A multi-source fusion positioning method, system and medium for unmanned aerial vehicle clusters

Abstract

The present application provides a multi-source fusion positioning method, system and medium for drone clusters, belonging to the field of drone and big data technology. The method includes: obtaining flight status information to obtain multi-source positioning information of multiple drones that meet the flight trajectory, processing the obtained drone navigation data to control the flight of the drone, obtaining a multi-source positioning accuracy index according to the flight results, performing correction processing to obtain a multi-source positioning accuracy correction index, and comparing it with the preset accuracy index to determine whether the multi-source positioning accuracy of the drone meets the requirements. The present application comprehensively considers the advantages of each data source, makes up for the shortcomings of a single data source in positioning accuracy, improves the overall positioning accuracy, and provides a more reliable and efficient solution for positioning and navigation of drones and other fields through the advantages of multi-source information fusion, real-time monitoring, accuracy evaluation and correction, and improving system robustness.

Description

Technical Field

[0001] The present application relates to the field of drone and big data technology, and more specifically, to a multi-source fusion positioning method, system and medium for drone clusters. Background Art

[0002] Drones have been widely used in various fields due to their flexibility and efficiency. For example, in the agricultural field, drones can be used for planting, pesticide spraying, etc., which improves work efficiency and reduces labor costs. Drones mainly rely on the Global Navigation Satellite System (GNSS) for positioning, but inaccurate or ineffective positioning may occur in environments with signal obstruction or interference. In environments without GNSS signals, such as indoors or underground, drone autonomous navigation technology still needs to be further improved.

[0003] The current drone navigation technology is still not perfect. There are obvious deviations in positioning accuracy. It is too greatly affected by environmental factors and lacks a multi-dimensional fusion positioning technology method.

[0004] In view of the above problems, effective technical solutions are currently awaited. Summary of the invention

[0005] The purpose of this application is to provide a multi-source fusion positioning method, system and medium for drone clusters, which can obtain multi-source positioning information of multiple drones that meet the flight trajectory by acquiring flight status information, process the obtained drone navigation data to control the flight of the drone, obtain the multi-source positioning accuracy index according to the flight results, perform correction processing to obtain the multi-source positioning accuracy correction index, compare it with the preset accuracy index, and judge whether the multi-source positioning accuracy of the drone meets the requirements. This application comprehensively considers the advantages of each data source, makes up for the shortcomings of a single data source in positioning accuracy, improves the overall positioning accuracy, and provides a more reliable and efficient solution for positioning and navigation of drones and other fields through the advantages of multi-source information fusion, real-time monitoring, accuracy evaluation and correction, and improved system robustness.

[0006] The present application provides a multi-source fusion positioning method for drone clusters, comprising the following steps:

[0007] Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features, compare them with preset trajectory image features, and obtain multi-source positioning information of multiple drones that meet the flight trajectory;

[0008] Extract satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, process them through a preset fusion positioning model, obtain UAV navigation data and send it to the control system to control the flight of the UAV;

[0009] Obtain flight results for processing and obtain multi-source positioning accuracy index;

[0010] Obtaining an average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory, and performing correction processing on the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index;

[0011] The multi-source positioning accuracy correction index is compared with a preset accuracy index to determine whether the multi-source positioning accuracy of the UAV meets the requirements.

[0012] Among them, in the multi-source fusion positioning method for drone clusters described in the present application, the flight status information of multiple drones in a preset area is obtained and the flight trajectory image features are extracted, and compared with the preset trajectory image features to obtain multi-source positioning information of multiple drones that meet the flight trajectory, specifically:

[0013] Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features;

[0014] Comparing the flight trajectory image features with preset trajectory image features to obtain trajectory similarity data;

[0015] Comparing the trajectory similarity data with a preset similarity threshold;

[0016] If the trajectory similarity data is greater than or equal to the similarity threshold, multi-source positioning information of multiple UAVs that meet the flight trajectory is obtained.

[0017] Among them, in a multi-source fusion positioning method for drone clusters described in the present application, the satellite positioning data, inertial navigation data and radar ranging data are extracted according to the multi-source positioning information, processed by a preset fusion positioning model, and the drone navigation data is obtained and sent to the control system to control the drone flight, including:

[0018] Extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information;

[0019] The satellite positioning data includes latitude and longitude data, altitude data and heading data;

[0020] The inertial navigation data includes acceleration data, angular velocity data and attitude data;

[0021] The radar ranging data includes distance data, angle data, speed data and echo intensity data;

[0022] The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain UAV navigation data and send it to the control system to control the flight of the UAV.

[0023] Among them, in the multi-source fusion positioning method for drone clusters described in this application, the flight results are obtained and processed to obtain the multi-source positioning accuracy index, which is specifically:

[0024] Obtain flight results to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data;

[0025] The satellite positioning offset data, the inertial navigation drift data and the radar ranging deviation data are processed through a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index.

[0026] Among them, in the multi-source fusion positioning method for drone clusters described in the present application, the multi-source positioning average accuracy index of the multiple drones that meet the flight trajectory is obtained, and the multi-source positioning accuracy index is corrected to obtain the multi-source positioning accuracy correction index, which is specifically:

[0027] Acquire and process a plurality of multi-source positioning accuracy indexes of the plurality of UAVs that meet the flight trajectory to obtain a multi-source positioning average accuracy index;

[0028] The multi-source positioning accuracy index is corrected by using a preset accuracy correction model according to the multi-source positioning average accuracy index to obtain a multi-source positioning accuracy correction index.

[0029] Among them, in the multi-source fusion positioning method for drone clusters described in the present application, the comparison between the multi-source positioning accuracy correction index and the preset accuracy index is used to determine whether the multi-source positioning accuracy of the drone meets the requirements, specifically:

[0030] Comparing the multi-source positioning accuracy correction index with a preset accuracy index to obtain an accuracy index deviation rate;

[0031] Comparing the accuracy index deviation rate with a preset index deviation rate threshold;

[0032] If the accuracy index deviation rate is greater than or equal to the index deviation rate threshold, the positioning deviation of the drone is too large and the multi-source positioning accuracy does not meet the requirements;

[0033] If the accuracy index deviation rate is less than the index deviation rate threshold, the drone positioning deviation is within an acceptable range and the multi-source positioning accuracy meets the requirements.

[0034] In a second aspect, the present application provides a multi-source fusion positioning system for drone clusters, the system comprising: a memory and a processor, the memory comprising a program of a multi-source fusion positioning method for drone clusters, the program of the multi-source fusion positioning method for drone clusters being executed by the processor to implement the following steps:

[0035] Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features, compare them with preset trajectory image features, and obtain multi-source positioning information of multiple drones that meet the flight trajectory;

[0036] Extract satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, process them through a preset fusion positioning model, obtain UAV navigation data and send it to the control system to control the flight of the UAV;

[0037] Obtain flight results for processing and obtain multi-source positioning accuracy index;

[0038] Obtaining an average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory, and performing correction processing on the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index;

[0039] The multi-source positioning accuracy correction index is compared with a preset accuracy index to determine whether the multi-source positioning accuracy of the UAV meets the requirements.

[0040] Among them, in the multi-source fusion positioning system for drone clusters described in the present application, the flight status information of multiple drones in a preset area is obtained and the flight trajectory image features are extracted, and compared with the preset trajectory image features to obtain multi-source positioning information of multiple drones that meet the flight trajectory, specifically:

[0041] Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features;

[0042] Comparing the flight trajectory image features with preset trajectory image features to obtain trajectory similarity data;

[0043] Comparing the trajectory similarity data with a preset similarity threshold;

[0044] If the trajectory similarity data is greater than or equal to the similarity threshold, multi-source positioning information of multiple UAVs that meet the flight trajectory is obtained.

[0045] Among them, in the multi-source fusion positioning system for drone clusters described in the present application, the satellite positioning data, inertial navigation data and radar ranging data are extracted according to the multi-source positioning information, processed by a preset fusion positioning model, and the drone navigation data is obtained and sent to the control system to control the drone flight, including:

[0046] Extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information;

[0047] The satellite positioning data includes latitude and longitude data, altitude data and heading data;

[0048] The inertial navigation data includes acceleration data, angular velocity data and attitude data;

[0049] The radar ranging data includes distance data, angle data, speed data and echo intensity data;

[0050] The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain UAV navigation data and send it to the control system to control the flight of the UAV.

[0051] In the third aspect, the present application also provides a computer-readable storage medium, which includes a multi-source fusion positioning method program for drone clusters. When the multi-source fusion positioning method program for drone clusters is executed by a processor, the steps of a multi-source fusion positioning method for drone clusters as described in any one of the above items are implemented.

[0052] From the above, it can be seen that the embodiment of the present application provides a multi-source fusion positioning method, system and medium for drone clusters, which obtains the flight status information of multiple drones in a preset area and extracts the flight trajectory image features, obtains the multi-source positioning information of multiple drones that meet the flight trajectory, extracts satellite positioning data, inertial navigation data and radar ranging data, processes them through a preset fusion positioning model, obtains the drone navigation data and sends it to the control system to control the drone flight, obtains the flight results for processing, obtains the multi-source positioning accuracy index, obtains the multi-source positioning average accuracy index of the multiple drones that meet the flight trajectory, corrects the multi-source positioning accuracy index, obtains the multi-source positioning accuracy correction index, compares it with the preset accuracy index, and determines whether the multi-source positioning accuracy of the drone meets the requirements. This application makes full use of the advantages of each data source in specific scenarios by simultaneously utilizing multi-source information such as satellite positioning data, inertial navigation data and radar ranging data, making up for the shortcomings of a single data source in positioning accuracy and improving the overall positioning accuracy. It introduces a multi-source positioning accuracy evaluation mechanism, and quantitatively evaluates the performance of the positioning system by calculating the multi-source positioning accuracy index. Through the advantages of multi-source information fusion, real-time monitoring, accuracy evaluation and correction, and improved system robustness, it provides a more reliable and efficient solution for positioning and navigation of drones and other fields.

[0053] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 A flowchart of a multi-source fusion positioning method for drone clusters provided in an embodiment of the present application;

[0056] Figure 2 A flowchart of a multi-source fusion positioning method for a drone cluster provided in an embodiment of the present application for obtaining multi-source positioning information of multiple drones that meet the flight trajectory;

[0057] Figure 3A flowchart of obtaining drone navigation data in a multi-source fusion positioning method for drone clusters provided in an embodiment of the present application;

[0058] Figure 4 A flowchart of obtaining a multi-source positioning accuracy index of a multi-source fusion positioning method for a drone cluster provided in an embodiment of the present application;

[0059] Figure 5 A flowchart of obtaining a multi-source positioning accuracy correction index for a multi-source fusion positioning method for a drone cluster provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0061] It should be noted that similar numbers and letters represent similar items in the following figures, so once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance. 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 need to comply with relevant laws, regulations and standards of relevant countries and regions.

[0062] Please refer to Figure 1 , Figure 1 The flowchart of a multi-source fusion positioning method for drone clusters in some embodiments of the present application. The multi-source fusion positioning method for drone clusters is used in terminal devices, such as computers, mobile phone terminals, etc. The multi-source fusion positioning method for drone clusters includes the following steps:

[0063] S101, obtaining flight status information of multiple drones in a preset area and extracting flight trajectory image features, and comparing them with preset trajectory image features to obtain multi-source positioning information of multiple drones that meet the flight trajectory;

[0064] S102, extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, processing them through a preset fusion positioning model, obtaining UAV navigation data and sending it to a control system to control the flight of the UAV;

[0065] S103, obtaining flight results for processing to obtain a multi-source positioning accuracy index;

[0066] S104, obtaining the average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory, and correcting the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index;

[0067] S105: Compare the multi-source positioning accuracy correction index with a preset accuracy index to determine whether the multi-source positioning accuracy of the UAV meets the requirement.

[0068] Among them, the present application obtains the flight status information of multiple drones in a preset area and extracts the flight trajectory image features, obtains the multi-source positioning information of multiple drones that meet the flight trajectory, extracts satellite positioning data, inertial navigation data and radar ranging data, processes them through a preset fusion positioning model, obtains the drone navigation data and sends it to the control system to control the drone flight, obtains the flight results for processing, obtains the multi-source positioning accuracy index, obtains the average multi-source positioning accuracy index of the multiple drones that meet the flight trajectory, corrects the multi-source positioning accuracy index, obtains the multi-source positioning accuracy correction index, compares it with the preset accuracy index, and determines whether the multi-source positioning accuracy of the drone meets the requirements. This application makes full use of the advantages of each data source in specific scenarios by simultaneously utilizing multi-source information such as satellite positioning data, inertial navigation data and radar ranging data, making up for the shortcomings of a single data source in positioning accuracy and improving the overall positioning accuracy. It introduces a multi-source positioning accuracy evaluation mechanism, and quantitatively evaluates the performance of the positioning system by calculating the multi-source positioning accuracy index. Through the advantages of multi-source information fusion, real-time monitoring, accuracy evaluation and correction, and improved system robustness, it provides a more reliable and efficient solution for positioning and navigation of drones and other fields.

[0069] Please refer to Figure 2 , Figure 2This is a flowchart of a multi-source fusion positioning method for drone clusters in some embodiments of the present application for obtaining multi-source positioning information of multiple drones that meet the flight trajectory. According to an embodiment of the present invention, the flight status information of multiple drones in a preset area is obtained and the flight trajectory image features are extracted, and compared with the preset trajectory image features to obtain the multi-source positioning information of multiple drones that meet the flight trajectory, specifically:

[0070] S201, obtaining flight status information of multiple UAVs in a preset area and extracting flight trajectory image features;

[0071] S202, comparing the flight trajectory image features with preset trajectory image features to obtain trajectory similarity data;

[0072] S203, comparing the trajectory similarity data with a preset similarity threshold;

[0073] S204: If the trajectory similarity data is greater than or equal to the similarity threshold, multi-source positioning information of multiple drones that meet the flight trajectory is obtained.

[0074] Among them, in order to quickly and intuitively determine whether the flight of the drone is normal, first obtain the flight status information of multiple drones in the preset area and extract the flight trajectory image features, including the shape, direction change, curvature, etc. of the trajectory, compare the flight trajectory image features with the preset trajectory image features, obtain the trajectory similarity data, and compare the trajectory similarity data with the preset similarity threshold. If the trajectory similarity data is greater than or equal to the similarity threshold, then obtain the multi-source positioning information of multiple drones that meet the flight trajectory. For example, the trajectory similarity data is 98%, and the preset similarity threshold is 97%. For those drones whose trajectory similarity meets the requirements, the system will further obtain multi-source positioning information.

[0075] Please refer to Figure 3 , Figure 3 The present invention is a flowchart of a multi-source fusion positioning method for a drone cluster in some embodiments of the present invention for obtaining drone navigation data. According to an embodiment of the present invention, the satellite positioning data, inertial navigation data and radar ranging data are extracted according to the multi-source positioning information, processed by a preset fusion positioning model, and the drone navigation data is obtained and sent to the control system to control the drone flight, including:

[0076] S301, extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information;

[0077] S302, the satellite positioning data includes latitude and longitude data, altitude data and heading data;

[0078] S303, the inertial navigation data includes acceleration data, angular velocity data and attitude data;

[0079] S304, the radar ranging data includes distance data, angle data, speed data and echo intensity data;

[0080] S305: Process the satellite positioning data, inertial navigation data and radar ranging data through a preset fusion positioning model to obtain UAV navigation data and send it to a control system to control the flight of the UAV.

[0081] Among them, in order to obtain the navigation data of the UAV and control its flight, the satellite positioning data, inertial navigation data and radar ranging data are extracted according to the multi-source positioning information. The satellite positioning data is received through GPS or other satellite navigation systems, including latitude and longitude data, altitude data and heading data. The inertial navigation data is obtained through IMU, including acceleration data, angular velocity data and attitude data. The radar ranging data is obtained using the airborne radar system, including distance data, angle data, speed data and echo intensity data. The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain the UAV navigation data and send it to the control system to control the UAV flight. The preset fusion positioning model is such as the extended Kalman filter and the unscented Kalman filter. Through the above steps, the multi-source positioning information can be effectively integrated to provide accurate navigation data for the UAV, and the precise control of the UAV can be achieved accordingly.

[0082] Please refer to Figure 4 , Figure 4 The flowchart of obtaining a multi-source positioning accuracy index of a multi-source fusion positioning method for a drone cluster in some embodiments of the present application is as follows. According to an embodiment of the present invention, the flight results are obtained and processed to obtain a multi-source positioning accuracy index, specifically:

[0083] S401, obtaining flight results to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data;

[0084] S402: Process the satellite positioning offset data, the inertial navigation drift data and the radar ranging deviation data through a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index.

[0085] Among them, in order to more intuitively reflect the positioning accuracy of the multi-source positioning system, the flight results are obtained to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data. The satellite positioning offset data is obtained by recording the deviation between the satellite positioning data and the known or high-precision reference point during the flight, and the error accumulated over time in the inertial navigation drift data. The radar ranging deviation data is obtained by evaluating the distance difference between the radar ranging data and the actual distance or known landmarks, and is processed through a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index.

[0086] The calculation formula of the positioning accuracy evaluation model is:

[0087] ;

[0088] in, is the multi-source positioning accuracy index, They are satellite positioning offset data, inertial navigation drift data and radar ranging deviation data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset positioning monitoring database).

[0089] Please refer to Figure 5 , Figure 5 The flowchart of obtaining a multi-source positioning accuracy correction index of a multi-source fusion positioning method for a drone cluster in some embodiments of the present application. According to an embodiment of the present invention, the multi-source positioning average accuracy index of the plurality of drones that meet the flight trajectory is obtained, and the multi-source positioning accuracy index is corrected to obtain the multi-source positioning accuracy correction index, specifically:

[0090] S501, obtaining and processing a plurality of multi-source positioning accuracy indexes of the plurality of UAVs that meet the flight trajectory to obtain a multi-source positioning average accuracy index;

[0091] S502: Correct the multi-source positioning accuracy index according to the multi-source positioning average accuracy index by using a preset accuracy correction model to obtain a multi-source positioning accuracy correction index.

[0092] Among them, in order to more accurately reflect the multi-source positioning accuracy, multiple multi-source positioning accuracy indexes of the multiple drones that meet the flight trajectory are obtained and processed to obtain a multi-source positioning average accuracy index, and the multi-source positioning accuracy index is corrected by a preset accuracy correction model to obtain a multi-source positioning accuracy correction index;

[0093] The calculation formula of the accuracy correction model is:

[0094] ;

[0095] in, is the multi-source positioning accuracy correction index, is the multi-source positioning accuracy index, is the average accuracy index of multi-source positioning, It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset positioning monitoring database).

[0096] According to an embodiment of the present invention, the comparison between the multi-source positioning accuracy correction index and the preset accuracy index is used to determine whether the multi-source positioning accuracy of the drone meets the requirements, specifically:

[0097] Comparing the multi-source positioning accuracy correction index with a preset accuracy index to obtain an accuracy index deviation rate;

[0098] Comparing the accuracy index deviation rate with a preset index deviation rate threshold;

[0099] If the accuracy index deviation rate is greater than or equal to the index deviation rate threshold, the positioning deviation of the drone is too large and the multi-source positioning accuracy does not meet the requirements;

[0100] If the accuracy index deviation rate is less than the index deviation rate threshold, the drone positioning deviation is within an acceptable range and the multi-source positioning accuracy meets the requirements.

[0101] Among them, in order to improve the multi-source positioning accuracy of the UAV, the multi-source positioning accuracy correction index is compared with the preset accuracy index to obtain the accuracy index deviation rate, which is compared with the preset index deviation rate threshold. If the accuracy index deviation rate is greater than or equal to the index deviation rate threshold, the UAV positioning deviation is too large and the multi-source positioning accuracy does not meet the requirements. If the accuracy index deviation rate is less than the index deviation rate threshold, the UAV positioning deviation is within an acceptable range and the multi-source positioning accuracy meets the requirements.

[0102] According to an embodiment of the present invention, it also includes:

[0103] Extracting power consumption rate data according to the flight status information;

[0104] Comparing the power consumption rate data with a preset consumption rate threshold;

[0105] If the power loss rate data is greater than or equal to the loss rate threshold, the battery power is lost too quickly and the drone needs maintenance;

[0106] If the power consumption rate data is less than the consumption rate threshold, the battery status is normal.

[0107] Among them, in order to ensure the normal operation of the UAV, the power loss rate data is extracted according to the flight status information, and the power loss rate data is compared with the preset loss rate threshold. If the power loss rate data is greater than or equal to the loss rate threshold, the battery power is lost too quickly and the UAV needs to be maintained. If the power loss rate data is less than the loss rate threshold, the battery status is normal.

[0108] The present invention also discloses a multi-source fusion positioning system for unmanned aerial vehicle clusters, including a memory and a processor, wherein the memory includes a multi-source fusion positioning method program for unmanned aerial vehicle clusters, and when the multi-source fusion positioning method program for unmanned aerial vehicle clusters is executed by the processor, the following steps are implemented:

[0109] Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features, compare them with preset trajectory image features, and obtain multi-source positioning information of multiple drones that meet the flight trajectory;

[0110] Extract satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, process them through a preset fusion positioning model, obtain UAV navigation data and send it to the control system to control the flight of the UAV;

[0111] Obtain flight results for processing and obtain multi-source positioning accuracy index;

[0112] Obtaining an average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory, and performing correction processing on the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index;

[0113] The multi-source positioning accuracy correction index is compared with a preset accuracy index to determine whether the multi-source positioning accuracy of the UAV meets the requirements.

[0114] Among them, the present application obtains the flight status information of multiple drones in a preset area and extracts the flight trajectory image features, obtains the multi-source positioning information of multiple drones that meet the flight trajectory, extracts satellite positioning data, inertial navigation data and radar ranging data, processes them through a preset fusion positioning model, obtains the drone navigation data and sends it to the control system to control the drone flight, obtains the flight results for processing, obtains the multi-source positioning accuracy index, obtains the average multi-source positioning accuracy index of the multiple drones that meet the flight trajectory, corrects the multi-source positioning accuracy index, obtains the multi-source positioning accuracy correction index, compares it with the preset accuracy index, and determines whether the multi-source positioning accuracy of the drone meets the requirements. This application makes full use of the advantages of each data source in specific scenarios by simultaneously utilizing multi-source information such as satellite positioning data, inertial navigation data and radar ranging data, making up for the shortcomings of a single data source in positioning accuracy and improving the overall positioning accuracy. It introduces a multi-source positioning accuracy evaluation mechanism, and quantitatively evaluates the performance of the positioning system by calculating the multi-source positioning accuracy index. Through the advantages of multi-source information fusion, real-time monitoring, accuracy evaluation and correction, and improved system robustness, it provides a more reliable and efficient solution for positioning and navigation of drones and other fields.

[0115] According to an embodiment of the present invention, the flight status information of multiple drones in a preset area is obtained, flight trajectory image features are extracted, and compared with preset trajectory image features to obtain multi-source positioning information of multiple drones that meet the flight trajectory, specifically:

[0116] Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features;

[0117] Comparing the flight trajectory image features with preset trajectory image features to obtain trajectory similarity data;

[0118] Comparing the trajectory similarity data with a preset similarity threshold;

[0119] If the trajectory similarity data is greater than or equal to the similarity threshold, multi-source positioning information of multiple UAVs that meet the flight trajectory is obtained.

[0120] Among them, in order to quickly and intuitively determine whether the flight of the drone is normal, first obtain the flight status information of multiple drones in the preset area and extract the flight trajectory image features, including the shape, direction change, curvature, etc. of the trajectory, compare the flight trajectory image features with the preset trajectory image features, obtain the trajectory similarity data, and compare the trajectory similarity data with the preset similarity threshold. If the trajectory similarity data is greater than or equal to the similarity threshold, then obtain the multi-source positioning information of multiple drones that meet the flight trajectory. For example, the trajectory similarity data is 98%, and the preset similarity threshold is 97%. For those drones whose trajectory similarity meets the requirements, the system will further obtain multi-source positioning information.

[0121] According to an embodiment of the present invention, the satellite positioning data, inertial navigation data and radar ranging data are extracted according to the multi-source positioning information, processed by a preset fusion positioning model, and the UAV navigation data is obtained and sent to the control system to control the flight of the UAV, including:

[0122] Extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information;

[0123] The satellite positioning data includes latitude and longitude data, altitude data and heading data;

[0124] The inertial navigation data includes acceleration data, angular velocity data and attitude data;

[0125] The radar ranging data includes distance data, angle data, speed data and echo intensity data;

[0126] The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain UAV navigation data and send it to the control system to control the flight of the UAV.

[0127] Among them, in order to obtain the navigation data of the UAV and control its flight, the satellite positioning data, inertial navigation data and radar ranging data are extracted according to the multi-source positioning information. The satellite positioning data is received through GPS or other satellite navigation systems, including latitude and longitude data, altitude data and heading data. The inertial navigation data is obtained through IMU, including acceleration data, angular velocity data and attitude data. The radar ranging data is obtained using the airborne radar system, including distance data, angle data, speed data and echo intensity data. The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain the UAV navigation data and send it to the control system to control the UAV flight. The preset fusion positioning model is such as the extended Kalman filter and the unscented Kalman filter. Through the above steps, the multi-source positioning information can be effectively integrated to provide accurate navigation data for the UAV, and the precise control of the UAV can be achieved accordingly.

[0128] According to an embodiment of the present invention, the flight results are obtained and processed to obtain a multi-source positioning accuracy index, specifically:

[0129] Obtain flight results to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data;

[0130] The satellite positioning offset data, the inertial navigation drift data and the radar ranging deviation data are processed through a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index.

[0131] Among them, in order to more intuitively reflect the positioning accuracy of the multi-source positioning system, the flight results are obtained to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data. The satellite positioning offset data is obtained by recording the deviation between the satellite positioning data and the known or high-precision reference point during the flight, and the error accumulated over time in the inertial navigation drift data. The radar ranging deviation data is obtained by evaluating the distance difference between the radar ranging data and the actual distance or known landmarks, and is processed through a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index.

[0132] The calculation formula of the positioning accuracy evaluation model is:

[0133] ;

[0134] in, is the multi-source positioning accuracy index, They are satellite positioning offset data, inertial navigation drift data and radar ranging deviation data. It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset positioning monitoring database).

[0135] According to an embodiment of the present invention, the multi-source positioning average accuracy index of the plurality of drones that meet the flight trajectory is obtained, and the multi-source positioning accuracy index is corrected to obtain a multi-source positioning accuracy correction index, specifically:

[0136] Acquire and process a plurality of multi-source positioning accuracy indexes of the plurality of UAVs that meet the flight trajectory to obtain a multi-source positioning average accuracy index;

[0137] The multi-source positioning accuracy index is corrected by using a preset accuracy correction model according to the multi-source positioning average accuracy index to obtain a multi-source positioning accuracy correction index.

[0138] Among them, in order to more accurately reflect the multi-source positioning accuracy, multiple multi-source positioning accuracy indexes of the multiple drones that meet the flight trajectory are obtained and processed to obtain a multi-source positioning average accuracy index, and the multi-source positioning accuracy index is corrected by a preset accuracy correction model to obtain a multi-source positioning accuracy correction index;

[0139] The calculation formula of the accuracy correction model is:

[0140] ;

[0141] in, is the multi-source positioning accuracy correction index, is the multi-source positioning accuracy index, is the average accuracy index of multi-source positioning, It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset positioning monitoring database).

[0142] According to an embodiment of the present invention, the comparison between the multi-source positioning accuracy correction index and the preset accuracy index is used to determine whether the multi-source positioning accuracy of the drone meets the requirements, specifically:

[0143] Comparing the multi-source positioning accuracy correction index with a preset accuracy index to obtain an accuracy index deviation rate;

[0144] Comparing the accuracy index deviation rate with a preset index deviation rate threshold;

[0145] If the accuracy index deviation rate is greater than or equal to the index deviation rate threshold, the positioning deviation of the drone is too large and the multi-source positioning accuracy does not meet the requirements;

[0146] If the accuracy index deviation rate is less than the index deviation rate threshold, the drone positioning deviation is within an acceptable range and the multi-source positioning accuracy meets the requirements.

[0147] Among them, in order to improve the multi-source positioning accuracy of the UAV, the multi-source positioning accuracy correction index is compared with the preset accuracy index to obtain the accuracy index deviation rate, which is compared with the preset index deviation rate threshold. If the accuracy index deviation rate is greater than or equal to the index deviation rate threshold, the UAV positioning deviation is too large and the multi-source positioning accuracy does not meet the requirements. If the accuracy index deviation rate is less than the index deviation rate threshold, the UAV positioning deviation is within an acceptable range and the multi-source positioning accuracy meets the requirements.

[0148] According to an embodiment of the present invention, it also includes:

[0149] Extracting power consumption rate data according to the flight status information;

[0150] Comparing the power consumption rate data with a preset consumption rate threshold;

[0151] If the power loss rate data is greater than or equal to the loss rate threshold, the battery power is lost too quickly and the drone needs maintenance;

[0152] If the power consumption rate data is less than the consumption rate threshold, the battery status is normal.

[0153] Among them, in order to ensure the normal operation of the UAV, the power loss rate data is extracted according to the flight status information, and the power loss rate data is compared with the preset loss rate threshold. If the power loss rate data is greater than or equal to the loss rate threshold, the battery power is lost too quickly and the UAV needs to be maintained. If the power loss rate data is less than the loss rate threshold, the battery status is normal.

[0154] The third aspect of the present invention provides a computer-readable storage medium, which includes a multi-source fusion positioning method program for drone clusters. When the multi-source fusion positioning method program for drone clusters is executed by a processor, the steps of the multi-source fusion positioning method for drone clusters as described in any one of the above items are implemented.

[0155] The present invention discloses a multi-source fusion positioning method, system and medium for unmanned aerial vehicle clusters. The method obtains flight status information of multiple unmanned aerial vehicles in a preset area and extracts flight trajectory image features to obtain multi-source positioning information of multiple unmanned aerial vehicles that meet the flight trajectory, extracts satellite positioning data, inertial navigation data and radar ranging data, processes them through a preset fusion positioning model, obtains unmanned aerial vehicle navigation data and sends it to a control system to control the flight of the unmanned aerial vehicle, obtains flight results for processing, obtains a multi-source positioning accuracy index, obtains an average multi-source positioning accuracy index of the multiple unmanned aerial vehicles that meet the flight trajectory, corrects the multi-source positioning accuracy index, obtains a multi-source positioning accuracy correction index, compares it with a preset accuracy index, and determines whether the multi-source positioning accuracy of the unmanned aerial vehicle meets the requirements. This application makes full use of the advantages of each data source in specific scenarios by simultaneously utilizing multi-source information such as satellite positioning data, inertial navigation data and radar ranging data, making up for the shortcomings of a single data source in positioning accuracy and improving the overall positioning accuracy. It introduces a multi-source positioning accuracy evaluation mechanism, and quantitatively evaluates the performance of the positioning system by calculating the multi-source positioning accuracy index. Through the advantages of multi-source information fusion, real-time monitoring, accuracy evaluation and correction, and improved system robustness, it provides a more reliable and efficient solution for positioning and navigation of drones and other fields.

[0156] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0157] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0158] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0159] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above-mentioned method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a readable storage medium, which, when executed, executes the steps of the above-mentioned method embodiment; and the aforementioned storage medium includes: mobile storage devices, read-only memories, random access memories, magnetic disks or optical disks, and other media that can store program codes.

[0160] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A multi-source fusion positioning method for drone clusters, characterized in that: The following steps are involved: Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features, compare them with preset trajectory image features, and obtain multi-source positioning information of multiple drones that meet the flight trajectory; Extract satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, process them through a preset fusion positioning model, obtain UAV navigation data and send it to the control system to control the flight of the UAV; Obtain flight results for processing and obtain multi-source positioning accuracy index; Obtaining an average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory, and performing correction processing on the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index; Comparing the multi-source positioning accuracy correction index with the preset accuracy index, determining whether the multi-source positioning accuracy of the UAV meets the requirements; The flight results are obtained and processed to obtain a multi-source positioning accuracy index, which is specifically: Obtain flight results to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data; The satellite positioning offset data, the inertial navigation drift data and the radar ranging deviation data are processed by a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index; The step of obtaining the average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory and correcting the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index is specifically: Acquire and process a plurality of multi-source positioning accuracy indexes of the plurality of UAVs that meet the flight trajectory to obtain a multi-source positioning average accuracy index; According to the multi-source positioning average accuracy index, the multi-source positioning accuracy index is corrected by a preset accuracy correction model to obtain a multi-source positioning accuracy correction index; The calculation formula of the positioning accuracy evaluation model is: ； in, is the multi-source positioning accuracy index, They are satellite positioning offset data, inertial navigation drift data and radar ranging deviation data. is the preset characteristic coefficient; The calculation formula of the accuracy correction model is: ； in, is the multi-source positioning accuracy correction index, is the multi-source positioning accuracy index, is the average accuracy index of multi-source positioning, is the preset characteristic coefficient.

2. The multi-source fusion positioning method for UAV clusters according to claim 1 is characterized in that: The acquisition of flight status information of multiple drones in a preset area and extraction of flight trajectory image features are compared with preset trajectory image features to obtain multi-source positioning information of multiple drones that meet the flight trajectory, specifically: Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features; Comparing the flight trajectory image features with preset trajectory image features to obtain trajectory similarity data; Comparing the trajectory similarity data with a preset similarity threshold; If the trajectory similarity data is greater than or equal to the similarity threshold, multi-source positioning information of multiple UAVs that meet the flight trajectory is obtained.

3. The multi-source fusion positioning method for UAV clusters according to claim 2 is characterized in that: The method extracts satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, processes them through a preset fusion positioning model, obtains UAV navigation data and sends it to a control system to control the flight of the UAV, including: Extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information; The satellite positioning data includes latitude and longitude data, altitude data and heading data; The inertial navigation data includes acceleration data, angular velocity data and attitude data; The radar ranging data includes distance data, angle data, speed data and echo intensity data; The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain UAV navigation data and send it to the control system to control the flight of the UAV.

4. The multi-source fusion positioning method for UAV clusters according to claim 1 is characterized in that: The method of comparing the multi-source positioning accuracy correction index with the preset accuracy index to determine whether the multi-source positioning accuracy of the UAV meets the requirements is as follows: Comparing the multi-source positioning accuracy correction index with a preset accuracy index to obtain an accuracy index deviation rate; Comparing the accuracy index deviation rate with a preset index deviation rate threshold; If the accuracy index deviation rate is greater than or equal to the index deviation rate threshold, the positioning deviation of the drone is too large and the multi-source positioning accuracy does not meet the requirements; If the accuracy index deviation rate is less than the index deviation rate threshold, the drone positioning deviation is within an acceptable range and the multi-source positioning accuracy meets the requirements.

5. A multi-source fusion positioning system for drone clusters, characterized in that: The invention comprises a memory and a processor, wherein the memory comprises a multi-source fusion positioning method program for unmanned aerial vehicle clusters, and the multi-source fusion positioning method program for unmanned aerial vehicle clusters is executed by the processor to implement the following steps: Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features, compare them with preset trajectory image features, and obtain multi-source positioning information of multiple drones that meet the flight trajectory; Extract satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, process them through a preset fusion positioning model, obtain UAV navigation data and send it to the control system to control the flight of the UAV; Obtain flight results for processing and obtain multi-source positioning accuracy index; Obtaining an average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory, and performing correction processing on the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index; Comparing the multi-source positioning accuracy correction index with the preset accuracy index, determining whether the multi-source positioning accuracy of the UAV meets the requirements; The flight results are obtained and processed to obtain a multi-source positioning accuracy index, which is specifically: Obtain flight results to extract satellite positioning offset data, inertial navigation drift data and radar ranging deviation data; The satellite positioning offset data, the inertial navigation drift data and the radar ranging deviation data are processed by a preset positioning accuracy evaluation model to obtain a multi-source positioning accuracy index; The step of obtaining the average multi-source positioning accuracy index of the plurality of UAVs that meet the flight trajectory and correcting the multi-source positioning accuracy index to obtain a multi-source positioning accuracy correction index is specifically: Acquire and process a plurality of multi-source positioning accuracy indexes of the plurality of UAVs that meet the flight trajectory to obtain a multi-source positioning average accuracy index; According to the multi-source positioning average accuracy index, the multi-source positioning accuracy index is corrected by a preset accuracy correction model to obtain a multi-source positioning accuracy correction index; The calculation formula of the positioning accuracy evaluation model is: ； in, is the multi-source positioning accuracy index, They are satellite positioning offset data, inertial navigation drift data and radar ranging deviation data. is the preset characteristic coefficient; The calculation formula of the accuracy correction model is: ； in, is the multi-source positioning accuracy correction index, is the multi-source positioning accuracy index, is the average accuracy index of multi-source positioning, is the preset characteristic coefficient.

6. The multi-source fusion positioning system for UAV clusters according to claim 5 is characterized in that: The acquisition of flight status information of multiple drones in a preset area and extraction of flight trajectory image features are compared with preset trajectory image features to obtain multi-source positioning information of multiple drones that meet the flight trajectory, specifically: Obtain flight status information of multiple drones in a preset area and extract flight trajectory image features; Comparing the flight trajectory image features with preset trajectory image features to obtain trajectory similarity data; Comparing the trajectory similarity data with a preset similarity threshold; If the trajectory similarity data is greater than or equal to the similarity threshold, multi-source positioning information of multiple UAVs that meet the flight trajectory is obtained.

7. The multi-source fusion positioning system for UAV clusters according to claim 6 is characterized in that: The method extracts satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information, processes them through a preset fusion positioning model, obtains UAV navigation data and sends it to a control system to control the flight of the UAV, including: Extracting satellite positioning data, inertial navigation data and radar ranging data according to the multi-source positioning information; The satellite positioning data includes latitude and longitude data, altitude data and heading data; The inertial navigation data includes acceleration data, angular velocity data and attitude data; The radar ranging data includes distance data, angle data, speed data and echo intensity data; The satellite positioning data, inertial navigation data and radar ranging data are processed through a preset fusion positioning model to obtain UAV navigation data and send it to the control system to control the flight of the UAV.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a multi-source fusion positioning method program for drone clusters. When the multi-source fusion positioning method program for drone clusters is executed by a processor, the steps of a multi-source fusion positioning method for drone clusters as described in any one of claims 1 to 4 are implemented.

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