A low-altitude intelligent cluster flight management method, equipment, medium and product

Abstract

This application discloses a low-altitude intelligent cluster flight management method, equipment, medium, and product, relating to the field of aviation technology. The method includes: constructing a knowledge graph based on the structured and unstructured data of low-altitude networked intelligent vehicles; using an LSTM model to analyze the airspace characteristics in the knowledge graph and predict the vehicle's traffic spatiotemporal graph; dividing the low-altitude airspace based on the traffic spatiotemporal graph and determining the low-altitude airspace allocation result; based on the low-altitude airspace allocation result, using the vehicle's flight safety as the reward function and the time taken for the flight path as the value function, using the Q-learning + traffic factor penalty algorithm to iterate the initialized low-altitude networked intelligent vehicle path to obtain the optimal path; and constructing a formation flight collaborative control model based on the low-altitude networked intelligent vehicle cluster with the goal of minimizing the delay rate. This application can improve the airspace utilization efficiency and flight safety of low-altitude networked intelligent vehicles.

Description

Technical Field

[0001] The present application relates to the field of aviation technology, and in particular to a low-altitude intelligent cluster flight management method, equipment, medium and product. Background Art

[0002] With rapid advances in electric propulsion, high-energy-density batteries, precise sensing, and stable communications, low-altitude connected intelligent vehicles are becoming a reality. These vehicles have broad application prospects in logistics, surveillance, rescue, and other fields.

[0003] However, similar to ground transportation, low-altitude connected intelligent vehicles require a comprehensive and efficient management system to ensure safety, comfort, and efficiency. Given the low barrier to entry, flexible operation, and large-scale availability of these new low-altitude connected intelligent vehicles, effective management strategies are currently lacking. Summary of the Invention

[0004] The purpose of this application is to provide a low-altitude intelligent cluster flight management method, equipment, medium and product, which can improve the airspace utilization efficiency and flight safety of low-altitude networked intelligent vehicles.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a low-altitude intelligent cluster flight management method, comprising:

[0007] Obtain structured and unstructured data of low-altitude connected intelligent vehicles.

[0008] Based on the structured data and the unstructured data, a structured low-altitude networked intelligent vehicle knowledge graph is constructed; the nodes in the structured low-altitude networked intelligent vehicle knowledge graph are entities, and the edges are the relationships between entities; the relationships between entities are obtained after analyzing the structured data and the unstructured data through information extraction technology and knowledge representation technology.

[0009] The LSTM model is used to analyze the airspace features in the structured low-altitude networked intelligent vehicle knowledge graph to predict the flow space-time diagram of low-altitude networked intelligent vehicles; the airspace features are the passable direction and maximum flow of the airspace.

[0010] The low-altitude airspace is divided based on the traffic space-time diagram to determine the low-altitude airspace allocation result; the low-altitude airspace allocation result is the passable direction and maximum traffic of the unit airspace.

[0011] Initialize the low-altitude networked intelligent vehicle path based on the low-altitude airspace allocation result.

[0012] Taking the flight safety of the low-altitude connected intelligent vehicle as the reward function and the time taken for the flight path of the low-altitude connected intelligent vehicle as the value function, the Q-learning + traffic factor penalty algorithm is used to iterate the initialized low-altitude connected intelligent vehicle path to obtain the optimal path of the low-altitude connected intelligent vehicle.

[0013] Based on a cluster of low-altitude networked intelligent vehicles, a formation flight collaborative control model is constructed with the goal of minimizing the delay rate; the low-altitude networked intelligent vehicle cluster is composed of low-altitude networked intelligent vehicles with the same path; the formation flight collaborative control model is used to control the flight of the low-altitude networked intelligent vehicle cluster according to the formation control method.

[0014] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the above-mentioned low-altitude intelligent cluster flight management methods.

[0015] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any one of the above-mentioned low-altitude intelligent cluster flight management methods.

[0016] In a fourth aspect, the present application provides a computer program product, comprising a computer program, which, when executed by a processor, implements any one of the above-mentioned low-altitude intelligent cluster flight management methods.

[0017] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0018] This application provides a method, equipment, medium, and product for low-altitude intelligent swarm flight management. This method builds a detailed knowledge graph based on the structured and unstructured data of low-altitude connected intelligent vehicles. Using a long short-term memory (LSTM) model, the airspace features in the knowledge graph are deeply analyzed to predict the spatiotemporal distribution of vehicle traffic. Based on this spatiotemporal distribution of traffic, this application proposes a low-altitude airspace partitioning method to determine an airspace allocation plan. Subsequently, based on the airspace allocation results, the initial low-altitude connected intelligent vehicle path is iteratively optimized using a Q-learning algorithm combined with a traffic penalty strategy, using the vehicle's flight safety as the reward function and the flight path time as the value function, to obtain the optimal flight path. Furthermore, this application constructs a formation flight collaborative control model for low-altitude connected intelligent vehicle swarms, targeting delay minimization. Through these technical approaches, this application significantly improves the airspace utilization efficiency and flight safety performance of low-altitude connected intelligent vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is an application environment diagram of a low-altitude intelligent cluster flight management method in one embodiment of the present application.

[0021] Figure 2 A flowchart of a low-altitude intelligent cluster flight management method provided in one embodiment of the present application.

[0022] Figure 3 A schematic diagram of formation control based on a goose-like bionic formation is provided in one embodiment of the present application.

[0023] Figure 4 A diagram of the overall system architecture provided for one embodiment of the present application.

[0024] Figure 5 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0026] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0027] The low-altitude intelligent cluster flight management method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 102 communicates with the server 104 via a network. The data storage system can store data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. Terminal 102 can send the structured data and unstructured data of the low-altitude networked intelligent vehicle to be processed to server 104. After server 104 receives the structured data and unstructured data of the low-altitude networked intelligent vehicle to be processed, server 104 constructs a structured low-altitude networked intelligent vehicle knowledge graph based on the structured data and the unstructured data; the nodes in the structured low-altitude networked intelligent vehicle knowledge graph are entities, and the edges are the relationships between entities; the relationships between entities are obtained by analyzing structured data and unstructured data through information extraction technology and knowledge representation technology; the airspace features in the structured low-altitude networked intelligent vehicle knowledge graph are analyzed using the LSTM model to predict the spatiotemporal flow diagram of the low-altitude networked intelligent vehicle; the airspace features are the passable directions of the airspace and maximum flow; based on the flow time-space diagram, the low-altitude airspace is divided to determine the low-altitude airspace allocation result; the low-altitude airspace allocation result is the passable direction and maximum flow of the unit airspace; according to the low-altitude airspace allocation result, the low-altitude networked intelligent vehicle path is initialized; the flight safety of the low-altitude networked intelligent vehicle is used as the reward function, the time used for the flight path of the low-altitude networked intelligent vehicle is used as the value function, and the Q-learning + traffic factor penalty algorithm is used to iterate the initialized low-altitude networked intelligent vehicle path to obtain the optimal path of the low-altitude networked intelligent vehicle; according to the low-altitude networked intelligent vehicle cluster, a formation flight collaborative control model with the lowest delay rate as the goal is constructed; the low-altitude networked intelligent vehicle cluster is composed of low-altitude networked intelligent vehicles with the same path; the formation flight collaborative control model is used to perform flight control on the low-altitude networked intelligent vehicle cluster according to the formation control method. The server 104 can feed back the obtained formation flight collaborative control model to the terminal 102. In addition, in some embodiments, the low-altitude intelligent cluster flight management method can also be implemented independently by the server 104 or the terminal 102. For example, the terminal 102 can directly perform data processing on the structured data and unstructured data of the low-altitude networked intelligent vehicle to be processed, or the server 104 can obtain the structured data and unstructured data of the low-altitude networked intelligent vehicle to be processed from the data storage system for data processing.

[0028] Terminal 102 may include, but is not limited to, various desktop computers, laptops, smartphones, tablet computers, IoT devices, and portable wearable devices. IoT devices may include smart speakers, smart TVs, smart air conditioners, and smart car devices. Portable wearable devices may include smart watches, smart bracelets, and head-mounted devices. Server 104 may be implemented as a standalone server or a server cluster consisting of multiple servers, or may be a cloud server.

[0029] In an exemplary embodiment, Figure 2 As shown, a low-altitude intelligent cluster flight management method is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the process, including the following steps 201 to 207.

[0030] Step 201: Obtain structured data and unstructured data of the low-altitude networked intelligent vehicle.

[0031] Step 202: Based on the structured data and the unstructured data, a structured low-altitude networked intelligent vehicle knowledge graph is constructed; the nodes in the structured low-altitude networked intelligent vehicle knowledge graph are entities, and the edges are relationships between entities; the relationships between entities are obtained by analyzing the structured data and the unstructured data through information extraction technology and knowledge representation technology.

[0032] Step 203: Use the LSTM model to analyze the airspace features in the structured low-altitude networked intelligent vehicle knowledge graph to predict the flow space-time graph of the low-altitude networked intelligent vehicle; the airspace features are the airspace passable direction and maximum flow.

[0033] Step 204: Divide the low-altitude airspace based on the traffic space-time graph and determine the low-altitude airspace allocation result; the low-altitude airspace allocation result is the passable direction and maximum traffic of the unit airspace.

[0034] Step 205: Initialize the low-altitude networked intelligent vehicle path based on the low-altitude airspace allocation result.

[0035] Step 206: Taking the flight safety of the low-altitude connected intelligent vehicle as the reward function and the time taken for the flight path of the low-altitude connected intelligent vehicle as the value function, the Q-learning + traffic factor penalty algorithm is used to iterate the initialized low-altitude connected intelligent vehicle path to obtain the optimal path of the low-altitude connected intelligent vehicle.

[0036] Step 207: Based on the low-altitude networked intelligent vehicle cluster, a formation flight collaborative control model is constructed with the goal of minimizing the delay rate; the low-altitude networked intelligent vehicle cluster is composed of low-altitude networked intelligent vehicles with the same path; the formation flight collaborative control model is used to perform flight control on the low-altitude networked intelligent vehicle cluster according to the formation control method.

[0037] When executing step 201, the specific steps may be as follows:

[0038] The structured data obtained for low-altitude connected intelligent vehicles are flight logs, and the unstructured data obtained for low-altitude connected intelligent vehicles are data entries that do not follow a fixed format or structure.

[0039] When executing step 202, the specific steps may be as follows:

[0040] Step 301: Preprocess the structured data and unstructured data of the low-altitude networked intelligent vehicle to obtain preprocessed information.

[0041] Step 302: Use a feature-based machine learning method to extract traffic entities from the preprocessed information.

[0042] Step 303: Use the semi-supervised learning Traffic-Snowball algorithm to extract the association relationship in the traffic information corpus; the traffic entity information corpus is composed of information of several traffic entities.

[0043] Step 304: Using data mining methods, mining the relationship patterns between traffic entity attributes and attribute values ​​from the travel information of low-altitude networked intelligent vehicles.

[0044] Step 305: Based on the extracted traffic entities, association relationships and relationship patterns, create nodes and edges in the structured low-altitude networked intelligent vehicle knowledge graph to obtain a structured low-altitude networked intelligent vehicle knowledge graph.

[0045] Specifically, structured data (such as flight logs) and unstructured data (such as entries) from low-altitude connected intelligent vehicles are preprocessed to remove privacy information and noise. Data cleaning and filtering methods are used to ensure data quality and consistency. Structured and unstructured data from different sources are matched and aligned to ensure data consistency and integrity.

[0046] Among them, information extraction is: using feature-based machine learning methods to extract traffic entities, using BiLSTM model. Given an input sequence , for t The flow of low-altitude connected intelligent vehicles at all times, the output of the model is:

[0047] (1).

[0048] Where, and are the hidden states of the forward and backward LSTM at time t, It is the final feature representation at time t, forward means forward and backword means backward. BiLSTM is used for information extraction, emphasizing bidirectionality. The output result is a combination of sequential analysis and reverse analysis. is the hidden state of the forward LSTM at time t-1, is the hidden state of the backward LSTM at time t+1.

[0049] Specifically, the Traffic-Snowball algorithm based on semi-supervised learning is used to extract correlations from traffic information corpus, including:

[0050] 1) Initial seed set: First, some initial seed relation instances (seed samples) are selected from the basic traffic corpus. These instances include known entity pairs and their relations.

[0051] 2) Feature extraction: Extract features from seed relationship instances to represent these relationship instances. These features can be word vectors, context patterns, part-of-speech tags, etc. is the feature set, For the initial seed set:

[0052] (2).

[0053] 3) Pattern generation: Generate relational patterns based on the extracted features. These patterns can be string patterns, part-of-speech patterns, etc. For a collection of patterns:

[0054] (3).

[0055] 4) Pattern matching: Use the generated patterns to match new relation instances in unlabeled corpus. is an unlabeled corpus, and represents the two entities in an entity pair, For existing relationships, The set of new relationship instances that are matched:

[0056] (4).

[0057] 5) Instance filtering: Filter the newly matched relationship instances, retain the instances with high confidence, and remove noise. is the confidence threshold, is a confidence function that returns the probability that the correct content is extracted from the two entities e1 and e2 during training under the existing relationship r. To filter the results:

[0058] (5).

[0059] 6) Iterative process: Add the filtered new instances to the seed set, repeat the above process, and gradually expand the set of relationship instances.

[0060] Specifically, data mining methods are used to mine the relationship patterns between traffic entity attributes and attribute values ​​from the travel information of low-altitude connected intelligent vehicles, including:

[0061] 1) Generate candidate item sets: Starting from a single item set, gradually generate candidate item sets for the item set.

[0062] 2) Calculate support: Calculate the support of each candidate item set and retain the item sets whose support is greater than or equal to the minimum support threshold.

[0063] 3) Generate frequent itemsets: Generate candidate +1 itemsets based on frequent itemsets, and repeat the above steps until no new candidate itemsets can be generated.

[0064] 4) Generate association rules: Generate association rules from frequent item sets, calculate the confidence and lift of each rule, and retain the rules that meet the minimum confidence threshold.

[0065] 5) Represent the extracted knowledge as entities, relationships, and attributes in a knowledge graph, using RDF (Resource Description Framework) as the semantic representation format and eliminating ambiguity and duplication. Based on the extracted knowledge and integrated data, nodes (entities) and edges (relationships) in the knowledge graph are created to construct a structured knowledge graph for low-altitude connected intelligent vehicles.

[0066] When executing step 203, the specific steps may be as follows:

[0067] The LSTM model is used to analyze the airspace features in the structured low-altitude networked intelligent vehicle knowledge graph, predict the spatiotemporal flow graph of low-altitude networked intelligent vehicles, and thus perform airspace division:

[0068] (6).

[0069] Where, is the input gate, For the Gate of Forgetfulness, is the output gate, is the sigmoid function, and is the weight matrix and bias vector of the model, subscript x and hCorresponding respectively X and h vector, i 、 f 、 o They correspond to the input gate formula, forget gate formula and output gate formula respectively. for t The traffic volume of low-altitude connected intelligent vehicles at all times, yes t The final feature representation at time -1. The cell state update equation is:

[0070] (7).

[0071] Where, C t for t The cell state at the moment, t The information left behind at all times, C t-1 for t -1 time cell state, W is the weight matrix, subscript x and h To represent the corresponding vector, c Coefficient representing the cell state calculation.

[0072] The hidden state update equation is:

[0073] (8).

[0074] Finally, the output of the LSTM model As input data The encoding is then passed through a linear layer or convolutional layer to predict the spatiotemporal graph .

[0075] When executing steps 204-206, the specific steps may be as follows:

[0076] The continuous low-altitude airspace is subdivided into rasterized areas, and then the low-altitude airspace is divided based on the traffic space-time diagram to determine the low-altitude airspace allocation result; the low-altitude airspace allocation result is the passable direction and maximum traffic of the unit airspace.

[0077] An environmental model is constructed based on the low-altitude airspace allocation results; the environmental model includes low-altitude intelligent vehicles, route networks, flight rules and potential conflict points.

[0078] Using reinforcement learning methods, the optimal path and speed under the current environmental state are determined in the environmental model.

[0079] The reward function is based on the flight safety composed of time consumption, speed change smoothness, power consumption and conflict risk level, and the value function is based on the time taken by the low-altitude networked intelligent vehicle's flight path. The Q-learning + traffic factor penalty algorithm is used to iterate the current environmental state to obtain the optimal path.

[0080] Specifically, define the state transfer function:

[0081] (9).

[0082] Where, This is the initial state of the low-altitude networked intelligent vehicle. Act for this moment, The posterior probability of taking this action in this state.

[0083] Define the reward function:

[0084] (10).

[0085] Where, Indicates the time taken for different paths. is the longest time consumed in the currently explored path, is the delay coefficient, and are the comfort penalty factors for acceleration magnitude and direction, For speed, is the acceleration, For the energy consumed, For spatial location. For time consumption, For the speed change smoothness, For power consumption, The conflict risk level.

[0086] The "Q-learning + traffic penalty" algorithm is used to iteratively find the optimal path based on the current airspace conditions.

[0087] (11).

[0088] in is the learning rate, is the discount factor, is to execute the action to get the next state, and is the traffic penalty coefficient, Is in state The maximum Q value after executing all possible actions, t is the reduction coefficient determined based on the city’s historical traffic conditions. R(s,d)Indicates that the status s Take action d Instant rewards when you win.

[0089] During the execution of step 207, the specific operation steps can be described in detail as follows:

[0090] First, a cluster of low-altitude connected intelligent vehicles is established: Based on their path planning results, low-altitude connected intelligent vehicles with the same path are grouped into the same cluster. This process involves analyzing and classifying the vehicle paths to ensure that the vehicles in the cluster have similar flight paths, thereby improving the efficiency of cluster management and the effectiveness of collaborative work.

[0091] Secondly, a dynamics model is constructed: a six-degree-of-freedom dynamics model of the low-altitude connected intelligent vehicle is established to describe the vehicle's motion characteristics in detail. This model will include parameters such as the vehicle's position, velocity, acceleration, and attitude in three-dimensional space, enabling accurate simulation of the vehicle's dynamic behavior during flight.

[0092] Next, we will establish a collaborative control model: a formation flight collaborative control model with the goal of minimizing delays. By adjusting the formation flight strategy, we can achieve optimal formation control. Specifically, this model includes the following aspects:

[0093] 1) Pilot Mode: In this mode, the low-altitude intelligent vehicle acts as a communication node between the entire swarm and the central control system. Based on the overall route plan and real-time environmental conditions, it determines the flight attitude of the entire swarm and issues commands to each following low-altitude connected intelligent vehicle. This mode ensures unified and coordinated swarm flight.

[0094] 2) Follow Mode: In this mode, the low-altitude intelligent vehicle acts as the execution terminal for flight decisions and as a sensor to transmit key information such as flight speed, atmospheric pressure, wind speed, and wind direction. The follower vehicle flies according to the instructions of the pilot vehicle, ensuring the safety and efficiency of the entire swarm.

[0095] 3) Transformation Mode: This mode is used to adapt to varying weather conditions and alter the overall formation's three-dimensional configuration. The pilot low-altitude connected intelligent vehicle issues the transformation command, causing the following vehicles to disengage and search for the optimal position from the center outwards. This process ensures the formation's stability and safety in adverse weather conditions.

[0096] 4) Disengagement Mode: In this mode, a low-altitude connected intelligent vehicle reaches its destination airspace and disengages from the formation. The piloting low-altitude connected intelligent vehicle responds to the disengagement request and provides a disengagement gesture. This mode ensures the vehicle can safely and orderly disengage from the formation and complete its mission.

[0097] Specifically, the basic control methods of the flight process include:

[0098] (12).

[0099] Where, and is the position and velocity of the navigator, is the time step, and the current relative position to the pilot vehicle is , the relative position of the target is , and To control the gain, is the control input, and The position and velocity of the vehicle that has left the formation are calculated. The fitness is then selected and the optimal solution is found through iterative updates.

[0100] In real-scene control, algorithms with different focuses are selected to control different modes.

[0101] In pilot mode, low-altitude connected intelligent vehicles act as communication nodes of the cluster, determining the flight posture of the entire team based on the overall route planning and real-time environmental conditions.

[0102] In follow mode, the low-altitude connected intelligent vehicle executes flight decisions and transmits sensor data back.

[0103] The transformation mode is used to cope with different weather conditions, and the three-dimensional configuration transformation of the formation is achieved by issuing commands through the pilot low-altitude networked intelligent vehicle.

[0104] In the separation mode, the low-altitude connected intelligent vehicle reaches the destination airspace and separates from the formation.

[0105] The present application also provides an application scenario, which applies the above-mentioned low-altitude intelligent cluster flight management method. Specifically: Use search engines, navigation software and other data sources to collect triple information of space and time occupied by vehicles, affiliations, and paths to build a knowledge graph, and produce a spatiotemporal diagram of low-altitude networked intelligent vehicle traffic. Use the LSTM model to dynamically divide the low-altitude airspace and assign headings, and use the spatiotemporal attention mechanism to reinforce learning of key spatiotemporal features. Establish an environmental model that matches the real environment, including low-altitude intelligent vehicles, road networks, traffic rules and potential conflict points. Use reinforcement learning methods to select the best path and speed adjustment action under the current environmental conditions. Through time consumption , speed change smoothness , power consumption and conflict risk level Set the reward function and get the optimal path.

[0106] (12).

[0107] In the formula, it represents Different paths take time. is the longest time consumed in the currently explored path, is the delay coefficient, and are the comfort penalty factors for acceleration magnitude and direction, For speed, is the acceleration, For the energy consumed, The optimal solution is obtained through the interaction between low-altitude networked intelligent vehicles and the environment.

[0108] The flight process includes pilot mode, follow mode, change mode and disengage mode. The basic control methods include:

[0109] (13).

[0110] in, and is the position and velocity of the navigator, is the time step, and the current relative position to the pilot vehicle is , the relative position of the target is , and To control the gain, is the control input, and is the position and velocity of the vehicle that has left the formation. Calculate the fitness and select a solution, and find the optimal solution through iterative updates:

[0111] like Figure 3 As shown in the figure, the control method based on the formation control diagram of the goose flock bionic formation is as follows:

[0112] In real-scene control, algorithms with different focuses are selected to control different modes.

[0113] In pilot mode, low-altitude connected intelligent vehicles act as communication nodes of the cluster, determining the flight posture of the entire team based on the overall route planning and real-time environmental conditions.

[0114] In follow mode, the low-altitude connected intelligent vehicle executes flight decisions and transmits sensor data back.

[0115] The transformation mode is used to cope with different weather conditions, and the three-dimensional configuration transformation of the formation is achieved by issuing commands through the pilot low-altitude networked intelligent vehicle.

[0116] In the separation mode, the low-altitude connected intelligent vehicle reaches the destination airspace and separates from the formation.

[0117] In an exemplary embodiment, Figure 4 As shown, the low-altitude connected intelligent vehicle control system employs a hierarchical control strategy to manage all vehicles, including those in high-altitude flight. The system predicts the number of empty flights for all vehicles and leverages a knowledge graph to obtain the following information: 1) travel planning clues; 2) regional resource distribution; and 3) historical route data. Furthermore, trajectory planning achieves collective optimization through reinforcement learning. For high-altitude flight vehicles, the system implements high-altitude formation flight and employs goose-like cluster control technology to determine piloting, following, change, and disengagement modes.

[0118] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 5 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store control data of the formation flight collaborative control model. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a low-altitude intelligent cluster flight management method is implemented.

[0119] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0120] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0121] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0122] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0123] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0124] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0125] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0126] In summary, this application has the following technical effects:

[0127] By combining search terms and travel orders with historical data, we can generate highly accurate predictions of the number of empty trips per day. This knowledge graph-based prediction method can clearly identify the starting and ending points of most trips, as well as the most likely routes. This helps alleviate congestion by pre-assigning airspace. In terms of real-time route planning, it can reduce the risk of finding a better route mid-trip, ensuring that the trip route is, in most cases, the optimal solution for the travel time.

[0128] A reinforcement learning-based path planning algorithm can find a route that minimizes the overall delay for all low-altitude connected intelligent vehicles per unit time while ensuring safety. Its primary advantage is reducing overall delays, thereby improving the reliability of individual trip time estimates, reducing the time passengers have to prepare for emergencies, and improving time efficiency.

[0129] A low-altitude connected intelligent vehicle formation system, based on goose-like cluster control, significantly reduces the total energy consumption of low-altitude connected intelligent vehicles in a formation by utilizing the wake of the preceding vehicle. Because the entire formation simultaneously responds to airflow disturbances and interacts with each other as a system, it reduces flight turbulence and improves journey safety, stability, and comfort.

[0130] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0131] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A low-altitude intelligent cluster flight management method, characterized in that: include: Acquire structured and unstructured data of low-altitude connected intelligent vehicles; The structured data obtained for low-altitude connected intelligent vehicles are flight logs, and the unstructured data obtained for low-altitude connected intelligent vehicles are data entries that do not follow a fixed format or structure; Based on the structured data and the unstructured data, a structured low-altitude networked intelligent vehicle knowledge graph is constructed; the nodes in the structured low-altitude networked intelligent vehicle knowledge graph are entities, and the edges are relationships between entities; the relationships between entities are obtained by analyzing the structured data and the unstructured data through information extraction technology and knowledge representation technology; The LSTM model is used to analyze the airspace features in the structured low-altitude networked intelligent vehicle knowledge graph to predict the spatiotemporal flow diagram of low-altitude networked intelligent vehicles; the airspace features are the airspace's passable direction and maximum flow rate; Dividing the low-altitude airspace based on the traffic spatiotemporal graph and determining a low-altitude airspace allocation result; The low-altitude airspace allocation result is the passable direction and maximum flow rate of the unit airspace; Initialize the low-altitude networked intelligent vehicle path according to the low-altitude airspace allocation result; Taking the flight safety of the low-altitude connected intelligent vehicle as the reward function and the time taken for the flight path of the low-altitude connected intelligent vehicle as the value function, the Q-learning + traffic penalty algorithm is used to iterate the initialized low-altitude connected intelligent vehicle path to obtain the optimal path of the low-altitude connected intelligent vehicle; Based on a cluster of low-altitude networked intelligent vehicles, a dynamic model is constructed, and a six-degree-of-freedom dynamic model of a low-altitude networked intelligent vehicle is established to describe the motion characteristics of the vehicle in detail, thereby constructing a formation flight collaborative control model with the goal of minimizing delay rate; the low-altitude networked intelligent vehicle cluster is composed of low-altitude networked intelligent vehicles with the same path; the formation flight collaborative control model is used to control the flight of the low-altitude networked intelligent vehicle cluster according to a formation control method; the formation control method includes a pilot mode, a follow mode, a transformation mode, and a disengagement mode; The structured data and the unstructured data are used to construct a knowledge graph of structured low-altitude networked intelligent vehicles, specifically including: Preprocess the structured and unstructured data of low-altitude connected intelligent vehicles to obtain preprocessed information; Use feature-based machine learning methods to extract traffic entities from pre-processed information; Using the semi-supervised learning Traffic-Snowball algorithm to extract the association relationship in the traffic information corpus; the traffic entity information corpus is composed of information of several traffic entities; Using data mining methods, we can mine the relationship patterns between the attributes of traffic entities and their values ​​from the travel information of low-altitude connected intelligent vehicles. Based on the extracted traffic entities, associations, and relationship patterns, nodes and edges in the structured low-altitude networked intelligent vehicle knowledge graph are created to obtain a structured low-altitude networked intelligent vehicle knowledge graph; The flight safety of the low-altitude connected intelligent vehicle is used as the reward function, and the time taken for the flight path of the low-altitude connected intelligent vehicle is used as the value function. The Q-learning + traffic penalty algorithm is used to iterate the initialized low-altitude connected intelligent vehicle path to obtain the optimal path of the low-altitude connected intelligent vehicle. Specifically, it includes: Constructing an environmental model based on the low-altitude airspace allocation results; the environmental model includes low-altitude intelligent vehicles, route networks, flight rules, and potential conflict points; Using reinforcement learning methods, the optimal path and speed are determined in the environment model under the current environment state; The reward function is based on flight safety, which is composed of time consumption, speed change smoothness, power consumption, and conflict risk level. The value function is based on the time taken by the low-altitude connected intelligent vehicle's flight path. Using the Q-learning + traffic penalty algorithm, the current environmental state is iterated to obtain the optimal path. Among them, the Q-learning + traffic factor penalty algorithm is used for iteration, and the formula expression for the optimal path of the low-altitude networked intelligent vehicle is obtained as follows: Where α is the learning rate, γ is the discount factor, s1 is the action to get the next state, and t α and t γ is the traffic penalty coefficient, is the maximum Q value after performing all possible actions in state s1, t is the reduction coefficient determined according to the historical traffic conditions of the city, and R(s,d) represents the immediate reward obtained when taking action d in state s; The LSTM model is used to analyze the airspace features in the structured low-altitude networked intelligent vehicle knowledge graph and predict the spatiotemporal flow graph of low-altitude networked intelligent vehicles, including: According to the formula Predict the spatiotemporal flow of low-altitude connected intelligent vehicles; Among them, i t is the input gate, f t For the forget gate, o t is the output gate, σ is the sigmoid function, W and b are the weight matrix and bias vector of the model, subscripts x and h correspond to X and h vectors respectively, i, f, o correspond to the input gate formula, forget gate formula and output gate formula respectively, X t is the traffic volume of low-altitude networked intelligent vehicles at time t, h t-1 is the final feature representation at time t-1.

2. A low-altitude intelligent cluster flight management method according to claim 1, characterized in that: The structured data of the low-altitude networked intelligent vehicle is a flight log, and the unstructured data of the low-altitude networked intelligent vehicle is a data entry that does not follow a fixed format or structure.

3. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement a low-altitude intelligent cluster flight management method according to any one of claims 1-2.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, a low-altitude intelligent cluster flight management method according to any one of claims 1 to 2 is implemented.

5. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, a low-altitude intelligent cluster flight management method according to any one of claims 1 to 2 is implemented.

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