Switching chain decision-making method and device and electronic equipment

Abstract

The invention discloses a switching chain decision-making method and device and electronic equipment, and relates to the technical field of wireless communication, and the method comprises the steps: constructing a low-altitude environment model according to building information and terrain information; collecting state information of the aircraft, and predicting trajectory data of the aircraft in the target time period based on the state information; determining the coverage range of the base stations based on the low-altitude environment model, and determining candidate base stations from the base stations based on the coverage range and the trajectory data; determining a target switching chain corresponding to the switching path of the candidate base station, and controlling the switching of the base station according to a target decision of the target switching chain; wherein the target switching chain is obtained through optimization in a multi-target optimization mode; the candidate base stations are determined through the low-altitude environment model and aircraft trajectory prediction, and the target switching chain is obtained through multi-target optimization, so that the switching stability and communication quality of the low-altitude aircraft base stations are guaranteed, and efficient utilization of network resources is realized.

Description

Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to a switching chain decision method, apparatus and electronic device. Background Technology

[0002] With the rapid development of fifth-generation mobile communication technology, the low-altitude economy is gradually emerging. Application scenarios such as drone logistics and air taxis have put forward higher demands on communication networks. As an evolution of fifth-generation mobile communication technology, fifth-generation enhanced mobile communication technology has higher speed, lower latency and greater connection density, making it suitable for supporting the communication needs of low-altitude applications.

[0003] In related technologies, handover chain schemes are formulated through rule-based handover algorithms or learning-based handover optimization techniques. However, these technologies are usually designed for specific scenarios and do not fully consider issues such as insufficient base station coverage and signal interference in low-altitude environments. They also lack the ability to dynamically allocate network resources, resulting in uneven distribution of network resources and making it difficult to guarantee the communication quality of aircraft in complex low-altitude environments. Summary of the Invention

[0004] This disclosure provides a handover chain decision-making method, apparatus, and electronic device to solve problems in related technologies. Candidate base stations are determined by low-altitude environment model and aircraft trajectory prediction, and the target handover chain is obtained through multi-objective optimization, which ensures the stability and communication quality of low-altitude aircraft base station handover and realizes efficient utilization of network resources.

[0005] According to a first aspect embodiment of this disclosure, a switching chain decision method is provided, comprising: Based on building and terrain information, a low-altitude environment model is constructed. Collect the status information of the aircraft, and based on the status information, predict the trajectory data of the aircraft within the target time period; The coverage area of ​​the base station is determined based on the low-altitude environment model, and candidate base stations are determined from the base stations based on the coverage area and the trajectory data. The target handover chain corresponding to the handover path of the candidate base station is determined, and the handover of the base station is controlled according to the target decision of the target handover chain; wherein, the target handover chain is obtained by optimizing through a multi-objective optimization method.

[0006] In some embodiments of this disclosure, constructing a low-altitude environment model based on building information and terrain information includes: Obtain the building information; wherein the building information includes at least the building height, building shape, and building material; The terrain information is obtained; wherein the terrain information includes at least ground elevation, ground slope, and vegetation cover. By fusing the building information and the terrain information, environmental fusion information is obtained, and the low-altitude environment model is generated based on the environmental fusion information.

[0007] In some embodiments of this disclosure, the step of collecting the aircraft's state information and predicting the aircraft's trajectory data within a target time period based on the state information includes: The status information of the aircraft is collected; wherein the status information includes at least position information, speed information, and heading information; The historical trajectory data of the aircraft is acquired, and the flight trajectory of the aircraft is predicted based on the historical trajectory data and the status information to obtain the trajectory data.

[0008] In some embodiments of this disclosure, determining the coverage area of ​​a base station based on the low-altitude environment model, and determining candidate base stations from the base stations based on the coverage area and the trajectory data, includes: Obtain the location data of the base station, and determine the base station parameters based on the location data; Based on the aforementioned low-altitude environment model, the propagation loss of the signal along different paths is determined; wherein, the propagation loss includes free space loss and obstacle loss. The coverage area of ​​the base station is determined based on the base station parameters and the propagation loss. The candidate base stations are determined based on the coverage area and the trajectory data.

[0009] In some embodiments of this disclosure, determining the target handover chain corresponding to the handover path of the candidate base station includes: The current base station where the aircraft is located is determined based on the location data of the base station; Plan the handover path from the current base station to the candidate base station, and construct the handover chain based on the handover path; The target switching chain is obtained by optimizing the switching chain using the multi-objective optimization method.

[0010] In some embodiments of this disclosure, optimizing the switching chain through the multi-objective optimization method to obtain the target switching chain includes: Define a state space; wherein the state space includes the position information, velocity information and heading information of the aircraft; Define an action space; wherein the action space includes selecting the base station in the handover chain; The reward value is calculated based on the state space, the action space, and the multi-objective optimization method. The preset learning algorithm is updated based on the reward value to obtain the target switching chain; The multi-objective optimization method optimizes at least one of signal strength, handover latency, network load balancing, energy consumption cost, and signal interference level. Before optimizing the handover chain through the multi-objective optimization method to obtain the target handover chain, the method includes: The signal strength is determined based on the coverage area of ​​the base station and the location information of the aircraft; The handover delay is determined based on the handover path of the base station; Based on the load information of the base station, the network load balance is determined; Based on the energy consumption information of the base station, the energy consumption cost is determined; The interference source is identified based on the low-altitude environment model, and the signal interference level is determined based on the interference source.

[0011] In some embodiments of this disclosure, controlling the handover of the base station according to the target decision of the target handover chain includes: The target decision is generated based on the target switching chain; A handover instruction is generated based on the target decision, and the aircraft is controlled to switch to the corresponding target base station at the handover time point based on the handover instruction; wherein, the handover instruction includes the handover time point of the handover chain and the target base station.

[0012] In some embodiments of this disclosure, after controlling the handover of the base station according to the target decision of the target handover chain, the method further includes: By deploying edge servers, the target switching chain is optimized to obtain the target decision, and weather data is added to the low-altitude environment model to optimize prediction accuracy.

[0013] According to a second aspect embodiment of this disclosure, a switching chain decision-making apparatus is provided, comprising: The building unit is used to construct a low-altitude environment model based on building and terrain information; The prediction unit is used to collect the state information of the aircraft and, based on the state information, predict the trajectory data of the aircraft within a target time period. The first determining unit is used to determine the coverage area of ​​the base station based on the low-altitude environment model, and to determine candidate base stations from the base stations based on the coverage area and the trajectory data. The second determining unit is used to determine the target handover chain corresponding to the handover path of the candidate base station; A control unit is used to control the handover of the base station according to the target decision of the target handover chain; wherein the target handover chain is obtained through multi-objective optimization.

[0014] In some embodiments of this disclosure, the building unit includes: The first acquisition module is used to acquire the building information; wherein, the building information includes at least the building height, building shape and building material; The second acquisition module is used to acquire the terrain information; wherein the terrain information includes at least ground elevation, ground slope and vegetation cover; The first generation module is used to fuse the building information and the terrain information to obtain environmental fusion information, and generate the low-altitude environment model based on the environmental fusion information.

[0015] In some embodiments of this disclosure, the prediction unit includes: The acquisition module is used to acquire the status information of the aircraft; wherein the status information includes at least position information, speed information, and heading information; The prediction module is used to acquire the historical trajectory data of the aircraft, and predict the flight trajectory of the aircraft based on the historical trajectory data and the status information to obtain the trajectory data.

[0016] In some embodiments of this disclosure, the first determining unit includes: The first determining module is used to acquire the location data of the base station and determine the base station parameters based on the location data. The second determining module is used to determine the propagation loss of the signal along different paths based on the low-altitude environment model; wherein the propagation loss includes free space loss and obstacle loss. The third determining module is used to determine the coverage area of ​​the base station based on the base station parameters and the propagation loss; The fourth determining module is used to determine the candidate base station based on the coverage area and the trajectory data.

[0017] In some embodiments of this disclosure, the second determining unit includes: The fifth determining module is used to determine the current base station where the aircraft is located based on the location data of the base station; A construction module is used to plan the handover path from the current base station to the candidate base station, and to construct the handover chain based on the handover path; An optimization module is used to optimize the switching chain through the multi-objective optimization method to obtain the target switching chain.

[0018] In some embodiments of this disclosure, the optimization module includes: The first definition submodule is used to define the state space; wherein, the state space includes the position information, velocity information and heading information of the aircraft; The second definition submodule is used to define the action space; wherein, the action space includes selecting the base station in the handover chain; The calculation submodule is used to calculate the reward value based on the state space, the action space, and the multi-objective optimization method; The update submodule is used to update the preset learning algorithm based on the reward value to obtain the target switching chain; The multi-objective optimization method optimizes at least one of signal strength, handover delay, network load balancing, energy consumption cost, and signal interference level. The second determining unit further includes: The sixth determining module is used to determine the signal strength based on the coverage area of ​​the base station and the location information of the aircraft before the optimization module optimizes the handover chain through the multi-objective optimization method to obtain the target handover chain; The seventh determining module is used to determine the handover delay based on the handover path of the base station; The eighth determining module is used to determine the network load balancing degree based on the load information of the base station; The ninth determining module is used to determine the energy consumption cost based on the energy consumption information of the base station; The tenth determining module is used to identify interference sources based on the low-altitude environment model and determine the signal interference level based on the interference sources.

[0019] In some embodiments of this disclosure, the control unit includes: The second generation module is used to generate the target decision based on the target switching chain; The control module is configured to generate a handover instruction based on the target decision, and control the aircraft to switch to the corresponding target base station at the handover time point according to the handover instruction; wherein, the handover instruction includes the handover time point of the handover chain and the target base station.

[0020] In some embodiments of this disclosure, the apparatus further includes: An optimization unit is configured to optimize the target handover chain by deploying an edge server after the control unit controls the handover of the base station according to the target decision of the target handover chain, so as to obtain the target decision, and add weather data to the low-altitude environment model to optimize the prediction accuracy.

[0021] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect embodiment.

[0022] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect of the present disclosure.

[0023] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method described in the first aspect of the preceding embodiments.

[0024] In summary, the handover chain decision-making method, apparatus, and electronic equipment provided in this disclosure include: constructing a low-altitude environment model based on building and terrain information; collecting aircraft status information and predicting aircraft trajectory data within a target time period based on the status information; determining the coverage area of ​​base stations based on the low-altitude environment model and identifying candidate base stations from among the base stations based on the coverage area and trajectory data; determining the target handover chain corresponding to the handover path of the candidate base stations and controlling the handover of base stations according to the target decision of the target handover chain; wherein the target handover chain is obtained through multi-objective optimization; by determining candidate base stations through the low-altitude environment model and aircraft trajectory prediction, and obtaining the target handover chain through multi-objective optimization, the stability and communication quality of low-altitude aircraft base station handover are ensured, and efficient utilization of network resources is achieved.

[0025] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0026] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a switching chain decision-making method provided in an embodiment of this disclosure; Figure 2 A flowchart illustrating another switching chain decision-making method provided in an embodiment of this disclosure; Figure 3 A flowchart illustrating another switching chain decision-making method provided in an embodiment of this disclosure; Figure 4 A flowchart illustrating another switching chain decision-making method provided in an embodiment of this disclosure; Figure 5 A flowchart illustrating another switching chain decision-making method provided in an embodiment of this disclosure; Figure 6 A flowchart illustrating another switching chain decision-making method provided in an embodiment of this disclosure; Figure 7 A flowchart illustrating another switching chain decision-making method provided in an embodiment of this disclosure; Figure 8 This is a schematic diagram of the structure of a switching chain decision device provided in an embodiment of the present disclosure; Figure 9 This is a schematic diagram of another switching chain decision device provided in an embodiment of the present disclosure; Figure 10 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0027] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0028] The switching chain decision-making method, apparatus, and electronic device of this disclosure are described below with reference to the accompanying drawings.

[0029] Figure 1 This is a flowchart illustrating a switching chain decision-making method provided in an embodiment of this disclosure.

[0030] like Figure 1 As shown, the method includes the following steps: Step 101: Construct a low-altitude environment model based on building and terrain information.

[0031] In some embodiments, building information can be obtained through urban planning database queries, lidar scanning, etc., and building information includes, but is not limited to, building height, building shape, and building material; terrain information can be obtained through satellite remote sensing data collection, on-site surveys, etc., and terrain information includes, but is not limited to, ground elevation, ground slope, and vegetation cover; the obtained building information and terrain information are fused, and the data fusion methods include, but are not limited to, data splicing, format standardization, feature extraction, etc., to obtain environmental fusion information, and then a low-altitude environment model that reflects the actual geographical characteristics of the low-altitude area is generated based on the environmental fusion information.

[0032] The above methods can accurately replicate the geographical environmental characteristics of low-altitude areas, providing basic data support for calculating base station coverage and analyzing signal propagation status.

[0033] Step 102: Collect the status information of the aircraft, and based on the status information, predict the trajectory data of the aircraft within the target time period.

[0034] In some embodiments, status information is collected by sensor devices on the aircraft, including information that reflects the operating status of the aircraft; at the same time, historical trajectory data of the aircraft is retrieved, and the historical trajectory data is combined with the currently collected status information to predict the flight trajectory of the aircraft within a target time period to obtain trajectory data. The trajectory prediction method includes, but is not limited to, methods based on data statistics or model inference.

[0035] By combining the aircraft's historical operating data with its current status information, the flight path can be accurately predicted, providing directional reference for candidate base station selection.

[0036] Step 103: Determine the coverage area of ​​the base station based on the low-altitude environment model, and determine candidate base stations from the base stations based on the coverage area and the trajectory data.

[0037] In some embodiments, the location data of the base stations are acquired, and the propagation impact of signals in the low-altitude environment is analyzed in combination with the low-altitude environment model. The coverage range of each base station is determined by the signal coverage calculation method. The predicted trajectory data is matched and compared with the coverage range of each base station, and the base stations involved in the flight path of the aircraft are selected as candidate base stations.

[0038] The above method can combine the low-altitude environment and the aircraft's flight path to accurately select suitable candidate base stations, providing a foundation for the construction of handover links.

[0039] Step 104: Determine the target handover chain corresponding to the handover path of the candidate base station, and control the handover of the base station according to the target decision of the target handover chain; wherein, the target handover chain is obtained by optimizing through a multi-objective optimization method.

[0040] In some embodiments, the base station currently connected to the aircraft is determined based on the base station location data, a handover path from the current base station to a candidate base station is planned, and an initial handover chain is constructed based on the handover path; the handover chain is optimized using a multi-objective optimization method to obtain a target handover chain, which requires comprehensive consideration of multiple factors affecting the handover effect of the base station; a target decision is generated based on the target handover chain, a handover command is generated based on the target decision, and the aircraft is controlled to perform the handover operation of the base station.

[0041] Using the above method, base station handover can be performed through the optimized target handover chain, ensuring the orderly handover of base stations during aircraft flight.

[0042] In summary, the handover chain decision-making method disclosed herein includes: constructing a low-altitude environment model based on building and terrain information; collecting aircraft status information and predicting aircraft trajectory data within a target time period based on the status information; determining the coverage area of ​​base stations based on the low-altitude environment model and identifying candidate base stations from among the base stations based on the coverage area and trajectory data; determining the target handover chain corresponding to the handover path of the candidate base stations and controlling the handover of base stations according to the target decision of the target handover chain; wherein the target handover chain is obtained through multi-objective optimization; by determining candidate base stations through the low-altitude environment model and aircraft trajectory prediction, and obtaining the target handover chain through multi-objective optimization, the stability and communication quality of low-altitude aircraft base station handover are ensured, and efficient utilization of network resources is achieved.

[0043] Figure 2 A flowchart illustrating a switching chain decision-making method proposed in an embodiment of this disclosure is further shown. Based on Figure 1 The illustrated embodiment further explains step 101. Figure 2 This may include the following steps: Step 201: Obtain the building information; wherein the building information includes at least the building height, building shape, and building material.

[0044] In some embodiments, building information is obtained through high-precision digital map extraction, lidar scanning, etc., and information such as building height, building shape and building material is obtained or processed through data processing.

[0045] Step 202: Obtain the terrain information; wherein the terrain information includes at least ground elevation, ground slope and vegetation cover.

[0046] In some embodiments, terrain data is acquired through satellite remote sensing data collection, lidar scanning, etc., and information such as ground elevation, ground slope and vegetation cover is obtained after screening and sorting.

[0047] Step 203: Merge the building information and the terrain information to obtain environmental fusion information, and generate the low-altitude environment model based on the environmental fusion information.

[0048] In some embodiments, building information and terrain information are integrated to form environmental fusion information, and a low-altitude environment model that reflects the geographical characteristics of the low-altitude area is constructed based on the environmental fusion information.

[0049] The above methods enable the construction of models that closely reflect the actual low-altitude environment based on accurate building and terrain information, providing a reliable data foundation for determining base station coverage and signal correlation analysis.

[0050] Figure 3A flowchart illustrating a switching chain decision-making method proposed in an embodiment of this disclosure is further shown. Based on Figure 1 The illustrated embodiment further explains step 102. Figure 3 This may include the following steps: Step 301: Collect the status information of the aircraft; wherein the status information includes at least position information, speed information and heading information.

[0051] In some embodiments, location information, including longitude, latitude, and altitude, is collected in real time by a positioning device (GPS device) onboard the aircraft; speed information, including horizontal and vertical speed, is collected in real time by a speed sensor; and heading information, including heading angle and yaw angle, is collected in real time by a heading sensor. The collected information is then integrated to obtain the aircraft's status information.

[0052] Step 302: Obtain the historical trajectory data of the aircraft, and predict the flight trajectory of the aircraft based on the historical trajectory data and the status information to obtain the trajectory data.

[0053] In some embodiments, historical trajectory data of the aircraft is retrieved, including position, speed, and heading information from past flights. A training dataset is constructed based on the historical trajectory data. A pre-trained machine learning algorithm is used to train a model on the training dataset to obtain a prediction model. The pre-trained machine learning algorithm includes, but is not limited to, support vector machines, random forests, and neural networks. The current position information is then collected. Current speed information Current heading information Input the trained prediction model, and use the formula (in (This represents a predictive model) predicting the future target time period of the aircraft. The flight path, i.e., the trajectory data.

[0054] The above methods can be used to obtain operational data of the aircraft, and combined with prediction models, accurate prediction of flight trajectories can be achieved, providing a reliable basis for the determination of candidate base stations.

[0055] Figure 4 A flowchart illustrating a switching chain decision-making method proposed in an embodiment of this disclosure is further shown. Based on Figure 1 The illustrated embodiment further explains step 103. Figure 4 This may include the following steps: Step 401: Obtain the location data of the base station and determine the base station parameters based on the location data.

[0056] In some embodiments, the location data of each base station is obtained through digital map queries and base station database retrieval. Base station parameters include base station transmit power. Base station antenna gain Receiver antenna gain Other losses Parameters such as these.

[0057] Step 402: Based on the low-altitude environment model, determine the propagation loss of the signal along different paths; wherein the propagation loss includes free space loss and obstacle loss.

[0058] In some embodiments, based on a pre-constructed low-altitude environment model, a ray tracing algorithm is used to predict the propagation loss of the signal along different propagation paths. Propagation loss is expressed by the formula The calculation yielded the result; among which, The free space loss is expressed by the formula calculate( Indicates the distance the signal travels. (Indicates the signal wavelength). The barrier loss is represented by the formula. calculate( Indicates the number of obstacles on the path. This represents the loss caused by the i-th obstacle, and its calculation method is determined based on the material and shape of the obstacle.

[0059] Step 403: Determine the coverage area of ​​the base station based on the base station parameters and the propagation loss.

[0060] In some embodiments, combined with base station transmit power Base station antenna gain Receiver antenna gain Other losses and propagation loss Through formula The coverage area of ​​the base station is calculated. .

[0061] Step 404: Determine the candidate base stations based on the coverage area and the trajectory data.

[0062] In some embodiments, the coverage area of ​​each base station is matched with the trajectory data of the aircraft, and the base stations corresponding to the coverage area that the future flight trajectory of the aircraft will pass through are selected and identified as candidate base stations.

[0063] Using the above method, the coverage area of ​​base stations can be accurately calculated and suitable candidate base stations can be selected by combining low-altitude environmental characteristics, base station parameters and aircraft trajectories.

[0064] Figure 5 A flowchart illustrating a switching chain decision-making method proposed in an embodiment of this disclosure is further shown. Based on Figure 1 The embodiment shown, Figure 5 This may include the following steps: Step 501: Determine the current base station where the aircraft is located based on the location data of the base station.

[0065] In some embodiments, the acquired base station location data is retrieved and matched with the aircraft's current location information to determine the current base station that the aircraft is currently connecting to.

[0066] Step 502: Plan the handover path from the current base station to the candidate base station, and construct the handover chain based on the handover path.

[0067] In some embodiments, a preset path planning algorithm is used to plan the handover path from the current base station to each candidate base station. The preset path planning algorithm includes, but is not limited to, graph theory algorithms. Based on the flight sequence corresponding to the predicted trajectory of the aircraft, the current base station and candidate base stations are arranged in the order of the handover path to construct an initial handover chain, the expression of which is as follows: ,in, Indicates switching chains. This represents the nth candidate base station in the switching chain.

[0068] Step 503: Optimize the switching chain using the multi-objective optimization method to obtain the target switching chain.

[0069] In some embodiments, the multi-objective optimization method includes, but is not limited to, at least one of signal strength, handover latency, network load balancing, energy consumption cost, and signal interference level; a preset learning algorithm is used to optimize the handover chain, and the preset learning algorithm includes, but is not limited to, reinforcement learning and other algorithms, to select the target handover chain.

[0070] Using the above method, a handover chain can be constructed based on the current connection status of the aircraft and candidate base stations, and an adapted target handover chain can be obtained through multi-objective optimization.

[0071] Figure 6 A flowchart illustrating a switching chain decision-making method proposed in an embodiment of this disclosure is further shown. Based on Figure 5 The embodiment shown, Figure 6 This may include the following steps: Step 601: Determine the signal strength based on the coverage area of ​​the base station and the location information of the aircraft.

[0072] In some embodiments, the coverage data of each base station is combined with the aircraft's position information on the predicted trajectory, using a formula... Calculate the signal strength, where, This indicates the number of base stations involved in the calculation. This represents the signal strength of the i-th base station.

[0073] Step 602: Determine the handover delay based on the handover path of the base station.

[0074] In some embodiments, based on the transmission distance between adjacent base stations in the handover path and the network transmission characteristics, the formula is used. Calculate the handover delay, where, Indicates the number of base stations in the handover chain. This represents the handover delay from the i-th base station to the (i+1)-th base station.

[0075] Step 603: Determine the network load balance based on the load information of the base station.

[0076] In some embodiments, real-time load information of each base station is retrieved and processed using a formula. Calculate network load balancing, where, This indicates the number of base stations involved in the calculation. This represents the load information of the i-th base station.

[0077] Step 604: Determine the energy consumption cost based on the energy consumption information of the base station.

[0078] In some embodiments, the operating energy consumption parameters of each base station are obtained using the formula... Calculate energy consumption costs, where, This indicates the number of base stations involved in the calculation. This represents the energy consumption information of the i-th base station.

[0079] Step 605: Identify the interference source based on the low-altitude environment model, and determine the signal interference level based on the interference source.

[0080] In some embodiments, based on a pre-constructed low-altitude environment model, obstacle-like interference sources that may cause signal interference on the path are identified; a preset ray tracing algorithm is used to predict the signal interference level on different paths, and the result is expressed by a formula. Calculate the total interference level, where, Indicates the number of interference sources on the path. This represents the interference level caused by the i-th interference source, and its calculation method is determined based on the material and shape of the interference source.

[0081] Step 606: Define the state space; wherein the state space includes the position information, velocity information and heading information of the aircraft.

[0082] In some embodiments, the real-time position information, speed information and heading information of the aircraft are used as parameters to form a state space in the multi-objective optimization process. The state space is used to reflect the operating state of the aircraft.

[0083] Step 607, define the action space; wherein the action space includes selecting the base station in the handover chain.

[0084] In some embodiments, the selection operation of each base station in the handover chain is taken as an executable action to construct an action space, and the output of the action space is the selection result of the base station in the handover chain.

[0085] Step 608: Calculate the reward value based on the state space, the action space, and the multi-objective optimization method.

[0086] In some embodiments, a reward function is designed using indicators such as signal strength, handover latency, network load balancing, energy consumption cost, and signal interference level. The reward value is calculated by combining the aircraft's operating state in the state space and the base station's selection action in the action space. It should be noted that the number of optimization objectives in the above multi-objective optimization method is not limited.

[0087] Step 609: Update the preset learning algorithm based on the reward value to obtain the target switching chain; In some embodiments, the calculated reward value is fed back to a preset learning algorithm. The strategy is updated through algorithm iteration, the selection logic of the switching chain is continuously optimized, and finally the target switching chain that meets the multi-objective optimization requirements is selected. The preset learning algorithm includes, but is not limited to, reinforcement learning related algorithms such as Q-learning and DQN.

[0088] The above methods can comprehensively consider the multi-dimensional factors affecting base station handover, and combine learning algorithms to achieve precise optimization of the handover chain, ensuring the adaptability and reliability of the target handover chain.

[0089] Figure 7 A flowchart illustrating a switching chain decision-making method proposed in an embodiment of this disclosure is further shown. Based on Figure 1 The embodiment shown, Figure 7 This may include the following steps: Step 701: Generate the target decision based on the target switching chain.

[0090] In some embodiments, by combining the coverage area of ​​each target base station in the target handover chain and the trajectory data of the aircraft, the time point at which the aircraft enters the coverage area of ​​each target base station on the predicted trajectory is analyzed to determine the handover time point; based on the handover time point and the corresponding target base station information, a target decision containing preset rules for handover execution is generated.

[0091] Step 702: Generate a handover instruction based on the target decision, and control the aircraft to switch to the corresponding target base station at the handover time point based on the handover instruction; wherein, the handover instruction includes the handover time point of the handover chain and the target base station.

[0092] In some embodiments, information such as the handover time point and the corresponding target base station in the target decision are integrated according to a preset instruction format to generate a handover instruction; the handover instruction is sent to the aircraft through a wireless communication link, and the communication connection status of the aircraft, the handover instruction reception status and the base station handover execution progress are monitored in real time to ensure that the aircraft accurately switches to the corresponding target base station at the set handover time point.

[0093] The above method enables the generation and precise execution of clear handover instructions based on the target handover chain, while real-time monitoring ensures the orderly progress of the handover process and the smooth completion of the aircraft base station handover.

[0094] In some embodiments of this disclosure, the method further includes: optimizing the target switching chain by deploying an edge server to obtain the target decision, and adding weather data to the low-altitude environment model to optimize prediction accuracy.

[0095] In some embodiments, an edge server is deployed, and a preset distributed learning algorithm is deployed on the edge server. The preset distributed learning algorithm includes, but is not limited to, a distributed Q-learning algorithm. The target handover chain is optimized in real time through the preset distributed learning algorithm, thereby generating target decisions and reducing the signaling load of the core network. Weather data is acquired and added to the constructed low-altitude environment model. Based on the low-altitude environment model that integrates terrain information, building information and weather data, a 3D ray tracing channel model is constructed. The low-altitude signal propagation path loss is predicted through the 3D ray tracing channel model. A cooperative beamforming algorithm is adopted to control adjacent base stations to jointly adjust the beam direction and suppress co-channel interference.

[0096] The above methods can improve the real-time performance of target decision generation and reduce the operational load of the core network. At the same time, they can optimize the prediction accuracy of low-altitude signal propagation path loss, effectively reduce the impact of co-channel interference on communication, and provide further assurance for the stability of base station handover.

[0097] In some embodiments, the following beneficial effects also exist: 1. Limited base station coverage: By constructing a low-altitude environment model, the trajectory data of the aircraft in the future target time period is predicted, and a handover chain is constructed based on the trajectory data and the base station coverage. The handover chain is optimized by a multi-objective optimization method to obtain the target handover chain, ensuring that the aircraft can connect to the corresponding target base station when crossing the coverage of multiple base stations, thereby reducing the impact of frequent handover on communication quality.

[0098] 2. Severe signal interference: By using a 3D ray tracing channel model, integrating terrain information, building information, and weather data, the path loss of low-altitude signal propagation is predicted. A cooperative beamforming algorithm is also used to control adjacent base stations to jointly adjust the beam direction, suppress co-channel interference, and improve signal quality.

[0099] 3. Uneven distribution of network resources: A multi-objective optimization approach is adopted, which comprehensively considers signal strength, handover latency, network load balance and energy consumption costs. The target handover chain is obtained by optimizing the handover chain through a preset learning algorithm, which ensures efficient utilization of network resources and meets the network resource needs of different applications.

[0100] 4. Edge computing-assisted handover decision-making: Deploy edge servers and deploy a preset distributed learning algorithm on the edge servers. The preset distributed learning algorithm is used to optimize the target handover chain in real time to generate target decisions, reduce the signaling load of the core network, and improve the real-time performance and accuracy of handover decisions.

[0101] Corresponding to the switching chain decision method described above, this invention also proposes a switching chain decision device. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0102] Figure 8 This is a schematic diagram of the structure of a switching chain decision device provided in an embodiment of the present disclosure, as shown below. Figure 8 As shown, it includes: a construction unit 81, a prediction unit 82, a first determination unit 83, a second determination unit 84, and a control unit 85.

[0103] Construction unit 81 is used to construct a low-altitude environment model based on building information and terrain information; The prediction unit 82 is used to collect the state information of the aircraft and predict the trajectory data of the aircraft within a target time period based on the state information. The first determining unit 83 is used to determine the coverage area of ​​the base station based on the low-altitude environment model, and to determine candidate base stations from the base stations based on the coverage area and the trajectory data. The second determining unit 84 is used to determine the target handover chain corresponding to the handover path of the candidate base station; Control unit 85 is used to control the handover of the base station according to the target decision of the target handover chain; wherein the target handover chain is obtained by optimizing through a multi-objective optimization method.

[0104] In summary, the handover chain decision-making device provided in this disclosure includes: constructing a low-altitude environment model based on building and terrain information; collecting aircraft status information and predicting aircraft trajectory data within a target time period based on the status information; determining the coverage area of ​​base stations based on the low-altitude environment model and identifying candidate base stations from among the base stations based on the coverage area and trajectory data; determining the target handover chain corresponding to the handover path of the candidate base stations and controlling the handover of base stations according to the target decision of the target handover chain; wherein the target handover chain is obtained through multi-objective optimization; by determining candidate base stations through the low-altitude environment model and aircraft trajectory prediction, and obtaining the target handover chain through multi-objective optimization, the stability and communication quality of low-altitude aircraft base station handover are ensured, and efficient utilization of network resources is achieved.

[0105] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the building unit 81 includes: The first acquisition module 811 is used to acquire the building information; wherein, the building information includes at least the building height, building shape and building material; The second acquisition module 812 is used to acquire the terrain information; wherein, the terrain information includes at least ground elevation, ground slope and vegetation cover; The first generation module 813 is used to fuse the building information and the terrain information to obtain environmental fusion information, and generate the low-altitude environment model based on the environmental fusion information.

[0106] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the prediction unit 82 includes: The acquisition module 821 is used to acquire the status information of the aircraft; wherein the status information includes at least position information, speed information and heading information; The prediction module 822 is used to acquire the historical trajectory data of the aircraft, predict the flight trajectory of the aircraft based on the historical trajectory data and the status information, and obtain the trajectory data.

[0107] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the first determining unit 83 includes: The first determining module 831 is used to acquire the location data of the base station and determine the base station parameters based on the location data. The second determining module 832 is used to determine the propagation loss of the signal along different paths based on the low-altitude environment model; wherein the propagation loss includes free space loss and obstacle loss. The third determining module 833 is used to determine the coverage area of ​​the base station based on the base station parameters and the propagation loss. The fourth determining module 834 is used to determine the candidate base station based on the coverage area and the trajectory data.

[0108] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the second determining unit 84 includes: The fifth determining module 841 is used to determine the current base station where the aircraft is located based on the location data of the base station; The construction module 842 is used to plan the handover path from the current base station to the candidate base station, and construct the handover chain according to the handover path; The optimization module 843 is used to optimize the switching chain through the multi-objective optimization method to obtain the target switching chain.

[0109] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the optimization module 843 includes: The first definition submodule 8431 is used to define the state space; wherein, the state space includes the position information, velocity information and heading information of the aircraft; The second definition submodule 8432 is used to define an action space; wherein, the action space includes selecting the base station in the handover chain; The calculation submodule 8433 is used to calculate the reward value based on the state space, the action space and the multi-objective optimization method; The update submodule 8434 is used to update the preset learning algorithm based on the reward value to obtain the target switching chain; The multi-objective optimization method optimizes at least one of signal strength, handover delay, network load balancing, energy consumption cost, and signal interference level. The second determining unit 84 further includes: The sixth determining module 844 is used to determine the signal strength based on the coverage area of ​​the base station and the location information of the aircraft before the optimization module 843 optimizes the handover chain through the multi-objective optimization method to obtain the target handover chain; The seventh determining module 845 is used to determine the handover delay based on the handover path of the base station; The eighth determining module 846 is used to determine the network load balance degree based on the load information of the base station; The ninth determining module 847 is used to determine the energy consumption cost based on the energy consumption information of the base station; The tenth determining module 848 is used to identify interference sources based on the low-altitude environment model and determine the signal interference level based on the interference sources.

[0110] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the control unit 85 includes: The second generation module 851 is used to generate the target decision based on the target switching chain; The control module 852 is configured to generate a handover instruction based on the target decision, and control the aircraft to switch to the corresponding target base station at the handover time point based on the handover instruction; wherein, the handover instruction includes the handover time point of the handover chain and the target base station.

[0111] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the device further includes: The optimization unit 86 is used to optimize the target handover chain by deploying an edge server after the control unit 85 controls the handover of the base station according to the target decision of the target handover chain to obtain the target decision, and to add weather data to the low-altitude environment model to optimize the prediction accuracy.

[0112] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0113] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0114] Figure 10 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0115] like Figure 10As shown, the electronic device 900 includes a computing unit 901, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 902 or loaded from storage unit 908 into RAM (Random Access Memory) 903. The RAM 903 can also store various programs and data required for the operation of the electronic device 900. The computing unit 901, ROM 902, and RAM 903 are interconnected via bus 904. An I / O (Input / Output) interface 905 is also connected to bus 904.

[0116] Multiple components in electronic device 900 are connected to I / O interface 905, including: input unit 906, such as keyboard, mouse, etc.; output unit 907, such as various types of displays, speakers, etc.; storage unit 908, such as disk, optical disk, etc.; and communication unit 909, such as network card, modem, wireless transceiver, etc. Communication unit 909 allows electronic device 900 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0117] The computing unit 901 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as the switching chain decision method. For example, in some embodiments, the switching chain decision method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 908. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 900 via ROM 902 and / or communication unit 909. When the computer program is loaded into RAM 903 and executed by the computing unit 901, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the aforementioned switching chain decision method by any other suitable means (e.g., by means of firmware).

[0118] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0120] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0121] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0122] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0123] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0124] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0125] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0126] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A switching chain decision method, characterized in that, The method includes: Based on building and terrain information, a low-altitude environment model is constructed. Collect the status information of the aircraft, and based on the status information, predict the trajectory data of the aircraft within the target time period; The coverage area of ​​the base station is determined based on the low-altitude environment model, and candidate base stations are determined from the base stations based on the coverage area and the trajectory data. The target handover chain corresponding to the handover path of the candidate base station is determined, and the handover of the base station is controlled according to the target decision of the target handover chain; wherein, the target handover chain is obtained by optimizing through a multi-objective optimization method.

2. The method according to claim 1, characterized in that, The process of constructing a low-altitude environment model based on building and terrain information includes: Obtain the building information; wherein the building information includes at least the building height, building shape, and building material; The terrain information is obtained; wherein the terrain information includes at least ground elevation, ground slope, and vegetation cover. By fusing the building information and the terrain information, environmental fusion information is obtained, and the low-altitude environment model is generated based on the environmental fusion information.

3. The method according to claim 1, characterized in that, The process of collecting the aircraft's status information and predicting the aircraft's trajectory data within a target time period based on the status information includes: The status information of the aircraft is collected; wherein the status information includes at least position information, speed information, and heading information; The historical trajectory data of the aircraft is acquired, and the flight trajectory of the aircraft is predicted based on the historical trajectory data and the status information to obtain the trajectory data.

4. The method according to claim 1, characterized in that, The step of determining the coverage area of ​​a base station based on the low-altitude environment model, and determining candidate base stations from the base stations based on the coverage area and the trajectory data, includes: Obtain the location data of the base station, and determine the base station parameters based on the location data; Based on the aforementioned low-altitude environment model, the propagation loss of the signal along different paths is determined; wherein, the propagation loss includes free space loss and obstacle loss. The coverage area of ​​the base station is determined based on the base station parameters and the propagation loss. The candidate base stations are determined based on the coverage area and the trajectory data.

5. The method according to claim 1, characterized in that, Determining the target handover chain corresponding to the handover path of the candidate base station includes: The current base station where the aircraft is located is determined based on the location data of the base station; Plan the handover path from the current base station to the candidate base station, and construct the handover chain based on the handover path; The target switching chain is obtained by optimizing the switching chain using the multi-objective optimization method.

6. The method according to claim 5, characterized in that, The step of optimizing the switching chain through the multi-objective optimization method to obtain the target switching chain includes: Define a state space; wherein the state space includes the position information, velocity information and heading information of the aircraft; Define an action space; wherein the action space includes selecting the base station in the handover chain; The reward value is calculated based on the state space, the action space, and the multi-objective optimization method. The preset learning algorithm is updated based on the reward value to obtain the target switching chain; The multi-objective optimization method optimizes at least one of signal strength, handover latency, network load balancing, energy consumption cost, and signal interference level. Before optimizing the handover chain through the multi-objective optimization method to obtain the target handover chain, the method includes: The signal strength is determined based on the coverage area of ​​the base station and the location information of the aircraft; The handover delay is determined based on the handover path of the base station; Based on the load information of the base station, the network load balance is determined; Based on the energy consumption information of the base station, the energy consumption cost is determined; The interference source is identified based on the low-altitude environment model, and the signal interference level is determined based on the interference source.

7. The method according to claim 1, characterized in that, The step of controlling the handover of the base station based on the target decision of the target handover chain includes: The target decision is generated based on the target switching chain; A handover instruction is generated based on the target decision, and the aircraft is controlled to switch to the corresponding target base station at the handover time point based on the handover instruction; wherein, the handover instruction includes the handover time point of the handover chain and the target base station.

8. The method according to claim 1, characterized in that, After controlling the handover of the base station according to the target decision of the target handover chain, the method further includes: By deploying edge servers, the target switching chain is optimized to obtain the target decision, and weather data is added to the low-altitude environment model to optimize prediction accuracy.

9. A switching chain decision-making device, characterized in that, include: The building unit is used to construct a low-altitude environment model based on building and terrain information; The prediction unit is used to collect the state information of the aircraft and, based on the state information, predict the trajectory data of the aircraft within a target time period. The first determining unit is used to determine the coverage area of ​​the base station based on the low-altitude environment model, and to determine candidate base stations from the base stations based on the coverage area and the trajectory data. The second determining unit is used to determine the target handover chain corresponding to the handover path of the candidate base station; A control unit is used to control the handover of the base station according to the target decision of the target handover chain; wherein the target handover chain is obtained through multi-objective optimization.

10. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

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