8, characterized in that The method further comprises: Receiving a target detection result sent by a terminal device, wherein the target detection result is obtained by the target simulation UAV performing target detection based on the acquired environment image when performing the target flight mission; Based on the target detection result, a flight adjustment instruction is generated, and the flight adjustment instruction is sent to the terminal device.

16. The method according to claim 15, characterized in that The method further comprises: receiving picture description information, where the picture description information is obtained based on the target detection result; Generating a flight adjustment instruction based on the target detection result includes: Based on the target detection result and the picture description information, the line adjustment instruction is generated.

17. A dispatching device for simulating a drone, characterized in that: The device comprises: A scene construction module is used to construct a simulated virtual scene for a target area based on a simulation platform, wherein the simulated virtual scene includes a simulated geographical environment and at least one simulated drone, each of the simulated drones having simulated remaining power information; An information sending module, configured to send the simulated remaining power information of each simulated drone to a server; An instruction receiving module is configured to receive a target flight instruction sent by the server, the target flight instruction carrying identification information of a target simulated UAV and flight target position information; the target simulated UAV is obtained by the server from the at least one simulated UAV based on the simulated remaining power information of each simulated UAV and the user flight instruction; A flight control module is used to control the target simulation UAV to perform a target flight mission according to the flight target position information; The scene construction module is specifically used for: Acquiring satellite image data, elevation data, and a vector map for the target area, and fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area; Performing parametric modeling on a target element in the three-dimensional fused map data to generate a three-dimensional model corresponding to the target element, and embedding real physical attribute information into the three-dimensional model to obtain a target three-dimensional model; the target three-dimensional model has a semantic label; Using the simulation platform to simulate the three-dimensional fused map data to obtain the simulated geographical environment, and using the simulation platform to simulate the at least one called drone model to obtain the at least one simulated drone; The simulated virtual scene is constructed based on the simulated geographical environment and the at least one simulated drone.

18. A dispatching device for simulating a drone, characterized in that: The device comprises: An information receiving module, configured to obtain flight instructions from a user and receive simulated remaining power information of at least one simulated drone sent by a terminal device; a drone screening module, configured to determine a target simulated drone from the at least one simulated drone based on the simulated remaining power information of each simulated drone and the user flight instruction; An instruction generation module, configured to generate a target flight instruction based on the identification information of the target simulated UAV and the user flight instruction; An instruction sending module is used to send the target flight instruction to the terminal device, wherein the target flight instruction is used to instruct the target simulated UAV to perform the target flight mission in the simulated virtual scene; the simulated virtual scene is constructed by the following steps: Acquiring satellite image data, elevation data, and a vector map for the target area, and fusing the satellite image data, the elevation data, and the vector map to generate three-dimensional fused map data for the target area; Performing parametric modeling on a target element in the three-dimensional fused map data to generate a three-dimensional model corresponding to the target element, and embedding real physical attribute information into the three-dimensional model to obtain a target three-dimensional model; the target three-dimensional model has a semantic label; Using a simulation platform to simulate the three-dimensional fused map data to obtain a simulated geographical environment, and using the simulation platform to simulate at least one called drone model to obtain the at least one simulated drone; The simulated virtual scene is constructed based on the simulated geographical environment and the at least one simulated drone.

19. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor and the memory communicate via the bus. When the machine-readable instructions are executed by the processor, the scheduling method for a simulated drone as described in any one of claims 1 to 16 is executed.

20. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, executes the scheduling method for a simulated drone as described in any one of claims 1 to 16.

Images